EGU General Assembly 2022  

Today, the automotive industry is a leading technology driver for lidar systems, because the largest challenge for achieving the next level of vehicle automation is to improve the reliability of the vehicles’ perception system. High costs of mechanically spinning lidars are still a limiting factor, but prices have already dropped significantly during the last decade and are expected to drop by another order of magnitude in the upcoming years thanks to new technologies like micro-electro-mechanical systems (MEMS) based mirrors, optical phased arrays, and vertical-cavity surface-emitting laser (VCSEL) sources. To exploit the potential of these newly emerging cost-effective technologies for geoscientific applications, we developed a novel stand-alone, modular Sensorbox that allows the use of automotive lidar sensors without the necessity of a complete vehicle setup. The novel Sensorbox includes a real-time kinematic differential global positioning system (RTK DGPS) and an inertial measurement unit (IMU) for georeferenced positioning and orientation. This setup enables measuring geoscientific processes and landforms reliably, at any remote location, with very high spatial and temporal resolution, and at relatively low costs. The current setup of the Sensorbox has a 360° field of view with 45° vertical angle, a range of 120m, a spatial resolution of a few cm and a temporal resolution of 20Hz. Compared to terrestrial laser scanners (TLS), such as the Riegl VZ-6000, automotive lidar sensors provide advantages in terms of size (40cm vs. 10cm), weight (20kg vs. 1kg), price (150k€ vs. 10k€), robustness (IP64 vs. IP68), acquisition time/frame rate (1h vs. 20Hz) and eye safety (class 3 vs. class 1). They can therefore provide a very useful complement to currently used TLS systems that have their strengths in range (6000m vs. 100m) and accuracy (1cm vs 5cm) performance. Automotive lidar sensors record high-resolution point clouds with very high acquisition frequencies, resulting in a data stream with order 10^6 points per second. To efficiently work with such large point cloud datasets, the open-source python package ‘pointcloudset’ was developed for handling, analysing, and visualizing large datasets that consists of multiple point clouds recorded over time.

How to cite: Muckenhuber, S., Schlager, B., Gölles, T., Hammer, T., Bauer, C., Exposito Jimenez, V., Schöner, W., Schratter, M., Schrei, B., and Senger, K.: A stand-alone, modular Sensorbox to exploit the potential of automotive lidar for geoscientific applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1625, https://doi.org/10.5194/egusphere-egu22-1625, 2022.

Abstract

The comprehensive study of the stability of ravines includes various aspects to be considered. One of them, interesting in this case, is the characterization of the morphology of the slopes, which due to their high inclination, presence of vegetation and irregularity in the surface, making it difficult to achieve this objective.

In the present study, we proceeded to study a ravine, located on the right bank of the Paraná de las Palmas river, in the town of Campana, northeast of the province of Buenos Aires. This work was focused on the surroundings of the coordinates 39º9'30.40 ”S 58º57'14.60” where lays a slope with structural conditions of interest. The morphology of the ravine in this area was studied in order to obtain a more precise assessment of the exposed surface and therefore a correct geometric and mass estimation of the slope.

For this propuse, an analysis of elevation models obtained from topographic surveys carried out with Drone and Total Station and, georeferenced with GPS equipment along the slope, was carried out. From there, the reliability of the applied methods and the results obtained could be evaluated comparatively.

All this information was complemented with a photographic record and available information on the environment to achieve a complete evaluation of the condition of the ravine in this area.

The importance of this work lies in the possibility of testing different methods and contrasting the results obtained using topographic and photogrammetric equipment and a combination of them. This will allow the characterization of slopes to be scaled over larger portions considering that this is part of a larger study along the Paraná river ravine. The greater reliability in the morphological results obtained is considered to be of significant utility for estimating the stability of the slopes, an aspect of interest to evaluate the geological danger and evaluate different engineering solutions.

Key Words: Ravine, Slope, Río Paraná, Argentina, Geological Risk, Drone.

How to cite: Serraiocco, L., Barbero, D., Santomauro, M., Peyrot, S., and Maroni, A.: Topographic and photogrametric techniques applied to the study of the morphology of ravines in Campana city, Buenos Aires, Argentina, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2035, https://doi.org/10.5194/egusphere-egu22-2035, 2022.

The recent development of optical spectroscopy enabled the development of the use of water isotopes in climate, environment and hydrological studies. An increasing number of studies also includes the most recent parameter ¹⁷O-excess as an indicator for kinetic fractionation effects in the water cycle. However, for some applications such as ice core science, the ¹⁷O-excess signal to be measured is very small, of the order of 10 – 20 ppm and it is a big analytical challenge to obtain the requested precision.

Here, we present results of performance of the new express mode and the standard mode developed for d¹⁸O, dD and now also ¹⁷O-excess for a Picarro analyzer. In the standard mode, there is a new injection of water vapor lasting 4.5 minutes every 10 minutes. To get rid of memory effect, the first injections are discarded or a correction is applied which depends on the difference in water isotopic composition between the measured sample and the previous one. For each new sample measured with the express mode, the sequence begins with 6 injections of water vapor in the cavity of 40 secondes each to get rid of the memory effect. It is followed by injections of water vapor lasting 2 minutes every 4 minutes. The advantage of the express mode is to avoid the memory correction and to decrease the measurement time. It thus permits to run more replicates which is important to improve the accuracy of the measurements, especially ¹⁷O-excess. We present here results of several series of samples and standards of different water isotopic composition (d¹⁸O ranging from -54 to 0 ‰) ran three times with both the standard and the express modes and compare the performances of the two modes.  

How to cite: Landais, A., Minster, B., Zuhr, A., Hoffmann, M., and Fourré, E.: Performances of express mode vs standard mode for d18O, dD and 17O-excess with a Picarro analyzer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4278, https://doi.org/10.5194/egusphere-egu22-4278, 2022.

This study provides an overview of the Earth observation and remote sensing activities of Svalbard Integrated Arctic Earth Observing System (SIOS) undertaken when building an observing system for sustained measurements in and around Svalbard to address Earth System Science (ESS) questions. SIOS research infrastructures are distributed across and around Svalbard for acquiring long-term in situ observations. These in situ measurements are not only useful for various ground-based studies, but also applicable for calibration and validation (Cal/Val) of current and future satellite missions e.g. Copernicus Imaging Microwave Radiometer (CIMR), Radar Observing System for Europe - L-Band (ROSE-L ) or Sentinel-1,2, Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL), Sentinel-5 Precursor, and Copernicus Hyperspectral Imaging Mission for Environment (CHIME). Better integration of in situ and satellite-based measurements is crucial for building a coherent network of observations to fill observational gaps. Additionally. complementing in situ measurements with satellite data is a prime necessity to generate operational reliable geoinformation products using traditional and advanced methods, for example, mapping vegetation extent in Svalbard using Sentinel-2 data complemented with in situ measurements of spectral reflectance collected by SIOS infrastructure. SIOS’s remote sensing activities are developed in SIOS knowledge centre (SIOS-KC) under the direction of the remote sensing working group (RSWG). This study highlights our current activities, goals for the next five years (2022-2026) and future activities with the intention of attracting potential collaborations to support achieving these goals. The study discusses SIOS’s present activities, including (1) capacity building e.g., webinar series, online conference, and training courses on EO and RS studies in Svalbard to train the next generation of polar scientists, (2) infrastructure development (like the current infrastructure investment programme SIOS-InfraNor) that can attract Cal/Val activities to Svalbard (3) SIOS’s airborne remote sensing activities, and (4) SIOS remote sensing service tools for field scientists. Ongoing and future activities include (1) the development of unified platform for satellite data availability for Svalbard, (2) establishing an EO and RS researcher’s forum on SIOS website, (3) community-based observations e.g. developing a citizen science project model for supporting satellite cal/val activities in Svalbard, (4) ongoing surveys on user requirements, product inventory and citizen science project, and (5) the ‘Satellite image of the week campaign’ on social media for outreach. The sustained and coordinated efforts by SIOS to develop a long-term monitoring system are expected to contribute to integrated monitoring, modelling and supporting decision making in Svalbard in the coming decades.

How to cite: Fjæraa, A. M., Jawak, S. D., Harcourt, W., Aparício, S., Pohjola, V., Andersen, B., Hübner, C., Jennings, I., Matero, I., Godøy, Ø., and Lihavainen, H.: SIOS’s Earth observation and remote sensing activities toward building an efficient regional observing system in Svalbard, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7123, https://doi.org/10.5194/egusphere-egu22-7123, 2022.

Seismic monitoring systems are continuously reducing in size and power consumption to facilitate larger scale and more remote experiments.

Güralp have been leading the way to develop a portable, user-friendly broadband seismometer that is robust, omnidirectional in its operation and maintains excellent low-noise performance. The Certimus, released in 2020, incorporates this omnidirectional sensor technology with the Minimus digitizer to provide a proven broadband station. Now, the analogue sensor component has been packaged into a robust and compact stainless-steel housing that is suitable for post-hole and surface deployments, known as the Certis.  

Certis enables users to deploy in dynamic environments, without the need for cement bases or precise levelling, as the sensor will automatically adjust to tilt up to +/- 90 degrees. Due to its small size, low weight and low power consumption, Certis significantly reduces the logistical requirements for broadband posthole deployments. In addition, the lack of levelling required allows for Certis to be easily deployed down hole without the need to manually adjust the sensor’s orientation.

Certis has a wide frequency range of 120s to 100Hz with a remote, user-selectable long period corner. The Certis design is compatible with any commercially available broadband digitizer, however increased functionality is available with the Minimus digitizer, including access to advanced state-of-health parameters.

Güralp has developed a range of accompanying accessories that expand on the functionality of Certis and Certimus. The Portable Power Module offers a compact power solution that can power offline stations for up to 6 weeks. Due to portability of both Certis and Certimus, custom-designed backpacks and smart cases allow for users to easily transport multiple systems into the field. After installation of a buried Certimus, users can easily access data from the microSD card without disturbing the sensor using a Surface Storage Module in line with the GNSS receiver.

How to cite: Mohr, S., Reis, W., Barbara, R., Cilia, M., Watkiss, N., and Hill, P.: The low-power, user-configurable, digital broadband seismometer, analogue iteration: Güralp Certis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7588, https://doi.org/10.5194/egusphere-egu22-7588, 2022.

Carbon dioxide (CO₂) and methane (CH₄) are the most important greenhouse gases, and there is an increasing need to measure these greenhouse gases with mobile measurement devices. Picarro’s G4301 Cavity Ring-Down Spectroscopy (CRDS) analyzer is a high-performance, light-weight, portable, battery-powered gas concentration analyzer that has enabled real-time measurements of CO₂ and CH₄ in challenging environments in the field of ecosystem [1]–[3], soil science [4] , glaciology [5], limnology [6] and indoor air quality [7]. Here we evaluate the performance of this portable greenhouse gas analyzer for atmospheric measurements, and discuss data obtained with this analyzer during balloon flights.  

The performance of the G4301 analyzer was assessed at the Metrology Laboratory (MLab) that is part of the Atmospheric Thematic Center of ICOS. The MLab regularly tests greenhouse gas analyzers that are used within the European monitoring network ICOS (Integrated Carbon Observation System). We will present CO₂ and CH₄ performance data on the continuous measurement repeatability (CMR), the short-term repeatability (STR), the long-term repeatability (LTR), the ambient temperature sensitivity, the inlet pressure sensitivity, and the built-in water vapor correction. We will discuss these findings in light of measurement requirements for different atmospheric applications.

To assess the performance of the analyzer in mobile field measurements, the G4301 was deployed at several balloon flights over Paris.

References

[1]         J. H. Matthes, A. K. Lang, F. V. Jevon, and S. J. Russell, “Tree stress and mortality from emerald ash borer does not systematically alter short-term soil carbon flux in a mixed northeastern U.S. forest,” Forests, vol. 9, no. 1, pp. 1–16, 2018.

[2]         L. Kohl et al., “Technical note: Interferences of volatile organic compounds (VOCs) on methane concentration measurements,” Biogeosciences, vol. 16, no. 17, pp. 3319–3332, 2019.

[3]         L. Jeffrey et al., “Are methane emissions from mangrove stems a cryptic carbon loss pathway? Insights from a catastrophic forest mortality,” no. June, 2019.

[4]         L. L. Chai et al., “A methane sink in the Central American high elevation páramo: Topographic, soil moisture and vegetation effects,” Geoderma, vol. 362, no. April 2019, p. 114092, 2020.

[5]         J. R. Christiansen and C. J. Jørgensen, “First observation of direct methane emission to the atmosphere from the subglacial domain of the Greenland Ice Sheet,” Sci. Rep., vol. 8, no. 1, p. 16623, Dec. 2018.

[6]         J. A. Villa et al., “Methane and nitrous oxide porewater concentrations and surface fluxes of a regulated river,” Sci. Total Environ., vol. 715, p. 136920, 2020.

[7]         Z. Merrin and P. W. Francisco, “Unburned Methane Emissions from Residential Natural Gas Appliances,” Environ. Sci. Technol., vol. 53, no. 9, pp. 5473–5482, May 2019.

How to cite: Lienhardt, L., Laurent, O., and E. G. Hofmann, M.: Performance assessment of the mobile G4301 Cavity Ring-Down Spectroscopy analyzer for atmospheric CO2, CH4 and H2O measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9283, https://doi.org/10.5194/egusphere-egu22-9283, 2022.

Among new technologies that enable representation of the submarine cultural landscapes, marine geophysical surveys provide fast and cost-effective tools now widely applied to the reconnaissance and management of underwater cultural and natural resources. In addition, passive and active sensors such as LiDAR and optical one mounted on Unmanned Aircraft Systems (UAS)  represent very effective tools for coastal remote sensing applications that require high spatial resolutions. In this work we use ultra-high resolution acoustic and LiDAR-derived data to characterize and map the marine and coastal area in the Baia archeological site (Naples, Italy). This area belongs to the Campi Flegrei volcanic field, which is affected by vertical ground movement called “Bradyseism” that strongly influenced the morphology of the coast over the last 2 Ka. As a consequence, Roman artifacts and structures dating from 1^st Century BC to 4^st Century AC, including Villas, luxury buildings and landing ports are now below the sea water surface, and partly buried within the marine sediments. Marine geophysical investigations included ultra-high resolution swath-bathymetry and parametric sub-bottom profiler surveys that allowed to characterize and map cultural and natural resources at seabed and in the shallow subseafloor. At same time optical (both visible and multispectral) images and LiDAR-derived elevation provided detailed information of the archaeological features and their natural setting along the adjacent coast. The main aim of this approach was to implement non-destructive geophysical methods for investigating and reconstruct the interrelationships between cultural and natural heritage at sea-land interface in the Baia archeological site. Such approach is now crucial for the evaluation of future trends induced by climate change and for a number of policy and management issues.

References

Masini N., Soldovieri F. (Eds) (2017). Sensing the Past. From artifact to historical site. Series: Geotechnologies and the Environment, Vol. 16. Springer International Publishing, ISBN: 978-3-319-50516-9, doi: 10.1007/978-3-319-50518-3, pp. 575

Violante C., Gallocchio E., Pagano F. (2022) Marine archaeological investigation in the submerged Roman site of Baiae using parametric sub-bottom profiler system. Phlegrean Fields Archaeological Park (Naples, Italy). Proceedings of the 2021 IEEE International Conference on Metrology for Archaeology and Cultural Heritage. Journal of Physics Conference Series, in press.

Violante C. (2020) Acoustic remote sensing for seabed archaeology. Proceedings of the International Conference on Metrology for Archaeology and Cultural Heritage. Trento, Italy, October 22-24, 2020, 21-26. ISBN: 978-92-990084-9-2.

Violante C., (2018) A geophysical approach to the fruition and protection of underwater cultural landscapes. Examples from the Bay of Napoli, southern Italy. In: Aveta, A., Marino, B.G., Amore R. (eds.), La Baia di Napoli. Strategie per la conservazione e la fruizione del paesaggio culturale. V. 1, 66-70. Artstudiopaparo, ISBN: 978-88-99130.

How to cite: Violante, C., Masini, N., and Abate, N.: Integrated remote sensing technologies for multi-depth seabed and coastal cultural resources: the case of the submerged Roman site of Baia (Naples, Italy)., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9939, https://doi.org/10.5194/egusphere-egu22-9939, 2022.

The nature of the Estonian crust was studied using global topography, magnetic data, and gravity data to estimate its tectonic regime. The Estonian Precambrian crystalline basement, composed of Paleo- to MesoProterozoic metamorphic and igneous rocks, is covered by a Paleozoic sedimentary rock deposit 100–780 m thick. To visualize crustal sources of the Estonian basement, we employed spectrum analysis of magnetic and gravity data, as well as two-dimensional (2D) forward modeling of gravity data. The gravimetric data was also evaluated to identify the depth of the Moho and Conrad discontinuities in Estonia. The magnetic data has also been evaluated to calculate the Curie point depth, which was then utilized to predict heat flow values inside the research zone. The subsurface of Estonia is divided into six petrological-structural zones: Tallinn, Alutaguse, Johvi, West-Estonian, Tapa and South-Estonian. To assess the structural variations of the crust at these locations, profiles of topographic, gravity, magnetic and heat flow data were constructed in each of the petrological-structural zones. The spectrum analysis and 2D gravity forward models yielded residual and regional gravity anomaly maps that show a significant amplitude potential maximum across the precambrian Rapakivi granitoid plutons and the Paldiski-Pskov tectonic zone. The Curie point depth reveals values ranging from 7 to 26 km, whereas the Moho depth suggests values ranging from 48 to 72 km and the Conrad depth values ranging from 14 to 20 km.

How to cite: Solano Acosta, J. D., Hints, R., and Soesoo, A.: Insights on the tectonic styles across Estonia using satellite potential fields derived from WGM-2012 gravity data and EMAG2 magnetic data., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11398, https://doi.org/10.5194/egusphere-egu22-11398, 2022.

Remote sensing technology based on laser scanning (LiDAR) has found a wide range of applications in cave mapping for a high degree of accuracy, level of detail, and time efficiency of this method. Besides the multidisciplinary research, the acquired data representing the cave morphology in a form of a dense point cloud became an essential part of the exploration for understanding the cave speleogenesis alongside capturing the current state that is of great importance in natural and cultural heritage documentation. Traditional cave cartography can benefit from using the LiDAR point clouds by a highly detailed 3D cave model enabling the creation of contours, shaded relief, or geomorphometric parameters, and a practically unlimited number of cross-sections. Compared to the passive remote sensing methods, such as photogrammetry, limited by the light conditions and cave dimensions, laser scanning is an active light-independent method that records additional attributes for each captured point in addition to its 3D coordinates. The recorded intensity of the backscattered laser pulse is very applicable for mapping purposes as it reveals spectral properties of the surface material bringing new aspects not only for the point cloud visualization but also for material differentiation, identification, and spatial localization of the cave paintings. The presented study introduces innovation in the methodology of creating a high-detail cave map from the acquired LiDAR data by combining derived cave floor model and semi-automatic procedure for identification of surface type based on the geomorphometric analysis and recorded intensity. The main benefit of the proposed approach is in the reduction of the author´s subjectivity and cave geometry generalization. By further automatization of this process, maps for large cave systems can be produced in a high level of detail.

How to cite: Nováková, M., Šupinský, J., Kaňuk, J., and Gallay, M.: Semi-automatic production of highly detailed cave maps from LiDAR point clouds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12392, https://doi.org/10.5194/egusphere-egu22-12392, 2022.

Volcanic areas frequently have diffuse gas emission of CO₂, SO₂, H₂S and even more. Normally these diffuse emissions do not vary only by time but also with location. Therefore, the spatial variation of concentrations cannot be monitored with one measurement system alone. On the other hand, the strength of gaseous volcanic emissions is often correlated with volcanic activity and can potentially endanger population in the vicinity.

For this reason, we developed a light new low-cost unit for the parallel measurement of various gases like CO₂, SO₂ and H₂S, which has the remarkable advantage of being able to transmit the measured data and GPS position with LTE to a remote server. Moreover, it can operate in an unattended way for days or even weeks, depending on the customizable operation modus of the unit and the capacity of the attached rechargeable battery. A solar-powered version is currently in development. The evaluation of the received data can be performed online on the server and the results are displayed continuously. The software is programmed by us in a way that alarms can be started in case that concentrations exceed predefined alarm thresholds also via email.

The electronic hardware unit is designed in a way that it can be equipped with low-cost NDIR sensors, electrochemical sensors or photoacoustic sensors (e.g. for CO₂: Sensirion SCD41). There are 4 analog ports (selectable voltage or current).

The operation procedure of the sensors electronics is customizable as well: The operation can be changed from continuous running to following mode:

• Measurement period,
• low power sleep period,
• sensor warm up-period,
• measurement period etc.

The length of all these time periods of the operation procedure can be varied depending on the measurement needs. That means for long runtime of the measurement a long sleeping time between the measurement periods can be chosen. On the other hand, if low power consumption and long runtime are not necessary, short sleeping periods or even the continuous running mode can be chosen. The operation configuration, e.g. sleeping time, can be changed by remote firmware update.

How to cite: Weber, K., Fischer, C., and Amend, D.: New development of a small customizable system for the measurement of volcanic gas concentrations with LTE data transfer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13132, https://doi.org/10.5194/egusphere-egu22-13132, 2022.

The Structure-from-Motion (SfM) approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for several decades. Using appropriate algorithms, SfM software combines images taken from different angles and distances based on the characteristic points determined in each image. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method, but still the impact of input processing parameters in software on the quality of photogrammetric products has not been fully recognized. This study aimed to identify the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired with an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: 1) with the shortest calculations time; 2) as accurate as possible, regardless of the time taken for the calculations; 3) the optimum, which is a compromise between accuracy and computation time.

Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. Three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analysis of root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in values of errors’ standard deviation. Furthermore, it has been shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies.

The research was funded by the National Science Center OPUS project number 2019/35/B/ST10/03928.

How to cite: Śledź, S. and Ewertowski, M.: Evaluation of the influence of Structure-from-Motion software processing parameters on the quality of digital elevation models and orthomosaics in the context of earth surface dynamics., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1375, https://doi.org/10.5194/egusphere-egu22-1375, 2022.

In order to explore the influence of slope shape on the development of ephemeral gully, 225 ephemeral gullies were obtained by visual interpretation based on the unmanned aerial photography of Langerzigou in Jingbian County, Shaanxi Province. The number and length of ephemeral gullies, the distance from the gully head to the watershed and the gully density were calculated. The original slope DEM was obtained by interpolating the elevation points on the ephemeral gully watershed, and the DEM was used to extract the terrain curvature to describe the hillslope shape, and then analyze the relationship between the slope shape and the ephemeral gully index. The results showed that: (1) the DEM after the elevation point interpolation on the ephemeral gully watershed was used to synthesize the ephemeral gully, which can well describe the original slope topographic features before the development of the ephemeral gully. (2) From the point of view of single slope shape, the gully density of the transverse concave slope was the highest, and the number of ephemeral gullies, the average distance from the gully head to the watershed of the longitudinal concave slope were the largest. the average length of the ephemeral gully and the number of the longitudinal convex slope were the largest. From the point of view of the combined slope shape, the average length of the ephemeral gully and the average distance from the gully head to the watershed on the biconvex slope and the convex slope were larger than those on the biconvex slope and the concave-convex slope. The ephemeral gully length of double concave slope was significantly different from that of double convex slope and convex concave slope (P<0.05); The ephemeral gully length of concave convex slope was significantly different from that of double convex slope and convex concave slope (P<0.05); There was a significant difference in the distance from the trench head to the watershed between the concave convex slope and the concave convex slope (P<0.1). (3) The curvature distribution characteristics of different forms of slope shallow ditch development were analyzed.

How to cite: Wang, H., Pang, G., Wang, C., Wang, L., and Long, Y.: Study on the Influence of Slope Shape on the Development of Ephemeral Gully Based on UAV, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1610, https://doi.org/10.5194/egusphere-egu22-1610, 2022.

Unmanned Aerial Vehicles (UAVs) have provided a cost-effective way to fill in gaps between in-situ (ground-based) and remote-sensing observations. In this study, a lightweight CO₂ sensor system suitable for operations on board small UAVs has been developed and validated. The CO₂ system autonomously performs in situ measurements, allowing for its integration into various platforms. It is based on a low-cost commercial nondispersive near-infrared (NDIR) CO₂ sensor (Senseair AB, Sweden), with a total weight of 1058 g, including batteries. A series of accuracy and linearity tests showed that the precision is within ±1 ppm for 1σ at 1 Hz. Variability due to temperature and pressure changes was derived from environmental chamber experiments. Additionally, the system has been validated onboard a manned aircraft against a reference instrument (Picarro, USA), revealing an accuracy of ±2 ppm (1σ) at 1 Hz and ±1 ppm (1σ) at 1 min (0.02 Hz). Integration on a quad-copter led to improvements in the calibration strategy for practical applications. The developed system has been deployed in an intensive flight campaign (a total of 16 flights per day), with horizontal flights performed at a low altitude (100 m AGL). The designed system highlights the capacity to detect CO₂ concentration changes at 1 Hz and spatial gradients and to provide accurate plume dispersion maps. It proved to be a good complementary measurement tool to the ground-based co-located observations performed by the Picarro G2401. This study gives a practical example of the process to be followed for the integration of a lightweight atmospheric sensor into a mobile (UAV) platform. Details of the measurement system and field implementations are described in this study to support future UAV platform applications for atmospheric trace gas measurements and closing the gaps in the monitoring of the current carbon cycle.

How to cite: Liu, Y., Paris, J.-D., Vrekoussis, M., Antoniou, P., Constantinides, C., Desservettaz, M., Keleshis, C., Laurent, O., Leonidou, A., Philippon, C., Vouterakos, P., Quéhé, P.-Y., Bousquet, P., and Sciare, J.: Improvements of a low-cost CO2 commercial NDIR sensor for UAV atmospheric mapping applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5278, https://doi.org/10.5194/egusphere-egu22-5278, 2022.

Investigating the chemical composition of volcanic plumes is an important method for obtaining geochemical information of volcanic systems, determining the environmental impact of volcanic outgassing and providing indications of impending activity of the volcano under investigation. However, sampling is not easy, particularly because of immediate meteorological influences on volcanic plume dispersion, but also, of course, because of potential hazards associated with sampling immediately at the rim of volcanic craters. 

When remote sensing methods are not available, UAVs offer the possibility of bringing measurement systems to the scene. Standard parameters that are commonly measured are SO₂ and CO₂, as well as a number of atmospheric state parameters such as pressure, temperature, and relative humidity. In flight data transmission via radio telemetry plays a significant role, as of course both orography and current meteorology make it otherwise difficult to locate the volcanic plume from several kilometers away. In addition to key components such as SO₂, CO₂, and water, there are also a number of other components of interest to geoscientists, such as H₂S, CO, H₂, and halogen compounds. Larger drones, such as the DJI Matrice M210 or the DJI M300, can be used to fly those research based measurement systems in parallel. This allows for the chemical characterization of highly transient plume structures simultaneously at two locations or at large distances from the source including the free troposphere. Results of such measurements carried out at Mt Etna and Vulcano Island, Italy during the last two years are presented in this contribution. Larger drone systems (with the DJI Matrice M210, DJI M300) have the disadvantage that they have a comparatively high weight and therefore make it difficult to bring to the sampling site which might not be accessible by car. Smaller drones like the DJI Mavic 3 significantly reduce the weight one has to carry. In addition, the relatively high cost of the larger drone systems prevents their use for daily monitoring tasks. Therefore, we have equipped a comparatively small drone (DJI Mavic 3) with suitable radio telemetry and sensors to gather basic chemical information in volcanic plumes with an extra-lightweight system. We will introduce this new miniaturized instrumentation and present first results of measurements with the new setup.

How to cite: Karbach, N., Geil, B., Gutiérrez, X., Hoor, P., Dötterl, A., Bobrowski, N., and Hoffmann, T.: Chemical characterization of volcanic plumes with multifunction UAVs and extra-lightweight drones: Concepts and first applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5354, https://doi.org/10.5194/egusphere-egu22-5354, 2022.

Accurate estimates of canopy cover (CC),tree and stand structure are required to manage poplar plantations effectively. However, traditional measurements are limited by the cost and time-consuming nature of field methods, which inherently have limited the large scale adoption of in situ approaches. Satellite remote sensing has the advantage of broader geographical coverage, but its spatial and temporal resolution is often not suited for tree- to stand-scale applications as required in precision plantation forestry. Recently, unmanned aerial vehicles (UAVs) have become very popular in forestry. In this contribution, we tested the use of UAVs for retrieving plot-level canopy and stand attributes in hybrid poplar plantations, which were sampled in Northern Italy. A multispectral camera sensor was equipped to a multi-rotor UAV, and was used to acquire orthorectified images of 50 poplar plantations, each 0.25 ha in size, with varying age and plant density. In addition, field optical measurements of canopy structure made by digital cover photography and mensurational attributes derived from tree inventory were also performed and used as ground truth data.

The very high resolution of UAV imagery (<10 cm) allowed to efficiently perform a Simple Linear Iterative Clustering (SLIC) algorithm for superpixels generation, which was used to delineate individual poplar crowns automatically. The segmented images were then processed using Gray-Level Co-occurrence Matrices (GLCM) to calculate specific texture attributes, which were benchmarked against ground truth measurements.

Results indicated that multispectral UAVs can estimate canopy and stand structure attributes in poplar plantations reliably and accurately. Based on model performance indicators, the best model is that relating stand features to image dissimilarity. Its RMSE is in line with the standard deviations of the observed values, meaning that the error associated with the prediction is in line with the uncertainty of the calibration dataset.

The basal area, the volume of the trunk and the crown volume were the most correlated attributes with image dissimilarity valued from GLCM.By contrast, crown cover (CC) and leaf area index (LAI) were the model's attributes that could fit the worse following the clustering effect of plants’ age and the leverage occurring in some stands that results in ground truth data overestimation.

We concluded that use of UAVs can be considered an efficient tool in poplar plantation forestry. Considering the multi-scale nature of poplar plantation interventions, UAVs are particularly relevant as they can bridge between field and satellite measurements. Regarding the latter, the high resolution of UAV imagery also allows calibrating metrics from coarser scale satellite products, avoiding or reducing the need for field calibration efforts.

How to cite: Romano, E., Brambilla, M., Bisaglia, C., Giannetti, F., Tattoni, C., Puletti, N., and Chianucci, F.: Estimating canopy and stand structure in hybrid poplar plantations from multispectral UAV imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5661, https://doi.org/10.5194/egusphere-egu22-5661, 2022.

Mountain agriculture is a vital social-economic activity in Europe, including the alpine Province of South Tyrol, Italy. Here, apple orchards and vineyards are extensively cultivated. Besides the difficulty to cultivate in mountain terrain (steep slopes, difficult accessibility, extreme weather conditions), the plants are exposed to a combination of biotic and abiotic stresses that can result in diseases caused by pathogens. It results in the loss of the yield and quality of products, economic losses, reducing food security with severe ecological impacts, and affects many ecosystem services (such as agrotourism).

This work presents a proximal sensing technique based on an unmanned aerial platform with a payload consisting of multi and hyperspectral optical cameras. Such platforms are suitable to access rugged terrains in a short time to map the presence of diseases and pests, as well they provide imagery for the optimal management of farms. We study three different experiments: apple orchard, vineyard, and forestry, observing Apple proliferation, Flavescence dorée, and Pine processionary, respectively. We aim at a non-invasive and non-destructive method to monitor plant diseases in the direction of high-precision mapping agriculture applications by exploring supervised classification methods based on ground data to distinguish healthy and unhealthy trees.

How to cite: Mejia-Aguilar, A., Barthel, D., Chuprikova, E., McLeod, B. A., Trenti, M., Kerschbamer, C., Prechsl, U., and Janik, K.: UAV-based precision mapping techniques for disease and pest identification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10511, https://doi.org/10.5194/egusphere-egu22-10511, 2022.

In some environmental applications, satellite acquisitions could not be able to provide all the information necessary to characterize the problem at hand due, for example, to limited spatial resolution or inadequate revisit time. However, in these cases, they can be used for preliminary investigation of the area of interest with the purpose to guide subsequent acquisitions with higher spatial resolution made by means of aerial sensing. This work presents an innovative application combining both satellite acquisitions and aerial close-range sensing implemented via drones in autonomous and coordinated flight. The case study concerns the discovery of illegal micro-dumps and other environmental hazards in Campania Region (Italy). The envisioned workflow includes the detection of target environmental criticalities in very high-resolution optical satellite images and a methodology to plan and adaptively re-plan a survey mission of a team of drones aimed at confirming the presence of a micro-dump and at its characterization. The processing of satellite images is validated on real data in a significative application context, while the performance of the acquisition strategy performed by the drone team are characterized trough simulations on a pre-analysed geographical area.

How to cite: Cicala, L., Amitrano, D., Cesario Vincenzo, A., Gargiulo, F., Gigante, G., Nebula, F., Palumbo, R., Parrilli, S., Pascarella, D., and Tufano, F.: Discovery and characterization of environmental hazards by means of dynamic coordination of drones driven by satellite detection maps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11912, https://doi.org/10.5194/egusphere-egu22-11912, 2022.

We present a platform for open-path tunable diode laser absorption spectroscopy between a drone and a base station. This is a step towards an open-path measurement between two collaborating drones, which to our knowledge has not yet been achieved. Such a system enables mapping of remote (permafrost tundra, e.g.) or hazardous areas (landfills, e.g.) and localization of emissions. We use a commercially available quad-copter drone that carries a reflector and a LED for being tracked. The base station consists of a self-made pan-tilt unit that carries a camera to track the drone, and the optical measurement system. The base station is controlled through a field--programmable gate array. We decided to built the base station ourselves to ensure a fast response, enabling tracking of the drone. To demonstrate the concept, our tunable diode laser absorption setup is tailored towards the detection of ammonia (NH₃) because of its fairly strong absorption, and thus comparatively easy detectability. The distributed feedback laser operates at a centre wavelength of 1512 nm, with a bandwidth of approximately 2 nm (full width at half maximum), and a typical output power of 10 mW. We characterize the stability of the drone, the reflector, and the laser system. We aim to further develop this concept such that it (a) can be implemented on two collaborating drones, without the need for a base station, and (b) to measure other greenhouse gases or pollutants, such as methane or hydrogen sulphide.

How to cite: Dunker, T., Berge, A., Haugholt, K. H., Moore, R. J. D., and Tørring, H.: Concept for open-path gas measurements between a drone and a base station, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12130, https://doi.org/10.5194/egusphere-egu22-12130, 2022.

Providing suitable initial states is a long-standing problem in numerical modelling of glaciers and ice sheets, as well as in other areas of the geosciences, due to the frequent lack of observations. This is particularly acute in glaciology, where important parameters such as the underlying bed may be only very sparsely observed or even completely unobserved. Glaciological models also often require lengthy relaxation periods to dissipate incompatibilities between input datasets gathered over different timeframes, which may lead to the modelled initial state diverging significantly from the real state of the glacier, with consequent effects on the accuracy of the simulation. Sequential data assimilation using an ensemble offers one possibility for resolving both these issues: by running the model over a period for which various observational datasets are available and loading observations into the model at the time they were gathered, the model state can be brought into good agreement with the real glacier state at the end of the observational window. The mean values of the ensemble for unknown parameters, such as the bed, then also represent best guesses for the true parameter values. This assimilated model state can then be used to initialise prognostic runs without introducing model artefacts or a distorted picture of the actual glacier.

In this study, we present a framework for conducting sequential data assimilation and retrieving the bed of a glacier in a 3D setting of the open-source, finite-element glacier flow model, Elmer/Ice, and solving the Stokes equations rather than using the shallow shelf approximation. Assimilation is undertaken using the open-source PDAF library developed at the Alfred Wegener Institute. We demonstrate that the set-up allows us to accurately retrieve the bed of a synthetic glacier and present our plans to extend it to a real-world example.

How to cite: Cook, S. and Gillet-Chaulet, F.: 3D sequential data assimilation in Elmer/Ice with Stokes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-753, https://doi.org/10.5194/egusphere-egu22-753, 2022.

Mass loss from the Antarctic ice sheet is dominated by the integrity of the ice shelves that buttress it. The evolution and stability of ice shelves is dependent on a variety of parameters that cannot be directly observed, such as basal melt and ice rheology. Constraining these parameters is of great importance in making predictions of the future changes in ice shelves that have a quantifiable uncertainty. This inference task is difficult in practice as the number of unknown parameters is large, observations are often sparse, and the computational cost of ice flow models is high.

We aim to develop a framework for inferring joint distributions of mass balance and rheological parameters of ice shelves from observations such as ice geometry, surface velocities, and radar isochrones. Here, we begin by inferring a posterior distribution over basal melt parameters in along-flow sections of synthetic and real world ice shelves (Roi Baudouin). We use the technique of simulation-based inference (SBI), a machine learning framework for performing Bayesian inference when the likelihood function is intractable. The inference procedure relies on the availability of a simulator to model the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack.  First, we show that by combining these two tools we can recover the underlying parameters of synthetic 2D data with meaningful uncertainty estimates. In a second step, we apply our method to real observations and get estimates for the basal melt rates which are coherent with the data when running the forward model over a centennial timescale.

How to cite: Moss, G., Višnjević, V., Schröder, C., Macke, J., and Drews, R.: Uncertainty quantification for melt rate parameters in ice shelves using simulation-based inference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-896, https://doi.org/10.5194/egusphere-egu22-896, 2022.

Sea ice is a key element in our climate system, and it is very sensitive to the current observed climate change. Sea ice volume is a sensitive indicator of the health of Arctic although very challenging to estimate precisely since it is a combination of sea ice area and sea ice thickness. Arctic sea ice volume has decreased by as much as 75% at the end of the summer season if compared with the conditions 40 years ago. The ongoing decline of Arctic sea ice exposes the ocean to anomalous surface heat and freshwater fluxes that can have potential implication for the Arctic region and beyond, for the general oceanic circulation itself.

For more than a decade, Mercator Ocean International develops and produces Global Ocean Reanalysis with a 1/4° resolution system. Based on the NEMO modelling platform, observations are assimilated by a reduced-order Kalman filter. In-situ CORA database, altimetric data, sea surface temperature, and sea ice concentration are jointly assimilated to constrain the ocean and sea ice model.

In previous reanalysis, long-term sea ice volume drift has been observed in the Arctic. To obtain a better constraint on the sea ice thickness, Cryosat-2 radar Freeboard data are assimilated jointly with the sea ice concentration in a multidata/multivariate sea ice analysis. The coupled ocean and ice assimilation system runs on a 7-day cycle, using IAU (Incremental Analysis Update) and a 4D increment. The “white ocean” is modelled with the multi-categories LIM3.6 sea ice numerical model. The aim of this study is to initiate the development of the future operational multi-variate and multi-data sea ice analysis system with freeboard radar assimilation.

After describing this global sea ice reanalysis system, we present results on the abilities of this configuration to reproduce sea ice extent and volume interannual variability in both hemispheres. Comparisons between experiments with and without assimilation show that the joint assimilation of CryoSat-2 radar freeboard and sea ice concentration reduces most of model biases of sea ice thickness, e.g., in the north of the Canadian Arctic Archipelago and in the Beaufort Sea in the Arctic. Moreover, radar freeboard assimilation does not hinder the good results in simulating sea ice extent previously obtained with the assimilation of only sea ice concentration. Validation with non-assimilated satellite data and in-situ data supports these findings. Lastly, snow depth significantly influences the Freeboard measurement: this study also reveals the importance of including snow information on freeboard retrieval and on the ice volume assimilation methodology.

These experiments take place in a context of increasing interest in polar regions and prepare the launch of Copernicus Sentinel expansion satellite missions.

How to cite: Chenal, A., Testut, C.-E., Garnier, F., Laurent, P., and Gilles, G.: Assimilation of CryoSat-2 radar Freeboard data in a global ocean-sea ice modelling system., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2061, https://doi.org/10.5194/egusphere-egu22-2061, 2022.

Modelling the past and future evolution of the West Antarctic Ice Sheet (WAIS) to climate and ocean forcing is challenged by the availability and quality of observed palaeo boundary conditions. Aside from point-based geochronological measurements, the only available proxy to query past ice-sheet processes on large spatial scales is Internal Reflecting Horizons (IRHs) as sounded by ice-penetrating radar. When isochronal, IRHs can be used to determine palaeo-accumulation rates and patterns, as previously demonstrated using shallow, centennially dated layers. Whilst similar efforts using deeper IRHs have previously been conducted over the East Antarctic Plateau where ice-flow is slow and ice thickness has been stable through time, much less is known of millennial-scale accumulation rates over the West Antarctic plateau due to challenging ice dynamical conditions in the downstream section of the ice sheet. Using deep and spatially extensive ice-core dated IRHs over Pine Island and Thwaites glaciers and a local layer approximation model, we quantify Holocene accumulation rates over the slow-flowing parts of these sensitive catchments. The results from the one-dimensional model are also compared with modern accumulation rates from observational and modelled datasets to investigate changes in accumulation rates and patterns between the Holocene and the present. The outcome of this work is that together with present and centennial-scale accumulation rates, our results can help determine whether a trend in accumulation rates exists between the Holocene and the present and thus test to what extent these glaciers are controlled by ice dynamics rather than changes in accumulation rates.

How to cite: Bodart, J., Bingham, R., Young, D., Blankenship, D., and Vaughan, D.: Quantifying Holocene Accumulation Rates from Ice-Core Dated Internal Layers from Ice-Penetrating Radar Data over the West Antarctic Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2535, https://doi.org/10.5194/egusphere-egu22-2535, 2022.

Accurately predicting ice crystal fabrics is key to understanding the processes and deformation in ice-sheets. Here we use SpecCAF, a continuum fabric evolution model validated against laboratory experiments, to predict the fabric evolution with an active ice stream. This is done by predicting the fabrics at the East Greenland Ice core Project (EGRIP) site. We do this using satellite data and inferred particle paths, combined with the shallow ice approximation (with basal slip) to infer a leading order approximation for the deformation through the ice sheet. We find that SpecCAF is able to predict the patterns observed at EGRIP - a girdle/horizontal maxima fabric perpendicular to the flow direction. By reducing the rate of rotational recrystallization in the model we are also able to predict the fabric strength at EGRIP. This suggests the effect of rotational recrystallization on the fabric may be primarily strain-rate/stress dependent. These results show SpecCAF can be applied to real-world conditions and provide insights into the deformation and basal-conditions of the ice sheet. As the model only considers deformation and recrystallization through dislocation creep, the results imply that - for the ice stream modelled - no other process is significantly influencing both the produced ice fabric and the deformation. We find that the model gives best results for full slip at the base of the ice sheet, implying that the level of sliding at the base of the ice sheet in the North Greenland Ice stream may be very high. The methodology used here can be extended to other ice core locations in Greenland and Antarctica.

How to cite: Richards, D., Pegler, S., and Piazolo, S.: Numerical modelling of ice stream fabrics: Implications for recrystallization processes and basal slip conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3743, https://doi.org/10.5194/egusphere-egu22-3743, 2022.

Lubrication by subglacial water or saturated subglacial sediments is crucial to controlling the movement of fast-flowing outlet glaciers and ice streams.  However, the subglacial environment is difficult to observe directly.  Here, we combine inverse modeling with ice-penetrating radar observations to characterize the ice sheet bed in the Filchner-Ronne sector of Antarctica, with a specific focus on the Recovery Glacier catchment.  First, we use the Ice Sheet System Model (ISSM; Larour et al., 2012) to assimilate satellite observations of ice sheet surface velocity (Mouginot et al., 2019) in order to solve for basal drag and ice rheology across the Filchner-Ronne sector of Antarctica.  Next, we compare these results with ice-penetrating radar observations sensitive to the presence of ponded water at the ice sheet base (Humbert et al., 2018; Langley et al., 2011), along with remotely sensed observations of active lakes (Smith et al., 2009) and putative large subglacial lakes inferred from the ice sheet surface slope (Bell et al., 2007).  We find that the main fast-flowing region of Recovery Glacier is mostly low-drag, with the exception of localized sticky spots and bands.  The boundary between rugged subglacial highlands and a deep subglacial basin near the onset of the ice stream is associated with a sharp reduction in basal drag, although surface velocity changes smoothly rather than abruptly across this transition.  An upstream shear margin, visible in satellite radar images of the ice surface, is associated with low basal drag.  The putative large lakes have low drag but are not strongly distinguished from their surroundings, and radar evidence for ponded subglacial water within them is weak.  The active lakes identified from satellite altimetry are similarly situated in areas of low basal drag, but have limited radar evidence for ponded subglacial water.  An L-curve analysis indicates that our inverse model results are robust against changes in regularization, yet the radar-identified lake candidates do not have a clear relationship with low-drag areas in the fast-flowing ice stream.  We conclude that the deep-bedded regions of Recovery Glacier are underlain by saturated subglacial sediments, but classic ponded subglacial lakes are much more rare.  Isolated sticky spots and bands within the ice stream are either due to protrusions of bedrock out of the sediments or to localized areas of frozen and/or compacted sediments.

How to cite: Wolovick, M., Höyns, L.-S., Kleiner, T., Nickel, N., Helm, V., and Humbert, A.: Basal Properties of the Filchner-Ronne Sector of Antarctica from Inverse Modeling and Comparison with Ice-Penetrating Radar Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4027, https://doi.org/10.5194/egusphere-egu22-4027, 2022.

Ongoing global glacier retreat leads to sea-level rise and changes in regional freshwater availability. For an adequate adaptation to these changes, knowledge about the ice volume and the current dynamic state of glaciers is crucial. At regional to global scales, sparse observations made the dynamic state of glaciers very difficult to assess. Thanks to recent advances in global geodetic mass-balance and velocity assessments, new ways to initialize numerical models and ice thickness estimation emerge. In this contribution, we present the COst Minimization Bed INvErsion model (COMBINE), which aims to be a cheap, flexible global data assimilation and inversion method. COMBINE uses an existing numerical model of glacier evolution (the Open Global Glacier Model, OGGM) rewritten in the machine learning framework PyTorch. This makes the model fully differentiable and allows to iteratively minimize a cost function penalizing mismatch to observations. Thanks to the flexible nature of automatic differentiation, various observational sources distributed in time can be considered (e.g. surface elevation and area changes, ice velocities). No assumption about the dynamic glacier state is needed, releasing the equilibrium assumption often required for large scale ice volume computations. In this contribution, we will demonstrate the capabilities of COMBINE in several idealized and real-world applications, and discuss its added value and upcoming challenges for operational application.

How to cite: Schmitt, P., Maussion, F., and Gregor, P.: Estimating large scale dynamic mountain glacier states with numerical modelling and data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5113, https://doi.org/10.5194/egusphere-egu22-5113, 2022.

Radar reflections from the interior of the Greenland ice sheet contain a comprehensive archive of past accumulation rates, ice dynamics, and basal melting. Combining these data with dynamic ice sheet models may greatly aid model calibration, improve past and future sea level estimates, and enable insights into past ice sheet dynamics that neither models nor data could achieve alone.

In this study, we present the first three-dimensional ice sheet model that explicitly simulates the Greenland englacial stratigraphy. Individual layers of accumulation are represented on a grid whose vertical axis is time so that they do not exchange mass with each other as the flow of ice deforms them. This isochronal advection scheme does not influence the ice dynamics and only requires modest input data from a host thermomechanical ice sheet model.

Using an ensemble of simulations, we show that direct comparison with the dated radiostratigraphy data yields notably more accurate results than calibrating simulations based on total ice thickness. We show that the isochronal scheme produces a more reliable simulation of the englacial age profile than Eulerian age tracers. Lastly, we outline how the isochronal model can be linearized as a foundation for inverse modeling and data assimilation.

How to cite: Born, A., Robinson, A., and Theofilopoulos, A.: Modeling the Greenland englacial stratigraphy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5425, https://doi.org/10.5194/egusphere-egu22-5425, 2022.

Meltwater forms at the base of the Antarctic Ice Sheet due to geothermal heat flux (GHF) and basal frictional dissipation. Despite the relatively small volume, this meltwater has a profound effect on ice-sheet stability, controlling the dynamics of the ice sheet and the interaction of the ice sheet with the ocean. However, observations of subglacial melting and hydrology in Antarctica are limited. Here we use numerical modelling to assess subglacial melt rates and hydrology beneath the Antarctic Ice Sheet. Our case study, focused on the Amery Ice Shelf catchment, shows that total subglacial melting in the catchment is 6.5 Gt yr^-1, over 50% larger than previous estimates. Uncertainty in estimates of GHF leads to a variation in total melt of ±7%. The meltwater provides an extra 8% flux of freshwater to the ocean in addition to contributions from iceberg calving and melting of the ice shelf. GHF and basal dissipation contribute equally to the total melt rate, but basal dissipation is an order of magnitude larger beneath ice streams. Remote-sensing observations, from CryoSat-2, indicating active subglacial lakes and ice-shelf basal melting constrain subglacial hydrology modelling. We observe a network of subglacial channels that link subglacial lakes and trigger isolated areas of sub-ice-shelf melting close to the grounding line. Building upon this Amery case study, we expand our analysis to quantify subglacial melt rates and hydrology beneath the entire Antarctic Ice Sheet.

How to cite: Wearing, M., Goldberg, D., Dow, C., Hogg, A., and Gourmelen, N.: Coupling modelling and satellite observations to constrain subglacial melt rates and hydrology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8605, https://doi.org/10.5194/egusphere-egu22-8605, 2022.

Objective: Estimate Soil Freezing Characteristic Curves (SFCCs) and uncertainty bounds based on a compilation of existing measured SFCCs.

Key Findings

• Uncertainty in measured SFCCs is estimated based on measurement technique, water content, and soil disturbance
• An open-source tool for estimating and constraining SFCCs is developed for use in parameterizing freeze/thaw models

Abstract

Cold-regions landscapes are undergoing rapid change due to a warming climate. This change is impacting many elements of the landscape and is often controlled by soil freeze/thaw processes. Soil freeze/thaw is governed by the Soil Freezing Characteristic Curve (SFCC) that relates the soil temperature to its unfrozen water content. This relation is needed in all physically based numerical models including soil freeze/thaw processes. A repository of all collected SFCC data and an R package for accessing and processing this data was presented in "A Repository of 100+ Years of Measured Soil Freezing Characteristic Curves".

This rich SFCC dataset is synthesized with a focus on potential sources of error due to the combination of measurement technique, data interpretation, and physical freeze-thaw process in a specific soil. Particular attention is given to combining sources of error and working with datasets given incomplete and missing metadata. A tool is developed to extract an SFCC for a soil with specified properties alongside its uncertainty bounds. This tool is intended for use in freeze/thaw models to improve freeze/thaw estimates, and better represent the ice and liquid water content of freezing soils. As phase change accounts for a vast majority of the energy budget in freezing soils, accurately representing the process is essential for realistic predictions. In addition, SFCC type curves are provided for the common sand, silt, clay, and organic soil textures when additional data is unavailable to define the SFCC more precisely.

How to cite: Devoie, É., Gruber, S., and McKenzie, J.: Constraining Soil Freezing Models using Observed Soil Freezing Characteristic Curves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8938, https://doi.org/10.5194/egusphere-egu22-8938, 2022.

The analysis of englacial layers using ice penetrating radar enables the characterisation and reconstruction of current and past ice sheet flow. To date, little research has been undertaken on the ice flow and englacial stratigraphy of the upper catchment of the Lambert Glacier. The Lambert Glacier catchment is one of the largest in East Antarctica, discharging ~16% of East Antarctica’s ice. Quantitative analysis of the continuity of englacial stratigraphy and ice flow has the potential to provide insight into the present-day and past flow regimes of the upper catchment of Lambert Glacier. Radar data from the British Antarctic Survey Antarctica’s Gamburtsev Province Project North (AGAP-N) aerogeophysical survey was analysed using the Internal Layer Continuity Index (ILCI). This approach identified, and characterised, a range of englacial structures and stratigraphy, including buckled layers in areas of increased ice velocity (>20ma^-1) and continuous layering across subglacial highlands with low ice velocity adjacent to the central Lambert Glacier trunk. Overall, the analysis is consistent with the present-day ice-flow velocity field and long-term stability of ice flow across the Lambert catchment. However, disrupted layer geometry at the onset of the Lambert Glacier suggests a past shift in the position of the onset of ice flow. These results have implications for the future evolution of this major ice flow catchment, and East Antarctica, under a changing climate. The ILCI method represents a valuable tool for rapidly characterising englacial stratigraphy, and the study demonstrates the transferability of the method across Antarctica. The use of quantitative tools such as ILCI for the analysis of large radar datasets will be critical for projects such as AntArchitecture (https://www.scar.org/science/antarchitecture/home/) which aims to investigate the long-term stability of the Antarctic ice sheets directly from the internal architecture of the ice sheet.

How to cite: Sanderson, R., Ross, N., Callard, L., Winter, K., Napoleoni, F., Bingham, R., and Jordan, T.: Assessing the continuity of englacial layers across the Lambert Glacier catchment., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9143, https://doi.org/10.5194/egusphere-egu22-9143, 2022.

How to cite: Sievers, I., Stenseng, L., and Rasmussen, T.: Assimilating Cyrosat2 freeboard into a coupled ice-ocean model , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9262, https://doi.org/10.5194/egusphere-egu22-9262, 2022.

No continuously updated glacier and glacial lake inventories exist for Norway. Previous inventories have been developed for the time periods of 1947-1985, 1988-1997 and 1999-2006 for glaciers and 1988-1997, 1999-2006, 2014 and 2018 for glacial lakes, by manual digitization, and semi-automated mapping. However, these methods are both time consuming and do not allow for an analysis of glacial lake behaviour on shorter timescales or on a seasonal basis. Therefore, one aim of this study is to present consistent inventories for glaciers and glacial lakes in Norway using semi-automated mapping and machine learning techniques applied on satellite imagery of different spatial and temporal resolution (Landsat 30m, 16 days, and Sentinel 10m, 5 days). An automated method that allows frequent monitoring of glacier variables can provide essential knowledge for the understanding of glacial lake dynamics in a changing climate.

In addition to glacial lake inventories, smaller ice caps with active glacial lakes are investigated more closely, aiming at following the development of glacial lakes throughout seasons. Here we are also analyzing the suitability of PlanetScope imagery compared to the Sentinel and Landsat imagery to detect the known glacial lake outburst flood events and identify currently unrecognized hazard-prone glacial lakes. Since the field-based investigations of glacial lake changes (especially of the ice-dammed lakes) are sparse in Norway, developing methods for remote-sensed, automated monitoring of glacial lake changes and glacial lake outburst floods is essential in order to develop early warning systems, detect potentially hazardous lakes and prevent human losses and damages to infrastructure and local businesses.

How to cite: Abderhalden, J. and Rogozhina, I.: Automated Tracking of Glacial Lake Outburst Floods in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9886, https://doi.org/10.5194/egusphere-egu22-9886, 2022.

Over the past few decades, polar research teams around the world have deployed long-term measurement sites to monitor changes in permafrost environments. Many of these sites include borehole sensor arrays which provide measurements of ground temperature as deep as 50 meters or more below the surface. Recent studies have attempted to leverage these borehole data from the Global Terrestrial Network of Permafrost to quantify changes in permafrost temperatures at a global scale. However, temperature measurements provide an incomplete picture of the Earth's subsurface thermal regime. It is well known that regions with warmer permafrost, i.e. where mean annual ground temperatures are close to zero, often show little to no long-term change in ground temperature due to the latent heat effect. Thus, regions where the least warming is observed  may also be the most vulnerable to rapid permafrost thaw. Since direct measurements of soil moisture in the permafrost layer are not widely available, thermal modeling of the subsurface plays a crucial role in understanding how permafrost responds to changes in the local energy balance. In this work, we explore a new probabilistic method to link observed annual temperatures in boreholes to permafrost thaw via Bayesian parameter estimation and Monte Carlo simulation with a transient heat model. We apply our approach to several sites across the Arctic and demonstrate the impact of local landscape variability on the relationship between long term changes in temperature and latent heat.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: A probabilistic analysis of permafrost temperature trends with ensemble modeling of heat transfer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10509, https://doi.org/10.5194/egusphere-egu22-10509, 2022.

Transitions from basal no slip to basal sliding are a common feature of ice sheets, yet one that has remained difficult to observe. In this study we leverage recent advances in the processing of radar sounding data to study these transitions through their signature in englacial layers. Englacial layers encode information about strain and velocity, and the relation between their geometry and the onset of basal sliding has been demonstrated in ice flow models (the so-called "Weertman effect"). Here we leverage this relation to test the long-standing hypothesis that sliding onset takes the form of an abrupt no slip/sliding transition. By comparing the modeled signature of an abrupt sliding onset in englacial layer slopes against slope observations from the onset region of a West Antarctic ice stream (Institute Ice Stream), we conclude that observed layer geometry does not support an abrupt no slip/sliding transition. Our findings instead suggest a much smoother sliding onset, as would be consistent with temperature-dependent friction between ice and bed. Direct measurements of basal temperature at the catchment scale would allow us to confirm this hypothesis.

How to cite: Mantelli, E., Bryant, M., Seroussi, H., Raess, L., Castelletti, D., Schroeder, D., Suckale, J., and Siegert, M.: Layer geometry as a constraint on the physics of sliding onset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11310, https://doi.org/10.5194/egusphere-egu22-11310, 2022.

Contemporary mass loss from the Antarctic ice sheet primarily originates from the discharge of
marine-terminating glaciers and ice streams. The rate of mass loss is influenced by warming ocean
and atmospheric conditions, which lead to subsequent thinning or disintegration of ice shelves and
increased outflow of upstream grounded ice. It is currently unclear how the basal thermal state of
grounded ice could evolve in the future - for example as a result of accelerated ice flow or changes
in the ice sheet geometry - but a change in the basal thermal state could impact rates of mass loss
from Antarctica. Here, we use a combination of numerical simulations and ice-penetrating radar
analysis to investigate the influence of basal thawing on 100yr simulations of the Antarctic ice
sheet’s evolution. Using the Ice-sheet and Sea-level System Model, we find that thawing patches
of frozen bed near the ice sheet margin could drive mass loss extending into the continental
interior, with the highest rates of loss coming from the George V - Adélie - Wilkes Land coast and
the Enderby - Kemp Land regions of East Antarctica. This suggests that the thawing of localized
frozen bed patches is sufficient to cause these East Antarctic regions to transition to an unstable
mass loss regime. We constrain model estimates of the basal thermal state using ice-penetrating
radar surveys and analyze radar characteristics including bed reflectivity and attenuation. In
combination, our work identifies critical regions of Antarctica where the ice-bed interface could
be close to thawing and where basal thaw could most impact mass loss.

How to cite: Dawson, E., Schroeder, D., Chu, W., Mantelli, E., and Seroussi, H.: Investigating basal thaw as a driver of mass loss from the Antarctic ice sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13501, https://doi.org/10.5194/egusphere-egu22-13501, 2022.

Every year, extreme events caused by climate-related hazards result in severe impacts globally. These impacts are expected to increase in the future due to both climate change and population growth in exposed locations. However, impacts are not only driven by exposure to extreme events, but also by the population’s vulnerability to these hazards, determined by individual characteristics such as age, gender, and income. Thus far, global-scale climate risk assessments account for social vulnerability to a limited degree. To address this gap, we produce spatially explicit global datasets of variables that can be used for characterizing social vulnerability. We further combine these data into a globally consistent and spatially explicit Social Vulnerability Index (SoVI), which will be made publicly available along with the input variables. To explore the value of the SoVI in characterizing social vulnerability, we validate it with the observed impacts (e.g., fatalities, damages) of past extreme events. To do so, we overlay the spatial vulnerability characteristics with recently published flood maps of observed flooding events across the globe, also testing how each vulnerability variable performs individually in explaining the observed impacts. Our analysis helps to develop a more in-depth understanding of the characteristics that drive social vulnerability globally, along with their spatial distribution. Therefore, our results can support decision-making in developing strategies that reduce social vulnerability to climate-related hazards, for instance related to spatial planning, socioeconomic development, and adaptation.

How to cite: Reimann, L., Koks, E., de Moel, H., and Aerts, J.: Characterizing social vulnerability for climate impact assessment at global scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-135, https://doi.org/10.5194/egusphere-egu22-135, 2022.

How to cite: Messori, G., Rusca, M., and Di Baldassarre, G.: Scenarios of social-environmental extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1073, https://doi.org/10.5194/egusphere-egu22-1073, 2022.

Hydrological extremes still cause severe damage worldwide. Understanding people’s perceptions of drought and flood risk, and their changes over time, can help researchers, practitioners, and policymakers assist communities at risk. In particular, identifying and highlighting gender differences in the perception of hydrological risk is fundamental to promote fair disaster risk reduction policies which take such differences into account. To this end, we collected national survey data three times over a year on risk perception, knowledge, and preparedness in regard to floods and droughts in Italy and Sweden. Preliminary results show that: i) the perceptions of drought and flood risk are heavily intertwined; and ii) women show a higher fluctuation in perception over time compared to men, especially when it comes to floods. These results and their implications show how important it is to integrate gender into the management of floods and drought and into risk communication, as well as to promote policies that simultaneously address flood and drought risk.

How to cite: Mondino, E., Raffetti, E., and Di Baldassarre, G.: Public perceptions of flood and drought risk: Gender differences in Italy and Sweden, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2691, https://doi.org/10.5194/egusphere-egu22-2691, 2022.

Adapting to the effects of climate change will increasingly become a task of municipal planning and implementation in the coming years. This ranges from the consideration of increasing heat days to the retention of heavy rainfall. Climate related hazards, together with their dynamic interplay of exposure and vulnerability pose considerable adverse consequences for municipalities and need to be addressed through risk management plans. While this is understood in research and is increasingly being implemented in cities, it is found that particularly small and medium-sized municipalities often lack (1) the necessary evidence base for planning, (2) adequate capacities to engage in adaptation, and (3) practical analytical tools and informal planning instruments for adapting to the unavoidable consequences of climate change. Identifying communities that are similarly impacted and thus show comparable adaption needs can help local stakeholders in forming climate adaption networks. Here they can pool resources, develop solutions and exchange knowledge on the highly contextual challenges of climate change adaptation.

We derive cluster based typologies of communities in the German state of Baden-Württemberg, which show assimilable characteristics in climatic hazards, exposure and vulnerability.   While cluster analysis is often used to differentiate patterns of climate change, few assessments have included societal variables. We therefore couple a ten-member regional climate model ensemble (RCP8.5, 1971-2000, 2021-2050, 2071-2100) with socio-economic data in so-called bivariate climate impact maps. This allows for statewide community specific conclusions on climate related risks. Statistical cluster analysis enables grouping of communities based on similar risks and adaption needs. Our approach provides a data driven basis for so-called climate adaption networks, which may foster the implementation of communal adaption efforts.

How to cite: Riach, N. and Glaser, R.: Typologies of community risk to climate change: fostering climate adaption networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5347, https://doi.org/10.5194/egusphere-egu22-5347, 2022.

Resilience to natural hazards varies widely within and between populations. People living in the same area affected by the same hazard event will experience it differently depending on their specific vulnerabilities and capacities. The social inequalities which drive differential resilience vary based on the norms of a given context, but result in resources being harder for some people to reach and use than others.

These inequalities are often invisible in traditional data, and therefore the needs of the most vulnerable are not addressed in disaster risk reduction and management policy and practice. The impacts of disasters therefore reinforce and worsen existing inequalities as already vulnerable people are left further and further behind.

This presentation will focus on new learning about the relationship between gender and social vulnerabilities and resilience to natural hazard-related disasters in a range of contexts with three key aims:

• To share key learning about differential disaster resilience and requirements of early warning and disaster risk management implementation
• To explore key tools which have been piloted, tested, and developed to improve knowledge and understanding of resilience
• To discuss effective and practical ways to apply these tools going forward in research, policy, and practice.

The presentation will draw on experiences and findings from projects conducted in the Philippines, Bangladesh, Malawi, Nepal, and Dominica to research gender and social inclusion in relation to early warning systems, disaster preparedness and response, and disaster risk financing.

The session will examine the drivers of social inequalities and their impacts relating to risk knowledge, monitoring and warning, communication and dissemination, and response capability, sharing examples of the different needs, considerations, and priorities relating to early warning and disaster risk management within communities.

We’ll then explore approaches to data layering and our Missing Voices methodology as key tools to identify and understand factors, including intersectional factors, influencing social and economic resilience to natural hazards.

How to cite: Sneddon, A., Budimir, M., Brown, S., and Nelder, I.: Gender and social inclusion in disaster risk reduction and management: Key learning and effective practices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5537, https://doi.org/10.5194/egusphere-egu22-5537, 2022.

Severe flooding in Western Germany, Belgium and the Netherlands in July 2021, particularly along the rivers Erft, Ahr and Meuse rivers has led to more than 240 causalities and an estimated damage of 29,2 billion EUR in Germany alone. The high human and economic costs of the event brought systemic problems in the flood risk management system to light and raised questions about the limits of disaster risk management and climate change adaptation. Using a forensic disaster analysis approach, the Post Event Review Capability (PERC), we systematically analyse the strengths and weaknesses of the flood risk management systems in the affected regions, the emergency response and recovery to draw lessons for future disaster risk management and climate change adaptation strategies. For that, PERC synthesizes existing information about the event from the hydro-meteorological characteristics of the physical impact and combines it with qualitative interviews with first responders, flood risk managers and other directly affected stakeholders. We will present key findings from the PERC study on the 2021 floods including the main drivers behind the high casualties and potential shortcomings in the emergency response and recovery as well as recommendations and opportunities for improvement.

How to cite: Rözer, V., Ulrich, J., Szönyi, M., Ianni, F., Laurien, F., Deubelli, T., MacClune, K., and Norton, R.: Forensic disaster analysis of the 2021 summer floods in Western Germany, Belgium and the Netherlands – Findings from the PERC study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6022, https://doi.org/10.5194/egusphere-egu22-6022, 2022.

Denmark is one of the most vulnerable countries in Europe with respect to increasing risk of sea surges. A two hundred year paradigm of land reclamation close to the sea must therefore be revisited with the intent of retaining flexibility and avoiding lock-ins while recognizing the unintended consequences of new adaptation strategies. Potential solutions continue to face considerable structural, spatial, temporal and definitional challenges requiring collaboration between communities, local actors and scientists. In the “Cities and rising sea levels” project scientists from different research disciplines including (landscape) architecture, regional and local planning, and hydrology collaborate with local actors in order to tackle these challenges. The aim is to establish a common terminology and identify common scenarios, strategies, and indicators of successful and less successful urban developments in coastal areas over space and time.

One of the objectives in the project is to establish a coherent, spatially explicit framework for assessing strategies for sustainable urban development (SUD) of coastal communities to facilitate mediation and decision-making for stakeholders involved in adaptation and urban planning processes. As a starting point, our study identified a total of >2200 indicators across 50 references on SUD and respective additional >1600 indicators across 28 references on coastal adaptation. By means of systemic reviews and analyses, the study builds upon previous reviews on indicators and expands beyond by laying a clear focus on sustainable adaptation in coastal areas.

Extracted indicators sets of SUD and coastal adaptation are compared and similarities as well as differences are pointed out and analysed. Interestingly none of the identified indicators of SUD include a direct representation of climate risks or determinants of risk i.e. vulnerability and exposure, neither as conceptual variables driving risk, nor the assessment of adaptive capacity. At the same time, indicators of coastal adaptation disregard liveability and human wellbeing as crucial aspects of urban planning, in contrast to SUD indicators where they represent guiding principles. This illustrates a clear gap between adaptation practices and other professions involved in urban planning processes.

In order to uncover sustainable pathways to adapt, adaptation must be an integral part of sustainable development. The study aims at understanding differences in performance assessments and to suggest steps forward to better integrate SUD and coastal adaptation. Here, the study will proceed by operationalizing a combined and integrated indicator framework in the form of spatio-temporal assessments. The first results of these assessments will be presented and synergies and tradeoffs between a risk lens and SUD will be highlighted.

How to cite: Eggert, A., Arnbjerg-Nielsen, K., and Löwe, R.: Comparative Analysis of Indicators for Sustainable Urban Development and Coastal Adaptation - Uncovering Barriers and Potentials of Integrated Assessments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6341, https://doi.org/10.5194/egusphere-egu22-6341, 2022.

Land use planning has been espoused as a key measure to decrease the risk of climate change-relatd disasters including landslides, however there is a dearth of research on how it affects the psychosocial wellbeing of affected people. This ethnographic study examines the risk management of the Svínafellsheiði fracture in south-east Iceland, where 60 to 100 million cubic metres of debris is predicted to fall onto the glacier below, and cause flooding from or a tsunami in the proglacial lake. A no-build zone was put in place between 2018 and 2020 to prevent a further increase in the number of people exposed to the hazard. Our results indicate that the no-build zone had both direct and indirect adverse effects on the psychosocial wellbeing of those affected. It caused frustration about a perceived inability to make changes to home and businesses, people feeling that their future was in limbo or on hold, and people questioning their future in the area. These direct psychosocial effects also had the knock-on effect of causing people to talk more about the risk, thereby undermining a key coping mechanism. 

How to cite: Matti, S., Ögmundardottír, H., Aðalgeirsdóttir, G., and Reichardt, U.: Psychosocial response to risk mitigation measures in Iceland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6600, https://doi.org/10.5194/egusphere-egu22-6600, 2022.

The effects of flooding on urban environment and social vulnerability are challenging issues in flood risk management and long-term planning. Flood risk is among the main causes of social crisis, as it can drastically affect the socioeconomic status of a community and an increase in flood events can significantly inhibit the political system of land and emergency management, social security, human welfare, and the economy.

In recent decades, several studies have illustrated how the probability of occurrence of a flood event can be modified by human-dependent factors, such as, among others, climate and land-use changes. 

For this reason, flood risk management policies are evolving to redirect the actions of policymakers from purely physical defensive measures toward integrated management and planning strategies, placing greater emphasis on the complexity of the interaction between social and physical processes.

The complexity of physical processes lies in the wide variety of underlying phenomena that produce different types of flooding, while that of social processes can be reconducted to their characterization, given by human-related factors such as risk perception, emotions, bonds, context, and behaviors. Structuring the complexity of these two systems could support flood risk to define the elements/classes of citizens that make a social system vulnerable.

Based on these premises, the present work aims in modelling the relationship between flood risk and community, starting from an analysis of social perception and knowledge of protective measures, and exploiting a methodology based on an online survey used to collect data, and on Mann-Whitney and Kruskal-Wallis tests used for their analysis.

The methodology was experimentally applied to the city of Brindisi (Puglia region, Southern Italy), which is potentially subject to floods of different nature, as fluvial, coastal and pluvial floods and dam overflows.

The results suggest that perceptions of flood risk depend on intrinsic components of individuals, primarily related to dimensions of perception such as trust in public strategies and risk communication. Slightly higher perception emerged for those living in risk areas, but the results of the remainder show that there is a non-negligible perception even where there is apparently no source of risk. This is reflected in the varying nature of the flooding that has affected the city. The presence of disabled persons in the household does not act in any way neither in the perception nor in the knowledge of the measures; the previous experience seems to have little weight in reference to the perception and almost null on the knowledge of the measures. This element is probably linked to the temporal distance from the last event that caused serious damage to the community. Knowledge of protective measures appears to be uniformly low for each category of citizens and territorial area, in particular for adolescents, a recurring category also on other investigated dimensions.

This work represents the first step for the development of a multi-agent model, as developed by the science of intelligent systems, able to analyze more deeply the relationships between natural and social systems and to bring out elements to support flood risk management.

How to cite: Santoro, S., Totaro, V., Lovreglio, R., Camarda, D., Iacobellis, V., and Fratino, U.: Structuring citizens’ risk perception and knowledge of flooding events for planning purposes: The case study of Brindisi, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10272, https://doi.org/10.5194/egusphere-egu22-10272, 2022.

On average, more than 21 million forced human displacements were reported as result of weather-related events between 2008 and 2020 worldwide. This is a major concern due to the increment trend in intensity and frequency of weather hazards. Breaking down the figures, the impact is more severe in low-middle income countries, where most of the natural hazards take place and adaptation strategies are lacking. Implementing efficient and operational policy responses requires a quantitative analysis of the nexus between climate-induced displacement. So far the study of this phenomenon has been often limited to qualitative assessments or to correlation measures from regression linear models, not accounting for the inherent complexity of the problem. The multicausal nature of human mobility and data availability present significant research challenges. We apply two methodological approaches that use machine-learning to close these gaps, namely addressing both rapid-onset (e.g. floods) and slow-onset (e.g. droughts) disaster types. The former uses the Internal Displacement Monitoring Centre (IDMC) global database of displacements triggered by floods and storms at disaster level, socioeconomic (RWI Meta Data4Good, Global Human Modification Layer, Education Expenditure), and Earth-Observation variables: meteorological (CHIRPS, ERA5) and environmental (NASA ASTER SRTM DEM, MODIS NDVI vegetation index). Explainable AI techniques enable to open the black box of random forest models and were applied at the global scale: Shapley values are used to investigate the contributions of the main drivers thereby quantitatively addressing the climate-displacement nexus. Results are consistent with the hazard, exposure and vulnerability concept discussed in literature and findings reveal that socioeconomic factors greatly mediate displacement magnitudes. The slow-onset study is being explored at the local scale at district level, currently focused on the effects of droughts on displaced populations in Somalia using UNCHR PRMN displacement dataset, remote sensing variables (CHIRPS, MODIS LST), conflict (ACLED) and market prices time-series (FSNAU, WFP VAM Unit). Beyond correlations analysis, causation alongside time-lag effects for the drivers of drought-induced displacement are assessed using the PCMCI algorithm. Results in specific districts indicate that decreases in vegetation in conjunction with cattle price drops are driving drought displacement, revealing these factors are in need for targeted intervention. Albeit the same method applied to other districts in Somalia returns no causal link among considered variables, taking these findings into account, we are able to propose district-wise recommendations on how to improve the quality of the data: eg. field data collection guidelines, what other data input is required, and where sampling efforts should be directed. 

How to cite: Tárraga Habas, J. M., Ronco, M., Miranda, M. T., Sevillano Marco, E., Wang, Q., Piles, M., Muñoz, J., and Camps-Valls, G.: Inspecting the link between climate and human displacement with Explainable AI and Causal inference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11200, https://doi.org/10.5194/egusphere-egu22-11200, 2022.

River floods are a common and often devastating environmental hazard causing severe damages, loss of lives and livelihoods, notably for the most vulnerable. Understanding the root causes, drivers, patterns and dynamics of flood risks and associated uncertainties is important to inform adequate risk management. Yet, a lack of understanding the highly dynamic processes, interactions, uncertainties, and the inclusion of participatory methods and transdisciplinary approaches in risk assessments remains a limiting factor. In many flood-prone regions of the world, data scarcity poses another serious challenge for risk assessments. Addressing the above, we developed an impact chain via desk study and expert consultation to reveal key drivers of flood risk for agricultural livelihoods in the Lower Mono River Basin of Benin and their interlinkages – a region that is both highly prone to flooding and can be considered data-scarce. Particularly, the dynamic formation of vulnerability and its interplay with hazard and exposure components is highlighted.

Based on a simplified version of the impact chain which was validated in a participatory manner during a virtual expert workshop, an alpha-level Bayesian Network was created to further explore these interactions. The model was applied to an exemplary what-if scenario for the study area in Benin. Based on the above, this study critically evaluates the benefits and limitations of integrating the two methodological approaches to better understand and simulate risk dynamics in data scarce environments. The study finds that impact chains are a useful approach to conceptualize interactions of risk drivers. Particularly in combination with a Bayesian Network approach the method enables an improved understanding of how different risk drivers interact within the system and allows for dynamic assessments of what-if scenarios, for example, to inform resilience building strategies.

How to cite: Wetzel, M., Schudel, L., Almoradie, A., Komi, K., Adounkpe, J., Walz, Y., and Hagenlocher, M.: The use of impact chains and Bayesian Network Analysis to assess flood risk dynamics in the Lower Mono River Basin, Benin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11251, https://doi.org/10.5194/egusphere-egu22-11251, 2022.

Residents in Álftaver, south Iceland, are very familiar with the 1918 Katla volcanic eruption, which caused rapid and catastrophic glacial outburst flooding of the area. Descriptions of the 1918 events, passed down by older generations, have become an important part of the collective memory. Based on oral and written history, this paper provides a vivid account, including detailed maps, of what people experienced and felt during the 1918 Katla eruption. It also considers how these experiences influence current-day perceptions and the impact this may have on behavior in relation to emergency response strategies. Until now, much of this history has only been accessible in Icelandic text and through oral stories. The aim of this paper is to unlock these stories for an international audience in an effort to advance understanding of volcanic eruptions and their impacts and, inform future emergency planning. Importantly, these descriptions tell us about the nature of the glacial outburst flood, with a ‘pre-flood’ devoid of ice and travelling at a much faster rate than the ice-laden main flood. As a future eruption of Katla may impact Álftaver, emergency response plans for glacial outburst floods were developed, and in March 2006 preliminary plans were tested in a full-scale evacuation exercise involving residents and emergency response groups. But Álftaver residents questioned the plans and were reluctant to follow evacuation orders during the exercise, as they felt their knowledge and the experience of their relatives during the 1918 Katla eruption, had not been taken into consideration. Residents were concerned that flood hazards, as well as tephra and lightning, were not appropriately accounted for by officials. In response to residents’ concerns, officials developed an alternative evacuation plan (Plan B) that builds on some of the experience and knowledge of Álftaver residents. However, residents were not involved in the development of ‘Plan B’ and they are not aware of what it constitutes or when it is to be implemented. This paper argues that more needs to be done to promote inclusive dialogue and the co-production of knowledge to ensure emergency response strategies adequately reflect and accommodate local knowledge, perspectives and planned behavior.

How to cite: Gísladóttir, G., Bird, D., and Pagneux, E.: What can we learn from previous generations? Álftaver’s experience of the 1918 Katla eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12884, https://doi.org/10.5194/egusphere-egu22-12884, 2022.

Amidst a period of complete lockdown due  to COVID-19, the severe cyclonic storm Amphan made landfall in the Indian Sundarbans on 20 May 2020. The occurrence of a cyclone during  the pandemic warranted investigation of interconnected risks and impacts in this climate hotspot and eco-critical region. Based on a desk study, field observations, key informant interviews and expert consultations, this research focussed on better understanding direct and cascading risks and the associated impacts from the concurrence of the two hazards occurring simultaneously. Our analysis reveals that although the region has not experienced a high number of COVID-cases between March and August 2020, the presence of underlying vulnerabilities exposed the population to cascading effects caused by the pandemic-induced lockdown along with the compounding effect of the Cyclone Amphan. In the Indian Sundarbans, COVID-19 acted as an exogenous shock, but its interplay with interconnected vulnerabilities resulted in the emergence of disruptions of a systemic nature. This was particularly the case in the economic domain, with cascading impacts observed across the welfare, education, and employment sectors.  Cyclone Amphan, led to additional cascading impacts on these sectors, and affected other sectors such as health and infrastructure as well as biodiversity. Interventions such as introduction of new social protection schemes and community participation in cyclone preparedness measures have helped the system from facing a total collapse. However, some interventions that were implemented to mitigate impacts of these two concurring hazards somewhat counteracted one another. For example, while stringent COVID-19 interventions stressed on safety norms (including social distancing and stay at home orders), the hazard response protocol for Cyclone Amphan directed communities to evacuate their homes and move to communal shelters which were being used as quarantine units for returning migrant workers till before the cyclone. This caused concerns among the evacuated population, thus undermining the efficacy of the response effort. This case study underpins the need for moving from hazard-by-hazard approaches of understanding and managing risks towards integrated approaches that consider interconnected vulnerabilities, risks and impacts based on a systems perspective. Further, it also provides lessons for risk management in a multi-hazard and multi-risk setting besides sharing recommendations for better risk management in the Indian Sundarbans.

How to cite: Banerjee, S., Shekhar, H., Cotti, D., Sparkes, E., Werners, S., and Hagenlocher, M.: Revisiting risk in a multi-hazard setting: the case of Cyclone Amphan occurring within the COVID-19 pandemic in the Indian Sundarbans, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13432, https://doi.org/10.5194/egusphere-egu22-13432, 2022.

Metal pollution in surface soils of industrial and urban areas is of concern owing to risk to human health and ecosystem and to its transport via winds and water. This study was aimed to determine total concentrations, contamination levels and source identification of metals in surface soil (n=37) from the Bhiwadi Industrial Cluster (BIC; a satellite industrial township to New Delhi). Average metal concentrations in surface soil exceeded their corresponding values in Upper Continental Crust (UCC, taken as background here) and varied depending upon metal(s) and sampling sites(s). Intensive industrial emissions/activities in BIC lead to high contamination factors (CFs > 6) and high pollution load indices (PLI > 1) for metals in surface soil. Average CFs followed the order Cr > Cd > Ni > Cu > Zn > Pb > Mn > V > Fe. Geo-accumulation index (Igeo) of metals in surface soils fall under unpolluted to extremely polluted for Cd, Cr, Cu, Ni and Zn, unpolluted to heavily polluted for Mn and Pb and unpolluted to moderately polluted for Fe and V. Ecological risk assessment in surface soil samples showed low to extremely high potential ecological risk for Cr, Cu and Ni, considerable to extremely high ecological risk for Cd, low to considerable ecological risk for Pb and low ecological risk for Mn, V and Zn. Risk (RI) values indicated that 37.8% of surface soil samples carried very high risk (RI > 600) of metal contamination in this industrial cluster. Findings suggested that proper waste collection and disposal techniques should be employed to safeguard human health and ecological risk in the region.  

How to cite: Verma, A. and Yadav, S.: Metal Pollution And Ecological Risk Assessment In Surface Soil Of An Emerging Industrial Cluster Near New Delhi, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-461, https://doi.org/10.5194/egusphere-egu22-461, 2022.

Recycling and disposal of e-waste by informal sector in developing nations raise concerns due to its environmental consequences and human health hazards. In this study, metal toxicity and leaching behaviour of 13 metals (Ag, As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn).  were investigated in surface dust samples (n=20) of informal e-waste recycling area in New Delhi by using Waste Extraction Test (WET) and Toxicity Characteristic Leaching Procedure (TCLP). The WET and TCLP tests were developed by California’s Department of Toxic Substances Control (CDTSC) and the United States Environmental Protection Agency (USEPA) respectively to simulate landfill conditions for metal leaching under laboratory conditions. All metals were leached more in WET compared to TCLP. In WET test, Cd, Cr, Cu, Ni, Pb and Zn exceeded the prescribed threshold limits of CDTSC and failed the test whereas Cd and Pb exceeded the threshold limits of USEPA in TCLP. Though Cu, Ni and Zn are not regulatory metals in TCLP, but their leaching concentrations exceeded the threshold limits of CDTSC. In both the tests, Fe, Mn and Sn were also leached in considerable amounts. In WET, Sn (37.7) leached in maximum percentage followed by Cd (28.7), Zn (27.9), Pb (27.7), Co (21.1), Mn (14.8), Ni (11.4), Fe (8.5), V (7.6), Cu (7.5), Ba (3.5), Cr (2.9) and As (0.4) respectively; whereas in TCLP Co (20.7) leached maximum followed by Cd (17.1), Zn (12.8), Mn (7.1), Ni (6.7), Sn (4.9), Cu (3.1), Pb (2.2), Ba (1.1), Fe (0.4), V (0.3), Cr (0.2) and As (0.1) respectively. The WET test was found to be more aggressive in leaching of metals when compared to TCLP due to citrate ion chelation property. Leaching of metals higher than the threshold limits can cause contamination of soil, surface water and ground water in nearby areas and can affect the human health and environment. Therefore, there is needs to regulate policies and environmentally sound new technologies for e-waste recycling to safeguard the human health and environment.

How to cite: Kumari, H. and Yadav, S.: Metal Leaching from Surface Dust of an Informal E-Waste Recycling area in New Delhi, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-464, https://doi.org/10.5194/egusphere-egu22-464, 2022.

In the Urov sub-region of the biosphere (Eastern Transbaikalia), a local increased content of Sr in soils and plants was found due to high concentrations of Sr in soil-forming rocks (carbonated granites). The purpose of this study is to assess the concentrations of Ca and Sr in the technozem of dumps and quarries of seven developed deposits (W-Mo, Mo-Cu, Pb-Zn, Au). The fraction of technozem (< 1 mm) was ground to a grain size of 150-200 mesh and analyzed by XRF. The content of Ca and Sr in plant mowing was determined by the flame variant of AAS. It was found that the content of Ca and Sr in 25 samples of technozem varied between 4970-37200 mg/kg (Ca) and 100-620 mg/kg (Sr). The average content of Sr is 308 ± 122 mg/kg. The increased Sr content was characteristic of carbonate technozems with an increased level of Ca (Mo-Cu ore occurrence). Increased accumulation of Ca and Sr in mowing plants was found in the technozems of the Zhireken Mo-Cu deposit: 35100 mg/kg (Ca) and 397 mg/kg (Sr). In general, the concentrations of Ca and Sr in technozem approach to their content in conditionally background soils and do not significantly contribute to the pollution of natural landscapes within the Urov-region of the biosphere.

How to cite: Uliana, G., Tatyana, K., and Vadim, E.: Ca and Sr in the technozem of various ore deposits of Eastern Transbaikalia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2348, https://doi.org/10.5194/egusphere-egu22-2348, 2022.

Environmental geochemical studies of urban territories involve heterogeneous information that can be most effectively processed within a unified database (DB). Since a significant portion of the accumulated data is georeferenced, geographic information technologies should be used at all stages of the researches. The purpose of this work is to consider the structure of the DB for information support of ecological and geochemical studies of different urban environments in Moscow within the framework of the Russian Science Foundation project No. 19-77-30004 "Integrated technology for environmental assessment of Moscow megacity based on chemical analysis of microparticle composition in the "atmosphere - snow - road dust - soil - surface water" system (Megacity)".

The project aims to develop technologies for the chemical analysis of the urban environments impacted by the pollutants coming from vehicles, industry, and construction sites, as well as the assessment of the environmental state of the megacity. Various components of the environment are analyzed at several spatial scales: for the entire Moscow city, for administrative districts, for drainage basins of two urban rivers (Moskva and its tributary Setun). The composition of pollutant emissions is characterized using monitoring aerosol data at the Meteorological Observatory of Lomonosov Moscow State University. Microparticles PM₁₀ and PM_2.5 are analyzed for the content of elemental carbon, ionic and organic compounds, as well as potentially toxic elements, under different meteorological conditions and seasonal variations. The fallout of aerosols during winter is determined by the chemical analysis of dissolved and solid fractions of snow samples and its comparison with a natural background. Water migration of pollutants is assessed by analyzing river flows (water and suspended/bottom sediments) at reference stations in the Moskva River basin. The ecological state of road dust and soils that accumulate pollutants is estimated in geochemical surveying. Finally, source apportionment is quantified using statistical methods of multivariate analysis.

The development of a DB with the integrated geographic information system (GIS) allows systematizing the spatial and non-spatial information accumulated in field works, chemical and analytical studies, and organizing effective data storage and processing along with providing geoinformation support for DB users. We created four DB subsystems designed for: (1) processing georeferenced data (GIS); (2) working with time series; (3) handling regulatory and reference information; (4) assessing pollution and environmental hazard with computational models. For Moscow megacity, GIS brings together two large blocks of information: spatial layers stored within the geodatabase and spreadsheets with the results of field studies and chemical analyses. The main functions of the GIS are geoprocessing, execution of non-spatial and spatial queries, data analysis (including exploratory spatial data analysis and modeling), visualization of the results.

The report will present subsystems of the DB and the interrelationships between them. The use of the database in practice will be considered on the example of assessing the pollution of road dust with benzo(a)pyrene, accounting for anthropogenic and natural factors.

How to cite: Chernitsova, O., Kosheleva, N., Popovicheva, O., Vlasov, D., and Erina, O.: Database for geochemical assessment of the urban environments: a spatially oriented approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2506, https://doi.org/10.5194/egusphere-egu22-2506, 2022.

The application of nanoparticles (NPs) is increasing drastically, especially in crop production. The repeated inputs of metal-based NPs in agri-field could increase their concentration in soil, and cause a threat to sustainable crop production. Thus, the present study was designed to determine the role of spore-forming metal tolerant bacteria (MTB) and biochar (B) to alleviate the toxic effects of high dose of ZnO NPs (2000 mg kg-1) on plants (Hordeum sativum L.) spiked to the soil. For detailed evaluation, the five treatments were used such as 1) clean soil, 2) soil+NPs, 3) soil+NPs+MTB, 4) soil+NPs+B and 5) soil+NPs+B+MTB in plastic vessels in triplicate. The addition of MTB and B showed a promising impact on H. sativum growth in combination and individual inputs. The application of MTB to the contaminated soil reduced the mobility of Zn by 7%, mainly due to exchangeable compounds, and B reduced mobility up to 33%, because of a decrease in equally exchangeable, complex, and specifically sorbed forms. The combined introduction of MTB and B reduced most effectively the actual and potential content of Zn compounds in soil. The content of Zn in H. sativum tissues was increased drastically, especially in ZnO NPs contaminated soil. MTB and B in the contaminated soil reduced Zn accumulation in H. sativum roots by 20% and 63%, and in the aboveground tissues by 11% and 68%, respectively, compared to ZnO NPs polluted soil without amendments. The combined application of MTB and B showed the greatest decrease in Zn accumulation in H. sativum tissues. The root length and H. sativum height was decreased by 52% and 40% in contaminated soil. However, the addition of B, both separately and in combination with MTB reduced root length by 48% and 85%, and plant height by 53% and 40%, respectively, compared to polluted control. The anatomical results also showed an improvement in cellular- sub-cellular organelles, especially in chloroplast by B and in combination with MTB. The results indicate that metal-tolerant bacteria and biochar could be an effective soil amendment to decrease metal toxicity enhance crop growth, and improve soil health.

The research was financially supported by the Russian Foundation for Basic Research, project no. 19-34-60041.

How to cite: Bauer, T., Rajput, V. D., Minkina, T., Natalya, C., Beschetnikov, V., Fedorenko, A., Sushkova, S., and Mandzhieva, S.: Insights into biochar and metals tolerant bacteria in alleviating ZnO nanoparticles toxicity in plant, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2770, https://doi.org/10.5194/egusphere-egu22-2770, 2022.

The purpose of this study is to describe the Tl distribution and accumulation rates in Czech peat soils with contrasting anthropogenic loads. Nine peat cores were sampled in the mountain areas of the Czech Republic (6 cores in the northern part affected by emissions from coal-burning power plants and 3 in the pristine southern part). In addition, 3 cores were collected close to the Pb mining and smelting area of Pribram. Cores were 210-Pb dated and trace metals/metalloids were measured in the digests by ICP-MS. Maximum Tl concentrations in peat were significantly higher in the polluted northern areas (1.16 mg/kg) and close to the Pb smelter (0.83 mg/kg) than in the pristine area (0.45 mg/kg). Thallium distribution well correlated with other metals (Pb, Hg) and metalloids (As, Sb). Thallium enrichment factors (EFs) calculated against Sc reached the maximum value of 17 indicating significant input of anthropogenic Tl. Thallium accumulation rates in peat varied between 20 and 50 µg/m2/y until 1930s, followed by a significant increase related to industrial activities in the northern part of the Czech Republic (up to 290 µg/m2/y in 1980s). In contrast, maximum Tl accumulation rate at the pristine site was 88 µg/m2/y. Data from the vicinity of Pb mines/smelter indicated higher accumulation rates even in the second half of the 19th century (between 50 and 200 µg/m2/y) followed by a significant decrease in late 1970s as a result of more efficient flue gas cleaning technology installed in the smelter during this period. 

How to cite: Mihaljevic, M., Ettler, V., and Vanek, A.: Is thallium in peat a good indicator of anthropogenic contamination?  Examples from Czech sites with contrasting pollution histories., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4373, https://doi.org/10.5194/egusphere-egu22-4373, 2022.

The problem of retreating glaciers is pronounced in almost all high-altitude and high-latitude landscapes. Black carbon is considered as one of the most important pollutants that contributes to global climate change and the melting of glaciers, especially in polar and mountainous regions due to formation of cryoconite. It is a supraglacial sediment which represents a mixture of black carbon, mineral particles and organic matter. Cryoconites are considered as accumulators of various pollutants such as polycyclic aromatic hydrocarbons, trace elements and radionuclides, which can be transported by aeolian and water flows to the downstream ecosystems and affect the safety of the region both directly and indirectly, through the cultivation of crops and grazing. Moreover, cryoconites considerably reduce the albedo of the glacier and take part in formation of primary soils after its retreat which is especially important in the context of global climate change.

The main purpose of this research is to study the pollution of cryoconites, other sediments and soils by trace elements at the Central Caucasus mountainous region, Russia. Cryoconite, moraines and mudflows were sampled from the biggest valley glacier at the Caucasus mountains, Bezengi Glacier; cryoconite derived soils were collected from the Khulamo-Bezengi Gorge. Chernozems and fresh mudflow samples were collected at Baksan Gorge. Trace elements content was determined by flame and electrothermal atomic absorption spectrometric method according to the standard ISO 11047-1998 at Atomic absorption spectrophotometer. We determined concentrations of Cu, Pb, Zn, Ni, Cd due to the facts that they are the most toxic for human health as well as they are mostly accumulated in a black carbon.

High concentrations of Zn (70.9 mg/kg) and Pb (30.0 mg/kg) in cryoconites have been determined on the Bezengi Glacier, which may be due to both local human activities and allochthonous pollution associated with the arrival of contaminated air masses from other regions. The content of Cu (max. 17.4 mg*kg), Ni (max. 19.0 mg*kg) and Cd (max. 0.052 mg*kg) was relatively low. However, concentrations of Zn (max. 89.2 mg*kg) and Cd (max. 0.313 mg*kg) in cryoconite derived soils were higher than in cryoconite which indicates high input of polluted material from the glacier into downstream ecosystems. The highest level of pollution with some trace elements has been determined in fresh mudflow: Cu = 40.7 mg*kg, Zn = 89.3 mg*kg, Ni = 42.0 mg*kg which also indicates that sediments act as a source of pollutants for mountain ecosystems. Pollution of Chernozems with trace elements was higher than in moraine sediments, however, it was lower than in cryoconites which shows possible impact of these sediments on pollution status of soils in mountainous region.

This work was supported by Russian Foundation for Basic Research, project No 19-05-50107 “The role of microparticles of organic carbon in degradation of ice cover of polar regions of the Earth”.

How to cite: Tembotov, R., Kushnov, I., Abakumov, E., and Zubrzycki, S.: Trace elements accumulation in cryoconites and periglacial soils of the Central Caucasus, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4773, https://doi.org/10.5194/egusphere-egu22-4773, 2022.

Topsoil is a filter that can absorb all wastes of production and anthropogenic activities. During the last 35 years, following several large industrial disasters and artificial radionuclides entering ecosystems, the ways of their migration and impact on living and biosphere systems are attracting close attention. As a result, the determination of both artificial and natural radionuclides in the soil seems relevant and is part of the radiation monitoring of the soil cover in Russia and the world. The purpose of the work was to carry out ecological monitoring of park-recreational, residential areas, as well as specially protected natural areas of the South of European Russia.

The maximum average value of activity for the artificial radionuclide ¹³⁷Cs was revealed in the soils of specially protected natural territories, there is a maximum variation of values. Significant variation of the obtained activity results relates to large sampling and wide geography of studied objects and proximity to the place of the Chernobyl accident (April 26, 1986). Specific activity of natural radionuclides is at the level of average values typical for the Rostov region, which are confirmed by the previously conducted data. The specific activity in recreational areas and specially protected natural territories is approximately at the same level and has a similar distribution pattern. The arithmetic average of specific activity of the studied radionuclides for the inhabited zones is:¹³⁷Cs - 13,5 ± 1,3 Bq/kg, ²²⁶Ra - 19,0 ± 1,1 Bq/kg, ²³²Th - 20,6 ± 0,8 Bq/kg, 334 ± 13,3 Bq/kg - ⁴⁰K; for recreational:15,8 ± 0,9  Bq/kg - 137Cs, ²²⁶Ra – 24,0 ± 0,4 Bq/kg, ²³²Th – 31,5 ± 0,4 Bq/kg, 436 ± 6 Bq/kg - ⁴⁰K and for specially protected natural areas: 25,6 ± 3,6 Bq/kg - ¹³⁷Cs, ²²⁶Ra – 23,8 ± 0,7 Bq/kg, ²³²Th – 26,4 ± 0,8 Bq/kg, 365,8 ± 13,1 Bq/kg - ⁴⁰K.

This study  was performed with financially supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of the state task in the field of scientific activity (no. 0852-2020-0029)

How to cite: Kozyrev, D., Gorbov, S., Buraeva, E., and Salnik, N.: Activity concentration of radionuclides of natural and anthropogenic-transformed soils in Rostov region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5308, https://doi.org/10.5194/egusphere-egu22-5308, 2022.

Polycyclic aromatic hydrocarbons (PAHs) are organic compounds of the benzene series, which differ in the number of benzene rings. Due to their carcinogenic and mutagenic properties, they have been included in the list of priority pollutants by the US Environmental Protection Agency and the European Community. Among all PAHs, there is a mutagen and a carcinogen of the 1st hazard class - benzo (a) pyrene (BaP), which is most often used as a marker of environmental pollution with PAHs. Up to 95% of the emitted pollutants are accumulated by the soil in various chemical forms. Since plants are inextricably linked with the soil, it becomes necessary to study the behaviour of PAHs in the formed plant-soil system. The aim of the study was to evaluate the effect of BaP on the morphometric characteristics of tomato plants under the conditions of a model experiment.

The studies were carried out under the conditions of a vegetation experiment. The soil was sifted through a sieve with a diameter of 1 mm and placed in 2 kg pots in 4 L pots. A BaP solution in acetonitrile was added to the soil surface based on the creation of a pollutant concentration in the soil of 400 and 1200 ng / g, which corresponds to 20 and 60 MPC of BaP. The original uncontaminated soil was used as a control. The soil was sown with tomato plants (Solánum lycopérsicum) of the early maturing variety White filling 241. The experiment was repeated three times. We analyzed such morphometric parameters as root length and stem height, as well as dry biomass of plants.

The root length and stem height in the control sample is set at 32 and 63 cm, respectively. In the samples contaminated with 20 MPC BaP, these indicators were lower, so the root length was 19 cm, and the stem height was 40 cm. In the samples with the introduction of 60 MPC BaP, the root length decreased to 14 cm and the stem height - to 27 cm.

In the control sample, the dry biomass of the roots was 10.3 g and the vegetative part was 80.2 g. When 20 MPC BaP was applied, these parameters decreased to 6.8 g of roots and 67 g of the vegetative part. In the samples with the introduction of 60 MPC BaP, the biomass of the roots was 3.1 g and the biomass of the vegetative part was 44 g, which is lower than the control values.

Thus, a decrease in the length of roots and the height of plant stems, as well as a decrease in their biomass relative to the control values, was established, which indicates that tomato plants are quite susceptible to soil pollution with BaP.

The research was financially supported by the Ministry of Science and Higher Education of the Russian Federation project on the development of the Young Scientist Laboratory (no. LabNOTs-21-01AB).

How to cite: Barbashev, A., Dudnikova, T., Minkina, T., Sushkova, S., Bakoeva, G., Tikhonenko, E., Chernikova, N., Rahman, M. M., and Amin, H.: Effect of benzo(a)pyrene on the morphometric characteristics of tomato plants (Solanum Lycopersicum) under the conditions of a model experiment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5779, https://doi.org/10.5194/egusphere-egu22-5779, 2022.

Bryansk region is only Russian, where total radionuclide contamination exceeding 1480 kBq/m² was detected after the Chernobyl accident. At the same time, a definite increase in the incidence of thyroid cancer (ICD-10 code C73) was recorded in this area. From 1990 to 2020, thyroid cancer morbidity in the region increased up to 18.7 cases per 100 000 population compared to the mean value of this parameter for Russia is 6.2 (Kaprin et al., 2020) and 6.0 global (Deng et al., 2020).

To study the geochemical factors responsible for the distribution of thyroid gland diseases, we applied some specialized geographic information system methods. Our approach is based on the idea of a two-layers spatial structure of the modern noosphere (Korobova, 2017). According to the developed approach, the natural geochemical background presented by the soil cover structure is overlain by technogenic contamination fields. In this case, we hypothesize that revealing the causes of the diseases is possible by evaluating the correlation between the two structures: the geochemical and the diseases'.

To analyze the spatial distribution of morbidity, we used the method of kernel density (Silverman, 1986) and the analysis of the obtained maps of thyroid cancer allowed us to identify five territories (with an area of 100-200 km²) characterized by high morbidity (18.0-55.7 cases) and four territories with low morbidity (2.7-10.6 cases). Spatial evaluation of the difference between the original experimental data on iodine content in soils, drinking water, and ¹³⁷Cs deposition in settlements located in areas with high and low thyroid mobidity was performed to estimate natural and anthropogenic geochemical factors contributing to the spread of thyroid diseases. Non-parametric Mann-Whitney U test showed significantly higher iodine content in centralized water supply (Z=1.46, p=0.06), pasture soils (Z=2.10, p=0.03), local milk (Z=1.71, p=0.08), and lower ¹³⁷Cs deposition, which is used to the restoration of ¹³¹I contamination of the territory (Z=-4.43, p<0.001) in areas with low thyroid morbidity). In our opinion, this witnesses a definite contribution of geochemical factors (iodine deficiency and radioiodine contamination) to the specific spatial distribution of thyroid gland diseases.

The study was partly funded by RFBR (project #20-55-00012) and BRFBR (project #X20P-386).

References:

Kaprin, A., Starinsky, V., Prteova, G. (Eds.) (2021). Malignant neoplasms in Russia in 2020 (morbidity and mortality). National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, Moscow (in Russian)

Deng, Y. et al. (2020). Global Burden of Thyroid Cancer From 1990 to 2017. JAMA Network Open, 3(6), e208759. https://doi.org/10.1001/jamanetworkopen.2020.8759

Korobova, E.M. (2017). Principles of spatial organization and evolution of the biosphere and the noosphere. Geochem. Int. 55, 1205–1282 (2017) doi:10.1134/S001670291713002X

Silverman, B.W. (1986). Density estimation for statistics and data analysis: Monographs on statistics and applied probability. London; New York: Chapman and Hall

How to cite: Baranchukov, V., Korobova, E., Romanov, S., and Kurnosova, I.: Comparison of the Spatial Distribution of Thyroid Cancer Morbidity and Geochemical Factors in Areas of the Bryansk Region (Russia), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8325, https://doi.org/10.5194/egusphere-egu22-8325, 2022.

Long-term anthropogenic impact as a result of urbanization leads to environmental pollution by potentially toxic elements (PTEs). Soil metal contamination poses significant risks for the conjugated landscape components and for the public health. Taganrog is the second largest city in the Rostov Oblast with a population of 248,600 people, with a developed metallurgy and mechanical engineering. The aim of the study is to evaluate possible ecological and human health risks related to PTEs in urban topsoils of Taganrog.

Topsoil samples (0–20 cm deep) were collected in summer 2021. The total concentrations of Cr, Mn, Ni, Cu, Zn, Cd, and Pb were determined by X-ray fluorescence analysis using a Spectroscan MAX-GV spectrometer (Spectron, Russia). Individual environmental risks were assessed using the potential ecological risk factors (Er and MEr), integral risks were identified using the potential ecological risk indices (RI and MRI). Human health risk assessment was based on the US EPA model (1989). The noncarcinogenic risk, expressed as a hazard quotient (HQ), was evaluated by comparing the average daily dose of pollutant with a reference dose. To assess the cumulative noncarcinogenic risk, a total hazard index (HI) was used. The carcinogenic risk (CR) was calculated as lifetime average daily dose of a pollutant multiplied by the corresponding carcinogen slope factor. The total carcinogenic risk (TCR) of exposure to elements along all routes of intake was calculated as sum of CR.

The individual ecological risks of all elements were low (Er and MEr <40), with the exception of Cd. The environmental risk due to Cd pollution, assessed by Er, was moderate (55.8–70.1) in 27.3% of the samples and considerable (89.4–106.6) in 18.2% of the samples. In 36.4% of the samples was moderate Cd risk (MEr 41.6–71.1). According to RI, moderate risk was detected only in 9.1% of samples; the rest of the samples are characterized by a low risk. Values of RI ranged from 20.6 to 197.1 with a mean of 84.0. The integral environmental risk, assessed by MRI, was low in all studied samples and ranged from 13.7 to 131.4.

Noncarcinogenic risks were more likely caused by intake of As and Pb (HQ>1). For both children and adults, the risk associated with the oral intake of pollutants was the greatest. The HI values for children varied from 0.9 to 5.6, on average 2.3, for adults - from 0.1 to 0.7, on average 0.3. Most of the territory was characterized by a medium non-carcinogenic risk for children (90.9% of samples) and a low risk for adults (100%). Significant CR (>1 × 10−6) was associated with long-term exposure to As and Pb. The TCR values under the combined effect of PTEs ranged from 2.1 × 10-5 to 1.5 × 10-4, on average 5.5 × 10-5. In general, the level of carcinogenic risk in the city was assessed as moderate in 81.8% of samples and as unacceptable in 18.2% of samples.

This work was funded by the Council for Grants of the President of the Russian Federation, grant no. MK-4654.2022.1.5.

How to cite: Konstantinova, E., Barakhov, A., Chernikova, N., Dudnikova, T., Barbashev, A., and Lobzenko, I.: Preliminary risk assessment of metal contamination of urban soils in Taganrog, Russia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8951, https://doi.org/10.5194/egusphere-egu22-8951, 2022.

The extraction of solid fossil fuels results in the accumulation of overburden and host rocks stored on the Earth's surface. Coal mining sites are among the most disturbed and polluted areas. Soils are affected by these transformations in multiple ways, including structural changes, the loss or suppression of vegetation cover, and the migration and accumulation of chemical elements in soils and water. To assess the global concentrations of chemical elements in the coal mine soils, we discussed and developed a meta-study on pollutants in Technosols and altered natural soils. For this, we collected data from papers published in peer-reviewed journals between 2000 and 2022, covering 25 major coal-producing countries of Eurasia, Africa, Australia, and the Americas. To understand better the patterns of soil pollution driven by coal extraction itself, we gathered the concentrations measured in soils, spoils, and dumps near open-cut and underground coal mines. For the same reason, the data on pollutants in remediated or reclaimed soils, as well as in soils near coal power plants (or other pollution sources) were excluded. Likewise, we did not consider other abiotic (e.g., coal ash, mine water) or biotic media (e.g., grasses, trees, and plants in general)  even though they are undoubtedly interlinked. Moreover, the data on soil pollution are far more abundant and thus statistically significant.

The typical set of keywords used for searching in databases included “coal mine”, “soil/dumps”, “pollution/contamination”, and “elements/metals”. Obviously, other terms like “colliery”, or “wasterock”, or “geochemical transformation” were applied too but gave fewer search results. To harmonize measurement units, we recalculated all data to mg/kg or ppm. When necessary, concentrations were recalculated from oxides into elemental forms. To confirm the representativeness of the figures, we checked the number of specimens analyzed in each research. The total number of samples used in the meta-study was over 7,000. For the standard statistical processing, the mean concentrations were collected alongside the minimum and maximum contents, and standard deviation values; when not provided in a paper, they were calculated from the raw data. After that, we obtained the average contents of chemical elements that characterize each coalfield separately.

The preliminary results reveal that priority pollutants are inherited from the world averages for trace element contents in coals rather than the natural background. In other words, concentrations of priority pollutants are predominantly determined by coal extraction and the release of related pollutants. The research outcomes indicate that the oxidation-reduction and alkaline-acid milieu, water and temperature regimes, sorption capacity, and other landscape-geochemical conditions are being ambiguously transformed in new ecosystems and can be derived from both natural conditions and the man-inflicted damage. The geochemical cycles in biocenoses are altered and the tasks for their restoration may vary significantly. The established global concentrations of chemical elements in coal mine soils can be used for comparative assessments and the management of legacy contamination and soil/landscape rehabilitation in post-mining regions. However, remediation efforts will also need to consider site-specific geological, hydrological, and climatic characteristics as well as socio-economic conditions and other regional development objectives.

How to cite: Bech, J., Alekseenko, A., Machevariani, M., and Karthe, D.: Global pollutant concentrations in coal mine soils: Discussing an approach to the meta-study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9123, https://doi.org/10.5194/egusphere-egu22-9123, 2022.

The Lower Don basin and the adjacent coastal zone of the Azov Sea are considered one of the most economically developed and anthropogenically transformed regions within Southern Russia. This territory is characterized by a high degree of urbanization, intensive agriculture, and diverse transport infrastructure facilities. Long-term anthropogenic activities have resulted in a strong transformation of the natural environment of the Lower Don floodplain, the Don Delta and Taganrog Bay coast. One of the main consequences of human activities is related to the degradation of vegetation and soil cover of subaquatic landscapes caused by pollution of potentially toxic elements (PTEs). The main aim of this study was to assess potential environmental risks of Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb in soils of the Lower Don floodplain and the Taganrog Bay coast.

The floodplain and coastal landscapes of the study area are dominated by Eutric and Calcaric Gleyic Fluvisols, Gleyic Fluvisols (Humic), Gleyic Phaeozems and Haplic Chernozems which are background soils of the region are less common. Soil samples were collected in summer 2020 from the surface soil horizon (0–20 cm deep). The total concentrations of Cr, Mn, Ni, Cu, Zn, Cd, and Pb were determined in air-dried powder samples by X-ray fluorescence analysis using a Spectroscan MAX-GV spectrometer (Spectron, Russia). Environmental risks were assessed using potential ecological risk factor (Er) and the potential ecological risk index (RI) based on the single pollution index (PI) and modified potential ecological risk factor (MEr) and the modified potential ecological risk index (MRI) based on the Müller geoaccumulation index (Igeo).

The obtained results showed that Er and MEr indicated a low ecological risk for most of the PTEs studied, with the exception of Cd, which was found to be moderate in 8% and 3.5% of the samples, respectively. The highest values of both Er and MEr for Cd were detected in the soils of the Don Delta. Integral ecological risk assessed using RI and MRI, based on the sum of all Er and MEr, respectively, was low in all samples studied. Values of RI ranged from 10.52 to 86.87 with a mean of 32.2. Similar results were observed for MRI, which ranged from 7.01 to 57.91 with a mean of 21.46. The highest values of both RI and MRI were observed in soils of the Don Delta in the vicinity of urbanized territories, which indicates an additional supply of PTEs due to more significant anthropogenic pressure. Thus, according to the results of the study, the risk of a potential negative impact of soil pollution on adjacent components of the landscapes of the Lower Don and Taganrog Bay does not cause serious concerns. Apparently, a relatively favourable land-use regime with a predominance of agriculture has developed in the region.

This work was funded by the Russian Science Foundation, grant no. 20-14-00317.

How to cite: Minkina, T., Konstantinova, E., Dina, N., Bauer, T., Mandzhieva, S., Rajput, V., Deryabkina, I., Beschetnikov, V., Lobzenko, I., Sushkova, S., and Tukur Bayero, M.: Ecological risks of PTEs pollution in soils of the Lower Don floodplain and the Taganrog Bay coast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9248, https://doi.org/10.5194/egusphere-egu22-9248, 2022.

The geochemical features of stable strontium distribution in groundwater of the Upper Devonian hydrogeological complex within the southwestern flank of the Moscow artesian basin used for centralized drinking water supply in the northeastern part of the Bryansk region were considered in order to detail the potential influence of additional geochemical factors on the manifestation of endemic decease caused by natural iodine deficiency.

Strontium concentration in water samples varied from 0.21 to 28.8 mg/l (median (Me) = 1.03 mg/l, n=34). The analysis of strontium distribution with considering the genetic features of water-bearing rocks showed no significant differences in the content of this element in the waters of depositions of the Frasnian (Me=0.86 mg/l, n=25) and Famennian stages (Me=1.09 mg/l, n=9) (p<0.01). The main sources of strontium in investigated groundwater are strontium-containing minerals (celestine) or strontium impurities in limestones of varying degrees of gypsification associated with the Upper Devonian carbonate rocks (Sr correlation with SO₄: r_<0.05=0.78). The maximum levels of strontium, which significantly exceed the Russian hygienic standard for drinking waters (7 mg/l), were detect in groundwater of Famennian sediments of the Rognedinsky district of the Bryansk region (>20 mg/l). Given the lack of significant correlation between strontium content and water salinity, which is usually observed for strontium-enriched artesian waters of regional hydrogeochemical provinces (Kraynov et al., 2012) it can be explained by the existence of natural local strontium anomaly in this area (Сa/Sr <7).

Membrane filtration of water samples allowed suggesting that strontium migrate in fresh and low-salinity waters mainly within dissolved fraction of groundwater (divalent cation and complexes with sulfate, chloride and hydrocarbonate) with sizes not exceeding 0.45 µm.

The presence of a local anomaly of strontium-containing waters within the Moscow artesian basin, which impair the quality of drinking water in this area, can be a factor of potential risk to the health of the local population living under conditions of iodine deficiency.

The reported study was funded by the Vernadsky Institute federal budget (research task #0137-2019-0006). The Field work was partly funded by RFBR and BRFBR project #20-55-00012 and BRFBR project # Х20Р-386.

How to cite: Kolmykova, L. and Korobova, E.: Concentrations and migration forms of strontium in groundwater used for drinking within the Moscow artesian basin (Russia, Bryansk region), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9523, https://doi.org/10.5194/egusphere-egu22-9523, 2022.

Iodine deficiency is the most common cause of endemic goiter and other diseases, largely due to the geographical features of the regions. Iodine deficiency diseases can occur not only in inland regions and high-altitude areas, but also in those regions where iodine is poorly involved in the food chains of the local population. Thus, for some territories, an important factor of iodine deficiency may be the diversity of rocks and the difference in soils and aquifers caused by them.

The purpose of the article is to identify the contrast in the concentration of iodine content in the soil cover and natural drinking waters in the Mountainous Crimea, on different rocks. Soil and water samples collected in several regions of the Mountainous Crimea, mostly in Bakhchisarai, were examined.

Samples of natural drinking water (n=34) were taken in three districts of the Mountainous Crimea (Bakhchisarai, Alushta and Simferopol) from various sources (rivers, wells, ponds, aqueduct) in 2017. Soil samples (n=23) were taken in the Bodrak River valley (Bakhchisarai district) from the upper horizons (sampling depth up to 20 cm) in 2019. Iodine was determined by kinetic thiocyanate-nitrite method in the laboratory of the Institute of Geochemistry of the Russian Academy of Sciences.

The iodine content in the surveyed drinking water sources corresponds to the existing standards (2-10 μg/l), however, for some sources, extremely low values of iodine content are observed (both for wells 0.89 μg/l and for private pumps and aqueduct 1.11 μg/L), which can be determined primarily by the composition of the water-bearing rocks. The highest median values are marked for springs (Me=5.34 μg/L; n=8) and rivers (Me=6.77 μg/L; n=8), the lowest for aqueduct (Me=1.74 μg/L; n=7). The high variability of iodine in the soils of the automorphic landscapes of the Crimean Mountains was established from 0.43 mg/kg (mountain cambisols) to 15.4 mg/kg (regosols), depending on the humus content and the pH. The highest median values are marked for regosols (Me=5.6 mg/kg; n=13) and cambisols (Me=1.7 mg/kg; n=6), the lowest for fluvisols (Me=1.1 mg/kg; n=4).

The dependence of the iodine content in the upper horizons of different types of soils, primarily on the content of humus and soil pH-water, has been established. It has been confirmed that the content of iodine in natural waters is primarily determined by the difference in aquifers. The study was carried out without financial support, with the partial support of the Laboratory of Biogeochemistry of the Russian Academy of Sciences, which provided equipment for measuring iodine.

How to cite: Berezkin, V., Glebov, V., Kayukova, E., and Korobova, E.: A study of iodine concentration in soils and drinking waters of the Mountainous Crimea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10028, https://doi.org/10.5194/egusphere-egu22-10028, 2022.

Biochar is the perfect solution to reduce the adverse effects of climate change by adopting viable solutions inspired by nature. Since biochar can be made from a variety of different sources, the paper aims to compare the properties of biochar made from different sources, including wood, sunflower, and rice husk. The results obtained from the elemental analysis showed that there are no exceeding the maximum permissible concentrations of trace elements in any of the samples. Moreover, it was found silicon oxide is presented in rice husk. IR spectroscopy of wood biochar and sunflower husk biochar showed the presence of hydroxyl functional groups and aliphatic C-H groups of cellulose, as well as phenolic functional groups and esters. In addition, the total surface area of the wood biochar and rice husk biochar is found to be highest and lowest, respectively. It was found that the total volume of pores in the following descending order rice husk>wood>sunflower. The SEM and 3D confocal microscopy results indicate that wood biochar contains the surface with the most upside-down as compared to other samples. The XRD demonstrated that wood and sunflower husk biochar samples take crystallinity from cellulose compared to rice husk biochar. TGA results manifested that the wood biochar is more stable, and the new step as the decomposition of lignin part results by increasing the temperature up to 500 °C. The addition of all the biochars to the soil (Сalcaric Fluvic Arenosols) increases the sorption capacity of the soil under mono- and polyelement contamination by copper, zinc, and lead.

This study was supported by RFBR project no. 19-05-50097, Grant of the President of Russian Federation project no. МК-6137.2021.1.5 and by the Strategic Academic Leadership Program of the Southern Federal University ("Priority 2030").

How to cite: Lobzenko, I., Bauer, T., Burachevskaya, M., Minkina, T., Fedorenko, A., Mazarji, M., Sushkova, S., Mandzhieva, S., Rajput, V., Zamulina, I., Scherbakov, A., and Severina, V.: Analysis and comparison of the composition, functional groups, sorption characteristics and surface structure of biochar affected by biomass feedstock, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10054, https://doi.org/10.5194/egusphere-egu22-10054, 2022.

A series of maps using different GIS spatial analysis techniques were constructed to perform spatial analysis of the distribution of oncological diseases in Belorussia. Mapping was based on the data of the national cancer register, which contains considerable information of all cancer cases of different localization and allows separation of different sex and age groups of the population. Preliminary data verification showed a high variation of cancer cases in different areas. The second step of the research confirmed the high spatial heterogeneity of medical data when the maps characterizing different variation levels of cancer cases were made using a specialized GIS. After that, the regional zoning was carried out for the Gomel and Mogilev regions most subjected to the Chernobyl radionuclides fallout in Belarus and the areas with a significant difference in the level of general and localized cancer rates were separated. The general picture showed that the actual risk level of the oncological diseases (including those of different localization) spatially varies by four times or even more. Such a significant change in the frequency of occurrence of cancer cases of mans and women within limited areas univocally showed on the local factors that can provoke such an increase in morbidity. Considerable radioactive contamination after the Chernobyl accident within this area obvious could be such a factor. However, the obtained maps showed a high level of differentiation before the Chernobyl catastrophe and no definite correlation with radionuclide fallout maps. In any case, in our opinion, the revealed zones of enhanced cancer morbidity and those where the morbidity appeared to be minimal should become the objects of priority study. Those which represent the highest density of cancer cases need priority examination and prevention.

The study was partly funded by RFBR and BRFBR project #20-55-00012 and BRFBR project # Х20Р-386. 

How to cite: Romanov, S., Chervan, A., and Korobova, E.: Spatial analysis of cancer distribution in Gomel and Mogilev oblasts of Belarus as a preliminary stage for revealing the provoking local factors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10118, https://doi.org/10.5194/egusphere-egu22-10118, 2022.

There is a continuous increase of the use of plastic materials globally, which makes difficult to manage their waste, constituting an important source of pollution for the different environmental areas. Specifically, the long-term quality and productivity of agricultural soils is affected by the contamination of these plastic residues, being these pollutants mainly present as microplastics coming from the degradation of the larger initial plastic contaminants. In addition, plastics contain different additives to improve their properties which are normally toxic organic compounds which may have a negative impact to the agricultural environment.

The purpose of this research is to develop and validate an analytical method based in a solid-liquid extraction stage followed by gas chromatography coupled to mass spectrometry (GC-MC) to determine volatile organic compounds related to plastics (monomers, additives, and degradation products) in soil samples of agricultural areas. For this purpose, a number of samples were collected in a wide zone located in the Rambla del Beal (Cartagena, Spain).

The optimized method has allowed the quantification of 14 volatile compounds, such as styrene, phthalates or bisphenol A that may be released from plastic residues, because they are monomeric species or additives. Other species associated to the degradation (environmental conditions over time) of the plastic residues such as 2,4-diterbutylphenol have been also found in the samples.

In addition, a non-targeted approach has been developed for the identification of other pollutants present in the soil samples without the use of standards. This goal was achieved by the use of the mass spectrometer detector working in the full scan mode and the application of MS database libraries (NIST and Wiley).

This analytical methodology represents a basis for a reliable evaluation of the presence of plastic pollutants in soils through the determination of their additives, monomers and degradation compounds.

The authors are grateful to the Spanish MICINN (Project PGC2018-098363-B-100) for financial support

How to cite: Hernandez, M., Peñalver, R., Arroyo-Manzanares, N., Campillo, N., López-García, I., and Viñas, P.: An approach for evaluating the level of plastic residues in agricultural soils, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10220, https://doi.org/10.5194/egusphere-egu22-10220, 2022.

Arsenic is a Potentially Toxic Element (PTE), which is present in the soils/sediments of abandoned mining areas, such as the Sierra Minera de Cartagena La-Unión and the mining site of Mazarron (SE Spain) and its areas of influence. In order to assess the risk to human health and the ecosystem, it is necessary to know the nature of the materials that contain this PTE, their alterability and their speciation.

On the one hand, there is a geogenic relationship between this element and materials rich in phyllosilicates and Fe minerals. These minerals can constitute primary mineralisation such as sulphide veins (pyrite, arsenopyrite, etc.) or secondary mineralisation such as haematite, goethite, siderite, jarosite, etc., and can even be found as a mineral phase forming various arsenates. Another very important aspect is the climatology of the area, which coincides with a semi-arid Mediterranean climate with infrequent but very heavy rainfall.

The As concentration range in the studied areas is very wide (5000 -70 mg.Kg-¹), with an average value of 150 mg.Kg^-1, being As (V) the predominant species. Only soils located in wetland areas with permanent waterlogging can show significant concentrations of As(III). 

The As content in surface waters, such as runoff water, is low, only reaching significant values (>2 mg.L^-1) when these waters are acid mine drainage and have pH values <2, coinciding in these cases with the presence of reduced As forms.

Particulate As is associated both with Fe oxides and hydroxides, through surface adsorption processes on Fe(OH)₃ particles, and with carbonates, through precipitation reactions as calcium arsenate. These reactions are evident in some places such as wadis that transport particulate and dissolved materials from areas affected by mining, and mainly take place both in the riverbed and in flooding areas when rainfall events occur.

For an appropriate understanding of the main processes involved, a detailed scheme is given. It should be noted that the dynamics of this PTE is of a particular interest in the zones studied due to the proximity of urban sites.

How to cite: Pérez-Sirvent, C., Martínez Sánchez, M. J., Martínez López, S., Martínez Martínez, L. B., Hernández Pérez, C., Gomez Martinez, C., Hernández-Córdoba, M., and Bech, J.: Arsenic dynamics in soils placed near old mining sites in SE Spain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10446, https://doi.org/10.5194/egusphere-egu22-10446, 2022.

Improper dumps are one of the most common indicators of accumulated harm and are a source of a wide range of pollutants entering the environment. The waste of packaging materials, household chemicals, agrochemicals, used industrial catalysts, ash from thermal waste disposal, and other contaminants have been identified as sources of their introduction into soils from dumps. The accelerated applications of nano-forms of metals are one of the emerging concerns. Like other contaminants, the soil is the main sink for nanoparticles (NPs). Undoubtedly, in the last decade, metal NPs have been recognized for their numerous roles in research and development but due to their increasing amount in the environment, these emerging issues cannot be ignored. Therefore, with this background, the current work was proposed, in which, Pisum sativum L. was exposed to nano-disperse (30-50 nm) and macro-disperse (3-5 μm) forms of metal oxide viz., Cu, Zn, Cr, Mn, Cd, Ti, Ni, and Pb at the doses of 3, 30, and 90 background contamination (in mg/kg). After 3-4 days of exposure, the emerged roots were harvested, cleaned with distilled water, and fixed in Clark’s fluid (aceto-alcohol) for further analyses. For microscopic observations, slides were prepared using the squash technique. In this work, the mitotic index and frequency of chromosomal aberrations were recorded to depict the extent of cytotoxic and genotoxic effects, respectively. The experimental outcomes revealed that the maximal genotoxicity was found in all soil samples at the level of 90 background contamination, regardless of the macro- or nano-state of the metals. Besides, the commonly observed chromosomal aberrations were bridges and fragments. Also, cell ruptures at the metaphase stage, forming a metaphase plate was found but rarely. Thus, the current observation depicted the cytotoxicity and genotoxicity of different nano- and macro-disperse forms of metals, however further studies are needed to explore the responsible mechanisms for these toxicological vulnerabilities.  

This study was supported by Russian Science Foundation project no. 21-77-20089.

How to cite: Chernikova, N., Kumari, A., Chokheli, V., Rajput, V., Mandzhieva, S., Shuvaeva, V., Tsitsuashvili, V., Barakhov, A., Nevidomskaya, D., Kirichkov, M., and Timoshenko, A.: Cytotoxic and genotoxic effects of macro- and nano-form of heavy metals in Pisum sativum L. grown in soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11146, https://doi.org/10.5194/egusphere-egu22-11146, 2022.

Long-term coal mining (more than 50 years) in the Moscow basin has a complex negative effect on soils. Because of underground mining at coal fields spoil heaps with a high content of iron sulfides, aluminosilicates and organic carbon of coal origin were formed. Oxidation of sulfides and acid hydrolysis of aluminosilicates in waste dumps results in the producing of toxic sulfuric acid, Al and Fe sulfates (Nordstrom and Alpers 1999). Acid mine drainage (AMD) entering from eroded spoil heaps, leads to physico-chemical and morphological changes in soil characteristics. On foreslopes around spoil heaps technogenically transformed soils are common. Our study aimed at evaluation of post-mining geochemical evolution of chemical composition and properties of solid and liquid soil phases.

We examined two key sites within abandoned coal mine fields in the central part of the Moscow basin. Predominant natural soils are Greyic Phaeozems and Haplic Chernozems (WRB 2014) (Grey forest and Leached Chernozems in Russian classification).

Soil samples and displaced soil solutions (by ethanol) were analysed for acid-base properties, content and composition of readily soluble salts, content of Fe²⁺ and Fe³⁺, H⁺ and Al³⁺, composition of exchangeable cations, heavy metals (HM) and organic carbon) by standard methods. The composition of clay minerals in soils were determined by X-ray diffractometry. The saturation degree of soil solutions by gypsum, iron and aluminum hydroxides was estimated.

Properties of technogenic soils differ significantly from natural soils. We observed the transformation of the composition of soil solutions. Key geochemical processes at mine sites in contaminated soils were: (1) acidification and Fe-Al-SO₄ salinization of entire soil profile along with the increment in H⁺ and Al³⁺ ions content; (2) cation exchange, leading to displacement of Cа²⁺ and Mg²⁺ ions by Al³⁺, H⁺ and, probably, by Fe²⁺ and Fe³⁺ in soil cation-exchange complex (CEC); (3) alteration of radial differentiation of organic carbon and carbonates in soils; (4) clay mineral transformations.

Topsoil features a high content of technogenic organic carbon (reaches 12%) due to the inflow of coal material particles from the dump. Ca²⁺ and Mg²⁺ ions predominate (for 70 to 90%) in CEC of natural soils. Exchangeable Al³⁺ accounts for more than 75% of the acidity formation in transformed soils. The share of exchangeable Ca²⁺ and Mg²⁺ in CEC of contaminated soils depletes on 22-38%.

Extracted soil solutions from polluted soils are heavily oversaturated by Al hydroxides. Even though the activity of Ca²⁺ and SO₄^2- ions in some samples reaches the gypsum saturation level, gypsum neoformations are not distinguished morphologically.

The content of Co, Сu, Ni and Zn in displaced solutions of transformed soils in tens or even hundreds times exceeds the background values. The clay minerals of natural soils are represented by kaolinite, illite, vermiculite and mixed-layer minerals. The sharp increase in smectite fraction (up to 75-80%) and slightly in chlorite fraction was revealed in transformed soils.

Post-technogenic soils have no analogues in natural forest-steppe landscapes of the Russian Plain.

How to cite: Kostin, A., Krechetov, P., Chernitsova, O., and Terskaya, E.: Geochemical transformations in liquid and solid phases of forest-steppe soils in the affected area of Moscow brown coal basin (Russia), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12209, https://doi.org/10.5194/egusphere-egu22-12209, 2022.

Campania Region, Southern Italy, in the last 10 years was facing potential environmental issues which needed to be addressed, with the proper scientific approach, to alleviate pressure from public opinion, based more on emotions than on scientific data. Such pressure indicated an increase of oncological incidence, not supported by scientific data on the presence of anomalous pollutants in different natural media (soil, water, air, agricultural products). To face environmental/health alarm, the Campania Regional Government in 2015 funded a large, multidisciplinary, environmental project known as Campania Trasparente, to Istituto Zooprofilattico del Mezzogiorno (IZSM), to get a deeper and scientific knowledge of the Campania territory carrying out geochemical investigations, to: 1) characterize the geochemical composition of agricultural soil, air and groundwater at regional and local scale; 2) define the level of bio-availability of the toxic elements; 3) try to demonstrate a direct relationship between the presence of contaminants in the environmental matrices, in agricultural products and finally in the human matrices (hair, urine, blood). Within this project we got data on the presence of the potentially toxic metals (PTMs) and hazardous persistent organic pollutants (POPs: OCPs, PCBs, PAHs, PAEs, PBDEs) in different media of the entire Region. The new large dataset complemented our research and monitoring activities, which before 2015, were focused mostly on PTMs in soils, both at regional and local scale. In Campania Trasparente project, samples (9,000) of top and bottom soils, air and bulk deposition (150 passive air samplers, over 7 seasons), waters (1,200), vegetation (2,500) and biological (4,200) media, were collected to characterize the status of PTMs and POPs. The results obtained showed that: a) most of these elements and compounds, in higher concentrations, occur predominantly in critical areas of Napoli Urban and Metropolitan Area (NMA) and in the Sarno river basin; b) the infamous area, in the Caserta and Napoli provincial territory, known as Terra dei Fuochi (Land of Fires), is only marginally interested by anomalous occurrence of PTMs and POPs in some spot areas, not justifying the emotional alarms calling for an increase of oncological cases due to diffuse illegal practice of wastes disposal in the area; c) the agricultural crops of the Terra dei Fuochi are not affected by anomalous PTM. Specifically, the ecological risk conditions for PAHs and some OCPs (Endosulfan) occur, mostly in NMA; PCBs are sourced mostly in urban areas, being dissipated in rural areas, whereas PAEs and PBDEs occur, in general, in concentrations similar to those in other Italian regions, with some higher hot spot values in NMA and south of Salerno town. The interactional complexity between metropolitan and the surrounding rural areas is also confirmed, as it is the role that urban areas play in the migration and transformation process of POPs. High urban-rural gradients for atmospheric PAHs, PCBs and OCPs are observed mostly in the NMA and the urban areas, identified as the main emission source of POPs.  Only OCPs, originating from the nearby agricultural areas, experienced long-term soil re-emission, continuously influencing conterminous urban environment via atmospheric transport processes.

How to cite: De Vivo, B., Lima, A., Cicchella, D., Qu, C., Hope, D., Cerino, P., Esposito, M., Pizzolante, A., Albanese, S., and Korobova, E.: Potentially Toxic Metals and high resolution monitoring at regional and local scale of Persistent Organic Pollutants in the soil, air, and bulk deposition of the Campania Region, southern Italy: Sources and environmental processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13164, https://doi.org/10.5194/egusphere-egu22-13164, 2022.

The public and scientific discourse on how to mitigate the COVID-19 pandemic is often focused on the impact of individual protective measures, in particular on vaccination. In view of changing virus variants and conditions, however, it seems not clear if vaccination or any other protective measure alone may suffice to contain the transmission of SARS-CoV-2. Accounting for both droplet and aerosol transmission, we investigated the effectiveness and synergies of vaccination and non-pharmaceutical interventions like masking, distancing & ventilation, testing & isolation, and contact reduction as a function of compliance in the population. For realistic conditions, we find that it would be difficult to contain highly contagious SARS-CoV-2 variants by any individual measure. Instead, we show how multiple synergetic measures have to be combined to reduce the effective reproduction number (Re) below unity for different basic reproduction numbers ranging from the SARS-CoV-2 ancestral strain up to measles-like values (R0 = 3 to 18).

Face masks are well-established and effective preventive measures against the transmission of respiratory viruses and diseases, but their effectiveness for mitigating SARS-CoV-2 transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance (virus-limited vs. virus-rich) and are related to population-average infection probability and reproduction number. Under virus-limited conditions, both surgical and FFP2/N95 masks are effective at reducing the virus spread, and universal masking with correctly applied FFP2/N95 masks can reduce infection probabilities by factors up to 100 or more (source control and wearer protection).

Masks are particularly effective in combination with synergetic measures like ventilation and distancing, which can reduce the viral load in breathing air by factors up to 10 or more and help maintaining virus-limited conditions. Extensive experimental studies, measurement data, numerical calculations, and practical experience show that window ventilation supported by exhaust fans (i.e. mechanical extract ventilation) is a simple and highly effective measure to increase air quality in classrooms. This approach can be used against the aerosol transmission of SARS-CoV-2. Mechanical extract ventilation (MEV) is very well suited not only for combating the COVID19 pandemic but also for sustainably ventilating schools in an energy-saving, resource-efficient, and climate-friendly manner.  Distributed extract ducts or hoods can be flexibly reused, removed and stored, or combined with other devices (e.g. CO2 sensors), which is easy due to the modular approach and low-cost materials (www.ventilationmainz.de).

The scientific findings and approaches outlined above can be used to design, communicate, and implement efficient strategies for mitigating the COVID-19 pandemic.

References:

Cheng et al., Face masks effectively limit the probability of SARS-CoV-2 transmission, Science, 372, 1439, 2021, https://doi.org/10.1126/science.abg6296 

Klimach et al., The Max Planck Institute for Chemistry mechanical extract ventilation (MPIC-MEV) system against aerosol transmission of COVID-19, Zenodo, 2021, https://doi.org/10.5281/zenodo.5802048  

Su et al., Synergetic measures to contain highly transmissible variants of SARS-CoV-2, medRxiv, 2021, https://doi.org/10.1101/2021.11.24.21266824

How to cite: Pöschl, U., Cheng, Y., Helleis, F., Klimach, T., and Su, H.: Efficiency and synergy of simple protective measures against COVID-19: Masks, ventilation and more, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1024, https://doi.org/10.5194/egusphere-egu22-1024, 2022.

The time evolution of the Covid-19 death cases exhibits several distinct episodes since the start of the pandemic early in 2020. We propose an analysis of several Southern Europe regions that highlights how the beginning of each episode correlates with a strong increase in the concentrations level of pollution particulate matter smaller than 2.5 µm (PM2.5). Following the original PM2.5 spike, the evolution of the Covid-19 spread depends on the (partial) lockdowns and vaccinate races, thus the highest level of confidence in correlation can only be achieved when considering the beginning of each episode. The analysis is conducted for the 2020-2022 period at different locations: the Lombardy region (Italy), where we consider the mass concentrations measurements obtained by air quality monitoring stations (µg.m^-3), and the cities of Paris (France), Lisbon (Portugal) and Madrid (Spain) using in-situ measurements counting particles (cm^-3) in the 0.5-2.5 µm size range obtained with hundreds of mobile aerosol counters. The particle counting methodology is more suitable to evaluate the possible correlation between PM pollution and Covid-19 spread because we can better estimate the concentration of the submicronic particles compared with a mass concentration measurement methodology which would result in skewed results due to larger particles. Very fine particles of lesser than one micron go deeper inside the body and can even cross the alveolar-capillary barrier, subsequently attacking most of the organs through the bloodstream, potentially triggering a pejorative systemic inflammatory reaction. The rapidly increasing number of deaths attributed to the covid-19 starts between 2 weeks and one month after PM events that often occur in winter, which is coherent with the virus incubation time and its lethal outcome. We suggest that the pollution by the submicronic particles alters the pulmonary alveoli status and thus significantly increase the lungs susceptibility to the virus.

How to cite: Renard, J.-B., Delaunay, G., Poincelet, E., and Surcin, J.: Possible effect of the particulate matter (PM) pollution on the Covid-19 spread in southern Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1890, https://doi.org/10.5194/egusphere-egu22-1890, 2022.

In the past two years, numerous advances have been made in the ability to predict the progress of COVID19 epidemics.  Basic forecasting of the health state of a population with respect to a given disease is based on the well-known family of SIR models (Susceptible Infected Recovered). The models used in epidemiology were based on deterministic behavior, so the epidemiological picture tomorrow depends exclusively on the numbers recorded today. The forecasting shortcomings of the deterministic SEIR models previously used in epidemiology were difficult to highlight before the advent of COVID19  because epidemiology was mostly not concerned with real-time forecasting.  From the first wave of COVID19 infections, the limitations of using deterministic models were immediately evident: to use them, one should know the exact status of the population and this knowledge was limited by the ability to process swabs. Futhermore, there is an intrinsic variability of the dynamics which depends on age, sex, characteristics of the virus, variants and vaccination status. 

Our main contribution was to define a SEIR model that assumes these parameters as constants could not be used for reliable predictions of COVID19 pandemis and that more realistic forecasts can be obtained by adding fluctuations in the model. The fluctuations in the dynamics of the virus induced by these factors do not just add variaiblity around the deterministic solution of the SIR models, the also introduce another timing of the pandemics which influence the epidemic peak. With our model we have found that even with a basic reprdocution number R_t less than 1 local epidemic peaks can occur that resume over a certain period of time. 

Introducing noise and uncertainty allows  to define a range of possible scenarios, instead of making a single prediction. This is what happens when we replace the deterministic approach, with a probabilistic approach. The probabilistic models used to predict the progress of the Covid-19 epidemic are conceptually very similar to those used by climatologists, to imagine future environmental scenarios based on the actions taken in the present.  As human beings we can intervene in both systems. Based on the choices we will make and the fluctuations of the systems, we can predict different responses. In the context of the emergency that we faced, the collaboration between different scientific fields was therefore fundamental, which, by comparing themselves, were able to provide more accurate answers. Furthermore, a close collaboration has arisen between epidemiologists and climatologists. A beautiful synergy that can give a great help to society in a difficult moment.

References

-Faranda, Castillo, Hulme, Jezequel, Lamb, Sato & Thompson (2020). Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(5), 051107.

-Alberti & Faranda (2020).  Communications in Nonlinear Science and Numerical Simulation, 90, 105372.

-Faranda & Alberti (2020). Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(11), 111101.

-Faranda, Alberti, Arutkin, Lembo, Lucarini. (2021).  Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(4), 041105.

-Arutkin, Faranda, Alberti, & Vallée. (2021). Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(10), 101107.

How to cite: Faranda, D.: How concepts and ideas from Statistical and Climate physics improve epidemiological modelling of the COVID 19 pandemics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2801, https://doi.org/10.5194/egusphere-egu22-2801, 2022.

Recurrent epidemic outbreaks such as the seasonal flu and the ongoing COVID-19 are disastrous events to our societies both in terms of fatalities, social and educational structures, and financial losses. The difficulty to control COVID-19 spread in the last two years has brought evidence that basic mechanisms of transmission for such pathogens are still poorly understood.

             Three different routes of virus transmission are known: direct contact (e.g. through handshakes) and indirect contact through fomites; ballistic droplets produced by speaking, sneezing or coughing; and airborne transmission through aerosols which can also be produced by normal breathing. The latter route, which has long been ignored, even by the World Health Organization during the COVID-19 pandemics, now appears to play the predominant role in the spread of airborne diseases (e.g. Chen et al., 2020).

             Further scientific research thus needs to be conducted to better understand the mechanistic processes that lead to inactivate airborne viruses, as well as the environmental conditions which favour these processes. In addition to modelling and epidemiological studies, chamber experiments, where viruses are exposed to various types of humidity, temperature and/or UV dose, offer to simulate everyday life conditions for virus transmission. However, the current standard instrumental solutions for virus aerosolization to the chamber and sampling from it use high fluid forces and recirculation which can cause infectivity losses (Alsved et al., 2020) and also do not compare to the relevant production of airborne aerosol in the respiratory tract.

             In this study, we utilized two of the softest aerosolization and sampling techniques: the sparging liquid aerosol generator (SLAG, CH Technologies Inc., Westwood, NJ, USA), which forms aerosol from a liquid suspension by bubble bursting, thus mimicking natural aerosol formation in wet environments (e.g. the respiratory system but also lakes, sea, toilets, etc…); and the viable virus aerosol sampler (BioSpot-VIVAS, Aerosol Devices Inc., Fort Collins, CO, USA), which grows particle via water vapour condensation to gently collect them down to a few nanometres in size. We characterized these systems with particle sizers and biological analysers using non-pathogenic viruses such as bacteriophages suspended in surrogate lung fluid and artificial saliva. We compared the size distribution of produced aerosol from these suspensions against similar distributions generated with standard nebulizers, and assess the ability of these devices to produce aerosol that much more resembles that produced in human exhaled air. We also assess the conservation of viral infectivity with the VIVAS vs. conventional biosamplers.

Acknowledgment

We acknowledge the IVEA project in the framework of SINERGIA grant (Swiss National Science Foundation)

References

Alsved, M., Bourouiba, L., Duchaine, C., Löndahl, J., Marr, L. C., Parker, S. T., Prussin, A. J., and Thomas, R. J. (2020): Natural sources and experimental generation of bioaerosols: Challenges and perspectives, Aerosol Science and Technology, 54, 547–571.

Chen, W., Zhang, N., Wei, J., Yen, H.-L., and Li, Y. (2020): Short-range airborne route dominates exposure of respiratory infection during close contact, Building and Environment, 176, 106859.

How to cite: Motos, G., Violaki, K., Schaub, A., David, S., Kohn, T., and Nenes, A.: Improving the conservation of virus infectivity during airborne exposure experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3690, https://doi.org/10.5194/egusphere-egu22-3690, 2022.

COVID-19 effects on measurements of the Earth Magnetic Field in the urbanized area of Brest (Brittany, France)

Jean-François OEHLER¹, Sylvain LUCAS¹, Alexandre LEON¹, André LUSVEN¹, Gildas DELACHIENNE¹

¹Shom (Service Hydrographique et Océanographique de la Marine), Brest, France

Since September 2019, Shom’s Magnetic Station (SMS) has been deployed in the north neighbourhoods of the medium-sized city of Brest (Brittany, France, about 210,000 inhabitants). SMS continuously measures the intensity of the Earth Magnetic Field (EMF) with an absolute Overhauser sensor. The main goal of SMS is to derive local external variations of the EMF mainly due to solar activity. These variations consist of low and high parasitic frequencies in magnetic data and need to be corrected. Magnetic mobile stations or permanent observatories are usually installed in isolated areas, far from human activities and electromagnetic effects. It is clearly not the case for SMS, mainly for practical reasons of security, maintenance and data accessibility. However, despite its location in an urbanized area, SMS stays the far western reference station for processing marine magnetic data collected along the Atlantic and Channel coasts of France.

The corona pandemic has had unexpected consequences on the quality of measurements collected by SMS. For example, during the French first lockdown between March and May 2020, the noise level significantly decreased of about 50%. Average standard deviations computed on 1 Hz-time series over 1 min. periods fell from about 1.5 nT to 0.8 nT. This more stable behavior of SMS is clearly correlated with the drop of human activities and traffic in the city of Brest.

Keywords: Shom’s Magnetic Station (SMS), Earth Magnetic Field, COVID19.

How to cite: Oehler, J.-F., Leon, A., Lucas, S., Lusven, A., and Delachienne, G.: COVID-19 effects on measurements of the Earth Magnetic Field in the urbanized area of Brest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3936, https://doi.org/10.5194/egusphere-egu22-3936, 2022.

Economic activities and the associated emissions have significantly declined during the 2019 novel coronavirus (COVID-19) pandemic, which has created a natural experiment to assess the impact of the emitted precursor control policy on ozone (O₃) pollution. In this study, we utilized comprehensive satellite, ground-level observations, and source-oriented chemical transport modeling to investigate the O₃ variations during the COVID-19 pandemic in China. Here, we found that the significant elevated O₃ in the North China Plain (40%) and Yangtze River Delta (35%) were mainly attributed to the enhanced atmospheric oxidation capacity (AOC) in these regions, associated with the meteorology and emission reduction during lockdown. Besides, O₃ formation regimes shifted from VOC-limited regimes to NO_x-limited and transition regimes with the decline of NO_x during lockdown. We suggest that future O₃ control policies should comprehensively consider the effects of AOC on the O₃ elevation and coordinated regulations of the O₃ precursor emissions.

How to cite: Wang, P., Zhu, S., and Zhang, H.: Comprehensive Insights Into O3 Changes During the COVID-19 From O3 Formation Regime and Atmospheric Oxidation Capacity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4170, https://doi.org/10.5194/egusphere-egu22-4170, 2022.

In the last few decades, Nature-based Solutions (NBS) has become widely considered a sustainable development strategy for the development of urban environments. Assessing the performances of NBS is significant for understanding their efficiency in addressing a large range of natural and societal challenges, such as climate change, ecosystem services and human health. With the rapid onset of the COVID-19 pandemic, the inner relationship between humans and nature becomes apparent. However, the current catchment management mainly focuses on reducing hydro-meteorological and/or climatological risks and improving urban climate resilience. This single-dimensional management seems insufficient when facing epidemics, and multi-dimensional management (e.g., reduce zoonosis) is necessary. With this respect, policymakers pay more attention to NBS. Hence, it is significant to increase the connectivity of the landscape to improve the ecosystem services and reduce the health risks from COVID-19 with the help of NBS.

This study takes the Guyancourt catchment as an example. The selected catchment is located in the Southwest suburb of Paris, with a total area of around 5.2 km². The ArcGIS software is used to assess the patterns of structural landscape connectivity, and the heterogeneous spatial distribution of current green spaces over the catchment is quantified with the help of the scale-independent indicator of fractal dimension. To quantify opportunities to increase landscape connectivity over the catchment, a least-cost path approach to map potential NBS links urban green spaces through vacant parcels, alleys, and smaller green spaces. Finally, to prioritise these potential NBS in multiscale, a new scale-independent indicator within the Universal Multifractal framework is proposed in this study.

The results indicated that NBS can effectively improve the connectivity of the landscape and has the potential to reduce the physical and mental risks caused by COVID-19. Overall, this study proposed a scale-independent approach for enhancing the multiscale connectivity of the NBS network in urban areas and providing quantitative suggestions for on-site redevelopment.

How to cite: Qiu, Y., Tchiguirinskaia, I., and Schertzer, D.: Nature-based Solutions in actions: improving landscape connectivity during the COVID-19, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5126, https://doi.org/10.5194/egusphere-egu22-5126, 2022.

The Coronavirus (COVID-19) epidemic, which was first reported in December 2019 in Wuhan, China, has been becoming one of the most important public health issues worldwide. Previous studies have shown the importance of weather variables and air pollution in the transmission or prognosis of infectious diseases, including, but not limited to, influenza and severe acute respiratory syndrome (SARS). In the early stage of the COVID-19 epidemic, there was intense debate and inconsistent results on whether environmental factors were associated with the spread and prognosis of COVID-19. Therefore, our team conducted a series studies to explore the associations between atmospheric parameters (temperature, humidity, UV radiation, particulate matters and nitrogen dioxygen) and the COVID-19 (transmission ability and prognosis) at the early stage of the COVID-19 epidemic with data in early 2020 in China and worldwide. Our results showed that meteorological conditions (temperature, humidity and UV radiation) had no significant associations with cumulative incidence rate or R₀ of COVID-19 based on data from 224 Chinese cities, or based on data of 202 locations of 8 countries before March 9, 2020, suggesting that the spread ability of COVID-19 among public population would not significantly change with increasing temperature or UV radiation or changes of humidity. Moreover, we found that particulate matter pollution significantly associated with case fatality rate (CFR) of COVID-19 in 49 Chinese cities based on data before April 12, 2020, indicating that air pollution might exacerbate negative prognosis of COVID-19. Our studies provided an environmental perspective for the prevention and treatment of COVID-19.

How to cite: Meng, X., Yao, Y., Wang, W., and Kan, H.: The associations between environmental factors and COVID-19: early evidence from China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5150, https://doi.org/10.5194/egusphere-egu22-5150, 2022.

The COVID-19 lockdowns worldwide provided a prospect to evaluate the impacts of restricted movements and emissions on air quality. In this study, we analyze the data obtained from the ground-based observation stations for six air pollutants (PM₁₀, PM_2.5, CO, NO₂, O₃ and SO₂) and meteorological parameters from March 25th to May 31st in 22 cities representative of five regions of India and from March 16th to May 14th in 21 districts of Finland from 2017 to 2020. The NO₂ concentrations dropped significantly during all phases apart from East India's exception during phase 1. O₃ concentrations for all four phases in West India reduced significantly, with the highest during Phase 2 (~38%). The PM_2.5 concentration nearly halved across India during all phases except South India, where a very marginal reduction (2%) was observed during Phase 4. SO₂ (~31%) and CO (~41%) concentrations also reduced noticeably in South India and North India during all the phases. The air temperature rose by ~10% (average) during all the phases across India when compared to 2017-2019. In Finland, NO₂ concentration reduced substantially in 2020. Apart from Phase 1, the concentrations of PM₁₀ and PM_2.5 reduced markedly in all the Phases across Finland. Also, O₃ and SO₂ concentrations stayed within the permissible limits in the study period for all four years but were highest in 2017 in Finland, while the sulfurous compounds (OSCs) levels increased during all the phases across Finland. The changes in the mobility patterns were also assessed and were observed to have reduced significantly during the lockdown. The benefits in the overall mortality due to the reduction in the concentrations of PM_2.5 have also been estimated for India and Finland. Therefore, this research illustrates the effectiveness of lockdown and provides timely policy suggestions to the regulators to implement interventions to improve air quality.

How to cite: Sharma, S., Heibati, B., Suneja, J., and Kota, S. H.: The Effects of COVID-19 Lockdown on Air Quality and Health in India and Finland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9213, https://doi.org/10.5194/egusphere-egu22-9213, 2022.

Since 2020, countries around the world have implemented various interventions in response to a global public health crisis. The interventions included restrictions on mobility, promotion of working from home and the limiting of local and international travel. These, along with other behavioural changes from people in response to the crisis affected various sources of air pollution, not least the transport sector. Whilst the method through which these changes were implemented is not something to be repeated, understanding the effects of the changes will help direct policy for further improving air quality. 

We analysed NO_x, O₃ and PM_2.5 data from many 100s of air quality monitoring sites in urban areas around the world, and examined 2020 in relation to the previous 5 years. The data were examined alongside mobility metrics to contextualise the magnitude of changes and were viewed through the lens of World Health Organisation guidelines as a metric to link air quality changes with human health. Interestingly, reductions in polluting activities did not lead to wholesale improvements in air quality by all metrics due to the more complex processes involved with tropospheric O₃ production.

How to cite: Drysdale, W., Stapleton, C., and Lee, J.: Changes in Global Urban Air Quality due to Large Scale Disruptions of Activity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9812, https://doi.org/10.5194/egusphere-egu22-9812, 2022.

Defining optimal COVID-19 mitigation strategies remains at the top of public health agendas around the world. It requires a better understanding and refined modeling of the intrinsic dynamics of the epidemic. The common root of most models of epidemics is a cascade paradigm that dates to their emergence with Bernoulli and d’Alembert, which predated Richardson’s famous quatrain on the cascade of atmospheric dynamics. However, unlike other cascade processes, the characteristic times of a cascade of contacts that spread infection and the corresponding rates are believed to be independent on the cascade level. This assumption prevents having cascades of scaling contamination.

In this presentation, we theoretically argue and empirically demonstrate that the intrinsic dynamics of the COVID-19 epidemic during the phases of growth and decline, is a cascade with a rather universal scaling, the statistics of which differ significantly from those of an exponential process. This result first confirms the possibility of having a higher prevalence of intrinsic dynamics, resulting in slower but potentially longer phases of growth and decline. It also shows that a fairly simple transformation connects the two phases. It thus explains the frequent deviations of epidemic models rather aligned with exponential growth and it makes it possible to distinguish an epidemic decline from a change of scaling in the observed growth rates. The resulting variability across spatiotemporal scales is a major feature that requires alternative approaches with practical consequences for data analysis and modelling. We illustrate some of these consequences using the now famous database from the Johns Hopkins University Center for Systems Science and Engineering.

Due to the significant increase over time of available data, we are no longer limited to deterministic calculus. The non-negligible fluctuations with respect to a power-law can be easily explained within the framework of stochastic multiplicative cascades. These processes are exponentials of a stochastic generators Γ(t), whose stochastic differentiation remains quite close to the deterministic one, basically adding a supplementary term σdt to the differential of the generator. When the generator Γ(t) is Gaussian, σ is the “quadratic variation”. Extensions to Lévy stable generators, which are strongly non-Gaussian, have also been considered. To study the stochastic nature of the cascade generator, as well as how it respects the above-mentioned symmetry between the phases of growth and decline, we use the universal multifractals. They provide the appropriate framework for joint scaling analysis of vector-valued time series and for introducing location and other dependencies. This corresponds to enlarging the domain, on which the process and its generator are defined, as well as their co-domain, on which they are valued. These clarifications should make it possible to improve epidemic models and their statistical analysis.

More fundamentally, this study points out to a new class of stochastic multiplicative cascade models of epidemics in space and time, therefore not limited to compartments. By their generality, these results pave the way for a renewed approach to epidemics, and more generally growth phenomena, towards more resilient development and management of our urban systems.

How to cite: Tchiguirinskaia, I. and Schertzer, D.: Scaling Dynamics of Growth Phenomena: from Epidemics to the Resilience of Urban Systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11475, https://doi.org/10.5194/egusphere-egu22-11475, 2022.

There have been a series of sessions on the generic theme of “Covid-19 and Geosciences” on the occasion of AGU, AOGS and EGU conferences, since 2020 including during the first lockdown that required a very fast adaptation to unprecedented health measures. We think it is interesting and useful to have an overview of these sessions and try to capture what could be the lessons to learn.

To our knowledge, the very first such session was the Great e-Debate “Epidemics, Urban Systems and Geosciences” (https://hmco.enpc.fr/news-and-events/great-e-debate-epidemics-urban-systems-and-geosciences-invitations-and-replays/). It was virtually organised with the help of the UNESCO UniTwin CS-DC (Complex Systems Digital Campus) thanks to its expertise in organising e-conferences long before the pandemic and the first health measures. This would not have been possible without the strong personal involvement of its chair Paul Bourgine. It was held on Monday 4th May on the occasion of the 2020 EGU conference, which became virtual under the title “EGU2020: Sharing Geoscience Online” (4-8 May 2020). The Great e-Debate did not succeed in being granted as an official session of this conference, despite the fact that the technology used (Blue Button) by the Great e-Debate was much more advanced. Nevertheless, it was clearly an extension of the EGU session ITS2.10 / NP3.3: “Urban Geoscience Complexity: Transdisciplinarity for the Urban Transition”. 

Thanks to a later venue (7-11 December 2020) and the existence of a GeoHealth section of the AGU, the organisation of several regular sessions for the 2020 Fall Meeting was easier. For EGU 2021 (19-30 April 2021), a sub-part of the  inter- transdisciplinary sessions ITS1 “Geosciences and health during the Covid pandemic”, a Union Session US “Post-Covid Geosciences” and a Townhall meeting TM10 “Covid-19 and other epidemics: engagement of the geoscience communities” were organised. A brief of the special session SS02 “Covid-19 and Geoscience” of the (virtual) 18th Annual Meeting of AOGS (1-6 August 2021) is included in the proceedings of this conference (in press). 

We will review materials generated by these sessions that rather show a shift from a focus on the broad range of scientific responses to the pandemic, to which geoscientists could contribute with their specific expertise (from data collection to theoretical modelling), to an expression of concerns about the broad impacts on the geophysical communities that appear to be increasingly long-term and constitute a major transformation of community functioning (e.g., again data collection, knowledge transfer).

How to cite: Schertzer, D., Dimri, V., and Fraedrich, K.: Geophysicists facing Covid-19, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11584, https://doi.org/10.5194/egusphere-egu22-11584, 2022.

The COVID-19 pandemic has brought forth the question of the need for draconian interventions before concrete evidence for their need and efficacy is presented. Such interventions could be critical if necessary for avoiding threats, or a threat in themselves if harms caused by the intervention are significant.

The interdisciplinary nature of such issues as well as the unpredictability of various local responses considering their potential for global impact further complicate the question.

The study aims to review the available evidence and discuss the problem of weighting the predictability of interventions vis-à-vis their intended results against the limits of knowability regarding complex non-linear systems and thus the predictability in non-interventionist approaches.

How to cite: Chiotinis, M., Dimitriadis, P., Illiopoulou, T., Mamassis, N., and Koutsoyiannis, D.: To act or not to act. Predictability of intervention and non-intervention in health and environment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11747, https://doi.org/10.5194/egusphere-egu22-11747, 2022.

How to cite: Alberti, T. and Faranda, D.: COVID-19 waves: intrinsic and extrinsic spatio-temporal dynamics over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12302, https://doi.org/10.5194/egusphere-egu22-12302, 2022.

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the COVID-19 pandemic led to a major reduction in human activity during the past thirty years. Here, the concentration of BC in the urban, urban-industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multi-wavelength Aethalometer to estimate the impact of the COVID-19 lockdown on BC emissions. The citywide BC decreased by 44% from 2.30 μg/m³ to 1.29 μg/m³ following the COVID-19 lockdown period. The source apportionment based on the Aethalometer model shows that vehicle emission reduction responded to BC decline in the urban area and biomass burning in rural areas around the megacity had a regional contribution of BC. We highlight that the emission controls of vehicles in urban areas and biomass burning in rural areas should be more efficient in reducing BC in the megacity Hangzhou.

How to cite: Li, W. and Xu, L.: Responses of concentration and sources of black carbon in a megacity during the COVID-19 pandemic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12907, https://doi.org/10.5194/egusphere-egu22-12907, 2022.

How to cite: Ghil, M.: Time-dependent forcing in the geosciences and in epidemiology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13522, https://doi.org/10.5194/egusphere-egu22-13522, 2022.

Standard epidemic models based on compartmental differential equations are investigated under continuous parameter change as external forcing. We show that seasonal modulation of the contact parameter superimposed upon a monotonic decay needs a different description from that of the standard chaotic dynamics. The concept of snapshot attractors and their natural distribution has been adopted from the field of the latest climate change research. This shows the importance of the finite-time chaotic effect and ensemble interpretation while investigating the spread of a disease. By defining statistical measures over the ensemble, we can interpret the internal variability of the
epidemic as the onset of complex dynamics—even for those values of contact parameters where originally regular behaviour is expected. We argue that anomalous outbreaks of the infectious class cannot die out until transient chaos is presented in the system. Nevertheless, this fact becomes apparent by using an ensemble approach rather than a single trajectory representation. These findings are applicable generally in explicitly time-dependent epidemic systems regardless of parameter values and time scales.

How to cite: Kovács, T.: How can contemporary climate research help understand epidemic dynamics? -- Ensemble approach and snapshot attractors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13534, https://doi.org/10.5194/egusphere-egu22-13534, 2022.

How to cite: Abad, L., Hölbling, D., Dabiri, Z., and Robson, B.: An open-source Python package for DEM generation and landslide volume estimation based on Sentinel-1 imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-693, https://doi.org/10.5194/egusphere-egu22-693, 2022.

Spaceborne digital elevation models (DEMs) are fundamental data for mapping and analyzing geomorphic features at regional and continental scale, but are limited by both their spatial resolution and accuracy. Typically, accuracy is measured using point- or profile-based geodetic measurements (e.g., sparse GNSS). We develop new methods to quantify the vertical uncertainty in spaceborne DEMs relevant to geomorphic analysis, focusing on the pixel-to-pixel variability internal to a given DEM, which we term the inter-pixel consistency. Importantly, the methods we develop are not based on external, geodetic measurements. Our codes are published open-source (https://github.com/UP-RS-ESP/DEM-Consistency-Metrics), and we particularly highlight a novel sun-angle rotation and hillshade-filtering approach that is based on the visual, qualitative assessment of DEM hillshades. Since our study area is in the arid Central Andes and contains diverse steep (volcano) and flat (salar) features, the environment is ideal for vegetation-free assessments of DEM quality across a range of topographic settings. We compare global 1 arcsec (~30 m) resolution DEMs (SRTM, ASTER, ALOS, TanDEM-X, Copernicus), and find high quality (high inter-pixel consistency) of the newest Copernicus DEM. At higher spatial resolution, we also seek to improve the stereo-processing of 3 m SPOT6 optical DEMs using the open-source AMES Stereo-Pipeline. This includes optimizing key parameters and processing steps, as well as developing metrics for DEM uncertainty masks based on the underlying image texture of the optical satellite scenes used to triangulate elevations. Although higher resolution spaceborne DEMs like SPOT6 are only available for limited spatial areas (depending on funds and processing power), the improvement in geomorphic feature identification and quantification at the hillslope scale is significant compared to 30 m datasets. Improved DEM quality metrics provide useful constraints on hazard assessment and geomorphic analysis for the Earth and other planetary bodies.

How to cite: Purinton, B. and Bookhagen, B.: DEM quality assessment and improvement in noise quantification for geomorphic application in steep mountainous terrain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1191, https://doi.org/10.5194/egusphere-egu22-1191, 2022.

One of the main environmental threats to sustainability and crop productivity in the agricultural sector is soil erosion. For the mitigation of this problem in agricultural fields, no-till management is considered a key approach. The measurement of soil erosion is particularly challenging, especially when surficial morphological changes are relatively small. Conventional experiments are commonly time-consuming and labour-intensive in terms of both field surveys and laboratory methods. However, the Structure from Motion (SfM) photogrammetry technique has enhanced the experimental activities by enabling the temporal evolution of soil erosion to be assessed through detailed micro-topography. This work presents a multitemporal quantification of soil erosion, using SfM through Uncrewed Aerial Vehicles (UAV) survey for understanding the evolution of no-till (NT) and conventional tillage (CT) in experimental plots. Considering that plot-scale soil surface (mm grid size) by several orders of magnitude, it was necessary to minimise SfM errors (e.g., co-registration and interpolation) in volumetric estimates to reduce noise as much as possible. Therefore, a methodological workflow was developed to analyse and identify the effectiveness of multi-temporal SfM-derived products, e.g. the conventional Difference of Digital Terrain Models (DoDs) and the less used Differences of Meshes (DoMs), for soil volume computations. To recognise the most suitable estimation method, the research validated the erosion volumetric changes calculated from the SfM outputs with the amount of soil directly collected through conventional runoff and sediment measurements in the field. This study presents a novel approach for using DoMs instead of DoDs to accurately describe the micro-topography changes and sediment dynamics. Another key and innovative aspect of this research, often overlooked in soil erosion studies, was to identify the contributing sediment surface, by delineating the channels potentially routing runoff directly to water collectors. The sediment paths and connected areas inside the plots were identified using a multi-temporal analysis of the sediment connectivity index for achieving the volumetric estimates. The DoM volume estimates showed better results with respect to DoDs and a mild overestimation compared to in-situ measurements. This difference was attributable to other factors (e.g., the soil compaction processes) or variables rather than to photogrammetric or geometric ones. The developed workflow enabled a very detailed quantification of soil erosion dynamics for assessing the mitigation effects of no-till management that can also be extended in the future to different scales with low-costs, based on SfM and UAV technologies.

How to cite: Cucchiaro, S., Carretta, L., Nasta, P., Cazorzi, F., Masin, R., Romano, N., and Tarolli, P.: Assessment of soil erosion induced by different tillage practices through multi-temporal geomorphometric analyses, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2000, https://doi.org/10.5194/egusphere-egu22-2000, 2022.

The eroding rocky coasts export sediment to the ocean, the amount of which is poorly known. At the global scale it could amounts 0.15-0.4 Gt/a (1). Recent evaluations of large retreat rates on monitored sections of sea cliffs indicate it can be comparable to the sediment input from medium to large rivers. We quantify rocky coast input to the ocean sediment budget at the European scale, the continent characterized by the best dataset.

The sediment budget from European rocky coasts has been computed from cliff lengths, heights and retreat rates. For that, we first compiled a large number of well-documented retreat rates; the analysis of whom showed that the retreat rates are at first order explained by cliff lithology (GlobR2C2, 2). Median erosion rates are 2.9 cm/a for hard rocks, 10 cm/a for medium rocks and 23 cm/a for weak rocks. These retreat rates were then applied to the European coast classification (EMODnet), giving the relative coast length for cliffs of various lithology types. Finally the cliff height comes from the EU-DEM (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/elevation).

Due to data availability, we only worked on ~70% of the whole Europe, corresponding to a 127,000 km-long coastline (65,000 km of rocky coast). We calculated it originates 111±65 Mt/a, corresponding to 0.38 times the sediment input from rivers from the equivalent area (3.56 10⁶ km²), calculated after Milliman and Farnsworth (3)’s database (290 Gt/a). A crude extrapolation to the 1.5 10⁶ km-long Earth’s coastline reaches an amount of 0.6-2.4 Gt/a, an order of magnitude less that the sediment discharge from rivers (11-21 Gt/a, e.g., 3).

This up-to-now overlooked sedimentary source must further be explored for: (i) its effects on the geochemical ocean budget; (ii) the rising sea level control on the cliff retreat rates; and (iii) the characteristics and location of sediment deposition on ocean margins.

References

(1) Mahowald NM, Baker AR, Bergametti G, Brooks N, Duce RA, Jickells TD, Kubilay N, Prospero JM, Tegen I (2005). Atmospheric global dust cycle and iron inputs to the ocean: ATMOSPHERIC IRON DEPOSITION. Global Biogeochemical Cycles 19. DOI: 10.1029/2004GB002402

(2) Prémaillon M, Regard V, Dewez TJB, Auda Y (2018). How to explain variations in sea cliff erosion rates? Insights from a literature synthesis. Earth Surface Dynamics Discussions:1–29. DOI: https://doi.org/10.5194/esurf-2018-12

(3) Milliman J, Farnsworth K (2011). River Discharge to the Coastal Ocean: A Global Synthesis. Cambridge University Press

How to cite: Regard, V., Prémaillon, M., Dewez, T., Carretier, S., Jeandel, C., Godderis, Y., Bonnet, S., Schott, J., Pedoja, K., Martinod, J., Viers, J., and Fabre, S.: Coastal erosion: an overlooked source of sediments to the ocean. Europe as an example, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2877, https://doi.org/10.5194/egusphere-egu22-2877, 2022.

Linear dunes show a wide variety of morphometrical patterns; their sizes, spacing, defect density, and orientations differ not only between but also within dunefields (Thomas 1986; Bullard et al. 1995; Hesse 2011; Hugenholtz et al. 2012). The first step towards characterising dune patterning is to accurately and precisely map dunefields, which is challenging, especially when dunefields are too large to be mapped manually. Thus, (semi-)automatic approaches have been brought forward (Telfer et al. 2015; Shumack et al. 2020; Bryant & Baddock 2021). Here, we are presenting the prototype of a deep learning workflow that allows for the automated mapping of large linear dunefields through semantic segmentation.

The algorithm includes the following components: 1) the download of satellite imagery; 2) pre-processing of training and prediction data; 3) training of a Neural Network; and 4) applying the trained Neural Network to classify satellite imagery into dune and non-dune pixels. The workflow is python-based and uses the deep learning API keras as well as a variety of spatial analysis libraries such as earthengine and rasterio.

A case study to apply and test the algorithm’s performance was conducted on Sentinel-2 satellite imagery (10 m spatial resolution) of the southwest Kalahari Desert. The resulting predictions are promising, despite the small amount of data the model was trained on.

The presented prototype is work in progress. Further developments will include parameter optimisation, exploring ways to improve the objectiveness of training data, and the conduction of case studies applying the algorithm to digital elevation rasters.

How to cite: Nowatzki, M., Bailey, R., and Thomas, D.: Prototype of a deep learning workflow to map dunes in the Kalahari, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3002, https://doi.org/10.5194/egusphere-egu22-3002, 2022.

Pit-and-mound (treethrow, windthrow) topography is a result of tree uprooting caused by the impact of hurricane-speed wind events. Analyzing its location and morphometric features can improve our knowledge about the influence of winds on forest ecosystem dynamics and on changes in the forest floor microrelief. This is important in terms of hillslope denudation and soil evolution.

The occurrence and evolution of pit-mound topography can be studied with the use of high-resolution elevation data. Such data can be obtained from LiDAR (Light Detection and Ranging) surveys. Polish Institute of Geodesy and Cartography carried the LiDAR survey in the years 2010-2015. Point cloud data for the entire area of Poland with the minimal density of 4 points per m² is currently available on the Internet.

Under the present project, we have analyzed Digital Elevation Models (DEMs) produced from the above-mentioned LiDAR data in order to develop and test a new method for automatic detection of pit-mound topography. As far as we know, no such method exists at the moment. We generated DEMs with 0.5 m spatial resolution for three study sites with the confirmed occurrence of pit-mound topography, located in Southern Poland. A script with the method was written in the R programming language.

The proposed method is based on contour lines. We found that the detection of pit and mound topography formed on gentle hillslopes is possible when closed contours are delineated. Detected forms can be classified into “pits” and “mounds” by investigating point positions with the highest and the lowest elevation within the closed contour. On the other hand, for steep surfaces pit-mound topography can be detected by calculating distances between contours and selecting slope segments with between-contours distances above a certain threshold value. This leads to the identification of gently-sloped areas within the study site. With a high probability, such areas indicate places, where pit-mound topography was formed. To validate our methods, we performed the on-screen assessment of DEMs for the presence of forms that could be interpreted as pit-mound topography.

The study has been supported by the Polish National Science Centre (project no 2019/35/O/ST10/00032).

How to cite: Godziek, J. and Pawlik, Ł.: Automatic detection of pit-mound topography from LiDAR based DEMs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3781, https://doi.org/10.5194/egusphere-egu22-3781, 2022.

Data-driven reconstructions of palaeo-ice sheets based on their landform records are required for validation and improvement of numerical ice sheet models. In turn, such models can be used to better predict the future responses of the Antarctic and Greenland ice sheets to climate change. We are exploiting the recent expansion in availability and coverage of very-high-resolution (1–2 m) digital elevation models (DEMs) within the domain of the former Fennoscandian Ice Sheet to reconstruct its flow pattern evolution from the glacial landform record.

The Fennoscandian Ice Sheet reached its maximum extent at 21–20 ka. Previous data-driven reconstructions over the whole ice sheet domain (encompassing Fennoscandia, northern continental Europe and western Russia) have necessarily relied upon landform mapping from relatively coarse-resolution (decametre-scale) data, predominantly from satellite images and aerial photographs. However, high-resolution (1–2 m/pixel resolution) LiDAR DEMs have recently become available over a large portion of the ice sheet domain above contemporary sea level. This reveals previously unobserved assemblages of landforms which record past ice sheet flow, including fine-scale cross-cutting and superposition relationships between landforms. These observations are likely to reveal previously unidentified complexity in the flow evolution of the ice sheet. However, the richness of the data available over such a large area amplifies labour-intensity challenges of data-driven whole-ice-sheet reconstructions; it is not possible to map every flow-related landform (or even a majority of the landforms) manually in a timely manner. We therefore present a new multi-scale sampling approach for systematic and comprehensive ice-sheet-scale mapping, which aims to overcome the data-richness challenge while maintaining rigor and providing informative data products for model-data comparisons.

We present in-progress mapping products covering Finland, Norway and Sweden produced using our new multi-scale sampling approach. The products include mapping of >200 000 subglacial bedforms and bedform fields, and a summary map of ‘landform linkages’. Landform linkages summarise the detailed landform mapping but do not extrapolate over large distances between observed landforms. Thus, they provide a reduced data product that is useful for regional-scale flow reconstruction and model-data comparisons and remains closely tied to landform observations. The landform linkages will be reduced further into longer interpretative flowlines, which we will then use to generate ‘flowsets’ describing discrete ice flow patterns within the ice sheet. We will use cross-cutting relationships observed in the detailed landform mapping to ascribe a relative chronology to overlapping flowsets where relevant. We will then combine the flowsets into a new reconstruction of the flow pattern evolution of the ice sheet.

How to cite: Butcher, F. E. G., Hughes, A. L. C., Ely, J. C., Clark, C. D., Lewington, E. L. M., Boyes, B. M., Scoffield, A. C., Howcutt, S., and Dowling, T. P. F.: A new, multi-scale mapping approach for reconstructing the flow evolution of the Fennoscandian Ice Sheet using high-resolution digital elevation models., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4765, https://doi.org/10.5194/egusphere-egu22-4765, 2022.

The Bradanic Trough (Southern Italy) is the Pliocene-present-day south Apennines foredeep. It is filled by a thick Pliocene to Pleistocene sedimentary succession constituted by hemipelagites (Blue Clay Fm.) in the lower part, and coarse grained deposits (sands and conglomerates) in the upper part, shaped in marine or continental terraced environment.

On the eastern border of the Bradanic Trough along the Murgian Plateau (Apulia, Italy) numerous morphological lineaments are associated with sequential lowering and rotation of the surface, aligned with the carbonate substrate dip direction.

These morphologies have been interpreted so far as erosion products; their association with medium-deep water circulations and surface phenomena, like mud volcanoes, now allows their interpretation as a lumped mass, detached and tilted along shear surfaces.

The surface patterns of such surfaces may be easily detected for the presence, at some distance, of a quite similar twin track, which overlaps with good agreement.

The numerical analysis of the tracks extracted from accurate DTMs allows us to reconstruct the kinematic patterns of the tectonic displacement (distance of the detachment; rotation; angle of the shear plane). This type of analysis might reveal very useful in some fields of engineering geology, such as underground works, and for interpreting many hydrogeological phenomena within the study area. Finally, the correct 3D representation of the detached masses helps to identify the true causes of the direct faulting, which is not always linked to the tectonics, not active in the concerned regions.

How to cite: Spilotro, G., Andriani, G. F., Di Prizio, G., Decaro, K., Parisi, A., and Fidelibus, M. D.: Kinematic patterns of tectonic displacements in the Blue Clay outcrops along the eastern border of the Bradanic Trough (Southern Italy) from DTM data processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5872, https://doi.org/10.5194/egusphere-egu22-5872, 2022.

The Gulf of Mexico is characterized by a high geodiversity that influences hydrodynamics patterns and drives biological and human uses of the seafloor. In 2017, the United States Bureau of Ocean Energy Management released a 1.4-billion-pixel bathymetric dataset of the deep northern Gulf of Mexico, with a pixel size of about 12m. The computational power required to analyze this dataset has limited its use so far. Here, geomorphometry was used to characterize the seafloor of the deep northern Gulf of Mexico at multiple spatial resolutions. Flat areas and slopes cover more than 70% of the studied area, yet thousands of smaller morphological features like peaks and pits were identified. Spatial comparisons confirmed that analyses at different spatial scales capture different features. A composite product combining seafloor classification at multiple scales helped highlight the dominant seafloor features and the scale at which they are best captured.

How to cite: Lecours, V.: Geomorphometry of the deep Gulf of Mexico, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5990, https://doi.org/10.5194/egusphere-egu22-5990, 2022.

The long-term (ka to ma) degradation of a volcanic edifice is controlled by both regional (e.g., climate, tectonics) and local factors (e.g., original morphology, lithology), resulting in both long-lasting weathering and river incision and short-term hazardous events, such as flank collapses and lahars. Trends among the morphometry of stratovolcanoes, their drainage network, denudation, and regional factors were recently characterised for composite volcanoes along the Indonesian arc. Denudation was shown to be negatively correlated with drainage density; the across-arc variations expose a tectonic control on the level of denudation and volcanoes’ irregularity. This study applies the same method on age-constrained volcanoes in Japan to find coherent trends between arcs despite the different local and regional factors. We aim to better understand the factors that control erosion rates and patterns, and the evolutionary phases of volcano degradation.       

We first compile a dataset of 35 singular, non-complex composite volcanoes with known eruption ages and spatially spread throughout the Japanese Island arc system. Using 30m TanDEM-X Digital Elevation Models, morphologies, and drainage metrics (e.g., volume, height, slopes, irregularity index, Hack’s Law exponent, and drainage density) are extracted for each volcano, using the MORVOLC algorithm adapted in MATLAB as well as the newly developed DrainageVolc algorithm. Correlations between the morphometric parameters and potential controlling factors (e.g., age, climate, lithology, and tectonics) are analysed to determine quantitative relationships of edifice degradation throughout the arc. Finally, we compare relationships and correlation values of the Japanese Arc system to those from the Indonesian Arc.   

The analysis shows that volcano age is positively correlated with irregularity and negatively correlated with height and volume. From the drainage parameters, we find that basins become wider and merge, resulting in lower drainage densities. The variation in erosion rates along the Japanese arc provides evidence for the degree of climatic control on the volcano degradation. The between-arc comparison shows which trends are susceptible to arc-scale variations and highlights consistent trends that have the potential to be extrapolated to other volcanic arcs and be used as a relative age determination tool for composite volcanoes.

How to cite: van Wees, R. M. J., O'Hara, D., Grosse, P., Kereszturi, G., Lahitte, P., and Kervyn, M.: Quantifying the morphometry and drainage patterns of composite volcanoes: A comparison of the Japanese and Indonesian volcanic arcs  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6152, https://doi.org/10.5194/egusphere-egu22-6152, 2022.

The Augšdaugava spillway valley located in SE Latvia has a system of river terraces formed by both glacio-fluvial and fluvial processes. The flight of terraces forms a staircase-like relief in the riverine landscape and represents the evidence of valley evolution during the transition from glacial to post-glacial conditions in this region. Hence terraces are substantial ‘archives’ of paleoenvironmental data and their geomorphometry could provide key information for untangling geomorphological history of the spillway valley. Hence the need for precise identification and mapping of terraces is obvious. However, these landforms, particularly upper terraces commonly are poorly preserved. It is a result of the interplay of many geological processes – channel incision, lateral erosion in the course of the river Daugava meandering, mass wasting etc., leaving discontinuous remnants of terraces along to the present-day long profile of the river. Previously, mapping of these features was performed via extensive field surveys and to some extent by interpretation of aerial images or topographic maps, because the presence of tree cover hinders the identification of terraces by conventional geomorphological techniques. Thereby due to the poor preservation of fluvial landforms and the abundant vegetation cover, the previously mapped terrace surfaces and inferred levels may be questionable.

Yet the now available high-resolution LiDAR data in Latvia and application of modern GIS-based techniques offer an opportunity to resolve these problems. Hence the main goal of the study was to apply a methodology based on using LiDAR-derived DEM and combining different semi-automated GIS analysis tools for the identification, mapping and morphometric analysis of fluvial terraces in the valley. In this study, LiDAR data coverage (courtesy of the Latvian Geospatial Information Agency) was used to generate a DEM. LiDAR coverage consists of 317 data folders in *.LAS format, each one of 1 km² extent. DEM with 0.5 x 0.5 m pixel resolution and <15 cm vertical accuracy was created by ArcGIS PRO tool ‘LAS Dataset to Raster’ following the standard procedure of the IDW interpolation. After the construction of DEM, the TerEx toolbox integrated into the ArcGIS environment was used for the extraction and delineation of terrace surfaces. After the completion of GIS works, the ground-truthing of the obtained data on the location of fluvial terraces was performed during field geomorphological reconnaissance.

DEM analysis allowed to identify the terrace sequence in the Augšdaugava spillway valley consisting of eight different terrace levels – T1 to T8. From the applied methodology, authors were able to delineate surfaces of river terraces in those parts of the valley, where in the course of previous research terraces were interpreted incorrectly or even not identified at all. However, only terraces T1 and T2 can only be unambiguously identified by GIS-based extraction. Upper terraces with smoothened edges due to mass wasting and surfaces dissected by gullies are not easily recognizable. Hence, the presence of minor landforms which increase the topographical roughness of the surface directly influences the quality of extracted data, thus leading to the necessity of an extensive amount of manual editing.

How to cite: Soms, J. and Vorslavs, V.: Identification, GIS-based mapping and morphometric analysis of river terraces from airborne LiDAR data in the Augšdaugava spillway valley, South-eastern Latvia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6177, https://doi.org/10.5194/egusphere-egu22-6177, 2022.

Bankfull channel dimensions are of fundamental importance in fluvial geomorphology, to describe the geomorphic character of a river, as inputs to models which explain variations in morphology through time and space, and as initial processing steps in more detailed morphometric techniques. With ever-increasing availability of high-resolution elevation data (e.g. LiDAR), manual delineation of channel extents is a bottleneck which limits the geomorphic insights that can be gained from that data.

We developed and tested two automated channel delineation methods that define bankfull according to different criteria and thus reflect different conceptualisations of bankfull extent: (1) a cross-sectional method (termed HydXS) that identified the elevation which maximises hydraulic depth (cross-section area/wetted width); and (2) a neural network image segmentation model trained on images derived from a LiDAR digital elevation model.

HydXS outperformed the neural network method overall, but the two methods were comparable in larger streams (> 20 m bankfull width; Dice coefficient ~0.85). Prediction accuracy of HydXS was generally high (overall precision 89%; recall 81%), performing well even in small streams (bankfull width ~ 10 m). HydXS performed worst in incised and recovering stream sections (precision 93%; recall 64%) where the choice between macro-channel and inset channel was somewhat arbitrary (both for the algorithm and manual delineation). The neural network outperformed HydXS where an inset channel was present. The neural network method performed worst in small streams and where other features (e.g. road embankments, small ditches) were misclassified as channels. Neural network performance was improved markedly by trimming the area of interest to a 100-m wide buffer along the stream, eliminating many areas prone to misclassification.

The two methods provide different ways to effectively leverage high-resolution LiDAR datasets to gain information about channel morphology. These methods are a significant step forward as they can delineate bankfull elevation, as well as bankfull width, and operate using morphology alone. HydXS is an objective method that doesn’t require training, can be run on consumer-level hardware, and can perform well in small streams, but requires manual work to develop the necessary spatial framework of an accurate channel centerline. The neural network model is a promising method to delineate larger channels (>20 m wide) without requiring detailed centerline or cross-section data, given adequate training data for the stream type of interest (i.e. expert-delineated bankfull channel extents). We envisage that further improvement of the neural network method is possible by scaling the input image extents to catchment area, and training on a larger dataset from multiple regions to increase generalizability. 

How to cite: Russell, K., Garber, J., Thompson, K., Kunapo, J., Burns, M., and Zhang, G.: Automated tools for identifying bankfull river channel extents: developing and comparing objective and machine-learning methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6681, https://doi.org/10.5194/egusphere-egu22-6681, 2022.

Despite the long record of applications and the well-known theoretical framework, geostatistical based image/surface texture tools have still not gained a wide diffusion in the context of geomorphometric analysis, even for the evaluation of surface roughness. Many geomorphometric studies dealing with various aspect of surface roughness use well-known approaches based on vector dispersion of normals to surface or even the popular Topographic Ruggedness Index. In many comparative studies on roughness metrics, geostatistical approaches are cited but not tested; in other studies, geostatistical approaches are tested using algorithms not adapted to the analysis of morphometric data. In remote sensing, geostatistical approaches are more popular, even if there is not a consensus on which are the most suited metrics for computing image texture indices. In metrology of manufactured surfaces, equipped by various industrial standards for surface texture measurements, approaches based on autocorrelation are widely adopted.  However, “natural” surfaces and related morphogenetic factors are much more complex than manufactured surfaces and ad-hoc concepts and algorithms should be devised. This presentation is mainly focused on topographic surface analysis, but the considerations and results are applicable also in the context of image analysis. This presentation aims to clarify some aspects of the geostatistical methodologies, highlighting the effectiveness and flexibility in the context of multiscale and directional evaluation of surface texture. In doing this, the connections with other methodologies and concepts related to spatial data analysis are highlighted. Finally, it is introduced a simplified algorithm for computing surface roughness indices, which does not require the preliminary detrending of the input DEM.

References

ATKINSON, P.M. and LEWIS, P., 2000. Geostatistical classification for remote sensing: An introduction. Computers and Geosciences, 26(4), pp. 361-371.

BALAGUER, A., RUIZ, L.A., HERMOSILLA, T. and RECIO, J.A., 2010. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Computers and Geosciences, 36(2), pp. 231-240.

GUTH, P.L., 2001. Quantifying terrain fabric in digital elevation models. GSA Reviews in Engineering Geology, 14, pp. 13-25.

HERZFELD, U.C. and HIGGINSON, C.A., 1996. Automated geostatistical seafloor classification - Principles, parameters, feature vectors, and discrimination criteria. Computers and Geosciences, 22(1), pp. 35-41.

TREVISANI, S., CAVALLI, M. and MARCHI, L., 2009. Variogram maps from LiDAR data as fingerprints of surface morphology on scree slopes. Natural Hazards and Earth System Science, 9(1), pp. 129-133.

TREVISANI, S., CAVALLI, M. and MARCHI, L., 2012. Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin. Geomorphology, 161-162, pp. 26-39.

TREVISANI, S. and ROCCA, M., 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, 81, pp. 78-92.

TREVISANI, S. and CAVALLI, M., 2016. Topography-based flow-directional roughness: Potential and challenges. Earth Surface Dynamics, 4(2), pp. 343-358.

How to cite: Trevisani, S.: Returning to geostatistical-based analysis of image/surface texture: from generalization to a basic one-click short-range surface roughness algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6924, https://doi.org/10.5194/egusphere-egu22-6924, 2022.

With the development of remote sensing technologies the application of different geospatial models in research has become increasingly important. Terrain relief is the difference in elevation between the high and low points of a land surface, that is, the change in the height of the ground over the area. Terrain relative relief (or elevation) is the relative difference in elevation between a morphological feature and those features surrounding it (e.g. height difference between a peak and surrounding peaks, a depression and surrounding depressions etc.). Together with terrain morphology, ppland other terrain attributes, it is useful for describing how the terrain affects intertidal and subtidal processes.

 Appropriate decision-making tools are required for urban and rural planning, design and management. The usage of DEM (Digital Elevation Model), DSM (Digital Surface Model) and DTM (Digital Terrain Model) helps researchers and designers to analyse issues connected with drainage, geology, earth crust movements, sound and radio-wave distribution, wind effects, exposure to sun, etc. Analysis of the future scenarios of geospatial models has an essential role in the field of water management and various environmental topics. This research aims to focus on the environmental issues in a context of water quality and hydrology.

How to cite: Grinberga, L., Celms, A., Sietins, K., Lidumnieks, T., Brinkmanis-Brimanis, M., and Luksa, J.: The Application of Relief Models for Environmental Solutions: Review, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7860, https://doi.org/10.5194/egusphere-egu22-7860, 2022.

High-resolution Light Detection and Ranging (LiDAR) data provide unique opportunities for landscape-scale mapping of hydrological features. LiDAR-derived digital elevation models are particularly valuable for identifying channel networks in densely forested landscapes, where satellite imagery-based mapping approaches are challenged by forest canopies. Artificial drainage practices have caused widespread alteration of northern landscapes of Europe and North America which likely have had significant impacts on hydrological connectivity and ecosystem functioning. However, these artificial channels are rarely considered in ecosystem management and poorly represented in existing geomorphological datasets. In this study, we conducted a landscape-scale analysis across 11 selected study regions in Sweden using LiDAR data for the virtual reconstruction of artificial drainage ditches to understand the extent of their ecological impacts.

We utilized a 0.5 m resolution digital elevation model for mapping natural channel heads and artificial ditches across the study regions. We also implemented a unique approach by back-filling ditches in the current digital elevation model to recreate the prehistoric landscape. This enabled us to map and model the channel networks of prehistoric (natural) and current (drained) landscapes. We found that 58% of the prehistoric natural channels had been converted to ditches. Moreover, the average channel density increased from 1.33 km km^‑2 in the prehistoric landscape to 4.66 km km^-2 in the current landscape, indicating substantial ditching activities in the study regions.

Our study highlights the need for accurate delineation of natural and artificial channel networks in northern landscapes for effective ecosystem restoration and management. We presented an innovative technique for comparing the channel networks between the prehistoric natural landscape and current modified landscape by integrating advanced LiDAR data, extensive manual digitization, and modeling; a highly suitable combination for channel network mapping in dense forest landscapes. The developed methodology can be implemented in any landscape for understanding the extent of human modification of natural channel networks to guide future environmental management activities and policy formulation.

How to cite: Paul, S. S., Hasselquist, E. M., Lidberg, W., and Ågren, A. M.: Mapping of natural and artificial channel networks in forested landscapes using LiDAR data to guide effective ecosystem management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8728, https://doi.org/10.5194/egusphere-egu22-8728, 2022.

Ecodiversity stems from the interaction between the biosphere and the geosphere, and it is one of the necessary conditions for achieving a sustainable planet. Thus, the relationship between geodiversity and biodiversity should be clearly defined. The  relationship between climate and topography in roughened mountain areas at low-latitudes, as constrains for the high values of biodiversity, has already been established. As a consequence, topography is the first and most important input parameter for investigating the connections between abiotic and biotic variety. Spatial analysis in a GIS framework is the key approach to better understand the role of topographic and hydrographic variables in evaluating geodiversity (geomorphodiversity) .

In this paper we focused on analyzing urban areas, where in 2030 60% of the world's population is expected to live. A science of cities is the future challenge for Earth Sciences: urban geomorphology could be the key to have a complete overview on the abiotic and biotic parameters in sustainable cities. To achieve this aim, the conservation of urban biodiversity is fundamental. Analysing the correlation between substantial geodiversity and biodiversity may be a guideline for science of cities and for designing and managing sustainable urban areas.

These ideas, if transposed in an urban context, should go beyond morphometric analysis of topography and take into account anthropogenic features and natural landforms modified by humans in time.  To this end, geomorphological mapping is fundamental to calibrate the quantitative models in a truly multidisciplinary approach to a science of cities and urban biodiversity. We consider our contribution as a new model for the analysis of geodiversity in urban areas.

How to cite: Burnelli, M., Alvioli, M., Melelli, L., and Pica, A.: Geodiversity as a key component for the evaluation of urban biodiversity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9650, https://doi.org/10.5194/egusphere-egu22-9650, 2022.

State-of-the-art applications in various earth science domains shows that different classification methods are playing an increasingly important role in mapping due to their improving accuracy. However, in the field of geological mapping, the exclusive use of morphometric and spectral indices in classification models are still often considered as subsidiary mapping tools. This is particularly true in areas where the surface is covered by vegetation and the soil layer is relatively thick, since in such places geological structures can only be observed at first hand at rock outcrops. The aim of our research is to investigate the automatic mapping of rock outcrops in the Dorog Basin in Hungary, where outdated geological maps are currently being updated. In this research, we applied the random forest classification combined with a wider range of input data including satellite imagery and ecosystem information.

The Dorog Basin, located in northern central Hungary, has a medium-density settlement network, with built-up and cultivated areas alternating with areas of wooded or scrub-covered terrain with rugged topography. The region is tectonically fragmented, where former fluvial erosion is of great importance. In several cases the Mesozoic carbonates, Paleogene limestones or limnic coal sequences outcrop the Quaternary sediments resulting a diverse, although a well identifiable surface. In the 86.86 km² study area, the input of the model included 14 morphometrical raster layers derived from SRTM-1, six raster layers with mineral indices derived from Sentinel II, and one ecosystem layer [1], all set to a uniform ~25m resolution. To test the performance of random forest classification in modelling pre-Quaternary formations, we applied two different approaches. In the first one, we used conventional training areas to model pre-Quaternary outcrops, as well as we modelled the physical characteristics of the surface formations. Whereas in the second one, we modelled the pre-Quaternary outcrops and physical characteristics of the surface formations by using randomly selected zones on the study area with around 6000-10000 random training polygons. The randomly generated training polygons were circles of about 1-2 pixels in size around points.  The training areas were derived from the former geological map of the Dorog Basin [2]. The importance of input parameters were also observed for further use. A six-fold cross-validation of the selected training areas showed that the two methods were equally accurate, but the automatic processing of randomly selected training areas was faster.

Based on the modelling results, the pre-Quaternary rock outcrops of the area can be determined with at least 80% confidence using random forest classification. These results will be used in future field mapping, which will also provide a field validation of the method.

From the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.

[1] Ecosystem Map of Hungary. DOI: 10.34811/osz.alapterkep

[2] Gidai, L., Nagy, G., & Sipass, S. (1981). Geological map of the Dorog Basin 1: 25 000. [in Hungarian] Geological Institute of Hungary, Budapest.

How to cite: Pogácsás, R. and Albert, G.: Automatic detection of rock outcrops on vegetated and moderately cultivated areas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10469, https://doi.org/10.5194/egusphere-egu22-10469, 2022.

Remotely piloted aircrafts (UAVs) and Structure-from-Motion photogrammetry (SfM) have become a widely used approach for producing high-resolution topographical measurements of river systems. This approach has the benefit of capturing data over large spatial scales while requiring little time in the field. In small, forested rivers, the dense canopy has hindered the use of remote sensing techniques, limiting topographic data collection to more time-consuming and lower-resolution methods. This complicates monitoring the response of these systems to individual floods, as in many situations there is not enough time to complete more time-consuming surveys between events.

In this study, we pilot the use of sub-canopy UAV surveys (flown at 1-3 m altitude) to monitor the response of a small mountain stream (1-3 m wide) in British Columbia to a sediment pulse generated by the removal of an upstream culvert. Using eleven surveys flown over a three-year period, we track the downstream propagation of the pulse and the subsequent responses in bed topography and roughness along the 240 m reach. We observe a “build-and-carve” response of the channel, where some channel segments aggrade during the first floods after pulse generation, whereas others undergo little morphologic activity. In subsequent floods, these aggradational segments rework through the carving of well-defined channels that release this aggraded sediment downstream. These “build-and-carve” segments serve as temporary storage reservoirs that caused the pulse to fragment as it progressed downstream. The locations of these storage reservoirs were set by the initial channel morphology and the movement of in-stream wood and debris. This study highlights the importance of temporary sediment storage reservoirs for fluvial morphodynamics and provides some insights and suggestions for the future monitoring of forested river systems using sub-canopy drone surveys.

How to cite: McDowell, C., Carina, H., David A., R., and Marwan, H.: Response of a small mountain river to a sediment pulse tracked using sub-canopy UAV surveys, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10675, https://doi.org/10.5194/egusphere-egu22-10675, 2022.

Advancements in processing strategies of time series interferometric synthetic aperture radar (InSAR) has resulted in improved deformation monitoring and DEM generation. Both of the applications use phase unwrapping, which involves finding and adding the unknown correct number of phase cycles to the wrapped phase. It is an inverse process of recovering the absolute phase from the wrapped phase, and the objective is to remove the 2π-multiple ambiguity. Ideally, it could be achieved by addition or subtraction of 2π at each pixel depending on the phase difference between the neighboring pixels. The problem appears effortless but brings challenges due to noise and inconsistencies. The conventional methods require improvements in terms of accurately estimating the unknown number of phase cycles and dealing with phase jumps. Recently, deep learning methods have been used extensively in the domain of remote sensing to solve complex image processing problems such as object detection and localization, image classification, etc. Since all the pixels in a stack of interferograms are not used in unwrapping, and the pixels used are scattered irregularly, modeling the unwrapping problem as an image classification problem is infeasible. In this work, we deploy Graph Neural Networks (GNNs), a class of deep learning methods designed to infer information from input graphs to solve the unwrapping problem. Phase unwrapping can be posed as a node classification problem using GNN, where each pixel is treated as a node. The method is aimed to exploit the capability of GNNs in correctly predicting the phase count of each pixel. The proposed work aims to improve the computational efficiency and accuracy of the unwrapping process, resulting in reliable estimation of displacement.

How to cite: Srivastava, A., Tiwari, A., Narayan, A. B., and Dikshit, O.: InSAR phase unwrapping using Graph neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11010, https://doi.org/10.5194/egusphere-egu22-11010, 2022.

Understanding the mechanism of fault rupture is important to minimize earthquake damage and to estimate the impacts of future earthquakes. In this study, we observed surface displacements caused by the Hovsgol earthquake (Mw 6.7) in January 2021 using three Differential Interferometric SAR (DInSAR) pairs of Sentinel-1B at descending node and ALOS-2 at ascending and descending nodes, and then estimated the source parameters of the earthquake by the inversion of the observed displacement fields. The maximum surface displacement in the radar look direction was 21 cm at the Sentinel-1 descending node, and 32 cm and 26 cm at the ALOS-2 ascending and descending node, respectively. All differential interferograms showed three fringe patterns near the epicenter, which suggests that there were three rupture planes with different slips. We performed the inversion modeling of the DInSAR-observed surface displacements assuming three rupture planes with different slip magnitudes and directions. The values of normalized root mean square error (NRMSE) between the modelled and observed displacements were smaller than 4% for all DInSAR observations. The spatial distribution of modelled displacements was matched to the observed one. The source parameters of fault estimated by the inversion were closely consistent with the measurements by United States Geological Survey and Global Centroid Moment Tensor. The inversion results demonstrated that the assumption of our inversion modeling (three rupture planes) is reasonable.

How to cite: Kim, T. and Han, H.: Source parameters of the 2021 Hovsgol earthquake (Mw 6.7) in Mongolia estimated by using Sentinel-1 and ALOS-2 DInSAR, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11152, https://doi.org/10.5194/egusphere-egu22-11152, 2022.

1.    INTRODUCTION

Monitoring land use and land cover change (LULCC) is one of the best methods to understand the interactive changes of agriculture, climate change, and ecological dynamics. In eastern Asia, Taiwan is characterized by high population density, rich biodiversity, and complex terrain. However, recent climate change has impacted the people and ecosystems in Taiwan.  Therefore, we applied landscape metrics and the deep learning U-net semantic segmentation model to enhance the remote sensing images based LULCC monitoring efficiency and take a case study in suburban areas of central Taiwan, a place that plays an important economic role in Taiwan occupied with intensive agricultural activities.

2.    METHOD

This study focuses on six townships in Nantou County in Central Taiwan, where the major agricultural products are rice, tea, and fruit. We obtained four dates of Sentinel-2 images in February for 2018 and 2021 and classified the landscape into five classes: agricultural, forest, built-up, free water bodies, and bare land. The spectral bands information (Blue, Green, Red, NIR), the normalized difference vegetation index (NDVI), and soil-adjusted vegetation index (SAVI) were obtained for establishing the deep learning U-net semantic segmentation model. The accuracy and the loss function of the training model results are 0.89 and 0.02, respectively. In addition, the ground truth data was consulted with the official land-use classification information and the high spatial resolution imagery in Google Earth Pro. Finally, we analysed the classified images' results to detail the study area's changing trajectory to explore the complex spatiotemporal landscape patterns.

3.    RESULTS AND DISCUSSIONS

According to the result, the forest area on the eastern side accounts for more than 70% of the study area. The construction area and the agricultural area have an upward trend during the research period (16% and 5%); in addition, except for the number of patches in free water bodies decreased, all other categories had an upward trend, especially the construction and agricultural area are the largest. The Shannon's Evenness Index reflects that all patches are evenly distributed in space and the area-weighted average fractal dimension index decreases reflecting possible influences of anthropogenic activities. Thus, the results indicate an increasing level of fragmentation, supported by the decrease of the area-weighted average fractal dimension index. In conclusion, using satellite imagery with the deep learning U-net semantic segmentation model can sufficiently discern a detailed LULCC. Furthermore, with the combination of landscape matrix information, the interactions between humans and the environment can be understood better quantitatively.

References

Huete, A. R., Hua, G., Qi, J., Chehbouni, A., & Van Leeuwen, W. J. D., 1992: Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment, 41(2-3), 143-154.

Ronneberger, O., Fischer, P., & Brox, T., 2015: U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D., Deering, W. 1973: Monitoring vegetation systems in the Great Plains with ERTS, ERTS Third Symposium, NASA SP-351 I, pp. 309-317.

How to cite: Zhuang, Z.-H. and Tsai, H. P.: Application of Deep Learning Model to LULCC Monitoring using Remote Sensing Images-A case study in suburban areas of central Taiwan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11764, https://doi.org/10.5194/egusphere-egu22-11764, 2022.

To enhance the understanding of aeolian landforms and their processes, the assessment of origin, migration and evolution of newly-born sand dunes is vital. In this regard, Lake Urmia, in NW Iran, was considered as a representative case study, given that it has lost approximately two-thirds of its water volume in the past two decades and, consequently, the newly-born sand ridges and sand dunes on its western shores were formed. The emerging sand dunes are located close to the villages, adjacent to the agricultural and farmlands, international transit road, and industrial zone, encompassing the whole area. The present study aims to assess the sand dunes’ origin and their migration both in speed and direction in the past decade.

To understand the questions above, remote sensing techniques and in-field studies were coupled. Therefore, wind data from the closest meteorological station were employed to calculate the wind rose, drift potential (DP), the resultant drift potential (RDP), and the resultant drift direction (RDD) across the region. Change detection techniques using high-resolution satellite images were chosen to detect the migration rate and morpho-dynamic changes of Lake Urmia sand dunes. To classify the geomorphological features and land uses in the region, a hybrid supervised classification approach including a customised decision tree classifier was used to distinguish sand dune units from other signatures. Using the minimum bounding geometry method, feature classes were created. These feature classes represent the length, width, and orientation of sand dunes, retrieved after the image classification process. Also, fieldwork surveying was carried out on the sixteen sand dunes in different periods to measure the morphological and evolutionary changes.

 As the wind results show, the trend of DP parameters between the years 2006-2009 and the years 2015-2020, the percentage of wind speeds above the threshold velocity (V>Vt%) to DP has significant gaps, suggestive of weaker winds in those periods. However, between the years 2009-2015, the V>Vt% and DP values are corresponding and coequal. This indicates that the most erosive and shifting winds are between 2009-2015, with the weakest wind power in tails. Moreover, the annual variability of DPt is well correlated with Lake Urmia water level changes; but there is no correlation between the DPt and precipitation amount. The evaluation of image processing results depicted that after 2003, the area of sand dunes had dramatically increased. On average, the smallest area belongs to 2010 (287.3 m2), and the largest area is for years 2019 (775.96 m2), 2018 (739.08 m2), and 2017 (739.74 m2). In addition, between the years 2010 and 2014, a significant increase in area of the sand dunes from 287.25 to 662.8 m2 was observed. The migration rate is the highest between 2010 and 2015, with the lowest values before 2010 and after 2015.

The results of this study have broad implications in the context of sustainable development and climate-related challenges, ecosystem management and policy-making for regions with sand dune challenges, hence crucial insights can be gained by coupling remote sensing techniques and in-situ studies.

How to cite: Ahmady-Birgani, H., Ravan, P., Yao, Z., and Afrasinei, G. M.: Newly-Born Sand Dunes of Lake Urmia: Assessing Migration Rate and Morphodynamic Changes Using Remote Sensing Techniques and Field Studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12041, https://doi.org/10.5194/egusphere-egu22-12041, 2022.

The supervised mapping of landforms last years got high levels based on classic classification methods and new artificial intelligence techniques. However, it is often difficult to create train data for large and diverse areas, and we can face up with differences between expert-to-expert landforms interpretation. It can be solve using unsupervised classification - a less effective in general case, but more objective. The way to make more effective classification - to create special input variables (to account local specificity of landforms) aimed to show real terrain structure. Study region - Yamal Peninsula (Arctic coast of Russia), covered sea accumulative and erosional plains, reshaped by some cryogenic processes, especially thermokarst, with many lake hollows. We used ArcticDEM 32m and decomposition of DEM with 2D FFT by moving windows with sequence of sizes from 1.5 to 3 km (by the interval of 0.3 km) and with lag around 150 m (overlapping - 90-95 %). The 9 variables were computed: 1) magnitude of the main wave in the height field, 2) wavelength of the main wave, 3) importance (share of the height variation) of the fix pool of biggest harmonic waves, 4-6) orthogonal (N-S and W-E) components of the general direction of the height fluctuations (and the significance of the direction), 7-9) coefficients of the exponential trend equation for approximation wave's frequencies/magnitudes distribution. We then trained the model of landforms clustering for the study area using Kohonen network and the hierarchic clustering was used for additional generalization. The medium-scale (750 m / pix, it is matched to maps at the scale 1:500 000 - 1:1 000 000) map of Yamal Peninsula landforms was created. Seven classes of landforms were recognized. The study was supported by Russian Science Foundation (project no. 19-77-10036).

How to cite: Kharchenko, S.: Medium-scale unsupervised landform mapping of the Yamal Peninsula (Russia) using 2D Fourier decomposition of the ArcticDEM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12383, https://doi.org/10.5194/egusphere-egu22-12383, 2022.

Corrosion is one of the most critical issues leading to damage in reinforced concrete structures. In most cases, the detection of corrosion damage is performed by visual inspection. Other techniques (drilling cores with petrography or chemical examination, potential measurements, and resistivity measurements) require minimum destruction since they can be utilized by reaching the reinforcement bar [1]. Recently, there has been an increasing trend to use Ground Penetrating Radar (GPR) as one of the emerging non-destructive testing (NDT) techniques in the diagnosis of corrosion [2].

This paper focuses on a series of GPR tests on specimens constructed from poor-quality concrete and plain round bar. These specimens were subjected to accelerated corrosion tests under laboratory conditions. The corrosion intensity of those specimens is non-destructively assessed with GPR, by collecting data before and after corrosion tests. For GPR tests, the IDS Aladdin system was used with a double polarized 2 GHz antenna. Based on GPR measurement, Relative Dielectric Permittivity (RDP) values of concrete, are calculated based on the known dimension of specimens and two-way travel time (twt) values obtained from A-scans. The change in RDP values of specimens before and after exposure to corrosion is then computed. Moreover, amplitude change and variation in frequency spectrum before and after corrosion exposure are analyzed.

The results of this experimental study thus indicate that corrosion damage in reinforced concrete can be determined by using several GPR signal attributes. More laboratory tests are required for better quantification of the impact of the corrosion phenomenon in reinforced concrete.

All GPR tests were conducted in Educational and Research Centre in Transport; Faculty of Transport Engineering; University of Pardubice. This work is supported by the University of Pardubice (Project No: CZ.02.2.69/0.0/0.0/18_053/0016969).

[1]        V. Sossa, V. Pérez-Gracia, R. González-Drigo, M. A. Rasol, Lab Non Destructive Test to Analyze the Effect of Corrosion on Ground Penetrating Radar Scans, Remote Sensing. 11 (2019) 2814. https://doi.org/10.3390/rs11232814.

[2]        K. Tešić, A. Baričević, M. Serdar, Non-Destructive Corrosion Inspection of Reinforced Concrete Using Ground-Penetrating Radar: A Review, Materials. 14 (2021) 975. https://doi.org/10.3390/ma14040975.

How to cite: Artagan, S., Borecky, V., Yurdakul, Ö., and Luňák, M.: Experimental assessment of corrosion influence in reinforced concrete by GPR, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-74, https://doi.org/10.5194/egusphere-egu22-74, 2022.

As a recognised non-destructive testing (NDT) tool, Ground Penetrating Radar (GPR) is becoming increasingly common in the field of environmental engineering [1]-[3]. GPR uses electromagnetic (EM) waves which travel at specific velocity determined by the permittivity of the material. With the development of new GPR signal processing methodologies, finding information on the physical properties of hidden targets has become a key target. Currently, only three types of approach could be applied for the quantitative estimation of permittivity from GPR data, i.e., hyperbola curve fitting, common middle point (CMP) velocity analysis and full-waveform inversion. However, the main challenges for the estimation of permittivity from GPR backscattered signals are to provide effective and accurate strategy for prediction.

In this research, we used a dual-polarimetric GPR system to estimate the dielectric constant of targets. The system is equipped with two 2GHz antennas polarised perpendicularly each to one another (HH and VV). The dual polarisation enables deeper surveying, providing images of both shallow and deeper subsurface features. Polarimetry is a property of EM waves that generally refers to the orientation of the electric field vector, which plays here an important role as it allows either direct or parameterisation permittivity effects within the scattering problem in the remote sensing [4].

The aim of this research is to provide a novel and more robust approach for dielectric constant prediction using a dual-polarimetric GPR system. To this extent, the relationship between the relative permittivity and the polarimetric alpha angle have been investigated based on data collected by a GPR system with dual-polarised antennas. The approach was then assessed by laboratory experiments where different moisture sand targets (simulating the effect of different relative permittivity targets) were measured. After signal processing, a clear relationship between the alpha angle and the relative permittivity was obtained, proving the viability of the proposed method.

Acknowledgements

The authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

References

[1] Zou, L. et al., 2020. Mapping and Assessment of Tree Roots using Ground Penetrating Radar with Low-Cost GPS. Remote Sensing, vol.12, no.8, pp:1300.

[2] Zou, L. et al., 2020. On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System. IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 6, pp. 4215-4224.

[3] Zou, L. et al., 2021. Study on Wavelet Entropy for Airport Pavement Debonded Layer Inspection by using a Multi-Static GPR System. Geophysics, vol. 86, no. 3, pp. WB69-WB78.

[4] J. Lee and E. Pottier, Polarimetric Imaging: From Basics to Applications, FL, Boca Raton: CRC Press, 2009.

How to cite: Zou, L., Tosti, F., and Alani, A. M.: Dielectric Constant Estimation through Alpha Angle with a Polarimetric GPR System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1544, https://doi.org/10.5194/egusphere-egu22-1544, 2022.

Road pavements play a crucial role in the development of any construction as they provide safe surface on which vehicles can travel comfortably [1]. Pavements are multi-layered structures of processed and compacted materials in different thicknesses and in both unbound and bound forms with the function of supporting vehicle loads as well as providing a smooth riding quality. The condition of road pavement structures is susceptible to the impact of uncertain environmental factors and traffic loads, resulting in pavement deterioration over time. Therefore, the mechanical properties of pavements (such as strength, stiffness, etc.) need to be monitored on a regular basis to make sure that the pavement condition meets its prescribed threshold. The ground-penetrating radar (GPR) and deflection-based methods (e.g., the falling weight deflectometer (FWD)) are the most popular non-destructive testing (NDT) methods in pavement engineering science that are often used in combination to evaluate the damage and strength of pavements [2-4]. The layer thickness data from GPR scans are used as an input for deflection-based measurements to back-calculate the elastic moduli of the layers [2]. During the recent years, problems concerning the automatic interpretation of data from NDTs have received good attention and have simulated peer to peer interests in many industries like transportation. The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques for the interpretation of NDT data can offer many advantages such as the improved speed and accuracy of analysis, especially for large-volume datasets. This study aims to train a dataset collected from GPR (2 GHz horn antenna) and the Curviameter deflection-based equipment using AI and ML algorithms to classify road flexible pavements based on their mechanical properties. Curviameter data are used as ground-truth measurements of pavement stiffness, whereas the GPR data provide geometric and physical attributes of the pavement structure. Several methods such as support vector machine (SVM), artificial neural network (ANN), and k nearest neighbours (KNN) are proposed and their performance in terms of accuracy of estimation of the strength and deformation properties of pavement layers are compared with each other as well as with the classical statistical methods. The results of this study can help road maintenance officials to identify and prioritise pavements at risk and make cost-effective and informed decisions for maintenance.

References

[1] Tosti, F., Bianchini Ciampoli, L., D’Amico, F. and Alani, A.M. (2019). Advances in the prediction of the bearing capacity of road flexible pavements using GPR. In: 10th International Workshop on Advanced GPR, European Association of Geoscientists & Engineers, pages 1-5.

[2] Plati, C., Loizos, A. & Gkyrtis, K. Assessment of Modern Roadways Using Non-destructive Geophysical Surveying Techniques. Surv Geophys 41, 395–430 (2020). 

[3] A. Benedetto, F. Tosti, Inferring bearing ratio of unbound materials from dielectric properties using GPR, in: Proceedings of the 2013 Airfield and Highway Pavement Conference: Sustainable and Efficient Pavements, June 2013, pp. 1336–1347.

[4] Tosti, F., Bianchini Ciampoli, L., D’Amico, F., Alani, A.M., Benedetto, A. (2018). An experimental-based model for the assessment of the mechanical properties of road pavements using GPR. Construction and Building Materials, Volume 165, pp. 966-974.

How to cite: Dinmohammadi, F., Bianchini Ciampoli, L., Tosti, F., Benedetto, A., and Alani, A. M.: On the use of Artificial Intelligence for classification of road pavements based on mechanical properties using ground-penetrating radar and deflection-based non-destructive testing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1849, https://doi.org/10.5194/egusphere-egu22-1849, 2022.

To the waterborne ground-penetrating radar detection, reverse-time migration (RTM) method can image the structure of the bottom of the water and locate the buried bodies. However, the image quality is limited by the attenuation of electromagnetic waves. How to compensate the attenuation becomes a critical problem. Some RTM methods related to the attenuation-compensated have been developed in recent years. We use the attenuation-compensated RTM based on the minus conductivity. However, the method is limited by the estimation of the attenuation coefficient. Here, we propose an attenuation-coefficient estimation method based on the centroid frequency downshift method (CFDS). In EM attenuation tomography, the centroid frequency downshift method works for attenuation estimation. Compared with the CFDS method in tomography, our proposal is based on the centroid frequency of the bottom-interface of water instead of the source wavelet. Thus, we can avoid the problem of the unknown source wavelet. The method is based on two assumptions: 1) GPR data can be regarded as zero-offset records. 2) Reflections from underwater interfaces are independent of frequency. In addition, the formula about the attenuation coefficient shows when the ratio between the conductivity and the product of the dielectric constant and the angular frequency is greater than one, the attenuation coefficient tends to be a constant. This does not meet the assumption that the attenuation coefficient is linearly related to frequency. We will select a proper frequency range to meet the linear relation by the spectral ratio method. Because the ratio of the signal spectrum of the bottom interface to the spectrum of the underwater interface is consistent with the change of the attenuation coefficient with frequency. Then, the CFDS method will acquire a linear attenuation coefficient with the frequency. Finally, we choose half of the central frequency to acquire the estimated attenuation coefficient. We design a layered waterborne GPR detection model, the conductivity of the silt layer varies between 0.1 and 0.01. The error of the conductivity estimation is below 10%. After acquiring the attenuation coefficient, the attenuation-compensated RTM works correctly and effectively.

How to cite: Shen, R., Zhao, Y., Cheng, H., and Ge, S.: Attenuation-compensated reverse-time migration of waterborne GPR based on attenuation coefficient estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2166, https://doi.org/10.5194/egusphere-egu22-2166, 2022.

It is a truism that pavements deteriorate due to the combined effects of traffic loads and environmental conditions. The manner or ability of a road to meet the demands of traffic and the environment and to provide at least an acceptable level of performance to road users throughout its life is referred to as pavement performance. An important indicator of pavement performance is ride quality. This is a rather subjective measure of performance that depends on (i) the physical properties of the pavement surface, (ii) the mechanical properties of the vehicle, and (iii) the acceptance of the perceived ride quality by road users. Due to the subjectivity of ride quality assessment, many researchers have worked in the past to develop an objective indicator of pavement quality. The International Roughness Index (IRI) is considered a good indicator of pavement performance in terms of road roughness. It was developed to be linear, transferable, and stable over time and is based on the concept of a true longitudinal profile. Following the identification and quantification of ride quality by the IRI, pavement activities include the systematic collection of roughness data in the form of the IRI using advanced laser profilers, either to "accept" an as-built pavement or to monitor and evaluate the functional condition of an in-service pavement.

On the other hand, pavement performance can vary significantly due to variations in layer thickness, primarily due to the construction process and quality control methods used. Even if a uniform design thickness is specified for a road section, the actual thickness may vary. It is expected that the layer thickness will have some probability distribution, with the highest density being around the target thickness. Information on layer thickness is usually obtained from as-built records, from coring or from Ground Penetrating Radar (GPR) surveys. GPR is a powerful measurement system that provides pavement thickness estimates with excellent data coverage at travel speeds. It can significantly improve pavement structure estimates compared to data from as-built plans. In addition, GPR surveys are fast, cost effective, and non-destructive compared to coring.

The present research developed a sensing approach that extends the capability of GPR beyond its ability to estimate pavement thickness. Specifically, the approach links GPR thickness to IRI based on the principle that a GPR system and a laser profiler are independent sensors that can be combined to provide a more complete image of pavement performance. To this end, field data collected by a GPR system and a laser profiler along highway sections are analyzed to evaluate pavement performance and predict future condition. The results show that thickness variations are related to roughness levels and specify the deterioration of the pavement throughout its lifetime.

How to cite: Plati, C., Loizos, A., and Georgouli, K.: An approach to integrate GPR thickness variability and roughness level into pavement performance evaluation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2253, https://doi.org/10.5194/egusphere-egu22-2253, 2022.

Monitoring of bridges and viaducts has become a priority for asset owners due to progressive infrastructure ageing and its impact on safety and management costs. Advancement in data processing and interpretation methods and the accessibility of Synthetic Aperture Radar (SAR) datasets from different satellite missions have contributed to raise interest for use of near-real-time bridge assessment methods. In this context, the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) space-borne monitoring technique has proven to be effective for detection of cumulative surface displacements with a millimetre accuracy [1-3].

This research aims to investigate the viability of using satellite remote sensing for structural assessment of the Rochester Bridge in Rochester, Kent, UK. To this purpose, high-resolution SAR datasets are used as the reference information and complemented by additional data from different sensing technologies (e.g., medium-resolution SAR datasets and ground-based (GB) non-destructive testing (NDT)). In detail, high-resolution SAR products of the COSMO-SkyMed (CSK) mission (2017-2019) provided by the Italian Space Agency (ASI) in the framework of the Project “Motib - ID 742”, approved by ASI, are processed using a MT-InSAR approach.

The method allowed to identify several Persistent Scatterers (PSs) – which have been associated to different structural elements (e.g., the bridges piers) over the four main bridge decks – and monitor bridge displacements during the observation time. The outcomes of this study demonstrate that information from the use of high-resolution InSAR data can be successfully integrated to datasets of different resolution, scale and source technology. Compared to stand-alone technologies, a main advantage of the proposed approach is in the provision of a fully-comprehensive (i.e., surface and subsurface) and dense array of information with a larger spatial coverage and a higher time acquisition frequency. This results in a more effective identification and monitoring of decays at reduced costs, paving the way for implementation into next generation Bridge Management Systems (BMSs).

Acknowledgements: This research is supported by the Italian Ministry of Education, University and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. Funding from MIUR, in the frame of the“Departments of Excellence Initiative 2018–2022”,attributed to the Department of Engineering of Roma Tre University, is acknowledged.Authors would also like to acknowledge the Rochester Bridge Trust for supporting research discussed in this paper. The COSMO-SkyMed (CSK) products - ©ASI- are provided by the Italian Space Agency (ASI) under a license to use in the framework of the Project “ASI Open-Call - Motib (ID 742)” approved by ASI.

References

[1] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Alani A. M., Tosti F., Battagliere M. L., Benedetto A., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”, Proc. SPIE 11525, SPIE Future Sensing Technologies. 2020. doi: 1117/12.2579700

[2] Jung, J.; Kim, D.-j.; Palanisamy Vadivel, S.K.; Yun, S.-H. "Long-Term Deflection Monitoring for Bridges Using X and C-Band Time-Series SAR Interferometry". Remote Sens. 2019

[3] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Tosti F., Alani A. and Benedetto A. “A Novel Geo-Statistical Approach for Transport Infrastructure Network Monitoring by Persistent Scatterer Interferometry (PSI)”. In: 2020 IEEE Radar Conference, Florence, Italy, 2020, pp. 1-6, doi: 10.1109/RadarConf2043947.2020.9266336

How to cite: Gagliardi, V., Bianchini Ciampoli, L., D’Amico, F., Battagliere, M. L., Threader, S., Alani, A. M., Benedetto, A., and Tosti, F.: Monitoring of Bridges by Satellite Remote Sensing Using Multi-Source and Multi-Resolution Data Integration Techniques: a Case Study of the Rochester Bridge, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2341, https://doi.org/10.5194/egusphere-egu22-2341, 2022.

Maintenance of airport runways is crucial to comply with strict safety requirements for airport operations and air traffic management [1]. Therefore, monitoring pavement surface defects and irregularities with a high temporal frequency, accuracy and spatial density of information becomes strategic in airport asset management [2-3]. In this context, Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets, proving their viability for the long-term evaluation of ground scatterers. However, the implementation of C-band SAR data as a routine tool in Airport Pavement Management Systems (APMSs) for the accurate measurement of differential displacements on runways is still an open challenge [4]. This research aims to demonstrate the viability of using medium-resolution (C-band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, Sentinel-1A SAR products, available through the European Space Agency (ESA) Copernicus Program, were acquired and processed to monitor displacements on “Runway n.3” of the “L. Da Vinci International Airport” in Fiumicino, Rome, Italy.A geostatistical study is performed for exploring the spatial data structure and for the interpolation of the Sentinel-1A SAR data in correspondence of ground control points.The analysis provided ample information on the spatial continuity of the Sentinel 1 data, also in comparison with the high-resolution COSMO-SkyMed and the ground-based topographic levelling data, taken as the benchmark.Furthermore, a comparison between the MT-InSAR outcomes from the Sentinel-1A SAR data, interpolated by means of Ordinary Kriging, and the ground-truth topographic levelling data demonstrated the accuracy of the Sentinel 1 data. Results support the effectiveness of using medium-resolution InSAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways. Outcomes of this study can pave the way for the development of more efficient and sustainable maintenance strategies for inclusion in next-generation APMSs.  

Acknowledgments and fundings: The authors acknowledge the European Space Agency (ESA), for providing the Sentinel 1 SAR products for the development of this research. The COSMO-SkyMed Products—©ASI (Italian Space Agency)- are delivered by ASI under the license to use.This research falls within the National Project “EXTRA TN”, PRIN 2017, supported by MIUR. The authors acknowledge funding from the MIUR, in the frame of the “Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University

 References

[1]Gagliardi V., Bianchini Ciampoli L., D'Amico F., Tosti F., Alani A. and Benedetto A. “A Novel Geo-Statistical Approach for Transport Infrastructure Network Monitoring by Persistent Scatterer Interferometry (PSI)”. In: 2020 IEEE Radar Conference, Florence, Italy, 2020, pp. 1-6

[2]Gagliardi V, Bianchini Ciampoli L, Trevisani S, D’Amico F, Alani AM, Benedetto A, Tosti F. "Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis". 2021; 21(17):5769. https://doi.org/10.3390/s21175769

[3]Gao, M.; Gong, H.; Chen, B.; Zhou, C.; Chen, W.; Liang, Y.; Shi, M.; Si, Y. "InSAR time-series investigation of long-term ground displacement at Beijing Capital International Airport, China". Tectonophysics 2016, 691, 271–281.

[4]Department of Transportation Federal Aviation Administration (FAA), Advisory Circular 150/5320-6F, Airport Pavement Design and Evaluation, 2016

How to cite: Gagliardi, V., Trevisani, S., Bianchini Ciampoli, L., D’Amico, F., Alani, A. M., Benedetto, A., and Tosti, F.: Monitoring of Airport Runways by Satellite-based Remote Sensing Techniques: a Geostatistical Analysis on Sentinel 1 SAR Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2533, https://doi.org/10.5194/egusphere-egu22-2533, 2022.

Maintenance and rehabilitation policies represent a task of paramount importance for managers and administrators of railway networks to maintain the highest standards of transport safety while limiting as much as possible the costs of maintenance operations.

To this effect, high-productivity survey methods become crucial as they allow for timely recognition of the quality of the asset elements, among which the ballast layers are the most likely to undergo rapid deterioration processes. Particularly, Ground Penetrating Radar (GPR) has received positive feedback from researchers and professionals due to the capability of detecting signs of deterioration within ballasted trackbeds that are not recognizable by a visual inspection at the surface, through high-productivity surveys. On the other hand, satellite-based surveys are nowadays being increasingly applied to the monitoring of transport assets. Techniques such as Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) allows evaluating potential deformations suffered by railway sections and their surroundings by analyzing phase changes between multiple images of the same area acquired at progressive times. 

For both of these techniques, despite the wide recognition by the field-related scientific literature, survey protocols and data processing standards for the detection and classification of the quality of ballast layers are still missing. In addition, procedures of integration and data fusion between GPR and InSAR datasets are still very rare.

The present study aims at demonstrating the viability of the integration between these two survey methodologies for a more comprehensive assessment of the condition of ballasted track-beds over a railway stretch. Particularly, a traditional railway section going from Cava de’ Tirreni to Salerno, Campania (Italy), was subject to both GPR and MT-InSAR inspections. An ad hoc experimental setup was realized to fix horn antennas with different central frequencies to an actual inspection convoy that surveyed the railway stretch in both the travel directions. Time-frequency methods were applied to the data to detect subsections of the railway affected by the poor quality of ballast (i.e. high rate of fouling). In parallel, a two-years MT-InSAR analysis was conducted to evaluate possible deformations that occurred to the railway line in the period before the GPR test. In addition, results from both the analyses were compared to the reports from visual inspections as provided by the railway manager.

The results of the surveys confirm the high potential of GPR in detecting the fouling condition of the ballast layers at various stages of severity. The integration of this information to the outcomes of InSAR analysis allows for identifying whether the deterioration of the track-beds is related to poorly bearing subgrades or rather to excessive stresses between the aggregates resulting in their fragmentation.

Acknowledgments

This research is supported by the Italian Ministry of Education, University, and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. Funding from MIUR, in the frame of the“Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University, is acknowledged. The authors would also like to express their gratitude to RFI S.p.a. in the person of Eng. Pasquale Ferraro for the valuable support to the tests.

How to cite: Bianchini Ciampoli, L., Gagliardi, V., D'Amico, F., Clementini, C., Latini, D., and Benedetto, A.: Quality assessment in railway ballast by integration of NDT methods and remote sensing techniques: a study case in Salerno, Southern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2712, https://doi.org/10.5194/egusphere-egu22-2712, 2022.

Detecting decay in tree trunks is essential in considering tree health and safety. Continual monitoring of tree trunks is possible using a digital model, which can contain incremental assessment data on tree health. Researchers have previously employed non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) to study both the external and internal physical dimensions of objects and structures [1], including tree trunks [2]. Light Detection and Ranging (LiDAR) technology is also continually employed in infrastructure and asset management to generate models and to detect surface displacements with millimeter accuracy [3]. Nevertheless, the scanning of structures using these existing state-of-the-art technologies can be time consuming, technical, and expensive.

This work investigates the design and implementation of a smartphone app for scanning tree trunks to generate a 3D digital model for later visualization and assessment. The app uses LiDAR technology, which has recently become available in smart devices, for instance, the Apple iPhone 12+ and the iPad Pro. With the prevalence of internet-of-things (IoT) sensors, digital twins are being increasingly used in a variety of industries, for example, architecture and manufacturing. A digital twin is a digital representation of an existing physical object or structure. With our app, a digital twin of a tree can be developed and maintained by continually updating data on its dimensions and internal state of decay. Further, we can situate and visualize tree trunks as digital objects in the real world using augmented reality, which is also possible in modern smart devices. We previously investigated tree trunks using GPR [2] to generate tomographic maps, to denote level of decay. We aim to adopt a data integration and fusion approach, using such existing (and incremental GPR data) and an external LiDAR scan to gain a full 3D ‘picture’ of tree trunks.

We intend to validate our app against state-of-the-art techniques, i.e., laser scanning and photogrammetry. With the ability to scan tree trunks within reasonable parameters of accuracy, the app can provide a relatively low-cost environmental modelling and assessment solution for researchers and experts.

Acknowledgments: Sincere thanks to the following for their support: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

References

[1] Alani A. et al., Non-destructive assessment of a historic masonry arch bridge using ground penetrating radar and 3D laser scanner. IMEKO International Conference on Metrology for Archaeology and Cultural Heritage Lecce, Italy, October 23-25, 2017.

[2] Tosti et al., "The Use of GPR and Microwave Tomography for the Assessment of the Internal Structure of Hollow Trees," in IEEE Transactions on Geoscience and Remote Sensing, Doi: 10.1109/TGRS.2021.3115408.

[3] Lee, J et al., Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit. 2019; 26:e2428.

How to cite: Uzor, S., Tosti, F., and Alani, A. M.: Low-cost scanning of tree trunks for analysis and visualization in augmented reality using smartphone LiDAR and digital twins, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3247, https://doi.org/10.5194/egusphere-egu22-3247, 2022.

The need to monitor and evaluate the impact of natural phenomena on structures, infrastructures, as well as on the natural environment, in recent years, plays a role of considerable importance for society also due to the continuous occurrence of "catastrophic events" which recently faster change our Planet.

Innovation and research have allowed a profound change in the data acquisition and acquisitions methodology coming to develop increasingly complex and innovative technologies. From an application point of view, remote sensing gives the possibility to easily manage the layer information which is indispensable for the best characterization of the environment from a numerical and a chemical-physical point of view.

NeMeA Sistemi srl, observant to the environment and its protection for years, began to study it using RADAR / SAR (Synthetic Aperture RADAR) data thanks to the opportunity to use in the best way the COSMO-SkyMed data through the tender Open Call for SMEs (Small and Medium Enterprises) of the Italian Space Agency in 2015.

Since then, NeMeA Sistemi srl has started a highly focused and innovative training that led us to observe the Earth in a new way. The path undertaken in NeMeA Sistemi srl is constantly growing and allowed us to know the RADAR / SAR data and the enormous potential.

The COSMO-SkyMed data provided is treated, processed and transformed by providing various information, allows you to identify changes, classify objects and artifacts measuring them.

In this context, NeMeA Sistemi srl in 2016 proposed a first project for the monitoring of illegal buildings in the Municipality of Ventimiglia (Liguria), with positive results. In this context, the final product was obtained with classic standard classification techniques of the SAR data.

 Following this positive experience, NeMeA Sistemi srl applied also to the regional call issued by Sardegna Ricerche for the Sardinia Region where the source of funding is the European Regional Development Fund (ERDF) 2014-2020.

The SardOS project (Sardinia Observed from Space), proposed by NeMeA Sistemi srl, aims to monitor and safeguard environmental and anthropogenic health in the territory of 4 Sardinian municipalities (Alghero, Capoterra, Quartu and Arzachena), also identifying the coast profiles, the evolutionary trend of sediments in the riverbed and buildings not present in the land registry. For environmental monitoring purposes, COSMO-SkyMed data are exploited and combined with bathymetric measurements acquired using the Hydra aquatic drone owned by NeMeA Sistemi srl. SAR data were processed using innovative specific territorial analysis algorithms in urban environment.

After these successful cases studies, which allowed the development of new services for the territorial monitoring and control, NeMeA Sistemi srl is working on a new project, 3xA (Creation of Machine Learning and Deep Learning algorithms dedicated to pattern recognition in SAR data). By exploiting Artificial Intelligence, the implemented algorithms use innovative unsupervised techniques to identify any changes.

The objective of this document is to provide an overview of the experience gained in NeMeA Sistemi srl, the value-added products and innovative services developed in the company aimed at environmental monitoring, the prevention of dangers and natural risks.

How to cite: Pennino, I.: A strategy of territorial control: from the standard comparison techniques to the Advanced Unsupervised Deep Learning Change Detection in high resolution SAR images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3799, https://doi.org/10.5194/egusphere-egu22-3799, 2022.

Rebar Corrosion is one of the main causes of deterioration of engineering reinforced structure. This degradation reduces the service life and durability of the structures. Such degradation can result in the collapse of engineering structures. When the first cracks are noticed on the concrete surface, corrosion has generally reached an advanced stage and maintenance action is required. The early detection of rebar corrosion of bridges, tunnel, buildings and other civil engineering structures is important to reduce the expensive cost to repair the deteriorated structure. Several techniques have been developed for understanding the mechanism and kinetics of the corrosion of rebar, but the paper defines the interest of combining several NDT for field inspection to overcome the limitation of measuring instantaneous corrosion rates and to improve the estimation of the service life of RC structures. Non-destructive testing and evaluation of the rebar corrosion is a major issue for predicting the service life of reinforced concrete structures.

This paper introduces a laboratory test, that was performed at Geophysical Laboratory of Ferrara University. The test consisted in a multisensor application concerning rebar corrosion monitoring using different geophysical methods on a concrete sample of about 50 x 30 cm with one steel rebar of 10 mm diameter. An accelerating reinforcement bar corrosion using direct current (DC) power supply with 5% sodium chloride (NaCl) solution was used to induce rebar corrosion. The 2GHz GPR antenna by IDS, the ERT with Abem Terrameter and Self-Potential with Keithley multivoltmeter at high impedance were used for rebar corrosion monitoring. A multisensor approach should reduce the errors resulting from measurements, and improve synergistically the estimation of service life of the RC.

Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. The collected data were used for an integration approach to obtain an evolution of the phenomenon of corrosion of the reinforcement bar. All the three methods were able to detect the physical parameter variation during the corrosion phenomena, but more attention is necessary on natural corrosion, that is a slow process and the properties of the experimental steel–concrete interface may not be representative of natural corrosion. However, each of these geophysical methods possesses certain advantages and limitations, therefore a combination of these geophysical techniques, with an multisensor approach is recommended to use to obtain the corrosion condition of steel and the condition of concrete cover.  Moreover, extrapolating laboratory results performed with a single rebar to a large structure with interconnected rebars thus remains challenging. Therefore, during the next experiments, special care must be taken regarding the design and preparation of the samples to obtain meaningful information for field application.

How to cite: Rizzo, E., Fornasari, G., Capozzoli, L., De Martino, G., and Giampaolo, V.: Rebar corrosion monitoring with a multisensor non-destructive geophysical techniques., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4437, https://doi.org/10.5194/egusphere-egu22-4437, 2022.

In the last years, unmanned surface vehicles (USVs) in marine environment have attracted considerable interest since they are flexible observation platforms suitable to operate in remote areas on demand. Accordingly, their usage has been proposed in several contexts such as research activities, military operations, environmental monitoring and oil exploration [1]. However, most of current USV remote control techniques are based on human-assisted technology thus a fully autonomous USV system is still an open issue [2].

The safety of the vehicle and the ability to complete the mission depends crucially on the capability of detecting objects on the sea surface, which is necessary for collision avoidance. Anti-collision systems for USVs typically require measurements collected from multiple sensors (e.g. Lidar, cameras, etc.), where each sensor has its own advantages and disadvantages in terms of resolution, field of view (FoV), operative range and so on [3].

Among the available sensing technologies, radar is capable of operating regardless of weather and visibility conditions, has moderate costs and can be easily adapted to operate within the marine environment. Furthermore, radar is characterized by an excellent coverage and high resolution along the range coordinate and it is also able to guarantee a 360° FoV in the horizontal plane.

Nautical radars are the most popular solutions to detect floating targets on the sea surface; however, they are bulky and not always effective in detecting small objects located very close to the radar.

This contribution investigates the applicability of a compact and lightweight 24 GHz multiple-input multiple-output (MIMO) radar originally developed for automotive applications to localize floating targets at short ranges (from tens to few hundreds of meters). In this frame, we propose an ad-hoc signal processing strategy combining MIMO technology, detection, and tracking algorithms to achieve target localization and tracking in a real-time mode. A validation of the proposed signal processing chain is firstly performed thanks to numerical simulations. After, preliminary field tests carried out in the marine environment are presented to assess the performance of the radar prototype and of the related signal processing.

References

• [1] Zhixiang et al. "Unmanned surface vehicles: An overview of developments and challenges", Annual Reviews in Control, vol. 41, pp. 71-93, 2016
• [2] Caccia, M. Bibuli, R. Bono, G. Bruzzone, “Basic navigation, guidance and control of an unmanned surface vehicle”, Autonomous Robots, vol. 25, no. 4, pp. 349-365, 2008
• [3] Robinette, M. Sacarny, M. DeFilippo, M. Novitzky, M. R. Benjamin, “Sensor evaluation for autonomous surface vehicles in inland waterways”, Proc. IEEE OCEANS 2019, pp. 1-8, 2019.

How to cite: Ludeno, G., Gennarelli, G., Noviello, C., Esposito, G., Catapano, I., and Soldovieri, F.: A 24 GHz MIMO radar for the autonomous navigation of unmanned surface vehicles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4826, https://doi.org/10.5194/egusphere-egu22-4826, 2022.

Ground penetrating radar (GPR) is a geophysical method that uses high frequency electromagnetic waves to detect underground or internal structures of objects. It has been widely used in the Geo-engineering and environment detection. In recent years, GPR has played an increasingly important role in shallow underwater structure survey due to its advantages of economy, high efficiency and high accuracy. However, due to the strong reflection coefficients of water surface and bottom for electromagnetic waves, there are multiples in the GPR profile acquired in waters, which will reduce the signal-to-noise ratio of the data and even lead to false imaging, finally seriously affect the reliability of the interpretation result. With the increasing requirement of high-precise GPR detection in waters, multiple suppression has become an essential issue in expanding the application fields of GPR. In order to suppress multiple waves in waterborne GPR profile, a novel multiple wave suppression method based on the combination scheme of the predictive deconvolution and free surface multiple wave suppression (SRME). Based on the validity test of one-dimensional data, the adaptive optimizations of these two methods are carried out according to the characteristics of GPR data in waters. First, the prediction step of predictive deconvolution can be determined by picking up the bottom reflection signal. Second, the water layer information provided by the bottom reflection is used in continuation from the surface to the bottom to suppress the internal multiples. The numerical model and real data test results show that each single method can suppress most of the multiples of the bottom interface and the combination strategy can further remove the additional residues. The research provides a basis for the precise interpretation of GPR data in hydro-detection.

How to cite: Zhao, Y., Shen, R., and Cheng, H.: Multiples suppression scheme of waterborne GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4912, https://doi.org/10.5194/egusphere-egu22-4912, 2022.

Modern roadways provide road users with both a comfortable and safe ride to their destinations. Increases in traffic demands and maximum allowable loads imply that roadway authorities should also care for the structural soundness of pavements. In parallel, budgetary limitations and frequent road closures for rehabilitation activities, especially in heavy-duty motorways, might guide the related authorities to focus their strategies on the preservation of pavements functional performance. However, structural issues concerning pavement damage remain on the forefront, as pavement’s service life extends beyond its design life; thus structural condition assessment is required to ensure pavement sustainability in the long-term.

Non-Destructive Testing (NDT) has played a major role during condition monitoring and evaluation of rehabilitation needs. Together with input from visual inspections and/or sample destructive testing (e.g. coring), NDT data help to define indicators and threshold values that assist the related decision-making for pavement condition assessment. The most indicative tool for structural evaluation is the Falling Weight Deflectometer (FWD) that senses roadway surfaces through geophones recording load-induced deflections at various locations. Additional geophysical inspection data with the Ground Penetrating Radar (GRP) is used to estimate pavement’s stratigraphy. Integrating the above sensing data enables the estimation of pavement’s performance and its damage potential.

To this end, a major challenge that pavement engineers face, concerns the assumptions made about the mechanical characterization of pavement materials. Asphalt mixtures, located on the upper pavement layers, behave in a viscoelastic mode because of temperature- and loading frequency- dependency, whereas in the contrary, simplified assumptions for linear elastic materials are most commonly made during the conventional NDT analysis. In this research, an integration of mainly NDT data and sample data from cores extracted in-situ is followed to comparatively estimate the long-term pavement performance through internationally calibrated damage models considering different assumptions for asphalt materials. Two damage modes are considered including bottom-up and top-down fatigue cracks that are conceptually perceived as alligator cracks and longitudinal cracks respectively alongside a roadway’s surface. As part of an ongoing research for the long-term pavement condition monitoring, data from a new pavement was considered at this stage indicating a promising capability of NDT data towards damage assessment.

Overall, this study aims to demonstrate the power of pavement sensing data towards structural health monitoring of roadways pinpointing the significance of database development for a rational management throughout a roadway’s service life. Furthermore, data from limited destructive testing enriches the pavement evaluation processes with purely mechanistic perspectives thereby paving the way for developing integrated protocols with improved accuracy for site investigations, especially at project-level analysis, where rehabilitation design becomes critical.

How to cite: Gkyrtis, K., Loizos, A., and Plati, C.: Sensing roadway surfaces for a non-destructive assessment of pavement damage potential, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4914, https://doi.org/10.5194/egusphere-egu22-4914, 2022.

Seismic and ultrasound tomography can provide rich information about spatial variations of elastic properties inside a material rendering this method ideal for non-destructive testing. These tomographic methods primarily use direct and reflected waves, but are also strongly affected by waves scattering at small-scale structures below the resolution limit. As a consequence, conventional tomography has the ability to unveil the deterministic large-scale structure only, rendering scattered waves imaging noise. To image scattering and absorption properties, we presented the adjoint envelope tomography (AET) method that is based on a forward simulation of wave envelopes using Radiative Transfer Theory and an adjoint (backward) simulation of the envelope misfit, in full analogy to full-waveform inversion (FWI). In this algorithm, the forward problem is solved by modelling the 2-D multiple nonisotropic scattering in an acoustic medium with spatially variable heterogeneity and attenuation using the Monte-Carlo method. The fluctuation strength ε and intrinsic quality factor Q^-1 in the random medium are used to describe the spatial variability of scattering and absorption, respectively. The misfit function is defined as the differences between the full squared observed and modelled envelopes. We derive the sensitivity kernels corresponding to this misfit function that is minimized during the iterative adjoint inversion with the L-BFGS method. This algorithm has been applied in some numerical tests (Zhang et al., 2021). In the present work, we show real data results from an ultrasonic experiment conducted in a reinforced concrete specimen. The later coda waves of the envelope processed from the 60 KHz ultrasonic signal are individually used for intrinsic attenuation inversion whose distribution has similarity to the temperature distribution of the concrete block. Based on the inversion result of intrinsic attenuation, scattering strength is inverted from early coda waves separately, which successfully provides the structure of the small-scale heterogeneity in the material. The resolution test shows that we recover the distribution of heterogeneity reasonably well.

How to cite: Zhang, T., Sens-Schönfelder, C., Epple, N., and Niederleithinger, E.: Ultrasonic Scattering and Absorption Imaging for the Reinforced Concrete using Adjoint Envelope Tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5731, https://doi.org/10.5194/egusphere-egu22-5731, 2022.

Street trees are a critical asset for the urban environment due to the variety of environmental and social benefits provided [1]. However, the conflicting coexistence of tree root systems with the built environment, especially with road infrastructure, frequently results in extensive damage, such as the uplifting and cracking of sidewalks and curbs, endangering pedestrians, cyclists, and road drivers’ safety.

Within this context, ground penetrating radar (GPR) is gaining recognition as an accurate non-destructive testing (NDT) method for tree roots’ assessment and mapping [2]. Nevertheless, the investigation methods developed so far are often inadequate for application on street trees, as these are often difficult to access. Recent studies have focused on implementing new survey and processing techniques for rapid tree root assessment based on combined time-frequency analyses of GPR data [3].  

This research also explores the adoption of a geostatistical approach for the spatial data analysis and interpolation of GPR data. The radial development of roots and the complexity of root network constitute a challenging setting for the spatial data analysis and the recognition of specific spatial features.

Preliminary results are therefore presented based on a geostatistical analysis of GPR data. To this end, 2-D GPR outputs (i.e., B-scans and C-scans) were analysed to quantify the spatial correlation amongst radar amplitude reflection features and their anisotropy, leading to a more reliable detection and mapping of tree roots. The proposed processing system could be employed for investigating trees difficult to access, such as road trees, where more comprehensive analyses are difficult to implement. Results' interpretation has shown the viability of the proposed analysis and will pave the way to further investigations.

Acknowledgements

The authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

References

[1]         Tyrväinen, L., Pauleit, S., Seeland, K., & de Vries, S., 2005. "Benefits and uses of urban forests and trees". In: Urban Forests and Trees. Springer, Berlin, Heidelberg.

[2]         Lantini, L., Tosti, F., Giannakis, I., Zou, L., Benedetto, A. and Alani, A. M., 2020. "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar," Remote Sensing 12(20), 3417.

[3]         Lantini, L., Tosti, F., Zou, L., Ciampoli, L. B., & Alani, A. M., 2021. "Advances in the use of the Short-Time Fourier Transform for assessing urban trees’ root systems." Earth Resources and Environmental Remote Sensing/GIS Applications XII. Vol. 11863. SPIE, 2021.

How to cite: Lantini, L., Trevisani, S., Gagliardi, V., Tosti, F., and Alani, A. M.: An investigation into road trees’ root systems through geostatistical analysis of GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6168, https://doi.org/10.5194/egusphere-egu22-6168, 2022.

The near-surface geophysical methods have been widely applied to investigations of shallow targets for scientific and engineering research. Various data processing algorithms are available to help visualize targets, data interpretation, and finally, achieve research goals.

Most of the available algorithms are Fourier-based with linear stationary assumptions. However, the real data are rarely the case and should be treated as nonlinear and non-stationary. In recent decades, a few newer algorithms are proposed for processing non-stationary, or nonlinear and non-stationary data, for instance, wavelet transform, curvelet transform, full-waveform inversion, Hilbert-Huang transform, etc. This progress is encouraging, but conventional algorithms still have many advantages, like strong theoretical bases, fast, and easy to apply, which the newer algorithms are short of.

In this study, we try to fuse both conventional and contemporary algorithms in near-surface geophysical methods. A cost-effective ground-penetrating radar (GPR) data processing scheme is introduced in shallow depth structure mapping as an example. The method integrates a nonlinear filtering technique, natural logarithmic transformed ensemble empirical mode decomposition (NLT EEMD), with the conventional pseudo-3D GPR data processing methods including background removal and migration to map the subsurface targets in 2D profile. The finalized pseudo-3D data volume is constructed by conventional linear interpolation. This study shows that the proposed technique could be successfully employed to locate the buried targets with minimal survey effort and affordable computation cost. Furthermore, the application of the proposed method is not limited to GPR data processing, any geophysical/engineering data with the similar data structure are applicable.

How to cite: Jeng, Y., Chen, C.-S., and Yu, H.-M.: Algorithms fusion for near-surface geophysical survey, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6251, https://doi.org/10.5194/egusphere-egu22-6251, 2022.

Nondestructive imaging and monitoring of the earth's subsurface using the geoelectric method require reliable and versatile numerical techniques for solving differential equation that govern the method's physic. The discrete operator should encompass fundamental properties of the original continuum model and differential operator for a robust numerical algorithm. In geoelectric modeling, critical model properties are anisotropy, irregular geometry, and discontinuous physical properties, whereas vital continuum operator properties are symmetry, the positivity of solutions, duality, and self-adjointness of differential operators and exact mathematical identities of the vector and tensor calculus. In this study, to simulate the response, we use the Mimetic Finite Difference Method (MFDM), where the discrete operator is constructed based on the support operator [1]. The MFDM operator mimics the properties mentioned above for structured and unstructured grids [2]. It is achieved by enforcing the integral identities of the continuum divergence and gradient operator to satisfy the integral identities by discrete analogs. 

The developed algorithm's accuracy is benchmarked using the analytical responses of dyke models of various conductivity contrasts for pole-pole configuration. After verifying the accuracy of the scheme, further tests are conducted to check the robustness of the algorithm involving the non-orthogonality of the grids, which is essential for simulating response for rugged topography. The surface potential is simulated using structured grids for a three-layer model. Subsequently, the orthogonal girds are distorted using pseudo-random numbers, which follow a uniform distribution. To quantify the distortion, we calculated the angles at all grid nodes. The node angles emulate a Gaussian distribution. We characterize those grids as highly distorted, for which the angle at the grid node is outside 20 to 160 degrees interval. The numerical tests are conducted by varying degrees of grid distortion, such that the highly distorted cells are from 1% to 10% of the total cells. The maximum error in surface potential stays below 1.5% in all cases. Hence, the algorithm is very stable with grid distortion and consequently can model the response of a very complex model. Thus, the developed algorithm can be used to analyze geoelectrical data of complex geological scenarios such as rugged topography and anisotropic subsurface. 

[1] Winters, Andrew R., and Mikhail J. Shashkov. Support Operators Method for the Diffusion Equation in Multiple Materials. No. LA-UR-12-24117. Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2012.

[2] Lipnikov, Konstantin, Gianmarco Manzini, and Mikhail Shashkov. "Mimetic finite difference method." Journal of Computational Physics 257 (2014): 1163-1227.

How to cite: Suryavanshi, D. and Dehiya, R.: Geoelectric data modeling using Mimetic Finite Difference Method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7009, https://doi.org/10.5194/egusphere-egu22-7009, 2022.

This paper deals with the evaluation of four measuring systems for the detection of potential deformations that can occur during structural rehabilitation measures. For this purpose, a test object resembling the shape of a tunnel structure was constructed. The structural properties of this test object are discussed in the related paper by Strauss et. al submitted for the same session.

In the paper, the installed measuring systems are presented first. These are a lamella system based on fibre optics, an array of accelerometers, a digital image correlation system and a profile laser scanner based system. The operating principles of the systems are briefly introduced.

A long-term measurement on the object in an unloaded state, which extended over several weeks, enables statements about the capturing of temperature-related deformations, the temperature dependence of the measured values and drift effects of the investigated systems. Selective loading of the test object was generated via four screw rods and applied both in the elastic as well as in the plastic deformation range. This enabled knowledge gain regarding the precision and the sensitivity of the analysed measuring systems.

Environmental conditions may have a strong influence on the measurement values. The former can be determined by permanent installations on the structure and its operating conditions as well as by the undertaken rehabilitation measures. Representative for the first category we investigated the influence of magnetic fields and light conditions on the measuring systems. For the second category, strong dust formation and increased humidity were generated during a test procedure.

An assessment regarding data handling, including storage, transfer and processing, completes the investigation of the four measuring systems. A summarising evaluation concludes the article.

How to cite: Neuner, H., Kostjak, V., Linzer, F., Loderer, W., Seywald, C., Strauss, A., Rigler, M., and Polt, M.: Assessing Deformation Monitoring Systems For Supporting Structural Rehabilitation under Harsh Conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7547, https://doi.org/10.5194/egusphere-egu22-7547, 2022.

The verification of the structural behaviour of existing structures and its materials characteristics requires the application of tests and monitoring to gather information about the actual response. The comparison of the actual performance and the designed performance enables the verification of the design assumptions in terms of implied loads and materials resistance. In case of non-compliance of the designed with the current performance, the design assumptions need to be updated. The objective of this contribution is to provide a guidance for the verification of the performance of reinforced concrete profiles of alpine infrastructure systems like tunnels assisted by monitoring, testing and material testing.

The application of defined loads to a structure to verify its load carrying capacity is a powerful tool for evaluating existing structures. In particular, in this research different types of load tests are employed depending on the limit state which is being investigated on tunnel profiles, on the other hand, the system responses to validate the structural performance are recorded with monitoring systems innovative in tunnel systems, such as accelerometer arrays, fibre optic sensors, laser distance sensors and digital image correlation system, see also the related paper by Neuner et. al. In these studies we also pay special attention to the capabilities of Digital Image Correlation and Nonlinear Finite Element Analysis. Digital Image Correlation (often referred to as "DIC") is an easy-to-use optical method for measuring deformations on the surface of an object. The method tracks changes in the grayscale pattern in small areas called subsets) during deformation. 

Finally, we will present the process for the implementation and validation of proof loading concepts based on the mentioned monitoring information in order to derive the existing safety level by using advanced digital twin models.  

How to cite: Strauss, A., Neuner, H., Rigler, M., Polt, M., Seywald, C., Kostjak, V., Linzer, F., and Loderer, W.: Verification of the performance of reinforced concrete profiles of alpine infrastructure systems assisted by innovative monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8512, https://doi.org/10.5194/egusphere-egu22-8512, 2022.

In the VHF frequencies, the sensitivity of the reflection coefficient at the air-soil interface with respect to the soil electromagnetic properties, i.e., the dielectric permittivity and electrical conductivity, varies with frequency. The lower the frequency is, the lower the sensitivity to permittivity is and the larger the sensitivity to conductivity is. In this study, we investigated low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil surface electrical conductivity characterization. In order to have a good sensitivity to electrical conductivity, we operated in the 15-45 MHz frequency range. We conducted both numerical and field experiments, under the assumptions that the soil magnetic permeability is equal to the magnetic permeability of free space, and that the soil permittivity and conductivity are frequency-independent. Through the numerical experiments, we analyzed the sensitivity of the soil permittivity and electrical conductivity by plotting the objective function in the inverse problem. In addition, we analyzed the effects of modelling errors on the retrieval of the permittivity and conductivity. The results show that the soil electrical conductivity is sensitive enough to be characterized by the low-frequency drone-borne GPR. The depth of sensitivity was found to be around 0.5-1 m in the 15-45 MHz frequency range. Yet, the effects of permittivity cannot be neglected totally, especially for relatively wet soils. For validating our approach, we conducted field measurements with the drone-borne GPR and we compared results with electromagnetic induction (EMI) measurements considering two different offsets, i.e., 0.5 and 1 m, respectively. The lightweight GPR system consists of a handheld vector network analyzer (VNA), a 5-meter half-wave dipole antenna, a micro-computer stick, a GPS receiver, and a power bank. The good agreement in terms of absolute values and field structures between the GPR and EMI maps demonstrated the feasibility of the proposed low-frequency drone-borne GPR method, which appears thereby to be promising for precision agriculture applications.

How to cite: Wu, K. and Lambot, S.: Analysis of low-frequency drone-borne GPR for soil surface electrical conductivity mapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8594, https://doi.org/10.5194/egusphere-egu22-8594, 2022.

Geological materials are mixtures of different endmember constituents with most of them having particles smaller in size than the path length of incident light. The obtained spectral response (reflectance) from such mixtures is nonlinear which can be attributed to multiple scattering of light and the receiver sensor’s height from the incident surface. Assuming a sensor’s fixed instantaneous field of view (IFOV), variation in its field of view (FOV) by shifting its height affects the spatial resolution of acquired spectra. We propose to estimate the point spread function (PSF) for which the spectral responses of fine-resolution pixels acquired by a sensor are mixed to produce a coarse-resolution pixel obtained by the same. Our approach is based on the sensor’s unchanged IFOV obtaining spectral information from a smaller ground resolution cell (GRC) at a lower FOV and a larger GRC with an increased sensor’s FOV. The larger GRC producing a coarse resolution pixel can be modeled as a gaussian PSF of its corresponding center and neighboring fine-resolution subpixels with the center exerting the maximum influence. Extensive experiments performed using a point-based sensor and a push broom scanner revealed such variational effects in PSF that are dependent on the sensor’s FOV, the spatial interval of acquisition, and optical properties. The coarse-resolution pixels’ spectra were regressed with their corresponding fine-resolution subpixels to provide estimates of the PSF values that assumed the shape of a two-dimensional Gaussian function. Constraining these values as sum-to-one introduced sparsity and explained variability in the spectral acquisition by different sensors.  The estimated PSFs were further validated through the linear spectral unmixing technique. It was observed that the fractional abundances obtained for the fine-resolution subpixels convolved with our estimated PSF to produce its corresponding coarse-resolution counterpart with minimal error. The obtained PSFs using different sensors also explained spectral mixing at different scales of observation and provided a basis for nonlinear unmixing integrating spatial as well as spectral effects and addressing endmember variability. We performed our experiments with various coarse-grained and fine-grained igneous and sedimentary rocks under laboratory conditions to validate our results which were compared with available literature. 

How to cite: Sahoo, M. M., Pattathal Vijayakumar, A., Herrmann, I., Mathew, S. K., and Porwal, A.: Estimation of point spread function for unmixing geological spectral mixtures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8712, https://doi.org/10.5194/egusphere-egu22-8712, 2022.

Water use efficiency (WUE) plays a vital role in planning and management of irrigation strategies. Considering the spatial scale, WUE can be quantified at scales ranging from leaf to whole-plant to ecosystem to region. However, the inter-relation and their associate is poorly understood. This study is aimed at stimulating WUE of irrigated cotton at leaf () and further investigate the role of environmental and biophysical conditions on WUE dynamics. This study was conducted in an agricultural croplands located in Sangareddy district, about 70 km west of Hyderabad, the capital city of southern state Telangana, India. Ground based observation were made such as soil moisture, photosynthetic parameters and meteorological parameters. Modelling leaf water use efficiency has been established. The stomatal conductance  and  of cotton leaves exposed to ambient CO₂ were simulated using Ball-Berry (mBB) model. Moreover, the stomatal conductance  and  of Cotton leaves exposed to ambient CO₂ is simulated using modified Ball-Berry model, with instantaneous gas exchanges measured around noon used to parameterize and validate the model. We observed a large diurnal (4.3±1.9 mmolCO₂ mol^-1H₂O) and seasonal (5.16±1.51 mmolCO₂ mol^-1H₂O) variations in  during the crop period. Model simulated  and  are in agreement with the measurements (R²>0.5, RMSE<0.3). Our results conclude that WUE_L­ is ruled by climatic as well as vegetative factors respectively, and are largely controlled by changes in transpiration over photosynthesis. This needs further investigation with extensive analysis by building library of in-situ measurements.

Keywords: Cotton, WUE, Irrigation, Stomatal conductance, Ball Berry Model

How to cite: Moharana, S. and Kambhammettu, P. B.: Water use efficiency (WUE) Modeling at Leaf level of Cotton (Gossypium hirsutum L.) in Telangana, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9441, https://doi.org/10.5194/egusphere-egu22-9441, 2022.

Monitoring of critical civil engineering infrastructures, and especially viaducts and bridges, has become a priority nowadays as the ageing of construction materials may cause damages and collapses with dramatic consequences. Following recent bridge collapses, specific guidelines on risk classification and management, safety assessment and monitoring of existing bridges have been issued in Italy, by the Minister of Infrastructure as a mandatory code [1]. Accordingly, several laws and regulations have been issued on the same topic, emphasizing the crucial role of BIM-based procedures for the design and management of civil infrastructures [2, 3]. Within this context, monitoring operations are generally conducted by on-site inspections and specialized operators, and rarely by high-frequency ground-based Non-Destructive Testing methods (NDTs). Furthermore, the implementation of satellite-based remote sensing techniques, have been increasingly and effectively used for the monitoring of bridges in the last few years [4]. Generally, these crucial pieces of information are analyzed separately, and the implementation of a multi-scale and multi-source interoperable BIM platform is still an open challenge [5].

This study aims at investigating the potential of an interoperable and upgradeable BIM platform supplemented by non-destructive survey data, such as Mobile Laser Scanner (MLS), Ground Penetrating Radar (GPR) and Satellite Remote Sensing Information (i.e. InSAR). The main goal of the research is to contribute to the state-of-the-art knowledge on BIM applications, by testing an infrastructure management platform aiming at reducing the limits typically associated to the separate observation of these assessments, to the advantage of an integrated analysis including both the design information and the routinely updated results of monitoring activities.

The activities were conducted in the framework of the Project “M.LAZIO”, approved by the Lazio Region, with the aim to develop an informative BIM platform of the investigated bridges interoperable within a Geographic Information System (GIS). As on-site surveys are carried out , a preliminary multi-source database of information  is created, to be operated as the starting point for the integration process and the development of  the infrastructure management platform. Preliminary results have shown promising viability of the data management model for supporting asset managers in the various management phases, thereby proving this methodology to be worthy for implementation in infrastructure integrated monitoring plans.

Acknowledgements

This research is supported by the Project “M.LAZIO”, accepted and funded by the Lazio Region, Italy. Funding from MIUR, in the frame of the “Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University, is acknowledged.

References

[1] MIT, 2020. Ministero delle Infrastrutture e dei Trasporti, DM 578/2020

[2] EU, 2014. Directive 2014/24/EU of the European Parliament and of the Council of 26 February 2014 on public procurement and repealing Directive 2004/18/EC.

[3] MIMS, 2021. Ministero delle Infrastrutture e della Mobilità Sostenibile, DM 312/2021

[4] Gagliardi, V. et al., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”. In SPIE Future Sensing Technologies; https://doi.org/10.1117/12.2579700

[5] D'Amico F. et al., "A novel BIM approach for supporting technical decision-making process in transport infrastructure management", Proc. SPIE 11863;  https://doi.org/10.1117/12.2600140

How to cite: Bertolini, L., Napolitano, A., Diezmos Manalo, J., Gagliardi, V., Bianchini Ciampoli, L., and D'Amico, F.: Implementation of an interoperable platform integrating BIM and GIS information for network-level monitoring and assessment of bridges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9845, https://doi.org/10.5194/egusphere-egu22-9845, 2022.

Knowledge of the monument for its conservation is the result of a multidisciplinary work based on the integration of different data sources obtainable from historical research, architectural survey, the use of different imaging technologies. The latter are increasingly within the reach of conservators, architects and restoration companies thanks to the reduction of costs and to the effort to produce increasingly user-friendly imaging technologies both in terms of data acquisition and processing. The critical element is the interpretation of the results on which depends the effectiveness of these technologies in answering various questions that the restoration poses. Scientific literature suggests different approaches aimed at making the interpretation of imaging diagnostics easier, particularly by means of : i) the comparison between direct data (carrots, visual inspection) and results from non-invasive tests; ii) the use of specimens or laboratory test beds; iii) Virtual and Augmented reality (VR/AR) to be used as a work environment to facilitate the interpretation of non invasive imaging investigations. In particular, the reading and visualization of multiparametric information using VR/AR contents increases the standard modes for the transmission of knowledge of physical characteristics and state of conservation of the architectural heritage. This approach represents an effective system for storing and analysing heterogeneous data derived from a number of diverse non invasive imaging techniques, including Ground Penetrating radar (GPR) at high frequency, Infrared Thermography (IRT), Seismic tomography and other diagnostics techniques. In the context of Heritage Within Project, a VR/AR platform to interrelate heterogeneous data derived from GPR, IRT, Ultrasonic and sonic measurements along with  results finite element computations has been developed and applied to the Convent of Our Lady of Mount Carmel  in Lisbon to understand cause-and-effect mechanisms between the constructive characteristics, degradation pathologies and stress/deformation maps.

References

Gabellone F., Leucci G., Masini N., Persico R., Quarta G., Grasso F. 2013. Non-destructive prospecting and virtual reconstruction of the chapel of the Holy Spirit in Lecce, Italy. Near Surface Geophysics, doi: 10.3997/1873-0604.2012030

Gabellone F., Chiffi M., “Linguaggi digitali per la valorizzazione”, in F. Gabellone, M. T. Giannotta, M. F. Stifani, L. Donateo (a cura di), Soleto Ritrovata. Ricerche archeologiche e linguaggi digitali per la fruizione. Editrice Salentina, 2015. ISBN 978-88-98289-50-9

Masini N., Nuzzo L., Rizzo E., GPR investigations for the study and the restoration of the Rose Window of Troia Cathedral (Southern Italy), Near Surface Geophysics, 5 (5)(2007), pp. 287-300, ISSN: 1569-4445; doi: 10.3997/1873-0604.2007010 

Masini N., Soldovieri F. (Eds) (2017). Sensing the Past. From artifact to historical site. Series: Geotechnologies and the Environment, Vol. 16. Springer International Publishing, ISBN: 978-3-319-50516-9, doi: 10.1007/978-3-319-50518-3, pp. 575

Javier Ortega, Margarita González Hernández, Miguel Ángel García Izquierdo, Nicola Masini, et al. (2021). Heritage Within. European Research Project, ISBN: 978-989-54496-6-8, Braga 2021.

How to cite: Masini, N., Gabellone, F., and Ortega, J.: VR/AR based approach for the diagnosis of the state of conservation of the architectural heritage. The case of the Convento do Carmo in Lisbon, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10538, https://doi.org/10.5194/egusphere-egu22-10538, 2022.

DIARIT^Sup is a chain of various softwares following the concept of ”system of systems”. It interconnects hardware and software layers dedicated to in-situ monitoring of structures or critical components. It embeds data assimilation capabilities combined with specific Physical or Statistical models like inverse thermal and/or mechanical ones up to the predictive ones. It aims at extracting and providing key parameters of interest for decision making tools. Its framework natively integrates data collection from local sources but also from external systems [1, 2]. DIARIT^Sup is a milestone in our roadmap for SHM Digital Twins research framework. Furthermore, it intends providing some useful information for maintenance operations not only for surveyed targets but also for deployed sensors.

Thanks to its Model-view-controller (MVC) design pattern, DIARIT^Sup can be extended, customized and connected to existing applications. Its core component is made of a supervisor task that handles the gathering of data from local sensors and external sources like the open source meteorological data (observations and forecasts) from Météo-France Geoservice [4] for instance. Meanwhile, a recorder manage the recording of all data and metadata in the Hierarchical Data Format (HDF5) [6]. HDF5 is used to its full potential with its Single-Writer-Multiple-Readers feature that enables a graphical user interface to represent the saved data in real-time, or the live computation of SHM Digital Twins models [3] for example. Furthermore, the flexibility of HDF5 data storage allows the recording of various type of sensors such as punctual sensors or full field ones. Finally, DIARIT^Sup is able to handle massive deployment thanks to Ansible [5] automation tool and a Gitlab synchronization for automatic updates. An overview of the developed software with a real application case will be presented. Perspectives towards improvements on the software with more component integrations (Copernicus Climate Data Store, etc.) and a more generic way to configure the acquisition and model configuration will be finally discussed.

References
[1] Nicolas Le Touz, Thibaud Toullier, and Jean Dumoulin. “Infrared thermography applied to the study of heated and solar pavement: from numerical modeling to small scale laboratory experiments”. In: SPIE - Thermosense: Thermal Infrared Applications XXXIX. Anaheim, United States, Apr. 2017. url: https://hal.inria.fr/hal-01563851.
[2] Thibaud Toullier, Jean Dumoulin, and Laurent Mevel. “Study of measurements bias due to environmental and spatial discretization in long term thermal monitoring of structures by infrared thermography”. In: QIRT 2018 - 14th Quantitative InfraRed Thermography Conference. Berlin, Germany, June 2018. url: https://hal.inria.fr/hal-01890292.
[3] Nicolas Le Touz, Thibaud Toullier, and Jean Dumoulin. “Study of an optimal heating command law for structures with non-negligible thermal inertia in varying outdoor conditions”. In: Smart Structures and Systems 27.2 (2021), pp. 379–386. doi: 10.12989/sss.2021.27.2.379. url: https://hal.inria.fr/hal-03145348.
[4] Météo France. Données publiques Météo France. 2022. url: https://donneespubliques.meteofrance.fr.
[5] Red Hat & Ansible. Ansible is Simple IT Automation. 2022. url: https://www.ansible.com/.
[6] The HDF Group. Hierarchical Data Format, version 5. 1997-2022. url: https://www.hdfgroup.org/HDF5/.

How to cite: Dumoulin, J., Toullier, T., Simon, M., and Andrade-Barroso, G.: DIARITSup: a framework to supervise live measurements, Digital Twins modelscomputations and predictions for structures monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11201, https://doi.org/10.5194/egusphere-egu22-11201, 2022.

In recent years, great interest has been paid to the risk that hydrogeological instability causes to the territory, especially in densely populated and geologically fragile areas. 
The forests, exerting a natural restraint, play an important protective function for the infrastructures and settlements underneath from the danger of falling rocks that fall from the rocky walls. This protective action is influenced not only by issues related to the vegetation itself but also by the morphology of the terrain, as a steeply sloping land surface can significantly increase the momentum of the rolling rock.
The aim of our work is to design a methodology based on the integration of remote sensing data, in detail optical satellite images and LiDAR data acquired by UAVs, to identify areas most prone to natural rockfall retention [1]. The results could then be used to identify areas that need to be reinforced artificially (rockfall nets) and naturally (protective forests).
The test area is located near Monte San Liberatore in the Campania region (Italy), which was affected in 1954 by a disastrous flood, in which heavy rains induced the triggering of a few complex landslides in a region that was almost geomorphologically susceptible.  Indeed, there are several areas subject to high risk of rockfalls, whose exposed value is represented by a complex infrastructural network of viaducts, tunnels, and galleries along the north-west slope of the mountain, which is partly covered by thick vegetation, which reduces the rolling velocity of rocks detaching from the ridge. 
According to the Carta della Natura, the vegetation most present in the area is the holm oak (Quercus Ilex), an evergreen, long-lived, medium-sized tree. Its taproot makes it resistant and stable, able to survive in extremely severe environments such as rocky soils or vertical walls, so it is ideal for slope protection.
The first processing step involved the multispectral analysis on Pleiades 1A four-band (RGB +NIR) high-resolution satellite images (HRSI). The computed vegetation indices (NDVI, RVI and NDWI) were used to assess the vegetation health status and its presumed age; thus, the most resilient areas of the natural compartment in terms of robustness and vigor were identified. The average plant height was determined using the normalized digital surface model (nDSM).
Next, starting from the Digital Terrain Model (DTM), we derived the morphometric features suitable for the description of the slope dynamics: slope gradient, exposure with respect to the North direction, plane, and convexity profile. The DTM and the DSM were created by interpolating on a grid the LiDAR point cloud acquired via UAV. Classification of areas having similar characteristics was made using SOM (Self-Organizing Maps), based on unsupervised learning.
The classified maps obtained delimit the similar areas from a morphological and vegetation point of view; in this way, all those areas that tend to have a higher propensity for rock roll reduction were identified.

[1] Fanos, Ali Mutar, and Biswajeet Pradhan. "Laser scanning systems and techniques in rockfall source identification and risk assessment: a critical review." Earth Systems and Environment 2.2 (2018): 163-182.

How to cite: Di Benedetto, A., Ambrosino, A., and Fiani, M.: Integrating Remote Sensing data to assess the protective effect of forests on rockfall:The case study of Monte San Liberatore (Campania, Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12743, https://doi.org/10.5194/egusphere-egu22-12743, 2022.

The identification of the processes underlining natural systems often requires the adoption of multiple investigation techniques for the assessment of the sites under study. In this work, the combination of information derived from non-invasive sensing techniques, such as geophysics, remote sensing and hydrogeochemistry, highlights the possible influence of global climate change on the future water availability related to an aquifer in a peculiar glacier context, located in central Ecuador. In particular, we show that the Chambo aquifer, which supplies potable water to the region, does not contain fossil water, and it’s instead recharged over time. Indeed, the whole Chambo river basin is affected by the Chimborazo volcano, which is a glacerised mountain located in the inner tropics, one of the most critical places  to be observed in the frame of climate impact on water resources. Thanks to the infomation gathered by the various surveying techniques, numerical modelling permitted an estimate of the recharge, which can be fully originated by the runoff from Chimborazo melting glaciers. Actually, the retreat of the glaciers on top of the Chimborazo is an ongoing process presumably related to global climate change.

How to cite: Scozzari, A., Catelan, P., Chidichimo, F., de Biase, M., Mendoza Trujillo, B. G., Carrettero Poblete, P. A., and Straface, S.: Integration of multiple geoscientific investigation methods for a better understanding of a water system: the example of Chimborazo glaciers melting effects on the Chambo aquifer, Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13153, https://doi.org/10.5194/egusphere-egu22-13153, 2022.

The permanent scatterer synthetic aperture radar interferometry (PS-InSAR) technique can detect the permanent scatterers(PSs) on the ground. But the deformation of PSs can’t be used to analyze the deformation of underground buildings below the ground surface directly, such as tunnels. In this paper, the process of tunnel deformation analysis using PSs data and stress-area method is proposed. The deformation data of PSs are used to fit the surface deformation of tunnel by kriging interpolation method. The stress area method is used to calculate the deformation of the soil above the tunnel, then the deformation of tunnel can be acquired. This process was applied to calculate the deformation of a tunnel in Shanghai, China. The results show that the fitted surface deformation rate data are accurate, with the maximum absolute difference of 1.45mm/y and the minimum difference of 0.11mm/y compared with the level monitoring data. The tunnel deformation rate calculated by this process is close to the measured deformation rate of the tunnel with error level in millimeters. The surface and tunnel deformation rate curves are similar in the tunnel extension direction. PS-InSAR technique has the advantages of acquiring large area, historical data of surface deformation. Combined with the process proposed in this paper, Large-scale tunnel deformation analysis can be achieved.

How to cite: Chai, L., Xie, X., Li, P., Zhou, B., and Zeng, L.: Tunnel deformation rate analysis based on PS-InSAR technique and stress-area method , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13401, https://doi.org/10.5194/egusphere-egu22-13401, 2022.

In recent years, China's construction demand for shield tunnel in soft soil continues to increase, and the control of ground settlement in tunnel boring process affects the safety of the tunnel itself and its superstructure directly. Paying close attention to controlling the strata loss and the ground settlement by multiple means is important to ensure construction safety. In this paper, the intelligent real-time monitoring system with dual-frequency ground penetrating radar (GPR) is used to detect the quality of back-fill grouting of shield tunnel, while monitoring points are arranged on the ground surface to acquire the settlement values in real time. The collaborative analysis of ground and underground monitoring results reveals the relationship between grouting and settlement values, and realizes the dynamic guidance on grouting operation, which helps to achieve the purpose of controlling ground settlement better. Last but not least, this paper proposes an outlook on a multiple-data fusion system based on cloud computing platform to adapt to more complex and multiple data in the future, so as to achieve the higher accuracy, efficiency and intelligence of monitoring data analysis.

How to cite: Li, K., Xie, X., Zhang, X., Zhou, B., Qu, T., and Zeng, L.: Collaborative use of ground monitoring and GPR data for the control of ground settlement in shield tunnel in soft soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13441, https://doi.org/10.5194/egusphere-egu22-13441, 2022.

The shield machine has become the mainstream of subway tunnels construction because of its safety and efficiency. But with the continuous development of urban construction, the environment of subway tunnel construction is becoming more and more complex. In the process of shield tunnels construction in southern cities of China, slurry balance shield machines often encounter various obstacles, such as large diameter boulders and concrete pile foundations, which result in accidents of shield machine sticking. Therefore, it is necessary to quickly and accurately detect the distribution of obstacles in front of shield excavation face in advance so that operators can in time take measures to reduce the occurrence of such accidents. Ground penetrating radar (GPR) is a method widely used in engineering geological exploration. It has advantages of small working space, high efficiency and no damage compared with other detecting methods. When the GPR antenna is mounted on the cutter head of the shield machine, the obstacles in the stratum ahead of the shield machine can be detected in real time. Under this condition the GPR antenna’s real work mode is that it will rotate with the cutter head to form a circumferential survey line. Based on Finite-Difference-Time-Domain-Method (FDTD), authors use the common numerical simulation software (GPRMAX) to make simulations of GPR circumferential detection under the antenna array rotating with the cutter head, which verifies the theoretical feasibility of this method. By simulating radar emission and reflection pattern of electromagnetic wave, we study the propagation pattern of the reflect wave after encountering the obstacles and conclude the image pattern to establish the foundation for image recognition of obstacles. Due to the radar wave being susceptible to electromagnetic interference, GPR is still lack of engineering practice in shield advanced detection. To reduce the interference of the surrounding metal cutter head, a new strip radar antenna with a shielding shutter is designed to improve the directivity of electromagnetic wave propagation. Several antennas are fixed at several slurry openings of the cutter head of slurry balance shield machine to form radar antenna array and improve detection efficiency and accuracy.

How to cite: Duan, W., Xie, X., Yang, Y., Zeng, K., Wu, H., Zeng, L., and Li, K.: Application of ground penetrating radar (GPR) in look-ahead detection of slurry balance shield machine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13515, https://doi.org/10.5194/egusphere-egu22-13515, 2022.

Wet deposition has been identified as a critical impactor for the modelling of ¹³⁷Cs in the Fukushima Daiichi Nuclear power plant (FDNPP) accident. However, it is difficult to simulate due to the involvement of close interaction between various complicated meteorological and physical processes during the wet deposition process. The limitation of measurement of the in-cloud and below-cloud scavenging also contribute to the uncertainty in wet deposition modeling, leading to the great variation of ¹³⁷Cs wet deposition parameterization. These variations can be amplified further by inaccurate meteorological input, making simulation of radionuclide transport sensitive to the choice of wet scavenging parameterization. Moreover, simulations can also be influenced by differences between radionuclide transport models, even if they adopt similar parameterization for wet scavenging. Although intensively investigated, wet deposition simulation is still subject to uncertainties of meteorological inputs and wet scavenging modeling, leading to biased ¹³⁷Cs transport prediction.

To improve modeling of ¹³⁷Cs transport, both in- and below-cloud wet scavenging schemes were integrated into the Weather Research and Forecasting-Chemistry (WRF-Chem) model, yielding online coupled modeling of meteorology and the two wet scavenging processes. Overall, 25 combinations of different in- and below-cloud scavenging schemes of ¹³⁷Cs, covering most wet scavenging schemes reported in the literature, were integrated into WRF-Chem. Additionally, two microphysics schemes were compared to improve the simulation of precipitation. These 25 models and the ensemble mean of 9 representative models were systematically compared with a previous below-cloud-only WRF-Chem model, using the cumulative deposition and atmospheric concentrations of ¹³⁷Cs measurements. The findings could elucidate the range of variation among these schemes both within and across the five in-cloud groups, reveal the behaviors and sensitivities of different schemes in different scenarios.

The results revealed that the Morrison's double moment cloud microphysics scheme improves the simulation of rainfall and deposition pattern. Furthermore, the integration of the in-cloud schemes in WRF-Chem substantially reduces the bias in the cumulative deposition simulation, especially in the Nakadori and Tochigi regions where light rain dominated. For atmospheric concentration of ¹³⁷Cs, those models with in-cloud schemes that consider cloud parameters showed better and more stable performance, among which Hertel-Bakla performed best for atmospheric concentration and Roselle-Apsimon performed best for both deposition and atmospheric concentration. In contrast, the in-cloud schemes that rely solely on rain intensity were found sensitive to the meteorological conditions and showed varied performance in relation to the plume events examined. The analysis based on the spatial pattern shows that the Roselle scheme, which considers cloud liquid water content and depth, can achieve a more balanced allocation of ¹³⁷Cs between the air and the ground in these two cases than that achieved by the empirical power function scheme Environ. The ensemble mean achieves satisfactory performance except for one plume event, but still outperforms most models. The range of variation of the 25 models covered most of the measurements, reflecting the reasonable capability of WRF-Chem for modeling ¹³⁷Cs transport.

How to cite: Zhuang, S., Dong, X., and Fang, S.: Sensitivity analysis on the wet deposition parameterization for 137Cs transport modeling following the Fukushima Daiichi Nuclear Power Plant accident, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-177, https://doi.org/10.5194/egusphere-egu22-177, 2022.

The nuclear emergency response for accidental release around the nuclear power plant site (NPPs) requires a fast and accurate estimate of the influence caused by gaseous hazardous pollutants spreading, which is critical for and preventing protecting lives, creatures, and the environment. However, as usual, the NPPs is consist of dense buildings and multi-type terrain, e.g. river and mountain, which poses challenges to atmospheric dispersion calculation for response tasks. Micro-SWIFT SPRAY (MSS) comprises both the diagnostic wind model and the dispersion model, which enables the airflows and atmospheric dispersion simulation with the meteorological and other inputs. For a small-scale scenario, especially, the separate module for obstacles influence modeling provides the potential capability of precise atmospheric dispersion. But the error behavior of such a scenario around a nuclear power plant site with complex topography remains to be further demonstrated. In this study, MSS is comprehensively evaluated against a wind tunnel experiment with a 1:600 scale for the small-scale (3 km × 3km) atmospheric dispersion modeling. Tens of buildings located in this scenario of a NPPs surrounded by a mountain and river. The evaluations for diagnostic wind modeling include the speed, direction, and distribution of horizontal airflows and vertical profile of speed at a representative site. And for the concentration calculation, horizontal distribution, axis profile, and vertical profile at a representative site. The results demonstrate the MSS can reproduce fine airflows near the buildings but overestimate the wind speed. The maximum deviation of vertical speed is around 2.09 m/s at the representative site. The simulated plume of concentration reproduces the highest concentration place and matches the observations well. The axis profile of concentration is underestimated and the vertical profile displays an increasing deviation with the height increase. Compared with the observations, the FAC5 and FAC2 of concentration simulation reach 0.945 and 0.891 in the entire calculation domain, which convinces the performance of MSS in small-scale modeling.

How to cite: Dong, X., Zhuang, S., and Fang, S.: Micro-SWIFT SPRAY modeling of atmospheric dispersion around a nuclear power plant site with complex topography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-190, https://doi.org/10.5194/egusphere-egu22-190, 2022.

Chlorine 36 (³⁶Cl, T_1/2 = 301,000 years) is a radionuclide with natural and anthropogenic origin that can be rejected accidentally during decommissioning of nuclear power plants or chronically during recycling of nuclear waste. Once emitted into the atmosphere, ³⁶Cl (gas and particles) can be transferred to the soil and vegetal cover by dry and wet deposition. However, knowledge of these deposits is very scarce. Because of its relatively high mobility in the geosphere and its high bioavailability, ³⁶Cl fate in the environment should be studied for environmental and human impact assessments. So, the objective of this work is to determine the dry deposition rates of chlorine 36 on grassland. Grass is studied, as it is a link in the human food chain via cow's milk.

In order to achieve this objective, a method for extracting the chlorine contained in plant leaves has been developed. This method consists in heating the dried and grounded plant sample in presence of sodium hydroxide. A temperature gradient up to 450°C allows the extraction to be carried out in two stages: (i) The chlorides with a strong affinity for alkaline environments are first extracted from the plant and preserved in sodium hydroxide; (ii) The organic matter is then destroyed by combustion and the sodium hydroxide crystallised. Brought out from the oven, the dry residue is dissolved in ultrapure water and chemically prepared for the measurement of chlorine 36. This extraction method was validated by its application to NIST standards of peach and apple leaves. The average extraction efficiency of chlorides was 83 ± 3%.

For the determination of dry deposition rates, 1m² of grass was exposed every 2 weeks at the IRSN La Hague technical platform (PTILH) located 2 km downwind from Orano la Hague, a chronic source of low-level chlorine 36 emissions. A mobile shelter with automatic humidity detection covered the grass during rainy episodes. In proximity to the grass, atmospheric chlorine was also sampled at the same frequency as the grass. Gaseous chlorine was sampled by bubbling in sodium hydroxide and by an AS3000 sampler containing activated carbon cartridge. Particulate chlorine was collected on a composite (teflon and glass fibre) filter. Chlorine 36 was measured by accelerated mass spectrometry ASTER (Accelerator for Earth Sciences, Environment and Risks) at CEREGE, Aix-en-Provence, France. All samples were subjected to a succession of chemical preparations in order to remove the sulphur 36 (an isobaric interferent) and to collect the chlorides in the form of AgCl pastilles. The results show a chlorine 36 deposition flux on the grass of 2.94.10² at/m².s with a deposition velocity in dry weather v_{d(gas+particles)} = 8.10^-4 m/s for a contribution of 65.5% of particulate chlorine 36 and 34.5% of gaseous chlorine 36. Based on these experimental results, a modelling of the dry and wet deposits will be carried out considering the parameters related to the canopy and the atmospheric turbulence.

How to cite: Deo, S., Hebert, D., Benedetti, L., Vitorge, E., Lourino Cabana, B., Guillou, V., and Maro, D.: Dry deposition velocity of chlorine 36 on grassland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-666, https://doi.org/10.5194/egusphere-egu22-666, 2022.

Lake Onuma on Mt. Akagi (Gunma Prefecture, Japan) is a closed lake with an average water residence time of 2.3 years. The activity concentration of radioactive cesium in the lake was high shortly after the Fukushima accident. According to Suzuki et al. [1] and Watanabe [2], after a filtration process, Cs-137 are separated into two groups: particulate form and dissolved form. These two forms appears to have very different concentration profiles with each other,  when the Cs-137 concentration plotted against the sampled water depths. In the present study, we are going to model those behavior of particulate/dissolved forms with an emphasis on the depth dependency.

We consider a creation-annihilation process of plankton for the model of the particulate form, since diatom shells are found to be a major constituent of the particulate Cs-137 [2]. We set  ∂P/∂t = f(x,t)  and  f(x,t) = χ(x) cos(ωt) (0 ≤ x ≤ L(water column height), t > 0),  where P=P(x,t) is the activity concentration of the particulate form. The term f(x,t) is the rate of the net production of the plankton at a specific location x at a specific time t. Seasonal cycle is also taken into account by the cosine function (we neglect the phase shift here). The function χ(x), depends solely on water depth x, is responsible for dynamics or inhomogeneity of lake water, such as circulation, stratification or a thermocline. We assume that such a water structure relates to the production rate of plankton through the function χ(x). Thus, we may obtain the concentration of particulate Cs-137. For the dissolved concentration S(x,t), we use the classical diffusion equation with the diffusivity K being dependent on both space and time (i.e. K(x,t)), namely ∂S/∂t =  ∇•(K(x,t) ∇S). Here S=S(x,t) is the activity concentration of the dissolved form. The total activity concentration C(x,t) is the sum of P(x,t) and S(x,t). Using the pair of the equations, we can reproduce the followings. (1) depth profiles of each of the soluble- and particulate activity concentration and (2) depth profiles of the total Cs-137 concentration.

 [1] Suzuki, K. et al., Sci. Tot. Env. (2018)

 [2] Watanabe, S. et al.,  Proc. 20^th Workshop on Environmental Radioactivity (2019)

How to cite: Hatano, Y., Akasaki, K., Suetomi, E., Okada, Y., Suzuki, K., and Watanabe, S.: Modeling the depth dependence of Cs-137 concentration in Lake Onuma, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1235, https://doi.org/10.5194/egusphere-egu22-1235, 2022.

How to cite: Igarashi, Y., Kenji, N., Wada, T., Wakiyama, Y., Onda, Y., and Moritaka, S.: Factors controlling the dissolved 137Cs seasonal fluctuations in the Abukuma River under the influence of the Fukushima Nuclear Power Plant accident, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3340, https://doi.org/10.5194/egusphere-egu22-3340, 2022.

This study presents the temporal variations in riverine ¹³⁷Cs concentrations and fluxes to the ocean during high-flow events in three coastal river catchments contaminated by the Fukushima Daiichi Nuclear Power Plant accident. River water samples were collected at points downstream in the Niida, Ukedo, and Takase Rivers during three high-flow events that occurred in 2019–2020. Variations in both the dissolved ¹³⁷Cs concentration and ¹³⁷Cs concentration in suspended solids appeared to reflect the spatial pattern of the ¹³⁷Cs inventory in the catchments, rather than variations in physico-chemical properties. Negative relationships between the ¹³⁷Cs concentration and δ¹⁵N in suspended sediment were found in all rivers during the intense rainfall events, suggesting an increased contribution of sediment from forested areas to the elevated ¹³⁷Cs concentration. The ¹³⁷Cs flux ranged from 0.33 to 18 GBq, depending on the rainfall erosivity. The particulate ¹³⁷Cs fluxes from the Ukedo River were relatively low compared with the other two rivers and were attributed to the effect of the Ogaki Dam reservoir upstream. The ratio of ¹³⁷Cs desorbed in seawater to ¹³⁷Cs in suspended solids ranged from 2.8% to 6.6% and tended to be higher with a higher fraction of exchangeable ¹³⁷Cs. The estimated potential release of ¹³⁷Cs from suspended solids to the ocean was 0.048–0.57 GBq, or 0.8–6.2 times higher than the direct flux of dissolved ¹³⁷Cs from the river. Episodic sampling during high-flow events demonstrated that the particulate ¹³⁷Cs flux depends on catchment characteristics and controls ¹³⁷Cs transfer to the ocean. 

How to cite: Wakiyama, Y., Niida, T., Takata, H., Taniguchi, K., Kurosawa, H., Fujita, K., and Konoplev, A.: A comparative study of riverine 137Cs dynamics during high-flow events at three contaminated river catchments in Fukushima, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3442, https://doi.org/10.5194/egusphere-egu22-3442, 2022.

    The Fukushima-Daiichi accident involved massive and complex releases of radionuclides in the atmosphere. The releases assessment is a key issue and can be achieved by advanced inverse modelling techniques combined with a relevant dataset of measurements. A Bayesian inversion is particularly suitable to deal with this case. Indeed, it allows for rigorous statistical modelling and enables easy incorporation of informations of different natures into the reconstruction of the source and the associated uncertainties.
    We propose several methods to better quantify the Fukushima-Daiichi 137Cs source and the associated uncertainties. Firstly, we implement the Reversible-Jump MCMC algorithm, a sampling technique able to reconstruct the distributions of the 137Cs source magnitude together with its temporal discretisation. Secondly, we develop methods to (i) mix both air concentration and deposition measurements, and to (ii) take into account the spatial and temporal information from the air concentration measurements in the error covariance matrix determination.
    Using these methods, we obtain distributions of hourly 137Cs release rates from 11 to 24 March and assess the performance of our techniques by carrying out a model-to-data comparison. Furthermore, we demonstrate that this comparison is very sensitive to the statistical modelling of the inverse problem.

How to cite: Dumont Le Brazidec, J., Bocquet, M., Saunier, O., and Roustan, Y.: Integrating measurement representativeness and release temporal variability to improve the Fukushima-Daiichi 137Cs source reconstruction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5397, https://doi.org/10.5194/egusphere-egu22-5397, 2022.

Bottom sediments of lakes and dam reservoirs can provide an insight into understanding the dynamics of ¹³⁷Cs strongly bound to sediment particles. On this premise, a number of cores of bottom sediments were collected in deep parts of lakes Glubokoe, Azbuchin, and Cooling Pond in close vicinity of the Chernobyl NPP in Ukraine, in Schekino reservoir (Upa River) in the Tula region of Russia (2018) and in Ogaki reservoir (Ukedo River) in Fukushima contaminated area (2019). Each layer of bottom sediments can be attributed to a certain time of suspended particles sedimentation. With ¹³⁷Cs activity concentration in a given layer of bottom sediments corresponding to ¹³⁷Cs concentration on suspended matter at that point in time, we were able to reconstruct the post-accidental dynamics of particulate ¹³⁷Cs activity concentrations. Using experimental values of the distribution coefficient K_d, changes in the dissolved ¹³⁷Cs activity concentrations were estimated. The annual mean particulate and dissolved ¹³⁷Cs wash-off ratios were also calculated for the period after the accidents. Interestingly, the particulate ¹³⁷Cs wash-off ratios for the Ukedo River at Ogaki dam were found to be similar to those for the Pripyat River at Chernobyl in the same time period after the accident, while the dissolved ¹³⁷Cs wash-off ratios in the Ukedo River were an order of magnitude lower than the corresponding values in the Pripyat River. The estimates of particulate and dissolved ¹³⁷Cs concentrations in Chernobyl cases were in reasonable agreement with monitoring data and predictions using the semi-empirical diffusional model. However, both the particulate and dissolved ¹³⁷Cs activity concentrations and wash-off ratios in the Ukedo River declined faster during the first eight years after the FDNPP accident than predicted by the diffusional model, most likely, due to greater natural attenuation and, to some extent, remediation measures implemented on the catchments in Fukushima.

This research was supported by Science and Technology Research Partnership for Sustainable Development (SATREPS), Japan Science and Technology Agency (JST)/Japan International Cooperation Agency (JICA) (JPMJSA1603), by bilateral project No. 18-55-50002 of Russian Foundation for Basic Research (RFBR) and Japan Society for the Promotion of Science (JSPS), and JSPS Project KAKENHI (B) 18H03389.

How to cite: Konoplev, A., Wakiyama, Y., Wada, T., Igarashi, Y., Laptev, G., Golosov, V., Ivanov, M., Komissarov, M., and Nanba, K.: Vertical distribution of 137Cs in bottom sediments as representing the time changes of water contamination: Chernobyl and Fukushima, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6698, https://doi.org/10.5194/egusphere-egu22-6698, 2022.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident occurred on March 11, 2011, and a large amount of Cs-137 was released into the environment. It is important to clarify the behavior of radioactive cesium-137 in headwater catchments because most of the Cs-137 falls and is deposited in forest areas and is transported in the environment through river systems.

The purpose of this study was to clarify the influence of water quality composition and organic matter on the seasonal variation of dissolved Cs-137 concentrations in stream water based on long-term monitoring since 2011 at four headwaters catchments in Yamakiya district, Fukushima Prefecture (Iboishiyama, Ishidairayama, Koutaishiyama, Setohachiyama), located about 35 km northwest of FDNPP.

Water temperature, pH, and EC were measured in the field, and SS and coarse organic matter were collected using a time-integrated SS (suspended sediments) sampler and organic matter net. The Cs-137 concentrations was measured in the laboratory using a germanium detector. Concentrations of cations (Na⁺,K⁺,Ca²⁺,Mg²⁺,NH₄⁺) and anions (Cl⁻,SO₄²⁻,NO₃⁻,NO₂⁻,PO₄²⁻) were measured by ion chromatography after 0.45μm filtration. In addition, dissolved organic carbon (DOC) concentrations was measured using a total organic carbon analyzer.

The results showed that K⁺, which is highly competitive with Cs-137, was detected at Iboisiyama, Ishidairayama, and Koutaishiyama, while NH₄⁺ was only detected in some samples at Iboishiyama. There was no obvious relationship between dissolved ion concentration and water temperature, and between dissolved ion concentration and dissolved ¹³⁷Cs concentration at all sites. However, a positive correlation between dissolved cesium concentration and water temperature and DOC and water temperature was observed at all sites regardless of the presence of K⁺ and NH₄⁺. On the other hand, there was no clear relationship between the cesium concentrations in SS and organic matter and water temperature. These results suggest that the seasonal variation in dissolved Cs-137 concentrations in stream water with water temperature could be caused by the seasonality of microbial decomposition of organic matter.

How to cite: Kawano, T., Onda, Y., Takahishi, J., Makino, F., and Iwagami, S.: Seasonal variation of dissolved Cs-137 concentrations in headwater catchments in Yamakiya district, Fukushima Prefecture, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7068, https://doi.org/10.5194/egusphere-egu22-7068, 2022.

A study of ¹³⁷Cs distribution in a landscape cross-section characterizing the ELGS system (top-slope-closing depression) in the “Vyshkov-2” test site located in the Chernobyl abandoned zone, the Bryansk region, Russia, has been performed in 2015 and 2021. The test site (70×100 m) is located on the Iput’ river terrace in a pine forest characterized by the undisturbed soil-plant cover. Sod-podzolic sandy illuvial-ferruginous soils present the soil cover. The initial level of ¹³⁷Cs contamination of the area varied from 1480 kBq/m² to 1850 kBq/m². Up to now, 89-99 % of the total ¹³⁷Cs is fixed in the upper 20 cm soil layer with 70-96 % in the upper 8 cm. It allows field spectrometry data to study the structure of the ¹³⁷Cs contamination field. The ¹³⁷Cs activity was measured in the soil and moss cover along cross-sections with 1 m step by adapted gamma-spectrometer Violinist-III (USA). Cs-137 content in the soil cores’ and plant samples was determined in the laboratory by Canberra gamma-spectrometer with HPGe detector. It was shown that there is no unidirectional movement of ¹³⁷Cs both in the soil and in the vegetation cover of the ELGS from the top to the closing depression. On the contrary, the data obtained allow us to state a pronounced cyclical variation of the ¹³⁷Cs activity in ELGS, which can be traced in the soil and the vegetation. The variation appeared to be rather stable in space 29 and 35 years after the primary pollution. Cyclic fluctuation (variation) of ¹³⁷Cs activity was described mathematically using Fourier-analysis, which was used to model the observed changes by the revealed three main harmonics. High and significant correlation coefficients obtained between the variation of ¹³⁷Cs activity and the model for the soil-vegetation cover (r_0,01= 0,868; n=17 - 2015; r_0,01= 0,675; n=17 - 2021), soils (r_0,01= 0,503-0,859; n=17) and moss samples (r_0,01= 0,883; n=17 - 2015; r_0,01= 0,678; n=17 - 2021) proved satisfactory fitting of models. The character of ¹³⁷Cs variability in moss cover was generally similar to surface soil contamination, but the level of contamination and amplitude was specific.

The performed study confirmed specific features of ¹³⁷Cs secondary migration in ELGS, which periodic functions describe. We infer that the observed cyclicity reflects elements’ migration in the ELGS system with water.

The reported study was funded by the Vernadsky Institute federal budget (research task #0137-2019-0006). The field works were supported partly by RFBR No 19-05-00816.

How to cite: Dolgushin, D. and Korobova, E.: Regularities of the 137Cs secondary distribution in the soil-moss cover of elementary landscape-geochemical systems and its dynamics within 6 years on the test site in the Chernobyl abandoned zone, Russia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8178, https://doi.org/10.5194/egusphere-egu22-8178, 2022.

Radiocaesium derived from the Fukushima Dai-ichi Nuclear Power Plant (FNPP1) accident was observed across a wide area of the North Pacific, not only in surface seawater, but also in the ocean interior. In this presentation, we summarized the time scale of Lagrangian transport of the FNPP1 derived radiocaesium in surface water during the period from the time of the accident to March 2021 in the North Pacific and the Arctic Oceans and its marginal seas as shown below.

Initial observation results until December 2012 in the surface layer in the North Pacific Ocean by the global observations revealed that a typical feature within one year after the accident was a westward movement across the North Pacific Ocean, speed of which was reported at 7 km day^-1 until August 2011. After that, the main body of FNPP1-derived radiocaesium moved east as 3 km day^-1 and is separated from Japan in 2013. The arrival of the FNPP1 signal at the west coast of the American continent was reported in 2014. The elevation in the FNPP1 derived radiocaesium concentration in the Bering Sea in 2017 and in the Arctic Ocean in 2019 was reported. The northward bifurcation of the Kuroshio Extension made these obvious transport of the FNPP1 derived radiocaesium to the subarctic and arctic region while the transport by southward bifurcation was not observed. At Hawaii Islands in the subtropical gyre, there was no signal of the FNPP1 derived radiocaesium during the period from March 2011 and February 2017. At Yonaguni Island where the Kuroshio enters the East China Sea, the FNPP1 signal arrived at Yonaguni Islands eight years after the time of the accident, and these might be transported mainly from the subtropical gyre.

At the marginal seas of the North Pacific Ocean, the elevation in the FNPP1 derived radiocaesium concentration in the northern East China Sea in 2014, in the Sea of Japan in 2014/2015 were observed.

We also briefly summarize study results on nuclides other than radiocaesium (e.g., ⁹⁰Sr, ²³⁹²⁴⁰Pu, and ¹²⁹I).

How to cite: Aoyama, M., Kumamoto, Y., and Inomata, Y.: Ten-year long-range transport of radiocaesium in the surface layer in the Pacific Ocean and its marginal seas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9022, https://doi.org/10.5194/egusphere-egu22-9022, 2022.

Radiocesium (¹³⁷Cs) was one of the radioactive materials released from the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident in March 2011. Highly ¹³⁷Cs contaminated water from groundwater to the sea was reduced after installation of the sea-side impermeable wall as a countermeasure against contaminated water in October 2015. As a result, ¹³⁷Cs contamination in water from other sources became more prominent and the levels of ¹³⁷Cs concentration in seawater was correlated with rainfall fluctuation. To determine the source of contamination, we estimated the fluctuation patterns of ¹³⁷Cs concentration in seawater, groundwater level, and discharge from the channels using the Antecedent Precipitation Index (Rw) method.
The results indicated that the fluctuation in seawater collected near the 1-4 Units had strong agreement with the 3 day half-life of Rw. The half-life is shorter than that estimated by groundwater level (7 to 30 day). Therefore, the ¹³⁷Cs concentration in seawater was influenced by relatively faster runoff than the deep groundwater flow. We also made the spatial distribution map of ¹³⁷Cs concentration in seawater to determine the sources of contamination. It showed that the ¹³⁷Cs contaminated area was the highest at “south- inside the intake of 1-4 Units” where the outlets of the K and BC discharge channels are located. In particular, the concentration of ¹³⁷Cs in the channel K was found to correlate with the concentration of ¹³⁷Cs in seawater near the 1-4 Units (average of R² = 0.5). These results indicate that the concentration of ¹³⁷Cs in seawater inside the FDNPP port can be estimated by the Rw method and that the source of the contamination could be determined using the half-life.

How to cite: Sato, H. and Onda, Y.: Determining sources of the 137Cs concentration in seawater at Fukushima Daiichi Nuclear Power Plant using Antecedent Precipitation Index, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9055, https://doi.org/10.5194/egusphere-egu22-9055, 2022.

European seas such as, Baltic, North, and Norwegian Seas are mostly affected areas by the accident at the Chernobyl nuclear power plant (CNPP) in 1986. Since Fukushima Daiichi nuclear power plant (FDNPP) is located on the coast of the North Pacific Ocean in east Japan, its accident resulted in the release of large amounts of radiocesium to the surrounding coastal marine environment (i.e. the waters off Fukushima and neighboring prefectures). The temporal change of radiocaesium concentration in seawater after both accidents was largely dependent on their submarine topography: The Baltic Sea is a semi-closed basin, while Norwegian and North Seas, and the waters off Fukushima and neighboring prefectures is directly connected to open-water. Although concentration of radioacesium (¹³⁷Cs) in the surface water of the Baltic Sea (central part) continuously decreased, the values in 1996, ten years after the accident, were even higher than pre-accident level in 1985. On the other hand, in the waters off Fukushima and neighboring prefectures ¹³⁷Cs concentrations in 2020, nine years after the accident, are approaching the pre-accident levels of 2010. The quick decrease is attributable to the intrusion or mixing of water masses with low ¹³⁷Cs.

How to cite: Takata, H.: Temporal trends of radio-cesium concentration in the marine environment after the Chernobyl and Fukushima Dai-ichi Nuclear Power Plant accidents, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10644, https://doi.org/10.5194/egusphere-egu22-10644, 2022.

For the Fukushima region in Japan, the large-scale decontamination in the catchments needed to require more attention because of their possible consequence in altering particulate Cs-137 flux from the terrestrial environment to the ocean. Here, combining the high-resolution satellite dataset and concurrent river monitoring results, we quantitively assess the impacts of land cover changes in large-area decontaminated regions on river suspended sediment (SS) and particulate Cs-137 dynamics during 2013-2018. We find that the decontaminated regions’ erodibility dramatically enhanced during the decontamination stage but rapidly declined in the subsequent natural-restoration stage. River SS dynamics show linear response to these land cover changes, where annual SS load (normalized by water discharge) at the end of decontamination increased by over 300% than pre-decontamination and decreased about 48% at the beginning of natural restoration. Fluctuations in particulate Cs-137 concentrations well reflect the process of sediment source alternation due to land cover changes in decontaminated regions. The “Fukushima decontamination experiment” can reveal the dramatic impact of decontamination-natural restoration processes, which highlights the need for quantitatively assessing human impacts on land use and resultant alternation in sediment transfer patterns in large scale catchments. 

How to cite: Onda, Y., Bin, F., Wakiyama, Y., Taniguchi, K., Hashimoto, A., and Zhang, Y.: Decontamination and subsequent natural restoration processes impact on terrestrial systems in Niida River Catchment in Fukushima, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10713, https://doi.org/10.5194/egusphere-egu22-10713, 2022.

Radiocesium deposited in the forest canopy is transferred to the forest floor by rainwater and　litterfall. Among them, stemflow likely increases the radiocesium inventory by concentrating rainwater around the trunk. However, the effects of stemflow on the influx of radiocesium into forest soil have not been evaluated quantitatively. In this study, the fluxes of rainwater via stemflow, throughfall, and soil infiltration water were observed. The concentration of dissolved ¹³⁷Cs was measured in a cedar forest in Fukushima Prefecture, Japan. Soil infiltration water was collected at 5 cm and 20 cm depths at the distant point from the tree trunk (Bt), and the base of the tree trunk (Rd), where the influence of stemflow was strong. The observations were conducted during the period from September 2019 to November 2021. During the observation period, an experiment was conducted to intercept the inflow of rainwater via the throughfall or stemflow, and the change in soil infiltration water was observed. The observation results showed that the infiltration flux of radiocesium into the forest soil was significantly higher at the Rd site and about three times larger than at the Bt site. Particularly at the 20 cm depth at the Rd site, the soil infiltration water flux increased with the stemflow. The stemflow exclusion resulted in the dcrease of radiocesium flux by about 70% at all depths at the Rd site. These results suggest that the stemflow increases the input of radiocesium to the base of the tree trunk and facilitates its transfer to the deeper soil layers.

How to cite: Kato, H., Iida, H., Shinozuka, T., Niwano, Y., and Onda, Y.: Effects of stemflow on radiocesium infiltration into the forest soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10817, https://doi.org/10.5194/egusphere-egu22-10817, 2022.

Radioactive cesium (¹³⁷Cs) is distributed in the global ocean due to global fallout by atmospheric nuclear weapons tests, releases from reprocessing plants in Europe, and supplied to the ocean by the Fukushima Daiichi Nuclear Power Plant (1F NPP) accident. In order to detect future contamination by radionuclides, it is necessary to understand the global distribution of radionuclides such as ¹³⁷Cs. For this purpose, observed data have been summarized in a historical database (MARIS) by IAEA. The spatio-temporal density of the observations varies widely, therefore simulation by an ocean general circulation model (OGCM) can be helpful in the interpretation of these observations.

In order to clarify the behavior of ¹³⁷Cs in the global ocean, OGSM simulations were conducted. Parallel Ocean Program version 2 (POP2) of the Community Earth System Model version 2 (CESM2) is employed. The horizontal resolution is 1.125 degree of longitude, and from 0.28 degree to 0.54 degree of latitude. There are 60 vertical levels with a minimum spacing of 10 m near the ocean surface, and increased spacing with depth to a maximum of 250 m. The simulated period was from 1945 to 2030 with the circulation forced by repeating (“Normal Year”) atmospheric conditions. As input sources of ¹³⁷Cs to the model, global fallout from atmospheric nuclear tests, releases from reprocessing plants in Europe, and input from the 1F NPP accident were considered. It was assumed that the input conditions in 2020 would continue after 2020.

The simulated ¹³⁷Cs activity agrees well with the observed data in the database, especially in the Atlantic and Pacific Oceans where the observation density is large. Since ¹³⁷Cs undergoes radioactive decay with a half-life of 30 years, the inventory for each basin is the difference between the decay corrected cumulative input and flux. In the North Pacific, the inventory reached its maximum in 1966 due to the global fallout by atmospheric nuclear weapons tests. Fluxes from the North Pacific to the Indian Ocean, Arctic Ocean, and Central Pacific were positive, and the North Pacific was a source of supply for other ocean basins. The 1F NPP accident caused a 20% increase in the inventory in 2011. In the North Atlantic, the inventory reaches its maximum in the late 1970s, due to the releases from the reprocessing plant. The outflow flux from the North Atlantic to the Greenland Sea is larger than the other fluxes and is a source of supply to other ocean basins. After 2000, the inflow flux to the North Pacific from the Labrador Sea and the South Atlantic is larger than the outflow flux.

The time series of ¹³⁷Cs inventory in each ocean basin and the fluxes among ocean basins were quantitatively analyzed by OGCM simulations, and the predictions for the next 10 years were made.  The ¹³⁷Cs activity concentrations by global fallout can be detected in the global ocean after 2030. The OGCM simulations will be useful in planning future observations to fill the gaps in the database.

How to cite: Tsumune, D., Bryan, F., Lindsay, K., Misumi, K., Tsubono, T., and Aoyama, M.: Estimation of 137Cs inventories in each ocean basin by a global ocean general circulation model for the global database interpolation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11022, https://doi.org/10.5194/egusphere-egu22-11022, 2022.

Considerable amounts of ¹⁴C in the nuclear reactor are generated by neutrons. It accumulates in reactor components, coolant, and cleaning systems, and partly is released into the environment as gaseous releases and as liquid effluents. Two RBMK-1500 type reactors were exploited at Ignalina NPP (Lithuania) 1983-2009. Releases from NPP radiocarbon accumulated in local biosphere by photosynthesis, including terrestrial and aquatic media, as INPP used Lake Drūkšiai as a cooling pond

Temporal variation of ¹⁴C in lake ecosystem was examined by analyzing measured radiocarbon concentration of the organic compounds (Alkali soluble-AS) and alkali insoluble-AIS) derived from the layers of the Drūkšiai lake bottom sediments. The lake sediment cores were sampled in 2013 and 2019, sliced to 1 cm layers and ¹⁴C concentration was measured of every layer. AS and AIS organic fractions of sediment samples were extracted by using the acid-base-acid method.

Tree ring cores were collected from Pinus Sylvestris pines around the Ignalina NPP site at different directions and distances. Cellulose extraction was performed with BABAB (base-acid-base-acid-bleach) procedure, and all samples were graphitized and measured by a single state accelerator mass spectrometry at Vilnius Radiocarbon facility. Tree rings ¹⁴C concentration analysis provides atmospheric radiocarbon concentration in locations around the nuclear object. This analysis provides an opportunity to evaluate the impact of a nuclear object on water and terrestrial ecosystems.

The results showed a pronounced increase of ¹⁴C above background up to 17.8 pMC in the tree rings during INPP exploitation as well during decommission (since 2010) periods. According to the recorded data in 2004-2017 of the local Ignalina NPP meteorological station, the prevailing wind direction was towards the North and East during warm and light time periods. The radiocarbon released from the INPP stack dilutes when it travels in a downwind direction from the INPP. However, even 6.6 km away from the INPP, the impact of the power plant is still clearly visible. By using our created Gaussian dispersion model, the estimated annual emissions of ¹⁴C activity from the Ignalina NPP to the air vary from year to year. When only the 1st INPP reactor Unit was operating in 1985-1987, average emissions were 1.2 TBq/year. Emissions almost doubled to 2.1 TBq/year in 1988, when the 2^nd Unit became operational. Later, emission levels increased. It could be explained by the large amount of ¹⁴C accumulating in the graphite of the RBMK reactor and its gradual release.

¹⁴C concentration profile analysis of the lake bottom sediments core revealed a significant impact of the Ignalina NPP on the Drūkšiai lake ecosystem. An increase of ¹⁴C concentration in the layers of bottom sediments by 80 pMC in the AS fraction and only by 9 pMC in the AIS fraction was observed, corresponding to the period about years of 1998-2003. The maximum peak in AS of 189 pMC was reached approximately in 2001, followed by gradual lake recovery. This radiocarbon peak in the lake represents a large single one-time pollution release. The critical period was in 2000s when maintenance works of the reactors were performed.

How to cite: Maceika, E., Barisevičiūtė, R., Juodis, L., Pabedinskas, A., Ežerinskis, Ž., Šapolaitė, J., Butkus, L., and Remeikis, V.: Retrospective assessment of 14C aquatic and atmospheric releases from Ignalina Nuclear Power Plant due to exploitation of two RBMK-1500 type reactors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11502, https://doi.org/10.5194/egusphere-egu22-11502, 2022.

All radiological measurements acquired from airborne detectors suffer from the issues of geometrical signal dilution, signal attenuation and a complex interaction of the effective sampling area of the detector system with the 3D structure of the surrounding environment. Understanding and accounting for these variables is essential in recovering accurate dose rate maps that can help protect responding workforces in radiologically contaminated environments.

Two types of terrain-cognisant methods of improving source localisation and the contrast of airborne radiation maps are presented in this work, comprising of ‘Point Cloud Constrained 3D Backprojection’ and ‘Point Cloud Constrained Randomised Kaczmarz Inversion’. Each algorithm uses a combination of airborne gamma-spectrometry and 3D scene information collected by UAS platforms and have been applied to data collected with lightweight, simple (non-imaging) detector payloads at numerous locations across the Chornobyl Exclusion Zone (CEZ).

Common to both the algorithms is the projection of the photopeak intensity onto a point cloud representation of the environment, taking into account the position and orientation of the UAS in addition to the 3D response of the spectrometer. The 3D Backprojection method can be considered a relatively fast method of mapping of through proximity, in which the measured photopeak intensity is split over the point cloud according to the above factors. It is an additive technique, with each measurement increasing the overall magnitude of the radiation field assigned to the survey area, meaning that more measurements continues to increase the total radiation of the site. The total measured intensity of the solution is then normalised according to the time spent in proximity to each point in the scene, determined by splitting and projecting the nominal measurement time at each survey point over the point cloud according to the distance from the survey position. Thus accounting for sampling biases during the survey.

The inversion approach adapts algorithms routinely used in medical imaging for the unconstrained world in which the detector is no longer completely surrounding the subject/target. A forward projection model, based on the contribution of distant point sources to the detector intensity, is used to determine the relationship between the full set of measurements and the 3D scene. This results in a hypercube of linear equations where it is assumed every point in the scene contributes to the measured intensity. The algorithm randomly adds measurements from within the aerial set and back-projects this onto the point cloud, with the initial state of the solution set to emit no radiation. After a given number of iterations, the fit of the current solution to the original measurements is assessed though a least squares method and updated when this produces a fit better than the current best estimate. This continues to happen until a minimum value is reached before the divergence of the system, representing the most confident solution. Based on examples from both simulations and real world data, the improvement in contrast of airborne maps using this inversion method can make them equivalent to ground-based surveys, even when operating at 20 m AGL and above.

How to cite: Connor, D., Megson-Smith, D., Wood, K., Mackenzie, R., Connolly, E., White, S., Russell-Pavier, F., Ryan-Tucker, M., Martin, P., Verbelen, Y., Richardson, T., Smith, N., and Scott, T.: Mapping of Post-Disaster Environments using 3D Backprojection and Iterative Inversion Methods Optimised for Limited-Pixel Gamma Spectrometers on Unoccupied Aerial Systems (UAS)., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11571, https://doi.org/10.5194/egusphere-egu22-11571, 2022.

Most of the territory of the Chernobyl Exclusion Zone (CEZ) is covered by forest. Forest of CEZ have accumulated a significant part of the radioactive release and for many years have served as a barrier to the non spreading of the radionuclide contamination outside the CEZ.

According to the classification of wildfire danger, the forests of CEZ belong to high, above average and medium classes, making cases of wildfires as quite common.

Poor, sod-podzolic soils of Ukrainian Polesye contribute to the entry the activity of ⁹⁰Sr and ¹³⁷Cs in plant biomass. During wildfires some of the radionuclides contained in combustion products of biomass are emitted into the atmosphere. Biologically important radionuclides such as ⁹⁰Sr, ¹³⁷Cs, plutonium isotopes and ²⁴¹Am bound to fine aerosols - combustion products - can be transported with atmospheric flows over the long range, causing secondary radioactive fallout and forming additional inhalation dose loads on the population.

Lack of the actual information on the source term (rate of emission of radionuclides) does not allow reliable modeling of the radiological impact of wildfires. To address this issue, we have proposed a methodology that allows for operational assessments of the dynamics of radionuclide emissions into the atmosphere from wildfires in the CEZ.

The basic parameters for the calculations are

• cartographic data on the density of radionuclide contamination of the territory of the CEZ;
• classification of the territory of the CEZ according to the distributive features of forests and meadows;
• classification of CEZ forests according to taxa characteristics to estimate amount of stored fuel biomass (kg/m²);
• experimental data on the transfer of radionuclides from soil to the main components of biomass for the calculation of radionuclide inventory in fuel biomass (Bq/m²). Thus, for meadows the main fuel component is grass turf, while for forest these are litter, wood, bark and pine needles.
• experimental data on emission factors of radionuclides from fuel biomass.

Implementation of the proposed algorithm in the form of GIS application makes it possible to assess the dynamics of radionuclide emission into the atmosphere by delineation the fire areas on the CEZ map. The NASA WorldView interactive mapping web application can be used to estimate the temporal and spatial characteristics of the wildfire while it is being developed. The contouring of the area affected by fire is carried out according to the analysis of the cluster of thermal points. Also, operational contouring of wildfire can be carried out using data delivered from unmanned aerial vehicles.

The application of the proposed algorithm for the analysis of the dynamics of ¹³⁷Cs emissions into the atmosphere from the April 2020 wildfire showed a good agreement with the data reported by various authors who used the method of inverse simulation. Improving the accuracy of calculations according to the proposed algorithm can be done by rectifying radionuclide emission factors and taking into account fire intensity data, which in turn can affect both the radionuclide emission factor and the degree of burnout of plant biomass.

How to cite: Protsak, V., Laptev, G., Voitsekhovych, O., Hinchuk, T., and Korychenskyi, K.: Methodology for estimating the emission of radionuclides into the atmosphere from wildfires in the Chernobyl Exclusion Zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11620, https://doi.org/10.5194/egusphere-egu22-11620, 2022.

Human activities such as mining and processing of naturally occurring radioactive materials have a potential to result in enhanced radioactivity levels in the environment. In South Africa, there has been extensive mining of gold and uranium which produced large mine tailings dams that are highly concentrated with radioactive elements. The purpose of this study was to carry out a preliminary survey on a large scale to assess the activity concentrations of ²³⁸U, ²³²Th and ⁴⁰K in mine tailings, soils and outcropping rocks in the West Rand District in South Africa. This was done to better understand the impact of the abandoned mine tailings on the surrounding soil. This study employed in-situ gamma spectrometry technique to measure the activity concentrations of ²³⁸U, ²³²Th and ⁴⁰K. The portable BGO SUPER-SPEC (RS-230) spectrometer, with a 6.3 cubic inches Bismuth Germanate Oxide (BGO) detector was used for in-situ measurements. In mine tailings the activity concentrations for ²³⁸U, ²³²Th and ⁴⁰K were found to range from 209.95 to 2578.68 Bq/kg, 19.49 to 108.00 Bq/kg and 31.30 to 626.00 Bq/kg, respectively. In surface soil, the activity concentration of ²³⁸U for all measurements ranged between 12.35 and 941.07 Bq/kg, with an average value of 59.15 Bq/kg. ²³²Th levels ranged between 12.59 and 78.36 Bq/kg, with an average of 34.91 Bq/kg. For ⁴⁰K the average activity concentration was found to be 245.64 Bq/kg, in a range of 31.30 - 1345.90 Bq/kg. For the rock samples analyzed, average activity concentrations were 32.97 Bq/kg, 32.26 Bq/kg and 351.52 Bg/kg for ²³⁸U, ²³²Th and ⁴⁰K, respectively. The results indicate that higher radioactivity levels are found in mine tailings than in rocks and soils. ²³⁸U was found to contribute significantly to the overall activity concentration in tailings dams as compared to ²³²Th and ⁴⁰K. It has been observed that the mine tailings have a potential to impact on the activity concentration of ²³⁸U in soil in the immediate vicinity. However, on a regional scale it was found that the radioactivity levels in surface soil mainly depend on the radioelement concentration of the underlying rocks. The contamination is only confined to areas where mine tailings materials are washed off and deposited on surface soils in close proximity to tailings sources. This serves as an indication that the migration of uranium from tailings dams is localized and occurs within short distances. It is recommended that further radiological monitoring be conducted in areas found to have elevated concentration of uranium-238.

Keywords-In-situ gamma-ray spectrometry, Mine tailings, Radioactivity, Soil.

How to cite: Moshupya, P., Abiye, T., and Korir, I.: In-situ measurements of natural radioactivity levels in the gold mine tailings dams of the West Rand District, South Africa., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11669, https://doi.org/10.5194/egusphere-egu22-11669, 2022.

The vast majority of the Antarctic ice sheet is covered with snow that compacts under its own weight and transforms into ice below the surface. However, in some areas, this typically blue-colored ice is directly exposed at the surface. These so-called "blue ice areas" represent islands of negative surface mass balance through sublimation and/or melt. Moreover, blue ice areas expose old ice that is easily accessible in large quantities at the surface, and some areas contain ice that extends beyond the time scales of classic deep-drilling ice cores.

Observation and modeling efforts suggest that the location of blue ice areas is related to a specific combination of topographic and meteorological factors. In the literature, these factors are described as (i) enhanced katabatic winds that erode snow, due to an increase of the surface slope or a tunneling effect of topography, (ii) the increased albedo of blue ice (with respect to snow), which enhances ablative processes, and (iii) the presence of nunataks (mountains protruding the ice) that act as barriers to the ice flow upstream, and prevent deposition of blowing snow on the lee side of the mountain. However, it remains largely unknown which role the physical processes play in creating and/or maintaining  blue ice at the surface of the ice sheet.

Here, we study how a combination of environmental and topographic factors lead to the observation of blue ice. We also quantify the relevance of the single processes and build an interpretable model aiming at not only predicting blue ice presence, but also explaining why it is there. To do so, data is fed into a convolutional neural network, a machine learning algorithm which uses the spatial context of the data to generate a prediction on the presence of blue ice areas. More specifically, we use a U-Net architecture that through convolutions and linked up-convolutions allows to obtain a semantic segmentation (i.e., a pixel-level map) of the input data. Ground reference data is obtained from existing products of blue ice area outlines that are based on multispectral observations. These products contain considerable uncertainties, as (i) the horizontal change from snow to ice is gradual and a single threshold in this transition is not applicable uniformly over the continent, and (ii) the blue ice area extent is known to vary seasonally. Therefore, we train our deep learning model with a loss function with increasing weight towards the center of blue ice areas.

Our first results indicate that the neural network predicts the location of blue ice relatively well, and that surface elevation data plays an important role in determining the location of blue ice. In our ongoing work, we analyze both the predictions and the neural network itself to quantify which factors posses predictive capacity to explain the location of blue ice. Eventually this information may allow us to answer the simple yet important question of why blue ice areas are located where they are, with potentially important implications for their role as paleoclimate archives and for their evolution under changing climatic conditions.

How to cite: Tollenaar, V., Zekollari, H., Tuia, D., Kellenberger, B., Rußwurm, M., Lhermitte, S., and Pattyn, F.: What determines the location of Antarctic blue ice areas? A deep learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1294, https://doi.org/10.5194/egusphere-egu22-1294, 2022.

The temporal variability of glacier calving front positions provides essential information about the state of marine-terminating glaciers. These positions can be extracted from Synthetic Aperture Radar (SAR) images throughout the year. To automate this extraction, we apply deep learning techniques that segment the SAR images into different classes: glacier; ocean including ice-melange and sea-ice covered ocean; rock outcrop; and regions with no information like areas outside the SAR swath, layover regions and SAR shadow. The calving front position can be derived from these regions during post-processing.   
A downside of deep learning is that hyper-parameters need to be tuned manually. For this tuning, expert knowledge and experience in deep learning are required. Furthermore, the fine-tuning process takes up much time, and the researcher needs to have programming skills.
    
In the biomedical imaging domain, a deep learning framework [1] has become increasingly popular for image segmentation. The nnU-Net can be used out-of-the-box. It automatically adapts the U-Net, the state-of-the-art architecture for image segmentation, to different datasets and segmentation tasks. Hence, no more manual tuning is required. The framework outperforms specialized deep learning pipelines in a multitude of public biomedical segmentation competitions.   
We apply the nnU-Net to the task of glacier segmentation, investigating whether the framework is also beneficial in the domain of remote sensing. Therefore, we train and test the nnU-Net on CaFFe (https://github.com/Nora-Go/CaFFe), a benchmark dataset for automatic calving front detection on SAR images. CaFFe comprises geocoded, orthorectified imagery acquired by the satellite missions RADARSAT-1, ERS-1/2, ALOS PALSAR, TerraSAR-X, TanDEM-X, Envisat, and Sentinel-1, covering the period 1995 - 2020. The ground range resolution varies between 7 and 20 m². The nnU-Net learns from the multi-class "zones" labels provided with the dataset. We adopt the post-processing scheme from Gourmelon et al. [2] to extract the front from the segmented landscape regions. The test set includes images from the Mapple Glacier located on the Antarctic Peninsula and the Columbia Glacier in Alaska. The nnU-Net's calving front predictions for the Mapple Glacier lie close to the ground truth with just 125 m mean distance error. As the Columbia Glacier shows several calving front sections, its segmentation is more difficult than that of the laterally constrained Mapple Glacier. This complexity of the calving fronts is also reflected in the results: Predictions for the Columbia Glacier show a mean distance error of 635 m. Concludingly, the results demonstrate that the nnU-Net holds considerable potential for the remote sensing domain, especially for glacier segmentation.
    
[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z 

[2] Gourmelon, N., Seehaus, T., Braun, M., Maier, A., Christlein, V.: Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery, In Prep.

How to cite: Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Dissecting Glaciers - Can an Automated Bio-Medical Image Segmentation Tool also Segment Glaciers?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2726, https://doi.org/10.5194/egusphere-egu22-2726, 2022.

The Himalayan glacierized region has experienced a substantial rise in number and area of glacial lakes in the past two decades. These glacial lakes directly influence glacier melt, velocity, geometry, and thus overall response of the glacier to climate change. The sudden release of water from these glacial lakes poses a severe threat to downstream communities and infrastructure. Thereby, regular monitoring and modelling of these lakes bear significance in order to understand regional climate change, and mitigating the anticipated impact of glacial lake outburst flood. Here, we proposed an automated scheme for Himalayan glacial lake extent mapping using multisource remote sensing data and a state-of-the-art deep learning technique. A combination of multisource remote sensing data [Synthetic Aperture Radar (SAR) coherence, thermal, visible, near-infrared, shortwave infrared, Advanced Land Observing Satellite (ALOS) DEM, surface slope and Normalised Difference Water Index (NDWI)] is used as input to a fully connected feed-forward Convolutional Neural Network (CNN). The CNN is trained on 660 images (300×300×10) collected from 11 sites spread across Himalaya. The CNN architecture is designed for choosing optimum size, number of hidden layers, convolutional layers, filters, and other hypermeters using hit and trial method. The model performance is evaluated over 3 different sites of Eastern Himalaya, representing heterogenous landscapes. The novelty of the presented automated scheme lies in its spatio-temporal transferability over the large geographical region (~8477, 10336 and 6013 km²). The future work involves Intra-annual lake extent mapping across High-Mountain Asian region in an automated fashion.

How to cite: Kaushik, S., Singh, T., Joshi, P. K., and Dietz, A. J.: Automated mapping of Eastern Himalayan glacial lakes using deep learning and multisource remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2904, https://doi.org/10.5194/egusphere-egu22-2904, 2022.

Artificial Intelligence for Cold Regions (AI-CORE) is a collaborative approach for applying Artificial Intelligence (AI) methods in the field of remote sensing of the cryosphere. Several research institutes (German Aerospace Center, Alfred-Wegener-Institute, Technical University Dresden) bundled their expertise to jointly develop AI-based solutions for pressing geoscientific questions in cryosphere research. The project addresses four geoscientific use cases such as the change pattern identification of outlet glaciers in Greenland, the object identification in permafrost areas, the detection of calving fronts in Antarctica and the firn-line detection on glaciers. Within this presentation, the four AI-based final approaches for each addressed use case will be presented and exemplary results will be shown. Further on, the implementation of all developed AI-methods in three different computer centers was realized and the lessons learned from implementing several ready-to-use AI-tools in different processing infrastructures will be discussed. Finally, a best-practice example for sharing AI-implementations between different institutes is provided along with opportunities and challenges faced during the present project duration.

How to cite: Dietz, A. and Baumhoer, C. and the AI-CORE Team: The AI-CORE Project - Artificial Intelligence for Cold Regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3446, https://doi.org/10.5194/egusphere-egu22-3446, 2022.

The scarcity and limited accuracy of snow and precipitation observation and estimation in high-mountain regions reduce our understanding of climatic-cryospheric processes. Thus, we compared the snow water equivalent (SWE) from winter mass balance observations of 95 glaciers distributed over the Alps, Canada, Central Asia and Scandinavia, with the cumulative gridded precipitation data from the ERA-5 and the MERRA-2 reanalysis products. We propose a machine learning model to downscale the gridded precipitation from the reanalyses to the altitude of the glaciers. The machine learning model is a gradient boosting regressor (GBR), which combines several meteorological variables from the reanalyses (air temperature and relative humidity are also downscaled to the altitude of the glaciers) and topographical parameters. Among the most important variables selected by the GBR model, are the downscaled relative humidity and the downscaled air temperature. These GBR-derived estimates are evaluated against the winter mass balance observations by means of a leave-one-glacier-out cross-validation (site-independent GBR) and a leave-one-season-out cross-validation (season-independent GBR). The estimates downscaled by the GBR show lower biases and higher correlations with the winter mass balance observations than downscaled estimates derived with a lapse-rate-based approach. Finally, the GBR estimates are used to derive SWE trends between 1981 and 2021 at high-altitudes. The trends obtained from the GBRs are more enhanced than those obtained from the gridded precipitation of the reanalyses. When the data is regrouped regionwide, significant trends are only observed for the Alps (positive) and for Scandinavia (negative), while significant positive or negative trends are observed in all the regions when looking locally at single glaciers and specific elevations. Positive (negative) SWE trends are typically observed at higher (lower) elevations, where the impact of rising temperatures is less (more) dominating.

How to cite: Guidicelli, M., Gabella, M., Huss, M., and Salzmann, N.: Snow accumulation over the world's glaciers (1981-2021) inferred from climate reanalyses and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3701, https://doi.org/10.5194/egusphere-egu22-3701, 2022.

The last few years have seen an increasing number of studies modeling glacier evolution using deep learning. Most of these techniques have focussed on artificial neural networks (ANN) that are capable of providing a regressed value of mass balance using topographic and meteorological input features. The large number of parameters in an ANN demands a large dataset for training the parameter values. This is relatively difficult to achieve for regions with a sparse in-situ data measurement set up such as the Himalayas. For example, of the 14326 point mass balance measurements obtained from the Fluctuations of Glaciers database for the period of 1950-2020 for glaciers between 60S and 60N, a mere 362 points over four glaciers exist for the Himalayan region. These are insufficient to train complex neural network architectures over the region. We attempt to overcome this data hurdle by using transfer learning. Here, the parameters are first trained over the 9584 points in the Alps following which the weights were used for retraining for the Himalayan data points. Fourteen meteorological from the ERA5Land monthly averaged reanalysis data were used as input features for the study. A 70-30 split of the training and testing set was maintained to ensure the authenticity of the accuracy estimates via independent testing. Estimates are assessed on a glacier scale in the temporal domain to assess the feasibility of using deep learning to fill temporal gaps in data. Our method is also compared with other machine learning algorithms such as random forest-based regression and support vector-based regression and we observe that the complexity of the dataset is better represented by the neural network architecture. With an overall normalized root mean squared loss consistently less than 0.09, our results suggest the capability of deep learning to fill the temporal data gaps over the glaciers and potentially reduce the spatial gap on a regional scale.

How to cite: Anilkumar, R., Bharti, R., and Chutia, D.: Point Mass Balance Regression using Deep Neural Networks: A Transfer Learning Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5317, https://doi.org/10.5194/egusphere-egu22-5317, 2022.

In the terrestrial cryosphere, freeze/thaw (FT) state transition plays an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes.

Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Many algorithms have been developed and evaluated over time in this scope. With the advancement of data science and artificial intelligence methods, the potential of better understanding the cryosphere is emerging.

This work is dedicated to exploring an effective approach to retrieve FT state based on microwave remote sensing data using machine learning methods, which is expected to fill in some hidden blind spots in the deterministic algorithms. Time series of remote sensing data will be created as training data. In the initial stage, the work aims to test the feasibility and establish the basic neural network based on fewer training factors. In the advanced stage, we will improve the model in terms of structure, such as adding more complex dense layers and testing optimizers, and in terms of discipline, such as introducing more influencing factors for training. Related parameters, for example, land cover types, will be included in the analysis to improve the method and understanding of FT-related processes.

How to cite: Chen, Y., Wang, L., Bernier, M., and Ludwig, R.: Retrieving freeze/thaw-cycles using Machine Learning approach in Nunavik (Québec, Canada), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5612, https://doi.org/10.5194/egusphere-egu22-5612, 2022.

In this talk, we propose to use neural networks in a hybrid modelling setup to learn sub-grid-scale dynamics of sea-ice that cannot be resolved by geophysical models. The multifractal and stochastic nature of the sea-ice dynamics create significant obstacles to represent such dynamics with neural networks. Here, we will introduce and screen specific neural network architectures that might be suited for this kind of task. To prove our concept, we perform idealised twin experiments in a simplified Maxwell-Elasto-Brittle sea-ice model which includes only sea-ice dynamics within a channel-like setup. In our experiments, we use high-resolution runs as proxy for the reality, and we train neural networks to correct errors of low-resolution forecast runs.

Since we perform the two kind of runs on different grids, we need to define a projection operator from high- to low-resolution. In practice, we compare the low-resolution forecasted state at a given time to the projected state of the high resolution run at the same time. Using a catalogue of these forecasted and projected states, we will learn and screen different neural network architectures with supervised training in an offline learning setting. Together with this simplified training, the screening helps us to select appropriate architectures for the representation of multifractality and stochasticity within the sea-ice dynamics. As a next step, these screened architectures have to be scaled to larger and more complex sea-ice models like neXtSIM.

How to cite: Finn, T., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carrassi, A., and Dansereau, V.: Learning and screening of neural networks architectures for sub-grid-scale parametrizations of sea-ice dynamics from idealised twin experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5910, https://doi.org/10.5194/egusphere-egu22-5910, 2022.

The cryosphere is a highly active and dynamic environment that rapidly responds to changing climatic conditions. In particular, the physical processes behind glacial dynamics are poorly understood because they remain challenging to observe. Glacial dynamics are strongly intermittent in time and heterogeneous in space. Thus, monitoring with high spatio-temporal resolution is essential.

In course of the RESOLVE (‘High-resolution imaging in subsurface geophysics : development of a multi-instrument platform for interdisciplinary research’) project, continuous seismic observations were obtained using a dense seismic network (100 nodes, Ø 700 m) installed on Glacier d’Argentière (French Alpes) during May in 2018. This unique data set offers the chance to study targeted processes and dynamics within the cryosphere on a local scale in detail.

To identify seismic signatures of ice beds in the presence of melt-induced microseismic noise, we applied the supervised ML technique gradient tree boosting. The approach has been proven suitable to directly observe the physical state of a tectonic fault. Transferred to glacial settings, seismic surface records could therefore reveal frictional properties of the ice bed, offering completely new means to study the subglacial environment and basal sliding, which is difficult to access with conventional approaches.

We built our ML model as follows: Statistical properties of the continuous seismic records (variance, kurtosis and quantile ranges), meteorological data and a seismic source catalogue obtained using beamforming (matched field processing) serve as features which we fit to measures of the GPS displacement rate of Glacier d’Argentière (labels). Our preliminary results suggest that seismic source activity at the bottom of the glacier strongly correlates with surface displacement rates and hence, is directly linked to basal motion. By ranking the importance of our input features, we have learned that other than for reasonably long monitoring time series along tectonic faults, statistical properties of seismic observations only do not suffice in glacial environments to estimate surface displacement. Additional beamforming features however, are a rich archive that enhance the ML model performance considerably and allow to directly observe ice dynamics.

How to cite: Umlauft, J., Roux, P., Lecointre, A., Gimbert, F., Nanni, U., Walpersdorf, A., Rouet-LeDuc, B., Hulbert, C., Trugman, D., and Johnson, P.: Mapping Glacier Basal Sliding with Beamforming and Artificial Intelligence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6948, https://doi.org/10.5194/egusphere-egu22-6948, 2022.

Ice lead analysis is an essential task for evaluating climate change processes in the Arctic. Ice leads are narrow cracks in the sea-ice, which build a complex network. While detecting and modeling ice leads has been performed in numerous ways based on airborne images, the dynamics of ice leads over time remain hidden and largely unexplored. These dynamics could be analyzed by interpreting the ice leads as more than just airborne images, but as what they really are: a dynamic network. The lead’s start, end, and intersection points can be considered nodes, and the leads themselves as edges of a network. As the nodes and edges change over time, the ice lead network is constantly evolving. This new network perspective on ice leads could be of great interest for the cryospheric science community since it opens the door to new methods. For example, adapting common link prediction methods might make data-driven ice lead forecasting and tracking feasible.
To reveal the hidden dynamics of ice leads, we performed a spatio-temporal and network analysis of ice lead networks. The networks used and presented here are based on daily ice lead observations from Moderate Resolution Imaging Spectroradiometer (MODIS) between 2002 and 2020 by Hoffman et al. [1].
The spatio-temporal analysis of the ice leads exhibits seasonal, annual, and overall trends in the ice lead dynamics. We found that the number of ice leads is decreasing, and the number of width and length outliers is increasing overall. The network analysis of the ice lead graphs reveals unique network characteristics that diverge from those present in common real-world networks. Most notably, current network science methods (1) exploit the information that is embedded into the connections of the network, e.g., in connection clusters, while (2) nodes remain relatively fixed over time. Ice lead networks, however, (1) embed their relevant information spatially, e.g., in spatial clusters, and (2) shift and change drastically. These differences require improvements and modifications on common graph classification and link prediction methods such as Preferential Attachment and EvolveGCN on the domain of ice lead dynamic networks.
This work is a call for extending existing network analysis toolkits to include a new class of real-world dynamic networks. Utilizing network science techniques will hopefully further our understanding of ice leads and thus of Arctic processes that are key to climate change mitigation and adaptation.

Acknowledgments

We would like to thank Prof. Gunnar Spreen, who provided us insights into ice lead detection and possible challenges connected to the project idea. Furthermore, we would like to thank Shenyang Huang and Asst. Prof. David Rolnick for their valuable feedback and support. J.K. was supported in part by the DeepMind scholarship, the Mitacs Globalink Graduate Fellowship, and the German Academic Scholarship Foundation.

References

[1] Jay P Hoffman, Steven A Ackerman, Yinghui Liu, and Jeffrey R Key. 2019. The detection and characterization of Arctic sea ice leads with satellite imagers. Remote Sensing 11, 5 (2019), 521.

How to cite: Kaltenborn, J., Ramesh, V., and Wright, T.: Ice Lead Network Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8945, https://doi.org/10.5194/egusphere-egu22-8945, 2022.

In recent years, the importance of dynamics in the Arctic Ocean have proven itself with respect to climate monitoring and modelling. Data used for creating models often include temperature & salinity profiles. Such profiles in the Arctic region are sparse and acquiring new data is expensive and time-consuming. Thus, efficient methods of interpolation are necessary to expand regional data. In this project, 3D temperature & salinity profiles are reconstructed using 2D surface measurements from ships, floats and satellites. The technique is based on a stacked Long Short-Term Memory (LSTM) neural network. The goal is to be able to reconstruct the profiles using remotely sensed data.

How to cite: Jensen, M., Bang-Hansen, C., Andersen, O. B., Ludwigsen, C. B., and Ehrhorn, M.: Using LSTM on surface data to reconstruct 3D Temperature & Salinity profiles in the Arctic Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9753, https://doi.org/10.5194/egusphere-egu22-9753, 2022.

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors.

Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution.

Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS)  from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers.

Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term.

The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region.

How to cite: Sangiorgio, M., Bianco, E., Iovino, D., Materia, S., and Castelletti, A.: Arctic sea ice dynamics forecasting through interpretable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10386, https://doi.org/10.5194/egusphere-egu22-10386, 2022.

Climate change intensifies glacier melt which effectively leads to the formation of numerous new glacial lakes in the overdeepenings of former glacier beds. Additionally, the area of many existing glacial lakes is increasing. More than one thousand glacial lakes have emerged in Switzerland since the Little Ice Age, and hundreds of lakes are expected to form in the 21st century. Rapid deglaciation and formation of new lakes severely affect downstream ecosystem services, hydropower production and high-alpine hazard situations. Day by day, glacier lake inventories for high-alpine terrains are increasingly becoming available to the research community. However, a high-frequency mapping and monitoring of these lakes are necessary to assess hazards and to estimate Glacial Lake Outburst Flood (GLOF) risks, especially for lakes with high seasonal variations. One way to achieve this goal is to leverage the possibilities of satellite-based remote sensing, using optical and Synthetic Aperture Radar (SAR) satellite sensors and deep learning.

There are several challenges to be tackled. Mapping glacial lakes using satellite sensors is difficult, due to the very small area of a great majority of these lakes. The inability of the optical sensors (e.g. Sentinel-2) to sense through clouds creates another bottleneck. Further challenges include cast and cloud shadows, and increased levels of lake and atmospheric turbidity. Radar sensors (e.g. Sentinel-1 SAR) are unaffected by cloud obstruction. However, handling cast shadows and natural backscattering variations from water surfaces are hurdles in SAR-based monitoring. Due to these sensor-specific limitations, optical sensors provide generally less ambiguous but temporally irregular information, while SAR data provides lower classification accuracy but without cloud gaps.

We propose a deep learning-based SAR-optical satellite data fusion pipeline that merges the complementary information from both sensors. We put forward to use Sentinel-1 SAR and Sentinel-2 L2A imagery as input to a deep network with a Convolutional Neural Network (CNN) backbone. The proposed pipeline performs a fusion of information from the two input branches that feed heterogeneous satellite data. A shared block learns embeddings (feature representation) invariant to the input satellite type, which are then fused to guide the identification of glacial lakes. Our ultimate aim is to produce geolocated maps of the target regions where the proposed bottom-up, data-driven methodology will classify each pixel either as lake or background.

This work is part of two major projects: ESA AlpGlacier project that targets mapping and monitoring of the glacial lakes in the Swiss (and European) Alps, and the UNESCO (Adaptation Fund) GLOFCA project that aims to reduce the vulnerabilities of populations in the Central Asian countries (Kazakhstan, Tajikistan, Uzbekistan, and Kyrgyzstan) from GLOFs in a changing climate. As part of the GLOFCA project, we are developing a python-based analytical toolbox for the local authorities, which incorporates the proposed deep learning-based pipeline for mapping and monitoring the glacial lakes in the target regions in Central Asia.

How to cite: Tom, M., Frey, H., and Odermatt, D.: A deep learning approach for mapping and monitoring glacial lakes from space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10637, https://doi.org/10.5194/egusphere-egu22-10637, 2022.

Over the past four decades, the inexorable growth in technology and subsequently the availability of Earth-observation and model data has been unprecedented. Hidden within these data are the fingerprints of the physical processes that govern climate variability over a wide range of spatial and temporal scales, and it is the task of the climate scientist to separate these patterns from noise. Given the wealth of data now at our disposal, machine learning methods are becoming the tools of choice in climate science for a variety of applications ranging from data assimilation, to sea ice feature detection from space. This talk summarises recent developments in the application of machine learning methods to the study of polar climate, with particular focus on Arctic sea ice. Supervised learning techniques including Gaussian process regression, and unsupervised learning techniques including cluster analysis and complex networks, are applied to various problems facing the polar climate community at present, where each application can be considered an individual component of the larger sea ice prediction problem. These applications include: seasonal sea ice forecasting, improving spatio-temporal data coverage in the presence of sparse satellite observations, and illuminating the spatio-temporal connectivity between climatological processes.

How to cite: Gregory, W.: Machine learning tools for pattern recognition in polar climate science, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12785, https://doi.org/10.5194/egusphere-egu22-12785, 2022.

We have recently applied an objective mapping type approach to merge observations from multiple altimeters, both for enhancing the temporal/spatial resolution of freeboard samples and for analyzing crossovers between satellites (Gregory et al, 2021). This mapping provides optimal interpolation of proximal observations to a location in space and time based on the covariance of the observations and a priori understanding of their spatiotemporal correlation length scales. This offers a best linear estimator and error field for the observation (radar freeboard or snow depth), which can be used to better constrain pan-Arctic uncertainties. 

In addition we will explore here a newly developed inverse modelling framework  to synchronously retrieve the snow and ice thickness from bias corrected or calibrated radar freeboards from multiple satellite retrievals. The radar equations expressed in section can be rearranged to formulate the joint forward model at gridded level relating measured radar freeboards from multiple satellites (and airborne data) to the underlying snow and ice thickness. In doing so we have also introduced a penetration factor correction term for OIB radar freeboard measurements. To solve this inverse model problem for  and  we use the following two methodologies inspired from Earth Sciences applications (i.e. seismology):  

Space ‘uncorrelated’ inverse modelling. The method is called `space uncorrelated' inverse modelling as the algorithm is applied locally, for small distinct regions in the Arctic Ocean, multiple times, until the entire Arctic ocean is covered. To sample the parameter space  we use the publicly available Neighbourhoud Algorithm (NA) developed originally for seismic tomography of Earth’s interior and recently by us to a sea ice dynamic inversion problem (Hoerton et al, 2019).   

Space ‘correlated inverse modelling. For the second method of inverse modelling, we used what we call a `space correlated' approach. Here the main algorithm is applied over the entire Arctic region, aiming to retrieve the desired parameters at once. In contrast with the previous approach, in this method we take into account positional correlations for the physical parameters when we are solving the inverse problem, the output being a map of the Arctic composed of a dynamically generated a tiling in terms of Voronoi cells. In that way, regions with less accurate observations will be more coarsely resolved while highly sampled regions will be provided on a finer grid with a smaller uncertainty. The main algorithm used here to calculate the posterior solution is called `reverse jump Monte Carlo Markov Chain' (hereafter referred to as rj-MCMC) and its concept was designed by Peter Green in 1999 (Green, 1995). Bodin and Sambridge (2009) adapted this algorithm for seismic inversion, which is the basis of the algorithm used in this study.  

How to cite: Perez Ferrer, J., Tsamados, M., Fox, M., Suciu, T., Heorton, H., and Nab, C.: Inverse modelling techniques for snow and ice thickness retrievals from satellite altimetry , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12882, https://doi.org/10.5194/egusphere-egu22-12882, 2022.

The Caribbean Sea in the tropical Atlantic is one of the major heat engines of the Earth and a sensitive area for monitoring climate variability. Salinity changes in the Caribbean Sea record changes in ocean currents and can provide information about variations in ocean heat transport. Seawater salinity in the Caribbean Sea has been monitored in recent decades, nevertheless, of all oceanographic environmental parameters salinity information before the instrumental period remains limited, due to the difficulty of reconstructing salinity, arguably the most difficult natural archives to recreate. We were able to reconstruct salinity changes in the Caribbean Sea from 1700 to the present from southwest Puerto Rico using slowly growing and long-lived scelerosponges from southwest Puerto Rico. These well-dated sponges are known to precipitate their skeletons in isotopic equilibrium (i.e., their record is not affected much by vital effects) and were retrieved from various depths in the mixed layer, from the surface to 90 m depth. We were able to establish salinity changes by deconvoluting stable isotopes (d¹⁸O) and trace element (Sr/Ca) proxies taken from the sponges at regular intervals. In this contribution, we will present the salinity record and illustrate the process for salinity reconstruction. We will also discuss how we determine how salinity changes in our record relate to radiative forcing as well as connect them with dominant mechanisms operating in the region, including changes in the position of the InterTtropical Convergence Zone and intensity of the Atlantic meridional Overturning Circulation over time.

How to cite: Winter, A., Zanchettin, D., McCulloch, M., Rigo, M., Sherman, C., and Rubino, A.: A high-resolution record of vertically-resolved seawater salinity in the Caribbean Sea mixed layer since 1700 AD., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1388, https://doi.org/10.5194/egusphere-egu22-1388, 2022.

In recent years, our research group has tried to improve the knowledge of the historical climate of the Extremadura region, located in the interior of the southwest of the Iberian Peninsula. Some results can be highlighted:

• Temperature and precipitation indices were constructed for the period 1750-1840 from the correspondence of the Duke of Feria (Fernández-Fernández et al., 2014, 2015, 2017).
• We have recovered many “pro pluvia” rogation dates (Domínguez-Castro et al., 2021) and we have seen their relationship with the North Atlantic Oscillation (Bravo-Paredes et al., 2020).
• We have studied the catastrophic floods of the Guadiana River since AD1500 (Bravo-Paredes et al., 2021).
• We have recovered more than 700,000 meteorological data from the Extremadura region taken in the 19th and early 20th centuries (Vaquero et al., 2022), including some uncommon series (Bravo-Paredes et al., 2019).

In recent months, we have started a study of the meteorological information published by the regional press of Extremadura in the last 150 years and here we will present some preliminary results.

References

Bravo-Paredes, N. et al. (2019) Tellus B 71, 1663597.

Bravo-Paredes, N. et al. (2020) Atmosphere 11(3), 282.

Bravo-Paredes, N. et al. (2021) Science of the Total Environment 797, 149141.

Domínguez-Castro, F. et al. (2021) Scientific Data 8, 186.

Fernández-Fernández, M.I. et al. (2014) Climatic Change 126, 107.

Fernández-Fernández, M.I. et al. (2015) Climatic Change 129, 267.

Fernández-Fernández, M.I. et al. (2017) Climatic Change 141, 671.

Vaquero, J.M. et al. (2022) Geoscience Data Journal. https://doi.org/10.1002/gdj3.131

How to cite: Vaquero, J. M., Gallego, M. C., Bravo-Paredes, N., Carrasco, V. M. S., and Tovar, I.: Reconstructing the climate of the Extremadura region (SW Spain) from documentary sources, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3134, https://doi.org/10.5194/egusphere-egu22-3134, 2022.

Geophysical observatories around the world collect data on various natural phenomena within the Earth and on its surface. Many of these measurements are made automatically, sometimes at high sampling rates, so that enormous amounts of data accumulate over the years. Continuous analysis is important to classify current phenomena and decide which data are important and which can be downsampled later.

At Moxa Geodynamic Observatory, located in central Germany, several laser strainmeters have been installed in subsurface galleries in order to measure strain of the Earth's crust. These instruments run in north-south, east-west, and northwest-southeast directions. Nano-strain rates are determined with a sampling rate of 0.1 Hz almost continuously over distances of 26 and 38 m, respectively, since summer 2011.

Signals of tectonically induced crustal deformation are superimposed by other signals of greater amplitude, e.g., tides, changes in atmospheric pressure, hydrologic events such as heavy rainfall, and earthquakes. Classification of these events is important to better associate jumps in the temporal vicinity and to distinguish anomalies from instrument failures. To avoid time-consuming pattern recognition by hand, algorithms are required to do most of the work automatically. Due to recent advances in the field of artificial intelligence, it is possible to implement time series algorithms that are capable of unifying and automating many steps of data analysis. Although artificial intelligence applications are increasingly used to support data analysis, their use for time series of geophysical origin so far is not widespread outside of seismology.

In this contribution, an approach to automatically detect earthquakes in the strain data using 1D Convolutional Neural Networks is presented, including the generation of artificial training data with time series data augmentation. Also the training process and generation of new training data, based on classification by hand and false predictions of the trained model is described. The 1D Convolutional Neural Networks are able to identify almost all earthquakes in the strain data and have F1 values > 0.99, showing that their application has the potential to significantly reduce the time required in signal classification of observatory time series data.

How to cite: Kasburg, V., Breuer, A., Bücker, M., and Kukowski, N.: Earthquake detection in time-series of laser strainmeter measurements as a first step towards automatic signal classification., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3906, https://doi.org/10.5194/egusphere-egu22-3906, 2022.

The Mont Terri rock laboratory, located in the Swiss Jura Mountains, is dedicated to research on argillaceous rocks. Since its founding in 1996, the objective is the hydrogeological, geochemical, and geotechnical characterisation of Opalinus Clay in the context of nuclear waste repositories. More recently, the work has broadened to additional fields, covering potential uses of the deep geological subsurface such as geological storage of carbon dioxide and geothermal energy. With the excellent infrastructure, a comprehensive database, and the broad scientific and technological expertise, knowledge is enhanced e.g. through the advancement and comparison of approaches as well as the development and testing of novel investigation methods. These, as well as studies on feasibility and risk assessment, are of benefit also for underground laboratories in general and in situ explorations in different rock types worldwide. Due to the long-term commitment and the available gallery space of the research facility, elaborate as well as decade-long experiments can be implemented.

In order to detect, quantify, and understand short- and long-term deformations in the Mont Terri rock laboratory, quasi continuous time series are established employing various monitoring techniques. The latter complement each other in regard to their spatial dimensions, operational frequency optima, and their point or integral information. The approach combines

• a 50 m long uniaxial hydrostatic levelling system (HLS, Type “PSI”, positioned along a gallery wall, measuring principle: electrical plate capacitors),
• four mini-arrays of very-broad-band triaxial seismometers, installed in the rock laboratory (one under the HLS) as well as outside the rock laboratory at the surface,
• and an array of high resolution, biaxial platform tiltmeters, with instruments situated close to the HLS and in various parts of the rock laboratory, integrated in other in situ experiments.

The observed signals and their analysis differ in space and time. They range from the detection of local nanoseismic as well as large tele seismic events, to the determination of earth tides, and to the identification of seasonal trends versus other long term geodetic movements. Besides the mutual comparison of the three deformation measurements, the time series provide valuable input for numerous scientific questions such as the stability of the rock laboratory as a whole or in its parts, the influence of excavation, ventilation, or fluid injection on rock matrix and faults. Long data series of ambient parameters, essential for interpretation of the deformation records, such as temperature, pressure, and humidity, are recorded by sensors integrated in the above listed instruments and are also of interest in further experiments performed by the Mont Terri Consortium.

How to cite: Rebscher, D., Schefer, S., Reichertz, F., Guglielmi, Y., Foxall, W., Gutiérrez, I., and Meier, E.: Long term deformation and seismic observations at the Mont Terri rock laboratory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6814, https://doi.org/10.5194/egusphere-egu22-6814, 2022.

The geomagnetic field, the Earth’s primary barrier against charged particles from the sun, varies on time scales from million years to sub-second fluctuations. In the past decades significant advances in measurement techniques, both ground and space based, paleo- and rock magnetic methods, as well as numerical and analytical simulations, improved our understanding of underlying processes and their consequences on our planet and on our society. Geomagnetic storms, often related to coronal mass ejections on the sun and their interaction with the Earth‘s magnetic field, pose a threat to our modern society as they affect satellites, disturb radio communication, and, in particular, damage power grids and cause electrical blackouts on a massive scale. Ground based measurements, which are used together with satellite data to investigate these events, point towards the occurrence of global scale major storms once every 100 years. When further looking at such observatory data, which is existing for the last few hundred years, it is also striking that the global Earth‘s magnetic field is gradually weakening, by more the 10% in the past 200 years. Paleo- and archeomagnetic investigations are used to extend our observational range into the past in order to clarify the significance and reasons of this field reduction. When looking even further into the past, complete flips of the geomagnetic field are recorded in geological archives like volcanic rocks and sediments. These geomagnetic field reversals, the last one happening about 770kyrs ago, are accompanied by strong reductions of the geomagnetic field strength and complex field behavior on the Earths surface, effects which are sometimes brought into connection with our modern observation of field reduction. This presentation will provide a comprehensive overview about geomagnetic field variations, and the necessity of using long timeseries for interpretation of its current state and future evolution.

How to cite: Leonhardt, R.: Variations of the Earth magnetic field: From geomagnetic storms to field reversal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8343, https://doi.org/10.5194/egusphere-egu22-8343, 2022.

To achieve very low ambient noise and thus very good conditions for long-term geophysical observations at a high level of instrumental accuracy in order to decipher also faint signals from Earth and environmental processes, sensors often are installed in the subsurface in galleries or in boreholes. This however, makes it necessary to consider the potential influence of the geological setting and properties of the surrounding rock formations and overburden.
Moxa Geodynamic observatory, located in a remote part of the Thuringian slate mountains, approximately 30 km south of Jena, provides an ideal setting to address this topic as it comprises two galleries, which are running perpendicular to each other. As the observatory is built at the toe of a relatively steep slope, coverage of the galleries varies along them. Further, the tectonic structure and hydrological settings of the overburden is rather complex.
Instruments sensitive to deformation, which include three laser strain meters measuring nano-strain, borehole tiltmeters and a superconducting gravimeter CD-034, together with other instruments, e.g. a node for the Global Network of Optical Magnetometers for Exotic physics (GNOME), are installed in various positions in the building of the observatory, close to the building, and in the galleries. The laser strainmeters record along three galleries in north-south, east-west and NW-SE directions. Further, information on fluid flow is gained from downhole temperature measurements employing an optical fiber and several groundwater level indicators, some of them installed in shallow boreholes. Additionally, information on environmental parameters is coming from a climate station and on the subsurface tectonic structure from various near surface geophysical data sets. 
Here, we present first results of an ongoing project which combines actual deformation recordings, structural and drillhole information to decipher how the tectonic structure of the and groundwater movement within the overlying slope on top of the observatory’s galleries may impact on the various instrumental recordings.

How to cite: Kukowski, N., Kasburg, V., Goepel, A., Schwarze, C., Jahr, T., and Stolz, R.: Impact of the geological setting of the overburden on long-time series recorded at underground geophysical observatories: case study from the FSU Jena Geodynamic Observatory Moxa (Thuringia, central Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11079, https://doi.org/10.5194/egusphere-egu22-11079, 2022.

Climate information from in-situ observation networks can be used to significantly improve the accuracy of gridded climate datasets, even in data-scarce regions. We applied a bias correction and spatial disaggregation method on daily maximum and minimum ERA5-Land (ERA5L) 2-m air temperature dataset covering Ethiopia. Due to large gaps in the observed temperature data, the bias correction is based on the statistics rather than the complete time series. First, long-term daily, monthly and annual temperature statistics (mean and variance) were summarized for the time series obtained from 155 stations covering the period 1981-2010. Second, the temperature statistics were interpolated onto a 0.05° x 0.05° grid using an inverse non-Euclidean distance weighting approach. This method accounts for the effects of elevation, thus enabling downscaling of the temperature to a higher spatial resolution. Next, the ERA5L maximum and minimum temperature were bias-corrected using quantile mapping assuming a Gaussian distribution transfer function. The quantile mapping was performed at daily, monthly and annual time steps to reproduce the climatology, seasonality, and interannual variability of the data. The performance of the bias correction was evaluated using the leave-out-one cross-validation method. The cross-validation shows that the bias-corrected maximum (minimum) daily temperature has an improved mean absolute error value of 68% (52%) in comparison to the original ERA5L reanalysis air temperature bias. The bias-corrected dataset is therefore suggested as an alternative for the ERA5L and can be used in a wide range of applications in Ethiopia.

How to cite: Wakjira, M. T., Peleg, N., and Molnar, P.: Downscaling to high-resolution and correcting air temperature from the ERA5-Land over Ethiopia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11916, https://doi.org/10.5194/egusphere-egu22-11916, 2022.

Geomagnetic activity is a measure aimed to quantify the effect of solar wind upon the Earth's magnetic environment. The main structures in solar wind driving geomagnetic activity are the coronal mass ejections (CME) and the high-speed solar wind streams together with related co-rotating interaction regions (HSS/CIR). While CMEs are closely related to sunspots and other active regions on solar surface, the HSSs are related to solar coronal holes, forming a proxy of solar polar magnetic fields. This gives an interesting possibility to obtain versatile information on solar activity and solar magnetic fields from geomagnetic activity.

Various indices have been developed to quantify and monitor global geomagnetic activity. The most often used indices of overall geomagnetic activity are the aa index, developed by P. Mayaud and running already since 1868, and the Kp/Ap index, developed by J. Bartels and running since 1932. Both aa and Kp/Ap depict the increase of geomagnetic activity during the first half of the 20th century, and a steep decline in the 2000s. However, although the two indices are constructed from midlatitude observations using roughly the same recipe, they depict notable differences during the 90-year overlapping interval. While the Kp/Ap index reaches a centennial maximum in the late 1950s, at the same time as sunspots, the aa index has its maximum only in 2003. Also, the Kp/Ap is systematically relatively more active in the first decades until 1960s, while aa is more active thereafter. The Dst index was developed to monitor geomagnetic storms and the ring current since 1957. We have corrected some early errors in the Dst index and extended its time interval to 1932. This extended storm index is called the Dxt index. Here we study these long-term geomagnetic indices and their differences. We also use their different dependences on the main solar wind drivers in order to obtain new information on the centennial evolution of solar activity and solar magnetic fields.

How to cite: Mursula, K.: Long-term geomagnetic activity: Comparison and analysis of geomagnetic activity indices during the last 90 years, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12745, https://doi.org/10.5194/egusphere-egu22-12745, 2022.

In the coming years, food demand will increase sharply in line with population growth. So, production will have to increase mostly by area expansion, which could lead to biodiversity loss and an increase in greenhouse gas emissions. Furthermore, both land and water resources are limited and already under severe pressure, making it imperative to ensure a more productive but also sustainable agricultural system. A possible solution to this could be the reclamation of marginal lands, such as saline and drought-prone lands, or even abandoned mining areas. In this sense, phytostabilisation is considered a suitable method for their rehabilitation and reconversion to agricultural and livestock activities while protecting the food chain. Some pasture plants can tolerate adverse growth conditions, such as mine waste or soil conditions (e.g. high concentrations of potentially hazardous elements (PHE) and EC, low pH, organic C and nutrients, and poor structure and water holding capacity). However, low and slow plant growth can limit environmental rehabilitation success. The combined use of Technosols and pastures may be an effective green technology towards reclaiming these marginal areas for food production. To verify this hypothesis, we studied the development of a biodiverse pasture in two biogeochemically distinct Technosols. The pasture is composed of leguminous plants of the genus Trifolium sp. (T. michelianum var. paradana Savi, T. vesiculosum var. cefala Savi, T. resupinatum var. nitrofolus L., and T. squarrosum L.) and Medicago sp., and gramineous plants such as Lolium multiflorum Lam. Two Technosols have been built using a saline Fluvisol collected in the Tagus Estuary and a gossan waste from the São Domingos mine together with a mixture of organic and inorganic amendments. The microcosm assay consisted of four treatments set up in pots of 1.5 dm³ volume (four replicates): (i) Fluvisol (VF), control of salinity affection (EC: 7.9 dS/m; exchangeable sodium 25%); (ii) Technosol-Fluvisol (TVF); (iii) gossan waste (G), control of PHE contamination (g/kg As: 9.1; Pb: 29.6) ; and (iv) Technosol-Gossan (TG). One month after sowing (5 g seeds per pot), the pasture biomass generated so far was mowed to simulate livestock grazing. Pasture in FV showed no seed germination, thus no plant growth. In contrast, in G (10 cm stem length and 1.41 g DW), was observed seed germination followed by plant growth, with gramineous dominating over leguminous plants. In the Technosols, pasture growth improved extraordinarily, with plants reaching more than 15 cm stem length and higher biomass with 2.59 g DW on TVF and 3.8 g DW on TG. Moreover, the pasture was more biodiverse, with the presence of as many leguminous as gramineous. Upon the first cut, the pasture was left for another month for the plants to regrow, and then mowed for the last time. During this time, L. multiflorum has kept growing to similar sizes to the first mowing, while the leguminous have not regrown as effectively. Thus, an integrated biotechnological approach involving Technosols and pastures could be a useful green technology to convert marginal lands into food production areas (grazing or foraging).

How to cite: Aguilar-Garrido, A., Vidigal, P., Caperta, A. D., and Abreu, M. M.: Development capacity of a biodiverse pasture on Technosols for the rehabilitation of marginal lands (saline soils and mining waste), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-121, https://doi.org/10.5194/egusphere-egu22-121, 2022.

Method of isolation of soil microorganisms - destructors of biopolymers

Nataliia Chupakhina, Nadezda Nikolaeva, Dmitriy Nechaev, Nadezda Medjalo, Anastasija Novichkova, Valerija Lobanova and Galina Chupakhina

School of Life Sciences, Immanuel Kant Baltic Federal University, Universitetskaya str. 2, 236040 Kaliningrad, Russian Federation

Biological degradation of plastic by microorganisms and their enzymes is one of the ways to eliminate the waste resulting from mass production of plastic (Carr C. M., Clarke D. J., 2020).
We analyzed the soil microflora in the presence of fragments of oxo-biodegradable polyethylene with the addition of d2w. The experiment was conducted in the historical center of the city with medium-rise buildings and mass landscaping. We took soil samples at a depth of 10 cm in accordance with GOST 17.4.4.02-84. The soil was classified as heavy sandy loam with the pH of 7.4. Soil suspension (1 g of dry soil per 100 ml of sterile water) in an amount of 100 ml was distributed on solid nutrient media Nutrient dry agar, Nutrient broth with agar addition, GMF broth with agar addition (pH 7.3), sterilized in an autoclave for 20 min at 121 °C. The cultivation regime consisted of keeping the Petri dishes in a thermostat at a temperature of 37 ° C in the range from 1 to 7 days. When using dry soil, bacteria could not be isolated. We repeated the experiment using raw soil. The highest number of diverse colonies had grown on the Nutrient Dry agar medium. After the growth of a large number of microorganisms on Petri dishes, 20 non-repeating colonies of bacteria were isolated.

Next, we placed 5-7 polyethylene discs with the diameter of 7 mm on Petri dishes with 20 isolated colonies. We washed the discs with soap, soaked them in alcohol and rinsed them with autoclaved water. The bacteria were cultured in a thermostat at 37°C for 1 - 7 days. The maximum reliable biofouling of the polymer was recorded on day 7 in 50% of the cups with a double complete repetition of the experiment. 

We can conclude that in order to isolate the soil bacteria aiming to find out their destructive activity against biodegradable plastic, it is effective to use a soil from a depth of 10 cm in suspension with sterile water (1g per 100ml) and cultivate it on Nutrient dry agar (pH 7.3) at 37 ° C for 7 days.

How to cite: Chupakhina, N., Nikolaeva, N., Nechaev, D., Medjalo, N., Novichkova, A., Lobanova, V., and Chupakhina, G.: Method of isolation of soil microorganisms - destructors of biopolymers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2063, https://doi.org/10.5194/egusphere-egu22-2063, 2022.

This study aimed to assess how the root system of Sesbania cannabina behaves under various concentrations of Cr (VI) and whether it could be a suitable species for the phyto-management of Cr (VI) contaminated soils. The experiment was conducted in rhizoboxes under greenhouse conditions using a sandy loam soil dosed with potassium dichromate giving eight different Cr (VI) concentrations (0 ppm, 5 ppm, 10 ppm, 20 ppm, 40 ppm, 80 ppm, 160 ppm, and 360 ppm). Plant roots were photographed with a Canon 60D (18-megapixel) camera with a 50 mm prime lens and analysed with Image J image processing software.

At 360 ppm concentration, seeds of S. cannabina germinated but were unable to grow further. However, under concentrations of 0-80ppm there was no significant change observed in the root growth (Length) . At 160 ppm root growth was reduced by about 55±0.65% at 25 days and 35±0.25 % at 60 days compared to plants grown at 0 ppm. After 60 days no chromium (VI) was detected in the soil for (0 to 160 ppm) in comparison with the control (with no plants) where no changes in Cr (VI) were observed.

The absence of Cr (VI) in soil after 60 days suggests that S. cannabina can be considered as a candidate for phyto-management of soils containing up to 160 ppm Cr (VI).

How to cite: Ibne Kamal, A. K., Batty, L., and Bartlett, R.: Evaluation of the root system and phyto-management potential of Sesbania cannabina grown in hexavalent chromium contaminated soils utilizing modified rhizobox systems., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4921, https://doi.org/10.5194/egusphere-egu22-4921, 2022.

Although the indications contained in the European New Green Deal aim to inhibit the opening of new waste disposal sites through a circular economy of waste materials, the management of an extremely large number of municipal solid waste landfills (MSWLF) at different stages of their life cycle is a highly topical issue. Recent national regulations in Italy require the operators to monitor subsoil conditions for 30 years, but do not define clear and unambiguous guidelines.

According to analyses carried out at various landfill sites in northern Italy, monitoring activities were often found to be set up with wells equipped with piezometers. This approach is not optimal because when any contaminants are intercepted by the wells, the conditions of the subsoil may already be compromised. Therefore, the goal of our work is to develop methodologies to test and define an effective monitoring protocol that allows to mitigate the environmental and ecological risks associated with the subsurface propagation of pollutants at MSWLF sites.

The analysis of the subsoil conditions involves the design of monitoring methodologies and the interpretation of the obtained results, exploiting geological, hydrogeological and geophysical knowledge and skills. Accordingly, we rely on a research methodology based on the mutual and continuous exchange between the involved disciplines, starting from the initial geological assumptions that will be used to define a physical model of the subsurface. We mainly resort to indirect non-invasive techniques, in particular to the direct current (DC) electrical resistivity tomography (ERT) that on the one hand is indicated for identifying conductive anomalies associated with the propagation of pollutants, but on the other hand constitutes a complex ill-posed numerical problem. The major issues are related to the spatial resolution and the penetration depth of the technique that in turn control the capability to detect presence and the conditions of the extremely thin high-density polyethylene (HDPE) membrane used to isolate the landfill waste from the surroundings.

To tackle the abovementioned issues, we decided to perform properly downscaled laboratory experiments in order to test the effectiveness of DC methodology in controlled and well-known settings. Processing and interpretation of the collected geoelectrical data are supported by a new modelling code in Python programming language that is being developed.

We deem that the integration of lab and modelling tests is necessary to propose a sound standard approach to address complex and multidisciplinary problems related to landfill risk management.

How to cite: Panzeri, L., Papini, M., Formaggia, L., Scotti, A., Fumagalli, A., Arosio, D., and Longoni, L.: Lab and modelling tests to develop a geoelectric monitoring system for municipal solid waste landfills, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7788, https://doi.org/10.5194/egusphere-egu22-7788, 2022.

The influence of six different Technosols on solubility and potential toxicity of metal(loid)s from polluted soils was studied. Technosols were made with a soil affected by residual pollution coming from the Guadiamar Green Corridor and different combinations of three organic wastes [solid olive-mill (T1 and T4), sewage sludge (T2 and T5), and gardening vermicompost (T3 and T6)]. In addition, carbonate-rich material from a peatbog was applied to T1, T2, and T3; while marble sludge was added to T4, T5, and T6; iron-rich sludge (2%) was also applied to all Technosols. The comparison with a non-polluted soil from the study area is also included. Main soil properties (pH, EC, and OC) and solubility of potentially harmful elements (PHEs) in a soil:water extract (1:10) was measured both in Technosols as in soils after two months of incubation period. Potential toxicity was estimated by a germination bioassay with Trifolium pratense L.

The different Technosols showed changes in the solubility of PHEs with significant differences between elements. Cu and Zn strongly reduced the solubility in T3 and T6 in relation to the polluted soil; Cd was reduced in all cases without significant differences between Technosols; and Pb increased in Technosols treated with solid olive-mill (T1 and T4) and sewage sludge (T2 and T5). A significant increase in the solubility of As and Sb was detected in all cases. Likewise, soil treated with Technosols showed differences in solubility of PHEs. Cu and Zn were strongly reduced in the soil treated with T6, and Cd and Pb was reduced in all cases. Arsenic was reduced in all treated soils, although the soluble concentration remained slightly above the values of non-polluted soil. While Sb solubility increased in soils treated with Technosols made with carbonate-rich material from a peatbog (T1, T2, and T3), and maintained at the same level as in polluted soils for soils treated with Technosols made with marble sludge (T4, T5, and T6). These behaviours are mainly related to the increase in calcium carbonate content and the rise in pH in Technosols compared to the polluted soil. The germination bioassay with T. pratense showed very high toxicity (no germination) in soils treated with sewage sludge (T2 and T5), high toxicity (>75% reduction in germination in relation to non-polluted soil) in soils treated with solid olive-mill (T1 and T4), and low toxicity (15% reduction in germination in relation to non-polluted soil) in soils treated with gardening vermicompost (T3 and T6).

Our results indicate that the Technosol composed of gardening vermicompost were the most effective in the reduction of PHEs solubility and toxicity; however, additional studies should be made to assess the increase of mobility in As and Sb after Technosol treatment.

How to cite: Aguilar-Garrido, A., Romero-Freire, A., Paniagua-López, M., Martínez Garzón, F. J., and Martín-Peinado, F. J.: Changes in the solubility and potential toxicity of metal(loid)s in soils treated with Technosols, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8337, https://doi.org/10.5194/egusphere-egu22-8337, 2022.

A detailed study of ⁹⁰Sr distribution in the overground vegetation cover within an elementary landscape geochemical system (top-slope-closing depression, ELGS) was performed in several forested and meadow test sites located in the Eastern Urals Radioactive Trace highly contaminated with this technogenic radionuclide during its release in the Kyshtym accident in 1957 (INES level 6). After selecting the suitable test sites, they were crossed by several lateral profiles along which relative altitude with a step of 1 m and precision of 1 cm was measured using theodolite Boif-DJD10. Averaged plant samples were taken at each point within a standard steel ring (14 cm diameter) later separated into the groups of cereals, legumes and other different herbs. Particular species were also collected if present at no less than seven consequent points. Strontium-90 activity was measured by the portable complex ”Colibri” (SKS-08P) developed in the Kurchatov Institute (Potapov et al., 2021). It allowed the measurement of radionuclide activity in field conditions. The determination error did not exceed 15%. Analysis of data obtained revealed the ordered variation of ⁹⁰Sr in all groups of plants. This order was presented by the cyclic (periodic) change of ⁹⁰Sr activity downslope without definite radionuclide accumulation at the foot of the slope and in depression as usually expected. A similar cyclic pattern was found for ¹³⁷Cs variation in moss cover studied in the Chernobyl zone (Dolgushin & Korobova, 2021). We consider it reflects peculiarities of water migration in the soil-plant system at the ELGS scale. Specific ⁹⁰Sr activity in forest grasses correlated with biomass volume while plant groups and species with the different activity of radionuclides significantly differed in ⁹⁰Sr variation amplitude and its maximum values. The revealed features of variation likely reflect the peculiarities of 90Sr water migration in soils, the structure of root system, and the plants' ecological demands, such as their hydrophilicity.

References

Potapov, V.N., Ivanov, O.P., Luk’yanov, V.V. et al. Portable β-Spectrometer for 90Sr Activity Field-Measurements in Radioecology and Rehabilitation of Nuclear Energy Facilities. At Energy 129, 155–162 (2021). https://doi.org/10.1007/s10512-021-00728-5

Dolgushin, D. and Korobova, E.: New data on the character of 137Cs lateral and vertical migration in soil-litter-moss cover within undisturbed elementary landscape geochemical systems on the test site in the Chernobyl abandoned zone, Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7691, https://doi.org/10.5194/egusphere-egu21-7691, 2021.

How to cite: Korobova, E., Baranchukov, V., Dolgushin, D., Tarasov, O., and Mikhailovskaya, L.: About the character of variation of 90Sr concentration in plants within elementary landscape geochemical system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8381, https://doi.org/10.5194/egusphere-egu22-8381, 2022.

Abstract

The Remance gold mine, in Veraguas (central Panama), had its last mining operation in 1999, using the cyanidation process for Au separation. As a result of this activity, three waste tailings were exposed to the weather, in addition to mine dumps and the open pit mining areas. Currently the area is inhabited by peasants who develop subsistence agriculture and livestock. Therefore, the objective of this study has been to evaluate the environmental and human health risks that this area represents. The total concentrations of potentially toxic elements (PTEs) such as As, Cu, Zn, Ba, Sb and Hg were determined in mining process areas, surrounding soils and edible and inedible plants in the area; in addition to the cyanide species and the enzymatic activity by dehydrogenase (DHA) in soils. The accumulated contamination index (PLI) and potential ecological risk (RI) were calculated, the carcinogenic (CR) and non-carcinogenic (HQ) risk to human health represented by soils and edible plants was estimated.

Regarding the degree of contamination, it is observed that the contamination is considerable in the tailings and the sediments of the pithead, and it spreads to the surroundings mainly in the sediments of the streams and their terraces, and, to a lesser degree, to the soils around it, showing that the main route of dissemination is through runoff; the same trend is followed by the potential ecological risk, being extreme in the sediments of the pithead, serious in the tailings and terrace sediments, high in the stream sediments and medium in the surrounding soils. The enzymatic activity by DHA tells us that the health of the surrounding soils is better than that of the stream sediments and terrace sediments, but less than in other sites affected by mining activity in Spain. Cyanide species are linked to DHA and this in turn is favoured by organic matter (OM). On the other hand, it was observed that cyanide elutes from the tailing’s piles in a complex cyanide way, favouring the transport of PTEs associated with it to the stream sediments.

Regarding the risks to human health in soils, As and Cu concentrations exceed the limits for non-carcinogenic and carcinogenic risk in both children and adults, with the residential scenario being the worst scenario, and for adults also the agricultural scenario. In edible plants such as rice, corn, cassava and tea leaves, Sb exceeds the limit for non-carcinogenic risk, and Cu and As for carcinogenic risk. Due to the potential ecological and human health risks that the area represents, actions must be taken to reduce them.

Keywords: potentially toxic elements (PTEs), gold mine, risk assessment, edible plants, human health.

How to cite: González-Valoys, A. C., Esbrí, J. M., Jiménez Salgado, J. U., Rodríguez, R., García-Noguero, E. M., Monteza-Destro, T., Vargas-Lombardo, M., Garcia-Ordiales, E., Arrocha, J., Martínez, E., Campos, J. A., Gutierréz, E., García-Giménez, R., Jiménez-Ballesta, R., García-Navarro, F. J., and Higueras, P.: Environmental and human health risks due to mining activity in the soils and plants of the Remance mine, Panama, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9882, https://doi.org/10.5194/egusphere-egu22-9882, 2022.

Mobilization of heavy metal ions has increasingly become a serious environmental issue globally, in the contaminated soils, calling for an urgent need to find environmentally friendly materials. With the continuing maturation of research on using biochar (BC) for the remediation of contaminated soil, compositing metal-organic framework (MOF), which is a highly crystalline porous material, has gradually attracted increasing attention. Compared with BC, BC-MOF has unique underexplored potential as an amendment for immobilization of heavy metal ions, including a high specific surface area and a large number of individual functional groups. The efficacy of BC-MOF for immobilization of toxic heavy metal contaminants in soil systems was investigated in the small leaching columns. The amendments (except BC) decreased the water-soluble and exchangeable content of Cu in Technosol compared to the unpolluted soil. The presence of MOF on the BC surface contributed to the higher immobilization efficacy, which was probably due to the synergistic effects among them. The mechanism of the immobilization process on BC-MOF was proposed. The obtained results highlight the promise of utilizing BC-MOF as a remediation material for Cu immobilization in the soil.

The research was financially supported by the Ministry of Science and Higher Education of the Russian Federation project on the development of the Young Scientist Laboratory (no. LabNOTs-21-01AB) and the Russian Foundation for Basic Research, project no. 19-34-60041 and 19-29-05265.

How to cite: Bayero, M. T., Mazarji, M., Bauer, T., Minkina, T., Sushkova, S., Mandzhieva, S., Timofeeva, A., Kızılkaya, R., and Gülser, C.: Biochar and metal-organic framework nanocomposite: Application for immobilization of Cu in polluted industrial soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10121, https://doi.org/10.5194/egusphere-egu22-10121, 2022.

The use of indicators for soil monitoring is a long-established methodology that can be applied in the context of climate change. It makes it possible to establish the state of the variable to be measured and to obtain an objective signal of the changes that occur over time. Obtaining a climate adaptation indicator (CAI) for Mediterranean rainfed soils is a very useful monitoring tool for decision-making and for the incorporation of mitigation measures.

Within the LIFE AMDRYC4 project, a framework has been outlined in which a) environmental indicators of the chemical degradation of soils (salinity, alkalinity, fertility, phytotoxicity) and the erosion indicator, b) biodiversity indicators (vegetation indices, Shannon-Weaver indices, Simpson, ...) and c) organic carbon indicator have been used.  These primary indicators can be summarized resulting in a higher rank one represented by the soil ecosystem services indicator (SESI) which reflects the soil global condition obtained from the base data for each established monitoring point. By combining the SESI with a transformative indicator such as the one calculated on the basis of the increase of organic C in the soil (mitigation indicator, MI), the indicator of climate adaptation is obtained, using a procedure based on fuzzy methodology.

In the calculation of the phytotoxicity indicator, the assimilable values of selected trace elements that can cause phytotoxicity are determined and their variation over time is monitored. The selected trace elements, in this case Pb, Cd and B, may vary from one area to another depending on the geochemical background of the soil.  

The results obtained for several soil plots that have been subjected to organic matter incorporation treatments (sewage sludge, manure from different animals, composted plant remains) and their untreated counterparts (blank) clearly show an improvement of the soil characteristics after the application of the mentioned soil treatment strategies. The soils are not affected by the polluting processes, both in terms of potentially toxic elements and other emerging pollutants. The experimental data obtained indicate that the remediated soils can be useful to reduce the concentration of greenhouse gases in the atmosphere and represent a good tool for combating climate change.

The authors are grateful to LIFE16 CCA/ES/000123-LIFE AMDRYC4 Project for the financial support

How to cite: Martínez-Sanchez, M. J., Perez-Sirvent, C., Martínez-López, S., Martínez-Martínez, L., Gómez-Martínez, C., Bech, J., and Hernández-Córdoba, M.: Tools for the adaptation to climate change and monitoring of soil environmental quality., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10520, https://doi.org/10.5194/egusphere-egu22-10520, 2022.

Land use and management practices are the primary cause of land degradation, including soil loss, soil pollution, and biodiversity loss. The goal of achieving a healthy soil capable of providing a wide range of ecosystem services should be guaranteed by integrating these goals in the landscape planning system.

Landscape planning methodologies should integrate a perspective of understanding the ecological suitability for different activities to avoid the risk of taking land degradation to a level of difficulty to recover. Those methodologies also need to involve the landowners, define the best management practices, and inform about landowners' financial returns. In Portugal, municipal plans (PDM) are defined at a scale of 1:25000, binding private parties. However, they are very superficial in informing landowners about land potentiality and management practices, and they do not identify where ecosystem restoration should occur.

This work aims to show how priority areas for restoration can be identified and included in municipal landscape plans to provide a good chemical, biological and physical condition of soils. The methodology is defined in Geographic Information System (GIS), and it is based on ecological-based principles. In particular, it is shown how the headwater system's restoration could be planned. The Headwater System is located between the beginning of the water network and the ridgeline and plays an essential role in regulating water and returning quality to the soil. The best suitable land use in the headwater system is a mixed forest consisting of species of potential natural vegetation that will return nutrients to the soil, maximize organic matter, reduce the risk of erosion and regulate the water cycle, while being an essential tool for controlling human activities, also preventing actions that lead to soil pollution.

The headwaters were mapped in GIS considering a drainage area of 0.05 km², for the study area, which comprises 55 thousand hectares. The present study evaluates the current land uses in the headwaters, identifying Hotspots for ecological restoration and identifying opportunities for improving the landscape planning system.

The results show that about one-third of the study area is made up of headwaters located in a very hilly relief with very thin soils. The dominant land uses are eucalyptus forest, shrubs, and old areas of maritime pine that burned in the 2017 mega-fires. The potential natural vegetation shows that these sites are suitable for Quercus pyrenaica, Quercus suber, Quercus robur, and Castanea sativa.

The adequate land use of the headwater system will significantly impact the ecological function of its river basin. The restoration of these areas will provide better ecosystem services by avoiding soil loss and reducing floods downstream, improving water infiltration and its quality, and increasing biodiversity. The integration of headwater restoration in the landscape planning system can be a crucial tool for attaining healthy soils.

How to cite: Pena, S. B., Magalhães, M. R., and Abreu, M. M.: Planning for a healthy landscape to provide healthy soils. Restoring the Headwater System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10530, https://doi.org/10.5194/egusphere-egu22-10530, 2022.

The Commune of Santiago is an administrative unit belonging to Chile's capital city. It is the central hub for local transportation services and the centre of most national government functions. In 2017, a geochemical survey was carried out focusing on the topsoils of the commune. A total of 121 samples were homogeneously collected across an area of 22.4 sqkm to determine the spatial distribution of potentially toxic elements (PTE) and discover their primary and secondary sources. In the aim of the work, the assessment of human health risk for the local population was also included considering both the direct contact with soils and the breathing of airborne particles as relevant exposure pathways.
The geochemical baseline maps of 15 PTEs were generated using the Multifractal IDW (MIDW) interpolation, and map intervals were determined using a Concentration-Area plot considering the fractal structure of the geochemical data.
The contamination degree of the urban soil and its pattern was also calculated and mapped after developing a new index, named as Cumulative Contamination Degree (CCD), which integrate information about the severity of contamination and its spatial complexity.
A robust multivariate statistical analysis based on Principal Components (RPCA) was carried out considering the compositional nature of the geochemical data. In addition, to highlight the presence and the geochemical patterns depending on different contamination sources, a Sequential Binary Partition (SBP) was used to generate contrasts among those elements considered as proxies of specific processes (Urban traffic, productive settlements, etc.).
A probabilistic approach was chosen to assess the risk due to exposure to soils for the local population. It was based on Monte Carlo simulation to include uncertainty due to spatial variation of data and the relative mobility of people within the borders of the study area. 
The results obtained show that the innovation proposed to assess contamination and discriminate its sources, even when they are of secondary relevance, can generate positive feedback. Using a probabilistic approach in a non-site specific framework can even be considered a more reliable method to assess risks if we want to not underestimate the burden of uncertainty that substantially influences the results. 

How to cite: Albanese, S., Aruta, A., Daniele, L., Cannatelli, C., Buscher, J. T., De Vivo, B., Petrik, A., Cicchella, D., and Lima, A.: Integrating methods to discriminate the soil historical pollution sources and assess the degree of contamination and related health risks in an urban environment:  the Commune of Santiago (Chile) case study., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11920, https://doi.org/10.5194/egusphere-egu22-11920, 2022.

The present study evaluates the potential toxicity of the soils of the Guadiamar Green Corridor (GGC) (Seville, SW Spain) affected by the Aznalcóllar mine spill, one of the most important mining accidents in Europe in recent decades. Twenty years after the accident, soils affected by residual contamination are still present in the area, for which their potential toxicity was assessed by carrying out bioassays with lettuce (Latuca sativa L.), earthworms (Eisenia andrei) and determining the microbial activity by measuring the basal respiration and the microbial metabolic quotient (qCO2) of these soils at surface level (0-10 cm). A total of 84 soil samples were taken along the GGC, which were divided into four types (SS1-SS4) according to their physicochemical properties. Soils SS1 and SS2 showed a higher environmental toxicity risk, with a reduction in root elongation of lettuce seeds of 57% and 34% compared to the control, as well as a higher metabolic quotient (23.9 and 18.1 ng Cc_O2 µg C_microb^-1 h^-1), significantly higher than those measured in SS3 and SS4.

The potential risk to humans of these soils through ingestion, inhalation, and skin exposure routes for the main potentially toxic elements (PTEs) present in the GGC (Pb, and As) was also evaluated based on EPA empirical models, in which the total exposure through each of the three routes was considered. The results obtained indicate that there is no potential risk for human health throughout the GGC considering the exposure for both adults and children, although there are areas of the corridor where the Guideline values for both Pb and As are exceeded. This, together with the potential toxicity of the soils to the ecosystem shown by the bioassays, indicate the need to monitor over time the human and environmental risks in the area to guarantee the safety and enjoyment of this natural area.

How to cite: Paniagua-López, M., Pastor-Jáuregui, R., Aguilar-Garrido, A., Romero-Freire, A., and Sierra-Aragón, M.: Ecotoxicological risk assessment of the Guadiamar Green Corridor soils 20 years after the Aznalcóllar mining accident, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12397, https://doi.org/10.5194/egusphere-egu22-12397, 2022.

In Portugal, there are numerous abandoned mines, many of them derived from an intense activity in massive sulfides. These areas have several environmental problems associated to extreme physicochemical characteristics of their wastes and leachates.

In São Domingos mine, there is an additional environmental problem derived from the percolation of acid mine drainage over the slopes. This measure, used in environmental management of waters, contributed to the total degradation of the edaphic system and enrichment of bedrock in potentially toxic elements (PTE). A sustainable strategy for environmental recovery using Technosols, designed specifically for each contaminated or degraded material, has been evaluated in this scenario. A pilot area, with ​​1.5 ha, was rehabilitated by applying a 40 cm layer of Technosol with alkaline and eutrophic properties. A biodiverse plant system with herbaceous and shrubs was applied. The efficiency of the Technosol was evaluated at short-medium term by: a)several indicators associated to plant development (visual signs of phytotoxicity or nutritional deficiency, percentage of plant cover and height) after 15 days, 1, 3 and 6 months, and b)the maintenance of soil properties after 6 months. An area without Technosol application was used as control.

In Control area, plant cover was low, with maximum values ​​of 8.8% in the third month and height <10 cm. However this vegetation cover was not with the sown plants but only native species, like Spergularia purpurea (dominant species identified) that already existed in the area.

In the area with Technosol application, the plants did not show visible signs of phytotoxicity or nutritional deficiency. The percentage of plant cover increases rapidly. After 15 days plant cover was 10–30% and between first and third month, when there were warmer and dry conditions, the coverage increased to 70% and 90%, respectively.  After 6 months, coinciding with a change in the vegetative cycle and very dry and hot period from summer, the vegetation only decrease ≈10%. The height varied between 40 to 60 cm, reaching these maximum values in the third month. The plant development remained during the monitoring period. This plant evolution shows the significant water-holding capacity and fertility of the Technosol.

After 6 months, Technosol samples maintained the alkaline and eutrophic properties and physico-chemical characteristics (pH 7.5–7.8, 85 g C/kg, 6 g N/kg, Capacity of cation change 54.3–73 cmol+/kg). In the Technosol was not verified an enrichment by PTE, due to its direct contact with acidic and contaminated material. In Control area, the substrate maintained acidic reaction conditions (pH 3.5–3.6), very low fertility and high concentration of several elements.

Application of the Technosol contributed to the environmental recovery of mine areas, with very acid material with multielementar contamination where it is inexistent an edaphic system. This green technology is an effective solution to the conversion non-productive areas to productive areas.

Acknowledgment: This work was executed by EDM under a concession contract for environmental remediation of legacy mines in Portugal, in collaboration with ISA-ULisboa, LEAF and Inproyen, and was financed by POSEUR EC Cohesion Funds (145/POSEUR/2020). This research is into the scope of the project UID/AGR/04129/2020.

How to cite: Aran, D., Abreu, M. M., Diamantino, C., Carvalho, E., and S. Santos, E.: Recovering abandoned mine area under semi-arid conditions with Technosols: a pilot assay in São Domingos legacy site, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12578, https://doi.org/10.5194/egusphere-egu22-12578, 2022.

Sulfide mine tailings present particular challenges in terms of risks of environmental recovery due to the acid lixiviation rich in several metal(oids) and sulfates. The conventional closure systems of these tailings have very high cost of implementation and, especially, maintenance of plant cover and continuous leachates treatment. Therefore, the improvement of the chemical characteristics of the tailings and their leachates is a more cost-effective strategy, especially if sustainable technologies are used. The use of designed Technosols is an option. The current study advances past field and laboratory findings by integrating a circular-economy approach into the chemical and soil science-based treatment of such tailings.

A column assay under controlled conditions was set up in order to evaluate the efficiency of a designed Technosol, applied into two ways, on the chemical improvement of the leachates from sulfide-rich tailing. A designed Technosol with alkaline and eutrophic properties was mixed with tailing material (TEC1) or applied as a superficial and distinct layer (TEC2). Tailing without treatment was used as control. The evolution of pH, Electrical conductivity (EC), Fe and sulfates levels was evaluated during 6 weeks in percolated leachates.

The tailing material was previously assessed as having pH ≈2.5 and total concentrations of 104-110 g Fe/kg and 60.0-67.5 g S/kg. Percolated leachates had a pH values between 1.73 and 2.68  and high EC (≈10 mS/cm) that indicate the high amount of several elements and, consequently, their environmental risk.

The first week’s pH increased to ≈6.5 for the TEC1 while in TEC2 was ≈2.5. Following weeks, the pH stabilized at around 7.3 in the TEC1 and 2.6 in the TEC2..  The EC decreased in the first week 73% in the TEC1 and 81% in the TEC2, compared to control. In control, EC presented upwards spikes within the first two weeks, reaching almost 14 mS/cm, and then more stable values. . Notably, the EC for TEC 1 was low (<1.6 mS/cm) and rather stable throughout the experiment, for TEC2 it presented large, gradual drops in the first two weeks, followed by rather stable value  and slightly lower than control. Fe concentrations largely mirrored these EC patterns. Iron concentrations in control were high varying 60 and 7546 mg/L during the assay. Technosol application was effective in the diminution of Fe concentrations in the leachates (TEC1 > 1000-fold and TEC2 2-100 fold lower than control).

Overall, both application mode of the Tecnhosol contribute to improvements in the leachates quality. Nonetheless, these results showed that TEC1 approach might result in more stable and better chemical quality of the leachates. Importantly, these findings also suggest the improvement in the tailing structure of TEC1 compared to TEC2, contributing to lower risk of lixiviation into the lower layers.

Acknowledgment: This research was supported by Fundação para a Ciência e Tecnologia, within the scope of the project UID/AGR/04129/2020 (Project Non-foodCropMine).

How to cite: S. Santos, E., Pastrac Lungu, A., and Arán, D.: Effect of Technosol application way on chemical quality of percolated leachates from sulfide-rich tailing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12756, https://doi.org/10.5194/egusphere-egu22-12756, 2022.

The correct reproduction of sea-level dynamics is crucial for forecasting floods and managing the associated risk. On the other hand, sea-level monitoring through observations can provide a description only of past events and it is challenging and costly, both of time and money. In this context, oceanographic models are increasingly used to describe the sea dynamics, providing a spatial/temporal extension to the observations. The best solution, which merges the observation accuracy and the model spatial/temporal resolution, is the data assimilation analysis, which is particularly important in coastal regions with scarce monitoring resources. In this study, we investigate the benefits of assimilating sparse observations from tide gauges in an unstructured hydrodynamic model for simulating the sea level in the Mediterranean Sea. We use the Ensemble Kalman filter, both to obtain an analysis of the past and to produce accurate forecasts. In the analysis we tested the assimilation in storm-surge simulations, only-tide simulations, and total-level simulations, using the observations in the stations. The results of storm-surge simulations were compared with those of total-level simulations, by adding the tide obtained from harmonic analysis of the observations. RMSE and correlation show improvements for all the components of the sea level and all the stations considered (not assimilated). The averaged-over-station RMSE reduces from 9.1 to 3.4 cm for the total level. The greatest improvements happen when the model without assimilation, due to an error of the wind-pressure forcing, did not reproduce some barotropic free modes of oscillation triggered by an initial surge. The preliminary forecast simulations of storm surge show improvements due to the data assimilation extending up to 5 days of forecasting. Even in this case, the longer improvements seem to happen when a free mode of oscillation is triggered. The results of this study will be used to improve the sea level forecasting system in the Adriatic Sea, developed within the framework of the Interreg Italy-Croatia STREAM project (Strategic development of flood management, project ID 10249186).

How to cite: Ferrarin, C., Bajo, M., and Umgiesser, G.: Sea-level modelling in the Mediterranean Sea using data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-732, https://doi.org/10.5194/egusphere-egu22-732, 2022.

The second version of the Arctic ocean and sea ice reanalysis is based on the coupled ensemble data assimilation system (TOPAZ4b). Compared to its predecessor (Xie et al. 2017) it has benefited from enhancements to observation, model vertical resolution, and forcing datasets. TOPAZ4 relies on version 2.2 of the HYCOM ocean model and the ensemble Kalman filter data assimilation using 100 dynamical members. A 30-years reanalysis of the Arctic ocean and sea ice has been completed starting in 1991, and made available as the multi-year physical product by the Arctic Marine Forecasting Center (ARC MFC) under the Copernicus Marine Environment Monitoring Service. Contrary to the previous version of the Arctic reanalysis, the systematic errors due to fragmented time series of assimilated observations have been removed by using consistent ESA CCI data. The comparison to in situ profiles shows that the temperature and salinity stratification has been considerably improved by the increased vertical resolution in HYCOM, for example in the East Greenland Sea, the temperature root mean square error (RMSE) from surface to 1400 m has been reduced by 50%. These improvements encourage the use of this Arctic reanalysis for climate studies.

How to cite: Xie, J. and Bertino, L.: TOPAZ4b: a new version of the ocean and sea-ice Arctic reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-923, https://doi.org/10.5194/egusphere-egu22-923, 2022.

This study uses a variational method combined with satellite observations to reconstruct three-dimensional temperature and salinity profiles for the Northern Indian Ocean (NIO). Sensitivity experiments show that sea surface temperature (SST) dominantly improve the temperature reconstruction of upper 100 m; sea surface salinity (SSS) determines salinity estimation in the upper 100 m; sea surface height anomaly (SSHA) dominates the reconstruction of thermocline. The reconstructed temperature fields can be greatly improved in the thermocline by removing barotropic signal from the altimeter SSH data through a linear regression method. Ocean reanalysis and in situ temperature and salinity data are used to evaluate the results of reconstruction. Comparing with Simple Ocean Data Assimilation (SODA) in 2016, the spectral correlation between the reconstruction and the SODA density anomalies show that the reconstruction fields can retrieve mesoscale and large-scale signals better. Moreover, the reconstruction salinity is much more accurate than SODA salinity in the upper ocean over the Bay of Bengal (BoB). Compared with CTD section observations, the reconstruction fields can capture the mesoscale eddy structure in the Arabian Sea (AS) and BoB well, respectively. The long time series of reconstruction along Argo trajectory shows that the reconstruction fields can better reproduce the observed intraseasonal oscillations of thermocline/halocline in the BoB. Compared with the World Ocean Atlas 2013 (WOA13) climatology, the reconstruction fields can better characterize upper ocean water mass variability.

How to cite: He, Z., Wang, X., Wu, X., and Chen, J.: Projecting Three-dimensional Ocean Thermohaline Structure in the North Indian Ocean from the Satellite Sea Surface Data Based on a Variational Method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2107, https://doi.org/10.5194/egusphere-egu22-2107, 2022.

The objective of this study is to investigate if the assimilation of ocean color data into a complex marine ecosystem model can improve the hindcast of key biogeochemical variables in coastal seas. A localized Singular Evolutive Interpolated Kalman filter was used to make assimilation of the daily fully reprocessed product of Multi-Satellite chlorophyll observations into a three-dimensional ecosystem model of the Baltic Sea. Twin experiments are performed to evaluate the performance of the assimilation with respect to both satellite and in situ observations. Compared to the reference run, the assimilation was found to immediately and considerably reduce the bias, root mean square error, and increase the correlation with the spatial distributions of the assimilated chlorophyll data while this improvement is limited to the upper layer of the water column. This feature is explained by the weak correlation taken into account by the assimilation between the surface and deep phytoplankton. The assimilation scheme used is multivariate, updating all biogeochemical model state variables. The other variables were not degraded by the assimilation. More significantly, the skill metrics for non assimilated variables indicate that the hindcast of the mean data values at L4 was improved; however, improvements in the short-term forecast were not discernable. Our results provide general recommendations for the successful application of ocean color assimilation to hindcast key biogeochemical variables in coastal seas.

How to cite: Liu, Y. and Arneborg, L.: Assimilating the remote sensing ocean color data into a biogeochemical model of the Baltic Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2555, https://doi.org/10.5194/egusphere-egu22-2555, 2022.

Accurate knowledge of ocean surface currents is crucial for a gamut of applications. In this study, the way in which merging altimeters composited two-dimensional sea surface height (SSH, 1/4°) with remote sensing combined sea surface temperature (SST, 9km) image improves the surface current estimates is investigated. Based on the surface quasigeostrophic (SQG) theory, we reconstruct the surface current by resolving the large scale motions, the mesoscale dynamics, and the oceanic smaller processes. Its feasibility is validated using the altimeter-derived geostrophic current (GC) and drogued drifters in the South Indian Ocean (SIO) during 2011–2015. Results of the two cases show that the effective resolution of the reconstructed surface current (RSC) has improved to 30 km after merging the high-resolution SST information, compared to 70 km of the GC. Moreover, the RSC outperforms the altimeter-derived GC in reproducing the practical dynamical processes. Over the analyzed period, compared with 841 drifters, the statistical results indicate that the RSC reduces the reconstruction errors of zonal velocity, meridional velocity, and velocity phase by about 14.6%, 45.7%, 27.0% in the SIO relative to the GC, respectively. Our method particularly improves the meridional velocity and velocity phase along the Antarctic Circumpolar Current, Agulhas Retroflection, Greater Agulhas System, and South Equatorial Current. In addition, the lower Lagrangian separation distance and higher skill score of the RSC given by Lagrangian analysis also demonstrate that the proposed method is more promising to provide essential information on ocean surface currents applications, such as water property transports, search and rescue, etc.

How to cite: Chen, Z., Wang, X., and Chen, J.: Improving the Surface Currents from the Merging of Altimetry and Sea Surface Temperature Image in the South Indian Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2731, https://doi.org/10.5194/egusphere-egu22-2731, 2022.

We present a post-hoc smoothing algorithm for use with sequentially generated reanalysis products, utilizing the archive of “future” assimilation increments to update the “current” analysis. This is applied to the Lorenz 1963 model and then to the Met Office GloSea5 Global ¼° ocean reanalysis during 2016.  A decay time parameter is applied to the sequential increments which assumes that background error covariances remain spatially unchanged but decay exponentially away from analysis times. Only increments are smoothed so the reanalysis product retains modelled high-frequency variability, e.g., from atmospheric forcing. Results show significant improvement over the original reanalysis in the 3D temperature and salinity variability, as well as in the sea surface height (SSH) and ocean currents. Spatial gap filling from future data is particularly beneficial. The impact on the time variability of ocean heat and salt content, as well as kinetic energy and the Atlantic Meridional Overturning Circulation (AMOC), is demonstrated. 

How to cite: Dong, B., Haines, K., and Martin, M.: Improving High Resolution Ocean Reanalyses Using a Smoother Algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4313, https://doi.org/10.5194/egusphere-egu22-4313, 2022.

Eight of the top ten most populated cities in the world are located by the coast. The improvement of the coastal ocean representation is a key topic to understand the  present and near-future ocean state and predict its evolution under climate change conditions.

The coastal ocean is difficult to model due to the presence of complex coastlines, interaction with inland waters, rapid changes in  topography and highly space-time variability of the phenomena involved. Unstructured-grid models are used to partially attenuate this source of errors in cross-scale (from open sea to coastal regions) oceanographic modelling. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas is being developed only recently (e.g., Aydogdu et al., 2018; Bajo et al., 2019) and needs more advancements.  

Here, we show preliminary results from the coastal ocean forecasting system SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System, Federico et al., 2017) based on SHYFEM fully-baroclinic unstructured-grid model (Umgiesser et al., 2004)  interfaced with OceanVar (Dobricic and Pinardi, 2008; Storto et al., 2014), a state-of-art variational data assimilation scheme, adopted for several systems based on structured grid (e.g. regional CMEMS for Mediterranean and Black Seas, marine.cmems.eu).

In OceanVar, Empirical Orthogonal Functions (EOFs) method is used to reduce the dimensionality of computation removing the statistically less significant modes and to correlate observations and model background in the water column;  while the increments are spread horizontally using the recursive filter method. While this method is typically only used to model covariances between neighbouring points in a structured grid, the algorithm has now been generalised and successfully implemented also for unstructured grids.

Preliminary results show that temperature and salinity observations from Argo profilers improve the ocean state. Future steps will also include sea level assimilation. 

This work is a starting point in order to improve our forecast of local extreme events (e.g. heat waves and storm surge) which are statistically increasing in number and intensity in the Mediterranean region due to climate change.

How to cite: Stefanelli, M., Jansen, E., Aydogdu, A., Federico, I., Coppini, G., and Pinardi, N.: Variational data assimilation for advanced cross-scale ocean modelling., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4741, https://doi.org/10.5194/egusphere-egu22-4741, 2022.

Recent intercomparison studies among ocean/sea-ice Reanalyses (such as ORA-IP) have shown large discrepancies in many sea-ice-related fields, despite a rather general agreement in the sea-ice extension. The low accuracy of sea-ice thickness measurements together with the highly non-gaussian distributions of related uncertainty, made multivariate sea-ice data assimilation (DA) strategies still at an early stage, although nearly twenty years of thickness observations are now available. In a standard multivariate scheme, the break of Gaussianity can generate un-realistic corrections due to the poor linear relationship driven by the B matrix.

One approach to solve the problem is the implementation of anamorphous transformations that modify the probability density functions of ice anomalies into Gaussian ones (Brankart et al. 2012). In this study, a 3DVar DA scheme (called OceanVar), employed in the routinely production of global/regional ocean reanalysis CGLORS (Storto et al, 2016), has been recently extended to ingest sea-ice concentration (SIC) and thickness (SIT) data. An anamorphous operator, firstly developed and made freely available within the SANGOMA project (http://www.data-assimilation.net/), has been updated and adapted for the bivariate assimilation of SIC/SIT within the OceanVar framework.

We present the comparison among several sensitivity experiments that were performed assimilating different observation datasets and using different DA configurations at 1/4 degree global resolution. Specifically, we assess the impact of ingesting different SIT products, such as SMOS and CRYOSAT-2 data or the merged product CS2SMOS.

We show that the sole assimilation of SIC improves the spatial representation of SIT with respect to a free run. The inclusion of thickness correction, determined by empirical relations, appears to improve the sea ice characteristics in the Atlantic sector and degrade them in the Siberian region; therefore a refined tuning could probably be beneficial. The spatial error reduces sharply only once CRYOSAT-2 data are assimilated jointly with SIC data. In the present set up, all the experiments generally tend to overestimate the sea-ice volume in the case SMOS data are not assimilated. However, observational errors associated with SMOS data are generally too small, leading to jumps in the volume time series at the beginning of the accretion period if not calibrated correctly.

The proposed approach is suitable to be used for covarying ocean/sea-ice variables in future coupled ocean/sea-ice DA.

Storto, A. and Masina, S. (2016), Earth Syst. Sci. Data, 8, 679, doi: 0.5194/essd-8-679-2016

Brankart, et al. (2012), Ocean Sci., 8, 121, doi: 10.5194/os-8-121-2012

How to cite: Cipollone, A., Banerjee, D. S., Aydogdu, A., Iovino, D., and Masina, S.: Bivariate sea-ice assimilation for Global Ocean Analysis/Reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5698, https://doi.org/10.5194/egusphere-egu22-5698, 2022.

Predicting ocean circulation in strong currents remains challenging because of limits in modelling capabilities such as resolution. Coastal ocean circulation models typically have horizontal resolution starting from 1 km. To address this matter, we have developed a high resolution three-dimensional computational fluid dynamics (CFD) model for strong ocean currents such as the Gulf Stream. Our model domain contains three inlets and an outlet and has been verified with field data from the Straits of Florida. For model verification, a 6 ADCP mooring array in a rectangular shape was deployed 8 miles offshore on the Miami Terrace. The data from 5 ADCP moorings were used to produce the inlet boundary conditions, which were updated every 1 minute. The sixth ADCP in the center of the outlet was used for model verification. This approach has demonstrated good predictive ability for ocean circulation in the challenging environment of a strong western boundary current. We anticipate our work to be a starting point for the development of sophisticated prediction models applicable to western boundary currents in the range from small-scales to sub-mesoscales, based on advanced data assimilation techniques.

How to cite: Vanderplow, B., Kluge, J., Soloviev, A., Dodge, R., Wood, J., Evans, J., Venezia, W., and Ferrar, M.: Measurement and modeling of small-scale to mesoscale ocean circulation in the Straits of Florida, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6451, https://doi.org/10.5194/egusphere-egu22-6451, 2022.

As part of the ongoing development of a data assimilation system for reconstructing the Arctic ocean-sea ice state, we incorporated an adjoint of sea ice rheology, which was approximated by free drift assumption due to stability problem, into an adjoint model of a coupled ocean-sea ice model. The adjoint sensitivity experiments show that the internal stress effect, represented by the adjoint rheology, induced remarkable differences in the sensitivities to ice drift and wind stress in the central Arctic Ocean. In contrast, ice is mostly free drift in the marginal ice zone. The assimilation experiments reveal that including the adjoint of ice rheology helps extract observational information, especially the ice drift observations, which improves the estimation of the sea ice decline process in 2012. The results suggested great potentials for further improving the Arctic ocean-ice state estimation in the framework of the adjoint method with the adjoint sea ice rheology included. 

How to cite: Lyu, G. and Zhou, M.: Effects of inclusion of adjoint sea ice rheology on estimating ocean-sea ice state, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6848, https://doi.org/10.5194/egusphere-egu22-6848, 2022.

Extreme weather events (e.g., heavy rainfall) are natural hazards that pose increasing threats to many sectors and across sub-regions worldwide (IPCC, 2014), exposing people and assets to damaging effects. In order to predict pluvial flood risks under different spatio-temporal conditions, three generalized Machine Learning models were developed and applied to the Metropolitan City of Venice: Logistic Regression, Neural Networks and Random Forest. The models considered 60 historical pluvial flood events, occurred in the timeframe 1995-2020. The historical events helped to identify and prioritize sub-areas that are more likely to be affected by pluvial flood risk due to heavy precipitation. In addition, while developing the model, 13 triggering factors have been selected and assessed: aspect, curvature, distance to river, distance to road, distance to sea, elevation, land use, NDVI, permeability, precipitation, slope, soil and texture. A forward features selection method was applied to understand which features better face spatio-temporal overfitting in pluvial flood prediction based on AUC score. Results of the analysis showed that the most accurate models were obtained with the Logistic Regression approach, which was used to provide pluvial flood risk maps for each of the 60 major historical events occurred in the case study area. The model showed high accuracy and most of the occured events in the Metropolitan City of Venice have been properly predicted, demostrating that Machine Learning could substantially improve and speed up disaster risk assessment and mapping helping in overcoming most common bottlenecks of physically-based simulations such as the computational complexity and the need of large datasets of high-resolution information.

How to cite: Critto, A., Zanetti, M., Allegri, E., Sperotto, A., and Torresan, S.: Enhance pluvial flood risk assessment using spatio-temporal machine learning models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1346, https://doi.org/10.5194/egusphere-egu22-1346, 2022.

Recent advances in technology have transformed the Natural Language Technology (NLT) landscape, specifically, the use of transformers to build language models such as BERT and GPT3. Furthermore, it has been shown that the quality and the domain-specificity of input corpus to language models can improve downstream application results. However, Earth science research has minimal efforts focused on building and using a domain-specific language model. 

We utilize a transfer learning solution that uses an existing language model trained for general science (SciBERT) and fine-tune it using abstracts and full text extracted from various Earth science journals to create BERT-E (BERT for Earth Science). The training process utilized the input of 270k+ Earth science articles with almost 6 million paragraphs. We used Masked Language Modeling (MLM) to train the transformer model. MLM works by masking random words in the paragraph and optimizing the model for predicting the right masked word. BERT-E was evaluated by performing a downstream keyword classification task, and the performance was compared against classification results using the original SciBERT Language Model. The SciBERT-based model attained an accuracy of 89.99, whereas the BERT-E-based model attained an accuracy of 92.18, showing an improvement in overall performance.

We investigate employing language models to provide new semantic search capabilities for unstructured text such as papers. This search capability requires utilizing a knowledge graph generated from Earth science corpora with a language model and convolutions to surface latent and related sentences for a natural language query. The sentences in the papers are modeled in the graph as nodes, and these nodes are connected through entities. The language model is used to give sentences a numeric representation. Graph convolutions are then applied to sentence embeddings to obtain a vector representation of the sentence along with combined representation of the  surrounding graph structure. This approach utilizes both the power of adjacency inherently encoded in graph structures and latent knowledge captured in the language model. Our initial proof of concept prototype used SIMCSE training algorithm (and the tinyBERT architecture) as the embedding model. This framework has demonstrated an improved ability to surface relevant, latent information based on the input query. We plan to show new results using the domain-specific BERT-E model.

How to cite: Ramachandran, R., Muthukumaran Ramasubramanian, M., Koirala, P., Gurung, I., and Maskey, M.: Language model for Earth science for semantic search, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3131, https://doi.org/10.5194/egusphere-egu22-3131, 2022.

With the growing ubiquity of large multi-dimensional geodata cubes, clustering techniques have become essential to extracting patterns and creating insights from data cubes. Aiming to meet this increasing need, we present Clustering Geodata Cubes (CGC): an open-source Python package designed for partitional clustering of geospatial data. CGC provides efficient clustering methods to identify groups of similar data. In contrast to traditional techniques, which act on a single dimension, CGC is able to perform both co-clustering (clustering across two dimensions e.g., spatial and temporal) and tri-clustering (clustering across three dimensions e.g., spatial, temporal, and thematic), as well as of subsequently refining the identified clusters. CGC also entails scalable approaches that suit both small and big datasets. It can be efficiently deployed on a range of computational infrastructures, from single machines to computing clusters. As a case study, we present an analysis of spring onset indicator datasets at continental scale.

How to cite: Ku, O., Nattino, F., Grootes, M., Izquierdo-Verdiguier, E., Girgin, S., and Zurita-Milla, R.: CGC: an open-source Python module for geospatial data clustering, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3855, https://doi.org/10.5194/egusphere-egu22-3855, 2022.

Forecasting the evolution of natural hazards is a critical problem in natural sciences. Earthquake forecasting is one such example and is a difficult task due to the complexity of the occurrence of earthquakes. Until today, earthquake prediction is based on the time before the occurrence of the main earthquake and is based mainly on empirical methods and specifically on the seismic history of a given area. Τhe analysis and processing of its seismicity play a critical role in modern statistical seismology. In this work, a first attempt is made to study and draw safe conclusions regarding the prediction for the seismic sequence, specifically using appropriate statistical methods like Bayesian predictive, taking into account the uncertainties of the model parameters. The above theory was applied in the recent seismic sequence in the area of ​​Arkalochori in Crete Island, Greece (2021, M_w 6.0). Τhe rich seismic sequence that took place immediately after the main 5.6R earthquake with a total of events for the next three months, approximately 4,000 events of magnitude M_L > 1 allowed calculating the probability of having the most significant expected earthquake during a given time as well as calculating the probability that the most significant aftershock is expected to be above a certain magnitude after a major earthquake.

References:

• Ganas, A., Fassoulas, C., Moshou, A., Bozionelos, G., Papathanassiou, G., Tsimi, C., & Valkaniotis, S. (2017). Geological and seismological evidence for NW-SE crustal extension at the southern margin of Heraklion basin, Crete. Bulletin of the Geological Society of Greece, 51, 52-75. doi: https://doi.org/10.12681/bgsg.15004
• Konstantaras, A.J. (2016). Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100.
• Konstantaras, A. (2020). Deep learning and parallel processing spatio-temporal clustering unveil new Ionian distinct seismic zone. Informatics. 7 (4), 39.
• Moshou, A., Papadimitriou, E., Drakatos, G., Evangelidis, C., Karakostas, V., Vallianatos, F., & Makropoulos, K. (2014, May). Focal Mechanisms at the convergent plate boundary in Southern Aegean, Greece. In EGU General Assembly Conference Abstracts (p. 12185)
• Moshou, A., Argyrakis, P., Konstantaras, A., Daverona, A.C. & Sagias, N.C. (2021). Characteristics of Recent Aftershocks Sequences (2014, 2015, 2018) Derived from New Seismological and Geodetic Data on the Ionian Islands, Greece. 6 (2), 8.
• C.B., Nolet. G., 1997. P and S velocity structure of the Hellenic area obtained by robust nonlinear inversion of travel times. J. Geophys. Res. 102 (8). 349–367

How to cite: Moshou, A., Konstantaras, A., and Argyrakis, P.: The Analysis of the Aftershock Sequence of the Recent Mainshock in Arkalochori, Crete Island Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3940, https://doi.org/10.5194/egusphere-egu22-3940, 2022.

Underground faults cast energy storage elements of the accumulated strain energy in border areas of active tectonic plates. Particularly in the southern front of the Hellenic seismic arc, a steady yearly flow in the accumulation of strain energy is being due to the constant rate of motion at which the African plate sub-sinks beneath the Eurasian plate. Partial release of the stored energy from a particular underground fold manifests in the form of an earthquake once reaching the surface of the Earth’s crust. The information obtained for each recorded earthquake includes among others the surface location and the estimated hypocentre depth. Considering that hundreds of thousands earthquakes have been recorded in that particular area, the accumulated hypocentre depths provide a most valuable source of information regarding the in-depth extent of the seismically active parts of the underground faults. This research work applies expert knowledge spatio-temporal clustering in previously reported distinct seismic cluster zones, aiming to associate each individual main earthquake along with its recoded foreshocks and aftershocks to a single underground fault in existing two-dimensional mappings. This process is being enabled by heterogeneous parallel processing algorithms encompassing both proximity and agglomerative density-based clustering algorithms upon main seismic events only to mapped. Once a main earthquake is being associated to a particular known underground fault, then the underground fault’s point with maximum proximity to the earthquake’s hypocentre appends its location parameters, additionally incorporating the dimension of depth to the initial planar dimensions of latitude and longitude. The ranges of depth variations provide a notable indication of the in-depth extent of the seismically active part(s) of underground faults enabling their 3D model mapping.

Indexing terms: spatio-temporal proximity and clustering algorithms, heterogeneous parallel processing, Cuda, 3D underground faults’ mapping

References

Axaridou A., I. Chrysakis, C. Georgis, M. Theodoridou, M. Doerr, A. Konstantaras, and E. Maravelakis. 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 51-56, 2014.

Konstantaras A. Deep learning and parallel processing spatio-temporal clustering unveil new Ionian distinct seismic zone. Informatics. 7 (4), 39, 2020.

Konstantaras A.J. Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100, 2016.

Konstantaras A.J., E. Katsifarakis, E. Maravelakis, E. Skounakis, E. Kokkinos and E. Karapidakis. Intelligent spatial-clustering of seismicity in the vicinity of the Hellenic Seismic Arc. Earth Science Research 1 (2), 1-10, 2012.

Konstantaras A., F. Valianatos, M.R. Varley, J.P. Makris. Soft-Computing modelling of seismicity in the southern Hellenic Arc. IEEE Geoscience and Remote Sensing Letters, 5 (3), 323-327, 2008.

Konstantaras A., M.R. Varley, F. Valianatos, G. Collins and P. Holifield. Recognition of electric earthquake precursors using neuro-fuzzy methods: methodology and simulation results. Proc. IASTED Int. Conf. Signal Processing, Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 303-308, 2002.

Maravelakis E., A. Konstantaras, K. Kabassi, I. Chrysakis, C. Georgis and A. Axaridou. 3DSYSTEK web-based point cloud viewer. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 262-266, 2014.

How to cite: Moshou, A., Konstantaras, A., Menounos, N., and Argyrakis, P.: 3D Mapping of Active Underground Faults Enabled by Heterogeneous Parallel Processing Spatio-Temporal Proximity and Clustering Algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5487, https://doi.org/10.5194/egusphere-egu22-5487, 2022.

As the interpretability and explainability of artificial intelligence decisions has been gaining attention, novel approaches are needed to develop diagnostic tools that account for the unique challenges of geospatial and environmental data, including spatial dependence and high dimensionality, which are addressed in this contribution. Building upon the geostatistical tradition of distance-based measures, spatial prediction error profiles (SPEPs) and spatial variable importance proles (SVIPs) are introduced as novel model-agnostic assessment and interpretation tools that explore the behavior of models at different prediction horizons. Moreover, to address the challenges of interpreting the joint effects of strongly correlated or high-dimensional features, often found in environmental modeling and remote sensing, a model-agnostic approach is developed that distills aggregated relationships from complex models. The utility of these techniques is demonstrated in two case studies representing a regionalization task in an environmental-science context, and a classification task from multitemporal remote sensing of land use. In these case studies, SPEPs and SVIPs successfully highlight differences and surprising similarities of geostatistical methods, linear models, random forest, and hybrid algorithms. With 64 correlated features in the remote-sensing case study, the transformation-based interpretation approach successfully summarizes high-dimensional relationships in a small number of diagrams.

The novel diagnostic tools enrich the toolkit of geospatial data science, and may improve machine-learning model interpretation, selection, and design.

How to cite: Brenning, A.: Novel approaches to model assessment and interpretation in geospatial machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6955, https://doi.org/10.5194/egusphere-egu22-6955, 2022.

Global-scale maps are an important tool to provide ecologically relevant environmental variables to researchers and decision makers. Usually, these maps are created by training a machine learning algorithm on field-sampled reference data and the application of the resulting model to associated information from satellite imagery or globally available environmental predictors. However, field samples are often sparse and clustered in geographic space, representing only parts of the global environment. Machine learning models are therefore prone to overfit to the specific environments they are trained on - especially when a large set of predictor variables is utilized. Consequently, model validations have to include an analysis of the models transferability to regions where no training samples are available e.g. by computing the Area of Applicability (AOA, Meyer and Pebesma 2021) of the prediction models.

Here we reproduce three recently published global environmental maps (soil nematode abundances, potential tree cover and specific leaf area) and assess their AOA. We then present a workflow to increase the AOA (i.e. transferability) of the machine learning models. The workflow utilizes spatial variable selection in order to train generalized models which include only predictors that are most suitable for predictions in regions without training samples. We compared the results to the three original studies in terms of prediction performance and AOA. Results indicate that reducing predictors to those relevant for spatial prediction, leads to a significant increase of model transferability without significant decrease of the prediction quality in areas with high sampling density.

Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution 2041–210X.13650 (2021) doi:10.1111/2041-210X.13650.

How to cite: Ludwig, M., Moreno Martínez, Á., Hölzel, N., Pebesma, E., and Meyer, H.: Global maps from local data: Towards globally applicable spatial prediction models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7529, https://doi.org/10.5194/egusphere-egu22-7529, 2022.

Geotagged social media messages, especially from Twitter, can have a substantial impact on decision-making processes during natural hazards and disasters. For example, such geolocation information can be used to enhance natural hazard detection systems where real-time geolocated tweets can help identify the critical human-centric hotspots of an emergency where urgent help is required.

Our work can extract geolocation information from tweets by making use of five meta-data attributes provided by Twitter. Three of these are free-form text, namely tweet text, user profile description, and user location. The other two attributes are GPS coordinates and place tags.

Tweet text may or may not have relevant information to extract geolocation. In the cases where location information is available within tweet text, we follow toponym extraction from the text using Named Entity Recognition and Classification (NERC). The extracted toponyms are then used to obtain geolocation information using Nominatim (which is open-source geocoding software that powers OpenStreetMap) at various levels such as country, state, county, city.

Similar process is followed for user profile description where only location toponyms identified by NERC are stored and then geocoded using Nominatim at various levels.

User location field, which is also a free form text, can have mentions of multiple locations such as USA, UK. To extract location from this field a heuristic algorithm is adopted based on a ranking mechanism that allows it to be resolved to a single point of location which can be then mapped at various levels such as country, state, county, city.

GPS coordinates provide the exact longitude and latitude of the device's location. We perform reverse geocoding to obtain additional location details, e.g., street, city, or country the GPS coordinates belong to. For this purpose, we use Nominatim’s reverse API endpoint to extract city, county, state, and country information.

Place tag provides a bounding box or an exact longitude and latitude or name information of location-tagged by the user. The place field data contains several location attributes. We extract location information from different location attributes within the place using different algorithms. Nominatim’s search API endpoint to extract city, county, state, and country names from the Nominatim response if available.

Our geo-inference pipeline is designed to be used as a plug-in component. The system spans an elasticsearch cluster with six nodes for efficient and fast querying and insertion of records. It has already been tested on geolocating more than two billion covid-related tweets. The system is able to handle high insertion and query load. We have implemented smart caching mechanisms to avoid repetitive Nominatim calls since it is an expensive operation. The caches are available both for free-form text (Nominatim’s search API) and exact latitude and longitude (Nominatim’s reverse API). These caches help reduce the load on Nominatim and give quick access to the most commonly queried terms.

With this effort, we hope to provide the necessary means for researchers and practitioners who intend to explore social media data for geo-applications.

How to cite: Qazi, U., Ofli, F., and Imran, M.: Multi-attribute geolocation inference from tweets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8323, https://doi.org/10.5194/egusphere-egu22-8323, 2022.

Inspection of the complexity of urban morphology facilitates understanding of human behaviors in urban space, leading to better conditions for the sustainable design of future cities. Fractal indicators, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index, have been proposed as measures of such complexity. However, these major fractal indicators are statistical rather than spatial, which leads to failure of characterizing the spatial complexity of urban morphology, such as building footprints. To overcome this problem, in this paper a graph-based fractality index (GFI), based on a hybrid of fractal theories and deep learning techniques, is proposed. To quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Building footprints of London were used to test the method and the results show that the proposed framework performs better than traditional indices. Moreover, the possibility of bridging fractal theories and deep learning techniques on complexity issues opens up new possibilities of data-driven GIScience.

How to cite: Ma, L., Seipel, S., Brandt, S. A., and Ma, D.: A graph-based fractality index to characterize complexity of urban form using deep graph convolutional neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8648, https://doi.org/10.5194/egusphere-egu22-8648, 2022.

Missing climate data is a widespread problem in climate science and leads to uncertainty of prediction models that rely on these data resources. So far, existing approaches for infilling missing precipitation data are mostly numerical or statistical techniques that require considerable computational resources and are not suitable for large regions with missing data. Most recently, there have been several approaches to infill missing climate data with machine learning methods such as convolutional neural networks or generative adversarial networks. They have proven to perform well on infilling missing temperature or satellite data. However, these techniques consider only spatial variability in the data whereas precipitation data is much more variable in both space and time. Rainfall extremes with high amplitudes play an important role. We propose a convolutional inpainting network that additionally considers a memory module. One approach investigates the temporal variability in the missing data regions using a long-short term memory. An attention-based module has also been added to the technology to consider further atmospheric variables provided by reanalysis data. The model was trained and evaluated on the RADOLAN data set  which is based on radar precipitation recordings and weather station measurements. With the method we are able to complete gaps in this high quality, highly resolved spatial precipitation data set over Germany. In conclusion, we compare our approach to statistical techniques for infilling precipitation data as well as other state-of-the-art machine learning techniques. This well-combined technology of computer and atmospheric research components will be presented as a dedicated climate service component and data set.

How to cite: Meuer, J., Bouwer, L., Plésiat, É., Lehmann, R., Hoffmann, M., Ludwig, T., Karl, W., and Kadow, C.: Infilling Spatial Precipitation Recordings with a Memory-Assisted CNN, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8891, https://doi.org/10.5194/egusphere-egu22-8891, 2022.

The real world does not live on a regular grid. The observations with the best spatiotemporal resolution are generally irregularly distributed over space and time, even though as data they are generally stored in arrays in files. Storing the diverse data types of Earth science, including grid, swath, and point based spatiotemporal distributions, in separate files leads to computer-native array layouts on disk or working memory having little or no connection with the spatiotemporal layout of the observations themselves. For integrative analysis, data must be co-aligned both spatiotemporally and in computer memory, a process called data harmonization. For data harmonization to be scalable in both diversity and volume, data movement must be minimized. The SpatioTemporal Adaptive Resolution Encoding (STARE) is a hierarchical, recursively subdivided indexing scheme for harmonizing diverse data at scale. 

STARE indices are integers embedded with spatiotemporal attributes key to efficient spatiotemporal analysis. As a more computationally efficient alternative to conventional floating-point spatiotemporal references, STARE indices apply uniformly to all spatiotemporal data regardless of their geometric layouts. Through this unified reference, STARE harmonizes diverse data in their native states to enable integrative analysis without requiring homogenization of the data by interpolating them to a common grid first.

The current implementation of STARE supports solid angle indexing, i.e. longitude-latitude, and time. To fully support Earth science applications, STARE must be extended to indexing the radial dimension for a full 4D spatiotemporal indexing. As STARE’s scalability is based on having a universal encoding scheme mapping spatiotemporal volumes to integers, the variety of existing approaches to encoding the radial dimension arising in Earth science raises complex design issues for applying STARE’s principles. For example, the radial dimension can be usefully expressed via length (altitude) or pressure coordinates. Both length and pressure raise the question as to what reference surface should be used. As STARE’s goal is to harmonize different kinds of data, we must determine whether it is better to have separate radial scale encodings for length and pressure, or should we have a single radial encoding, for which we provide tools for translating between various (radial) coordinate systems. The questions become more complex when we consider the wide range of Earth science data and applications, including, for example, model simulation output, lidar point clouds, spacecraft swath data, aircraft in-situ measurements, vertical or oblique parameter retrievals, and earthquake-induced movement detection. 

In this work, we will review STARE’s unifying principle and the unique nature of the radial dimension. We will discuss the challenges of enabling scalable Earth science data harmonization in both diversity and volume, particularly in the context of detection, cataloging, and statistical study of fully 4D hierarchical phenomena events such as extratropical cyclones. With the twin challenges of exascale computing and increasing model simulation resolutions opening new views into physical processes, scalable methods for bringing best-resolution observations and simulations together, like STARE, are becoming increasingly important.

How to cite: Rilee, M. and Kuo, K.-S.: Design Considerations for the 3rd Spatial Dimension of the Spatiotemporal Adaptive Resolution Encoding (STARE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10799, https://doi.org/10.5194/egusphere-egu22-10799, 2022.

The study describes a generalized framework to extract and track features from large climate datasets. Unlike other feature extraction algorithms, Scalable Feature Extraction and Tracking (SCAFET) is independent of any physical thresholds making it more suitable for comparing features from different datasets. Features of interest are extracted by segmenting the data on the basis of a scale-independent bounded variable called shape index (Si). Si gives a quantitative measurement of the local shape of the field with respect to its surroundings. To illustrate the capabilities of the method, we have employed it in the extraction of different types of features. Cyclones and atmospheric rivers are extracted from the ERA5 reanalysis dataset to show how the algorithm extracts points as well as surfaces from climate datasets. Extraction of sea surface temperature fronts depicts how SCAFET handles unstructured grids. Lastly, the 3D structures of jetstreams is extracted to demonstrate that the algorithm can extract 3D features too. The detection algorithm is implemented as a jupyter notebook[https://colab.research.google.com/drive/1D0rWNQZrIfLEmeUYshzqyqiR7QNS0Hm-?usp=sharing] accessible to anyone to test out the algorithm.

How to cite: Nellikkattil, A., Lee, J.-Y., and Timmermann, A.: Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10823, https://doi.org/10.5194/egusphere-egu22-10823, 2022.

With the far-reaching impact of Artificial Intelligence (AI) becoming more acknowledgeable across various dimensions and industries, the Geomatics scientific community has reasonably turned to automated (in some cases, autonomous) solutions while looking to efficiently extract and communicate patterns in high-dimensional geographic data. This, in turn, has led to a range of AI platforms providing grounds for cutting-edge technologies such as data mining, image processing and predictive/prescriptive modelling. Meanwhile, coastal management bodies around the world, are striving to harness the power of AI and Machine Learning (ML) applications to act upon the wealth of coastal information, emanating from disparate data sources (e.g., geodesy, hydrography, bathymetry, mapping, remote sensing, and photogrammetry). The cross-disciplinarity of stakeholder engagement calls for thorough risk assessment and coastal defence strategies (e.g., erosion/flooding control), consistent with the emerging need for participatory and integrated policy analyses. This paper addresses the issue of seeking techno-centric solutions in human-understandable language, for holistic knowledge engineering (from acquisition to dissemination) in a spatiotemporal context; namely, the benefits of setting up a unified Visual Analytics (VA) system, which allows for real-time monitoring and Online Analytical Processing (OLAP) operations on-demand, via role-based access. Working from an all-encompassing data model could form seamlessly collaborative workspaces that support multiple programming languages (packaging ML libraries designed to interoperate) and enable heterogeneous user communities to visualize Big Data at different granularities, as well as perform task-specific queries with little, or no, programming skill. The proposed solution is an integrated coastal management dashboard, built natively for the cloud (aka leveraging batch and stream processing), to dynamically host live Key Performance Indicators (KPIs) whilst ensuring wide adoption and sustainable operation. The results reflect the value of effectively collecting and consolidating coastal (meta-)data into open repositories, to jointly produce actionable insight in an efficient manner.

How to cite: Anthis, Z.: Reading Between the (Shore)Lines: Real-Time Analytical Processing to Monitor Coastal Erosion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13102, https://doi.org/10.5194/egusphere-egu22-13102, 2022.

The European infrastructure In-service Aircraft for a Global Observing System (IAGOS) (www.IAOGS.org) has implemented an automatic workflow for data management organizing the dataflow starting at the sensor towards the central data-portal located in Toulouse. The workflow is realized and documented using the web-based Django framework with a model-based approach using Python.

This workflow performs all necessary data processing and QA/QC tests to automated upload NRT processed data and serves the PI as basis for approval decisions. This includes repeated cycles for different stages of data maturity. The PI can monitor the status of all tasks by web-based reports produced by the Task Manager.  An automated reprocessing is possible by storing metadata on all steps as well as decisions of the PI. The implementation of the workflow is one big step to make IAGOS data handling compliant with the FAIR principles (findable, accessible, interoperable, reusable).

The workflow is easy adaptable to manage the workflow of other Infrastructures or research institutes. Thus, we will open the development under MIT license and invite other datacenters to contribute to the development.

Acknowledgments:

This work was supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 824068 and by Helmholtz STSM Grant “DIGITAL EARTH”

How to cite: Bundke, U., Kennert, M., Mahnke, C., Rohs, S., and Petzold, A.: Implementation of a FAIR Compliant Automated Workflow for Infrastructures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6332, https://doi.org/10.5194/egusphere-egu22-6332, 2022.

Urban territories collect many types of geochemical and physico-chemical data relative to, e.g., soil quality or soil functions. Such data may serve for various purposes like verifying the compatibility with current or future uses, defining (pedo)geochemical backgrounds, establishing levels of exposure to soil pollutants, identifying management options for polluted sites or for excavated soils, verifying the evolution of infiltration ponds, assessing carbon storage, etc. They may also serve to prioritize soil functions and associated ecosystem services such as, e.g., soil fertility, surface and groundwater storage or supply, purification of infiltrated rainwater, etc. Gathering such data in national databases and making them available to stakeholders raises many issues that are technical, legal and social.  Should all of the data be made available or only selected portions? How can access and reuse of the data be ensured in a legal fashion? Are statistical and geostatistical methods able to deal with data from heterogeneous origins, allowing their reuse for other purposes than the initial one? In this context, it is necessary to take into account scientific as well as practical considerations and to collect the societal needs of end-users like urban planners.

To illustrate the complexity of these issues and ways to address them, we propose to share the French experience:

• on gathering urban soil geochemical data in the French national database BDSolU. We will present how this database was created, the choices made in relation with the national context, the difficulties encountered, and the questions that are still open.
• on a new interrogation system linking agricultural and urban soil databases (DoneSol and BDSolU), which have different requirements, and the corresponding standards. Such linkage based on interoperability is important in the context of changes of soil use, with for example agricultural soils becoming urbanised soils, or soils from brownfields intended for gardening. It is also necessary to ensure a territorial continuity for users.

The objective is to define a robust and standardised methodology for database conceptualisation, sharing and final use by stakeholders including scientists

How to cite: Le Guern, C., Brunet, J.-F., Négrel, P., Lemal, S., Taffoureau, E., Grellet, S., Beaufils, M., Lattelais, C., Le Bas, C., and Roussel, H.: French feedback from urban soil geochemical data archive to data sharing: state of mind and intent, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7228, https://doi.org/10.5194/egusphere-egu22-7228, 2022.

Automatic Weather Stations (AWS) deployed in the context of research projects provide very valuable data thanks to the flexibility they offer in term of measured meteorological parameters, choice of sensors and quick deployment and redeployment. However this flexibility is a challenge in terms of metadata and data management. Traditional approaches based on networks of standard stations can not accommodate these needs and often no tools are available to manage these research AWS, leading to wasted data periods because of difficult data reuse, low reactivity in identifying potential measurement problems, and lack of metadata to document what happened.

The Data Access Made Easy (DAME) effort is our answer to these challenges. At its core, it relies on the mature and flexible open source MeteoIO meteorological pre-processing library. It was originally developed as a flexible data processing engine for the needs of numerical models consuming meteorological data and further developed as a data standardization engine for the Global Cryosphere Watch (GCW) of the World Meteorological Organization (WMO). For each AWS, a single configuration file describes how to read and parse the data, defines a mapping between the available fields and a set of standardized names and provides relevant Attribute Conventions Dataset Discovery (ACDD) metadata fields, if necessary on a per input file basis. Low level data editing is also available, such as excluding a given sensor, swapping sensors or merging data from another AWS, for any given time period. Moreover an arbitrary number of filters can be applied on each meteorological parameter, restricted to specific time periods if required. This allows to describe the whole history of an AWS within a single configuration file and to deliver a single, consistent, standardized output file possibly spanning many years, many input data files and many changes both in format and available sensors. Finally, all configuration files are kept in a git repository in order to document their history.

A basic email-based interface has been developed that allows to create new configuration files, modify an existing configuration file or request data on-demand for any time period. Every hour, the data for all available configuration files is regenerated for the last 13 months and stored on a shared drive so all are able to access the current data without even having to submit a request. A table is generated showing all warnings or errors produced during the data generation along with some metadata such as the data owner email in order for the data owner to quickly spot troublesome AWS.

How to cite: Bavay, M., Fierz, C., and Nitu, R.: Data Access Made Easy: flexible, on the fly data standardization and processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8262, https://doi.org/10.5194/egusphere-egu22-8262, 2022.

The TRR170-DB data repository (https://planetary-data-portal.org/) is a Re3data (r3data.org) referenced repository that manages new machine-readable data and resources from the collaborative research center ‘Late Accretion onto Terrestrial Planets’ (TRR 170) and from other institutions in in the planetary science community. Data in the repository reflect the diverse methods and approaches applied in the planetary sciences, including astromaterials data, experimental studies, remote sensing data, images and geophysical modeling data. The TRR170-DB repository follows a data policy and practice that supports Open Science and the FAIR principles (Wilkinson et al., 2016) as promoted by the German National Research Data Infrastructure (www.nfdi.de) and various national and international funding agencies and initiatives. The TRR170-DB framework supports users to align their data storage with the data life cycle of data sharing, persistent data citation, and data publishing. The permanent host of the TRR170-DB is Freie Universität Berlin. This long-term preservation and access of TRR170-DB’s published data ensures them being reused by researchers and the interested public.

The TRR170-DB repository is operated on the open source data management software Dataverse (dataverse.org). A web portal provides access to the storage environment of the datasets. The web portal guides users through the process of data storage and publication. It also informs about legal conditions and embargo periods to safeguard the data publication process. Additional information is available informing the user about data management and data publication related news and training events.

A user can search metadata information to find specific published data collections and files without logging in to TRR170-DB. A recently integrated new tool, the data explorer, assists the user in advanced searches to browse and find published data content. Data suppliers receive data curation services, a permanent archive and a digital object identifier (DOI) to make the dataset unique and findable. We encourage TRR 170 members and other users to store replication datasets by implementing publishing workflows to link publications to data. These replication datasets are freely available, and no permission is required for reuse and verification of a study. TRR170-DB has a flexible data-driven metadata system that uses tailored metadata blocks for specific data communities. Once a dataset has been published, its metadata and files can be exported in various open metadata standards and file formats. This ensures that all data published in the repository are generally accessible for other external databases and repositories (“interoperability”).

We are currently expanding metadata templates to improve interoperability, findability, preservation, and reuse of geochemical data in TRR170-DB. New geochemical metadata templates will incorporate additional standardized information on samples and materials, analytical methods and additional experimental data.  Advancing metadata templates will be an ongoing process in which the international scientific community and various initiatives (OneGeochemistry, Astromaterials Data System, etc.) need to interact and discuss what is required.

How to cite: Lehmann, E. and Becker, H.: The TRR170-DB Data Repository: The Life Cycle of FAIR Planetary Data from Archive to Publication, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9960, https://doi.org/10.5194/egusphere-egu22-9960, 2022.

As volumes of geoanalytical data grow, research in geochemistry, volcanology, petrology, and other disciplines working with geoanalytical data is evolving to data-driven and computational approaches that have enormous potential to lead to new scientific discoveries. Application of advanced methods for data mining and analysis including Machine Learning, and Artificial Intelligence, as well as the generation of models for simulating natural processes all require seamless machine-readable access to large interoperable stores of consistently structured and documented geochemical data. Standard protocols, formats, and vocabularies are also critical in order to process, manage, and publish these growing data volumes efficiently with seamless workflows that are supported by interoperable tools.

Today, easy integration of data into Analysis Ready Data stores and the successful and efficient application of new research methodologies to these data stores is hindered by the fragmentation of the international geochemical data landscape that lacks the technical and semantic standards for interoperability; organizational structures to guide and govern these standards; and a scientific culture that supports and prioritizes a global sustainable data infrastructure. In order to harness the scientific treasures hidden in BIG volumes of geochemical data, the science community, geochemistry data providers, publishers, funders, and other stakeholders need to come together to develop, implement, and maintain standards and best practices for geochemical data, and commit to changing the current data culture in geochemistry. The benefits will be wide-ranging and increase the relevance of the discipline. 

Although many research data initiatives today focus on the implementation of the FAIR principles for Findable, Accessible, Interoperable, and Reusable data, most data is only human-readable, even though the original purpose of the FAIR principles has been to make data machine-actionable. The development of standards today should not focus on spreadsheet templates used to format and compile project-centric databases that are hard to re-purpose. These methods are not scalable. The focus should be on global solutions where any digital data are born connected to agreed machine readable standards so that researchers can utilize the latest AI and ML techniques.

Global standards for geochemical data should not be perceived as ‘one ring to rule them all’, but rather as a series of interoperable ‘rings’ of data, which like the Olympic rings will integrate data from the all continents and nations.

How to cite: Lehnert, K. and Wyborn, L.: Global Data Standards for Geochemistry: Not the ‘One Ring to Rule Them All’, but a set of ‘Olympic Rings’ that Link and Integrate across Continents, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10726, https://doi.org/10.5194/egusphere-egu22-10726, 2022.

Some disciplines, especially those that look at the Earth system, work with large to very large amounts of data. Storing this data, but also processing it, places completely new demands on scientific work itself.

Let's take the example of climate research and specifically climate modelling. In addition to long-term meteorological measurements in the recent past, results from climate models form the main basis for research and statements on past and possible future global, regional and local climate. Climate models are very complex numerical models that require high-performance computing. However, with the current and future increasing spatial and temporal resolution of the models, the demand for computing resources and storage space is also increasing. Previous working methods and processes no longer hold up and need to be rethought.

Taking the German Climate Computing Centre (DKRZ) as an example, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group subdivides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules.

In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.

The vision is that the global integrated data space is formed by standardised, independent and persistent entities that contain all information about diverse data objects (data, documents, metadata, software, etc.) so that human and, above all, machine agents can find, access, interpret and reuse (FAIR) them in an efficient and cost-saving way. At the same time, these units become independent of technologies and heterogeneous organisation of data, and will contain a built-in mechanism that supports data sovereignty. This will make the handling of data sustainable and secure.

So, each step in a research workflow can be a FDO. In this case, the research is fully reproducible, but parts can also be exchanged and, e.g. experiments can be varied transparently. FDOs can easily be linked to others. The redundancy of data is minimised and thus also the susceptibility to errors is reduced. FDOs open up the possibility of combining data, software or whole parts of workflows in a new and simple but at all times comprehensible way. FDOs will make an important contribution to the reproducibility of research results, but they are also crucial for saving storage space. There are already data that are FDOs, but also self-contained frameworks that store data via tracking workflows. Similar to the TCP/IP standard, DO interface protocols are already developed. However, there are still some open points that are currently being worked on and defined with regard to FDOs in order to make them a globally functioning system.

How to cite: Anders, I., Peters-von Gehlen, K., Thiemann, H., Bergemann, M., Buurman, M., Fast, A., Kadow, C., Kulüke, M., and Wachsmann, F.: Data amounts and reproducibility: How FAIR Digital Objects can revolutionise Research Workflows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11103, https://doi.org/10.5194/egusphere-egu22-11103, 2022.

Geochemical data are fundamental to understanding many planetary and environmental processes – yet in the absence of a community-endorsed data culture that adheres to common data standards, the geochemical data landscape is highly fragmented. The GEOROC and PetDB databases are leading, open-access resources for geochemical and isotopic rock and mineral data that have collaborated for nearly 25 years to provide researchers with access to large volumes of curated and harmonized data collections. PetDB is a global synthesis of published chemical, isotopic and mineralogical data for rocks, minerals and melt inclusions with a focus on data for igneous and metamorphic rocks from the ocean floor, ophiolites, xenolith samples from the Earth's mantle and lower crust and tephra, operated by the EarthChem data facility. Its counterpart, GEOROC hosts a collection of published analyses of volcanic and plutonic rocks, minerals and mantle xenoliths, predominantly derived from ocean islands and continental settings. These curated, domain-specific databases are increasingly valuable to data-driven and interdisciplinary research and form the basis of hundreds of new research articles each year across numerous earth data disciplines. 

Over the last two decades, both GEOROC and EarthChem have invested great efforts into operating data infrastructures for findable, accessible, interoperable and reusable data, while working together to develop and maintain the EarthChem Portal (ECP) as a global open data service to the geochemical, petrological, mineralogical and related communities. The ECP provides a single point of access to >30 million analytical values for >1 million samples, aggregated from independently operated databases (PetDB, NAVDAT, GEOROC, USGS, MetPetDB, DARWIN). Yet one crucial element of FAIR data is still largely missing: interoperability across different data systems, that allows data in separately curated databases, such as GEOROC and PetDB, to be integrated into comprehensive, global geochemical datasets.

Both EarthChem and GEOROC have recently embarked on major new developments and upgrades to their systems to improve the interoperability of their data systems. The new Digital Geochemical Data Infrastructure (DIGIS) initiative for GEOROC 2.0 aims to develop a connected platform to meet future challenges of digital data-based research and provide advanced services to the community. EarthChem has been developing an API-driven architecture to align with growing demands for machine-readable, Analysis Ready Data (ARD). This has presented an opportunity to make the two data infrastructures more interoperable and complementary. EarthChem and DIGIS have committed to cooperation on system architecture design, data models, data curation, methodologies, best practices and standards for geochemistry. This cooperation will include: (a) joint research projects; (b) optimized coordination and alignment of technologies, procedures and community engagement; and (c) exchange of personnel, data, technology and information. The EarthChem-DIGIS collaboration integrates with the international OneGeochemistry initiative to create a global geochemical data network that facilitates and promotes discovery and access of geochemical data through coordination and collaboration among international geochemical data providers, in close dialogue with the scientific community and with journal publishers.

How to cite: Klöcking, M., Lehnert, K., Profeta, L., Sarbas, B., Brase, J., Cao, S., Figueroa, J. D., Horstmann, W., Ji, P., Johansson, A., Kallas, L., Möller-McNett, S., Mukhumova, M., Nieschulze, J., Sturm, A., Sweets, H., Willbold, M., and Wörner, G.: GEOROC and EarthChem: Optimizing Data Services for Geochemistry through Collaboration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11348, https://doi.org/10.5194/egusphere-egu22-11348, 2022.

CaosDB is a flexible semantic research data management system, released as open source software. Its versatile data model and data integration toolkit allows for applications in complex and very heterogeneous scientific workflows and different scientific domains. Successful implementations include biomedical physics [1] and glaciology [2]. Here, we present a recent implementation of a use case in marine biogeochemistry which has a special focus on bridging between experimental work and numerical ocean modelling. CaosDB is used to store, index and link data during different stages of research on the marine carbon cycle: Data from experiments and field campaigns is integrated and mapped onto semantic data structures. This data is then linked to data from numerical ocean simulations. The ocean model, here with a specific focus on natural marine carbon sequestration of dissolved organic carbon (DOC), uses the georeferenced data to evaluate model performance. By simultaneously linking empirical data and the sampled model parameter space together with the model output, CaosDB enhances the efficiency of model development. In the current implementation simulated data is linked to georeferenced DOC concentration data. We plan to expand it to complex data sets including thousands of dissolved organic matter molecular formulae and metagenomes of pelagic microbial communities. The combined management of these heterogeneous data structures with semantic models allows us to perform complex searches and seamlessly connect to automated data analysis pipelines.


[1] Fitschen, T.; Schlemmer, A.; Hornung, D.; tom Wörden, H.; Parlitz, U.; Luther, S. CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows. Data 2019, 4, 83. https://doi.org/10.3390/data4020083
[2] Schlemmer, A.; tom Wörden, H.; Freitag, J.; Fitschen, T.; Kerch, J.; Schlomann, Y.; ... & Luther, S. Evaluation of the semantic research data management system CaosDB in glaciology. deRSE 2019. https://doi.org/10.5446/42478

How to cite: Schlemmer, A., Merder, J., Dittmar, T., Feudel, U., Blasius, B., Luther, S., Parlitz, U., Freund, J., and Lennartz, S. T.: Implementing semantic data management for bridging empirical and simulative approaches in marine biogeochemistry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11766, https://doi.org/10.5194/egusphere-egu22-11766, 2022.

In this abstract we introduce a suite of free applications to produce FAIR consistent, clean and easily available geoscience data for research and industry alike. 

Creation of data starts with sample collection in the field and the assigning of an unique global IGSN sample identifier to samples, these samples are stored along with any subsequent  analytical data in our fine-grained and detailed geochemical data models allowing visualising and publishing acquired datasets. This unique solution has been developed by Lithodat Pty Ltd in conjunction with the AuScope Geochemical Network (AGN), Australian geochemical laboratories and can be accessed by the public on the AusGeochem web platform. 

Using our fully integrated field application users can enter and store all sample details on-the-fly during field collection, the data will be stored in the user's private data collection. Once the researchers return from the field they can log into their account on the browser-based AusGeochem platform and view or edit all collected samples. After running subsequent geochemical analyses on the sample those results, including all metadata, can be stored in the database and attached to the sample. Once uploaded, data can be visualised within AusGeochem, using simple data analytics via technique-specific dashboards and graphs. The data can be shared with collaborators, downloaded in multiple formats and made public enabling FAIR data for the research community. 

Here we show a complete sample workflow example, from collection in the field to the final result as a thermochronology study. Sample analysis using fission track and (U-Th)/He and all associated data will be uploaded and stored in the AusGeochem platform. Once all analyses are complete, the data will be shared with collaborators and made available to the public. An important step during this process is by having an integrated IGSN minting option which will give the sample a unique global sample identifier, making the sample globally discoverable. 

Having all data stored in a clean and curated relational database with very detailed and fine-grained data models gives researchers free access to large amounts of structured and normalised data, helping them develop new technologies using machine learning and automated data integration in numerical models. Having all data in one place including all metadata such as ORCIDs from involved researchers, funding sources, grant numbers and laboratories enables the quantification and quality assessment of research projects over time.

How to cite: Theile, M., Noble, W., Beucher, R., McMillan, M., Boone, S., and Kohlmann, F.: From Field Application to Publication: An end-to-end Solution for FAIR Geoscience Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11980, https://doi.org/10.5194/egusphere-egu22-11980, 2022.

The European Multidisciplinary Seafloor and water-column Observatory (EMSO) European Research Infrastructure Consortium (ERIC) is a large-scale European Strategy Forum on Research Infrastructure (ESFRI) member with strategically placed sea observatories with the essential scientific objective of real-time, long-term monitoring of environmental processes related to the interaction between the geosphere, biosphere, and hydrosphere. EMSO ERIC collects, curates, and provides high-quality oceanographic measurements from surface to deep seafloor to assess long-term time series and oceanographic modeling. In addition, EMSO ERIC has developed a set of data services that harmonize its regional facilities’ data workflows, enhancing efficiency and productivity, supporting innovation, and enabling data- and knowledge-based discovery and decision-making. These services are developed in connection with the ESFRI cluster of Environmental Research Infrastructures (ENVRI) through the adoption of FAIR data principles (findability, accessibility, interoperability, and reusability) and supported by the ENVRI-FAIR H2020 project. 

EMSO ERIC’s efforts in adopting FAIR principles include the use of globally unique and resolvable persistent identifiers (PIDs) in alignment with the ENVRI-FAIR task forces. We present a service for the identification and long-lasting citability of dynamic data queries on harmonized data sets generated by EMSO ERIC users. The service is aligned with the Research Data Alliance (RDA) working group on data citation and has been integrated into the EMSO ERIC data portal. User-built queries on the data portal are served by the EMSO ERIC Application Programming Interface (API), which retrieves the user requested data and provides a Uniform Resource Identifier (URI) to the query, visualizations, and data sets in CSV and NetCDF formats. The data portal allows users to request a PID to the data query by providing mandatory and optional metadata information through an online form. The mandatory metadata consists of the description of the data and specific information about the creators, personal or organizational, including their identifiers and affiliations. The optional metadata consists of different types of titles and descriptions that the user finds compelling. The service provides a permalink to a web page maintained within the data portal with the PID reference, metadata information, and the URI to the data query. The web pages associated with PIDs also provide the option to request a Digital Object Identifier (DOI) if users are authorized via the EMSO ERIC Authorization and Authentication Infrastructure (AAI) system.

How to cite: Rodero, I., Fornós, A., Bardaji, R., Chiappini, S., and Dañobeitia, J.: Identification and Long-lasting Citability of Dynamic Data Queries on EMSO ERIC Harmonized Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12096, https://doi.org/10.5194/egusphere-egu22-12096, 2022.

The UCLA Cosmochemistry Database was initiated as a data rescue project aiming to archive a variety of cosmochemical data acquired at the University of California, Los Angeles. The database will ensure that future studies can use and reference these data in the examination, analysis and classification of new extraterrestrial samples.

The database is developed in collaboration with the Astromaterials Data System (AstroMat) that will provide persistent access to and archiving of the database. The database is a project in progress. We will continue to make additions, updates, and improvements to the database.

The database includes elemental compositions of extraterrestrial materials (including iron meteorites, chondrites, Apollo samples, and achondrites) analyzed by John T. Wasson, Paul H. Warren and their coworkers using atomic absorption spectrometry (AAS), neutron activation analysis (NAA), and electron microprobe analysis (EMPA) at UCLA over the last six decades. The team started to use INAA to analyze iron meteorites, lunar samples, and stony meteorites starting from the late 1970s [1]. Some achondrites and lunar samples were analyzed by EMPA. Some of the UCLA data have been published, but most of the data were neither digitized nor stored in a single repository.

Compositional data have been compiled by the UCLA team from publications, unpublished files, and laboratory records into datasets using Astromat spreadsheet templates. These datasets are submitted to the Astromat repository. Astromat curators review the datasets for metadata completeness and correctness, register them with DataCite to obtain a DOI and make them citeable, and package them for long-term archiving. To date, we have compiled data from 52 journal articles; each article has its own separate dataset. Data and metadata of these datasets are then incorporated into the Astromat Synthesis database.

The UCLA datasets are publicly accessible at the Astromat Repository, where individual datasets can be searched and downloaded. The UCLA cosmochemical data can also be accessed as part of the Astromat Synthesis database, where they are identified as a special ‘collection’. Users may search, filter, extract, and download customized datasets via the user interface of the Astromat Synthesis database (AstroSearch).  Users will be able to access the UCLA Cosmochemistry Database directly from the home page of AstroMat (https://www.astromat.org/).

We plan to include EMPA data of lunar samples and achondrites, and add scanned PDF files of laboratory notebooks and datasheet binders that are not commonly published in journals. These PDF files contain information on irradiation date, mass, elemental concentrations, and classification for each iron specimen, and John Wasson’s personal notes on meteorites. We will also add backscattered-electron (BSE) images, energy dispersive spectroscopy (EDS) images, and optical microscopy images.

The Astromat team is currently working to develop plotting tools for the interactive tables.

Acknowledgments: We thank John Wasson and his coworkers for collecting the cosmochemical data for the last 60 years. Astromat acknowledges funding from NASA (grant no. 80NSSC19K1102).

References: [1] Scott E.R.D et al. (1977) Meteoritics, 12, 425–436.

How to cite: Zhang, B., Warren, P. H., Rubin, A. E., Lehnert, K., Profeta, L., Johansson, A., Ji, P., Figueroa-Solazar, J. D., and Mays, J.: The UCLA Cosmochemistry Database, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13188, https://doi.org/10.5194/egusphere-egu22-13188, 2022.

When researchers collect or create physical samples they usually assign a user-generated number to each sample. Subsequently, that sample can be submitted to a laboratory for analysis of a variety of analytes. However, as geoanalytical laboratories are generating ever increasing volumes of data, most laboratories have automated workflows and it is no longer feasible for laboratories to use researcher-supplied sample numbers, particularly as it is not guaranteed that user-supplied numbers will be unique in comparison to numbers submitted by other users to the same laboratory. To address this issue new, laboratory-generated numbers may be assigned to that sample.

Moreover, as a single laboratory rarely has the capability to offer all analytical techniques, individual samples tend to move from laboratory to laboratory to acquire the desired suite of analytes.  Each laboratory may implement a different number to that sample. At the conclusion of their project, the researcher may submit the same sample to a museum or institutional repository, where the sample will be assigned yet another institution-generated number to ensure that all samples are uniquely identified in their repository. 

Ultimately, by the time the researcher submits an article to a journal and wants to identify samples in the text or tables, they may have a multitude of locally-generated numbers to choose from. Not one of the locally assigned numbers to that sample can be guaranteed to be globally unique. It is also unlikely that any of these local numbers will be persistent over the longer term (decades), or be resolvable to enable the location of the identified resource or any information about it elsewhere on the web (metadata, landing page, services related to it, etc).

Globally unique, persistent, resolvable identifiers such as the IGSN play a critical role in the unique identification of geoanalytical samples that pass between systems and organisations: they cannot be duplicated by another researcher, laboratory or sample repository. They persistently link to information about the origin of the sample; to personas in the creation of the sample (collector, institution, funder); to the laboratory data and their creation (analyst, laboratory, institution, funder, data software); and to the sample curation phase (curator, repository, funder). They connect the phases of a sample’s path from collection in the field to lab analysis to the synthesis/research phase to the publication to the archive. Globally unique sample identifiers also enable cross linkages to any artefacts derived from that sample (images, analytical data, other articles). Further, identifiers like IGSN enable sub samples or sample splits to be linked back to their parent sample, creating a holistic picture of any information derived from the initial sample. 

Hence, best practice is clearly to assign the globally unique resolvable identifier to the initial resource. Like a birth certificate, the identifier can be carried through the progressive stages of the research ‘life-cycle’ including laboratory analysis, generation of further data, images, publication, and ultimately curation and preservation. Where any subsamples are derived, they, and any data generated on them, can be linked back to the parent identifier.

How to cite: Lehnert, K., Klump, J., Ramdeen, S., Elger, K., and Wyborn, L.: The critical role of unique identification of samples for the geoanalytical data pipeline, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13317, https://doi.org/10.5194/egusphere-egu22-13317, 2022.