It is theoretically demonstrated  that, even with perfectly complete and perfectly accurate data, there is a fundamental ambiguity in the analysis of potential field data. The ambiguity may be easily illustrated by computing some of the various kinds of structures that can give rise to the same anomaly field. To solve the ambiguity and yield reasonable geophysical models we must therefore supply a priori information. In gravimetry, the ambiguity comes from the fact that only the excess mass is uniquely determined by the anomaly, neither the density nor the source volume. However, not only the excess mass can be uniquely estimated. Examples are the center of a uniformly dense (or magnetized) sphere or the top of a deeply extended homogeneously-dense cylinder. A priori information may consist of direct information (e.g., depth, shape) and/or of assuming that the source distribution has some specified properties (e.g., compactness, positivity). If one tries to classify the physical source-distributions in terms of their complexity, we may however use two different scaling laws, based on homogeneity and self-similarity, which allow modeling of the Earth in its complex heterogeneity. While monofractals or homogeneous functions are scaling functions, that is they do not have a specific scale of interest, multi-fractal and multi-homogeneous models need to be described within a multiscale dataset. Thus, specific techniques are needed to manage the information contained on the whole multiscale dataset. In particular,  any potential field  generated by a complex source may be modeled as  a multi-homogeneous field, which typically present a fractional and spatially varying homogeneity degree. For a source of irregular shape, it may be convenient to invert not the  field but a related quantity, the scaling function, which is a multiscale function having the advantage of not involving the density among the unknown parameters. For density or magnetic susceptibility tomographies, the degree of spatially variable homogeneity can be incorporated in the model weighting function, which, in this way, does not require prior assumptions because it is entirely deductible from the data. We discover that difficult quantities, such as the bottom of the sources, or multiple source systems are reasonably well estimated by abandoning the analysis at a single scale and unraveling the scale-related complexity of geophysical signals. The inherent self-consistency of these new multiscale tools is a significant step forward, especially in the analysis of areas where there is scarce other information about the sources.

How to cite: Fedi, M.: Solving the ambiguity in the potential field exploration of complex sources, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13597, https://doi.org/10.5194/egusphere-egu23-13597, 2023.

The wide use of the NDT technologies and the big database are produced, transmitted, collected, processed needs to be managed and well-presented towards monitoring of road transports. Using Ground Penetrating Radar (GPR) as one of the most efficient non-destructive tests in road transport monitoring.  Such database outcomes produced through on-site monitoring approaches are essential for providing the most reasonable decision-making tools to support best engineering judgment on-site. In addition to that the accuracy and precision of such decision-making tools are highly dependent on the data quality generated from different GPR images. Establishing performance indictor could avoid errors in dataset and unfavorable decisions in pavement management system. Consequently, GPR data management and transforming to local indicators is crucial to increase quality control of the dataset. This is still an ongoing challenging task for GPR support-knowledge.  

Establishing indicators are based on different criteria including intuitive outcomes, empirical outputs, and analytical results. Different GPR signal parameters can be correlated to the subsurface material changes and deterioration such as electromagnetic wave velocity, amplitude, centre frequency and signal attenuation to some local indicators. This can be under the category of the current challenges, the question is as follows, How GPR data can be converted to indicators based on the common defects in road transports. Therefore, establishing potential metrics to value GPR-related indicators in both a qualitative and quantitative approaches is crucial to provide better understanding of the defects and their propagation in road pavements.

How to cite: Rasol, M.: Towards sustainable road transport infrastructure: Insights from GPR performance indicator development and enhancement of data quality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1641, https://doi.org/10.5194/egusphere-egu23-1641, 2023.

In this poster contribution, we present a scheduling system for automated remote operation of instruments at high-altitude research facilities and similar remote sites. Via web-based interfaces, the system allows instrument owners as well as authorized third-party scientists to schedule and execute measurements and observations.

The system has been developed as a thesis project in the context of the AlpEnDAC-II ("Alpine Environmental Data Analysis Centre", www.alpendac.eu) collaboration (funded by the Bavarian State Ministry of the Environment and Consumer Protection). Consequently, the scheduler and interfaces have been integrated with the AlpEnDAC Operating-on-Demand functionalities. A first use case for the framework has been the operation of an airglow imager (FAIM) in Oberpfaffenhofen (DE).

We describe the design and implementation of our system for scheduling and execution of multi-user observations on instruments, including scheduling-data transfers and data retrieval. Our core implementation uses an optimization-based scheduler (Google's OR-Tools) to ensure maximum instrument use and to minimize idle times. Results show that the scheduler is reliable, fast, and is consistently able to provide optimal observation plans. The extensibility of the system is guaranteed by the usage of modern software in the core of the system, including well-defined and specified communication through REST APIs. Thus, it can easily be adapted to other settings and instruments, which is also facilitated by a modern deployment strategy using Docker and Kubernetes.

How to cite: Schumann, M., Munke, J., Hachinger, S., Hannawald, P., Beck, I., Götz, A., Goussev, O., Handschuh, J., Heller, H., Mair, R., Rehm, T., Wittmann, B., Wüst, S., Bittner, M., Schmidt, J., and Kranzlmüller, D.: Scheduling System for Remote Control of Instruments used for Atmospheric Observation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-237, https://doi.org/10.5194/egusphere-egu23-237, 2023.

The German Research Centre for Geosciences (GFZ) in Potsdam hosts a CAMECA 1280-HR large geometry secondary ion mass spectrometer (SIMS) with a web-based user node at the University of the Witwatersrand, South Africa. A major theme of our facility is high-precision, high-accuracy, high-spatial resolution analyses of light isotope ratios in a variety of natural and experimental materials.

The latest analytical developments from the GFZ SIMS laboratory focus on the development, assessment and use of new reference materials for stable isotope analysis. Particularly for oxygen, our repeatability from 15-µm diameter domains is now typically better than ±0.15‰ (1s). However, the total uncertainty on such analyses is commonly larger because of significant differences (in some cases more than one ‰) among the isotope ratios of reference materials reported by multiple, highly regarded gas source mass spectrometry laboratories. This issue of interlaboratory bias during reference material characterization inevitably impacts all in situ data employing such materials and must be duly considered.

How to cite: Scicchitano, M. R., Wiedenbeck, M., Couffignal, F., Glynn, S., Wudarska, A., Trumbull, R., and Rocholl, A.: Analytical developments at the Potsdam SIMS user facility and the metrological limits on in situ isotope ratio data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1220, https://doi.org/10.5194/egusphere-egu23-1220, 2023.

The wide use of the NDT technologies and the big database are produced, transmitted, collected, processed needs to be managed and well-presented towards monitoring of road transports. Using Ground Penetrating Radar (GPR) as one of the most efficient non-destructive tests in road transport monitoring.  Such database outcomes produced through on-site monitoring approaches are essential for providing the most reasonable decision-making tools to support best engineering judgment on-site. In addition to that the accuracy and precision of such decision-making tools are highly dependent on the data quality generated from different GPR images. Establishing performance indictor could avoid errors in dataset and unfavorable decisions in pavement management system. Consequently, GPR data management and transforming to local indicators is crucial to increase quality control of the dataset. This is still an ongoing challenging task for GPR support-knowledge.  

Establishing indicators are based on different criteria including intuitive outcomes, empirical outputs, and analytical results. Different GPR signal parameters can be correlated to the subsurface material changes and deterioration such as electromagnetic wave velocity, amplitude, centre frequency and signal attenuation to some local indicators. This can be under the category of the current challenges, the question is as follows, How GPR data can be converted to indicators based on the common defects in road transports. Therefore, establishing potential metrics to value GPR-related indicators in both a qualitative and quantitative approaches is crucial to provide better understanding of the defects and their propagation in road pavements.

How to cite: Rasol, M.: Towards sustainable road transport infrastructure: Insights from GPR performance indicator development and enhancement of data quality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1641, https://doi.org/10.5194/egusphere-egu23-1641, 2023.

Scalar calibration method is quite convenient and widely used in the coil sensitivity calibration for certain coil system. The uncertainties of the results are critical in evaluating the accuracy of the coil sensitivity. Based on the calibration principle, a mathematical description of the coil sensitivity uncertainty is given, which shows that the current, the environmental magnetic field and their uncertainties are the main factors attributing to the coil sensitivity uncertainty. Series of tests were conducted under different currents and stable environmental magnetic fields. The results show a very good accordance with the analytical description. With the increase of the current, the coil sensitivity uncertainty becomes smaller and goes to a limit decided by the current uncertainty while the influence of the environmental magnetic field is comparatively insignificant.

How to cite: Chen, M., Li, Y., Hao, X., Liu, K., Pan, Z., Li, X., and Zhang, T.: Analysis on the Uncertainty of the Coil Sensitivity based on the Principle of Scalar Calibration Method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1725, https://doi.org/10.5194/egusphere-egu23-1725, 2023.

Decades ago new opportunities in seismology were opened by the development of broadband seismic sensors with feedback. The three defining characteristics of these instruments were the bandwidth extension to longer periods, a much lower intrinsic noise and a higher dynamic range. However, the goal of further extending their bandwidth to frequencies above 100 Hz has proven elusive, because these sensors are plagued by parasitic resonances leading to modes not controllable by the feedback system.

Here we present a new low noise seismic borehole sensor with a truly VBB flat response over five frequency decades from 2.7 mHz (360 sec) to 270 Hz. The instrument has no mechanical resonances below 400 Hz. We achieved the bandwidth extension to high frequencies with improvements of the mechanical design, i.e. the arrangement of the pivots and the geometry of the spring.

The design is realized in a borehole arrangement, where three sensors are stacked in 90 degree angles to each other. Including a singe jaw holelock as a clamping mechanism the complete stack has a diameter of 89 mm, is 625 mm long and weighs about 24.5 kg. We show test results from three co-located complete borehole sensors with identical frequency responses.

How to cite: Guralp, C., Rademacher, H., Minchinton, P., Kirrage, R., Alimov, M., Jones, R., and Boustead, N.: A truly Very Broad Band (VBB) borehole seismometer with flat response over 5 decades of frequency, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2690, https://doi.org/10.5194/egusphere-egu23-2690, 2023.

Muography is a passive and non-destructive imaging technique that utilizes cosmic-ray muons for visualizing and monitoring the interior of geological structures and human-made objects. A rapid development of muographic observation technologies was achieved in the recent years, which allowed to resolve Earth's shallow subsurface with a resolution of a few meters and conduct long-term muon monitoring in harsh and varying environment. Muography can be applied as a complementary technique for Earth sciences and related engineering fields, e.g., for studying active volcanism, characterizing the overburden above underground sites, structural health monitoring infrastructures, exploring hidden cultural heritages, etc. We overview the recent progress in development of muography and we discuss case studies from volcanology to mining engineering from the Americas, Asia and Europe.

How to cite: Oláh, L.: Advances in cosmic-ray muography for Earth sciences and geophysical applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3288, https://doi.org/10.5194/egusphere-egu23-3288, 2023.

Inductively coupled plasma time-of-flight mass spectrometry (ICP-TOFMS) is increasingly used in various disciplines, especially for the characterization of single particles, because it allows truly simultaneous determination of isotopes over full mass range without sacrificing analytical sensitivity (Baalousha et al., 2021; Erhardt et al., 2019; Goodman et al., 2022). In particular, the extremely high time resolution of TOFMS allows us to detect individual mineral dust particles in the water stream obtained from Continuous Flow Analysis of Greenland Ice cores. Even though collision cell technology (CCT) and high-sensitivity sample introduction have been applied to the ICP-MS systems to overcome analytical limitations in spectral interferences and sensitivity (Burger et al., 2019; Lin et al., 2019), the impact of CCT and high-sensitivity desolvation sample introduction on analysis of single particles, unlikely to bulk analysis, are still relatively poorly understood. We investigated the effects of CCT and high-sensitivity desolvation sample introduction (individually and in combination) on the capability of single particle (sp) ICP-TOFMS including sensitivity and transport/transmission efficiency. To do so, we systemically investigated differences in the sensitivity of total Au, the transport efficiency of Au nano particles as well as the signal amplitude above the background of these nano particles. Application of the desolvation unit without CCT led to a significant improvement of the transport efficiency (number of particles introduced into the plasma) by a factor of about 5 but to a reduction of sensitivity (counts per particle) by about 30 percent. When CCT was used for sp-ICP-TOFMS without the high-sensitivity sample introduction system, the sensitivity for gold ion in particle signals increased by only about six percent. This is similar to the sensitivity improvement for gold ions in dissolved background signals from the ion focusing effect after the collision cell. However, when CCT was used in combination with the high-sensitivity sample introduction system, the sensitivity for gold ion in particle signals increased by another up to about 33 percent compared to the desolvation system without CCT. This could be because the collisional ion focusing is more effective under the high sample transport condition of the high-sensitivity sample introduction system. In conclusion, the high-sensitivity sample introduction system increased the number of detected particles by about 5 times while drying the sample and applying CCT enhanced the sensitivity of analyte ions in the ion optics of sp-ICP-TOFMS by about a third for gold particle signals and a factor of 2 for multi-elemental dissolved background signals. These enhancements will help us to analyze trace isotopes in ice core mineral dust particle analysis and to characterize the chemical composition of detected particles by sp-ICP-TOFMS.

How to cite: Lee, G., Erhardt, T., Hendriks, L., Tanner, M., Delmonte, B., and Fischer, H.: Improving single particle ICP-TOFMS using a desolvation sample introduction system and collision cell technology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3732, https://doi.org/10.5194/egusphere-egu23-3732, 2023.

Seismic networks often face logistical and financial challenges that require portability, longevity and interoperability with existing equipment.

Güralp have combined proven ocean bottom, borehole and digitiser technology to produce an analogue seismometer with intelligence that benefits networks of all sizes. The Güralp Certis is a broadband analogue instrument that incorporates specific aspects of its sister digital instrument (Certimus) while still remaining compatible with third-party digitisers.

Each Certis stores its own serial number, calibration and response parameters internally and will automatically communicate these to a connected Minimus digitiser. This allows seismometer-digitiser pairings to be changed without manual entry of new parameters. If using GDI-link streaming protocol with the Minimus, these metadata parameters are transmitted within (and therefore inseparable from) the datastream itself. Therefore, this small piece of intelligence in the analogue sensor removes the need for any manual re-entry of response parameters anywhere along the sensor-digitiser-client chain.

Certis enables users to install in locations with poor horizontal stability (e.g., glaciers, dynamic landslide scarps, water-saturated soils), without the need for cement bases or precise levelling, as the sensor can be deployed at any angle regardless of which model digitiser is connected. Due to its small size, low weight and ultra-low power consumption, Certis significantly reduces logistical efforts and makes short term temporary deployments far easier.

Certis addresses many challenges of traditional seismometer deployments, including cost, but provides a flexible and simple solution for seismic monitoring applications across all disciplines.

How to cite: Whealing, D., Lindsey, J., Watkiss, N., and Reis, W.: A Rugged, Portable and Intelligent Analogue Seismometer for Future and Pre-Existing Arrays – Güralp Certis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7349, https://doi.org/10.5194/egusphere-egu23-7349, 2023.

Synthetic Aperture Radar (SAR) is an active sensor that can be mounted onboard satellite or airborne platforms for observing of Earth’s surface in any weather condition and even during night [1]. In the last years, it has been shown that the interferometric SAR (InSAR) technique allows [2] generating high quality Digital Elevation Models (DEMs) [1] from spaceborne [3] and airborne [4–7] SAR data.

Airborne SAR systems, unlike satellite SAR ones, are particularly suitable for environmental monitoring in case of emergencies due to their capability to maintain very tight revisit times and to acquire data practically without orbital constraints. The contribution of this work fits very nicely within this context. Indeed, in this work, we show the results obtained from the data collected during the acquisition campaigns carried out with the AXIS [5] and MIPS [8] airborne X-band interferometric SAR systems over the Stromboli island (Italy). In particular, starting from multiple single-pass interferometric SAR surveys we present the differences of the generated DEMs with the aim of measuring the topographic changes induced by the eruptive activity over the whole island during the July 2019 – October 2022 time interval. The work is supported by an agreement between IREA-CNR and the Civil Protection Department of Italy.

References

1. Franceschetti, G.; Lanari, R. Synthetic aperture radar processing; 1999;

2. Moreira, A.; Prats-Iraola, P.; Younis, M.; Krieger, G.; Hajnsek, I.; Papathanassiou, K.P. A tutorial on synthetic aperture radar. IEEE Geosci. Remote Sens. Mag. 2013.

3. Rabus, B.; Eineder, M.; Roth, A.; Bamler, R. The shuttle radar topography mission - A new class of digital elevation models acquired by spaceborne radar. ISPRS J. Photogramm. Remote Sens. 2003.

4. Perna, S.; Esposito, C.; Amaral, T.; Berardino, P.; Jackson, G.; Moreira, J.; Pauciullo, A.; Junior, E.V.; Wimmer, C.; Lanari, R. The InSAeS4 airborne X-band interferometric SAR system: A first assessment on its imaging and topographic mapping capabilities. Remote Sens. 2016, 8.

5. Esposito, C.; Natale, A.; Palmese, G.; Berardino, P.; Lanari, R.; Perna, S. On the Capabilities of the Italian Airborne FMCW AXIS InSAR System. Remote Sens. 2020, 12.

6. Pinheiro, M.; Reigber, A.; Scheiber, R.; Prats-Iraola, P.; Moreira, A. Generation of highly accurate DEMs over flat areas by means of dual-frequency and dual-baseline airborne SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2018.

7. Wimmer, C.; Siegmund, R.; Schwäbisch, M.; Moreira, J. Generation of high precision DEMs of the Wadden Sea with airborne interferometric SAR. IEEE Trans. Geosci. Remote Sens. 2000.

8. Natale, A.; Berardino, P.; Esposito, C.; Palmese, G.; Lanari, R.; Perna, S. The New Italian Airborne Multiband Interferometric and Polarimetric SAR (MIPS) System: First Flight Test Results. Int. Geosci. Remote Sens. Symp. 2022, 2022-July, 4506–4509.

How to cite: Lanari, R., Esposito, C., Berardino, P., Natale, A., Palmese, G., and Perna, S.: Stromboli volcano monitoring with airborne SAR systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10047, https://doi.org/10.5194/egusphere-egu23-10047, 2023.

Direct analysis of micrometer-scale features embedded in solid samples of a wide chemical variety is an integral part of many fields in geo-, geobio-, and planetary sciences. Examples range from microscopic mineral inclusions in meteoritic material, (e.g., zircons, CAIs, chondrules, etc.) to biological inclusions in a variety of mineralogical host materials, e.g., (putative) fossils of microbial species. In many of these use-cases, the determination of the element and/or isotope composition, specifically those of minor or trace abundance, is of prime interest, meaning mass spectrometry is typically the preferred analysis technique.

As a result, a growing group of instruments has been (and are being) developed specifically with the purpose of element and/or isotope analysis of microscale features in solid hosts, each with its specific advantages and limitations. Perhaps the most well-known technique is (nano)SIMS, which boasts analysis spot sizes down to the nanometer level as well as ppm to ppb detection limits, but struggles with quantitativeness and capital and operating expenses. In the field of laser-based solid sampling mass spectrometric techniques, LA-ICP-MS has become a well-established technique, mainly due to its reproducibility and ease of operation. However, due to necessity to transport particles from the ablation plume to the ICP, this technique is inherently limited through fractionation effects and isobaric interferences with the plasma and carrier gas. Furthermore, sample dilution in the plasma and the subsequent loss of sample at the ICP-MS interface result in diminished limits of detection.

Another member of the laser-based solid sampling techniques is Laser Ablation Ionization Mass Spectrometry (LIMS), in which the ions present in the ablation plume are directly introduced into the mass spectrometer. This direct sampling of the ablation plume results in both a significant advantage over LA-ICP-MS (high sensitivity) and a challenge (mass resolution). The limited mass resolution of typical LIMS instruments often makes (quantitative) analysis challenging due to isobaric interferences, especially when applied to more complex materials. To solve this issue, the Laser Mass Spectrometer – Gran Turismo (LMS-GT) was developed at the University of Bern with the aim of achieving mass resolutions sufficient to resolve the most common isobaric interferences (M/ΔM = 10.000).

Over the last years, commissioning and continuous improvement of the instrument has been ongoing, which has led to a set of analytical performance characteristics which highlight the potential complementary value of LMS-GT. In this talk, we will discuss the latest technological developments¹, latest analytical performance metrics (mass resolution, mass accuracy, limits of detection, etc.), and element and isotope ratio accuracies^2,3. We will also discuss a case-study in which LMS-GT was used to study fossilized microbial inclusions in Gunflint chert⁴, highlighting both the potential strength and challenges for LMS-GT in a geo- and geobiosciences context.

1. Gruchola, S. et al., Int. J. Mass Spectrom. 474, 116803 (2022).

2. Wiesendanger, R. et al.,  J. Anal. At. Spectrom. 34, 2061–2073 (2019).

3. de Koning, C. P. et al.,  Int. J. Mass Spectrom. 470, 116662 (2021).

4. Lukmanov, R. A. et al., Front. Space Technol. 3, (2022).

How to cite: de Koning, C., Gruchola, S., Lukmanov, R., Keresztes Schmidt, P., Boeren, N., Riedo, A., Tulej, M., and Wurz, P.: Analytical performance assessment of LMS-GT, a newly-developed laboratory-scale Laser Ablation Ionization Mass Spectrometry instrument, in the context of geo- and planetary sciences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11295, https://doi.org/10.5194/egusphere-egu23-11295, 2023.

It is theoretically demonstrated  that, even with perfectly complete and perfectly accurate data, there is a fundamental ambiguity in the analysis of potential field data. The ambiguity may be easily illustrated by computing some of the various kinds of structures that can give rise to the same anomaly field. To solve the ambiguity and yield reasonable geophysical models we must therefore supply a priori information. In gravimetry, the ambiguity comes from the fact that only the excess mass is uniquely determined by the anomaly, neither the density nor the source volume. However, not only the excess mass can be uniquely estimated. Examples are the center of a uniformly dense (or magnetized) sphere or the top of a deeply extended homogeneously-dense cylinder. A priori information may consist of direct information (e.g., depth, shape) and/or of assuming that the source distribution has some specified properties (e.g., compactness, positivity). If one tries to classify the physical source-distributions in terms of their complexity, we may however use two different scaling laws, based on homogeneity and self-similarity, which allow modeling of the Earth in its complex heterogeneity. While monofractals or homogeneous functions are scaling functions, that is they do not have a specific scale of interest, multi-fractal and multi-homogeneous models need to be described within a multiscale dataset. Thus, specific techniques are needed to manage the information contained on the whole multiscale dataset. In particular,  any potential field  generated by a complex source may be modeled as  a multi-homogeneous field, which typically present a fractional and spatially varying homogeneity degree. For a source of irregular shape, it may be convenient to invert not the  field but a related quantity, the scaling function, which is a multiscale function having the advantage of not involving the density among the unknown parameters. For density or magnetic susceptibility tomographies, the degree of spatially variable homogeneity can be incorporated in the model weighting function, which, in this way, does not require prior assumptions because it is entirely deductible from the data. We discover that difficult quantities, such as the bottom of the sources, or multiple source systems are reasonably well estimated by abandoning the analysis at a single scale and unraveling the scale-related complexity of geophysical signals. The inherent self-consistency of these new multiscale tools is a significant step forward, especially in the analysis of areas where there is scarce other information about the sources.

How to cite: Fedi, M.: Solving the ambiguity in the potential field exploration of complex sources, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13597, https://doi.org/10.5194/egusphere-egu23-13597, 2023.

The detection of vessels is considered an attractive byproduct of satellite radar altimetry, because it may complement the conventional tracking systems with the possibility to build long-term global statistics of ship traffic based on relatively small and manageable datasets of freely available data. Satellite radar altimetry was initially conceived and applied to the observation of ocean topography, being later extended to the coastal zone and to the observation of inland water.

The potentiality of SAR altimetry for the detection of ships has already been demonstrated with Cryosat2, and today Sentinel-3 is the first operational mission offering global SAR coverage with a constellation of two satellites.

Thanks to the enhanced azimuth (along-track) resolution available in the synthetic aperture radar (SAR) mode, the radar altimeter on board the Sentinel-3 satellite could be beneficial to other applications than ocean topography. In particular, this work studies the performance of algorithms for the automatic detection of ship targets from SAR mode data. In addition, the pre-processing of altimeter data by reliable detection algorithms, filtering out signal outliers from the sea surface response, largely contributes to enhance geophysical products that are typical in ocean topography studies (e.g. mean sea level).  Thus, altimeter data of today could be regarded as an additional non-cooperative source for vessel traffic monitoring or to map global traffic patterns over long periods of time.

This work proposes a processing chain based on mathematical morphology filtering and robust statistics to estimate the structured background and detect target signatures from radargrams. The detection stage is followed by an additional binary morphological filtering phase that is useful to estimate target characteristics, such as the height. The study shows that robust statistics outperform non-robust ones, in terms of target signal to background ratio and of rejection of false alarms. The study finally provides a first attempt to validate the analysis comparing detected target contacts with automatic identification system (AIS) data. 

How to cite: Scozzari, A. and Grasso, R.: Radar altimetry for the detection of ship traffic: an improved byproduct of satellite radar altimetry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13618, https://doi.org/10.5194/egusphere-egu23-13618, 2023.

The SBUDNIC project was a collaboration between The National Research Council of Italy and Brown University’s School of Engineering. The project also got support from D-Orbit, AMSAT-Italy, La Sapienza-University of Rome and NASA Rhode Island Space Grant.

This scientific cooperation started in January 2021 and aimed to improve techniques and skills in Earth observation and its applications, with particular focus on teaching, research and knowledge transfer, as well as to promote open access to documentation, software and other information and resources for developing Earth observation capabilities.

The project was led by a team of students, professors and researchers and involved the construction of a 3U CubeSat according to an open and agile approach, using mostly commercial components commonly used on Earth, including an Arduino processor and AA Energizer batteries. The satellite was designed to allow the download of low resolution images in the amateur radio band.

SBUDNIC was launched on May 25, 2022 by a SpaceX Falcon 9 rocket and was released into orbit at an altitude of about 525 kilometers by the ION Satellite Carrier platform of the Space logistics company D-Orbit.

As a New Space experiment, SBUDNIC has provided useful insights on several organizational, technological and regulatory aspects of the implementation of a CubeSat mission and confirmed the increasingly rapid and affordable accessibility of Space to the scientific and academic community.

We hope that the SBUDNIC project may be able to inspire some of the future engineers in universities, research and industry, in their effort to advance Space exploration, Earth observation techniques and innovative satellite technologies, tools that may prove of fundamental importance in addressing global challenges.

How to cite: Bigagli, L. and Fleeter, R.: SBUDNIC: lessons learned in implementing a CubeSat mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16127, https://doi.org/10.5194/egusphere-egu23-16127, 2023.

The assessment of fossils in sediments and sedimentary rocks often involves the destruction of the sedimentary matrix and even of parts of the fossil assemblage (e.g. via removing and/or dissolution). Therefore, the destruction-free assessment of fossils in sediments (e.g. sediment cores) and sedimentary rocks is of great interest to the geoscience community. In addition, the three-dimensional examination of fossils becomes more and more important to evaluate morphological features and improve morphometrical analyses.

The "Holsteiner Gestein" is a sandstone and glacial erratic which is frequently found at certain outcrops in northern Germany. While the material was transported during the Pleistocene, the original deposition of this sediment took place during the Miocene, perhaps also the upper Oliocene (Schallreuter et al. 1984).

Its fossil content and paleoecology has not been investigated in detail, and since the 1980s, scientific publications on this sediment are rare. This type of material, if analysed at all, is generally subjected to destructive methods to isolate fossils such as marine snails or foraminifers (marine protists), which both comprise taxa with calcareous shells. These fossils support the reconstruction of the paleo-ecosystem and depositional environment.

In the framework of a case study, a piece of glacial erratics – “Holsteiner Gestein” was scanned with a Comet Yxlon FF35 CT system employing the directional beam tube: First, an overview scan of the whole sample (210 kV, 160 µA, 1.0 mm Cu filter, 50.00 µm iso-voxel size) was used to identify a region with high fossil count and potentially interesting fossils. The region of interest was then scanned (210 kV, 160 µA, 1.0 mm Cu filter 7.23 µm iso-voxel size) with higher resolution using a scan trajectory with a flexible rotation center that allowed for maximal resolution by adjusting the position of the sample such that it was located as close as possible to the x-ray tube but a collision was prevented. In addition, a laminography scan (180 kV, 70 µA, 0. 5 mm Cu filter, 5.02x5.02x9.58 µm voxel size) was performed to achieve the maximally possible sharpness and resolution in cross-sectional images. Data were visualized and analysed in the software Amira (version 6.0.1).

Our approach did not only enable us to three-dimensionally assess relatively big snails shells, but also foraminifera of less than 1 mm in size. The scans additionally allow quantifying the number of microfossils inside a certain part of the sample.

The foraminiferal taxa comprise agglutinating foraminifers which closely resemble the genus Entzia. These imply a former salt marsh environment.

This work is distributed under the Creative Commons Attribution 4.0 License. This licence does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 4.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

References:

Schallreuter, R., Vinx, R., Lierl, H.J. (1984): Geschiebe in Südholstein. In Degens et al. (eds.): Exkursionsführer Erdgeschichte des Nordsee- und Ostseeraumes, Geologisch-Paläontologisches Institut der Universität Hamburg.

How to cite: Kotthoff, U., Engelkes, K., Tshibalanganda, M., Beerlink, A., Hesemann, M., Milker, Y., and Schmiedl, G.: Case study for the application of micro-computed tomography on a Miocene sample of Holstein Erratics to identify and asses included molluscs and foraminifera, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16665, https://doi.org/10.5194/egusphere-egu23-16665, 2023.

Remote monitoring as well as remote waveform data retrieval is an enabling capability for portable stations used in seismic hazards studies. Remote monitoring provides intra-deployment visibility of system performance to allow prompt detection, and subsequent resolution, of faults which may otherwise go undetected for the duration of the deployment and jeopardize a seismic campaign's successful outcome.  Waveform data retrieval allows intra-deployment quality control (QC) and can enable faster science. These benefits must be considered against the associated power consumption and telemetry bandwidth costs. These tradeoffs may drive a system implementation that limits remote retrieval to low resolution data or, in other use cases, restricts the retrieval of full resolution waveform data to specific periods of interest only.  In the case of deployments in harsh environments, such as polar or ocean bottom environments where field visits are cost prohibitive or may not be possible, even high cost remote retrieval of a complete data set may be preferable if it offers a savings and / or a reduction in operational risk in comparison to a station visit.

As part of this session, we discuss how advancement in low power portable geoscience instrumentation, combined with low power communication technology, deliver these new capabilities with flexible implementation balancing function and operational cost.

How to cite: Hamilton, V., Pigeon, S., Parker, T., Perlin, M., and Laporte, M.: Cost Efficient Station Monitoring and Remote Data Retrieval for Portable Seismic Stations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16722, https://doi.org/10.5194/egusphere-egu23-16722, 2023.

The deposition of heavy metals on water bodies and soil has adverse consequences on
human health. The elevated Coal-based energy production and increased industrial emissions
have also prompted us to study about heavy metals reactive nitrogen species in the
atmosphere. In the present work, the samples of rain water were collected from a residential
site in south-west Delhi. The samples were analyzed for selected heavy metals by using ICP-
OES. The heavy metals analysis involved voltammetry method using 797 VA Computrace
(Metrohm, Switzerland) instrument. The analysis of Total Nitrogen (TN) and dissolved
organic carbon (DOC) was carried out by using chemiluminescence based TN/TOC analyzer
(Shimadzu model TOC-LCPH E200 ROHS). The mean values of Cu, Mn, Zn, Al, As and Hg
were calculated as 34.5 mg/l, 19.5 mg/l, 52.7 mg/l, 392.3 mg/l, 9.8 mg/l and 1.6 mg/l
respectively. The mean values for TN and DOC were 12.7mg/l and 2.8 mg/l respectively. The
detailed results will be discussed in the EGU General Assembly Meeting.

Keywords: Total Nitrogen, wet deposition, ICP-OES, voltammetry, agricultural area.

How to cite: sunaina, S.: Wet deposition of heavy metals, reactive nitrogen species and dissolved organic carbonat a residential site in Delhi region, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-345, https://doi.org/10.5194/egusphere-egu23-345, 2023.

Birmingham is a city within the West Midlands region of the United Kingdom. In June 2021, coinciding with the introduction of the Clean Air Zone by Birmingham City Council (BCC), multiple low-cost IoT sensor networks for air pollution were deployed across the city by both the University of Birmingham and BCC. Low-cost sensor networks are growing in popularity due to their lower costs compared to regulatory instruments (£10’s-£1000’s per unit compared to £10,000+ per unit) and the reduced need for specialised staff allow for deployments at greater spatial scales (1-3).  Although such low-cost sensing is often associated with uncertainty, the measurement of PM_2.5 optical particle counters have been generally shown to perform well, giving indicative insight into concentrations following calibrations and corrections for external influence such as humidity (4-7). 

One common problem with sensor networks is they tend to be isolated and unopen deployments, deployed and maintained by an interested party with the focus of their own monitoring goal. To tackle this, Birmingham Urban Observatory was an online platform created and used by researchers at the University of Birmingham to host and share open access meteorological and air pollution data from low-cost sensor deployments. Whilst hosting and displaying data from two of their own deployments of air quality sensors (Zephyrs by Earthsense and AltasensePM: an in-house designed PM sensor), the platform also pulled data from the DEFRA AURN sites and collaborated with local government to pull data from their own low-cost sensor network. The result was a real-time view of environmental data produced from a series of nested arrays of sensors.

This poster presents findings from this combined low-cost network, considering the successes and pitfalls of the low-cost monitoring network alongside insight into regional and local PM_2.5 concentrations. Colocations against reference instruments within the network demonstrate good performance of the low-cost sensors after calibration and data validation but the project experienced challenges in deploying the network and sensor reliability. Low-cost sensor data generally gives novel insight into spatial analysis of PM_2.5 across the city and this is presented alongside other experiences of deploying and using sensor networks for air quality.

1 Lewis et al., (2016) https://doi.org/10.1039/C5FD00201J

2 Chong and Kumar. (2003) doi: 10.1109/JPROC.2003.814918

3 Snyder et al., (2013) https://doi.org/10.1021/es4022602

4 Magi et al., (2020) https://doi.org/10.1080/02786826.2019.1619915

5 Crilley et al., (2018) https://doi.org/10.5194/amt-11-709-2018

6 Cowell et al., (2022) https://doi.org/10.3389/fenvs.2021.798485

7 Cowell et al., (2022) https://doi.org/10.1039/D2EA00124A

How to cite: Cowell, N., Baldo, C., Bloss, W., and Chapman, L.: What can we learn from nested IoT low-cost sensor networks for air quality?  A case study of PM2.5 in Birmingham UK., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1075, https://doi.org/10.5194/egusphere-egu23-1075, 2023.

Ammonia (NH₃) is the major alkaline gas in the atmosphere and the third most abundant N-containing species, after N₂ and N₂O. It plays an important role in N deposition processes, responsible of several damages on ecosystems, and it is also a precursor of fine particulate matter, known to cause numerous impacts on human health. Despite this, not many countries have implemented long-term monitoring of NH₃ in their air quality programs due to the lack of consensus on limit values for ambient levels and a reference method of measuring this gas. In the climate change context, governments and health organizations are increasingly concerned about NH₃ and its effects. As a proof, the revision of the EU air quality directives proposes the inclusion of NH₃ as a mandatory pollutant for several urban and rural supersites for all member states.

Currently, there are only 12 long term programs worldwide dedicated specifically to measure NH₃ or including gas-phase measurements of NH₃. The longest NH₃ time series come from UK and Africa, where measurements start in mid-1990. The rest of locations have started after 2000 and they have lower temporal coverage, between 5 and 22 years. The objectives pursued by these networks are to follow long term spatio-temporal trends, assess the N deposition on sensitive ecosystems, validate emission and/or chemistry transport models and help to understand the effectiveness of air pollution control and mitigation policies. Most of these networks operate using a combination of low-cost samplers with a high spatial density with few collocated sites with high time resolution instrumentation to help calibrate passive samplers and to better monitor the fine temporal variability of NH₃. This combined approach has proven to be successful for most of the proposed objectives.

However, there are several differences that may difficult harmonizing the information at both the technical and scientific level. At the technical level these include type and number of passive samplers per site, calibration protocol, data control and quality analysis, exposure duration and type of high time resolution sampling method. On the scientific level, increased difficulty understanding the operative parameters and scientific results may come from language barriers (non-English reports), availability of the data (whether it is public or not), and gaps on the knowledge of NH₃ levels on a spatial scale due to differences in the implementation of monitoring strategies within the same country.

This work aims to review synthetically the world current long-term NH₃ networks and provide some insight and recommendations for other countries and supranational programs aiming to establish long term monitoring networks of NH₃, based on cost-effective, technical, and operational criteria.

How to cite: Bourin, A., Espina-Martin, P., Font, A., Crunaire, S., and Sauvage, S.: Atmospheric ammonia in-situ long-term monitoring: review worldwide strategies and recommendations for implementation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2847, https://doi.org/10.5194/egusphere-egu23-2847, 2023.

Greenhouse gases (GHGs) play a crucial role regarding global warming. Therefore, precise and accurate observations of anthropogenic GHGs, especially carbon dioxide and methane, are of utmost importance for the estimation of their emission strengths, flux changes and long-term monitoring. Satellite observations are well suited for this task as they provide global coverage. However, like all measurements these need to be validated.

The COllaborative Carbon Column Observing Network (COCCON) performs ground-based observations to retrieve column-averaged dry air mole fractions of GHGs (XGAS) with reference precision. The instrument used by the network is the EM27/SUN, a solar-viewing Fourier Transform infrared (FTIR) spectrometer. COCCON data are of high accuracy as COCCON uses species dependent airmass-independent and airmass-dependent adjustments for tying the XGAS products to TCCCON (Total Carbon Column Observing Network) and thereby to the World Meteorological Organization (WMO) reference scale. Moreover, instrument specific characteristics are measured for each COCCON spectrometer, and taken into account in the data analysis.

Here we first introduce the COCCON network in general and summarize its capabilities for various challenges including satellite and model validation, long-term observation of GHGs, and local and regional GHG source emission strength estimations. By example of the COCCON Tsukuba station we highlight in detail its usefulness for the above-mentioned applications.

How to cite: Frey, M. M., Morino, I., Ohyama, H., Hori, A., Dubravica, D., and Hase, F.: The COllaborative Carbon Column Observing Network COCCON: Showcasing GHG observations at the COCCON Tsukuba site, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2984, https://doi.org/10.5194/egusphere-egu23-2984, 2023.

Emissions of nitrogen compounds, including nitrogen dioxide (NO₂) and ammonia (NH₃), have significant impacts on air quality and the environment. To effectively monitor the spatial and temporal variability of these emissions and the efficacy of emission mitigation measures, OnePlanet Research Center is developing a low-cost sensor system to monitor outdoor NO₂ and NH₃concentrations. This sensor system is designed to be deployable in fine-grained networks to accurately capture the dispersion from an emitting source. The deployment of multitudes of such sensor systems will result in large volumes of data. For this purpose, we developed a data infrastructure using the OGC SensorThings API and TimescaleDB, a time-series database extending PostgreSQL. This infrastructure allows for the efficient storage, management, and analysis of large volumes of spatiotemporal data from various sources, such as air quality monitoring networks, meteorological data, and agricultural practices. We demonstrate the potential of this infrastructure by using it in citizen science project COMPAIR, combining data from various sensors to gain insights on the air quality impact of urban circulation policies. The resulting data platform will facilitate the development of decision support tools and the implementation of targeted emission reduction strategies.

How to cite: Bertocci, D., Celikkol, B., Zhuang, S., and Fabius, J.: Data infrastructure for nitrogen compound emissions monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7462, https://doi.org/10.5194/egusphere-egu23-7462, 2023.

Even in the presence of more reliable air quality tools, low-cost sensors have the benefit of recording data on highly localized spatial and temporal scales, allowing for multiple measurements within a single satellite pixel and on pixel boundaries. However, they are less accurate than their regulatory-grade counterparts, requiring regular co-locations with accepted instruments to ensure their validity. Thus, the addition of low-cost sensors to a field campaign – where reference-grade air quality instruments are abundant – not only provides ample opportunities for low-cost sensor co-location and calibration, but also allows the low-cost instruments to be used for sub-pixel validation, covering more surface area than the regulatory instruments alone with a network of sensors. During the summer of 2014, our low-cost sensor network was deployed during the Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ) campaign conducted to sample the composition of air at and above ground level in northeastern Colorado, USA. The low-cost sensor platform included a suite of gas-phase sensors, notably NO₂ and two generalized volatile organic compound (VOC) sensors, which were leveraged together to quantify speciated hydrocarbons such as formaldehyde. These key pollutants were chosen for their impacts on human health and climate change, as well as their inclusion on the TEMPO satellite launching this year. Airborne campaign measurements included slant column optical observations of formaldehyde (HCHO), nitrogen dioxide (NO₂), and ozone (O₃). Myriad additional in-situ instruments described chemical composition up to approximately 5 km above surface level. Ground-based instrumentation included both stationary and mobile regulatory-grade instruments, which were used for sensor calibration. Machine learning techniques such as artificial neural networks (ANNs) were used to match the low-cost signals to that of the reference-grade instruments. Here, we compare calibrated low-cost sensor data collected at ground level in a variety of locations along Colorado’s Front Range to various data sources from the FRAPPÉ campaign to better understand how well airborne and regulatory ground-based measurements can be extrapolated to other locations. Further, as the slant column measurements act as satellite simulators, we explore how low-cost instruments can be used for satellite validation purposes. Comparisons among these different data types also have important implications in data fusion.

How to cite: Okorn, K., Iraci, L., and Hannigan, M.: Comparing Low-Cost Sensors with Ground-Based and Airborne In-Situ and Column Observations of NO2 and HCHO during the FRAPPE Field Campaign in Colorado, USA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7839, https://doi.org/10.5194/egusphere-egu23-7839, 2023.

The Atacama Desert in the Chilean Andes region is one of the dryest areas in the world. Due to its unique location with stable subtropical meteorological conditions and high mountains, it is an ideal site for the astronomical telescope facilities of the European Southern Observatory (ESO). The special meteorological conditions are continuously monitored at Cerro Paranal (the location of the Very Large Telescope) by measuring various parameters like temperature, pressure, humidity, precipitable water vapour (PWV), wind speed and direction, and sky radiance and bolometric sky temperature, respectively, the latter being crucial for astronomical observations in the thermal infrared regime. ESO operates several site monitoring systems for that purpose, e.g. the ESO MeteoMonitor, the Differential Image Motion Monitor (DIMM) and a Low Humidity And Temperature PROfiler (L-HATPRO) microwave radiometer providing detailed water vapour and temperate profiles up to a height of 12km in various directions. 

We have assembled all available data for a period of 4.5 years (2015-07-01 through 2019-12-31) and created a unique data set from it. This period also covers the strong El Niño event at the end of 2015. In this poster we present statistical results on the overall conditions and trends, and compare our measurements of the nocturnal sky brightness with an empirical model as function of the ambient temperature, PWV and zenith distance.

How to cite: Kausch, W., Kimeswenger, S., Noll, S., and Holzlöhner, R.: Ambient conditions and infrared sky brightness in the Chilean Atacama Desert, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8631, https://doi.org/10.5194/egusphere-egu23-8631, 2023.

Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes,  but obtaining high quality data can be costly, with each site in a monitoring network requiring instrumentation and skilled operator time. It is therefore important to ensure that each monitor in the network is providing unique data to maximize the value of the entire network.  Differences in measurement approaches for the same chemical between monitoring stations may also result in discontinuities in the network data.  Both of these factors suggest the need for objective, machine-learning methodologies for monitoring network data analysis.   

Air quality models are another valuable tool to augment monitoring networks.  The models simulate air quality over a large region where monitoring may be sparse. The gridded output from air-quality models thus contain inherent information on the similarity of sources, chemical oxidation pathways and removal processes for chemicals of interest, provided appropriate tools are available to identify these similarities on a gridded basis.  The output from these models can be immense, again requiring the use of special, highly optimized tools for post-processing analysis.

Spatiotemporal clustering is a family of techniques that have seen widespread use in air quality, whereby time-series taken at different locations are grouped based on the level of similarity between time-series data within the dataset.   Hierarchical clustering is one such algorithm, which has the advantage of not requiring an a priori assumption about how many clusters there might be (unlike K-means).  However, traditional approaches for hierarchical clustering become computationally expensive as the number of time-series increases in size, resulting in prohibitive computational costs  when the total number of time-series to be compared rises above 30,000, even on a supercomputer.  Similarly, the comparison and clustering of large numbers of discrete data (such as multiple mass spectrometer data sampled at high time resolution from a moving laboratory platform) becomes computationally prohibitive using conventional methods. 

In this study we present a high-performance hierarchical clustering algorithm which is able to run in parallel over many nodes on massively parallel computer systems, thus allowing for efficient clustering for very large monitoring network and model output datasets.   The new high-performance program is able to cluster 290,000 annual time series (from either monitoring network data or gridded model output) in 13 hours on 800 nodes. We present here some example results showing how the algorithm can be used to analyse very large datasets, providing new insights into “airsheds” depicting regions of similar chemical origin and history, different spatial regimes for nitrogen, sulphur, and base cation deposition, .  These analyses show how different processes control each species at different potential monitoring site locations, via cluster-generated airshed maps for each species. The efficiency and flexibility of the algorithm allows for extremely large datasets to be analysed in hours of wall-clock time instead of weeks or months. The new algorithm is being used as the numerical engine for a new tool for the analysis of EU monitoring network data. 

How to cite: Lee, C., Makar, P., and Soares, J.: Spatio-temporal clustering on a high-performance computing platform for high-resolution monitoring network analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8841, https://doi.org/10.5194/egusphere-egu23-8841, 2023.

22 cost-efficient (aka ‘low-cost’) commercially available particulate matter (PM) measurement devices were installed in a diverse urban area in Leipzig, Germany. The instruments measure mostly PM2.5, some additionally PM10, and are equipped with methods for quality assurance such as conditioning to a defined temperature and regular internal calibration. In order to investigate the spread between the instruments and to enable a pre-campaign calibration, all instruments were setup in the laboratory and the outside air and compared against the same reference measurements.

Since July 2022, the measurement network was installed. It covers roughly 2x2 km² and holds different urban features like residential and commercial buildings, important main roads, city parks, and small open building gaps. Within the network there is an official air quality monitoring station located directly at a main road. In addition, at two further official monitoring stations as well as at observation stations of the Leibniz Institute for Tropospheric Research instruments were installed to study the long-term performance, dependence on meteorological conditions and comparison to reference measurements. The measurements will take place until end of 2023.

The cost-efficient instruments perform generally quite well after the calibration. In particularly for higher PM loads > 10 µg m^-3 the agreement against references is mostly satisfying. However, under very high relative humidity and cold temperatures, some instruments lacked to condition the air sufficiently. Despite these difficulties, the chosen instruments have the potential for application in monitoring of air quality limit values, i.e. the answer the question how often are certain limits exceeded.

Furthermore, differences between different local features in the observation area could be observed in e.g., the diurnal cycle but also peak and mean concentrations.

This work is co-financed with tax funds on the basis of the budget passed by the Saxon State Parliament (funding number 100582357).

How to cite: Schrödner, R., Alas, H., and Voigtländer, J.: Application of cost-efficient particulate matter measurement devices in an urban network and comparison to state-of-the-art air quality monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9356, https://doi.org/10.5194/egusphere-egu23-9356, 2023.

The Global Environmental Measurement and Monitoring (GEMM) Initiative is an international project of Optica and the American Geophysical Union seeking to provide precise and usable environmental data for local impact. The Initiative brings together science, technology, and policy stakeholders to address critical environmental challenges and provide solutions to inform policy decisions on greenhouse gases (GHGs) and air and water quality. GEMM Centers are currently established in Scotland, Canada, New Zealand, and the United States. These Centers represent partnerships with leading institutions that are actively working toward developing or deploying new measurement technology and improved climate models. Additional Centers are under development in India and Australia with plans to expand to Asia and Africa.

In addition to establishing monitoring centers worldwide, GEMM actively engages with other sectors (including industry, standards organizations, and regional or national governments) to support the incorporation or adoption of these evidence-based approaches into decision making processes. For example, Glasgow, Scotland is piloting the GEMM Urban Air Project, deploying a low-cost, real-time, ground-based network of devices that continuously monitors GHGs and air pollutants at a neighborhood scale. The sensor network in Glasgow is increasing the precision of local models that can provide the city with information to assess current policies and support future action. Here we will share the progress and outputs of the GEMM Initiative to date and highlight paths forward to grow the network.

How to cite: Klingenberg, D., Bailey, D. M., Lang, D., and Shimamoto, M.: The Global Environmental Measurement and Monitoring Initiative – An International Network for Local Impact, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9537, https://doi.org/10.5194/egusphere-egu23-9537, 2023.

Since the discovery of the chlorofluorocarbons (CFCs) implication in stratospheric ozone destruction, the Montreal Protocol (1987) has aimed at controlling the production of CFCs and other ozone depleting substances (ODS) in order to protect and then recover the ozone layer. Consequently, temporary substitutes for CFCs have been developed and produced by the industry. First substitute molecules were hydrochlorofluorocarbons (HCFCs), which have smaller ozone depletion potentials (ODP) than CFCs since their atmospheric lifetimes are shorter. Nevertheless, HCFCs still contain chlorine atoms and hence, also deplete the stratospheric ozone, requiring them to be banned in turn. Thus, chlorine-free molecules, i.e. hydrofluorocarbons (HFCs) such as CH₂FCF₃ (HFC-134a) were introduced to replace both CFCs and HCFCs. Even if HFCs do not contribute to ozone depletion, they are very powerful greenhouse gases since they have great global warming potentials (GWPs). Consequently, the Kigali amendment (2016) to the Montreal Protocol aimed for their phase-out.

The atmospheric concentrations of CFCs have decreased in response to the phase-out and ban of their production by the Montreal Protocol and its subsequent amendments, while the HCFCs burden is now leveling off. In contrast, the atmospheric concentrations of HFCs have increased notably in the last two decades.

We present the first retrievals of HFC-134a from Fourier Transform Infra-Red (FTIR) solar spectra obtained from a remote site of the Network for the Detection of Atmospheric Composition Change (NDACC.org): the Jungfraujoch station (Swiss Alps). We discuss of the applicability of our retrieval strategy to other NDACC sites, for future quasi global monitoring from ground-based observations. We further perform first comparisons with other datasets as ACE-FTS satellite observations.

How to cite: Pardo Cantos, I. and Mahieu, E.: First HFC-134a retrievals and analysis of long-term trends from FTIR solar spectra above NDACC network stations: the Jungfraujoch case, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11033, https://doi.org/10.5194/egusphere-egu23-11033, 2023.

Monitoring networks, able to effectively provide high-frequency geochemical data for characterizing the geochemical behavior of the main greenhouse gases (i.e., CO₂ and CH₄) and pollutants (e.g., heavy metals) are crucial tools for the assessment of air quality and its role in climate changes. However, the provision of measurement stations dedicated to monitor gas species and particulate in polluted areas is complicated by the high cost of their set-up and maintenance. In the last decade, traditional instruments have tentatively been coupled with low-cost sensors for improving spatial coverage and temporal resolution of air quality surveys. The main concerns of this new approach regard the in-field accuracy of the low-cost sensors, being significantly dependent on: (i) cross-sensitivities to other atmospheric pollutants, (ii) environmental parameters (e.g., relative humidity and temperature), and (iii) detector signal degradation over time.

This study presents the results of a geochemical survey carried out in the Greve River Basin (Chianti territory, Central Italy) from May to September 2022 by adopting two measuring strategies: (i) deployment of a mobile station, along predefined transepts within the Greve valley, equipped with a Picarro G2201-i analyzer to measure CO₂ and CH₄ concentrations and δ¹³C-CO₂ and δ¹³C-CH₄ values (‰ vs. V-PDB) by Wavelength-Scanned Cavity Ring-Down Spectroscopy (WS-CRDS); (ii) continuous monitoring, at five fixed sites positioned at different altitudes, of CO₂ and CH₄ concentrations through prototyped low-cost stations, coupled with atmospheric deposition and rain samplers to collect particulate samples for chemical lab analysis. The low-cost monitoring stations housed (i) a non-dispersive infrared (NDIR) sensor for CO₂ concentrations, (ii) a solid-state metal oxide sensor (MOS) for CH₄ concentrations, (iii) a laser light scattering sensor (LSPs) for PM_2.5 and PM₁₀ concentrations, and (iv) a sensor for temperature and relative humidity in the air. The CO₂ and CH₄ sensors have been calibrated in-field based on parallel measurements with the Picarro G2201-i and elaborating the calibration data with the Random Forest machine learning-based algorithm.

The measurements carried out along the transepts showed that the downstream areas next to the metropolitan city of Florence were affected by the highest concentrations of CO₂ and CH₄, marked by isotopic signatures revealing a clear anthropogenic origin, mainly ascribed to vehicular traffic. The distribution of these carbon species reflected the evolution of the atmospheric boundary layer, displaying higher concentrations during the early morning, when gas accumulation occurred due to stable atmospheric conditions, and lower concentrations during daytime when the heating of the surface favored the dilution of air pollutants due to the establishment of convective turbulence. These observations were confirmed by the network of low-cost stations, which allowed to simultaneously monitor the distribution of the atmospheric pollutants at different altitudes in the valley. The distribution of particulate was consistent with that of the gaseous species, and the main sources were clearly distinguished based on the chemical composition of the atmospheric deposition in the collection sites. The promising results from the present study could result in an affordable approach to effectively improve air quality monitoring strategies and support data-driven policy actions to reduce carbon emissions.

How to cite: Biagi, R., Ferrari, M., Tassi, F., and Venturi, S.: Multi-instrumental approach for air quality monitoring: characterization and distribution of greenhouse gases and atmospheric metal deposition in the Greve River Basin (Chianti territory, Central Italy)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11385, https://doi.org/10.5194/egusphere-egu23-11385, 2023.

As climate change is amplified in the Arctic, it is crucial to have temperature records of high temporal resolution and quality in this area. This will help improve understanding of the involved physical mechanisms, assessment of the past changes and improve predictions for the future temperature development in the Arctic. In this study temperature measurements from the DMI Greenland station network spanning 1784-present day are corrected, gap-filled and homogenized on a daily level. Currently homogenized data is only available on a monthly level, and the more recent data has not been homogenized. The data is currently used for purposes like assessment and predictions of the surface mass balance of the Greenland Ice Sheet, temperature/climate reanalyses, validation of proxy data, etc.  

This study presents a method for improving the calculation of daily average temperatures, from the current practice of averaging the available measurements without considering what time of day they are from and how the measurements are distributed. The method is based on a moving average taking into consideration time of day, time of year and latitude/longitude of the station in question. An estimate of the related uncertainty is also calculated. Following the generation of daily average temperatures, different gap filling methods are tested. The different algorithms tested and compared are: simple gap filling by linear interpolation with other stations, single station temporal linear interpolation and MEM (Maximum Entropy Method). Finally, homogenization on daily level is performed. These steps will in turn also improve the monthly and annual average temperatures for the DMI Greenland station network. 

How to cite: Rapp, D., Møllesøe Vinther, B., L. Høyer, J., and Kaas, E.: Correction, gap filling and homogenization on daily level of the historical DMI station network temperature data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13997, https://doi.org/10.5194/egusphere-egu23-13997, 2023.

The Global Atmosphere Watch (GAW) Programme was established in 1989 in recognition of the need for improved scientific understanding of the increasing influence of human activities on atmospheric composition and subsequent societal impacts. It is implemented as an activity of the World Meteorological Organization, a specialized agency of the United Nations system, and is funded by the organization member countries.

As an international programme, GAW supports a broad spectrum of applications from atmospheric composition-related services to contribution to environmental policy. The examples of the later include provision of a comprehensive set of high quality and long-term globally harmonized observations and analysis of atmospheric composition for the United Nations Framework Convention on Climate Change (UNFCCC), the Montreal Protocol on Substances that Deplete the Ozone Layer and follow-up amendments, and the Convention on Long-Range Transboundary Air Pollution (CLRTAP).

The programme includes six focal areas: Greenhouse Gases, Ozone, Aerosols, Reactive Gases, Total Atmospheric Deposition and SolarUltraviolet Radiation.

The surface-based observational network of the programme includes Global (31 stations) and Regional (about 400 stations) stations where observations of various GAW parameters occur. These stations are complemented by regular ship cruises and various contributing networks. All observations are linked to common reference standards and the observational data are made available at seven designated World Data Centres (WDC).

Surface-based observations are complemented by airborne and space-based observations that help to characterize the upper troposphere and lower stratosphere, with regards to ozone, solar radiation, aerosols, and certain trace gases.

Requirements to become a GAW station are detailed in the GAW Implementation Plan 2016-2023 (WMO, 2017). A new IP is in preparation, the four strategic objectives will be presented.

• The GAW Quality Management comprises: Data Quality Objectives, Measurement Guidelines, Standard Operating Procedures and Data Quality Indicators. Throughout the programme the common quality assurance principles apply, that include requirements for the long-term sustainability of the observations, use of one network standard for each variable and implementation of the measurement practices that satisfy the set data quality objectives. GAW implements open data policy and requires observational data be made available in the dedicated data centers operated by WMO Member countries.

The programme relies on different types of central facilities: Central Calibration Laboratories, Quality Assurance/Science Activity Centres, World and Regional Calibration Centres, which are also directly supported and implemented by the individual Member countries for the global services.

Majority of the recommendations regarding measurement and quality assurance procedures are developed by the expert and advisory groups within the programme, often those rely on the expertise withing the contributing networks and collaborating organizations, like the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) or the Integrated Carbon Observation System (ICOS).

One of the GAW priorities is to expand and strengthen partnerships with contributing networks, through development of statements and strategies to articulate the mutual benefits for the collaborations and stream-line processes of data reporting and exchange of QA standards and metadata. This involves collaboration with national and regional environmental protection agencies and the development of harmonized metadata and data exchange and quality information.

How to cite: Moreno, S.: The WMO Global Atmosphere Watch Programme new implementation plan and strategic objectives, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14442, https://doi.org/10.5194/egusphere-egu23-14442, 2023.

With the increasing attempt to empower citizens and civil society in promoting virtuous behaviours and relevant climate actions, novel user-friendly and low-cost tools and sensors are nowadays being developed and distributed on the market. Most of these sensors are typically easy to install with a ready-to-use system, while measured data are automatically uploaded on a mobile application or a web dashboard which also guarantees secure and open access to measurements gathered by other users. However, the quality of the datum and the calibration of these sensors are often ensured against research-grade instrumentations only in the laboratory and rarely in real-world measurement. The discrepancies arising between these low-cost sensors and research-grade instrumentations are such that the first might be impossible to use if a validation (and re-calibration if needed) under environmental conditions is not performed. Here we propose a validation procedure applied to the MeteoTracker, a recently developed portable sensor to monitor atmospheric quantities on the move. The ultimate scope is to develop and implement a general procedure to test and validate the quality of the MeteoTracker data to compile user guidelines tailored for on-the-move sensors. The result will evaluate the feasibility of MeteoTracker (and potentially other on-the-move sensors) to integrate the existing monitoring networks on the territory, improve the atmospheric data local coverage and support the informed decision by the authorities. The procedure will include multi-sensor testing of all the sensor functionalities, validation of all data simultaneously acquired by several sensors under similar conditions, methods and applications of comparisons with research-grade instruments. The first usage of the MeteoTracker will be also presented for different geographical contexts where the sensors will be used for citizen science activities and develop a monitoring network of selected Essential Variables within the HORIZON-EU project I-CHANGE (Individual Change of HAbits Needed for Green European transition).

How to cite: Barbano, F., Brattich, E., Cintolesi, C., Iurato, J., Mazzarella, V., Milelli, M., Nizamani, A. G., Sarfraz, M., Parodi, A., and Di Sabatino, S.: Developing and testing a validation procedure to successfully use on-the-move sensors in urban environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14459, https://doi.org/10.5194/egusphere-egu23-14459, 2023.

Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes. In a European legislative context, the data collected constitutes the basis for reporting air quality status and exceedances under the Ambient Air Quality Directives (AAQD) following specific requirements. Consequently, the network's representativity and ability to accurately assess the air pollution situation in European countries become a key issue. The combined use of models and measurements is currently understood as the most robust way to map the status of air pollution in an area, allowing it to quantify both the spatial and temporal distribution of pollution. This spatial-temporal information can be used to evaluate the representativeness of the monitoring network and support air quality monitoring design using hierarchical clustering techniques.

The hierarchical clustering methodology applied in this context can be used as a screening tool to analyse the level of similarity or dissimilarity of the air concentration data (time-series) within a monitoring network. Hierarchical clustering assumes that the data contains a level of (dis)similarity and groups the station records based on the characteristics of the actual data. The advantage of this type of clustering is that it does not require an a priori assumption about how many clusters there might be, but it can become computationally expensive as the number of time-series increases in size. Three dissimilarity metrics are used to establish the level of similarity (or dissimilarity) of the different air quality measurements across the monitoring network: (1) 1-R, where R is the Pearson linear correlation coefficient, (2) the Euclidean distance (EuD), and (3) multiplication of metric (1) and (2). The metric based on correlation assesses dissimilarities associated with the changes in the temporal variations in concentration. The metric based on the EuD assesses dissimilarities based on the magnitude of the concentration over the period analysed. The multiplication of these two metrics (1-R) x EuD assesses time variation and pollution levels correlations, and it has been demonstrated to be the most useful metric for monitoring network optimization.

This study presents the MoNET webtool developed based on the hierarchical clustering methodology. This webtool aims to provide an easy solution for member states to quality control the data reported as a tier-2 level check and evaluate the representativeness of the air quality network reporting under the AAQD. Some examples from the ongoing evaluation of the monitoring site classification carried out as a joint exercise under the Forum for Air Quality Modeling (FAIRMODE) and the National Air Quality Reference Laboratories Network (AQUILA) are available to show the usability of the tool. MoNet should be able to identify outliers, i.e., issues with the data or data series with very specific temporal-magnitude profiles, and to distinguish, e.g., pollution regimes within a country and if it resembles the air quality zones required by the AAQD and set by the member states; stations monitoring high-emitting sources; background regimes vs. a local source driving pollution regime in cities.

How to cite: Soares, J., Stoll, C., Vallejo, I., Lee, C., Makar, P., and Tarrasón, L.: Applications of an advanced clustering tool for EU AQ monitoring network data analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15087, https://doi.org/10.5194/egusphere-egu23-15087, 2023.

How to cite: Feigel, G., Plein, M., Zeeman, M., Briegel, F., and Christen, A.: A compact and customisable street-level sensor system for real-time weather monitoring and outreach in Freiburg, Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15609, https://doi.org/10.5194/egusphere-egu23-15609, 2023.

EMER-Met is the new meteorological forecasting system for the protection of the population in Switzerland. It provides the meteorological basis for coping with all types of emergencies, especially in case of nuclear and chemical accidents. EMER-Met consists of a dedicated upper air measurement network and a high-resolution numerical weather prediction model. The measurement network is composed of state-of-the-art remote sensing instruments to measure accurate wind and temperature profiles in the boundary layer. At three sites, a radar wind profiler PCL1300, a Doppler lidar Windcube-200s and a microwave radiometer Hatpro-G5 are installed. The data from the measurement network are assimilated into the operational 1-km ensemble numerical weather prediction (NWP) system. In the case of the microwave radiometers, we assimilate the brightness temperatures using an adapted version of the RTTOV observation operator. To ensure best impact on the NWP results, the data quality of the measurements is of high importance and is monitored closely on a daily and monthly basis against radiosondes and the NWP model itself. EMER-Met is operational since 2022 and to our best knowledge, it is the first time that the brightness temperatures measured by surface-based microwave radiometers are assimilated operationally. This presentation will focus on the upper air network performance and its impact on NWP. 

How to cite: Haefele, A., Hervo, M., Bättig, P., Leuenberger, D., Merker, C., Regenass, D., Kaufmann, P., and Arpagaus, M.: An integrated meteorological forecasting system for emergency response, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16779, https://doi.org/10.5194/egusphere-egu23-16779, 2023.

How to cite: Salameh, T., Tison, E., Stratigou, E., Dusanter, S., Gaudion, V., Jamar, M., Tillmann, R., Rohrer, F., Winter, B., Verea, T., Muñoz, A., Bachelier, F., Daele, V., and Grandjean, A.: ACTRIS - CiGas side-by-side interlaboratory comparison of new and classical techniques for formaldehyde measurement in the nmol/mol range, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17535, https://doi.org/10.5194/egusphere-egu23-17535, 2023.

Integrated application of geo-analysis models is critical for geo-process research. Due to the continuity of the real world, the geo-analysis model cannot be applied immediately over the entire space. To date, the method of regrading space as a sequence of computing units (i.e. grid) has been widely used in geographic study. However, the model's variances in division algorithms result in distinct grid data structures. At first, researchers must install and setup the various software to generate the structure-specific grid data required by the models. This method of localized processing is inconvenient and inefficient. Second, in order to integrate the models that use different structural grid data, researchers need to design a specific conversion method based on the integration scenario. Due to difference of researcher’s development habits, it is difficult to reuse the conversion method in another runtime environment. The open and cross-platform character of web services enables users to generate data without the assistance of software programs. It has the potential to revolutionize the present time-consuming process of grid generation and conversion, hence increasing efficiency.

Based on the standardized model encapsulation technology proposed by OpenGMS group, this paper presents a grid-service method tailored to the specific requirements of open geographic model integration applications, and the research work is carried out in the following three areas:

• The basic strategy of grid servitization. The heterogeneity of the grid generation method is a major factor that prevents it from being invoked via a unified way by web services. To reduce the heterogeneous of the grid generation method, this study proposes a standardized description method based on the Model Description Language (MDL).
• Method for constructing a grid data generating service. A unified representation approach for grid data is proposed in order to standardize the description of heterogeneous grid data; an encapsulation method for grid generating algorithms is proposed; and grid-service is realized by merging the main idea of grid servitization.
• Method for constructing a grid data conversion service . A box-type grid indexing approach is provided to facilitate the retrieval of grid cells with a large data volume; two conversion types, topologically similar and topologically inaccessible grid data conversion, are summarized, along with the related conversion procedures. On this foundation, a grid conversion engine is built using the grid service-based strategy as a theoretical guide and integrated with the grid conversion strategy.

Based on the grid service approach proposed in this paper, researchers can generate and converse grid data without tedious steps for downloading and installing programs. Thus, there are more time spend on geography problem solving, hence increasing efficiency.

How to cite: He, Y., Chen, M., Wen, Y., and Yue, S.: A web-based strategy to reuse grids in geographic modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-229, https://doi.org/10.5194/egusphere-egu23-229, 2023.

Scientific progress requires research outputs to be reproducible, or at least persistently traceable and analysable for defects through time. This can be facilitated by coupling analysis tools that are already familiar to scientists, with reproducibility controls designed around common containerisation technologies and formats to represent metadata and provenance. Moreover, modern interactive tools for data analysis and visualisation, such as computational notebooks and visual analytics systems, are built to expose their functionalities through the Web. This facilitates the development of integrated solutions that are designed to support computational research with reproducibility in mind, and that, once deployed onto a Cloud infrastructure, benefit from operations that are securely managed and perform reliably. Such systems should be able to easily accommodate specific requirements concerning, for instance, the deployment of particular scientific software and the collection of tailored, yet comprehensive, provenance recordings about data and processes. By decoupling and generalising the description of the environment where a particular research took place from the underlying implementation, which may become obsolete through time, we obtain better chances to recollect relevant information for the retrospective analysis of a scientific product in the long term, enhancing preservation and reproducibility of results.

In this contribution we illustrate how this is achievable via the adoption of microservice architectures combined with a provenance model that supports metadata standards and templating. We aim at empowering scientific data portals with Virtual Research Environments (VREs) and provenance services, that are programmatically controlled via high-level functions over the internet. Our system SWIRRL deals, on behalf of the clients, with the complexity of allocating the interactive services for the VREs on a Cloud platform. It runs staging and preprocessing workflows to gather and organise remote datasets, making them accessible collaboratively. We show how Provenance Services manage provenance records about the underlying environment, datasets and analysis workflows, and how these are exploited by researchers to control different reproducibility use cases. Our solutions are currently being implemented in more contexts in Earth Science. We will provide an overview on the progress of these efforts for the EPOS and IS-ENES research infrastructures, addressing solid earth and climate studies, respectively.

Finally, although the reproducibility challenges can be tackled to a large extent by modern technology, this will be further consolidated and made interoperable via the implementation and uptake of the FDOs. To achieve this goal, it is fundamental to establish the conversation between engineers, data-stewards and researchers early in the process of delivering a scientific product. This fosters the definition and implementation of suitable best practices to be adopted by a particular research group. Scientific tools and repositories built around modern FAIR enabling resources can be incrementally refined thanks to this mediated exchange. We will briefly introduce success stories towards this goal in the context of the IPCC Assessment Reports.

How to cite: Spinuso, A., van der Neut, I., Veldhuizen, M., Pagé, C., and Bailo, D.: Provenance powered microservices: a flexible and generic approach fostering reproducible research in Earth Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2744, https://doi.org/10.5194/egusphere-egu23-2744, 2023.

The AuScope 3D Geomodels Portal is a website designed to display a variety of geological models and associated datasets and information from all over the Australian continent. The models are imported from publicly available sources, namely Australian government geological surveys and research organisations. Often the models come in the form of downloadable file packages designed to be viewed in specialised geological software applications. They usually contain enough information to view the model’s structural geometry, datasets and a minimal amount of geological textual information. Seldom do they contain substantial metadata, often they were created before the term ‘FAIR’ was coined or the importance of metadata had dawned upon many of us. This creates challenges for data providers and aggregators trying to maintain a certain standard of FAIR compliance across all their offerings. How to improve the standard of FAIR compliance of metadata extracted from these models? How to integrate these models into existing metadata infrastructure? For the Geomodels portal, these concerns are alleviated within the automated model transformation software. This software transforms the source file packages into a format suitable for display in a modern WebGL compliant browser. Owing to the nature of the model source files only a very modest amount of metadata can be extracted. Hence other sources of metadata must be introduced. For example, often the dataset provider will publish a downloadable PDF report file or a description on a web page associated with the model. Automated textual analysis is used to extract more information from these sources. At the end of the transformation process, an ISO-compliant metadata record is created for importing into a geonetwork catalogue. The geonetwork catalogue record can be used for integration with other applications. For example, AuScope’s flagship portal, the AuScope Portal displays information, download links and a geospatial footprint of models on a map. The metadata can also be displayed in the Geomodels Portal.

How to cite: Fazio, V.: How AuScope 3D Geomodels Portal integrates relatively metadata poor geological models into its metadata infrastructure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3006, https://doi.org/10.5194/egusphere-egu23-3006, 2023.

The Community Surface Dynamics Modeling System (CSDMS) is a US-based science facility that supports computational modeling of diverse Earth and planetary surface processes, ranging from natural hazards and contemporary environmental change to geologic applications. The facility promotes open, interoperable, and shared software. Here we review approaches and lessons learned in advancing FAIR principles for geoscience modeling. To promote sharing and accessibility, CSDMS maintains an online Model Repository that catalogs over 400 shared codes, ranging from individual subroutines to large and sophisticated integrated models. Thanks to semi-automated search tools, the Repository now includes ~20,000 references to literature describing these models and their applications, giving prospective model users efficient access to information about how various codes have been developed and used. To promote interoperability, CSDMS develops and promotes the Basic Model Interface (BMI): a lightweight, language-agnostic API standard that provides control, query, and data-modification functions. BMI has been adopted by a number of academic, government, and quasi-private institutions for coupled-modeling applications. BMI specifications are provided for common scientific languages, including as Python, C, C++, Fortran, and Java. One challenge lies in broader awareness and adoption; for example, self-taught code developers may be unaware of the concept of an API standard, or may not perceive value in designing around such a standard. One way to address this challenge is to provide open-source programming libraries. One such library that CSDMS curates is Landlab Toolkit: a Python package that includes building blocks for model development (such as grid data structures and I/O functions) while also providing a framework for assembling integrated models from component parts. We find that Landlab can greatly speed model development, while giving user-developers an incentive to follow common patters and contribute new components to the library. However, libraries by themselves do not solve the reproducibility challenge. Rather than reinventing the wheel, the CSDMS facility has approached reproducibility by partnering with the Whole Tale initiative, which provides tools and protocols to create reproducible archives of computational research. Finally, we have found that a central challenge to FAIR modeling lies in the level of community knowledge. FAIR is a two-way street that depends in part on the technical skills of the user. Are they fluent in a particular programming language? How familiar are they with the numerical methods used by a given model? How familiar are they with underlying scientific concepts and simplifying assumptions? Are they conversant with modern version control and collaborative-development technology and practices? Although scientists should not need to become software engineers, in our experience there is a basic level of knowledge that can substantially raise the quality and sustainability of research software. To address this, CSDMS offers training programs, self-paced learning materials, and online help resources for community members. The vision is to foster a thriving community of practice in computational geoscience research, equipped with ever-improving modeling tools written by and for the community as a whole.

How to cite: Tucker, G., Kettner, A., Hutton, E., Piper, M., Gan, T., Campforts, B., Overeem, I., and Rossi, M.: Lessons in FAIR software from the Community Surface Dynamics Modeling System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3711, https://doi.org/10.5194/egusphere-egu23-3711, 2023.

This is a report from the chapter editor's perspective of a high visibility publication effort to foster the adoption of the FAIR principles (Findable, Accessible, Interoperable, Reusable) by encouraging the adoption of Persistent Identifiers (PID) and repository-based workflows in geospatial open source software communities as good practices. Lessons learned are detailed about how to communicate the benefits of PID adoption to software project communities focussing on professional software-development and meritocracy. Also encountered communication bottleneck patterns, the significance of cross-project  multiplicators, remaining challenges and emerging opportunities for publishers and repository infrastructures are reported. For the second Edition of the Springer Handbook of Geographic Information, a team of scientific domain experts from several software communities was tasked to rewrite a chapter about Open Source Geographic Information Systems (DOI: 10.1007/978-3-030-53125-6_30). For this, a sample of representative geospatial open source projects was selected, based on the range of projects integrated in the OSGeo live umbrella project (DOI: 10.5281/zenodo.5884859). The chapters authors worked in close contact with the respective Open Source software project communities. Since the editing and production process for the Handbook of Geographic Information was delayed due to the pandemic, this provided the opportunity to explore, improve and implement good practices for state of the art PID-based citation of software projects and versions, but also project communities, data and related scientific video ressources. This was a learning process for all stakeholders involved in the publication project. At the completion of the project, the majority of the involved software projects had minted Digital Object Identifiers (DOI) for their codebases. While the adoption level of software versioning with automated PID-generation and metadata quality remains heterogeneous, the insights gained from this process can simplify and accelerate the adoption of PID-based best software community practices for other open geospatial projects according to the FAIR principles.

How to cite: Löwe, P.: Going FAIR by the book: Accelerating the adoption of PID-enabled good practices in software communities through reference publication., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4354, https://doi.org/10.5194/egusphere-egu23-4354, 2023.

The number of models, as well as data inputs and outputs, are continuously growing as scientists continue to push the boundaries of spatial, temporal, and sectoral details being captured. This study presents the framework being developed to manage the Global Change Intersectoral Modeling System (GCIMS) eco-system of human-Earth system models. We discuss the challenges of ensuring continuous deployment and integration, reproducibility, interoperability, containerization, and data management for the growing suite of GCIMS models. We investigate the challenges of model version control and interoperability between models using different software, operating on different temporal and spatial scales, and focusing on different sectors. We also discuss managing transparency and accessibility to models and their corresponding data products throughout our integrated modeling lifecycle.

How to cite: Khan, Z., Vernon, C., Thompson, I., and Patel, P.: GCIMS – Integration: Reproducible, robust, and scalable workflows for interoperable human-Earth system modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4525, https://doi.org/10.5194/egusphere-egu23-4525, 2023.

Open science is transformative, removing barriers to sharing science and increasing reproducibility and transparency. The benefits of open science are maximized when its principles are incorporated throughout the research process, through working collaboratively with community members and sharing data, software, workflows, samples, and other aspects of scientific research openly where it can be reused, distributed, and reproduced. However, the paths toward Open Science are not always apparent, and there are many concepts, approaches, tools to learn along the way.  

Open Science practices are along a continuum where researchers can make incremental adjustments to their research practices that may seem small but can have valuable benefits. Here we will share the first steps in a researcher’s open science journey and how to lead your own research team in adopting Open Science practices.

How to cite: Stall, S. and Vrouwenvelder, K.: Open Science: How Open is Open?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4939, https://doi.org/10.5194/egusphere-egu23-4939, 2023.

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) is a community-driven climate impact modeling initiative that aims to contribute to a quantitative and cross-sectoral synthesis of the various impacts of climate change, including associated uncertainties. ISIMIP is organized into simulation rounds for which a simulation protocol defines a set of common scenarios. Participating modeling groups run their simulations according to these scenarios and with a common set of climatic and socioeconomic input data. The model output data are collected by the ISIMIP team at the Potsdam Institute for Climate Impact Research (PIK) and made publicly available in the ISIMIP repository. Currently the ISIMIP Repository at data.isimip.org includes data from over 150 impact models spanning across 13 different sectors. It comprises of over 100 Tb of data.

As the world's largest data archive of model-based climate impact data, ISIMIP output data is used by a very diverse audience inside and outside of academia, for all kind of research and analyses. Special care is taken to enable persistent identification, provenience, and citablity. A set of workflows and tools ensure the conformity of the model output data with the protocol and the transparent management of caveats and updates to already published data. Datasets are referenced using unique internal IDs and hash values are stored for each file in the database.

In recent years, this process has been significantly improved by introducing a machine-readable protocol, which is version controlled on GitHub and can be accessed over the internet. A set of software tools for quality control and data publication accesses this protocol to enforce a consistent data quality and to extract metadata. Some of the tools can be used independently by the modelling groups even before submitting the data. After the data is published on the ISIMIP Repository, it can be accessed via web or using an API (e.g. for access from Jupyter notebooks) using the same controlled vocabularies from the protocol. In order to make the data citable, DOI for each output sector are registered with DataCite. For each DOI, a precise list of each contained dataset is maintained. If data for a sector is added or replaced, a new, updated DOI is created.

While the specific implementation is highly optimized to the peculiarities of ISIMIP, the general ideas should be transferable to other projects. In our presentation, we will discuss the various tools and how they interact to create an integrated curation and publishing workflow.

How to cite: Klar, J. and Mengel, M.: A machine-actionable workflow for the publication of climate impact data of the ISIMIP project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6375, https://doi.org/10.5194/egusphere-egu23-6375, 2023.

A vast amount of in situ cryospheric data has been collected during publicly funded field campaigns to the polar regions over the past decades. Each individual data set yields important insights into local thermo-physical processes, but they need to be assembled into informative data compilations to unlock their full potential to produce regional or global outcomes for climate change related research. The efficient and sustainable interdisciplinary reuse of such data compilations is of large interest to the scientific community. Yet, the creation of such compilations is often challenging as they have to be composed of often heterogeneous data sets from various data repositories. We will focus on the reuse of data sets in this contribution, while generating extendible data compilations with enhanced reusability.

Data reuse is typically conducted by researchers other than the original data producers, and it is therefore often limited by the metadata and provenance information available. Reuse scenarios include the validation of physics-based process models, the training of data-driven models, or data-integrated predictive simulations. All these use cases heavily rely on a diverse data foundation in form of a data compilation, which depends on high quality information. In addition to metadata, provenance, and licensing conditions, the data set itself must be checked for reusability. Individual data sets containing the same metrics often differ in structure, content, and metadata, which challenges data compilation.

In order to generate data compilations for a specific reuse scenario, we propose to break down the workflow into four steps:
1) Search and selection: Searching, assessing, optimizing search, and selecting data sets.
2) Validation: Understanding and representing data sets in terms of the data collectors including structure, terms used, metadata, and relations between different metrics or data sets.
3) Specification: Defining the format, structure, and content of the data compilation based on the scope of the data sets.
4) Implementation: Integrating the selected data sets into the compilation.

We present a workflow herein to create a data compilation from heterogeneous sea ice core data sets following the previously introduced structure. We report on obstacles encountered in the validation of data sets mainly due to missing or ambiguous metadata. This leaves the (re)user space for subjective interpretation and thus increases uncertainty of the compilation. Examples are challenges in relating different data repositories associated with the same location or the same campaign, the accuracy of measurement methods, and the processing stage of the data. All of which often require a bilateral iteration with the data acquisition team. Our study shows that enriching data reusability with data compilations requires quality-ensured metadata on the individual data set level.

How to cite: Simson, A., Yildiz, A., and Kowalski, J.: Data compilations for enriched reuse of sea ice data sets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6726, https://doi.org/10.5194/egusphere-egu23-6726, 2023.

Investigating the mechanics of physical processes involved in various geohazards, e.g. gravitational, flow-like mass movements, shallow landslides or flash floods, predicting their temporal or spatial occurrence, and analysing the associated risks clearly benefit from advanced computational process-based or data-driven models. Reproducibility is needed not only for the integrity of the scientific results, but also as a trustbuilding element in practical geohazards engineering. Various complex numerical models or pre-trained machine learning algorithms exist in the literature, for example, to determine landslide susceptibility in a region or to predict the run-out of torrential flows in a catchment. These use FAIR datasets with increasing frequency, for example DEM data to set up the simulation, or open access landslide databases for training and validation purposes. However, we maintain that workflow reproducibility is not ensured simply due to the FAIRness of input or output datasets. Underlying computational or machine learning model needs to be (re)structured to enable the reproducibility and replicability of every step in the workflow so that a model can be (re)built to either reproduce the same results, or can be (re)used to elaborate on new cases or new applications. We propose a data-integrated, platform-independent scientific model publication approach combining self-developed Python packages, Jupyter notebooks, version controlling, FAIR data repositories and high-quality metadata. Model development in the form of a Python package guarantees that model can be run by any end-user, and defining submodules of analysis or visualisation within the package helps the users to build their own models upon the model presented. Publishing the manuscript as a data- and model-integrated Jupyter notebook creates a transparent application of the model, and the user can reproduce any result either presented in the manuscript or in the datasets. We demonstrate our workflow with two applications from geohazards research herein while highlighting the shortcomings of the existing frameworks and suggesting improvements for future applications.

How to cite: Yildiz, A. and Kowalski, J.: Data-integrated executable publications for reproducible geohazards research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7417, https://doi.org/10.5194/egusphere-egu23-7417, 2023.

The flexible open-source research data management toolkit CaosDB is used in a diversity of fields such as turbulence physics, legal research, maritime research and glaciology. It is used to link research data and make it findable and retrievable and to keep it consistent, even if the data model changes.

CaosDB is used in the glaciology department at the Alfred Wegener Institute in Bremerhaven for the management of ice core samples and related measurements and analyses. Researchers can use the system to query for ice samples linked to, e.g., specific measurements for which they then can request to borrow for further analyses. This facilitates inter-laboratory collaborative research on the same samples. The system helped to solve a number of needs for the researchers, such as: A revision system which intrinsically keeps track of changes to the data and in which state samples were, when certain analyses were performed. Automated gathering of information for the publication in a meta-data repository (Pangaea). Tools for storing, displaying and  querying geospatial information and graphical summaries of all the measurements and analyses performed on an ice core. Automatic data extraction and refinement into data records in CaosDB so that users do not need to enter the data manually. A state machine which guarantees certain workflows, simplifies development and can be extended to trigger additional actions upon transitions.

We demonstrate how CaosDB enables researchers to create and work with semantic data objects. We further show how CaosDB's semantic data structure enables researchers to publish their data as FAIR Digital Objects.

How to cite: Spreckelsen, F., tom Wörden, H., Hornung, D., Fitschen, T., Schlemmer, A., and Freitag, J.: Integrating sample management and semantic research-data management in glaciology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7427, https://doi.org/10.5194/egusphere-egu23-7427, 2023.

Humankind is facing unprecedented global environmental and social challenges in terms of food, water and energy security, resilience to natural hazards, etc. To address these challenges, international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan (e.g., the UN SDGs). The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions and avoid potentially negative impacts on society and the environment.

Scientific models are key tools to transform into information and knowledge the huge amount of data currently available online. Executing a scientific model (implemented as an analytical software) commonly requires the discovery and use of different types of digital resources (i.e. data, services, and infrastructural resources). In the present geoscience technological landscape, these resources are generally provided by different systems (working independently from one another) by utilizing Web technologies (e.g. Internet APIs, Web Services, etc.). In addition, a given scientific model is often designed and developed for execution in a specific computing environment. These are important barriers to enable reproducibility, replicability, and reusability of scientific models –becoming key interoperability requirements for a transparent decision-making process.

This presentation introduces the Virtual Earth Cloud concept, a multi-cloud framework for the generation of information/knowledge from Big Earth Data analytics. The Virtual Earth Cloud allows the execution of computational models to process and extract knowledge from Big Earth Data, in a multi-cloud environment, and thus improving their reproducibility, replicability and reusability.

The development and prototyping of the Virtual Earth Cloud is carried out in the context of the GEOSS Platform Plus (GPP) project, funded by the European Union’s Horizon 2020 Framework Programme, aims to contribute to the implementation of the Global Earth Observation System of Systems (GEOSS) by evolving the European GEOSS Platform components to allow access to tailor-made information and actionable knowledge.

How to cite: Santoro, M., Mazzetti, P., and Nativi, S.: Virtual Earth Cloud: a multi-cloud framework for improving replicability of scientific models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7532, https://doi.org/10.5194/egusphere-egu23-7532, 2023.

Evaluating the quality of data is a major concern within the scientific community: before using any dataset for study, a careful judgement of its suitability must be conducted. This requires that the steps followed to acquire, select, and process the data have been thoroughly documented in a methodical manner, in a way that can be clearly communicated to the rest of the community. This is particularly important in the field of citizen science, where a project that can clearly demonstrate its protocols, transformation steps, and quality assurance procedures have much more chance of achieving social and scientific impact through the use and re-use of its data.

A number of specifications have been created to provide a common set of concepts and terminology, such as ISO 19115-3 or W3C PROV. These define a set of interchange formats, but in themselves, they do not provide tooling to create high-quality dataset descriptions. The existing tools built on these standards (e.g. GeoNetwork, USGS metadata wizard, CKAN) are overly complex for some users (for example, many citizen science project managers) who, despite being experts in their own fields, may be unfamiliar with the structure and context of metadata standards or with semantic modelling. 

In this presentation, we will describe a prototype authoring tool that was created using a Model-driven engineering (MDE) software development methodology. The tool was authored using JetBrains Meta Programming System (MPS) to implement a modelling language based on the ISO19115-3 model. A user is provided with a “text-like” editing environment, which assists with the formal structures needed to produce a machine-parable document.

This allows a user to easily describe data lineage and generic processing steps while reusing recognised external vocabularies with automated validation, autocompletion, and transformation to external formats (e.g. the XML format 19115-3 or JSON-LD). We will report on the results of user testing aimed at making the tool accessible to citizen scientists (through dedicated projections with simplified structures and dialogue-driven model creation) and evaluating with those users any new possibilities that comprehensive and machine-parsable provenance information may create for data integration and sharing. The prototype will also serve as a test pilot of the integration between ISO 19115-3 and existing/upcoming third-party vocabularies (such as the upcoming ISO data quality measures registry).

How to cite: Bastin, L., Reynolds, O., Garcia-Dominguez, A., and Sprinks, J.: Facilitating provenance documentation with a model-driven-engineering approach., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8321, https://doi.org/10.5194/egusphere-egu23-8321, 2023.

openEO Platform holds a large amount of free and open as well as commercial Earth Observation (EO) data which can be accessed and analysed with openEO, an open API that enables cloud computing and EO data access in a unified and reproducible way. Additionally, client libraries are available in R, Python and Javascript. A JupterLab environment and the Web Editor, a graphical interface, allow a direct and interactive development of processing workflows. The platform is developed with a strong user focus and various use cases have been implemented to illustrate the platform capabilities. Currently, three federated backends support the analysis of EO data from pixel to continental scale.  

The use cases implemented during the platform’s main development phase include a dynamic landcover mapping, an on-demand analysis-ready-data creation for Sentinel-1 GRD, Sentinel-2 MSI and Landsat data, time series-based forest dynamics analysis with prediction functionalities, feature engineering for crop type mapping and large-scale fractional canopy mapping. Additionally, three new use cases are being developed by platform users. These include large scale vessel detection based on Sentinel-1 and Sentinel-2 data, surface water indicators using the ESA World Water toolbox for a user-defined area of interest and monitoring of air quality parameters using Sentinel-5P data. 

The future evolution of openEO Platform in terms of data availability and processing capabilities closely linked to community requirements, facilitated by feature requests from users who design their workflows for environmental monitoring and reproducible research purposes. This presentation provides an overview of the completed use cases, the newly added functionalities such as user code sharing, and user interface updates based on the new use cases and user requests. openEO Platform exemplifies how the processing and analysing large amounts of EO data to meaningful information products is becoming easier and largely compliant with FAIR data principles supporting the EO community at large. 

How to cite: Schumacher, B., Griffiths, P., Pebesma, E., Dries, J., Jacob, A., Thiex, D., Mohr, M., and Briese, C.: openEO Platform – showcasing a federated, accessible platform for reproducible large-scale Earth Observation analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8526, https://doi.org/10.5194/egusphere-egu23-8526, 2023.

NuoroForestrySchool (a study center of the Department of Agriculture, University of Sassari, Italy) has developed and published a ‘data documentation procedure’ (link to NFS-DDP) enabling the improvement of the dataset FAIRness that any data collector wishes to share as open data. Datasets are frequently shared as spreadsheet files. While this tool is very handy in data preparation and preliminary analysis, its structure and composition are not very effective for storing and sharing consolidated data, unless data structures are extremely simple. NFS-DDP takes in input a spreadsheet in which data are organized as relational tables, one per sheet, while four additional sheets contain metadata standardized according to the Dublin Core specifications. The procedure outputs an SQLite relational database (including data and metadata) and a pdf-file documenting the database structure and contents. A first example application of the proposed procedure was shared by Giadrossich et al. (2022) on the PANGEA repository, concerning experimental data of erosion in forest soil measured during artificial rainfall. The zip-archive that can be downloaded contains the experiment data and metadata processed by NFS-DDP. At the following link is available a test document where basic statistics are computed to show how NFS-DDProcedure facilitates the understanding and correct processing of the shared dataset. 

The NFS-DataDocumentationProcedure provides a simple solution for organizing and archiving data aiming to i) achieve a more FAIR archive, ii) exploit data consistency and comprehensibility of semantic connections in the relational database, ii) produce a report documenting the collection and organization of data, providing an effective and concise overview of the whole with all details at hand.

Giadrossich, F., Murgia, I., Scotti, R. (2022). Experiment of water runoff and soil erosion with and without forest canopy coverage under intense artificial rainfall. PANGAEA. DOI:10.1594/PANGAEA.943451

How to cite: Giadrossich, F., Murgia, I., and Scotti, R.: Proposal of a simple procedure to derive a more FAIR open data archive than a spreadsheet or a set of CSV files, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9852, https://doi.org/10.5194/egusphere-egu23-9852, 2023.

Landlab is an open-source Python package designed to facilitate creating, combining, and reusing 2D numerical models. As a core component of the Community Surface Dynamics Modeling System (CSDMS) Workbench, Landlab can be used to build and couple models from a wide range of domains. We present how Landlab provides a platform that fosters a community of model developers and aids them in creating sustainable and FAIR (Findable, Accessible, Interoperable, Reusable) research software.

Landlab’s core functionality can be split into two main categories: infrastructural tools and community-contributed components. Infrastructural tools address the common needs of building new models (e.g. a gridding engine, and numerical utilities for common tasks). Landlab’s library of community-contributed components consists of several dozen components that each model a separate physical process (e.g. routing of shallow water flow across a landscape, calculating groundwater flow, or biologic evolution over a landscape). As these user-contributed components are incorporated into Landlab, they are able to attach to the Landlab infrastructure so that they also become both findable and accessible (through, for example, standardized metadata and versioning) and are maintained by the core Landlab developers.

One key aspect of Landlab’s design is its use of a standard programming interface for all components. This ensures that all Landlab components are interoperable with one another and with other software tools, allowing researchers to incorporate Landlab's components into their own workflows and analyses. By separating processes into individual components, they become reusable and allow researchers to combine components in new ways without having to write new components from scratch.

Overall, Landlab's design and development practices support the principles of FAIR research software, promoting the ability for scientific research to be easily shared and built upon. This design also provides a platform onto which model developers are able to attach their model components and take advantage of Landlab’s development practices and infrastructure and ensure their components also follow FAIR principles.

How to cite: Hutton, E. and Tucker, G.: Landlab: a modeling platform that promotes the building of FAIR research software, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12443, https://doi.org/10.5194/egusphere-egu23-12443, 2023.

Reproducible research necessitates full transparency and integrity in data collection (e.g. from observations) or generation of data, and further data processing and analysis to generate research products. However, Earth and environmental science data are growing in complexity, volume and variety and today, particularly for large-volume Earth observation and geophysics datasets, achieving this transparency is not easy. It is rare for a published data product to be created in a single processing event by a single author or individual research group. Modern research data processing pipelines/workflows can have quite complex lineages, and it is more likely that an individual research product is generated through multiple levels of processing, starting from raw instrument data at full resolution (L0) followed by successive levels of processing (L1-L4), which progressively convert raw instrument data into more useful parameters and formats. Each individual level of processing can be undertaken by different research groups using a variety of funding sources: rarely are those involved in the early stages of processing/funding properly cited.

The lower levels of processing are where observational data essentially remains at full resolution and is calibrated, georeferenced and processed to sensor units (L1) and then geophysical variables are derived (L2). Historically, particularly where the volumes of the L0-L2 datasets are measured in Terabytes to Petabytes, processing could only be undertaken by a minority of specialised scientific research groups and data providers, as few had the expertise/resources/infrastructures to process them on-premise. Wider availability of colocated data assets and HPC/cloud processing means that the full resolution, less processed forms of observational data can now be processed remotely in realistic timeframes by multiple researchers to their specific processing requirements, and also enables greater exploration of parameter space allowing multiple values for the same inputs to be trialled. The advantage is that better-targeted research products can now be rapidly produced. However, the downside is that far greater care needs to be taken to ensure that there is sufficient machine-readable metadata and provenance information to enable any user to determine what processing steps and input parameters were used in each part of the lineage of any released dataset/data product, as well as be able to reference exactly who undertook any part of the acquisition/processing and identify sources of funding (including instruments/field campaigns that collected the data).

The use of Persistent Identifiers (PIDs) for any component objects (observational data, synthetic data, software, model inputs, people, instruments, grants, organisations, etc.) will be critical. Global and interdisciplinary research teams of the future will be reliant on software engineers to develop community-driven software environments that aid and enhance the transparency and reproducibility of their scientific workflows and ensure recogniton. The advantage of the PID approach is that not only will reproducibility and transparency be enhanced, but through the use of Knowledge Graphs it will also be possible to trace the input of any researcher at any level of processing, whilst funders will be able to determine the impact of each stage from the raw data capture through to any derivative high-level data product. 

How to cite: Wyborn, L., Rees, N., Klump, J., Evans, B., Farrington, R., and Rawling, T.: Who Done It? Reproducibility of Data Products Also Requires Lineage to Determine Impact and Give Credit Where Credit is Due., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12864, https://doi.org/10.5194/egusphere-egu23-12864, 2023.

Environmental sensor networks produce ever-growing volumes of time series data with great potential to broaden the understanding of complex spatiotemporal environmental processes. However, this growth also imposes its own set of new challenges. Especially the error-prone nature of sensor data acquisition is likely to introduce disturbances and anomalies into the actual environmental signal. Most applications of such data, whether it is used in data analysis, as input to numerical models or modern data science approaches, usually rely on data that complies with some definition of quality.

To move towards high-standard data products, a thorough assessment of a dataset's quality, i.e., its quality control, is of crucial importance. A common approach when working with time series data is the annotation of single observations with a quality label to transport information like its reliability. Downstream users and applications are hence able to make informed decisions, whether a dataset in its whole or at least parts of it are appropriate
for the intended use.

Unfortunately, quality control of time series data is a non-trivial, time-consuming, scientifically undervalued endeavor and is often neglected or executed with insufficient rigor. The presented software, the System for automated Quality Control (SaQC), provides all basic and many advanced building blocks to bridge the gap between data that is usually faulty but expected to be correct in an accessible, consistent, objective and reproducible way. Its user interfaces address different audiences ranging from the scientific practitioner with little access to the possibilities of modern software development to the trained programmer. SaQC delivers a growing set of generic algorithms to detect a multitude of anomalies and to process data using resampling, aggregation, and data modeling techniques. However, one defining component of SaQC is its innovative approach to storing runtime process information. In combination with a flexible quality annotation mechanism, SaQC allows to extend quality labels with fine-grained provenance information appropriate to fully reproduce the system's output.

SaQC is proving its usefulness on a daily basis in a range of fully automated data flows for large environmental observatories. We highlight use cases from the TERENO Network, showcasing how reproducible automated quality control can be implemented into real-world, large-scale data processing workflows to provide environmental sensor data in near real-time to data users, stakeholders and decision-makers.

How to cite: Schäfer, D., Palm, B., Lünenschloß, P., Schmidt, L., and Bumberger, J.: Reproducible quality control of time series data with SaQC, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12971, https://doi.org/10.5194/egusphere-egu23-12971, 2023.

A new paradigm for Intergovernmental Panel on Climate Change (IPCC) Working Group I (WGI) data publication has been implemented.  IPCC Data Distribution Centre (DDC) partners at the Centre for Environmental Data Analysis (CEDA), the German Climate Computing Centre (DKRZ) and the Spanish Research Council (CSIC) have worked with the IPCC Technical Support Unit (TSU) for WGI to publish figure data from the Sixth Assessment Report (AR6). The work was guided by the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) recommendations for Open Science and FAIR data (making data Findable, Accessible, Interoperable, and Reusable) with a general aim to enhance the transparency and accessibility of AR6 outcomes.  We highlight the achievement of implementing FAIR for AR6 figure data and discuss the lessons learned on the road to FAIRness in the unique context of the IPCC.

• Findable - The CEDA catalogue record for each figure dataset enhances findability. Keywords can be easily searched. Records are organised into collections for each AR6 chapter. There is a two-way link between the catalogue record and the figure on the AR6 website. CEDA catalogue records are duplicated on the IPCC-DDC. 
• Accessible - Scientific language is understandable, acronyms and specific terminology are fully explained. CEDA services provide tools to access and download the data. 
• Interoperable - Where possible data variables follow standard file format conventions such as CF-netCDF and have standard names, where this is not feasible readme files describe the file structure and content. 
• Reusable - The data can be reused, shared and adapted elsewhere, with credit, under a Creative Commons Attribution 4.0 licence (CC BY 4.0). Catalogue records link to relevant documentation such as the Digital Object Identifier (DOI) for the code and other supplementary information. The code used to create the figures allows users to reproduce the figures from the report independently. 

CEDA catalogue records provide a platform to acknowledge the specific work of IPCC authors and dataset creators whose work supports the scientific basis of AR6. 

Catalogue records for figure datasets were created at CEDA with data archived in the CEDA repository and the corresponding code stored on GitHub and referenced via Zenodo.  For instances where the data and code were blended in a processing chain that could not be easily separated, we developed criteria to categorise the different blends of data and code and created a decision tree to decide how best to archive them. Key intermediate datasets were also archived at CEDA.

Careful definition of metadata requirements at the beginning of the archival process is important for handling the diversity of IPCC figure data which includes data derived from climate model simulations, historical observations and other sources of climate information. The reality of the implementation meant that processes for gathering data and information from authors were specified later in the preparation of AR6. This presented challenges with data management workflows and the separation of figure datasets from the intermediate data and code that generated them. 

We present recommendations for AR7 and scaling up this work in a feasible way.

How to cite: Pascoe, C., Sitz, L., Cammarano, D., Pirani, A., Stockhause, M., MacRae, M., and Anderson, E.: The reality of implementing FAIR principles in the IPCC context to support open science and provide a citable platform to acknowledge the work of authors., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13108, https://doi.org/10.5194/egusphere-egu23-13108, 2023.

In our project we are employing semantic data management with the Open Source research data management system (RDMS) CaosDB [1] to link empirical data and simulation output from Earth System Models [2]. The combined management of these data structures allows us to perform complex queries and facilitates the integration of data and meta data into data analysis workflows.

One particular challenge for analyses of model output is to keep track of all necessary meta data of each simulation during the whole digital workflow. Especially for open science approaches it is of great importance to properly document - in human- and computer-readable form - all the information necessary to completely reproduce obtained results. Furthermore, we want to be able to feed all relevant data from data analysis back into our data management system, so that we are able to perform complex queries also on data sets and parameters stemming from data analysis workflows.

A specific aim of this project is to re-analyse existing sets of simulations under different research questions. This endeavour can become very time consuming without proper documentation in an RDMS.

We implemented a workflow, combining semantic research data management with CaosDB and Jupyter notebooks, that keeps track of data loaded into an analysis workspace. Procedures are provided that create snapshots of specific states of the analysis. These snapshots can automatically be interpreted by the CaosDB crawler that is able to insert and update records in the system accordingly. The snapshots include links to the input data, parameter information, the source code and results and therefore provide a high-level interface to the full chain of data processing, from empirical and simulated raw data to the results. For example, input parameters of complex Earth System Models can be extracted automatically and related to model performance. In our use case, not only automated analyses are feasible, but also interactive approaches are supported.

• [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., 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.

How to cite: Schlemmer, A. and Lennartz, S.: Transparent and reproducible data analysis workflows in Earth System Modelling combining interactive notebooks and semantic data management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13347, https://doi.org/10.5194/egusphere-egu23-13347, 2023.

In the era of cloud computing, big data and Internet of things, research is very often data-driven: based on the analysis of data, increasingly available in large quantities and collected by experiments, observations or simulations. These data are very often characterized as being dynamic in space and time and as continuously expanding (monitoring) or change (data quality management or survey). Modern Spatial Data Infrastructures (e.g.  swisstopo or INSPIRE), are based on interoperable Web services which expose and serve large quantities of data on the Internet using widely accepted and used open standards defined by the Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO). These standards mostly comply with FAIR principles but do not offer any capability to retrieve a dataset how it was in a defined instant, to refer to its status in that specific instant and to guarantee its immutability. These three aspects hinder the replicability of research based on such a kind of services. We discuss the issue here and the state of the art  and propose a possible solution to fill this gap, using or extending when needed the existing standards and or adopting best practices in the fields of sensor data, satellite data and vector data.

How to cite: Cannata, M., Giuliani, G., Ingensand, J., Ertz, O., and Collombin, M.: Open geospatial standards and reproducible research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14845, https://doi.org/10.5194/egusphere-egu23-14845, 2023.

The Reproducible AGILE initiative (https://reproducible-agile.github.io/) successfully established a code execution procedure following the CODECHECK principles (https://doi.org/10.12688/f1000research.51738.2) at the AGILE conference series (https://agile-online.org/conference). The AGILE conference is a medium-sized community-led conference in the domains of Geographic Information Science (GIScience), geoinformatics, and related fields. The conference is organised under the umbrella of the Association of Geographic Information Laboratories in Europe (AGILE).

Starting with a series of workshops on reproducibility from 2017 to 2019, a group of Open Science enthusiasts with the support of the AGILE Council (https://agile-online.org/agile-actions/current-initiatives/reproducible-publications-at-agile-conferences) was able to introduce guidelines for sharing reproducible workflows (https://doi.org/10.17605/OSF.IO/CB7Z8) and establish a reproducibility committee that conducts code executions for all accepted full papers.
In this presentation, we provide details of the taken steps and the encountered obstacles towards the current state. We revisit the process and abstract a series of actions that similar events or even journals may take to introduce a shift towards higher reproducibility of research publications in a specific community of practice.

We discuss the taken approach in the light of the challenges for reproducibility in Earth System Sciences (ESS) around four main ideas.
First, Reproducible AGILE’s human-centered process is able to handle the increasingly complex, large and varying data-based workflows in ESS because of the clear guidance on responsibilities (What should the author provide? How far does the reproducibility reviewer need to go?).
Second, the communicative focus of the process is very well suited to, over time, help to establish a shared practice based on current technical developments, such as FAIR Digital Objects, and to reform attitudes towards openness, transparency and sharing. A code execution following the CODECHECK principles is a learning experience that may sustainably change researcher behaviours and practice. At the same time, Reproducible AGILE’s approach avoids playing catch-up with technology and does not limit researcher freedom or includes a need to unitise researcher workflows beyond providing instructions suitable for a human evaluator, similar to academic peer review.
Third, while being agnostic of technology and infrastructures, a supportive framework of tools and infrastructure can of course increase the efficiency of conducting a code execution. We outline how existing infrastructures may serve this need and what is still missing.
Fourth, we list potential candidates of event series or journals that could introduce a code checking procedure because of their organisational setup or steps towards more open scholarhip that were already taken.

How to cite: Nüst, D., Ostermann, F. O., and Granell, C.: A peer review process for higher reproducibility of publications in GIScience can also work for Earth System Sciences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15384, https://doi.org/10.5194/egusphere-egu23-15384, 2023.

German climate research initiative PalMod phase II (www.palmod.de) is presented here as an exclusive example where the project end-product is unique, scientific paleo-climate data. PalMod-II data products include output from three state-of-the-art coupled climate models of varying complexity and spatial resolutions simulating the climate of the past 130,000 years. In addition to the long time series of modeling data, a comprehensive compilation of paleo-observation data is prepared to facilitate model-model and model-proxy intercomparison and evaluation. Being a large multidisciplinary project, a dedicated RDM (Research Data Management) approach is applied within the cross-cutting working group for PalMod-II. The DMP (Data Management Plan), as a living document, is used for documenting the data-workflow framework that defines the details of paleo-climate data life-cycle. The workflow containing the organisation, storage, preservation, sharing and long-term curation of the data is defined and tested.  In order to make the modeling data inter-comparable across the PalMod-II models and easily analyzable by the global paleo-climate community, model data standardization (CMORization) workflows are defined for individual PalMod models and their sub-models. The CMORization workflows contain setup, definition, and quality assurance testing of CMIP6¹ based standardization processes adapted to PalMod-II model simulation output requirements with a final aim of data publication via ESGF². PalMod-II data publication via ESGF makes the paleo-climate data an asset which is (re-)usable beyond the project life-time.

The PalMod-II RDM infrastructure enables common research data management according to the FAIR³ data principles across all the working groups of PalMod-II using common workflows for the exchange of data and information along the process chain. Applying data management planning within PalMod-II made sure that all the data related workflows were defined, continuously updated if needed and made available to the project stakeholders. End products of PalMod-II which consist of unique long term scientific paleo-climate data (model as well as paleo-proxy data) are made available for re-use via the paleo-climate research community as well as other research disciplines (e.g., land-use, socio-economic studies etc.).

1. Coupled Model Intercomparison Project phase 6 (https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6)

2. Earth System Grid Federation (https://esgf.llnl.gov)

3. Findable, Accessible, Interoperable, Reusable

How to cite: Gehlot, S., Peters-von Gehlen, K., Lammert, A., and Thiemann, H.: Data Management for PalMod-II – data workflow and re-use strategy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15391, https://doi.org/10.5194/egusphere-egu23-15391, 2023.

Within the UK the National Centre for Atmospheric Science (NCAS) operates a suite of observational instruments for atmospheric dynamics, chemistry and composition studies. These are principally made available through two facilities: the Atmospheric Measurement and Observations Facility (AMOF) and the Facility for Airborne Atmospheric Measurements (FAAM). Between these two facilities instrumentation can be on either campaign or long-term deployed in diverse environments (from polar to maritime; surface to high altitude), on a range of platforms (aircraft, ships) or dedicated atmospheric observatories.

The wide range of instruments, spanning an operational time period from the mid 1990s to present, has traditionally been orientated to specific communities, resulting in a plethora of different operational practices, data standards and workflows. The resulting data management and usage challenges have been further exacerbated over time by changes of staff, instruments and end-user communities and their requirements. This has been accompanied by the wider end-user community seeking greater access to and improved use of the data, with necessary associated improvements in data production to ensure transparency, quality, veracity and, thus, overall reproducibility. Additionally, these enhancemed workflows further ensure FAIR data outputs, widening long-term re-use of the data. 

Seeking to address these challenges in a more harmonious approach across the range of AMOF and FAAM facilities, NCAS established the NCAS Data Project in 2018 bringing together key players in the data workflows to break down barriers and common standards and procedures through improved dialogue. The resulting NCAS ‘Data Pyramid’ approach, brings together representatives from the data provider, data archive and end-user communities alongside supporting software engineers within a common framework that enables cross-working between all partners. This has lead to new data standards and workflows being established to ensure 3 key objectives: 1) capturing and flow of the necessary metadata to automate data flows and quality control as much as possible in a timely fashion ‘from field to end-user’; 2) enhanced transparency and traceability in data production via linked externally visible documentation, calibration and code repositories; and, 3) data products meeting end-user requirements in terms of their content and established quality control. Finally, data workflows are further enhanced thanks to scriptable conformance checking throughout the data production lifecycle, built on the controlled data product and metadata standards.

Thus, through the established workflows of the NCAS Data Project, the necessary details are captured and conveyed by both internal file-level and catalogue-level metadata to ensure that all three corners of the triangle of reproducibility, quality information, and provenance are able to be achieved in combination.

How to cite: Parton, G., Brooks, B., Stephens, A., and Garland, W.: The UK’s NCAS Data Project: establishing transparent observational data workflows from field to user, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16288, https://doi.org/10.5194/egusphere-egu23-16288, 2023.

Some disciplines, e.g. Astrophysics or Earth system sciences, work with large to very large amounts of data. Storing this data, but also processing it, is a challenge for researchers because novel concepts for processing data and workflows have not developed as quickly. This problem will only become more pronounced with the ever increasing performance of High Performance Computing (HPC) – systems.

At the German Climate Computing Center, 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 main 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.

We will show the analysis of the different users, the Canonical workflow and the vision of the FDOs. Furthermore, we will present the framework Freva and further developments and implementations at DKRZ with respect to the reproducibility of simulation-based research in the ESS.

How to cite: Anders, I., Thiemann, H., Bergemann, M., Kadow, C., and Lucio-Eceiza, E.: Towards reproducible workflows in simulation based Earth System Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17263, https://doi.org/10.5194/egusphere-egu23-17263, 2023.

As deep learning (DL) is gathering remarkable attention for its capacity to achieve accurate predictions in various fields, enormous applications of DL in geosciences also emerged. Most studies focus on the high accuracy of DL models by model selections and hyperparameter tuning. However, the interpretability of DL models, which can be loosely defined as comprehending what a model did, is also important but comparatively less discussed. To this end, we select thin section photomicrographs of five types of sedimentary rocks, including quartz arenite, feldspathic arenite, lithic arenite, dolomite, and oolitic packstone. The distinguishing features of these rocks are their characteristic framework grains. For example, the oolitic packstone contains rounded or oval ooids. A regular classification model using ResNet-50 is trained by these photomicrographs, which is assumed as accurate because its accuracy reaches 0.97. However, this regular DL model makes their classifications based on the cracks, cements, or even scale bars in the photomicrographs, and these features are incapable of distinguishing sedimentary rocks in real works. To rectify the models’ focus, we propose an attention-based dual network incorporating the microphotographs' global (the whole photomicrographs) and local features (the distinguishing framework grains). The proposed model has not only high accuracy (0.99) but also presents interpretable feature extractions. Our study indicates that high accuracy should not be the only metric of DL models, interpretability and models incorporating geological information require more attention.

How to cite: Zheng, D., Cao, Z., Hou, L., Ma, C., and Hou, M.: High accuracy doesn’t prove that a deep learning model is accurate: a case study from automatic rock classification of thin section photomicrographs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-244, https://doi.org/10.5194/egusphere-egu23-244, 2023.

Starting from 2021 International Maritime Organization (IMO) introduced more demanding NO_x emission restrictions for ships operating in waters of the North and Baltic Seas. All methods currently used for ship compliance monitoring are financially and time-demanding. Thus, it is important to prioritize the inspection of ships that have a high chance of being non-compliant. 

TROPOMI/S5P instrument for the first time allows a distinction of NO₂ plumes from individual ships. Here, we present a method for the selection of potentially non-compliant ships using automated machine learning (AutoML) on TROPOMI/S5P satellite data. The study is based on the analysis of 20 months of data in the Mediterranean Sea region. To each ship, we assign a Region of Interest (RoI), where we expect the ship plume to be located. We then train a regression model to predict the amount of NO₂ that is expected to be produced by a ship with specific properties operating in the given atmospheric conditions. We use a genetic algorithm-based AutoML for the automatic selection and configuration of a machine-learning pipeline that maximizes prediction accuracy. The difference between the predicted and actual amount of produced NO₂ is a measure of inspection worthiness. We rank the analyzed ships accordingly. 

We cross-check the obtained ranks using a previously developed method for supervised ship plume segmentation.  We quantify the amount of NO₂ produced by a given ship by summing up concentrations within the pixels identified as a “plume”. We rank the ships based on the difference between the obtained concentrations and the ship emission proxy.

Ships that are also ranked as highly deviating by the segmentation method need further attention. For example, by checking their data for other explanations. If no other explanations are found, these ships are advised to be the candidates for fuel inspection.

How to cite: Kurchaba, S., van Vliet, J., Verbeek, F. J., and Veenman, C. J.: Detection of anomalous NO2 emitting ships using AutoML on TROPOMI satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1183, https://doi.org/10.5194/egusphere-egu23-1183, 2023.

Compaction of agricultural soil negatively affects its hydraulic proprieties, leading to water erosion and other negative effects on the quality of the environment. This study focused on the effect of compaction on soil hydrodynamic properties under unsaturated and saturated conditions using the Hydraulic Property Analyzer (HYPROP) system. We studied the impact of five levels of compaction among loam sand soils collected in a potato crop field in northern Québec, Canada. Soil samples were collected, and the soil bulk densities of the artificially compacted samples were developed by increasing the bulk density by 0% (C0), 30% (C30), 40% (C40), 50% (C50), and 70% (C70). First, the saturated hydraulic conductivity of each column was measured using the constant-head method. Soil water retention curve (SWRC) dry-end data and unsaturated hydraulic conductivities were obtained via the implementation and evaluation of the HYPROP evaporation measurement system and WP4-T Dew Point PotentioMeter equipment (METER group, Munich, Germany). Second, the soil microporosity was imaged and quantified using the micro-CT-measured pore-size distribution to visualize and quantify soil pore structures. The imaged soil microporosity was related to the saturated hydraulic conductivity, air permeability, porosity and tortuosity measured of the same samples.  Our results supported the application of the Peters–Durner–Iden (PDI) variant of the bimodal unconstrained van Genuchten model (VGm-b-PDI) for complete SWRC estimation based on the root mean square error (RMSE). The unsaturated hydraulic conductivity matched the PDI variant of the unconstrained van-Genuchten model (VGm-PDI) well. Finally, the preliminary results indicated that soil compaction could strongly influence the hydraulic properties of soil in different ways. The saturated conductivity decreased with increasing soil compaction, and the unsaturated hydraulic conductivity changed very rapidly with the ratio of water to soil. Overall, the HYPROP methodology performed extremely well in terms of the hydraulic behavior of compacted soils.

How to cite: Mbarki, Y. and Gumiere, S. J.: Study of the effect of compaction on the hydrodynamic properties of a loamy sand soil for precision agriculture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1583, https://doi.org/10.5194/egusphere-egu23-1583, 2023.

Scientific publications in space science contain valuable and extensive information regarding the links and relationships between the data interpreted by the authors and the associated observational elements (e.g., instruments or experiments names, observing times, etc.). In this reality of scientific information overload, researchers are often overwhelmed by an enormous and continuously growing number of articles to access in their daily activities. The exploration of recent advances concerning specific topics, methods and techniques, the review and evaluation of research proposals and in general any action that requires a cautious and comprehensive assessment of scientific literature has turned into an extremely complex and time-consuming task.

The availability of Natural Language Processing (NLP) tools able to extract information from scientific unstructured textual contents and to turn it into extremely organized and interconnected knowledge, is fundamental in the framework of the use of scientific information. Exploitation of the knowledge that exists in the scientific publications, necessitates state-of-the-art NLP. The semantic interpretation of the scientific texts can support the development of a varied set of applications such as information retrieval from the texts, linking to existing knowledge repositories, topic classification, semi-automatic assessment of publications and research proposals, tracking of scientific and technological advances, scientific intelligence-assisted reporting, review writing, and question answering.

The main objectives of TACTICIAN are to introduce Artificial Intelligence (AI) techniques to the textual analysis of the publications of all ESA Space Science missions, to monitor and evaluate the scientific productivity of the science missions, and to integrate the scientific publications’ metadata into the ESA Space Science Archive. Through TACTICIAN, we extract lexical, syntactic, and semantic information from the scientific publications by applying NLP and Machine Learning (ML) algorithms and techniques. Utilizing the wealth of publications, we have created valuable scientific language resources, such as labeled datasets and word embeddings, which were used to train Deep Learning models that assist us in most of the language understanding tasks. In the context of TACTICIAN, we have devised methodologies and developed algorithms that can assign scientific publications to the Mars Express, Herschel, and Cluster ESA science missions and identify selected named entities and observations in these scientific publications. We also introduced a new unsupervised ML technique, based on Nonnegative Matrix Factorization (NMF), for classifying the Planck mission scientific publications to categories according to the use of the Planck data products.

These methodologies can be applied to any other mission. The combination of NLP and ML constitutes a general basis, which has proved that it can assist in establishing links between the missions’ observations and the scientific publications and to classify them in categories, with high accuracy.

This work has received funding from the European Space Agency under the "ArTificiAl intelligenCe To lInk publiCations wIth observAtioNs (TACTICIAN)" activity under ESA Contract No 4000128429/19/ES/JD.

How to cite: Giannakis, O., Demiros, I., Koutroumbas, K., Rontogiannis, A., Antonopoulos, V., De Marchi, G., Arviset, C., Balasis, G., Daglis, A., Vasalos, G., Boutsi, Z., Tauber, J., Lopez-Caniego, M., Kidger, M., Masson, A., and Escoubet, P.: TACTICIAN: AI-based applications knowledge extraction from ESA’s mission scientific publications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1902, https://doi.org/10.5194/egusphere-egu23-1902, 2023.

Identification of constituent grains in carbonate rocks is primarily a qualitative skill requiring specialist experience. A carbonate sedimentologist must be able to distinguish between various grains of different ages, preserved in differing alteration stages, and cut in random orientations across core sections. Recent studies have demonstrated the effectiveness of machine learning in classifying lithofacies from thin section, core and seismic images, with faster analysis times and reduction of natural biases.  In this study, we explore the application and limitations of convolutional neural network (CNN) based object detection frameworks to identify and quantify multiple types of carbonate grains within close-up core images. Nearly 400 images of carbonate cores we compiled of high-resolution core images from three ODP and IODP expeditions. Over 9,000 individual carbonate components of 11 different classes were manually labelled from this dataset. Using transfer learning, we evaluate one-stage (YOLO v3) and two-stage (Faster R-CNN) detectors under different feature extractors (Darknet and Inception-ResNet-v2). Despite the current popularity of one-stage detectors, our results show Faster R-CNN with Inception-ResNet-v2 backbone provides the most robust performance, achieving nearly 0.8 mean average precision (mAP). Furthermore, we extend the approach by deploying the trained model to ODP Leg 194 Sites 1196 and 1190, developing a performance comparison with human interpretation. 

How to cite: Dawson, H. and John, C.: Deep learning based identification of carbonate rock components in core images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2388, https://doi.org/10.5194/egusphere-egu23-2388, 2023.

Water used in agricultural crops can be managed by irrigation scheduling based on plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing stomatal conductance, growth rate, leaf water potential, and stem water potential. Calculating thresholds of soil matric potential, and available water content improves the precision of irrigation management by preventing water limitations between irrigations. Crop monitoring and irrigation management make informed decisions using geospatial technologies, the internet of things, big data analysis, and artificial intelligence. Remote sensing (RS) could be applied whenever in situ data are not available. High-resolution crop mapping extracts information through index-based methods fed by the multitemporal and multi-sensor data used in detection and classification. Precision Agriculture (PA) means applying farm inputs at the right amount, at the right time, and in the right place. RS in PA captures images in different spatial, and spectral resolutions through in-field, satellites, aerial, and handheld or tractor-mounted such as unmanned aerial vehicles (UAVs) sensors. RS sensors receive the electromagnetic signals of plant responses in different spectral domains. Optical satellite data, including narrow-band multispectral remote sensing techniques and thermal imagery, is used for water stress detection. To process and analysis RS data, cloud storage and computing platforms simplify the complex mathematical of incorporating various datasets for irrigation scheduling. Machine learning (ML) algorithms construct models for the regression and classification of multivariate and non-linear crop mapping. The web-based software gathered from all different datasets makes a reliable product to reinforce farmers’ ability to make appropriate decisions in irrigating agricultural crops.

Keywords: Agricultural crops; Crop water stress detection; Irrigation scheduling; Precision agriculture; Remote Sensing.

How to cite: Koohi, E., Gumiere, S. J., and Bonakdari, H.: Artificial Intelligence Models for Detecting Spatiotemporal Crop Water Stress in schedule Irrigation: A review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3997, https://doi.org/10.5194/egusphere-egu23-3997, 2023.

Wetlands provide invaluable services for ecosystems and society and are a crucial instrument in our fight against climate change. Although Earth Observation satellites offer cost-effective and accurate information about wetland status at the continental scale; to date, there is no universally accepted, standardized, and regularly updated inventory of European wetlands <100m resolution. Moreover, previous satellite-based global land cover products seldom account for wetland diversity, which often impairs their mapping performances. Here, we mapped major wetland types (i.e., peatland, marshland, and coastal wetlands) across Europe for 2018, based on high resolution (10m) optical and radar time series satellite data as well as field-collected land cover information (LUCAS) using an ensemble model combining traditional machine learning and deep learning approaches. Our results show with high accuracy (>85%) that a substantial extent of European peatlands was previously classified as grassland and other land cover types. In addition, our map highlights cultivated areas (e.g., river floodplains) that can be potentially rewetted. Such accurate and consistent mapping of different wetland types at a continental scale offers a baseline for future wetland monitoring and trend assessment, supports the detailed reporting of European carbon budgets, and lays down the foundation towards a global wetland inventory.

How to cite: Kovács, G. M., Oehmcke, S., Horion, S., Gominski, D., Tong, X., and Fensholt, R.: Satellite-based continental-scale inventory of European wetland types at 10m spatial resolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6696, https://doi.org/10.5194/egusphere-egu23-6696, 2023.

Lagoons are highly valued coastal environments providing unique ecosystem services. However, they are fragile and vulnerable to natural processes and anthropogenic activities. Concurrently, climate change pressures, are likely to lead to severe ecological impacts on lagoon ecosystems. Among these, direct effects are mainly through changes in temperature and associated physico-chemical alterations, whereas indirect ones, mediated through processes such as extreme weather events in the catchment, include the alteration of nutrient loading patterns among others that can, in turn, modify the trophic states leading to depletion or to eutrophication. This phenomenon can lead, under certain circumstances, to harmful algal blooms events, anoxia, and mortality of aquatic flora and fauna, or to the reduction of primary production, with cascading effects on the whole trophic web with dramatic consequences for aquaculture, fishery, and recreational activities. The complexity of eutrophication processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on fragile lagoon environments. In this context, a novel framework combining Machine Learning (ML) and biogeochemical models is proposed, leveraging the potential offered by both approaches to unravel and modelling environmental systems featured by compounding pressures. Multi-Layer Perceptron (MLP) and Random Forest (RF) models are used (trained, validated, and tested) within the Venice Lagoon case study to assimilate historical heterogenous WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the biogeochemical model SHYFEM-BFM for 2049, and 2099 timeframes under RCP 8.5 are integrated to evaluate Chl-a variations under future bio-geochemical conditions forced by climate change projections. Annual and seasonal Chl-a predictions were performed out by classes based on two classification modes established on the descriptive statistics computed on baseline data: i) binary classification of Chl-a values under and over the median value, ii) multi-class classification defined by Chl-a quartiles. Results from the case study showed as the RF successfully classifies Chl-a under the baseline scenario with an overall model accuracy of about 80% for the median classification mode, and 61% for the quartile classification mode. Overall, a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) can be observed, with an opposite rising fashion for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2099 a strong increase in Chl-a is also expected during the spring one. The proposed novel framework represents a valuable approach to strengthen both eutrophication modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models.

How to cite: Zennaro, F., Furlan, E., Melaku Canu, D., Aveytua Alcazar, L., Rosati, G., Aslan, S., Solidoro, C., and Critto, A.: Evaluation of lagoon eutrophication potential under climate change conditions: A novel water quality machine learning and biogeochemical-based framework., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8409, https://doi.org/10.5194/egusphere-egu23-8409, 2023.

Marine coastal ecosystems (MCEs) are of vital importance for human health and well-being. However, their ecological condition is increasingly threatened by multiple risks induced by the complex interplay between endogenic (e.g. coastal development, shipping traffic) and exogenic (e.g. changes in sea surface temperature, waves, sea level, etc.) pressures. Assessing cumulative impacts resulting from this dynamic interplay is a major challenge to achieve Sustainable Development Goals and biodiversity targets, as well as to drive ecosystem-based management in marine coastal areas. To this aim, a Machine Learning model (i.e. Random Forest - RF), integrating heterogenous data on multiple pressures and ecosystems’ health and biodiversity, was developed to support the evaluation of risk scenarios affecting seagrasses condition and their services capacity within the Mediterranean Sea. The RF model was trained, validated and tested by exploiting data collected from different open-source data platforms (e.g. Copernicus Services) for the baseline 2017. Moreover, based on the designed RF model, future scenario analysis was performed by integrating projections from climate numerical models for sea surface temperature and salinity under the 2050 and 2100 timeframes. Particularly, under the baseline scenario, the model performance achieved an overall accuracy of about 82%. Overall, the results of the analysis showed that the ecological condition and services capacity of seagrass meadows (i.e. spatial distribution, Shannon index, carbon sequestration) are mainly threatened by human-related pressures linked to coastal development (e.g. distance from main urban centres), as well as to changes in nutrient concentration and sea surface temperature. This result also emerged from the scenario analysis, highlighting a decrease in seagrass coverage and related services capacity, in both 2050 and 2100 timeframes. The developed model provides useful predictive insight on possible future ecosystem conditions in response to multiple pressures, supporting marine managers and planners towards more effective ecosystem-based adaptation and management measures in MCEs.

How to cite: Bianconi, A., Furlan, E., Simeoni, C., Pham, V., Vascon, S., Critto, A., and Marcomini, A.: Evaluating the risk of cumulative impacts in the Mediterranean Sea using a Random Forest model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8702, https://doi.org/10.5194/egusphere-egu23-8702, 2023.

The standard way for earth observation experts or users to retrieve images from image archives (e.g., ESA's Copernicus Open Access Hub) is to use a graphical user interface, where they can select the geographical area of the image they are interested in and additionally they can specify some other metadata, such as sensing period, satellite platform and cloud cover.

In this work, we are developing the question-answering engine EarthQA that takes as input a question expressed in natural language (English) that asks for satellite images satisfying certain criteria and returns links to such datasets, which can be then downloaded from the CREODIAS cloud platform. To answer user questions, EarthQA queries two interlinked knowledge graphs: a knowledge graph encoding metadata of satellite images from the CREODIAS cloud platform (the SPARQL endpoint of CREODIAS) and the well-known knowledge graph DBpedia. Hence, the questions can refer to image metadata (e.g., satellite platform, sensing period, cloud cover), but also to more generic entities appearing in DBpedia knowledge graph (e.g., lake, Greece). In this way, the users can ask questions like “Find all Sentinel-1 GRD images taken during October 2021 that show large lakes in Greece having an area greater than 100 square kilometers”.

EarthQA follows a template-based approach to translate natural language questions into formal queries (SPARQL). Initially, it decomposes the user question by generating its dependency parse tree and then automatically disambiguates the components appearing in the question to elements of the two knowledge graphs.  In particular, it automatically identifies the spatial or temporal entities (e.g., “Greece”, “October 2021”), concepts (e.g., “lake”), spatial or temporal relations (e.g., “in”, “during”), properties (e.g., “area”) and product types (e.g., “Sentinel-1 GRD”) and other metadata (e.g., “cloud cover below 10%”) mentioned in the question and maps them to the respective elements appearing in the two knowledge graphs (dbr:Greece, dbo:Lake, dbp:area, etc). After this, the SPARQL query is automatically generated.

How to cite: Punjani, D., Tsalapati, E., and Koubarakis, M.: EarthQA: A Question Answering Engine for Earth Observation Data Archives, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10681, https://doi.org/10.5194/egusphere-egu23-10681, 2023.

In the current stage of scientific research, it is necessary to break the barriers between traditional disciplines and promote the cross integration of various related disciplines. As one of the important carriers of research achievements of various disciplines, maps can be superimposed and integrated to more intuitively display the results of multidisciplinary integration, promote the integration of disciplines and discover new scientific problems. Traditional geological mapping is often based on different scales for single scale mapping, aiming at the mapping mode of paper printing results. It is difficult to read maps between different scales at the same time. To solve this problem，an integrated platform named Global Layer is being built under the support of Deep-time Digital Earth (DDE). Global Layer is embedded with several core databases such as Geological Map of the World at a scale 1/5M, Global Geothermal Database etc. These databases presented in form of electronic map which enables the results of different scales to be displayed and browsed through one-stop hierarchical promotion. In addition, Users can also upload data in four ways: local file, database connection, cloud file and arcgis data service, and data or maping results can be shared to Facebook, Twitter and other platforms in the form of links, widgets, etc. Construction of Global Layer could provide experience and foundation for integrating global databases related to geological map and constructing data platforms.

How to cite: Yang, X., Song, Y., Wu, Z., and Wu, C.: Global Layer——An integrated, fully online, cloud based platform, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11527, https://doi.org/10.5194/egusphere-egu23-11527, 2023.

Streams and ditches are seldom identified on current maps due to their small dimensions and sometimes intermittent nature. Estimates point out that only 9% of all ditches are currently mapped, and the underestimation of natural streams is a global issue. Ditches have been dug in European boreal forests and some parts of North America to drain wetlands and increase forest production, consequently boosting the availability of cultivable land and a national-scale landscape modification. Target 6.6 of the Agenda 2030 highlights the importance of protecting and restoring water-related ecosystems. Wetlands are a substantial part of this, having a high carbon storage capability, the property of mitigating floods, and purifying water. All things accounted for, the withdrawal of anthropogenic environment alterations can be on the horizon, even more because ditches are also strong emitters of methane and other greenhouse gases due to their anoxic water and sediment accumulation. However, streams and ditches that are missing from maps and databases are difficult to manage.

The main focus of this study was to develop a method to map channels combining deep learning and national Aerial Laser Scans (ALS). The performance of different topographical indices derived from the ALS data was evaluated, and two different Digital Elevation Model (DEM) resolutions were compared. Ditch channels and natural streams were manually digitized from ten regions across Sweden, summing up to 1923km of ditch channels and 248km of natural streams. The topographical indices used were: high-passing median filter, slope, sky-view factor and hillshade (with azimuths of 0°, 45°, 90° and 135°); while 0.5m and 1m were the DEM resolutions analysed. A U-net model was trained to segment images between ditches and stream channels: all pixels from each image were labelled in a way that those with the same class display similar attributes.

Results showed that ditches can be successfully mapped with this method and it can generally be applied anywhere since only local terrain indices are required. Additionally, when the natural streams are present in the dataset the model underperformed in predicting the location of ditches, while a higher resolution had the opposite effect. Streams were more challenging to map, and the model only indicated the channels, not whether or not they contained water. Further research will be required to combine hydrological modelling and deep learning.

How to cite: Dos Santos Toledo Busarello, M., Ågren, A., and Lidberg, W.: Mapping streams and ditches using Aerial Laser Scanning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12373, https://doi.org/10.5194/egusphere-egu23-12373, 2023.

There is a soaring demand for up-to-date and spatially-explicit soil information to address various environmental challenges. One of the most basic pieces of information, essential for research and decision-making in multiple disciplines is soil classification. Conventional soil maps are often low in spatial resolution and lack the complexity to be practical for hands-on use. Digital Soil Mapping (DSM) has emerged as an efficient alternative for its reproducibility, updatablity, accuracy, and cost-effectiveness, as well as the ability to quantify uncertainties.

Despite DSM’s growing popularity and increasingly wider areas of application, soil information is still rare in forested areas and remote regions, and the integration with high-resolution data on a country scale remains limited. In Sweden, quaternary deposit maps created by the Geological Survey of Sweden (SGU) have been the main reference input for soil-related research and operation, though most parts of the country still warrant higher quality representation. This study utilizes machine learning to produce a high-resolution surficial deposits map with nationwide coverage, capable of supporting research and decision-making. More specifically, it: i) compares the performance of two tree-based ensemble machine learning models, Extreme Gradient Boosting and Random Forest, in predictive mapping of soils across the entire country of Sweden; ii) determines the best model for spatial prediction of soil classes and estimates the associated uncertainty of the inferred map; iii) discusses the advantages and limitations of this approach, and iv) outputs a map product of soil classes at 2-m resolution. Similar attempts around the globe have shown promising results, though at coarser resolutions and/or of smaller geographical extent. The main assumptions behind this study are: i) terrain indices derived from digital elevation model (DEM) are useful predictors of soil type, though different classification algorithms differ in their effectiveness; ii) machine learning can capture major soil classes that cover most of Sweden, but expert geological and pedological knowledge is required when identifying rare soil types.

To achieve this, approximately 850,000 labeled soil points extracted from the most accurate SGU maps will be combined with a stack of 12 LiDAR DEM-derived topographic and hydrological indices and 4 environmental datasets. Uncertainty estimates of the overall model and for each soil class will be presented. An independent dataset obtained from the Swedish National Forest Soil Inventory will be used to assess the accuracy of the machine learning model. The presentation will cover the method, data handling, and some promising preliminary results.

How to cite: Lin, Y., Lidberg, W., Karlsson, C., and Ågren, A.: Mapping Swedish Soils with High-resolution DEM-derived Indices and Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13099, https://doi.org/10.5194/egusphere-egu23-13099, 2023.

As human activities continue to expand and evolve, their impact on the planet is becoming more evident. These past years Murmuration has been studying one of the most recent and destructive trends that has taken off: mass tourism. In Malta, tourism has been on the rise since before the Covid-19 pandemic. Now that travel restrictions are beginning to lift, it's likely that this trend will go back to increasing in the coming years. While Malta’s economy is mostly based on tourism, it's essential that this activity does not alter the areas in which it takes place. To address these issues and ensure sustainable development, governments and organizations have developed a set of guidelines called Sustainable Development Goals (SDG). SDGs are a set of 17 goals adopted by the United Nations in 2015 to provide a framework to help countries pursue sustainable economic, social and environmental development. They include objectives for mitigating climate change, preventing water pollution and degradation of biodiversity, as well as providing economic benefits to local communities.

In order to help territories like the islands of Malta to cope with these environmental issues, Murmuration carries out studies on various ecological, human and economic indicators. Using the Sentinel satellites of the European Copernicus program for earth imagery data makes possible the collection of geolocated, hourly values on air quality indicators such as NO2, CO and other pollutants but also water quality and vegetation through the analysis of the vegetation health. Other data sources give access to land cover values at meter resolution, tourism infrastructures locations and many more human activity variables. This information is processed into understandable indicators, aggregated indexes which take international standards and SDGs in their design and usage. An example of these standards are the WHO air quality guidelines providing thresholds quantifying the impact on health of the air pollution in the area of interest. The last step is to gather all the data, maps and correlations computed and design understandable visualizations to make it usable by territory management instances, enabling efficient decision making and risk management. The goal here is to achieve a link between satellite imagery, internationally agreed political commitment  and ground level decision-making.

This meaningful aggregation comes in the shape of operational dashboards. A dashboard is an up-to-date, interactive, evolving online tool hosting temporal and geographical linked visualizations on various indicators. This kind of tool allows for a better understanding of the dynamic of a territory in terms of environmental state, human impact and ecological potential.

How to cite: Plantec, M. and Castel, F.: From satellite data and Sustainable Development Goals to interactive tools and better territorial decision making, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14519, https://doi.org/10.5194/egusphere-egu23-14519, 2023.

Karst aquifers cover a significant portion of the global water supply. However, a proper representation of groundwater recharge in karst areas is completely absent in the state-of-art global hydrological models. This study, based on the new version of the global hydrological model WaterGAP, (1) presented the first modeling of diffuse groundwater recharge (GWR) in all karst regions using the global map of karstifiable rocks; and (2) adjusted the current GWR algorithm with the up-to-date databases of slope and soil. A large number of ground-based recharge estimates on 818 half degree cells including 75 in karst areas were compared to model results. GWR in karst landscapes assuming equal to the runoff from soil leads to unbiased estimation. The majority of simulated mean annual recharge ranges from 0.6 mm/yr (10^th percentile) to 326.9 mm/yr (90^th) in nonkarst regions, and 7.5 mm/yr (10^th) to 740.2 mm/yr (90^th) in karst regions. The recharge rate ranges from 2% to 66% of precipitation according to ground-based estimates in karst regions, while the simulated GWR produces global recharge fractions between 4% (10^th) to 68% (90^th) in karst areas while that in nonkarst areas rarely exceeds 25%. Unlike the previous studies that claimed global hydrological models consistently underestimate recharge, we observed underestimation only in the very humid regions where recharge exceeds 300 mm/yr. These very high recharge estimates are likely to include preferential flow and adopt a finer spatial and temporal scale than the global model. In karst landscapes and arid regions, we demonstrate that WaterGAP incorporating karst algorithm gives a worthy performance.

How to cite: Wan, W. and Döll, P.: Karst integration into groundwater recharge simulation in WaterGAP, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15656, https://doi.org/10.5194/egusphere-egu23-15656, 2023.

In Marine Geology, scientists persistently perform extensive experiments to measure diverse features across the globe, hence to estimate environmental changes. For example, Mass Accumulation Rate (MAR) and Sedimentation Rate (SR) are measured by marine geologists at various oceanographic locations and are largely reported in research publications but have not been compiled in any central database. Furthermore, every MAR and SR observation normally carries i) exact locational information (Longitude and Latitude), ii) the method of measurement (stratigraphy, 210Pb), iii) a numerical value and units (2.4 g/m²/yr), iv) temporal feature (e.g. hundred years ago). The contextual information attached to MAR and SR observations is heterogeneous and manual approaches for information extraction from text are infeasible. It is also worth mentioning that MAR and SR are not denoted in standard international (SI) units.

We propose the comprehensive end-to-end framework GEOTEK (Geological Text to Knowledge) to extract targeted information from marine geology publications. The proposed framework comprises three modules. The first module carries a document relevance model alongside a PDF extractor, capable of filtering relevant sources using metadata, and the extraction module extracts text, tables, and metadata respectively. The second module mainly comprises of two information extractors, namely Geo-Quantities and Geo-Spacy, particularly trained on text from the Marine Geology domain. Geo-Quantities is capable of extracting relevant numerical information from the text and covers more than 100 unit variants for MAR and SR, while Geo-Spacy extracts a set of relevant named entities as well as locational entities, which are further processed to obtain respective geocode boundaries. The third module, the Heterogeneous Information Linking module (HIL), processes exact spatial information from tables and captions and forms links to the previously extracted measurements. Finally, the all-linked information is populated in an interactive map view.

How to cite: Suryani, M. A., Beth, C., Wallmann, K., and Renz, M.: GEOTEK: Extracting Marine Geological Data from Publications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16252, https://doi.org/10.5194/egusphere-egu23-16252, 2023.

Climate change is arguably the greatest environmental challenge facing humankind in the twenty-first century. The United Nations Framework Convention on Climate Change (UNFCCC) facilitates multilateral action to combat climate change and its impacts on humanity and ecosystems. To make decisions on climate change mitigation and adaptation, the UNFCCC requires systematic observations of the global climate system.

The objective of the ESA’s climate programme, currently delivered via the Climate Change Initiative (CCI), is to realise the full potential of the long-term, global-scale, satellite earth observation archive that ESA and its Member States have established over the last 35 years, as a significant and timely contribution to the climate data record required by the UNFCCC.

Since 2010, the programme has contributed to a rapidly expanding body of scientific knowledge on >22 Essential Climate Variables (ECVs), through the production of Climate Data Records (CDRs). Although varying across geophysical parameters, ESA CDRs follow community-driven data standards, facilitating inter- and cross-ECV research of the climate system.

In this work, we highlight the use of artificial intelligence (AI) in the context of the ESA CCI. AI has played a pivotal role in the production and analysis of these Climate Data Records. Eleven CCI projects - Greenhouse Gases (GHG), Aerosols, Clouds, Fire, Ocean Colour, Sea Level, Soil Moisture, High Resolution Landcover, Biomass, Permafrost, and Sea Surface Salinity - have applied AI in their data record production and research or have identified specific AI usage for their research roadmaps.

The use of AI in these CCI projects is varied, for example - GHG CCI algorithms using random forest machine learning techniques; Aerosol CCI algorithms to retrieve dust aerosol optical depth from thermal infrared spectra; Fire CCI algorithms to detect burned areas. Moreover, the ESA climate community has identified climate science gaps in context to ECVs with the potential for meaningful advancement through AI.

We specifically focus on showcasing the use of AI for data homogenization and super-resolution of ESA CCI datasets. For instance, both the land cover and fire CCI dataset were generated globally in low resolution, while high resolution data only exists for specific geographical regions. By adapting super-resolution algorithms to the specific science use cases, we can accelerate the generation of global, high-resolution datasets with the required temporal coverage to support long-term climate studies. 

How to cite: Jungbluth, A., Pechorro, E., Albergel, C., and Mecklenburg, S.: The Use of Artificial Intelligence in ESA’s Climate Change Initiative, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16813, https://doi.org/10.5194/egusphere-egu23-16813, 2023.

In most places on the planet vegetation thrives: it is known as “greening Earth”. However in certain regions, especially in the Arctic, there are areas exhibiting a browning trend. This phenomenon is well known but not fully understood yet, and grasping its impact on local ecosystems requires involvement of scientists from different disciplines, including social sciences and humanities, as well as local populations. Here we focus on the Troms and Finnmark counties in northern Norway to assess the extent of the problem and any link with local environmental conditions as well as potential impacts. 

We have chosen to adopt an open and collaborative process and take advantage of the services offered by RELIANCE on the European Open Science Cloud (EOSC). RELIANCE delivers a suite of innovative and interconnected services that extend the capabilities of the European Open Science Cloud (EOSC) to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The RELIANCE project has delivered 3 complementary  technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. 

We will show how we are using these technologies along with EGI notebooks to work open and share an executable Jupyter Notebook that is fully reproducible and reusable. We use a number of Python libraries from the Pangeo software stack such as Xarray, Dask and Zarr. Our Jupyter Notebook is bundled with its computational environment, datacubes and related bibliographic resources in an executable Research Object. We believe that this approach can significantly speed up the research process and can drive it to more exploitable results. 

Up to now, we have used indices derived from satellite data (in particular Sentinel-2) to assess how the vegetation cover in Troms and Finnmark counties has changed. To go a bit further we are investigating how to relate such information to relevant local parameters obtained from meteorological reanalysis data (ERA5 and ERA5-land from ECMWF). That should give a good basis for training an Artificial Intelligence algorithm and testing it, with the objective of getting an idea about the possibility of “predicting” what is likely to happen in the near future with certain types of vegetation like mosses and lichens which are essential for local populations and animals.

How to cite: Iaquinta, J. and Fouilloux, A.: Using FAIR and Open Science practices to better understand vegetation browning in Troms and Finnmark (Norway), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2579, https://doi.org/10.5194/egusphere-egu23-2579, 2023.

Although utilizing the cloud infrastructure for big data processing algorithms is increasingly common, the challenges of utilizing cloud infrastructures efficiently and effectively are often underestimated. This is especially true in multi-cloud scenarios where data are available only on a subset of the participating clouds. In this study, we have iteratively developed a solution enabling efficient access to ECMWF’s Numerical Weather Prediction (NWP) and EUMETSAT’s satellite data on the European Weather Cloud [1], in combination with UK Met Office assets in Amazon Web Services (AWS), in order to provide a common template for multi-cloud processing solutions in meteorological application development and operations in Europe.  

Dask [2] was chosen as the computing framework due to its widespread use in the meteorological community, its ability to automatically spread processing, and its flexibility in changing how workloads are distributed across physical or virtualized infrastructures while maintaining scalability. However, the techniques used here are generally applicable to other frameworks. The primary limitation in using Dask is that all nodes should be able to intercommunicate freely, which is a serious limitation when nodes are distributed over multiple clouds. Although it is possible to route between multiple cloud environments over the Internet, this introduces considerable administrative work (firewalls, security) as well as networking complexities (e.g., due to extensive use of potentially-clashing private IP ranges and NAT in clouds, or cost for public IPs). Virtual Private Networks (VPNs) can hide these issues, but many use a hub-and-spokes model, meaning that communications between workers pass through a central hub. By use of a mesh network VPN (WireGuard) between clusters using IPv6 private addressing, all these difficulties can be avoided, in addition to providing a simplified network addressing scheme with extremely high scalability. Another challenge was to ensure the Dask worker nodes were aware of data locality, both in terms of placing work near data and in terms of minimizing transfers. Here, the UK Met Office’s work on labeling resource pools (in this case, data) and linking scheduling decisions to labels was the key. 

In summary, by adapting Dask's concept of resourcing [3] into resource pools [4], building an automated start-up process, and effectively utilizing self-configuring IPv6 VPN mesh networks, we managed to provide a “cloud-native” transient model where all resources can be easily created and disposed of as needed. The resulting “throwaway” multi-cloud Dask framework is able to efficiently place processing on workers proximate to the data while minimizing necessary data traffic between clouds, thus achieving results more quickly and cheaper than naïve implementations, and with a simple, automated setup suitable for meteorological developers. The technical basis of this work was published on the Dask blog [5] but is covered more holistically here, particularly regarding the application side and challenges of developing cloud-native applications which can effectively utilize modern multi-cloud environments, with future applicability to distributed (e.g., Kubernetes) and serverless computing models. 

References: 

[1] https://www.europeanweather.cloud 
[2] https://www.dask.org 
[3] https://distributed.dask.org/en/stable/resources.html
[4] https://github.com/gjoseph92/dask-worker-pools  
[5] https://blog.dask.org/2022/07/19/dask-multi-cloud  

How to cite: Karatosun, A., Grant, M., Baousis, V., McGregor, D., Care, R., Nolan, J., and Tervo, R.: Data Proximate Computation; Multi-cloud approach on European Weather Cloud and Amazon Web Services , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3639, https://doi.org/10.5194/egusphere-egu23-3639, 2023.

The National Oceanic and Atmospheric Administration (NOAA) established the Earth Prediction Innovation Center (EPIC) to be the catalyst for community research and modeling focused on informing and accelerating advances in our nation’s operational NWP forecast modeling systems. The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. The UFS numerical applications span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. It is designed to support the Weather Enterprise and to be the source system for NOAA‘s operational numerical weather prediction applications. EPIC applies an open-innovation and open-development framework that embraces open-source code repositories integrated with automated Continuous Integration/Continuous Deployment (CI/CD) pipelines on cloud and on-prem HPCs. EPIC also supports UFS public releases, tutorials and training opportunities (e.g., student workshops, hackathons, and codesprints), and advanced user support via a virtual community portal (epic.noaa.gov). This framework allows community developers to track the status of their contributions, and facilitate rapid incorporation of innovation by implementing consistent and transparent, standardized and community-driven validation and verification tests. In this presentation, I will demonstrate capabilities in the EPIC framework using the UFS Short-range Weather (SRW) Application as an example in the follow aspects:

• Public Releases of a Cloud-ready UFS SRW application with a scalable container following a modernize continuous release paradigm 
• Test cases for challenging forecast environments released with datasets
• Training and Tutorials for users and developers
• Baseline for benchmarking in skill and computation on cloud HPCs , and
• An Automated CI/CD pipeline to enable seamless transition to operations

How to cite: Huang, M.: An Open-innovation and Open-development Framework for the Unified Forecast System Powered by the Earth Prediction Innovation Center, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3738, https://doi.org/10.5194/egusphere-egu23-3738, 2023.

BUILDSPACE aims to couple terrestrial data from buildings (collected by IoT platforms, BIM solutions and other) with aerial imaging from drones equipped with thermal cameras and location annotated data from satellite services (i.e., EGNSS and Copernicus) to deliver innovative services for the building and urban stakeholders and support informed decision making towards energy-efficient buildings and climate resilient cities. The platform will allow integration of these heterogeneous data and will offer services at building scale, enabling the generation of high fidelity multi-modal digital twins and at city scale providing decision support services for energy demand prediction, urban heat and urban flood analysis. The services will enable the identification of environmental hotspots that increase pressure to local city ecosystems and raise probability for natural disasters (such as flooding) and will issue alerts and recommendations for action to local governments and regions (such as the support of policies for building renovation in specific vulnerable areas). BUILDSPACE services will be validated and assessed in four European cities with different climate profiles. The digital twin services at building level will be tested during the construction of a new building in Poland, and the city services validating the link to digital twin of buildings will be tested in 3 cities (Piraeus, Riga, Ljubljana) across EU. BUILDSPACE will create a set of replication guidelines and blueprints for the adoption of the proposed applications in building resilient cities at large. 

How to cite: Rizou, S., Marinakis, V., Hernández Moral, G., Sánchez-Guevara, C., Sánchez-Aparicio, L. J., Brilakis, I., Baousis, V., Maes, T., Tsetsos, V., Boaria, M., Dymarski, P., Bourmpos, M., Pergar, P., and Brieze, I.: BUILDSPACE: Enabling Innovative Space-driven Services for Energy Efficient Buildings and Climate Resilient Cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4298, https://doi.org/10.5194/egusphere-egu23-4298, 2023.

The second phase (2023-2026) of the Center of Excellence for Exascale in Solid Earth (ChEESE-2P), funded by HORIZON-EUROHPC-JU-2021-COE-01 under the Grant Agreement No 101093038, will prepare 11 European flagship codes from different geoscience domains (computational seismology, magnetohydrodynamics, physical volcanology, tsunamis, geodynamics, and glacier hazards). Codes will be optimised in terms of performance on different types of accelerators, scalability, containerisation, and continuous deployment and portability across tier-0/tier-1 European systems as well as on novel hardware architectures emerging from the EuroHPC Pilots (EuPEX/OpenSequana and EuPilot/RISC-V) by co-designing with mini-apps. Flagship codes and workflows will be combined to farm a new generation of 9 Pilot Demonstrators (PDs) and 15 related Simulation Cases (SCs) representing capability and capacity computational challenges selected based on their scientific importance, social relevance, or urgency. The SCs will produce relevant EOSC-enabled datasets and enable services on aspects of geohazards like urgent computing, early warning forecast, hazard assessment, or fostering an emergency access mode in EuroHPC systems for geohazardous events including access policy recommendations. Finally, ChEESE-2P will liaise, align, and synergise with other domain-specific European projects on digital twins and longer-term mission-like initiatives like Destination Earth.

How to cite: Folch, A., DelaPuente, J., Costa, A., Halldórson, B., Gracia, J., Lanucara, P., Bader, M., Gabriel, A.-A., Macías, J., Lovholt, F., Montellier, V., Fournier, A., Raffin, E., Zwinger, T., Denamiel, C., Kaus, B., and le Pourhiet, L.: The EuroHPC Center of Excellence for Exascale in Solid Earth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5807, https://doi.org/10.5194/egusphere-egu23-5807, 2023.

Satellite-based Interferometric Synthetic Aperture Radar (InSAR) plays a significant role for numerous surface motion monitoring applications, e.g. civil-infrastructure stability, hydrocarbons extraction, etc. InSAR monitoring is based on a coregistered stack of Single Look Complex (SLC) SAR images. Due to the long temporal coverage, broad spatial coverage and high spatio-temporal resolution of an SLC SAR stack, handling it in an efficient way is a common challenge within the community. Aiming to meet this need, we present SarXarray: an open-source Xarray extension for SLC SAR stack processing. SarXarray provides a Python interface to read and write a coregistered stack of SLC SAR data, with basic SAR processing functions. It utilizes Xarray’s support on labeled multi-dimensional datasets to stress the space-time character of an SLC SAR stack. It also leverages Dask to perform lazy evaluation of the operations. SarXarray can be integrated to existing Python workflows in a flexible way. We provide a case study of creating a SAR Mean Reflectivity Map to demonstrate the functionality of SarXarray.

How to cite: Ku, O., Nattino, F., Grootes, M., Chandramouli, P., and van Leijen, F.: SarXarray: an Xarray extension for SLC SAR data processing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6768, https://doi.org/10.5194/egusphere-egu23-6768, 2023.

Typical end-to-end Earth System Modelling (ESM) workflows rely on different steps including data pre-processing, numerical simulation, output post-processing, as well as data analytics and visualization. The approaches currently available for implementing scientific workflows in the climate context do not properly integrate the entire set of components into a single workflow and in a transparent manner. The increasing usage of High Performance Data Analytics (HPDA) and Machine Learning (ML) in climate applications further exacerbate the issues. A more integrated approach would allow to support next-generation ESM and improve the workflow in terms of execution and energy consumption.

Moreover, a seamless integration of components for HPDA and ML into the ESM workflow will open the floor to novel applications and support larger scale pre- and post-processing. However, these components typically have different deployment requirements spanning from HPC (for ESM simulation) to Cloud computing (for HPDA and ML). It is paramount to provide scientists with solutions capable of hiding the technical details of the underlying infrastructure and improving workflow portability.

In the context of the eFlows4HPC project, we are exploring the use of innovative workflow solutions integrating approaches from HPC, HPDA and ML for supporting end-to-end ESM simulations and post-processing, with a focus on extreme events analysis (e.g., heat waves and tropical cyclones). In particular, the envisioned solution exploits PyCOMPSs for the management of parallel pipelines, task orchestration and synchronization, as well as PyOphidia for climate data analytics and ML frameworks (i.e., TensorFlow) for data-driven event detection models. This contribution presents the approaches being explored in the frame of the project to address the convergence of HPC, Big Data and ML into a single end-to-end ESM workflows.

How to cite: Elia, D., Scardigno, S., D'Anca, A., Accarino, G., Ejarque, J., Immorlano, F., Peano, D., Scoccimarro, E., Badia, R. M., and Aloisio, G.: Convergence of HPC, Big Data and Machine Learning for Earth System workflows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6857, https://doi.org/10.5194/egusphere-egu23-6857, 2023.

Remote-sensing (RS) and Earth observation (EO) data have become crucial in areas ranging from science to policy, with their use expanding beyond the ‘usual’ fields of geosciences to encompass ‘green’ life sciences, agriculture, and even social sciences. Within this context, the RS-DAT project has developed and made available a readily deployable framework enabling researchers to scale their analysis of EO and RS data on HPC systems and associated storage resources. Building on and expanding the established tool stack of the Pangeo Community, the framework integrates tools to access, retrieve, explore, and process geospatial data, addressing common needs identified in the EO domain. On the computing side RS-DAT leverages Jupyter (Python), which provides users a web-based interface to access (remote) computational resources, and Dask, which enables to scale analysis and workflows to large computing systems. Both Jupyter and Dask are well-established tools in the Pangeo community and can be deployed in several ways and on different infrastructures. RS-DAT provides an easy-to-use deployment framework for two targets: the generic case of SLURM-based HPC systems (for example, Dutch Supercomputer Snellius/Spider) which offer flexibility in computational resources; and the special case of an ansible-based cloud-computing infrastructure (Surf Research Cloud (SRC)) which is more straight-forward for the user but less flexible. Both these frameworks enable the easy scale-up of workflows, using HPCs, to access, manipulate and process large-scale datasets as commonly found in EO. On the data access and storage side RS-DAT integrates two python packages, STAC2dCache and dCacheFS, which were developed to facilitate data retrieval from online STAC catalogs (STAC2dCache) and its storage on the HPC system or local mass storage, specifically dCache.  This ensures efficient computation for large-scale analyses where data retrieval and handling can cause significant bottlenecks. User-defined input/output to Zarr file format is also supported within the framework. We present an application of the tools developed to the calculation of leaf-spring indices for North America using the Daymet dataset at a 1km resolution for 42 years (~940 GiB, completed in under 5 hours using 60 cores on the Dutch supercomputing system) and look forward to on-going work integrating both deployment targets in the case of the Dutch HPC ecosystem.

How to cite: Chandramouli, P., Nattino, F., Grootes, M., Ku, O., Alidoost, F., and Dzigan, Y.: Remote Sensing Deployable Analysis environmenT, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6960, https://doi.org/10.5194/egusphere-egu23-6960, 2023.

How to cite: Gans, F. and Cremer, F.: Scaling up a Sentinel 1 change detection pipeline using the Julia programming language, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7825, https://doi.org/10.5194/egusphere-egu23-7825, 2023.

In order to make use of a collection of datasets – for example, scenes from a SAR satellite – more efficient, it is important to be able to search for datasets relevant for a specific application. In particular, one might want to search for a specific period in time, for the spatial extent, or perform searches over multiple collections together.

For SAR data or data obtained from optical satellites, Spatio-Temporal Asset Catalogs (STAC) have become increasingly popular in the past few years. Defined as JSON and backed by databases with geospatial extensions, STAC servers (endpoints) have the advantage of being efficient, language-agnostic and following a standardized API.

Just like satellite scenes, in-situ data is growing in size very quickly and thus would benefit from being catalogued. However, the sequential nature of in-situ data and its sparse distribution in space makes it difficult to fit into STAC's standard model.

In the session, we present a experimental STAC extension that defines the most common properties of in-situ data as identified from ArgoFloat and  biologging data.

How to cite: Magin, J. and Odaka, T.: Spatio-Temporal Asset Catalog (STAC) for in-situ data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8096, https://doi.org/10.5194/egusphere-egu23-8096, 2023.

The ever increasing number of scientific datasets made available by authoritative data providers (NASA, Copernicus, etc.) and provided by the scientific community opens new possibilities for advancing the state of the art in many areas of the natural sciences. As a result, researchers, innovators, companies and citizens need to acquire computational and data analysis skills to optimally exploit these datasets. Several educational programs dispense basic courses to students, and initiatives such as “The Carpentries” (https://carpentries.org/) complement this offering but also reach out to established researchers to fill the skill gap thereby empowering them to perform their own data analysis. However, most researchers find it challenging to go beyond these training sessions and face difficulties when trying to apply their newly acquired knowledge to their own research projects. To this regard, hackathons have proven to be an efficient way to support researchers in becoming competent practitioners but organising good hackathons is difficult and time consuming. In addition, the need for large amounts of computational and storage resources during the training and hackathons requires a flexible solution. Here, we propose an approach where researchers  work on realistic, large and complex data analysis problems similar to or directly part of  their research work. Researchers access an infrastructure deployed on the European Ocean Science Cloud (EOSC)  that supports intensive data analysis (large compute and storage resources). EOSC is a European Commission initiative for providing a federated and open multi-disciplinary environment where data, tools and services can be shared, published, found and re-used. We used jupyter book for delivering a collection of FAIR training materials for data analysis relying on Pangeo EOSC deployments as its primary computing platform. The training material (https://pangeo-data.github.io/foss4g-2022/intro.html, https://pangeo-data.github.io/clivar-2022/intro.html, https://pangeo-data.github.io/escience-2022/intro.html) is customised (different datasets with similar analysis) for different target communities and participants are taught the usage of Xarray, Dask and more generally how to efficiently access and analyse large online datasets. The training can be completed by group work where attendees can work on larger scale scientific datasets: the classroom is split into several groups. Each group works on different scientific questions and may use different datasets. Using the Pangeo (http://pangeo.io) ecosystem is not always new for all attendees but applying Xarray (http://xarray.pydata.org)  and Dask (https://www.dask.org/) on actual scientific “mini-projects” is often a showstopper for many researchers. With this approach, attendees have the opportunity to ask questions, collaborate with other researchers as well as Research Software Engineers, and apply Open Science practices without the burden of trying and failing alone. We find the involvement of scientific computing research engineers directly in the training is crucial for success of the hackathon approach. Feedback from attendees shows that it provides a solid foundation for big data geoscience and helps attendees to quickly become competent practitioners. It also gives infrastructure providers and EOSC useful feedback on the current and future needs of researchers for making their research FAIR and open. In this presentation, we will provide examples of achievements from attendees and present the feedback EOSC providers have received.

How to cite: Fouilloux, A., Marasco, P. L., Odaka, T., Mottram, R., Zieger, P., Schulz, M., Coca-Castro, A., Iaquinta, J., and Eynard Bontemps, G.: Pangeo framework for training: experience with FOSS4G, the CLIVAR bootcamp and the eScience course, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8756, https://doi.org/10.5194/egusphere-egu23-8756, 2023.

Research projects heavily rely on the exchange and processing of data and in this context Pangeo (https://pangeo.io/), a world-wide community of scientists and developers, thrives to facilitate the deployment of ready to use and community-driven platforms for big data geoscience. The European Open Science Cloud (EOSC) is the main initiative in Europe for providing a federated and open multi-disciplinary environment where European researchers, innovators, companies and citizens can share, publish, find and re-use data, tools and services for research, innovation and educational purposes. While a number of services based on Jupyter Notebooks were already available, no public Pangeo deployments providing fast access to large amounts of data and compute resources were accessible on EOSC. Most existing cloud-based Pangeo deployments are USA-based, and members of the Pangeo community in Europe did not have a shared platform where scientists or technologists could exchange know-how. Pangeo teamed up with two EOSC projects, namely EGI-ACE (https://www.egi.eu/project/egi-ace/) and C-SCALE (https://c-scale.eu/) to demonstrate how to deploy and use Pangeo on EOSC and emphasise the benefits for the European community. 

The Pangeo Europe Community together with EGI deployed a DaskHub, composed of Dask Gateway (https://gateway.dask.org/) and JupyterHub (https://jupyter.org/hub), with Kubernetes cluster backend on EOSC using the infrastructure of the EGI Federation (https://www.egi.eu/egi-federation/). The Pangeo EOSC JupyterHub deployment makes use of 1) the EGI Check-In to enable user registration and thereby authenticated and authorised access to the Pangeo JupyterHub portal and to the underlying distributed compute infrastructure; and 2) the EGI Cloud Compute and the cloud-based EGI Online Storage to distribute the computational tasks to a scalable compute platform and to store intermediate results produced by the user jobs. 

To facilitate future Pangeo deployments on top of a wide range of cloud providers (AWS, Google Cloud, Microsoft Azure, EGI Cloud Computing, OpenNebula, OpenStack, and more), the Pangeo EOSC JupyterHub deployment is now possible through the Infrastructure Manager (IM) Dashboard (https://im.egi.eu/im-dashboard/login). All the computing and storage resources are currently supplied by CESNET (https://www.cesnet.cz/?lang=en) in the frame of EGI-ACE project (https://im.egi.eu/). Several deployments have been made to serve the geoscience community, both for teaching and for research work. To date, more than 100 researchers have been trained on Pangeo@EOSC deployments and more are expected to join, in particular with easy access to large amounts of Copernicus data through a recent collaboration established with the C-SCALE project. In this presentation, we will provide details on the different deployments, how to get access to JupyterHub deployments and more generally how to contribute to Pangeo@EOSC.

How to cite: Eynard-Bontemps, G., Iaquinta, J., Luna-Valero, S., Caballer, M., Paul, F., Fouilloux, A., Ragan-Kelley, B., Marasco, P. L., and Odaka, T.: Pangeo@EOSC: deployment of PANGEO ecosystem on the European Open Science Cloud, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9095, https://doi.org/10.5194/egusphere-egu23-9095, 2023.

The Joint Effort for Data assimilation Integration (JEDI), is an innovative data assimilation system for Earth system prediction, spearheaded by the Joint Center for Satellite Data Assimilation (JCSDA) and slated for implementation in major operational modeling systems across the globe in the coming years. Funded as an inter-agency development by NOAA, NASA, the U.S. Navy and Air Force, and with contributions from the UK Met Office, JEDI must operate on a wide range of computing platforms. The recent move towards cloud computing systems puts portability, adaptability and performance across systems, from dedicated High Performance Computing systems to commercial clouds and workstations, in the critical path for the success of JEDI.

JEDI is a highly complex application that relies on a large number of third-party software packages to build and run. These packages can include I/O libraries, workflow engines, Python modules for data manipulation and plotting, several ECMWF libraries for complex arithmetics and grid manipulations, and forecast models such as the Unified Forecast System (UFS), the Goddard Earth Observing System (GEOS), the Modular Ocean Model (MOM6), the Model for Prediction across Scales (MPAS), the Navy Environmental Prediction sysTem Utilizing the NUMA corE (NEPTUNE), and the Met Office Unified Model (UM).

With more than 100 contributors and rapid code development it is critical to perform thorough automated testing, from basic unit tests to comprehensive end-to-end-tests. This presentation summarizes recent efforts to leverage cloud computing environments for research, development, and near real-time applications of JEDI, as well as for developing a Continuous Integration/Continuous Delivery (CI/CD) pipeline. These efforts rest on a newly developed software stack called spack-stack, a joint effort of JCSDA, the NOAA Environmental Modeling Center (EMC) and the U.S. Earth Prediction Innovation Center (EPIC). Automatic testing in JEDI is implemented with modern software development tools such as GitHub, Docker containers, various Amazon Web Services (AWS), and CodeCov for testing and evaluation of code performance. End-to-end testing is realized in JCSDA’s newly developed Skylab Earth system data assimilation application, which combines JEDI with the Research Repository for Data and Diagnostics (R2D2) and the Experiments and Workflow Orchestration Kit (EWOK), and which leverages the AWS Elastic Compute Cloud (EC2) for testing, research, development and production.

How to cite: Heinzeller, D., Abdi-Oskouei, M., Herbener, S., Lingerfelt, E., Trémolet, Y., and Auligné, T.: The Joint Effort for Data Assimilation Integration (JEDI): A unified data assimilation framework for Earth system prediction supported by NOAA, NASA, U.S. Navy, U.S. Air Force, and UK Met Office, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10697, https://doi.org/10.5194/egusphere-egu23-10697, 2023.

Significant progress has been made in recent years to develop km-scale versions of global Earth System Models (ESM), combining the chance of replacing uncertain model parameterizations by direct treatment and the improved representation of orographic and land surface features (Schär et al., 2020, Hohenegger et al., 2022). However, adapting climate codes to new hardware and at the same time keeping the performance portability, still remains a major issue. Given the long development cycles, the various maturity of ESM modules and their large code bases, it is not expected that all code parts can be brought to the same level of exascale readiness in the near future. Instead, short term model adaptation strategies need to focus on software abilities as well as hardware availability. Moreover, energy use efficiency is of growing importance on both sides, supercomputer providers and scientific projects employing climate simulations.

Here, we present results from first simulations of the coupled atmosphere-ocean modelling system ICON-v2.6.6-rc on the supercomputing system JUWELS at the Jülich Supercomputing Centre (JSC) with a global resolution of 5 km, using significant parts of the HPC system. While the atmosphere part of ICON (ICON-A) is capable of running on GPUs, model I/O currently performs better on a CPU cluster and the ocean module (ICON-O) has not been ported to modern accelerators yet. Thus, we make use of the modular supercomputing architecture (MSA) of JUWELS and its novel batch job options for the coupled ICON model with ICON-A running on the NVIDIA A100 GPUs of JUWELS Booster, while ICON-O and the model I/O are running simultaneously on the CPUs of the JUWELS Cluster partition. As expected, ICON performance is limited by ICON-A. Thus we chose the performance-optimal Booster-node configuration for ICON-A considering also memory requirements (84 nodes) and adapted ICON-O configuration to achieve minimum waiting times for simultaneous time step execution and data exchange (63 cluster nodes).  We compared runtime and energy efficiency to cluster-only simulations (on up to 760 cluster nodes) and found only small improvements in runtime for the MSA case, but energy consumption is already reduced by 26% without further improvements in vector length applied with ICON. When switching to even higher ICON resolutions, cluster-only simulations are not fitting to most of current HPC systems and upcoming exascale systems will rely to a large extent on GPU acceleration. Thus exploiting MSA capabilities is an important step towards performance portable and energy efficient use of km-scale climate models.

References:

Hohenegger et al., ICON-Sapphire: simulating the components of the Earth System and their interactions at kilometer and subkilometer scales, https://doi.org/10.5194/gmd-2022-171, in review, 2022.

Schär et al., Kilometer-Scale Climate Models: Prospects and Challenges, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020.

How to cite: Stein, O., Bishnoi, A., Kornblueh, L., Hoffmann, L., Eicker, N., Suarez, E., and Meyer, C. I.: Modeling the Earth System on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11117, https://doi.org/10.5194/egusphere-egu23-11117, 2023.

The European Centre for Medium-Range Weather Forecasts (ECMWF) together with the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) have worked together to offer to their Member States a new paradigm to access and consume weather data and services. The “European Weather Cloud-(EWC)” (https://www.europeanweather.cloud/), concluded its pilot phase and is expected to become operational during the first months of 2023.

This initiative aims to offer a community cloud infrastructure on which Member and Co‐operating States of both organizations can create on demand virtual compute (including GPUs) and storage resources to gain easy and high throughput access to the ECMWF’s Numerical Weather Predication (NWP) and EUMETSAT’s satellite data in a timely and configurable fashion. Moreover, one of the main goals is to involve more National Meteorological Services to jointly form a federation of clouds/data offered from their Member States, for the maximum benefit of the European Meteorological Infrastructure (EMI). During the pilot phase of the project, both organizations have jointly hosted user and technical workshops to actively engage with the meteorological community and align the evolution of the EWC to reflect and satisfy their operational goals and needs.

The EWC, in its pilot phase hosted several use cases, mostly aimed at users in the developers’ own organisations. These broad categories of these cases are:

• Web services to explore hosted datasets
• Data processing applications
• Platforms to support the training of machine learning models on archive datasets
• Workshops and training courses (e.g., ICON model training, ECMWF training etc)
• Research in collaboration with external partners
• World Meteorological Organization (WMO) support with pilots and PoC.

Some examples of the use cases currently developed at the EWC are:

• The German weather service DWD, which is already feeding maps generated by a server it deployed on the cloud into its public GeoPortal service.
• EUMETSAT and ECMWF joint use case assesses bias correction schemes for the assimilation of radiance data based on several satellite data time series
• the Royal Netherlands Meteorological Institute (KNMI) hosts a climate explorer web application based on KNMI climate explorer data and ECMWF weather and climate reanalyses
• The Royal Meteorological Institute of Belgium prepares ECMWF forecast data for use in a local atmospheric dispersion model.
• NordSat, a collaboration of northern European countries which is developing and testing imagery generation tools in preparation for the Meteosat Third Generation (MTG) satellite products.
• UK Met Office with the DataProximateCompute use case, which distributes compute workload close to data, with the automatic creation and disposal of Dask clusters, as well as the data plane VPN network, on demand and in heterogeneous cloud environments.

In this presentation, the status of the project, the offered services and how these are accessed by the end users along with examples of the existing use cases will be analysed. The plans, next steps for the evolution of the EWC and its relationship with other projects and initiatives (like DestinE) will conclude the presentation.

How to cite: Cuccu, R., Baousis, V., Modigliani, U., Kominos, C., Abellan, X., and Tervo, R.: European Weather Cloud: A community cloud tailored for big Earth modelling and EO data processing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12539, https://doi.org/10.5194/egusphere-egu23-12539, 2023.

Managing huge volumes of data is a problem now, and will only become worse with the advent of exascale computing and next generation observational systems. An important recognition is that data needs to be more easily migrated between storage tiers. Here we present a new solution, the Near-Line Data store (NLDS), for managing data migration between user facing storage systems and tape by using an object storage cache.  NLDS builds on lessons learned from previous experience developing the ESIWACE funded Joint Data Migration App (JDMA) and deploying it at the Centre for Environmental Data Analysis (CEDA). 
 
CEDA currently has over 50PB of data stored on a range of disk based storage systems.  These systems are chosen on cost, power usage and accessibility via a network, and include three different types of POSIX disk and object storage. Tens of PB of additional data are also stored on tape. Each of these systems has different workflows, interfaces and latencies, causing difficulties for users.  

NLDS, developed with ESIWACE2 and other funding, is a multi-tiered storage solution using object storage as a front end to a tape library.  Users interact with NLDS via a HTTP API, with a Python library and command-line client provided to support both programmatic and interactive use.  Files transferred to NLDS are first written to the object storage, and a backup is made to tape.  When the object storage is approaching capacity, a set of policies is interrogated to determine which files will be removed from it.  Upon retrieving a file, NLDS may have to first transfer the file from tape to the object storage, if it has been deleted by the policies.  This implements a multi-tier of hot (disk), warm (object storage) and cold (tape) storage via a single interface. While systems like this are not novel, NLDS is open source, designed for ease of redeployment elsewhere, and for use from both local storage and remote sites. 

NLDS is based around a microservice architecture, with a message exchange brokering communication between the microservices, the HTTP API and the storage solutions.  The system is deployed via Kubernetes, with each microservice in its own Docker container, allowing the number of services to be scaled up or down, depending on the current load of NLDS.  This provides a scalable, power efficient system while ensuring that no messages between microservices are lost.  OAuth is used to authenticate and authorise users via a pluggable authentication layer. The use of object storage as the front end to the tape allows both local and remote cloud-based services to access the data, via a URL, so long as the user has the required credentials. 

NLDS is a a scalable solution to storing very large data for many users, with a user-friendly front end that is easily accessed via cloud computing. This talk will detail the architecture and discuss how the design meets the identified use cases.

How to cite: Massey, N., Leland, J., and Lawrence, B.: A Scalable Near Line Storage Solution for Very Big Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12785, https://doi.org/10.5194/egusphere-egu23-12785, 2023.

Tackling complex environmental issues requires accessing and processing a wide range of voluminous data. The Copernicus spatial data is a very complete and valuable source for many earth science domains, in particular thanks to its Core Services (Land, Atmosphere, Marine…). For almost five years now, Copernicus DIAS platforms have provided broad access to the core services products through the cloud. Among these platforms, the Wekeo platform operated by EUMETSAT, Mercator Ocean, ECMWF and EEA provides wider access to Copernicus Core Service data.

However, Copernicus data needs an additional layer of processing and preparation to be presented and understood by the general public and decision makers. Murmuration has developed data processing pipelines to produce environmental indicators from Copernicus data constituting powerful tools to put environmental issues at the centre of decision-making processes.

Throughout its use, limitations on the DIAS platforms were observed. Firstly, the cloud service offerings are basic in comparison to the market leaders (such as AWS and GCP). In particular, there is no built-in solution for automating and managing data processing pipelines, which must be set up at the user's expense. Secondly, the cost of resources is higher than market price. Limiting the activities on DIAS to edge data processing and relying on a cheaper offering for applications not requiring the direct access to raw Copernicus data is a cost effective choice.  FInally, the performance and reliability requirements to access the data can sometimes not be met when relying on a single DIAS platform. Implementing a multi-DIAS approach ensures backup data sources. This raises the question of the automation and orchestration of such a multi-cloud system.

We propose an approach combining the wide data offer of the DIAS platforms, the automation features provided by the Prefect platform and the usage of efficient cloud technologies to build a repository of environmental indicators. Prefect is a hybrid orchestration platform dedicated to automation of data processing flows. It does not host any data processing flow itself and rather connects in a cloud-agnostic way to any cloud environment, where periodic and triggered flow executions can be scheduled. Prefect centrally controls flows that run on different cloud environments through a single platform.

Technologies leveraged to build the system allow to efficiently produce and disseminate the environmental indicators: firstly, containerisation and clustering (using Docker and Kubernetes) to manage processing resources; secondly object storage combined with cloud native access (Zarr data format); and finally, the Python scientific software stack (including pandas, scikit-learn, etc.) complemented by the powerful Xarray library. Data processing pipelines ensure a path from the NetCDF Copernicus Core Services products to cloud-native Zarr products. The Zarr format allows windowed read/write operations, avoiding unnecessary data transfers. This efficient data access allows plugging into the data repository fast data dissemination services following well-established OGC standards and feeding interactive dashboards for decision makers. The cycle is complete, from the Copernicus satellite data to an environmentally aware field decision.

How to cite: Castel, F. and Rizzi, E.: From the Copernicus satellite data to an environmentally aware field decision, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12851, https://doi.org/10.5194/egusphere-egu23-12851, 2023.

Making assets in scientific research Findable, Accessible, Interoperable and Reusable (FAIR) is still overwhelming for many scientists. When considered as an afterthought, FAIR research is indeed challenging, and we argue that its implementation is by far much easier when considered at an early stage and focusing on improving the researchers' day to day work practices. One key aspect is to bundle all the research artefacts in a FAIR Research Object (RO) using RoHub (https://reliance.rohub.org/), a Research Object management platform that enables researchers to collaboratively manage, share and preserve their research work (data, software, workflows, models, presentations, videos, articles, etc.). RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. This approach provides the technical basis for implementing FAIR executable notebooks: the data and the computational environment can be “linked” to one or several FAIR notebooks that can then be executed via EGI Binder Service with scalable compute and storage capabilities. However, the need for defining clear practises for writing and publishing FAIR notebooks that can be reused to build upon new research has quickly arised. This is where a community of practice is required. The Environmental Data Science Book (or EDS Book) is a pan-european community-driven resource hosted on GitHub and powered by Jupyter Book. EDS Book provides practical guidelines and templates that help to translate research outputs into curated, interactive, shareable and reproducible executable notebooks. The quality of the FAIR notebooks is ensured by a collaborative and transparent reviewing process supported by GitHub related technologies. This approach provides immediate benefits for those who adopt it and can feed fruitful discussions to better define a reward system that would benefit Science and scientific communities. All the resources needed for understanding and executing the notebook are gathered into an executable Research Object in RoHub. To date, the community has successfully published ten FAIR notebooks covering a wide range of topics in environmental data science. The notebooks consume open-source python libraries e.g. intake, iris, xarray, hvplot for fetching, processing and interactively visualising environmental research.  While these notebooks are currently python-based, EDS Book supports other programming languages such as R and Julia, and we are aiming at engaging with computational notebooks communities alike towards improving the research practices in environmental science.

How to cite: Coca-Castro, A., Fouilloux, A., Hosking, J. S., and community, E. D. S. B.: FAIR Notebooks: opportunities and challenges for the geoscience community, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13768, https://doi.org/10.5194/egusphere-egu23-13768, 2023.

geokube is a Python package for data analysis and visualisation in geoscience that  provides high level abstractions in terms of both Data Model, inspired by Climate Forecast and Unidata Common Data Models, and Application Programming Interface (API), inspired by xarray. Key features of geokube are the capabilities to: (i) perform georeferenced axis-based indexing on data structures and specialised geospatial operations according to different types of geo scientific datasets like structured grids, point observations, profiles etc. (e.g. extracting a bounding box or a multipolygon of variable values defined on a rotated pole grid), (ii) perform operations on the variables that are either instantaneous or defined over intervals, (iii) convert to/from xarray data structures and to read/write CF-compliant netCDF datasets.

How to cite: Mancini, M., Stojiljkovic, M., and Walczak, J.: geokube: A Python Package for Data Analysis and Visualization in Geoscience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14507, https://doi.org/10.5194/egusphere-egu23-14507, 2023.

In this showcase, we present to you how Intake and its plugin Intake-ESM are utilized at DKRZ to provide highly FAIR data collections from different projects, stored on different types of storages in different formats.

The Intake Plugin Intake-ESM allows users to not only find the data of interest, but also load them as analysis-ready-like Xarray datasets. We utilize this tool to provide users with access to many available data collections at our institution from only one single access point, the main DKRZ intake catalog at www.dkrz.de/s/intake. The functionality of this package works independently of data standards and formats and therefore enables full metadata-driven data access including data processing. Intake-esm catalogs increase the FAIRness of the data collections in all aspects but especially in terms of Accessibility and Interoperability.

Started with a collection of DKRZ’s CMIP6 Data Pool, DKRZ now hosts catalogs for more than 10PB of data on different local storages. The Intake-ESM package has been well integrated into ESM data provisioning workflows.

• Early sharing and making accessible: The co-developed inhouse ICON model generates an intake-esm catalog on each run.
• Uptake from other technologies: E.g., intake-esm catalogs serve as templates for the more advanced DKRZ STAC Catalogs. 
• Making accessible all storage types: tools used for writing data to the local institutional cloud allow users to create Intake-ESM catalogs for the written data.
• Data archiving: Catalogs for projects in the archive can be created from its metadata database.

For future activities, we plan to make use of new functionalities like the support for kerchunked data and the derived variable registry.

The DKRZ data management team develops and maintains local services around intake-esm for a positive user experience. In this showcase, we will present excerpts of seminars, workflows and integrations.

How to cite: Wachsmann, F.: Intaking DKRZ ESM data collections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14515, https://doi.org/10.5194/egusphere-egu23-14515, 2023.

Earth observation and modelling is a major challenge for research and a necessity for environmental and socio-economic applications. It requires voluminous and heterogeneous data from distributed and domain-dependent data sources, managed separately by various national and European infrastructures.

In a context of unprecedented data wealth and growth, new challenges emerge to enable inter-comparison, inter-calibration and comprehensive studies and uses of earth system and environmental data.

To this end, the FAIR-EASE project aims to provide integrated and interoperable services through the European Open Science Cloud to facilitate the discovery, access and analysis of large volumes of heterogeneous data from distributed sources and from different domains and disciplines of Earth system science.

This presentation will explain how the PANGEO stack will be used within FAIR EASE to improve data access, interpolation and analysis, but will also explore its integration with existing services (e.g. Galaxy) and underlying IT infrastructure to serve multidisciplinary research uses.

How to cite: Vernet, M., Bodere, E., Detoc, J., Pierkot, C., Rizzo, A., and Carval, T.: PANGEO multidisciplinary test case for Earth and Environment Big data analysis in FAIR-EASE Infra-EOSC project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14547, https://doi.org/10.5194/egusphere-egu23-14547, 2023.

Observational meteorological data is central to understanding atmospheric processes, and is thus a key requirement for the calibration and validation of atmospheric and numerical weather prediction models. While recent decades have seen the development of notorious platforms to make satellite data easily accessible, observational meteorological data mostly remains scattered through the sites of regional and national meteorological service, each potentially offering different magnitudes, temporal coverage and data formats. 

In order to overcome these shortcomings, we propose meteostations-geopy, a Pythonic library to access data from meteorological stations. The central objective is to provide a common interface to retrieve observational meteorological data, therefore reducing the amount of time required to process and wrangle the data. The library interacts with APIs from different weather services, handling authentication if needed and transforming the requested information into geopandas data frames of geolocated and timestamped observations that are homogeneously structured independently of the provider. 

The project is currently in an early development stage with support for two providers only. Current and future work is organized in three interrelated main axes, namely integration of further providers, implementation of native support of distributed data structures and organization of the library into the intake technical structure with drivers, catalogs, metadata sharing and plugin packages that are provider specific.

How to cite: Bosch, M.: meteostations-geopy: a Pythonic interface to access data from meteorological stations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14774, https://doi.org/10.5194/egusphere-egu23-14774, 2023.

The coastal community widely anticipates that in the next years data-driven studies are going to make essential contributions to bringing about long-term coastal adaptation and mitigation strategies at continental scale. This view is also supported by CoCliCo, a Horizon 2020 project, where coastal data form the fundamental building block for an open-web portal that aims to improve decision making on coastal risk management and adaptation. The promise of data is likely triggered by several coastal analyses that showed how the coastal zone can be be monitored at unprecedented spatial scales using geospatial cloud platforms . However, we note that when analyses become more complex, i.e., require specific algorithms, pre- and post-processing or include data that are not hosted by the cloud provider, the cloud-native processing workflows are often broken, which makes analyses at continental scale impractical.

We believe that the next generation of data-driven coastal models that target continental scales can only be built when: 1) processing workflows are scalable; 2) computations are run in proximity to the data; 3) data are available in cloud-optimized formats; 4) and, data are described following standardized metadata specifications. In this study, we introduce these practices to the coastal research community by showcasing the advantages of cloud-native workflows by two case studies.

In the first example we map building footprints in areas prone to coastal flooding and estimate the assets at risk. For this analysis we chunk a coastal flood-risk map into several tiles and incorporate those into a coastal SpatioTemporal Asset Catalog (STAC). The second example benchmarks instantaneous shoreline mapping using cloud-native workflows against conventional methods. With data-proximate computing, processing time is reduced from the order of hours to seconds per shoreline km, which means that a highly-specialized coastal mapping expedition can be upscaled from regional to global level.

The analyses mostly rely on "core-packages" from the Pangeo project, with some additional support for scalable geospatial data analysis and cloud I/O, although they can essentially be run on a standard Python Planetary Computer instance. We publish our code, including self-explanatory Juypter notebooks, at https://github.com/floriscalkoen/egu2023.

To conclude, we foresee that in next years several coastal data products are going to be published, of which some may be considered "big data". To incorporate these data products into the next generation of coastal models, it is urgently required to agree upon protocols for coastal data stewardship. With this study we do not only want to show the advantages of scalable coastal data analysis; we mostly want to encourage the coastal research community to adopt FAIR data management principles and workflows in an era of exponential data growth.

How to cite: Calkoen, F., Baart, F., Kras, E., and Luijendijk, A.: A novel data ecosystem for coastal analyses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15964, https://doi.org/10.5194/egusphere-egu23-15964, 2023.

The ESGF Virtual Aggregation (EVA) is a new data workflow approach that aims to advance the sharing and reuse of scientific climate data stored in the Earth System Grid Federation (ESGF). The ESGF is a global infrastructure and network of internationally distributed research centers that together work as a federated data archive, supporting the distribution of global climate model simulations of the past, current and future climate. The ESGF provides modeling groups with nodes for publishing and archiving their model outputs to make them accessible to the climate community at any time. The standardization of the model output in a specified format, and the collection, archival and access of the model output through the ESGF data replication centers have facilitated multi-model analyses. Thus, ESGF has been established as the most relevant distributed data archive for climate data, hosting the data for international projects such as CMIP and CORDEX. As of 2022 it includes more than 30 PB of data distributed across research institutes all around the globe and it is the reference archive for Assessment Reports (AR) on Climate Change produced by the Intergovernmental Panel on Climate Change (IPCC). However, explosive data growth has confronted the climate community with a scientific scalability issue. Conceived as a distributed data store, the ESGF infrastructure is designed to keep file sizes manageable for both sysadmins and end users. However, use cases in scientific research often involve calculations on datasets spanning multiple variables, over the whole time period and multiple model ensembles. In this sense, the ESGF Virtual Aggregation extends the federation capabilities, beyond file search and download, by providing out of the box remote climate data analysis capabilities over data analysis ready, virtually aggregated, climate datasets, on top of the existing software stack of the federation. In this work we show an analysis that serves as a test case for the viability of the data workflow and provides the basis for discussions on the future of the ESGF infrastructure, contributing to the debate on the set of reliable core services upon which the federation should be built.

Acknowledgements

This work it’s been developed under support from IS-ENES3 which is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824084.

This work it’s been developed under support from CORDyS (PID2020-116595RB-I00) funded by MCIN/AEI/10.13039/501100011033.

How to cite: Cimadevilla, E., Iturbide, M., and Cofiño, A. S.: Virtual aggregations to improve scientific ETL and data analysis for datasets from the Earth System Grid Federation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16117, https://doi.org/10.5194/egusphere-egu23-16117, 2023.

The ballooning volume and complexity of geospatial data is one of the main inhibitors for advancements in climate & sustainability research. Oftentimes, researchers need to create bespoke and time-consuming workflows to harmonize datasets, build/deploy AI and simulation models, and perform statistical analysis. It is increasingly evident that these workflows and the underlying infrastructure are failing to scale and exploit the massive amounts of data (Peta and Exa-scale) which reside across multiple data centers and continents. While there have been attempts to consolidate relevant geospatial data and tooling into single cloud infrastructures, we argue that the future of climate & sustainability research relies on networked/federated systems. Here we present recent progress towards multi-cloud technologies that can scale federated geospatial discovery and modeling services across a network of nodes. We demonstrate how the system architecture and associated tooling can simplify the discovery and modeling process in multi-cloud environments via examples of federated analytics for AI-based flood detection and efficient data dissemination inspired by AI foundation models.

How to cite: Watson, C., Hamann, H., Weldemariam, K., Brunschwiler, T., Edwards, B., Jones, A., and Schmude, J.: Establishing a Geospatial Discovery Network with efficient discovery and modeling services in multi-cloud environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17029, https://doi.org/10.5194/egusphere-egu23-17029, 2023.

We are experimenting with hybrid access from Cloud and HPC environments using the Pangeo platform to make use of a data lake in an HPC infrastructure “DATARMOR”.  DATARMOR is an HPC infrastructure hosting ODATIS services (https://www.odatis-ocean.fr) situated at “Pôle de Calcul et de Données pour la Mer” in IFREMER. Its parallel file system has a disk space dedicated for shared data, called “dataref”.  Users of DATARMOR can access these data, and some of those data are cataloged by sextant service (https://sextant.ifremer.fr/Ressources/Liste-des-catalogues-thematiques/Datarmor-Donnees-de-reference ) and is open and accessible from the internet, without duplicating the data. 

In the cloud environment, the ability to access files in a parallel manner is essential for improving the speed of calculations. The Zarr format (https://zarr.readthedocs.io) enables parallel access to data sets, as it consists of numerous chunked “object data” files and some “metadata” files. Although it enables multiple data access, it is simple to use since all the collections of data stored in a Zarr format are accessible through one access point.  

For HPC centers, the numerous “object data” files create a lot of metadata on parallel file systems, slowing the data access time. Recent progress on development of Kerchunk (https://fsspec.github.io/kerchunk/), which recognize the chunks in a file (e.g. NetCDF / HDF5) as a Zarr chunk and its capability to recognize a series of files as one Zarr file, is solving these technical difficulties in our PANGEO use cases at DATARMOR. Thanks to Kerchunk and Intake (https://intake.readthedocs.io/) it is now possible to use different sets of data stored in DATARMOR in an efficient and simple manner.    

We are further experimenting with this workflow using the same use cases on the PANGEO-EOSC cloud.   We make use of the same data stored at the data lake in DATARMOR, but based on Kerchunk and Intake catalog through ODATIS access, without duplicating the source data. In the presentation we will share our recent experiences from these experiments. 

How to cite: Carval, T., Bodere, E., Meillon, J., Woillez, M., Le Roux, J. F., Magin, J., and Odaka, T.: Enabling simple access to a data lake both from HPC and Cloud using Kerchunk and Intake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17494, https://doi.org/10.5194/egusphere-egu23-17494, 2023.

This study presents a technique based on the use of quinine as a fluorescent tracer, to estimate sheet flow velocities over various surface coverings (e.g., bare; mulched; vegetated; paved) in low luminosity conditions (e.g., night; twilight; shielded environments). Quinine glows when exposed to UVA light and in the concentrations used is not harmful to the environment. Experimental work was conducted for studying sheet flows in the i) laboratory (using a soil flume), over bare and mulched surfaces, and ii) field, over vegetated and paved surfaces. Flow velocities were estimated based on the injection of a quinine solution into the water flow.  In these experiments, dye and thermal tracer techniques were used as a benchmark for assessing the performance of the quinine tracer. Optical and infrared cameras were used to record the movement of the tracers’ plumes in the flow. The surface velocity of the flow was estimated by tracking the tracers’ plumes leading-edge and calculating their travel distance over a certain time lapse. Overall, the visibility of the quinine tracer was better in comparison to the dye tracer. However, under some circumstances, lower than the visibility of the thermal tracer. Nonetheless, the results show that all three tracers yielded similar estimations of the flow velocities. Therefore, when exposed to UVA light the quinine tracer can be useful to estimate sheet flow velocities over a wide variety of soil and urban surfaces in low luminosity conditions. Despite some inherent limitations of this technique (e.g., invisible under bright light conditions or heavy mulched/vegetated cover; need of a UVA lamp), its main advantage is the high visibility of the quinine fluorescent tracer under UVA light for fade light conditions (e.g., night; twilight; shielded environments such as close conduits), which creates new opportunities for tracer-based surface flow velocity measurements in surface hydrology studies.

How to cite: Zehsaz, S., de Lima, J. L. M. P., de Lima, M. I. P., Isidoro, J. M. G. P., and Martins, R.: Estimating sheet flow velocities using quinine as a fluorescent tracer in low luminosity conditions: laboratory and field experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-515, https://doi.org/10.5194/egusphere-egu23-515, 2023.

Monitoring the depth changes in lakes is crucial to understanding hydrological dynamics and water quality changes. In developed countries, the authorities monitor the lake depths regularly; however, it might be different in developing and underdeveloped countries. In this study, we aim to develop a near-real-time SAR-based depth change monitoring system for lakes by focusing on shoreline pixels. For this purpose, we developed a framework using the Sentinel-1 GRD and Sentinel-2 Dynamic World land cover datasets available on the Google Earth Engine. Sentinel-1 data provides us with the necessary temporal resolution for frequent monitoring. For the initial development phase, we consider five ground monitoring stations in Sweden and one in Turkey. The approach starts by detecting water bodies within a selected area of interest using Sentinel-1. Then it extracts shoreline pixels to calculate the change in the VV and VH sigma naught and VV-VH and VV+VH Pauli vectors. Extracted differences are further classified according to the temporally closest Dynamic World data to handle the temporal difference for each land cover type. Next, we eliminate outlier values based on the percentiles, and from the remaining data, we sample each landcover class for modeling. From many of the tested frameworks, we obtained an R² of 0.79 with Gaussian Process Regression. Currently, in this framework, we observed an underestimation of higher values and an overestimation of lower values within a range of ±0.4 cm. Furthermore, considering the chosen six lakes, we observed a negative correlation between depth change and polarimetric features obtained from samples taken from land covers of grass and flooded vegetation, which is typical for natural lakes. In the second step of the development, we will increase the number of samples by including lakes from Switzerland and further develop the model.

How to cite: Ünalan, U. B., Yüzügüllü, O., and Aksoy, A.: Near Real-Time Depth Change Monitoring on Inland Water Bodies Using Sentinel-1 and Dynamic World Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-649, https://doi.org/10.5194/egusphere-egu23-649, 2023.

Monitoring dissolved methane in aquatic ecosystems contributes significantly to advancing our understanding of the carbon cycle in these habitats and capturing their impact on methane emissions. Low-cost metal oxide semiconductors (MOS) gas sensors are becoming an increasingly attractive tool to perform such measurements, especially at the air-water interface. However, the performance of MOS sensors in aquatic environmental sciences has come under scrutiny because of their cross-sensitivity to temperature, moisture, and sulfide interference. In this study, we evaluated the performance and limitations of a MOS methane sensor when measuring dissolved methane in waters. A MOS sensor was encapsulated in a hydrophobic ePTFE membrane to impede contact with water but allow gas perfusion. Therefore, the membrane enabled us to submerge the sensor in water and overcome cross-sensitivity to humidity. A simple portable, low-energy, flow-through cell system was assembled that included an encapsulated MOS sensor and a temperature sensor. Waters (with or without methane) were injected into the flow cell at a constant rate by a peristaltic pump. The signals from the two sensors were recorded continuously with a cost-efficient Arduino UNO microcontroller.. Our experiments revealed that the lower limit of the sensor was in the range of 0.1-0.2 uM and that it provided a stable response at water temperatures in the range of 18.5-28^oC. More information at Butturini, A., & Fonollosa, J. (2022). Use of metal oxide semiconductor sensors to measure methane in aquatic ecosystems in the presence of cross‐interfering compounds. Limnology and Oceanography: Methods, 20(11), 710-720.

How to cite: Butturini, A. and Fonollosa, J.: Metal oxide semiconductor (MOS) sensors to measure methane in aquatic ecosystems. An eficient DIY low  cost application., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1221, https://doi.org/10.5194/egusphere-egu23-1221, 2023.

The river discharge data is one of the most important pieces of information to regulate various water resources, including flood frequency analysis, drought and flood prediction, etc. The missing observer discharge data, even a short gap, influences the whole analysis and gives a totally different result. Filling data gaps in streamflow data is thus a critical step in any hydrological study. Interpolation, regression-based analysis, artificial neural networks, and modeling are all methods for generating missing data. While using the hydrological model to generate the data, we first need to calibrate the hydrological model. The single-site calibration of the hydrological model has its own limitations, due to which it does not correctly predict the streamflow at intermediate gauge locations. This is because, while calibrating the model for the final outlet, we tune the parameters that affect the results for the final outlet only and neglect the intermediate sites' output. In this study, we demonstrate the importance of multi-site calibration and use the calibrated hydrological model to generate the missing data at intermediate sites.

For this study, we selected the Godavari River basin and calibrated it at the final outlet (single-site calibration) and at 18 + 1 outlets (multi-site calibration). The whole basin is divided into 103 subbasins, and the Soil and Water Assessment Tool (SWAT) hydrological model is used for this study. After the successful multi-site calibration, we generated the missing data at 25 different gauging locations. The initial results from single-site calibration (NSE (0.57) and R2 (0.61)) show good agreement between observed and simulated discharge for the final outlet. The multi-site calibration analysis is in progress, and full results will be presented at the conference.

How to cite: Singh, A., Dhaka, H. K., Prajapati, P., and Jha, S. K.: Using the hydrological model for filling the missing discharge data by using multi-site calibration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1636, https://doi.org/10.5194/egusphere-egu23-1636, 2023.

Glacier movement has been measured over the years using commercial units such as those from Leica. The aim is to measure point movements on the glacier surface in order to capture fine-grained data about its movement. This can also help to calibrate satellite-based approaches which have much lower resolution. Commercial dGPS recorders cost thousands of Euros so our project is creating a solution using new lower cost dGPS boards which could enable their use by more earth scientists.

The u-blox Zed-F9P based boards from Sparkfun can be used as a base station to send dGPS corrections to “rover” units on the glacier via a radio link. Each measurement is accurate to about 2cm depending on conditions. In our design the radio is used by the rovers to forward good fixes back to the base station, which then uses off-site communications to send the data home. Two types of internet link have been enabled: using a nano-satellite board (by SWARM) and a more traditional GSM mobile phone board (for locations with coverage). Both these boards are also available from Sparkfun – making most of the modules off-the-shelf. However our power supply is optimised to save power and charge the lithium ion battery from a solar panel. A real-time clock chip is used to wake up the system to take readings and transmit data, so the sleep power is only 0.03 mW enabling a year-long lifetime. The whole system is controlled by a Sparkfun Thing Plus SAMD51 which provides the required four serial connections and a circuitpython  environment. The full system will be installed in Iceland in the summer of 2023 and replace the previous prototype based on Swift Piksi Multi units which had shown the measurement principle to be sound.

How to cite: Martinez, K., Hart, J., Attia, S., Bragg, G., Corbin, M., Jones, M., Kuhlmann, C., Weaver, E., Wells, R., Christou, I., and James, E.: A low cost real-time kinematic dGPS system for measuring glacier movement, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2681, https://doi.org/10.5194/egusphere-egu23-2681, 2023.

Hydrological data, especially water stage and discharge, is very important for water resources planning and development, hydraulic structure design, and water resources management. Thus the hydrological data has to be observed and collected regularly and continuously. The hydrological data can be affected by many factors such as people, instruments, and climate. Therefore, the collected hydrological data still need to be subject to quality control and inspection to eliminate unreasonable data to ensure the accuracy and reliability. Traditionally, the quality control and inspection of stream water-stage is mainly manual. The verification of water stage data needs experienced hydrologists to judge the correctness of the data, and cannot be processed automatically. It is time consumed, costly, and labor intensive to process the quality control of stream water stage. Therefore, it is necessary to develop a feasible model to automatically check stream water-stage for providing reliable and accurate hydrological data.

This study applies Hilbert-Huang Transform (HHT) to process stream water-stage. The HHT is composed of Empirical Mode Decomposition (EEMD) and Hilbert transform (HT). The EEMD decomposes stream water-stage into many intrinsic mode functions (IMFs) and a residual. The first IMF component is used for Hilbert transform conversion to obtain the time amplitude energy relationship diagram. The amplitude fluctuation of the corresponding component of the stream water-stage, the amplitude value of the outliers can be revealed. When the amplitude value is larger than usual, there may be outliers, and vice versa. It depends on the threshold that is established in this study as the basis for filtering the incorrect water-stage. Therefore automatically inspecting the water-stage data can be achieved. The model for automatic inspecting procedure developed by this study will greatly reduce the manual quality control, not only shorten the checking time, save manpower, but also provide reliable and correct river water stage data.

How to cite: Chen, Y.-C. and Hsiao, W.-H.: Quality control of stream water-stage using Hilbert-Huang Transform, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4844, https://doi.org/10.5194/egusphere-egu23-4844, 2023.

Understanding the transport behaviour of mismanaged plastic waste in riverine and estuarine environments is growing. However, many studies to date focus on the surface layer transport while a limited number look to measure the vertical distribution of plastic waste within these systems. Factors such as density, shape, the influence of wind and flow velocity can determine the vertical distribution of the plastic waste in a river, but many knowledge gaps remain. With this, and as technology developers move to create innovative river surface focused interception solutions to extract plastic waste, a greater understanding of the transport behaviour of sub-surface plastic debris is required. Here, we present a comprehensive overview of the development stages required to build and deploy a low-cost depth trawl tool designed to sample plastic waste at a depth of up to 5m in a heavily polluted river in Malaysia. Topics covered include tool design concepts, manufacturing methods, onsite testing, river deployment learnings and sampling results. Field data is compiled from over 60 sampling surveys conducted over 14 days in several locations along the Klang River, Malaysia. The depth trawl is mounted to a locally available fishing boat (sampan) and consists of two steel horizontal arms, a steel frame, two winches, cables, weights, five nets, and is operated manually with the assistance of a solar-powered motor. The dimensions of each net are 30cm (W) x 50cm (H) x 100cm (L) with a mesh size of 30mm x 30mm. To ensure that the nets remain aligned vertically during deployment, a weight of 15kg is tied to the bottom of the net system on both sides. Samples were collected every 1 metre to a depth of 5 metres. Each sampling was conducted for 15 minutes, six times per day with an interval of 1 hour between samples to allow for changes in the tide and river flow direction. An ADCP was deployed in parallel to the depth trawl to provide measurements of flow velocity variation at the river surface and with depth. In addition, this paper reviews the depth trawl system’s capabilities and recommendations for further studies and applications in the field.

How to cite: Higgins, D., Correia, R., Kang, H. S., Quen, L. K., Ken, T. L., Vollering, A., Pinson, S., H. Assumpção, T., and Mani, T.: Trials and design iterations experienced developing a low-cost depth trawl to sample macroplastic through the water column of a tidal river., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4878, https://doi.org/10.5194/egusphere-egu23-4878, 2023.

FreeStations are mature low-cost, networked, DIY environmental sensors and data loggers, developed since 2014  and now deployed around the world.  Build instructions are open source at www.freestation.org and based on high availability, low cost but accurate and robust components (with builds typically 3% the parts-cost of an equivalent proprietary monitoring systems).  This allows investment in a network of environmental loggers at the cost of a single, proprietary logger.  

FreeStations have been widely deployed in the DEFRA Natural Flood Management (NFM) national trials in the UK, and analytical methods developed to examine the performance of leaky dams, retention ponds, regenerative agricultural practices and other nature based solutions in mitigating flood risk at downstream assets.

These deployments usually consist of FreeStation weather stations: recording rainfall volume, rainfall intensity, air temperature, humidity and pressure as well as solar radiation, wind speed and direction.  The rainfall volume and instantaneous intensity are the most important for NFM studies.  Alongside weather stations, FreeStation sonar-based stage sensors are used, alongside river profile scan from a FreeStation LIDAR, to monitor change in river discharge due to an NFM intervention, relative to discharge at a downstream asset at risk.  Readings are taken at 10-minute intervals over multiple years.

A series of web based methods have been built as part of the FreeStation //Smart: platform to monitor and manage data from deployments and to analyse data to better understand flood mitigation by the key types of intervention.  In testing at more than 10 sites in the UK over a period of 2-3 years per site, large volumes of data have been collected at low cost and in support of local stakeholders during the H2020NAIAD and H2020ReSET projects.  

The data indicate the importance of careful design in leaky debris dams, the limited impact of inline retention ponds and the significant capacity of low-till farming methods to mitigate downstream flooding.  The effectiveness of NFM depends upon the number and scale of interventions, the proportion of the discharge at the downstream asset at risk which they affect (i.e. the downstream proximity of the asset at risk) and the capital and maintenance costs of the interventions. 

Low-cost approaches to environmental monitoring will be critical for developing the evidence base needed to better understand what nature based solutions work, where for water.  Low cost, internet-connected devices are easy to monitor and maintain, low risk and capable of extensive deployment to address the challenge of geographical variability which means that the impacts of specific NFM interventions are highly site specific. 

How to cite: Burke, S., van Soesbergen, A., and Mulligan, M.: Effectiveness-assessment of nature-based flood mitigation using networked, low cost DIY environmental monitoring from FreeStation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5922, https://doi.org/10.5194/egusphere-egu23-5922, 2023.

How to cite: Aigner, N., Moos, C., and Noyer, E.: Developing a smart sensor network for soil moisture monitoring in forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8165, https://doi.org/10.5194/egusphere-egu23-8165, 2023.

This study developed a synchronized mapping technique for water quantity and quality via an unmanned surface vehicle (USV). The USV with the acoustic doppler current profiler (ADCP) and the multiparameter sonde of water quality sensors (YSI EXO2) was used for identifying spatial and seasonal patterns of the Daljeon reservoir in South Korea. With this technique, we measured bathymetry and nitrate concentration from August 2021 through July 2022 at the high resolution spatial resolution and tested the sensitivity of estimated nitrate loads to spatial variations of input variables (water volumes and nitrate concentrations). Results showed that measured bathymetry and nitrate concentration varies over the water surface of the reservoir and time, which are associated with seasonal variations of temperature and precipitation. Despite weak spatial variations of the nitrate concentration, the water level of the reservoirs showed strong spatiotemporal variations depending on the topography of the reservoir and the  rainfall occurrence. Furthermore, we figured out using the mean for nitrate load was underestimated by -20% of the nitrate load estimates by considering spatial variation. High-resolution bathymetry measurement play a role in estimating nitrate loads with a minor impact of spatial variations of measured nitrate concentrations. We found that rainfall occurrences more likely increase estimated nitrate loads when it accounts for spatially variations of input variables, particularly water volumes. This study proved the potential utility of USV in simultaneously monitoring water quantity and quality for integrative water resource management for sustainably development of our communities.

How to cite: Lee, K.-H., Ali, S., Kim, Y., Lee, K.-T., Kwon, S. Y., and Kam, J.: Synchronized mapping of water quantity and quality of a reservoir through an unmanned surface vehicle: A case study of the Daljeon reservoir, South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10497, https://doi.org/10.5194/egusphere-egu23-10497, 2023.

We developed automated ablation stakes to measure colocated in-situ changes in ice-surface elevation and climatological drivers of ablation. The designs implement open-source hardware, including the Margay data logger, which records information from a MaxBotix ultrasonic rangefinder as well as a sensor to detect atmospheric temperature and relative humidity. The stakes and sensor mounts are assembled using commonly available building materials, including electrical conduit and plastic pipe. The frequent (typically 1–15 minute) measurement intervals permit an integral approach to estimating temperature-index melt factors for ablation. Regressions of ablation vs. climatological drivers improve when relative humidity is included alongside temperature. We present all materials required to construct an automated ablation stake, alongside examples of their deployment and use in Alaska (USA), Ecuador, Patagonia (Argentina), and the Antarctic archipelago.

a: Alaska, 2012
b: Alaska, 2013
c: Ecuador, 2016
d: Argentina, 2020
e: Antarctica, 2021

How to cite: Wickert, A. D., Barnhart, K. R., Armstrong, W. H., Romero, M., Schulz, B., Ng, G.-H. C., Sandell, C. T., La Frenierre, J. D., Penprase, S. B., Van Wyk de Vries, M., and MacGregor, K. R.: Automated ablation stakes to constrain temperature-index melt models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11411, https://doi.org/10.5194/egusphere-egu23-11411, 2023.

Determining greenhouse gas (GHG) fluxes, water (ET) fluxes and their interconnectivity within the soil-plant-atmosphere-intersphere is crucial, not only when aiming to find solutions for current agricultural systems to mitigate the global climate crises but also to adapt them to related challenges ahead, such as more frequent and severe droughts. In a first attempt for a better understanding, often laboratory and/or greenhouse pot experiments are performed, during which gas exchange is predominately measured using especially manual closed chamber systems. Commercially available systems to determine gas exchange in terms of CO₂ and ET are, however, costly and measurements itself labour-intensive. This limits the amounts of variables to be studied as well as possible repetitions during a study. Additionally, it resulted in the long-term focus on agroecosystems of the northern hemisphere while agroecosystems of sub-Saharan Africa as well as Southeast Asia are still being underrepresented.

We present an inexpensive (<1.000 Euro), Arduino based, multi-chamber system to semi-automatically measure 1) CO₂ and 2.) ET fluxes. The systems consists of multiple, self-sufficient, closet-shaped PVC “coffins”. The “coffins” a closed by a frontal door and periodically ventilated through a sliding window. Relays connected to the microcontroller are used to steer closure/opening (linear actuator) and ventilation (axial fans). CO₂ and ET fluxes are determined through the respective concentration increase during closure by a low-cost NDIR CO­₂ (K30FR; 0-10,000 ppm, ± 30 ppm accuracy) and rH sensor (SHT-41). Parallel measurements of relevant environmental parameters inside and outside the “coffins” are conducted by DS18B20 (temperature) and BMP280 (air pressure) sensors. Sensor control, data visualization and storage, as well as steering closure/opening and ventilation is implemented in terms of a wifi and bluetooth enabled, socket powered (9V), compact microcontroller (D1 RS32) based logger unit. Here, we present the design, and first results of the developed, low-cost multi-chamber system. Results were validated against results of customized CO2 and ET measurement systems using regular scientific sensors (LI-COR 850) and data logger components (CR1000), connected to each “coffin” by a multiplexer.  Flow-meter were used for measurement synchronization.

How to cite: Hoffmann, M., Al Hamwi, W., Lück, M., Schmidt, M., and Dubbert, M.: A low cost multi-chamber system (“Greenhouse Coffins”) to monitor CO2 and ET fluxes under semi-controlled conditions: Design and first results, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11777, https://doi.org/10.5194/egusphere-egu23-11777, 2023.

Mediterranean agriculture is facing the challenge to produce sustainably with a water resource under pressure. As irrigated areas expand in response to increasing vulnerability to drought, it is essential to support water users towards better agricultural water management. We set up two Fab Labs on the shores of the Mediterranean (France and Tunisia) to bring together water users around a collective project: co-constructing innovations to address local water management issues. A range of low-tech, low-cost and open source IoT-based sensors emerged from this process. The technologies were tested with users during the 2022 irrigation season. The aim of this study is to provide feedback on this participatory method as a facilitator for creating and sharing innovation in rural territories and to discuss the opportunities, benefits and limitations related to the use of these new technologies. We believe that this work contributes to make the measurement of water flows - and thus their understanding and better management - more accessible to the agricultural sector.     

How to cite: Vandôme, P., Leauthaud, C., Moinard, S., Mekki, I., Zairi, A., and Belaud, G.: Water user Fab Labs: co-design of low-tech sensors for irrigated systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12622, https://doi.org/10.5194/egusphere-egu23-12622, 2023.

This study proposes a novel method for rainfall intensity estimation from acoustic and vibration data with low-cost sensors. At first, a precipitation measurement device was developed to collect sound and touch signals from raindrops, composed of Raspberry Pi, a condenser microphone, and an accelerometer with 6 degrees of freedom. To figure out whether rainfall occurred or not, a binary classification model with the XGBoost algorithm was considered to analyze long-term time series of vibration data. Then, high-resolution acoustic data was used to investigate the main characteristics of rainfall patterns at a frequency domain for the period when it was determined that rainfall occurred. As a result of the Short Time Fourier Transform (STFT), the highest frequency, mean and standard deviation of amplitudes were selected as representative values for minute data. Finally, different types of regression models were applied to develop the method for rainfall intensity estimation from comparative analysis with other precipitation measurement devices (e.g., PARSIVEL, etc.). It should be noted that the new device with the proposed method functions reliably under extreme environmental conditions when the estimated rainfall intensity was compared with measured data from ground-based precipitation devices. It shows that low-cost sensors with sound and touch signals from raindrops can be effectively used for rainfall intensity estimation with easy installation and maintenance, indicating a strong possibility of being considered in a wide range of areas for precipitation measurement with high resolution and accuracy

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

How to cite: Hwang, S., Lee, J., Sung, J., Kim, H., Kim, B., and Jun, C.: Precipitation Measurement from Raindrops’ Sound and Touch Signals, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13072, https://doi.org/10.5194/egusphere-egu23-13072, 2023.

River networks in the Alps are very complex and hold many unanswered research questions. For example, various assumptions must be made to when studying tributaries and small rivers. Namely, there is not a widely accepted tool to measure streamflow in small, mountain streams that can overcome their specific challenges affordably without large installations. For example, alteration between extremely high and no discharge volume is characteristic of intermittent rivers and ephemeral streams (IRES). Conventional measuring devices all require streambed installation, which exposes them to displacement or destruction by abruptly rising water levels. One solution, thus, is to remove the sensor from the streambed and measure from a distance. We have experimented with an acoustic sound recorder mounted above the stream as an alternative tool to assess water level. We designed a low-cost audio sensor powered by a microcontroller with an audio shield specifically for recording IRES. To ensure reproducibility, we used Arduino for programming the Teensy 3.2. Images of the water level in an IRES were simultaneously captured when possible (daylight) and used for calibration. The water level visible in the images correlated well with that determined from the audio recordings from our self-developed audio sensor (R² = 95%). Based exclusively on the audio recording of an IRES, we can obtain a time series of the water level, at least when water was present. We are currently unable to determine consistently whether water is present nor state with certainty when the streambed is dry based solely on acoustic data. Nevertheless, this new sensor allows us to measure an alpine channel network at more locations and over longer time periods than previously feasible.

How to cite: Fässler, L., Ceperley, N., Leiser, P., and Schaefli, B.: Monitoring an ephemeral stream with a Teensy 3.2 + audio shield to determine water level only from the noise of a stream, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14370, https://doi.org/10.5194/egusphere-egu23-14370, 2023.

The development of artificial reservoirs plays a considerable role in regulating the spatial and temporal distribution of irrigated rainfall and guaranteeing sustainable agricultural development. Many studies have used the area-storage relationship to obtain the storage capacity of on-farm reservoirs (OFRs), but it does not work for OFRs with persistent water surface area. In this study, we proposed an effective method to estimate the water storage of irrigated OFRs by combining multi-source remote sensing data and ground observation. We quickly derived the location of irrigated OFRs by using seasonal characteristics of irrigated OFRs and obtained high-precision water surface area using an object-oriented segmentation. We estimated water storage of irrigated OFRs by combining three different ways (i.e., Lidar-based, ground observation-based (photos), and surface area-based). The method performs well in three aspects, i.e., identifying on-farm reservoirs, extracting water surface area, and calculating water storage. The accuracy of identification reaches 94.1%, and the derived water area agrees well with the surveyed results, i.e., an overall accuracy of 97.8%, the root mean square error (RMSE) and the mean absolute errors (MAE) are 962 m² and 766 m², respectively. The obtained water storage is reliable using three different ways (i.e., the area-storage, Lidar-based, and photo observations-based methods), with accuracy of 98.8%, 95.2%, and 94.1%, respectively. The proposed method enables monitoring of the storage of multiple types of irrigated OFRs, particularly the photo observation-based method can deal with the storage of OFRs with persistent water areas, showing huge potential to promote irrigated water resource utilization efficiency.

How to cite: wang, Y.: Monitoring water storage of on-farm reservoirs using remote sensing and ground observation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15372, https://doi.org/10.5194/egusphere-egu23-15372, 2023.

Hydrology is still one of the most data scarce natural sciences. The large number of variables to measure, their extreme spatiotemporal gradients, and the often harsh and hostile environmental conditions all contribute to this issue. This challenge is even more pronounced in remote and extreme environments such as the tropics, and mountain regions, where the need for robust data is most acute.

Many new and emergent technologies can help with building more cost-effective, robust, and versatile hydrological monitoring systems. However, the speed at which these new technologies are being incorporated in commercially available systems is slow and dictated by commercial interests and bottlenecks.

An alternative solution is for scientists to build their own systems using off the shelf components. Open-source hardware and software, such as the Arduino and Raspberry Pi ecosystems, make this increasingly feasible. As a result, a plethora of global initiatives for open-source sensing and logging solutions have emerged.

But despite these new technologies, it remains a major challenge to build open-source solutions that equal the reliability and robustness of the high-end commercial systems that are available on the market. Sharing experiences, best practices, and evidence on the real-world performance of different designs may help with overcoming this bottleneck.

In this contribution, I summarize the experience gained from developing and operating over 300 open-source data loggers, built around the Riverlabs platform. This platform is mostly a compilation of existing open-source hardware and software components and solutions, which were refined further and tweaked for robustness and reliability in extreme environments. Our loggers have been installed in locations as diverse as Arctic Norway, the high Andes of Peru and Chile, the Nepalese and Indian Himalayas, the Somali desert, and the Malaysian rainforest, providing a wide range of real-world test-cases and performances.

How to cite: Buytaert, W.: Towards a robust, open-source logging platform for environmental monitoring in challenging environments: the Riverlabs toolbox, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15989, https://doi.org/10.5194/egusphere-egu23-15989, 2023.

The air quality monitoring is a core topic for European environmental policies and worldwide. At the same time technologies such as electrochemical or NDIR gas sensors became affordable and easy to implement in a customized design. A highly flexible monitoring station has been designed and build in order to obtain a customizable and affordable device. It is composed of two boards, one in charge of connectivity and processing while the other allows to insert up to 11 gas sensors. Such number is achieved through the use of three multiplexers that allow to spare input pins of the processor. Moreover the flexibility at the moment is achieved using sensors with the same form factor but adapters are under development to increase the adaptability of the system, both hardware and software. An Arduino MKR zero runs the application that can be run in three different modes: single measurement, time driven or position driven. The last feature is obtained through an optional on-board U-blox GNSS module that allows to georeference the performed measurements. This mode is mainly used when the measurement cell is applied on moving object, such as drones. The system is able to send the data collected and receive commands using MQTT protocol (HiveMQ broker) through a NB-IoT connection and interact with the user from an online dashboard created using Thingsboard. The use of the MQTT protocol allows to send the data to multiple endpoints if the data should be provided also to third parties. Moreover, the data and some parameters are also saved on a sd card. All the system is built on stand alone boards to achieve easy maintaince of the system and to allow a rapid change in the used technology (a plug and play LoRaWan module is under development). Being a multi-application platform, price of the device is of course highly dependent on the chosen set of sensors thus, in the end, on the application itself (i.e. Air pollution or gas emission in barns). To sum up, the device described is a possible solution for an affordable gas concentration measurement system that can be adapted to fit a large variety of use cases combining software and hardware solutions.

How to cite: Renzi, F., Cammillozzi, F., Cecchini, G., Filippi, A., and Valentini, R.: Design of an affordable and highly flexible IoT station for multiple gas concentration monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17527, https://doi.org/10.5194/egusphere-egu23-17527, 2023.

Numerical stress or strain modeling has been a focused subject in many fields, especially in assessing the stability of key engineering structures and better understanding in local or tectonic stress patters and seismicity. Here we proposed a new stress modeling method governed by elastic wave equations using finite-difference scheme. Based on the modeling scheme of wave propagation, the proposed method is able to solve both the dynamic stress evolution and the static stress state of equilibrium by introducing an artificial damping factor to the particle velocity. We validate the proposed method in three geophysical benchmarks: (a) a layered earth model under gravitational load, (b) a rock mass model under nonuniform loads on its exterior boundaries and (c) a fault zone with strain localization driven by regional tectonic loading that measured by GPS velocity field.  Because the governing equations of the proposed method are wave equations instead of equilibrium equations, we are able to use the perfectly matched layer as the artificial boundary conditions for models in unbounded domain, which will substantially improve the accuracy of them. Also, the proposed scheme maps the physical model on simple computational grids and therefore is more memory efficient for grid points’ positions not been stored. Besides, the efficient parallel computing of the finite-different method guarantees the proposed method’s advantage in computational speed. As a minor modification to wave modeling scheme, the proposed stress modeling method is not only accurate for geological models through different scales, but also physically reasonable and easy to implement for geophysicists.

How to cite: Fan, Z., Cheng, F., and Liu, J.: A new finite-difference stress modeling method governed by elastic wave equations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1562, https://doi.org/10.5194/egusphere-egu23-1562, 2023.

Buildings that have existed for centuries undergo structural changes over time due to variations in use. In addition, many structures are severely damaged for example by moisture intrusion. To determine the distribution of moisture in the structure, they are often examined pointwise by core sampling. In addition to invasive methods, non-destructive methods may be applied to obtain three-dimensional hints on the moisture distribution with structures of interest.            
The purpose of this paper is to show that non-destructive determination of moisture distribution is possible by using and combining geophysical measurement methods such as infrared thermography (IR), ultrasound (US) and ground penetrating radar (GPR). There are examples for the combination of these methods for non-destructive examination, but it is not yet commonly applied in the field of restoration and conservation of historic buildings.            
We present results of geophysical investigations of medieval wall paintings in the cloister of the cathedral in Schleswig (Federal State Schleswig-Holstein, Northern Germany) in the framework of a project funded by the German Federal Foundation for the Environment (Deutsche Bundesstiftung Umwelt - DBU). In the cloister, large-scale alterations of the medieval red-line paintings occurred due to gypsum deposits and a shellac coating. In order to quantify the material properties of a vault section (yoke) in the cloister during the restoration ultrasound surface wave measurements, passive and active thermography and ground penetrating radar measurements were carried out.
Repeating measurements at intervals of several months made it possible to evaluate the effectiveness of the test treatments by different solvents to remove the shellac as well as the gypsum deposits. In addition, our results from the passive thermography measurements show that in one section a defect in the horizontal barrier could be responsible for moisture ingress and associated damage. The radargrams recorded in this area confirm that a significant change in reflection amplitudes is present in the areas of increased moisture.

How to cite: Esel, Y., Erkul, E., Schulte-Kortnack, D., Leonhardt, C., Heller, J., and Meier, T.: Non-destructive geophysical damage analysis of medieval plaster in the cloister of the St. Petri Cathedral Schleswig (Germany), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2228, https://doi.org/10.5194/egusphere-egu23-2228, 2023.

Pavements are an essential component of airport facilities. Airport infrastructures serve to safely transport people and goods on a day-to-day basis. They promote economic development, both regionally and internationally, by also boosting tourist flows. In times of crisis, they can be used for societal emergencies, such as managing migration flows. Therefore, airports need pavements in good physical condition to ensure uninterrupted operations. However, interventions on airfield pavements are costly and labor intensive. Aspects of pavement structural performance related to bearing capacity and damage potential remain of paramount importance as the service life of a pavement extends beyond its design life. Therefore, structural condition evaluation is required to ensure the long-term bearing capacity of the pavement. 

The design and evaluation of flexible airfield pavements are generally based on the Multi-Layered Elastic Theory (MLET) in accordance with Federal Aviation Administration (FAA) principles. The most informative tool for structural evaluation is the Falling Weight Deflectometer (FWD), which senses pavement surfaces using geophones that record load-induced deflections at various locations. Additional geophysical inspection data using Ground Penetrating Radar (GRP) is processed to estimate the stratigraphy of the pavement. The integration of the above data provides an estimate of the pavement's performance and potential for damage. However, GRP is not always readily applicable.

In addition, the most important concern in pavement evaluation is the mechanical characterization of pavement materials. At the top of pavement structures, asphalt mixtures behave as a function of temperature and loading frequency. This viscoelastic behavior deviates from MLET and this issue needs further investigation. Therefore, this study integrates measured NDT data and sample data from cores taken in-situ. The pavement under study is an existing asphalt pavement of a runway at a regional airport in Southern Europe. A comparative evaluation of the strain state within the pavement body is performed both at critical locations and at the pavement surface, taking into account elastic and viscoelastic behaviors. Strains are an important input to models of long-term pavement performance, which has a critical influence on aircraft maneuverability. In turn, the significant discrepancies found highlight the need for more mechanistic considerations in predicting the damage and stress potential of airfield pavements so that maintenance and/or rehabilitation needs can be better managed and planned.

Overall, this study highlights the sensing capabilities of NDT data towards a structural health monitoring of airfield pavements. Ground-truth data from limited destructive testing enrich pavement evaluation processes and enhance conventional FAA evaluation procedures. The study proposes a numerical development for accurate field inspections and improved monitoring protocols for the benefit of airfield pavement management and rehabilitation planning. 

How to cite: Gkyrtis, K., Plati, C., and Loizos, A.: Non-destructive testing methods and numerical development for enhancing airfield pavement management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2347, https://doi.org/10.5194/egusphere-egu23-2347, 2023.

The Laacher See Event- (LSE-) volcanism isochrone of 12.850 yrs BP (Bujatti-Narbeshuber, 1997), proxy for P/H boundary KISS (Bujatti-Narbeshuber, 1996), was improved from Gerzensee varves to 13.034 cal yrs BP (Van Raden, 2019).

    This LSE date now separates end Pleistocene, first, mainly oceanic-water KISS, from the second, Holocene-Younger Dryas Onset (YDO), continental-ice impact, as predicted by KISS-hypothesis, separating:„ a continental Koefels-comet ice-impact, from the mainly oceanic KISS, at the Pleistocene/Holocene boundary, associated with global warming, dendro C14 spikes, faunal mass extinction...“ (Bujatti-Narbeshuber, 1996; Max, 2022).

    Oceanic-water LSE-KISS (13.034 cal yrs BP, varves) of end Alleroed temperature maximum, separates by 157 yrs from continental-ice YDO-KISS (12.877 cal yrs BP, varve-date). A larger gap of 184 yrs results, taking C 14 dated YD-KISS (12.850 cal yrs BP), approaching 200 yrs of earlier varve-studies (Bujatti-Narbeshuber, 1997).

    LSE-KISS varve-date differs by 47 yrs from geo-magnetic Gothenberg Excursion Onset- (GEO-) isochrone of 13.081 cal yrs BP (Chen, 2020), suggesting geo-magnetic reversal, True Polar Wander (TPW) GEO-TPW-KISS from 2 Koefels-comet (Taurid-) fragments. This considers end-paleolithic Magdalenian Impact Sequelae Symbolisations (MISS).

    Questioning P/H isostatic-unloading volcanism (Zielinsky, 1996), LSE-KISS volcanism is from Mid Atlantic Ridge & Mid Atlantic Plateau (MAR&MAP) impact (Bujatti-Narbeshuber, 1997, 2022), as further corroborated by Greenland (NGRIP) ice-core sulfate monitoring: from LSE-KISS-volcanism (12.978 cal yrs) to YDO (12.867 cal yr BP), within 110 yrs, an unprecedented, bipolar-volcanic-eruption-quadruplet resulted (Lin, 2022).

    The first Taurid LSE-KISS (Varves-date: 13.034 cal yrs BP, GEO-date: 13.084 cal yrs BP.) into oceanic-water is evident from two 700 km Mid Atlantic Ridge & Plateau Lowering Events (MARPLES) releasing two separate Tsunamis (Bujatti-Narbeshuber, 2022): Resulting in submarine explosive-magmatism-silicates, seafloor-carbonates, volcanic-ash and sea-water in huge strato-meso-spheric overheated steam-plume moving eastward by eolian transport, descending in drowning rain-flood, largely contributing to Eurasian loess sediment layer (Muck, 1976).

    This is stratigraphically verified in e.g. relative stratigraphic positions in Netherland, Geldrop-Aalsterhut, with Younger Coversand I, bleached (!) (AMS 13.080- 12.915 cal yrs BP) underlying intercalated (!), charcoal rich (AMS 12.785-12.650 cal yrs BP) Usselo Horizon (Andronikov, 2016). It corresponds to US, Black Mats stratigraphy from second Taurid, continental-ice, YD-KISS (12.850 cal yrs BP, C14) plus Carolina Bays (CB) with: 1. Soft, white, loess sediment from first oceanic LSE-KISS. 2. YD-KISS proxies-stratum. 3. e.g. Carolina-Florida-coast-sand-disturbances, within 1.500 km radius of continental-ice YD-KISS ice-ejecta impact-curtain of 500.000 CB (LIDAR) 4. Black Mats after YD-KISS.

    After visiting Koefels-crater an “below continental-glacier-ice, circular geomagnetic-anomaly with paleoseismic Koefels-corridor of twelfe Holocene rockfalls”, Eugene Shoemaker (Vienna, May 5^th 1997), when asked about Carolina Bays causation, is quoted: “Eugene spoke of a late Pleistocene origin of the Bays and as glaciological features while I preferred the paleoseismic interpretation. I interprete them as paleoseismic impact-seismic liquefaction features. They … are the first evidence for a late Pleistocene impact event. Dated by me …12.850 BP (1950) in calendar years”. (Bujatti-Narbeshuber, NHM letter to John Grant III, Sept. 22^nd 1997).

    Both P/H-impacts break&make, Pleistocene criticality&Holocene damped flow, through 700 km geomorphological threshold (GLOVES) submersion & through (GTT) water, CO2 Greenhouse-gas-production, beyond glaciation threshold for hot climate prediction.

How to cite: Bujatti-Narbeshuber, M.: Pleistocene/Holocene (P/H) boundary oceanic Koefels-comet Impact Series Scenario (KISS) of 12.850 yr BP Global-warming Threshold Triad (GTT)-Part III, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2869, https://doi.org/10.5194/egusphere-egu23-2869, 2023.

To evaluate the feasibility of CO2 sequestration in offshore, South Korea, we studied numerical modelling with elastic velocity model. The CO2 storage candidate is a brine saturated aquifer formation overlain by basalt caprock in the Southern Continental Shelf of Korea. Basalt formation without joint and fracture can seal a storage volume preventing leakage of injected CO2. Result of preliminary two-dimensional seismic exploration estimated that storage potential would be from 42.07 to 143.79 Mt of CO2. The input model include P- and S-wave velocity and density of shallow sediment and vasalt layer. To simulate CO2 injection, we assumed an area of CO2 plume at the interval beneath the depth of basalt formation and artificially decreased P-, S-wave velocities, and density values. Synthesized seismic records are comparable with survey's gather as direct arrival and primary reflections. The ongoing work can be extended on a quantitative verification concerning serveral cased of varying velcoties and densities.

How to cite: Cheong, S., Kang, M., and Kim, K. J.: Numerical modelling of seismic field record with elastic velocity construction for CO2 sequestration in offshore, South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2980, https://doi.org/10.5194/egusphere-egu23-2980, 2023.

Health monitoring and disease mitigation of trees are essential to ensure the sustainability of wood industry, safety of ecosystems, and maintenance of climatic conditions. Several non-destructive testing methods have been applied to monitor and detect the decays inside the trunks. Among them, ground penetrating radar (GPR) has gained recognition due to its high efficiency and good resolution. However, due to the wide beam width of the antenna pattern and the complicated scattering caused by the trunk structure, the recorded GPR profile is far from the actual geometry of the tree trunk. Moreover, the irregular contour of the tree trunk makes traditional data processing algorithms difficult to be performed. Therefore, an efficient migration algorithm with high resolution, as well as a high accuracy survey-line positioning method for curved contour of the trunk should be developed.

In this paper, a combined approach is proposed to image the inner structures inside the irregular-shaped trunks. In the first step, the 3D contour of the targeted tree trunk is built up by a 3D point cloud technique via photographing around the trunk at various angles. Subsequently, the 2D irregular contour of the cross-section of trunk at the position of the GPR survey line is extracted by the Canny edge detection method to locate the accurate position of each GPR A-scans [1]. Thirdly, the raw GPR profile is pre-processed to suppress undesired noise and clutters. Then, an RTM algorithm based on the zero-time imaging condition is applied for image reconstruction using the extracted 2D contour [2]. Lastly, a denoising method based on the total variation (TV) regularization is applied for artifact suppression in the reconstructed images [3].

Numerical, laboratory and field experiments are carried out to validate the applicability of the proposed approach. Both numerical and laboratory experimental results show that the RTM can yield more accurate and higher resolution images of the inner structures of the tree cross section than the BP algorithm. The proposed approach is further applied to a diseased camphor tree, and an elliptical decay defect is found the in the migrated GPR image. The results are validated by a visual inspection after the tree trunk was sawed down.

Fig. 1 Field experiment. (a) Geometric reconstruction result using point cloud data, (b) migrated result by the RTM algorithm and (c) bottom view of the tree trunk after sawing down. The red and yellow ellipses indicate the cavity and the decay region in the trunk, respectively.

References:

[1] Canny, "A Computational Approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Interllgent, vol. PAMI-8, no. 6, pp. 679-698, 1986, doi: 10.1109/TPAMI.1986.4767851.

[2] S. Chattopadhyay and G. A. McMechan, "Imaging conditions for prestack reverse-time migration," Geophysics, vol. 73, no. 3, pp. S81-S89, 2008, doi: 10.1190/1.2903822.

[3] L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, vol. 60, pp. 259-268, 1992, doi: 10.1016/0167-2789(92)90242-F.

How to cite: Chen, Z., Liu, H., Xu, M., Yue, Y., and Zhang, B.: Decay diagnosis of tree trunks using 3D point cloud and reverse time migration of GPR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4861, https://doi.org/10.5194/egusphere-egu23-4861, 2023.

Satellite sensors need to make a trade-off between revisit frequency and spatial resolution. This work presents a spatio-temporal image fusion method called Unpaired Spatio-Temporal Fusion of Image Patches (USTFIP). This method combines data from different multispectral sensors and creates images combining the best of each satellite in terms of frequency and resolution. It generates synthetic images and selects optimal information from cloud-contaminated images, to avoid the need of cloud-free matching pairs of satellite images. The removal of this restriction makes it easier to run our fusion algorithm even in the presence of clouds, which are frequent in time series of satellite images. The increasing demand of larger datasets makes necessary the use of computationally optimized methods. Therefore, this method is programmed to run in parallel reducing the run-time with regard to other methods. USTFIP is tested through an experimental scenario with similar procedures as Fit-FC, STARFM and FSDAF. Finally, USTFIP is the most robust, since its prediction accuracy deprecates at a much lower rate as classical requirements become progressively difficult to meet.

How to cite: Goyena, H., Pérez-Goya, U., Montesino-San Martín, M., F. Militino, A., Atkinson, P. M., and Ugarte, M. D.: Relaxing requirements for spatio-temporal data fusion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6795, https://doi.org/10.5194/egusphere-egu23-6795, 2023.

Continual monitoring of tree roots, which is essential when considering tree health and safety, is possible using a digital model. Non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) have been used in the past to study both the external and internal physical dimensions of objects and structures [1], including trees [2,3]. Recent studies have shown that GPR is effective in mapping the root system's network in street trees [3]. Light Detection and Ranging (LiDAR) technology has also been employed in infrastructure management to generate 3D data and to detect surface displacements with millimeter accuracy [4]. However, scanning such structures using current state-of-the-art technologies can be expensive and time consuming. Further, continual monitoring of tree roots requires multiple visits to tree sites and, oftentimes, repeated excavations of soil.

This work proposes a Virtual Reality (VR) system using smartphone-based LiDAR and GPR data to capture ground surface and subsurface information to monitor the location of tree roots. Both datasets can be visualized in 3D in a VR environment for future assessment. LiDAR technology has recently become available in smartphones (for instance, the Apple iPhone 12+) and can scan a surface, e.g., the base of a tree, and export the data to a 3D modelling and visualization application. Using GPR data, we combined subsurface information on the location of tree roots with the LiDAR scan to provide a holistic digital model of the physical site. The system can provide a relatively low-cost environmental modelling and assessment solution, which will allow researchers and environmental professionals to a) create digital 3D snapshots of a physical site for later assessment, b) track positional data on existing tree roots, and c) inform the decision-making process regarding locations for potential future excavations.

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. The Authors would also like to thank Mr Dale Mortimer (representing the Ealing Council) and the Walpole Park for facilitating this research.

References

[1] Alani A. M. 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] Ježová, J., Mertens, L., Lambot, S., 2016. “Ground-penetrating radar for observing tree trunks and other cylindrical objects,” Construction and Building Materials (123), 214-225.

[3] Lantini, L., Alani, A. M., Giannakis, I., Benedetto, A. and Tosti, F., 2020. "Application of ground penetrating radar for mapping tree root system architecture and mass density of street trees," Advances in Transportation Studies (3), 51-62.

[4] 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., Lantini, L., and Tosti, F.: Low-cost assessment and visualization of tree roots using smartphone LiDAR, Ground-Penetrating Radar (GPR) data and virtual reality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6908, https://doi.org/10.5194/egusphere-egu23-6908, 2023.

Monitoring and protection of natural resources have grown increasingly important in recent years, since the effect of emerging illnesses has caused serious concerns among environmentalists and communities. In this regard, tree roots are one of the most crucial and fragile plant organs, as well as one of the most difficult to assess [1].

Within this context, ground penetrating radar (GPR) applications have shown to be precise and effective for investigating and mapping tree roots [2]. Furthermore, in order to overcome limitations arising from natural soil heterogeneity, a recent study has proven the feasibility of deep learning image-based detection and classification methods applied to the GPR investigation of tree roots [3].

The present research proposes an analysis of the effect of root orientation on the GPR detection of tree root systems. To this end, a dedicated survey methodology was developed for compilation of a database of isolated roots. A set of GPR data was collected with different incidence angles with respect to each investigated root. The GPR signal is then processed in both temporal and frequency domains to filter out existing noise-related information and obtain spectrograms (i.e. a visual representation of a signal's frequency spectrum relative to time). Subsequently, an image-based deep learning framework is implemented, and its performance in recognising outputs with different incidence angles is compared to traditional machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the use of novel ways to enhance the interpretation of tree root systems.

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. The Authors would also like to thank the Ealing Council and the Walpole Park for facilitating this research.

References

[1] Innes, J. L., 1993. Forest health: its assessment and status. CAB International.

[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., Massimi, F., Tosti, F., Alani, A. M. and Benedetto, F. "A Deep Learning Approach for Tree Root Detection using GPR Spectrogram Imagery," 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 2022, pp. 391-394.

How to cite: Lantini, L., Massimi, F., Sotoudeh, S., Mortimer, D., Benedetto, F., and Tosti, F.: A Study on the Effect of Target Orientation on the GPR Detection of Tree Roots Using a Deep Learning Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8384, https://doi.org/10.5194/egusphere-egu23-8384, 2023.

Structural health monitoring (SHM) is a necessary measure to maintain bridge infrastructure safe. To this purpose, remote sensing has proven effective in acquiring data with high accuracy in relatively short time. Amongst the available methods, the ground-based synthetic aperture radar (GB-SAR) can detect sub-zero deflections up to 0.01 mm generated by moving vehicles or the environmental excitation of the bridges [1]. Interferometric radars are also capable of data collection regardless of weather, day, and night conditions (Alba et al., 2008). However, from the available literature - there is lack of studies and methods focusing on the actual capabilities of the GB-SAR to target specific structural elements and components of the bridge - which makes it difficult to associate the measured deflection with the actual bridge section. According to the antenna type, the footprint of the radar signal gets wider in distance which encompasses more elements and the presence of multiple targets in the same resolution cell adds uncertainty to the acquired data (Michel & Keller, 2021). To this effect, the purpose of the present research is to introduce a methodology for pinpointing targets using GB-SAR and aid the data interpretation. An experimental procedure is devised to control acquisition parameters and targets, and being able to analyse the returned outputs in a more clinical condition. The outcome of this research will add to the existing literature in terms of collecting data with enhanced precision and certainty.

Keywords

Structural Health Monitoring (SHM), GB-SAR, Remote Sensing, Interferometric Radar

Acknowledgements

This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London.

References

[1] Benedettini, F., & Gentile, C. (2011). Operational modal testing and FE model tuning of a cable-stayed bridge. Engineering Structures, 33(6), 2063-2073.

[2] Alba, M., Bernardini, G., Giussani, A., Ricci, P. P., Roncoroni, F., Scaioni, M., Valgoi, P., & Zhang, K. (2008). Measurement of dam deformations by terrestrial interferometric techniques. Int.Arch.Photogramm.Remote Sens.Spat.Inf.Sci, 37(B1), 133-139.

[3] Michel, C., & Keller, S. (2021). Advancing ground-based radar processing for bridge infrastructure monitoring. Sensors, 21(6), 2172.

How to cite: Sotoudeh, S., Lantini, L., Munisami, K. J., Alani, A. M., and Tosti, F.: An Investigation into the Acquisition Parameters for GB-SAR Assessment of Bridge Structural Components, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8667, https://doi.org/10.5194/egusphere-egu23-8667, 2023.

As the Earth's lungs, trees are a natural resource that provide, amongst others, food, lumber, and oxygen. Therefore, monitoring these wooden structures with non-destructive testing (NDT) techniques such as ground penetrating radar (GPR) and ultrasound can provide valuable information about inner flaws and decays, which is an essential step for tree conservation.  

In recent years, GPR and ultrasound have been used to delineate the interior architecture of tree trunks [1-3]. However, more research is required to improve results and consequently have a more reliable interpretation. Due to limitations in depth penetration and signal-to-noise ratio [4], these approaches have a limited capacity for resolving features. The use of gain functions and higher frequencies to compensate for wave attenuation may exaggerate events and reduce resolution, respectively.

In this context, an integration between GPR multi-frequency and ultrasound data can be used to address this issue. Data were collected on a tree trunk log at the Faringdon Centre for Non-Destructive Testing and Remote Sensing using two high-frequency GPR systems (2GHz and 4GHz central frequencies) and an ultrasound (supporting a wide range of transducers from 24 kHz up to 500 kHz) testing equipment. Internal features of interest in terms of extended perimetric air gaps at the bark-wood interface, natural cracks and small artificial cavities were investigated through electromagnetic and mechanical waves. After compilation of data, a joint interpretation strategy for data analysis is developed. The processed data were mapped against the cut sections of the tree for validity purposes.

Although study of stand tree trunks would be more challenging, the findings of this research may be applied for wood timbers and pave the way to future research for living tree trunks.

Acknowledgements

This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London.

References

[1] Arciniegas, A., et al., Literature review of acoustic and ultrasonic tomography in standing trees. Trees, 2014. 28(6): p. 1559-1567. 

[2] Giannakis, I., et al., Health monitoring of tree trunks using ground penetrating radar. IEEE Transactions on Geoscience and Remote Sensing, 2019. 57(10): p. 8317-8326.

[3] Espinosa, L., et al., Ultrasound computed tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects. Annals of Forest Science, 2020. 77(3): p. 1-13.

[4] Tosti, F., et al., The use of GPR and microwave tomography for the assessment of the internal structure of hollow trees. IEEE Transactions on Geoscience and Remote Sensing, 2021. 60: p. 1-14.

How to cite: Parnow, S., Lantini, L., Uzor, S., Alani, A. M., and Tosti, F.: Joint Interpretation of Multi-Frequency Ground Penetrating Radar and Ultrasound Data for Mapping Cracks and Cavities in Tree Trunks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8762, https://doi.org/10.5194/egusphere-egu23-8762, 2023.

Ground subsidence is a typical geological hazard in urban areas. It endangers the safety of infrastructures, such as subways. In this study, the ground subsidence risk of Shanghai metro lines was mapped and assessed. Firstly, PS-InSAR was used for the ground subsidence survey, and subsidence intensity was divided into five classes according to subsidence velocity. 10 subsidence causal factors were collected and the frequency ratio method was applied to analyze the correlation between subsidence and its causal factors. Then LightGBM model was used to generate a ground subsidence susceptibility map. And receiver operating characteristic curve and area under the curve (AUC) were adopted to assess the model. And AUC is 0.904, which suggests the model's performance is excellent. Finally, a risk matrix was introduced to consider the intensity and susceptibility of ground subsidence. The risk of ground subsidence was mapped and classified into five levels: R1 (very low), R2 (low), R3 (medium), R4 (high), and R5 (very high). The results showed that the risk of subway ground subsidence exhibited a regional-related characteristic. Metro lines located in areas with higher ground subsidence risk levels also had higher ground subsidence risk levels. Meanwhile, the statistical results of subway ground subsidence risk levels showed that subway stations were safer than sections.

How to cite: Chai, L., Xie, X., Zhou, B., and Zeng, L.: Ground subsidence risk mapping and assessment along Shanghai metro lines by PS-InSAR and LightGBM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10874, https://doi.org/10.5194/egusphere-egu23-10874, 2023.

Geological mixtures having endmembers mixed at a fine scale pose a challenge to estimating their fractional abundances. Light incident on these mixtures interacts both at multilayered and surface levels, resulting in volumetric and albedo scattering, respectively. Accounting for these effects necessitates a nonlinear spectral mixing model approach rather than conventional linear mixing. In this study, we evaluate the performances of linear and various nonlinear spectral mixing models for an intimately mixed geological mixture, i.e., a banded hematite quartzite (BHQ) sample. The BHQ sample with distinct endmembers of hematite and quartzite facilitated our study of the behavior of light on two-component nonlinear mixtures. In a laboratory-based experimental setup, we used a spectroradiometer of full spectral range in the visible and near-infrared regions (350 to 2500nm) to acquire a hyperspectral image of the BHQ sample. It was followed by the identification of nonlinearly mixed regions and inferring changes in their spectral features. The nonlinearity induced in these regions was attributed to two significant causes- (1) the fine scale of spectral mixing and (2) the spectroradiometer sensor’s limited ability to spatially distinguish between focused and neighboring points, thereby producing a point spread effect. We observed the effects of nonlinear spectral mixing for our sample by changing the sensor’s height from 1mm to 5mm, to simulate fine and coarse-resolution images, respectively. The spectral mixing was modeled using the existing mapped ground truth fractional abundances and library endmembers’ spectra by linear mixing and established nonlinear techniques of the generalized bilinear model (GBM), polynomial post-nonlinear model (PPNM), kernel-based support vector machines (k-SVMs). The evaluated performance metric of reconstruction error revealed the nonlinearity effect in image pixels through statistical tests and nonlinearity parameters used in these models. It was further observed that the associated nonlinearity increases from fine to coarse-resolution images. The minimum error of image reconstruction was observed for the polynomial post-nonlinear model, with a single nonlinearity parameter and an average reconstruction error (ARE) of 0.05. Our study provided insights into the nature of nonlinear mixing with endmember composition and particle sizes.

How to cite: Sahoo, M. M., Rajendran, K., Pattathal Vijayakumar, A., Mathew, S. K., and Porwal, A.: Evaluation of Spectral Mixing Techniques for Geological Mixture in a Laboratory Setup: Insights on the nature of mixing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12226, https://doi.org/10.5194/egusphere-egu23-12226, 2023.

Non-destructive and high-resolution hyperspectral analyses are widely used in planetary and environmental sciences and in mining exploration. In recent years, the scanning method was applied to lacustrine sediment cores in complement to XRF core scanning. However, this approach was rarely applied to marine sediments. The Gaoping canyon, located south of Taiwan island, is connected to the Gaoping River and is a very active canyon with large sediment transfer capacity. In particular, about 4 typhoon-driven hyperpycnal flows have been recorded by mooring systems in every recent year. Studying their frequency and intensity responding to past climate and environmental changes is a key to understand future tropical storm frequency and related climate variability. Core MD18-3574 was collected on the western levee of the Gaoping canyon and displays numerous fine laminations (millimetric to centimetric) recording the deposition of the gravity flows occurring in the canyon and on the slope. In this study, we combined non-destructive analyses such as XRF core scanning and hyperspectral imaging with high-resolution grain size and XRD bulk mineralogy analyses to understand the sedimentological and geochemical variations at the scale of the laminae. Core MD18-3574 sediments consist mainly of fine silt, presenting an alternance of fine-grained and coarse-grained laminations. The average mean grain size is 13.4 µm ranging from 9 to 20.5 µm. Thick coarser grained laminations are showing grain size distributions and asymmetric sorting of typical turbidite sequence. Grain size and bulk mineralogy display great visual and statistical correlation with XRF (Fe/Ca, Si/Al) and hyperspectral proxies (sediment darkness (Rmean), Clay_R2200). Principal component analyses (PCA) demonstrates that darker laminae are composed of coarser sediments with high Si/Al (quartz and feldspar-rich) and Clay_R2200 values and low Fe/Ca (calcite-rich) resulting from gravity flows.  Inversely, lighter laminae consist of finer sediments with low Si/Al (muscovite and illite-rich), Clay_R2200 and high Fe/Ca resulting from hemipelagic deposition. Thus, such interpretation was extended to the core scale to identify gravity flows deposits layers. Moderate intensity tropical storm frequency is decreasing since the last 4 ka in response to the sea surface temperature (SST) decrease and enhanced East Asian winter monsoon since the middle Holocene. Tropical storm intensity increased after 2 ka in La Niña like periods indicating that the surge of super-typhoons hitting Taiwan could be triggered by El Niño Southern Oscillation (ENSO) state and variability. We can then assess that tropical storm activity is controlled by SST, monsoon system and ENSO conditions. This study brings new insights in the prediction of the ongoing climate change impacts on storms activity in the western Pacific Ocean.

How to cite: Bertaz, J., Jacq, K., Colin, C., Liu, Z., debret, M., Zhao, H., and Lin, A. T.-S.: High-resolution grain-size analysis and non-destructive hyperspectral imaging of sediments from the Gaoping canyon levee to establish past typhoon and monsoon activities affecting Taiwan during the late Holocene, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13163, https://doi.org/10.5194/egusphere-egu23-13163, 2023.

This paper describes laboratory tests performed with an NDT geophysical methods: Ground Penetrating Radar (GPR), Self Potential (SP) and Direct Current (DC) methods in order to monitor the corrosion of a rebar embedded in concrete. Even if the GPR is a common geophysical method for reinforced concrete structures, the SP and DC techniques are not widely used. Rebar corrosion is one of the main causes of deterioration of engineering reinforced structures and this degradation phenomena reduces their service life and durability. Non-destructive testing and evaluation of the rebar corrosion is a major issue for predicting the service life of reinforced concrete structures.

Several new experiments were performed at Applied Geophysical laboratory of University of Ferrara, following the experiences coming from previous tests (Fornasari et al., 2022), where two reinforced concrete samples of about 50 cm x 30 cm were cast, with a central ribbed steel rebar of 10 mm diameter and 35 cm long, were partially immersed in a plastic box with salty and distilled water. In this experiment, we applied a new protocol, where an epoxy resin was used in order to focalize the corrosion only along the exposed part of the rebar. The steel rebar was partially painted with a waterproof resin in order to leave only the central part uncovered for a length of 8 cm. The same waterproof epoxy resin was applied on part of the concrete sample, in order to have a specific chlorides diffusion across a freeway zone of about 10cm x 8cm defined below the exposed rebar.

The experiments were carried out on two identically constructed reinforced concrete samples, exposed to distilled water (sample “A”) and the second, exposed to a salty water with chlorides (sample “B”). Both samples were partially immersed for only 1 cm form the lower surface. The sample B was immersed in a salty water plastic box with different NaCl concentrations. An initial NaCl concentration of 0.1 % was adopted for 7 days, then the concentration was increased to 1% and finally to 3.5% for further 7 days. The experiment was set up in two phases. In the first phase of this study, we monitored the "natural" corrosion occurred on sample "B" due to the diffusion of chlorides towards the steel rebar comparing the obtained data with those of sample "A" exposed to distilled water. In the second phase of the study, accelerated corrosion was applied to sample "B" in order to induce an increment of the corrosion phenomena. The accelerated corrosion was designed in order to reach different theoretical levels of mass weight loss in the steel rebar, which were of 2%, 5%, 10% and 20%. During the experiments, 2GHz C-Thrue GPR antenna, Multivoltmeter with non-polarized calomel referenced electrode for SP and ABEM Terrameter LS for resistivity data, were used to monitor the rebar corrosion monitoring. The collected data were used for an integration observation to detect the evolution of the corrosion phenomenon on the reinforcement steel rebar and to define a quantitative analysis of the phenomena.

How to cite: Fornasari, G., Zanotto, F., Balbo, A., Grassi, V., and Rizzo, E.: Combined use of NDT methods for steel rebar corrosion monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13329, https://doi.org/10.5194/egusphere-egu23-13329, 2023.

A fully customizable data management system for Built Cultural Heritage surveys through NDT

The diagnosis of Built Cultural Heritage using non-invasive methods is useful to deepen the understanding of building characteristics, assessing the state of conservation of materials, and monitoring over time the effectiveness of restoration interventions.

Ultrasonic and sonic tests are Non-Destructive Techniques widely used to evaluate the consistency of historic masonry and stone elements and to identify on-site internal defects such as voids, detachments, fractures. These tests, in addition to being suitable for Cultural Heritage because they are non-invasive, provide a fundamental preliminary screening useful to better address further analysis.

Ultrasonic and Sonic velocity tests performed on monuments involve a lot of different information obtained from many surveys.  It is therefore important to optimize the amount of data collected both during documentation and diagnostic phase, making them easily accessible and meaningful for analysis and monitoring. In addition, investigations set-up should be following a standard methodology, repeatable over time, suitable for different types of artifacts, and prepared for comparison with other techniques.

An integrated data management system is then also useful to support the decision-making processes behind maintenance actions.

This work proposes the development of a complete management IT solution for the Ultrasonic and Sonic measurements of different types of masonry, and stone artifacts. The system consists of a browser-based collaboration and document management platform, a mobile/desktop application for data entry, and a data visualization and reporting tool. This set of tools enable the complete processing of data, from the on-site survey to their analysis and visualization.

The proposed methodology allows the standardization of the data entry workflow, and it is scalable, so it can be adapted to different types of masonry and artifacts. Moreover, this system provides real-time verification of data, optimizes survey and analysis times, and reduces errors. The platform can be integrated with machine learning models, useful to gain insight from data.

This solution, aimed to improve the approach to diagnostics of Cultural Heritage, has been successfully applied by the LAM Laboratory of the Department of Earth Sciences (University of Florence) on different case studies (e.g., ashlar, frescoed walls, plastered masonries, stone columns, coat-of-arms, etc.) belonging to many important monuments.

How to cite: Centauro, I., Salvatici, T., Calandra, S., and Garzonio, C. A.: A fully customizable data management system for Built Cultural Heritage surveys through NDT, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13720, https://doi.org/10.5194/egusphere-egu23-13720, 2023.

The possibility to estimate accurately the subsurface electric properties of the pavements from ground-penetrating radar (GPR) signals using inverse modeling is obstructed by the appropriateness of the forward model describing the GPR subsurface system. In this presentation, we improved the recently developed approach of Lambot et al. whose success relies on a stepped-frequency continuous-wave (SFCW) radar combined with an off-ground monostatic transverse electromagnetic horn antenna. The deep-learning based method were adopted to train an intelligent model including the waveform of the Green’s functions. The method was applied and validated in laboratory conditions on a tank filled with a two-layered sand subject to different water contents. Results showed agreement between the predictions of measured Green’s functions deep-learning model and the measured ones. Model inversions for the dielectric permittivity and heights of antenna further demonstrated for a comparison of presented method.

How to cite: Zeng, L., Zhou, B., Xie, X., and Lambot, S.: Pavements Layered Media Characterizations using deep learning-based GPR full-wave inversion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13934, https://doi.org/10.5194/egusphere-egu23-13934, 2023.

Discontinuity surfaces are well known to influence the mechanical behaviour of rocks under compression. Non-destructive techniques, such as ultrasonic pulse velocity and sclerometers, are increasingly used to estimate uniaxial compressive strength of rocks. In this study, several core samples derived from the doubling works of the railway network near Genga (Marche Region, Central Italy) were analysed in order to assess the influence of the structural geological context (proximity to folds, faults etc.) and tectonic deformation on rock strength. Tests were conducted in rock specimens through: i) conventional uniaxial compressive experiment, ii) non-destructive rebound-based methods such as Schmidt Hammer and Equotip  and iii) ultrasound. In this way, it was possible to make a critical analysis of the use of these techniques in the estimation of the uniaxial compressive strength (considering also information about discontinuity type, orientation and nature of the filling). Finally, a petrographic analysis using optical microscope has been undertaken as a support to the observations derived from the analysis at the sample scale. The results indicate that there are two main factors influencing the strength at the scale of the specimen. The first and most decisive factor is the presence of natural pre-existing fractures. The second is the tectonic deformation ratio: the greater the deformation is, the little the strength. Furthermore, through the combined use of uniaxial compressive experiment, non-destructive rebound-based methods and ultrasounds it was possible to highlights the advantages and limitations of each technique and define/propose new guidelines for their use. 

How to cite: Mammoliti, E., Gironelli, V., Jablonska, D., Mazzoli, S., Ferretti, A., Morici, M., and Francioni, M.: Influence of tectonic deformation on the mechanical properties of calcareous rocks: drawbacks of the non-destructive techniques , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14658, https://doi.org/10.5194/egusphere-egu23-14658, 2023.

One of the main priorities for road administrations and stakeholders is the management and monitoring of critical infrastructures, especially transportation infrastructures. In this context, Building Information Modeling (BIM) can be one of the more effective methodologies to be used to optimize the management process. In Italy, several laws and regulations have been issued, making the use of BIM procedures mandatory for the design of new infrastructures and emphasizing its role in the management of existing civil works [1, 2].

Monitoring operations of transportation infrastructures are generally conducted by on-site surveys. Non-Destructive Testing methods (i.e., GPR, LiDAR, Laser Profilometer, InSAR, etc.) have been used to perform these inspections as their outputs have been proven to be effective in determining the conditions of the infrastructure and its assets [3]. Moreover, BIM methodology could prove a valuable tool to manage the data provided by these surveys, as it consists in the creation of digital models capable of containing information related to the object that they are representing. These models can be used to store over time the different information obtained by the NDT surveys to carry out integrated analysis on the conditions of the infrastructure [4].

This study aims to analyze a potential BIM process capable of integrating different NDT surveys’ outputs, to generate an informative digital model of an infrastructure and its assets. The proposed methodology is then able to merge the data provided by the inspections, which is typically obtained by different operators and comes in different file formats, in a single BIM model. The main goal of the research is to provide a process to optimize the management procedures of transportation infrastructures, by creating digital models capable of reducing the problems typically associated with the monitoring and maintenance of these critical civil works. By merging different information in a single environment and relying on survey data that are commonly analyzed separately, an integrated analysis of the infrastructure can be carried out and data loss can be reduced.

The study was developed by relying on real data, obtained from on-site surveys carried out over Italian infrastructures. As different outputs have been collected, BIM models of different assets of the analyzed infrastructures were defined. Preliminary results have shown that the proposed methodology can be a viable tool for optimizing the management process of these critical civil works.

Acknowledgements

The research is supported by the Italian Ministry of Education, University and Research under the National Project “Extended resilience analysis of transport networks (EXTRA TN): Towards a simultaneously space, aerial and ground sensed infrastructure for risks prevention”, PRIN 2017. Prot. 20179BP4SM.

References

[1] MIT, 2018. Ministero delle Infrastrutture e dei Trasporti, D. Lgs 109/2018

[2] MIT, 2021. Ministero delle Infrastrutture e dei Trasporti, D.M. 312/2021

[3] D’Amico F. et al., 2020. Integration of InSAR and GPR Techniques for Monitoring Transition Areas in Railway Bridges. NDT&E Int

[4] D’Amico, F. et al., 2022. Integrating Non-Destructive Surveys into a Preliminary BIM-Oriented Digital Model for Possible Future Application in Road Pavements Management. Infrastructures 7, no. 1: 10

How to cite: Bertolini, L., D'Amico, F., Napolitano, A., Manalo, J. R. D., and Bianchini Ciampoli, L.: Combined NDT data for road management through BIM models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14846, https://doi.org/10.5194/egusphere-egu23-14846, 2023.

Measuring soil reflectance in the field, rather than in a laboratory setting, can be very useful when it comes to numerous applications such as mapping the distribution of various soil properties, especially when prompt estimations are needed.  Recent advances in spectroscopy, and specifically in the development of low-cost Micro-Electro-Mechanical-Systems (MEMS) based spectrometers, pave the way for developing real-time applications in agriculture and environmental monitoring. Compared to high-end spectrometers, whose spectral range extends from Visible (VIS) and Near-InfraRed (NIR) to Shortwave InfraRed (SWIR), MEMS cover limited parts of the electromagnetic spectrum resulting in missing important information. In parallel, new space missions such as Planet Fusion are operationally ready and provide optical imagery (RGB and NIR) with high spatial (3m) and temporal (daily) resolution. To this end, we assessed the potential of augmenting the bands captured from a commercial MEMS sensor (Spectral Engines Nirone S2.2 @ 1750 – 2150 nm) by adjoining the Planet Fusion bands at the exact sampling date and location that in-situ scans originate.

Employing the above, a set of portable MEMS was used at a pilot area in Cyprus (Agia Varvara, Nicosia district) to develop a regional in-situ Soil Spectral Library (SSL). A set of 60 distinct locations were selected for capturing in situ spectral reflectance after the stratification of Planet Fusion pixels of the pilot area, while a physical soil sample was analyzed at the laboratory for the determination of Soil Organic Carbon (SOC) content. During the visit, topsoil moisture was also measured.

The resulting SSL, containing the in-situ spectra, SOC, and moisture content was further augmented by the 4 bands of Planet Fusion imagery acquired on the exact date of the field visit. At this stage, three Random Forest models for SOC content estimation were fitted using as explanatory variables initially only the MEMS data with moisture content, then Planet Fusion bands, and finally all three available inputs.

The results presented an observable decrease in RMSE of SOC content estimations when fusing in-situ with spaceborne data, highlighting the importance of the information contained at VIS-NIR when modeling SOC. On the other hand, the synergy of the two sensors is mutually beneficial; SOC absorption bands can also be found in the SWIR region and are hard to detect with remote sensing means since they fall within the strong water absorption region (around 1950 nm). MEMS-based systems operating at the SWIR part can support this process, and if combined with ancillary environmental measurements such as soil moisture, can provide a cost-effective solution for measuring SOC and other soil-related parameters. To loosen the necessity of laboratory analysis, it is necessary to establish protocols and guidelines for spectral data collection and management to ensure that the data collected is consistent and of high quality and develop representative SSLs that can be used to serve different modeling scenarios. 

How to cite: Karyotis, K., Tsakiridis, N., and Zalidis, G.: Fusion of in-situ and spaceborne sensing for environmental monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14899, https://doi.org/10.5194/egusphere-egu23-14899, 2023.

Nowadays, there is an emerging demand from public authorities and managing bodies, to evaluate the overall health of infrastructures and identify the most critical transport assets. Considering the national-scale level, thousands of transport infrastructure are in critical conditions and require urgent maintenance actions. Currently, most of available Digital Twins (DT) allow to explore and visualize data including limited kind of information. This issue still limits the operative and practical use by infrastructure owners, that require fast solutions for managing several amount of data. Moreover, this idea is perfectly in line with European and national actions related to the development of a DT of the earth’s systems, including the “DestinE” programme of the European Commission by EUSPA and the European Space Agency (ESA). For this purpose, a dynamic DT model of a critical infrastructure is developed, using the available data about design information, historical maintenance operations and monitoring surveys based on satellite imageries.

In this context, this study presents an innovative concept of Digital Twin, which integrates all the details coming from NDTs surveys, on-site inspections and satellite-based information, to store, manage and visualize valuable information. This is made possible by analysing the main several gaps and limitations of existing platforms, providing a viable integrated solution developing an upgradable strategic analysis tool. To this purpose, remote sensing methods are identified as viable technologies for continuous monitoring operations. More specifically, data coming from satellites and the processing techniques, such as the Multi-Temporal SAR Interferometry approach, are strategic for the continuous monitoring of the displacements associated to transport infrastructures. An advantage of these techniques is the lighter data-processing required for the assessment of displacements and the detection of critical areas [1, 2].

The study introduces two main levels of innovation. The first one is associated to the integrated approach for transportation planning, integrating quantitative data from multi-sources, into the more traditional territorial analysis models. The second one is related to the technological engineering discipline, and it consists of the fusion of observation data from multi-source, with the last-generation dynamic data connected to the environment.

Acknowledgements

This research is supported by the Project “M.LAZIO”, accepted and funded by the Lazio Region, Italy.

References

[1] D'Amico, F. et al., “Implementation of an interoperable BIM platform integrating ground based and remote sensing information for network-level infrastructures monitoring”, Spie Remote Sensing 2022.

[2] Gagliardi, V. et al., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”, Spie Future Sensing Technologies 2020.

How to cite: Napolitano, A., Gagliardi, V., Bertolini, L., Manalo, J. R. D., Calvi, A., and Benedetto, A.: Implementation of a Digital Twin integrating remote sensing information for network-level infrastructure monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14981, https://doi.org/10.5194/egusphere-egu23-14981, 2023.

Infrastructure networks are crucial elements to ensure the sustainability of the current development model in which the movement of people and goods is essential. On the other hand, transport assets are increasingly exposed to several issues, including climatic conditions changing, vulnerability and exposure to natural hazards such as hydraulic, geomorphological, landslides and seismic phenomena, which can affect the structural integrity causing damages and deteriorations. The context is made even more serious by the degradation of materials and the progressive ageing of infrastructure, often accelerated by environmental conditions and inadequate, or not always effective, maintenance actions. This requires the investigation of novel methods for the large-scale detection of network-scale linear infrastructures, and simultaneously, of detail to diagnose causes and determine the priorities for the most effective countermeasures.

The proposed solution is based on a Data-Fusion approach, merging data coming from multi-source and multi-scale data, to enhance the interpretation process in a holistic sense. The information comes from spaceborne Multi-temporal SAR Interferometry, complemented by more detailed aerial data, detected by UAVs and Ground Based Non-Destructive Testing Methods, including laser scanner surveys for resolution and digital integrability, high-resolution camera measurements assisted by artificial intelligence for the surface degradation and from prospecting data collected by Ground Penetrating Radar technology. All these data can be simultaneously analyzed into a comprehensive digital platform, providing a useful tool to support operators and public bodies to prioritize maintenance actions.

The digital platform can be investigated also using augmented reality tools, capable of generating and reproducing the Digital Twin of the inspected infrastructure into a real environment. This allows any monitoring evaluation through a diagnostic technique that integrates spatial, aerial, ground-based and geophysical surveys, allowing navigation within the infrastructure. Potential applications are numerous, ranging from mapping of wide areas affected by potential criticality to the definition of the main vulnerabilities related to the seismic and hydraulic risks, the analysis of land changes surrounding the assets following extreme natural events, and the reconstruction of historical deformative trends of roads, railways and bridges through the interpretation of SAR data.

Acknowledgments

This research is supported by the Italian Ministry of Education, University, and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. In addition, this research is supported by the Project “MLAZIO” funded by Lazio Region (Italy).

How to cite: Gagliardi, V., Bianchini Ciampoli, L., D'Amico, F., Calvi, A., and Benedetto, A.: Novel perspectives in transport infrastructure management: Data-Fusion, integrated monitoring and augmented reality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15542, https://doi.org/10.5194/egusphere-egu23-15542, 2023.

The hydrogen isotope fractionation between leaf wax compounds and source water, the apparent fractionation (ε_app), necessary for the reconstruction of hydrogen isotopic composition (δD) of precipitation, is mainly assessed through field and transect studies. The current ε_app dataset, however, exhibit a bias toward mid-latitude regions of the Northern Hemisphere. Here we report the results of an outdoor experiment wherein four evergreen and three deciduous species were grown with water of known δD value (-1.8‰) in a tropical semi-arid monsoon region. This allowed us to estimate ε_app more accurately and also quantify ε_app variability within a species and among different species. Among-species ε_app values varied by -119 ± 23‰ (for n-alkane of chain length n-C₃₁) and -126 ± 27‰   (for n-alkanoic acid of chain length n-C₃₀). The similarity of the among-species variability in ε_app reported here and that observed in field and transect studies suggested the species-effect, rather than uncertainty in δD of source water, control the uncertainty in community-averaged ε_app. The fractionation of  δD between n-C₂₉ alkane and n-C₃₀ alkanoic acid (ε_29/30) and between n-C₃₁ alkane and n-C₃₂ alkanoic acid (ε_31/32) were 7 ± 25‰ and 6 ± 15‰, respectively, suggesting minimal fractionation of hydrogen isotopes during decarboxylation. Further, as we did not observe a systematic difference between the ε_app of deciduous and evergreen species; changes in the relative proportion of this vegetation in a community might not affect its ε_app value.

How to cite: Saishree, A., Managave, S., and Sarangi, V.: Hydrogen isotope fractionation between leaf wax compounds and source water in tropical angiosperms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16471, https://doi.org/10.5194/egusphere-egu23-16471, 2023.

Geoelectric non-destructive imaging and monitoring of the earth's subsurface requires robust and adaptable numerical methods to solve the governing differential equation. Most of the time, the DC data is acquired along a straight line. Hence, we solve the DC problem for the 2D case. But the source for the DC method exhibits a 3D nature. To account for the source's 3D nature, the 2D DC resistivity modeling is often carried out in the wavenumber domain. There have been studies that suggest ways for the selection of optimum wavenumbers and weights. But, this does not guarantee a universal choice of wavenumbers. The chosen wavenumbers and related weights strongly influence the precision of the resulting solution in the space domain. Many forward modeling studies demonstrate that selecting effective wavenumbers is challenging, especially for complicated models with topography, anisotropy, and significant resistivity differences. Moreover, forward modeling requires many wavenumbers as the models get more complex. 

This study focuses on developing a method that can completely omit wavenumbers for 2D DC resistivity modeling. The present work finds its motivation in a numerical experiment on a simple half-space model. Since the analytical response for such a model can be easily calculated, we match the analytical solution against the responses obtained from various wavenumbers and weights used in the literature. All the responses deviated from the analytical solution after a certain distance, and none of them were found to be accurate for large offsets. It was discovered after thorough testing of the numerical scheme that the wavenumbers selected for the forward modeling significantly impacted how practical the approach is for large offsets. 

To overcome this problem, a new boundary condition is derived and implemented in the existing numerical scheme. The numerical scheme chosen to perform the forward modeling is Mimetic Finite Difference Method (MFDM). We consider that the source is placed on the origin of the coordinate system. This removes the dependency of the source term, expressed in the Fourier domain, on the wavenumber. The solution obtained by solving the resulting equation will be an even function of the wavenumber and be real-valued. This ensures that the potential in the space domain for the 2D model will also be a real-valued even function with a symmetry about a plane perpendicular to the strike direction and passing through the origin. Because the first-order derivative of an even function at the plane of symmetry vanishes, mathematically, it can be expressed as a Neumann boundary condition at the considered plane. Therefore, we propose a scheme to solve the 2D resistivity problem in the space domain using the boundary condition mentioned here.

The developed algorithm is tested on isotropic and anisotropic two-layer models with large contrasts. It is found that the numerical solutions obtained using the modified boundary condition described above show considerable accuracy even for large offsets when compared with the analytical solution. On the other hand, the results obtained using available wavenumbers in the literature are also compared and are found to deviate considerably from the analytical solution at large offsets.

How to cite: Suryavanshi, D. and Dehiya, R.: Development of a flexible 2D DC Resistivity modelling technique for use in space domain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16632, https://doi.org/10.5194/egusphere-egu23-16632, 2023.

Approximately eight years ago, after a research activity that I started in the nineties on the application of GPR and, later, of NDTs to civil engineering, I realized that no technology can be considered as self standing. This is the consequence of the high complexity related to the civil engineering works and to the highly unpredictable impacts of ordinary processes and exceptional natural events. At the beginning of this century it was clear that a reliable and comprehensive monitoring of a phenomenon affecting bridges, tunnels, structures, or any civil engineering work is possible only by integrating data from different sources.

GPR was at that time a very promising technology, and many investigated in this field measuring e.g. pavement deformation, asphalt moisture, ballast degradation, also the mechanical properties of materials. The accurate outcomes represent a great step forward for the science in this sector, but the final results demonstrated to be partial, because the approach failed under a holistic perspective.

So, in the second decade of 2000, the need of a novel paradigm for investigation raised, in order not only to identify and quantify the problem, but also to diagnose its causes.

It was the stimulus to fuse data from different NDTs, under the assumption that information A and B give much more than A+B. It means that one information (A) can be explanatory of one or more characters contained in a second (B) that cannot be inferred by the knowledge of only one single standing information (B).

Based on this I decided, with very high level international colleagues, to establish a new session at EGU. It was the 2018. Today the sixth edition!

During these years a number ranging from 80 to 120 of researchers took part to each session. Also the number of countries involved is impressive, ranging for each session from 10 to 17. The institutions ranged from 36 to 50.

The number of contributions presented in the five editions is 141.

After 2018 we have seen several special issues of prominent journals were dedicated to data fusion. Recently, beyond the typical technologies as GPR, UT, ERT, a great attention was given to Lidar, Satellite and UAV.

Data fusion was also directed to other interesting and promising fields as archaeology, agriculture, urban planning, only to cite a few.

I would like to underline that this great interest started in Europe and in USA, but actually the geographical coverage is much wider and it includes at a same level also Asiatic and emerging countries.

There is now a new frontier that has to be. My vision is that this holistic approach can be used to develop an innovative immersive environment through the integration in augmented reality platforms on which a digital twin can be generated and dynamically upgraded through an adaptive interface, as well as using AI and machine learning paradigms.

How to cite: Benedetto, A.: Data fusion in civil engineering: personal experience, vision and historical considerations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16864, https://doi.org/10.5194/egusphere-egu23-16864, 2023.

Building Information Modeling is a software-based parametric design approach that allows a full interoperability between the various actors involved in a design or management process. Notwithstanding It has been specifically created for buildings projects, its use has been adapted to a wide range of applications, including transport infrastructure design and, more recently, cultural heritage. In regard to this field, it has been mainly applied to raise accuracy and effectiveness of restoring and stabilization activities for historical architectures.
The present study aims at demonstrating how the use of BIM may return remarkable outcomes in improving the current quality level of digital valorisation and virtual reconstructions of historical structures, especially when their rate of conservation is limited. Indeed, even though current digital reconstruction models are, usually, verified under an archaeological perspective, their structural consistency is never tested. This involves that many virtual reconstruction models are likely to represent structures that are historically accurate but that have no structural sense as, according to their geometric features and the construction materials/techniques, they would not stand their weight.
In this perspective, this study proposes a novel BIM-based methodology capable of both driving the archaeological reconstruction hypotheses and testing the reconstruction hypotheses on a structural basis. The model can be schematically represented by the following process:
1- Survey of the emerging: acquisition of data from superficial archaeological surveys (topographic data, laser scanner, aero photogrammetry, satellite images)
2- Survey of the hidden: acquisition of data from hypogeal surveys (georadar, electrical tomography, magnetometry);
3- Mechanical characterization: gathering of information concerning the materials of the find, proven in their mechanic qualities also through load stress tests;
4- Virtual reconstruction: proposal of a possible hypothesis of virtual reconstruction linked to structural and morphological features known to be present in the referred historical periods;
5- Structural test: engineering and structural confirmation of the forwarded hypothesis by means of finite element algorithms.
The proposed methodology was tested on the archaeological area of the Villa and Circus of Maxentius along the Ancient Appian Way in Rome; all the planned activities have been shared and authorized by the Sovrintendenza Capitolina ai Beni Culturali, within the context of the Project BIMHERIT, funded by Regione Lazio (DTC Lazio Call, Prot. 305-2020-35609).

How to cite: Santarelli, R. and Ten, A.: Integration of non-destructive surveys for BIM-based and structural-verified digital reconstruction of archaeological sites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17489, https://doi.org/10.5194/egusphere-egu23-17489, 2023.

In the wake of Chernobyl and Fukushima accidents radiocesium has become a radionuclide of most environmental concern. The ease with which this radionuclide moves through the environment and is taken up by plants and animals is governed by its chemical forms and site-specific environmental characteristics. Distinctions in climate and geomorphology, as well as ¹³⁷Cs speciation in the fallout result in differences in migration rates of ¹³⁷Cs in the environment and rates of its natural attenuation. In Fukushima areas ¹³⁷Cs was found to be strongly bound to soil and sediment particles, its bioavailability being reduced as a result.  Up to 80% of the deposited ¹³⁷Cs on the soil were reported to be incorporated in hot glassy particles (CsMPs) insoluble in water. Disintegration of these particles in the environment is much slower than of Chernobyl-derived fuel particles. The higher annual precipitation and steep slopes in Fukushima contaminated areas are conducive to higher erosion and higher total radiocesium wash-off. Typhoons Etou in 2015 and Hagibis in 2019 demonstrated the pronounced redistribution of ¹³⁷Cs on river watersheds and floodplains, and in some cases natural self-decontamination occurred. Among the common features in ¹³⁷Cs behavior in Chernobyl and Fukushima is a slow decrease in ¹³⁷Cs activity concentration in small, closed, and semi-closed lakes and its particular seasonal variations: increase in summer and decrease in winter.

How to cite: Konoplev, A.: Fukushima and Chernobyl: similarities and differences of radiocesium behavior in the soil-water environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1081, https://doi.org/10.5194/egusphere-egu23-1081, 2023.

After the Fukushima nuclear accident, atmospheric ¹³⁴Cs and ¹³⁷Cs measurements were taken in Fukushima city for 8 years, from March 2011 to March 2019. The airborne surface concentrations and deposition of radiocesium (radio-Cs) were high in winter and low in summer; these trends are the opposite of those observed in a contaminated forest area. The effective half-lives of ¹³⁷Cs in the concentrations and deposition before 2015 (0.754 and 1.30 years, respectively) were significantly shorter than those after 2015 (2.07 and 4.69 years, respectively), which was likely because the dissolved radio-Cs was discharged from the local terrestrial ecosystems more rapidly than the particulate radio-Cs. In fact, the dissolved fractions of precipitation were larger than the particulate fractions before 2015, but the particulate fractions were larger after 2016. X-ray fluorescence analysis suggested that biotite may have played a key role in the environmental behavior of particulate forms of radio-Cs after 2014. 

Resuspension of ¹³⁷Cs from the contaminated ground surface to the atmosphere is essential for understanding the long-term environmental behaviors of ¹³⁷Cs. We assessed the ¹³⁷Cs resuspension flux from bare soil and forest ecosystems in eastern Japan in 2013 using a numerical simulation constrained by surface air concentration and deposition measurements. In the estimation, the total areal annual resuspension of ¹³⁷Cs is 25.7 TBq, which is equivalent to 0.96% of the initial deposition (2.68 PBq). The current simulation underestimated the ¹³⁷Cs deposition in Fukushima city in winter by more than an order of magnitude, indicating the presence of additional resuspension sources. The site of Fukushima city is surrounded by major roads. Heavy traffic on wet and muddy roads after snow removal operations could generate superlarge (approximately 100 μm in diameter) road dust or road salt particles, which are not included in the model but might contribute to the observed ¹³⁷Cs at the site.

The current presentation based on the two published papers: Watanabe et al., ACP, https://doi.org/10.5194/acp-22-675-2022 (2022) and Kajino et al., ACP, https://doi.org/10.5194/acp-22-783-2022 (2022). The presenters would like to thank all of the co-authors of the two papers for their significant contributions.

How to cite: Kajino, M. and Watanabe, A.: Eight-year variations in atmospheric radiocesium in Fukushima city and simulated resuspension from contaminated ground surfaces in eastern Japan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1607, https://doi.org/10.5194/egusphere-egu23-1607, 2023.

Concentration-discharge relationships are widely used to understand the hydrologic processes controlling river water chemistry. We investigated how hydrological processes affect radionuclide concentrations (¹³⁷Cs and ⁹⁰Sr) in surface water in the headwater catchment at the Chornobyl exclusion zone in Ukraine. In flat wetland catchment, the depth of saturated soil layer changed little throughout the year, but changes in saturated soil surface area during snowmelt and immediately after rainfall affected water chemistry by changing the opportunities for contact between suface water and the soil surface. On the other hand, slope catchments with little wetlands, the water chemistry in river water is formed by changes in the contribution of "shallow water" and "deep water" due to changes in the water pathways supplied to the river. Dissolved and suspended ¹³⁷Cs concentrations did not correlate with discharge rate or competitive cations, but the solid/liquid ratio of ¹³⁷Cs showed a significant negative relationship with water temperature, and further studies are needed in terms of sorption/desorption reactions. ⁹⁰Sr concentrations in surface water were strongly related to water pathways for each the catchments. The contact between surface water and the soil surface and the change in the contribution of shallow and deep water to stream water could changes ⁹⁰Sr concentrations in surface water for in wetland and slope catchments, respectively. In this study, we revealed that the radionuclide concentrations in rivers in Chornobyl is strongly affected by the water pathways at headwater catchments.

How to cite: Igarashi, Y., Onda, Y., Matsushita, K., Sato, H., Wakiyama, Y., Lisovyi, H., Laptev, G., Samoilov, D., Kirieiev, S., and Konoplev, A.: Hydrological setting control 137Cs and 90Sr concentration at headwater catchments in the Chornobyl Exclusion Zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2540, https://doi.org/10.5194/egusphere-egu23-2540, 2023.

A generalized model of scavenging of the reactive radionuclide 239,240Pu was developed, in which the sorption-desorption processes of oxidized and reduced forms on multifraction suspended particulate matter are described by first-order kinetics. One-dimensional transport-diffusion-reaction equations were solved analytically and numerically. In the idealized case of instantaneous release of 239,240Pu on the ocean surface, the profile of concentrations asymptotically tends to the symmetric spreading bulge in the form of a Gaussian moving downward with constant velocity. The corresponding diffusion coefficient is the sum of the physical diffusivity and the apparent diffusivity caused by the reversible phase transitions between the dissolved and particulate states. Using the method of moments, we analytically obtained formulas for both the velocity of the center mass and apparent diffusivity. It was found that in ocean waters that have oxygen present at great depths, we can consider in the first approximation a simplified problem for a mixture of forms with a single effective distribution coefficient, as opposed to considering the complete problem. This conclusion was confirmed by the modeling results for the well-ventilated Eastern Mediterranean. In agreement with the measurements, the calculations demonstrate the presence of a maximum that is slowly descending for all forms of concentration. The ratio of the reduced form to the oxidized form was approximately 0.22-0.24. At the same time, 239,240Pu scavenging calculations for the anoxic Black Sea deep water reproduced the transition from the oxidized to reduced form of 239,240Pu with depth in accordance with the measurement data.

How to cite: Kim, K. O., Maderich, ., Brovchenko, ., Jung, . T., Kivva, ., Kovalets, ., and Kim, .: Dispersion of particle-reactive elements caused by the phase transitions in scavenging, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2561, https://doi.org/10.5194/egusphere-egu23-2561, 2023.

Radionuclides released and deposited by the 2011 Fukushima Daiichi Nuclear Power Plant accident caused an increase in air dose rates in forests in Fukushima Prefecture. It has been reported that air dose rates increase during rainfall, but we found that air dose rates decreased during rainfall in forests in Fukushima. This is said to be due to the shielding effect of soil moisture. This study aimed to develop a method for estimating changes in air dose rates due to rainfall even in the absence of soil moisture data. Therefore, we used the preceding rainfall (Rw), an indicator that also takes into account past rainfall; we calculated Rw in Namie-Town, Futaba-gun, Fukushima Prefecture from May to July 2020, and estimated air dose rates. In this area, air dose rates decreased with increasing soil moisture. Furthermore, air dose rates could be estimated by combining Rw with a half-life of 2 hours and 7 days, and by considering hysteresis in the absorption and drainage processes. The coefficient of determination (R²) exceeded 0.70 for the estimation of soil water content at this time. Furthermore, good agreement was also observed in the estimation of air dose rates from Rw (R² > 0.65). The same method was used to estimate air dose rates at the Kawauchi site from May to July 2019. Due to the high water repellency of the Kawauchi site, the increase in soil water content was very small and the change in air dose rate was almost negligible when soil water content was less than 15% and rainfall was less than 10 mm. This study enabled the estimation of soil water content and air dose rate from rainfall and captured the effect of rainfall on the decreasing trend of air dose rate. Therefore, in the future, This study can be used as an indicator to determine whether temporary changes in air dose rates are caused by influences other than rainfall. This study also contributes to the improvement of methods for estimating external dose rates for humans and terrestrial animals and plants in forests.

How to cite: Nakanishi, M., Onda, Y., Kato, H., Takahashi, J., Iida, H., and Takada, M.: Changes in Air Dose Rates due to Soil Water Content in Forests in Fukushima Prefecture, Japan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3049, https://doi.org/10.5194/egusphere-egu23-3049, 2023.

Wet scavenging modeling remains a challenge of the atmospheric transport of ¹³⁷Cs following the Fukushima Daiichi Nuclear Power Plant accident, which significantly influences the detailed spatiotemporal ¹³⁷Cs distribution. Till now, numerous wet deposition schemes have been proposed for ¹³⁷Cs, but it is often difficult to evaluate them consistently, due to the limited resolution of meteorological field data and detailed differences in model implementations. This study evaluated the detailed behavior of 25 combinations of in- and below-cloud wet scavenging models in the framework of the Weather Research and Forecasting-Chemistry model, using high-resolution (1 km × 1 km) meteorological input. The above implementation enables consistent evaluation with great details, revealing complex local behaviors of these combinations. The 1-km-resolution simulations were compared with simulations obtained previously using 3-km-resolution meteorological field data, with respect to the rainfall pattern of the east Japan during the accident, atmospheric concentrations acquired at the regional SPM monitoring sites and the total ground deposition. The capability of these models in reproducing local-scale observations were also investigated with a local-scale observations at the Naraha site, which his only 17.5 km from the Fukushima Daiichi Nuclear Power Plant. The performance of the ensemble mean was also evaluated. Results revealed that the 1-km simulations better reproduce the cumulative rainfall pattern during the Fukushima accident than those revealed by the 3-km simulations, but showing with spatiotemporal variability in accuracy. And rainfall below 1 mm/h is critical for the simulation accuracy. Those single-parameter wet deposition models that rely solely on the rainfall showed improvements in performance in the 1-km simulations relative to that in the 3-km simulations, because of the improved rainfall simulation in the 1-km results. Those multiparameter models that rely on both cloud and rainfall showed more robust performance in both the 3-km and -1km simulations, and the Roselle–Mircea model presented the best performance among the 25 models considered. Besides rainfall, wind transport showed substantial influence on the removal process of atmospheric ¹³⁷Cs, and it was nonnegligible even during periods in which wet deposition was dominant. The ensemble mean of the 1-km simulations better reproduces the high deposition area and the total deposition amount is closer to the observations than the 3-km simulation. At the local scale, the 1-km-resolution simulations effectively reproduced the ¹³⁷Cs concentrations observed at the Naraha site, but with deviations in peak timing, mainly because of biased wind direction. These findings indicate the necessity of a multi-parameter model for robust regional-scale wet deposition simulation and a refined wind and dispersion model for local-scale simulation of ¹³⁷Cs concentration.

How to cite: Zhuang, S., Dong, X., Xu, Y., and Fang, S.: Modeling and sensitivity study of wet scavenging models for the Fukushima accident using 1-km-resolution meteorological field data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4152, https://doi.org/10.5194/egusphere-egu23-4152, 2023.

The Fukushima-Daiichi Nuclear Power Plant (FDNPP) accident trigged by the Great East Japan Earthquake and subsequent tsunami in March 2011 released large quantities of radionuclides in terrestrial and marine environments of Fukushima Prefecture. Although radiocaesium (i.e. ¹³⁴Cs and ¹³⁷Cs) activity in these environments has decreased since the accident, the secondary inputs via the rivers draining and eroding the main terrestrial radioactive plume were shown to sustain high levels of ¹³⁷Cs in riverine and coastal sediments, which are likely deposited off the coast of the Prefecture. Accordingly, identifying the sources of sediment is required to elucidate the links between terrestrial and marine radiocaesium dynamics and to anticipate the fate of persistent radionuclides in the environment.

The objective of this study is to develop an original sediment source tracing technique to quantify the riverine sources of sediment and associated radionuclides accumulated in the Pacific Ocean. Target coastal sediment cores (n=6) with a length comprised between 20 and 60cm depth were collected during cruise campaigns between July and September 2022 at the Ota (n=2), Niida (n=1) and Ukedo (n=3) river mouths. Prior to gamma spectrometry measurements, sediment cores were opened and cut into 2 cm increments, oven-dried at 50°C for at least 48 hours, ground and passed through a 2-mm sieve.

Preliminary results regarding the spatial and depth distribution of radiocaesium in these samples show a strong heterogeneity, with highest radiocaesium levels (up to 134 ± 2 and 4882 ± 11 Bq kg^-1 for ¹³⁴Cs and ¹³⁷Cs, respectively) found in coastal sediment cores located at the Ukedo river mouth. On the opposite, no trace or low levels of Fukushima-derived radiocaesium were found in the Niida and in one sediment core of the Ota River mouths. Additional measurements will be conducted to determine the physico-chemical properties of this sediment, in order to select the optimal combination of tracers, which will then be introduced into un-mixing models. This increase knowledge will undoubtedly be useful for watershed and coastal management in the FDNPP post-accidental context.

How to cite: Chaboche, P.-A., Yoshifumi, W., Takata, H., Wada, T., Evrard, O., Misonou, T., Shiribiki, T., and Funaki, H.: Quantifying the riverine sources of sediment and associated radiocaesium deposited off the coast of Fukushima Prefecture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4697, https://doi.org/10.5194/egusphere-egu23-4697, 2023.

Radioactive cesium (¹³⁷Cs) is distributed in the global ocean due to global fallout from atmospheric nuclear tests, release from reprocessing plants in Europe, and supply to the ocean due to the Fukushima Daiichi Nuclear Power Plant 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, the IAEA is compiling a database of observation results (MARIS). However, since the spatio-temporal densities of observed data vary widely, it is difficult to obtain a complete picture from the database alone. Comparative validation using ocean general circulation model (OGCM) simulations is useful in interpreting these observations, and global ocean general circulation model (CESM2, POP2) simulations were conducted to clarify the behavior of ¹³⁷Cs in the ocean. The horizontal resolution is 1.125° longitude and 0.28° to 0.54° latitude. The minimum spacing near the sea surface is 10 m, and the spacing increases with depth to a maximum of 250 m with 60 vertical levels. Climatic values were used for driving force. As a source term for ¹³⁷Cs to the ocean, atmospheric fallout from atmospheric nuclear tests was newly established based on rainfall data and other data, and was confirmed to be more reproducible than before. Furthermore, the release from reprocessing plants in Europe and the leakage due to the accident at the Fukushima Daiichi Nuclear Power Plant were taken into account. 2020 input conditions were assumed to continue after 2020, and calculations were performed from 1945 to 2030. The simulated ¹³⁷Cs activities were found to be in good agreement, especially in the Atlantic and Pacific Oceans, where the observed densities are large. On the other hand, they were underestimated in the Southern Hemisphere, suggesting the need for further improvement of the fallout data. ¹³⁷Cs concentrations from the Fukushima Daiichi Nuclear Power Plant accident in March 2011 were generally in good agreement, although the reproducibility remained somewhat problematic due to insufficient model resolution. In other basins, the concentration characteristics were able to be determined, although the observed values were insufficient. Radioactivity concentrations of atmospheric nuclear test-derived ¹³⁷Cs may continue to be detected in the global ocean after 2030. The results of this simulation are useful for 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.: Verification of reproductivity of 137Cs activity concentration in the database by an ocean general circulation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4925, https://doi.org/10.5194/egusphere-egu23-4925, 2023.

A lot of radionuclides were scattered after the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. Previous studies showed that there were FDNPP-derived radioactive cesium-rich microparticles (CsMPs) with the size of a few μm in the soil and river water around Fukushima Prefecture^[1]. CsMPs have high radioactive cesium (Cs) concentration per unit mass, therefore they can be one of the factor in overestimating the Cs concentration in samples. Because Cs in CsMPs may not react directly with clay particles unlike the Cs ion in liquid phase, it is considered that CsMPs work as Cs carrier in soils^[2]. However, unlike ionic Cs and Cs adsorbed onto clay particles, the distribution and dynamics of CsMPs in soils have not been clarified. In this study, we investigated vertical distribution of CsMPs in the forest soil and the soil properties in Fukushima Prefecture, Japan.

Soil samples were collected from the forest in the difficult-to-return zone, approximately 10 km away from the FDNPP. The undisturbed soil samples were collected from 0-35 cm soil depth at 5 cm intervals using core sampler to investigate soil properties. Furthermore, litter samples on the surface soil layer were collected. Using these samples, the vertical distribution of Cs concentration in the soil and Cs derived from CsMPs were investigated. Cs concentration in samples placed in 100 mL of U8 container was measured using a germanium semiconductor detector. Cs derived from CsMPs was evaluated using an Imaging plate with reference to the method ffor quantification of CsMPs^[3].

Like Cs adsorbed on the soil, CsMPs were also mostly distributed in the soil surface layer between o and 5 cm of soil depth. We considered that straining may be one of the mechanism of CsMPs retention on the soil surface. Bradford et al. (2006) ^[4] showed that straining might be a significant mechanism for colloid retention when the average particle size in the porous medium is less than 200 times larger than the colloidal particle size. In this study, assuming the CsMPs size of approximately 1 µm, the average particle size of the soil collected from surface layer 0-5 cm was less than 200 times that of CsMPs. However, the average particle size decreased in deeper layer than 5 cm, therefore, it was considered that straining mechanism could be stronger.

This work was supported by FY2022 Sumitomo Foundation and FY2022 Internal Project of　Institute of Environmental Radioactivity, Fukushima University.

References

[1]　Igarashi, Y. et al., 2019. J. Environ. Radioact. 205–206, 101–118.

[2]  Tatsuno, T et al., 2022. J. Environ. Manage. 329, 116983.

[3]　Ikehara et al., 2018. Environ. Sci. Technol. 52, 6390–6398.

[4]　Bradford et al., 2003. Environ. Sci. Technol. 37, 2242–2250.

How to cite: Tatsuno, T., Waki, H., Nihei, N., and Ohte, N.: Vertical distribution of radioactive cesium-rich microparticles in forest soil of Hamadori area, Fukushima Prefecture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4947, https://doi.org/10.5194/egusphere-egu23-4947, 2023.

Migration of long-lived radioactive ⁹⁰Sr introduced by nuclear accidents and radioactive waste requires long-term monitoring and protection management due to its half-life of 28.8 years and high mobility in water. Presently, 37 years have passed since the largest worldwide ⁹⁰Sr contamination was released and deposited around the Chornobyl Nuclear Power Plant (ChNPP). In the vicinity of the ChNPP, the water level of the cooling pond (CP) has declined since May 2014 following the decommissioning phase of the Unit 3 reactor. The drawdown of the CP lowered the groundwater level in a massive vicinity (about 70 km²), and the change in the groundwater system due to the drawdown has caused concerns about possible changes in ⁹⁰Sr concentrations in water and transport dynamics to the Pripyat River. Therefore, this study evaluated how ⁹⁰Sr transport dynamics were influenced due to changes in the groundwater flow system from 2011 to 2020 based on observed data and results of the groundwater flow simulation in the CP vicinity.

The numerical simulation was conducted from 2011 to 2020 on monthly time-step using USGS MODFLOW with PM11 GUI and calibrated to groundwater heads measured at monitoring wells. In the location between the CP and the Pripyat River, estimated pore velocities near the river were reduced compared to velocities before the CP drawdown due to the decrease in the hydraulic gradient between the CP and the river. Decrease in groundwater velocity results decrease in groundwater discharge and delay of ⁹⁰Sr transport. Therefore, the amount of ⁹⁰Sr transported from the CP to the river is smaller than the period prior to the CP drawdown. The reduced ⁹⁰Sr transport is expected to have less impact on the radioactivity in the river water even in the Pripyat River floodplain northwest of the CP where ⁹⁰Sr concentrations significantly increased after the CP drawdown. In addition, the measured and simulated changes in groundwater flow direction and velocity suggested the possibility of ⁹⁰Sr accumulation at the floodplain caused by stagnant groundwater from reduced velocity and additional ⁹⁰Sr infiltration from surrounding ponds located at the Pripyat River floodplain. Therefore, enhancing the current monitoring of ⁹⁰Sr concentrations near the floodplain would be needed for long-term monitoring and protection management to prevent the risk.

How to cite: Sato, H., Shibasaki, N., Gusyev, M., Onda, Y., and Veremenko, D.: Changes in 90Sr transport dynamics in groundwater after large-scale groundwater drawdown in the vicinity of the cooling pond at the Chornobyl Nuclear Power Plant, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5042, https://doi.org/10.5194/egusphere-egu23-5042, 2023.

The 3D model THREETOX was applied for the long-term simulation of the planned release of radioactively contaminated water from Fukushima storage tanks to marine environment. Two radionuclides were considered: ³H that has the largest activity in tanks and ¹²⁹I that can caused the largest dose of radiation to human. The constant release rate of ³H equal to 22 TBq/y according to TEPCO estimations and the constant release rate of ¹²⁹I equal to 361 MBq/y according to estimations from the current study were used in the simulations.

The THREETOX model used monthly averaged currents from the KIOST-MOM model. A dynamic food web model was included in the THREETOX model. In the model, organisms uptake the activity directly from water and through the food chain. The food chain consists of phytoplankton, zooplankton, non-piscivorous (prey) fish, and piscivorous (predatory) fish. In case of ¹²⁹I, macro-algae was also considered. The modelling area covers Fukushima coastal waters and extends for 1600 km from the coast to the East. From North to South this area extends for 1300 km.

From model results, we can see how contamination will spread along the coast in different seasons. For example, in summer time the currents near the coast are directed to the North that leads to contamination of the Sendai Bay. This means that at different points along the coast, the concentration of radionuclides can periodically change according to currents that change during the year. Calculated concentrations of activity at several points along the coast of Japan, which correspond to largest cities in the area of interest, were extracted from model results. For example, calculated concentration of ³H in water in Tomioka point, which is quite close to FDNPP, sometimes can exceed 200 Bq/m³. In Soma point, the concentration will exceed 50 Bq/m³, while in point Iwaki-Onahama – 20 Bq/m³ at some moments of time. In other points, the calculated concentration of ³H in water will not exceed 10 Bq/m³ that is less than background concentration 50 Bq/m³. Concerning ¹²⁹I, its maximum concentration in water will be around 10^-3 – 10^-2 Bq/m³ in points close to FDNPP and around 10^-4 Bq/m³ in points further from the NPP that is around 100 000 times less than the calculated concentrations of ³H.

Calculated concentrations of OBT (organically bounded tritium) in predatory and prey fish are less than 0.01 Bq/kg in all points except FDNPP point where it is around 0.02 Bq/kg. This value is 10 times less than measured concentration of OBT in fish (0.2 Bq/kg) that was made in 2014 in the coastal area near the damaged NPP. Calculated concentrations of ¹²⁹I in predatory and prey fish are in the range 10^-6 – 10^-4 Bq/kg in all considered points. Concentrations of ¹²⁹I in macro-algae are about 100 times higher due to ability of iodine to accumulate in macro-algae. 

How to cite: Bezhenar, R., Takata, H., and Maderich, V.: Transport of H-3 and I-129 in water and their uptake by marine organisms due to the planned release of Fukushima storage water, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6019, https://doi.org/10.5194/egusphere-egu23-6019, 2023.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident on March 11, 2011 resulted in the release of large amounts of radioactive cesium-137 (137Cs) into the environment. It is important to characterize the Cs-137 dynamics throughout the river from the headwaters to the downstream. Previous studies have suggested the importance of dissolved forms of Cs-137 in organic matter in small watersheds and dissolved forms in suspended solids in large watersheds. Since the concentration of suspended-form Cs has been shown to decrease significantly after decontamination in evacuated areas (Feng et al. 2022), this rapid decrease in suspended-form Cs-137 concentration can be used to determine the cause of dissolved-form Cs. Therefore, we attempted to evaluate whether the dissolved Cs-137 was derived from organic matter or suspended solids by comparing data before and after decontamination.

 The objective of this study is to compare the decreasing trends of Cs-137 concentrations in decontaminated and undecontaminated areas based on long-term monitoring of suspended solids, dissolved solids, and coarse organic matter Cs-137 concentrations since 2011. The study area includes four headwater basins and four river basins (eight sites in total) in the Kuchibuto River watershed in the Yamakiya district of Fukushima Prefecture, located approximately 35 km northwest of the FDNPP.

In the Kuchibuto River watershed, a large inflow of decontaminated soil with low Cs-137 concentrations due to an increase in the amount of bare land caused by decontamination resulted in a rapid decrease in the concentration of suspended-form 137Cs in the decontaminated area in the headwaters and in the upper reaches of the river. However, no clear effect of decontamination was observed in the concentrations of dissolved Cs-137 and Cs-137 in coarse organic matter. Comparison of the slopes of Cs-137 concentrations in the suspended, dissolved, and coarse organic matter showed that the slope of the dissolved form was similar to that of the coarse organic matter in the source watersheds, and similar to that of the SS in the downstream watersheds. These results suggest that the contribution of dissolved Cs-137 from organic matter in small watersheds and that from suspended solids in large watersheds is significant.

How to cite: Onda, Y., Kawano, T., Taniguchi, K., and Takahashi, J.: Dynamic change of dissolved Cs-137 from headwaters to downstream in the Kuchibuto River catchment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6026, https://doi.org/10.5194/egusphere-egu23-6026, 2023.

Understanding riverine ¹³⁷Cs dynamics during high-flow events is crucial for improving predictability of ¹³⁷Cs transportation and relevant hydrological responses. It is frequently documented that the majority of ¹³⁷Cs is exported during high-flow events triggered by intensive rainfall. Studies on ¹³⁷Cs in coastal seawater suggested that a huge high-flow events resulted in high dissolved ¹³⁷Cs concentration in seawater. Different temporal patterns of ¹³⁷Cs concentrations in river water are found in the existing literature on ¹³⁷Cs dynamics during high-flow events. Although such differences may reflect catchment characteristics, there is no comprehensive analysis for the relationships. This study explores catchment characteristics affecting ¹³⁷Cs transport via river to ocean based on datasets obtained by sampling campaigns during high-flow events. ¹³⁷Cs datasets obtained at 13 points in 6 river water systems were subject to the analysis. The analyses intended to explore relationship between catchment characteristics (scale and land use composition) and ¹³⁷Cs dynamics in terms of variations in concentration, fluxes, and potential remobilization in seawater. We could not find any significant correlations between the parameters of catchment characteristics and mean values of normalized concentrations of ¹³⁷Cs and apparent K_d. However, when approximating ¹³⁷Cs concentrations and K_d value as a power function of suspended solid concentration (Y=α X^β), the power of β in the equations for dissolved ¹³⁷Cs concentration and K_d showed negative and positive correlations with the logarithm of the watershed area, respectively, and the positive β was found when the catchment area was on the order of 100 km² or larger and vice versa. This indicates that the concentration of dissolved ¹³⁷Cs tends to decrease with increased water discharge in larger catchments for smaller catchments. These results suggest that the temporal pattern of dissolved ¹³⁷Cs concentrations depends on watershed scale. ¹³⁷Cs flux during a single event ranged from 1.9 GBq to 1.1 TBq and accounted for 0.00074% to 0.22% of total ¹³⁷Cs deposited in relevant catchments. Particulate ¹³⁷Cs flux accounted for more than 92% of total ¹³⁷Cs flux, except for Ukedo River basin with a large dam reservoir. R-factor, an erosivity index in the Universal Soil Loss Equation model family, is a good parameter for reproducing sediment discharge and particulate ¹³⁷Cs flux. Efficiency of particulate ¹³⁷Cs flux, calculated by dividing the flux by R-factor of event, tended to be high in catchments with relatively low forest cover. Desorption ratio of ¹³⁷Cs, obtained by 1-day shaking experiment of SS in seawater, ranged from 2.8 to 6.6%. The ratio was almost proportional of ratio of exchangeable ¹³⁷Cs. The estimated amounts of desorbed ¹³⁷Cs, obtained by multiplying particulate ¹³⁷Cs and the desorption ratios, were greater than direct flux of dissolved ¹³⁷Cs. Reanalysis of riverine ¹³⁷Cs dataset in high flow events is revealing relationship between catchment characteristics and ¹³⁷Cs dynamics. Further analyses, such as evaluation of decontamination impacts and inter-catchment comparisons of ¹³⁷Cs fluxes, are required for better understanding.

How to cite: Wakiyama, Y., Takata, H., Taniguchi, K., Niida, T., Igarashi, Y., and Konoplev, A.: Riverine 137Cs dynamics and remoralization in coastal waters during high flow events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10093, https://doi.org/10.5194/egusphere-egu23-10093, 2023.

It is known that the semi-enclosed water area such as pond and dam reservoir is readily subject to ¹³⁷Cs accumulation because of the secondary inflow from the catchment area. We present the long-term monitoring data of the ¹³⁷Cs concentration in bottom sediment and pond water in an urban pond located in the central area of Koriyama City, Fukushima Prefecture to discuss the ¹³⁷Cs dynamics of the urban pond. The pond was decontaminated by the bottom sediment removal in 2017. The bottom sediment core and pond water were collected in 2015 and 2018-2021. The inflow and outflow water were collected in 2020-2021. The river water around the pond was collected in 2021. The bottom sediment and water samples were measured for ¹³⁷Cs concentration, particulate size distribution, and N and C stable isotopes. Compared between 2015 and 2018, the ¹³⁷Cs inventory and 0-10 cm depth of ¹³⁷Cs concentration in the bottom sediment at 7 points were decreased by 81 % (mean 1.50 to 0.28 MBq/m²) and 85 % (mean 31.5 to 4.8 kBq/kgDW), respectively. Although mean ¹³⁷Cs inventory in bottom sediment did not drastically change during 2018-2021, its variability became wider. Points with increased ¹³⁷Cs inventory in bottom sediment showed year-by-year increase in thickness of layer with concentrations higher than 8 kBq/kgDW, a criterion for considered decontamination. The ¹³⁷Cs concentration in suspended solids (SS) in pond water was lowered after decontamination, although it still remained above 8 kBq/kgDW. The ¹³⁷Cs concentrations in SS of inflow water were also high, exceeding 8 kBq/kgDW. The ¹³⁷Cs concentration in SS of the river water around the pond was higher when it passed through the urban area, suggesting that the inflow of particles from urban origin maintained high ¹³⁷Cs level in the pond. 

How to cite: Kurosawa, H., Nanba, K., Wada, T., and Wakiyama, Y.: Long-term dynamics of 137Cs accumulation at an urban pond, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10539, https://doi.org/10.5194/egusphere-egu23-10539, 2023.

Due to the accident at the Fukushima Daiichi Nuclear Power Plant (1F) in March 2011, radionuclides were introduced into the environment, and one of the release pathways to the ocean is the direct discharge from the 1F (on-going release). This was mainly caused immediately after the accident, but even now, the on-going release is continuing. In this study, firstly we estimated the on-going release of ¹³⁷Cs from 1F over 10 years after the accident, using the TEPCO’s ¹³⁷Cs monitoring results in the coastal area around 1F. Secondly, change in the monitoring data related to countermeasures by TEPCO (e.g. construction of iced walls) to reduce the introduction of contaminated water into the ocean or detect ¹³⁷Cs in nearby seawater, so their effects on the on-going release estimation were also discussed. A box model including inside and outside of the port was assumed for the area around 1F, and the amount of ¹³⁷Cs in the box was estimated (estimated value: modeled data). Then, the difference between the estimated value and the amount of ¹³⁷Cs obtained from actual observed concentrations (measured value: monitoring data) was calculated. The result showed that the measured value was higher than the estimated value, suggesting the on-going release from 1F. As for decrease in monitoring data after the countermeasures, it is implied that the estimation of rate of on-going release has been reduced by the countermeasures.

How to cite: Satoh, S. and Takata, H.: Estimation of annual Cesium-137 influx from the FDNPP to the coastal water, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10868, https://doi.org/10.5194/egusphere-egu23-10868, 2023.

The Fukushima Daiichi Nuclear Power Plant accident (FDNPP) in 2011 resulted in the release of large amounts of Cs-137 into the atmosphere. Cs-137 deposited on land was mainly distributed in forests, but some of it has been discharged to the sea through rivers. The dissolved and suspended forms of Cs-137 in rivers have been focused on, and it is known that the discharge mechanism and concentration formation of Cs-137 differ depending on the land use in the river basin. On the other hand, there are few cases that focus on the dynamics of Cs-137 in river bottom sediments. River-bottom sediment is less likely to flow downstream than suspended sediments, so contamination in the downstream area may be long-term.
We will clarify the migration mechanism of Cs-137 in rivers including river-bottom sediment.Therefore, we will analyze data collected from 2011 to 2018 in 89 watersheds in Fukushima prefecture. In analyzing the data, we removed sampling points with brackish water using electrical conductivity and corrected for particle size to standardize the surface area of particles that absorb Cs-137.As a result, it was found that unlike dissolved and suspended forms, the Cs concentration in river-bottom sediments can increase within the initial year. This is related to the average initial deposition in the watershed and the amount of initial deposition at the river-bottom sediment sampling sites, with a tendency to increase with relatively higher initial deposition in the upstream area. It was also known that the decrease in suspended Cs concentration was more pronounced when anthropogenic activities in the watershed were more active, but there was no clear relationship between land use in the watershed and changes in river-bottom sediment Cs concentration. This indicates that suspended sediment Cs concentrations are controlled by initial deposition to suspended sediment production sources, whereas river-bottom Cs concentrations are controlled by multiple factors such as sediment traction and Cs supply from river water.

How to cite: Wada, N., Onda, Y., Gao, X., and Tang, C.: Changes in Cs-137 concentrations in river-bottom sediments and their factors in Fukushima Prefecture rivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11671, https://doi.org/10.5194/egusphere-egu23-11671, 2023.

Government-led decontamination of agricultural land in the Fukushima accident (2011) region has lowered the on-site radiation risk considerably. From 2013 to early 2017, 11.9% of the land in the Fukushima disaster affected Niida watershed in Japan was remediated through topsoil removal. However, this resulted in a 237.1% increase in suspended sediment loads in the river for 2016 compared to 2013.  In contrast, sediment loads decreased by 41% from 2016 to 2017; this can be attributed to the effect of natural vegetation restoration on sediment yield and transfer patterns (Bin et al., 2022). Since radiocaesium firmly binds to the clay minerals in the soil, it is inevitably transported along with the sediments downstream to the river systems. These observations confirm that rapid, spatially targeted interventions, such as revegetation, e.g., through afforestation, have the potential to decrease the magnitude and period of increased exports of contaminated sediments. The CAMF tool (Cellular Automata-based Heuristic for Minimizing Flow) (Vanegas et al., 2012) was originally designed to find the cells in a raster representation of a watershed for which afforestation would lead to a maximal reduction of sediment exports with minimal effort or cost while taking sediment flow from cell to cell into account. In our research, we adapted the CAMF tool to account for the radiocaesium budgets associated with the transported sediments. We applied the approach to the Niida catchment, where land-cover changes in upstream decontaminated regions are detected using drone imagery and linked to increased sediment loads in the Niida river using long-term river monitoring systems. For example In 2014, agricultural land (18.02 km²) was one of the major land uses in the regions where decontamination was ordered, resulting in increased sediment loads from 2014 to 2016. By recognizing both the on- and off-site impacts of the remediation interventions and their temporal dynamics, the modified CAMF tool offers scope for supporting the formulation of spatio-temporal schemes for the remediation of agricultural land. These schemes aim to decrease the radiation risk for downstream communities and minimize the potential recontamination of already decontaminated sites.

How to cite: Abrams, F., Sweeck, L., Camps, J., Castillo-Reyes, G., Feng, B., Onda, Y., and Van Orshoven, J.: Minimizing the loss of radioactively contaminated sediment from the Niida watershed (Fukushima, Japan) through spatially targeted afforestation., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12670, https://doi.org/10.5194/egusphere-egu23-12670, 2023.

We compare the atmospheric concentrations of Cs-137 after a decade between Chernobyl and Fukushima cases. We plotted 8 datasets on log-log axes (5 cases in Chernobyl and 3 cases Fukushima) and found that they appear to follow a single function.

There have been measured the atmospheric concentration after the Chernobyl accident for more than 30 years [1]. On the other hand, several teams of Japanese researchers have been measured in Fukushima and its vicinity for almost 10 years. [2][3] In this study, we compare 5 sites in Chernobyl (Pripyat, Chernobyl, Baryshevka, Kiev, and Polesskoe) and 3 sites in Fukushima (FDNPP O-6 and O-7, Univ. Fukushima).

We adjust the magnitude of the data because it depends on the amount of the initial deposition. After the adjustment, we plot the 8 cases on a log-log plot. We found that the 8 cases collapse together, with the power index of -1.6. Namely,

C(t) ~ t^{-1.6}.               …(1)

Incidentally, we have been proposed a formula which reproduce the long-term behavior of atmospheric concentration at a fixed location as

C(t) = A exp(-bt) t^{-4/3}    …(2)

where A is a parameter which relates to the amount of the initial deposition and b as the reaction rate of all the first-order reactions (including the radioactive decay rate, the vegetation uptake rate, the runoff rate, etc). We will investigate the difference in the power-law index in Eq. (1) and (2). The parameter b is highly dependent on the environment. When we take a proper value of b, the apparent decrease of the concentration will change from t^{-4/3}. We may make the apparent power-index close to -1.6.

[1] E. K. Garger, et al., J. Env. Radioact., 110 (2012) 53-58.

[2] A. Watanabe, et al., Atmos. Chem. Phys. 22 (2022) 675-692.

[3] T. Abe, K. Yoshimura, Y. Sanada, Aerosol and Air Quality Research, 21 (2021) 200636.

How to cite: Akasaki, K., Mori, S., Suetomi, E., and Hatano, Y.: Similarity of long-term temporal decrease in atmospheric Cs-137 between Chernobyl and Fukushima, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13366, https://doi.org/10.5194/egusphere-egu23-13366, 2023.

For decommissioning of Fukushima Daiichi Nuclear Power Station (FDNPS), it is one of the biggest problems to treat the radioactive contaminated stagnant water in the building. It is difficult to remove H-3 from the contaminated water by only Advanced Liquid Processing System (ALPS) treatment. Thus, the Japanese Government announced to release the ALPS treated water containing H-3. To predict the alteration of the dose rate of the marine biota by the change of H-3 concentration in marine water after the release of ALPS water, it is necessary to understand the dynamics of H-3 in marine ecosystem. In this study, we studied the behavior of H-3 in the marine environment (water and biota) off Aomori and Iwate prefectures from FY2003 to FY2012, as the background data of the Pacific Ocean along the coast of the North East Japan. To clarify the dynamics of H-3 in marine biota, we compared H-3 and Cs-137. Excluding the period of the intermittent test operation of the Rokkasho Reprocessing Plant (FY2006-FY2008), the concentration of H-3 in seawater, tissue free water tritium (TFWT) and organically bound tritium (OBT) were 0.052-0.20 Bq/L with a mean of 0.12±0.031 Bq/L, 0.050-0.34 Bq/kg-wet with a mean of 1.1±0.039 Bq/kg-wet and 0.0070-0.099 Bq/kg-wet with a mean of 0.042±0.019 Bq/kg-wet, respectively. Before the FDNPS accident (FY2003-FY2010), Cs-137 concentration in seawater and marine biota were 0.00054-0.0027 Bq/L with a mean of 0.0016±0.00041 Bq/L and 0.022-1.8 Bq/kg-wet with a mean of 0.090±0.037 Bq/kg-wet, respectively. Concentration Ratio (CR), the ratio of the concentration of marine biota and seawater for TFWT, was to be 0.34-2.37 with a mean of 0.97±0.31 in all spices, meaning the concentration of marine biota was almost equal to seawater. For Cs-137, CR were 46-78 with a mean of 56±22. We compared CRs for TFWT of Gadus macrocephalus, Lophius litulon and Oncorhynchus keta with those of Cs-137. Comparing CR-TFWT and CR-Cs-137 for these three species, Spearman-R was <0.4 and p was >0.05, indicating that the dynamics of TFWT and Cs-137 in marine ecology is decoupled.

How to cite: Ohtsuki, S., Shirotani, Y., and Takata, H.: Distributions of tritium in the marine water and biota around Rokkasho Reprocessing Plant, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13486, https://doi.org/10.5194/egusphere-egu23-13486, 2023.

Groundwater systems and surface water can interact in a complex manner that influences catchment discharge, which then becomes more complex in forest slopes. A large amount of Radioactive cesium (¹³⁷Cs) deposited on forests due to the Fukushima Daiichi Nuclear Power Plant accident remains in terrestrial environments and is transported downstream as suspended or dissolved forms by surface water. Generally, the concentration of dissolved ¹³⁷Cs in surface water increases especially during runoff. While the leaching behavior of ¹³⁷Cs from contaminated forest materials and soils to surface water has been heavily studied, the influence of ¹³⁷Cs concentration in shallow groundwater systems in forest slopes have not been investigated. Therefore, detailed hydrological observations of groundwater on a forest hillslope will enable quantitative analysis of the influence of groundwater flow on the formation of dissolved ¹³⁷Cs concentrations in surface water during base flow and during runoff. Our results showed that the dissolved ¹³⁷Cs concentration in surface water increases during water discharge. The average concentration of dissolved ¹³⁷Cs in shallow groundwater was 0.64 Bq/L, which was higher than that in surface water (average 0.10 Bq/L). Furthermore, it was also observed that a part of the shallow groundwater on the slope moves toward the river channel at the time of water runoff. This suggests that shallow groundwater may have flowed into the surface water during the outflow and contributed to the increase of ¹³⁷Cs in the surface water. In this study, the contribution of groundwater in forest slopes to the dissolved ¹³⁷Cs concentration in surface water was estimated using the hydrodynamic gradient distribution of groundwater in forest slopes and the measured dissolved ¹³⁷Cs concentration in groundwater.

How to cite: Niwano, Y., Kato, H., Akaiwa, S., Anderson, D., Iida, H., Nakanishi, M., Onda, Y., Sato, H., and Niizato, T.: 137Cs transport flux to surface water due to shallow groundwater discharge from forest hillslope, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15515, https://doi.org/10.5194/egusphere-egu23-15515, 2023.

Understanding, modeling and predicting the future of the Earth System in response to global change is a challenge for the Earth system scientific community, but a necessity to address pressing societal needs related to the UN Sustainable Development Goals and risk monitoring and prediction. These “wicked” environmental problems require the building of integrated modeling tools . The latter will only provide reliable response if they integrate all existing multi-disciplinary data sources. Open science and data sharing using the FAIR (Findable, Accessible, Interoperable, Reusable) principles provide the framework for such data sharing. However, when trying to put it into practice, we face a large fragmentation of the landscape, with different communities having developed their own data management systems, standards and tools.

When starting to work on the Theia/OZCAR Information System (IS) that aims to Facilitate the discovery, to make FAIR, in-situ data of continental surfaces collected by French research organizations and their foreign partners, we performed a “Tour de France” to understand the critical zone science users’ needs when searching for data. The common criterion that emerged was the variables names. We believe that this need is general to all disciplines involved in Earth System sciences and is all the more important when data is searched by scientists of other disciplines that are not familiar with the vocabularies of the other communities. This abstract aim is to share our experience in building the tools aiming at harmonizing and sharing variables names using FAIR principles.

In the Theia/OZCAR critical zone research community, long term observatories that produce the data have heterogeneous data description practices and variable names. They may be different for the same variable (i.e.: "soil moisture", "soil water content", "humidité des sols", etc.). Moreover, it is not possible to infer automatically or semi-automatically similarities between these variables names. In order to identify these similarities and implement data discovery functionalities on these dimensions in the IS, we built the Theia/OZCAR variable thesaurus. To enable technical interoperability of the thesaurus, it is published on the web using the SKOS vocabulary description standard. Other thesauri used in environmental sciences in Europe and worldwide have been identified and the definition of associative relationships with these vocabularies ensures the semantic interoperability of the Theia/OZCAR thesaurus. However, it is quite common that the variable names used for the search dimensions remain general (e.g. "soil moisture") and are not specific enough for the end user to interpret exactly what has been measured (e.g. "soil moisture at 10 cm depth measured by TDR probe"). Therefore, to improve data reuse and interoperability, the thesaurus now follows a recommendation of the Research Data Alliance and implements the I-ADOPT framework to describe the variables more precisely. Each variable is composed and described by relationships with atomic concepts whose definition is specified. The use of these atomic concepts enhances interoperability with other catalogues or services and contributes to the reuse of the data by other communities that those who collected them.

How to cite: Braud, I., Coussot, C., Chaffard, V., and Galle, S.: Theia/OZCAR Thesaurus: a terminology service to facilitate the discovery, interoperability and reuse of data from continental surfaces and critical zone science in interdisciplinary research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1099, https://doi.org/10.5194/egusphere-egu23-1099, 2023.

A data integration and processing system was established to provide long-time data and real-time data to the researcher who are interested in long-term variation of ocean data in the Northwest Pacific area. All available ocean data of 6 variables (ocean temperature, salinity, dissolved oxygen, ocean CO2, nutrients) in the NWP area (0°N - 65°N, 95°E - 175°E) are collected from the Korean domestic organizations (KIOST, NFIS, KHOA, KOEM), the international data systems (WOD, GTSPP, SeaDataNet, etc.), and the international observation networks (Argo, GOSHIP, GLODAP, etc.). Total number of data collected is over 5 millions and observation dates are from 1938 to 2022. After referring to several QC manuals and related papers, QC procedures and test criteria for 6 data items were determined and documented. Several Matlab programs complying with QC procedures were developed and used to check quality of all collected data. We excluded duplicated data from the data set and saved them in 0.25° grid data files. Long-term average over 40 years and standard deviation of data at each standard depths and grid point were calculated. All quality controlled data, qc flag, average, standard deviation of each ocean variables are saved in format of netCDF and provided to ocean climate researchers and numerical modelers. We also have 2 plans using the collected data from 2023 to 2025. The one is production of long-term grid data set focused on the NWP area, the other is developing a data service system providing observation data and reanalysis data together.

Acknowledgement : This research was supported by Korea Institute of Marine Science & Technology Promotion(KIMST) funded by the Ministry of Oceans and Fisheries(KIMST-20220033)

How to cite: Kim, S. D., Park, H. M., Kwon, Y. S., and Han, H. G.: A data integration system for ocean climate change research in the Northwest Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1294, https://doi.org/10.5194/egusphere-egu23-1294, 2023.

The consequences of global change on the ocean are multiple such as increase in temperature and sea level, stronger storms, deoxygenation, impacts on ecosystems. But the detection of changes and impacts is still difficult because of the diversity and variability of marine environments. While there has been a clear increase in the number of marine and coastal observations, whether by in situ, laboratory or remote sensing measurements, each data is both costly to acquire and unique. The number and variety of data acquisition techniques require efficient methods of improving data availability via interoperable portals, which facilitate data sharing according to FAIR principles for producers and users. ODATIS, the ocean cluster of Data Terra, the French research infrastructure for Earth data, is the entry point to access all the French Ocean observation data (Ocean Data Information and Services ; www.odatis-ocean.fr/en/). The first challenge of ODATIS is to get data producers to share data. To that purpose, ODATIS offers several services to help them define Data Management Plan (DPM), implement the FAIR principles, make data more visible and accessible by being referenced in the ODATIS catalog, and better tracked and cited through a Digital Object Identifier (DOI). ODATIS also offers a service for publishing open scientific data on the sea, through SEANOE (www.seanoe.org) that provides a DOI that can be cited in scientific articles in a reliable and sustainable way. In parallel to the informatic development of the ocean cluster, further communication and training are needed to inform the research community of these new tools. Through technical workshops, Odatis offers data providers practical experience and support in implementing data access, visualization and processing services. Finally, ODATIS relies on scientific consortia in order to promote and develop innovative processing methods and products for remote, airborne, or in situ observations of the ocean and its interfaces (atmosphere, coastline, seafloor) with the other clusters of the RI Data Terra.

How to cite: Schmidt, S., Quimbert, E., Vernet, M., Sudre, J., Mercier, C., Obaton, D., Piollé, J.-F., Merceur, F., Dibarboure, G., and Maudire, G.: Overview of the services provided to marine data producers by ODATIS, the French ocean data center, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1599, https://doi.org/10.5194/egusphere-egu23-1599, 2023.

A digital twin is a digital representation of real-world physical product, system, or process. Digital twins potentially offer a much richer capability to model and analyze real-world systems and improve environment sustainability.

In this work, an integrated 3D GIS and VR model for scenarios modeling and interactive data visualisation has been developed and implemented through the Digital Twin technology at the Glensaugh research farm. Spatial Multi-criteria Analysis has been applied to decide where to plant new woodlands, recognizing a range of land-use objectives while acknowledging concerns about possible conflicts with other uses of the land. The virtual contents (e.g., forest spatial datasets, monitored climate data, analyzed carbon stocks and natural capital asset index) have been embedded in the virtual landscape model which help raise public awareness of changes in rural areas.

The Digital twin prototype for Glensaugh Climate-Positive Farming was used at the STFC workshop 2021, GISRUK 2022, 2022 Royal Highland Show which provides an innovative framework to integrate spatial data modelling, analytical capabilities and immersive visualization.

Audience feedback suggested that the virtual environment was very effective in providing a more realistic impression of the different land-use and woodland expansion scenarios and environmental characteristics. This suggests considerable added value from using digital twin technology to better deal with complexity of data analysis, scenarios simulation and enable rapid interpretation of solutions.

Findings show this method has a potential impact on future woodland planning and enables rapid interpretation of forest and climate data which increases the effectiveness of their use and contribution to wider sustainable environment.

How to cite: Wang, C., Miller, D., Gimona, A., Nijnik, M., and Jiang, Y.: An integration of digital twin technology, GIS and VR for the service of environmental sustainability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5626, https://doi.org/10.5194/egusphere-egu23-5626, 2023.

Anthrax is a rare but serious disease caused by gram-positive, stem-shaped bacteria Bacillus anthracis, which are toxin-producing, encapsulated, facultative anaerobic organisms. Anthrax is found naturally in the soil and mainly harms livestock and wildlife. It can cause serious illness in both humans and animals. Anthrax, an often fatal disease of animals, is spread to humans through contact with infected animals or their products. People get infected with anthrax when spores get into the body.

The study aims to monitor the anthill localization map of anthrax on geographical maps and identify geographical variables that are significantly associated with environmental risk factors for anthrax recurrence in Georgia (Caucasus), as specific diseases affect the geographical environment, soil, climate. etc.

We carefully analyzed a set of 1664 cases of anthrax in humans and 621 cases of anthrax in animals, up to 1430 locations in anthrax foci (animal burial sites, slaughterhouses, BP roads, construction, etc.) observed in Georgia. Literature and the National Center for Disease Control for over 70 years. We analyzed more than 30 geographical variables such as climate, topography, soil (soil type, chemical composition, acidity), landscape, etc., and created several digital thematic maps, and foci of ant distribution and detection. The identified variable will help you to monitor anthrax development foci.

How to cite: Chichinadze, T., Gulashvili, Z., Bolashvili, N., Malania, L., and Suknidze, N.: Mapping and Analysis of Anthrax Cases in Humans and Animals, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5866, https://doi.org/10.5194/egusphere-egu23-5866, 2023.

The urban is at the heart of many disciplinary projects covering very broad scientific areas. Acquired data or simulations are often accessible (when they are) via targeted thematic portals. However, the need for transdisciplinarity has been essential for several years to answer specific scientific questions or societal demands. For this, the crossing of human sciences data, health, air quality, land use, emissions inventories, biodiversity, etc., would allow new innovative studies in connection with the city.

PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) developed by AERIS was designed as the first brick of a data portal that can promote the discovery, access, cross-referencing and representation of urban data from various sectors with air quality and urban heat islands as a starting point. The portal and future developments will be discussed in this presentation.

How to cite: Douet, V., Bouffiès-Cloché, S., Dumont, J., Haeffelin, M., Dupoont, J.-C., Kotthaus, S., Masson, V., Lemonsu, A., Gros, V., Cantrell, C., Michoud, V., and Payan, S.: PANAME: a portal laboratory for city's environmental data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6357, https://doi.org/10.5194/egusphere-egu23-6357, 2023.

GEOMAR research covers a unique range of physical, chemical, biological and geological ocean processes. The department Digital Research Services develops and provides advice and tools to support scientific data workflows, including metadata description of expeditions, model experiments, lab experiments, and samples. Our focus lies on standardized internal data exchange in large interdisciplinary scientific projects and citable data and software publications in discipline specific repositories to meet the FAIR principles. GEOMAR aims at providing their services not only internally but as a collaborative RDM platform for marine projects as a community service. How to achieve this on the operational level is currently worked on jointly with other research institutions in community projects, e.g. within the DAM (German Alliance of Marine Research), the DataHUB, an initiative of several research centres within the Helmholtz research area Earth and Environment, and within the national research infrastructure NFDI4Earth, a network of more than 60 partners.  

Our latest use cases are the inclusion of the seismic data and numerical model simulations into the community portals to increase their visibility and reusability. We present the success stories and pitfalls of bringing a locally well established system in larger communities and address the challenges we are facing. 

How to cite: Mehrtens, H., Berndt, J., Getzlaff, K., Lehmann, A., and Lorenz, S.: From local to global: Community services in interdisciplinary research data management , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6873, https://doi.org/10.5194/egusphere-egu23-6873, 2023.

Agriculture is extremely vulnerable to climate change. Increase in air temperature alongside the more frequent extreme climate events are the main climate change’s negative impacts influencing the yields, safety, and quality of crops. One approach to assess the impacts of climate change on agriculture is the use of agro-climatic indicators (AgcIs). Agcls characterize plant-climate interactions and are practical and understandable for both farmers and decision makers.

Climate and climate change impact studies on crop require long samples of reliable past and future datasets describing both spatial and temporal variability. The lack of observed historical data with an appropriate temporal resolution (i.e., 30 years of continuous daily data) and a sufficient local precision (i.e., 1km) is a major concern. To overcome that, the reanalysis products (RPs) are often used as a potential reference data of observed climate in impact studies. However, RPs have some limitations as they contain some biases and uncertainties. In addition, the RPs’ evaluation is often conducted on climate indicators which raises questions about their suitability for agro-climatic indicators.

This work aims to evaluate the ability of five of the most used RPs to reproduce observed AgcIs for three specific crops (i.e., apple, corn, and vine) over France. The five RPs selected for this study are the SCOPE Climate, FYRE Climate, ERA5, ERA5 Land and the gridded dataset RFHR. They are compared to the SYNOP meteorological data provided by Météo-France, considered as a reference dataset from 1996 to 2021.

Our findings show a higher agreement between the five RPs and SYNOP for the temperature-based Agcls than the precipitation-based Agcls. RPs tend to overestimate the precipitation-based Agcls. We also note that, for each RP, the discrepancies between the AgcIs and the reference SYNOP dataset do not depend on the geographical location or the crop. This study emphasizes the need to quantify uncertainty in climate data in climate variability and climate change impact studies on agriculture.

How to cite: Er-rondi, M., Troin, M., Coly, S., Buisson, E., Serlet, L., and Azzaoui, N.: Evaluation of five reanalysis products over France: implications for agro-climatic studies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7015, https://doi.org/10.5194/egusphere-egu23-7015, 2023.

We present a method for publishing high performance compute (HPC) code and results in a scalable, portable and ready-to-use interactive environment in order to enable sharing, collaborating, peer-reviewing and teaching. We show how we utilize cloud native elements such as kubernetes, containerization, automation and webshells to achieve this and demonstrate such an OpenScienceLab for the MAGE (Multiscale Atmosphere Geospace Environment) model, being developed by the recently selected NASA DRIVE Center for Geospace Storms.
We argue that a key factor in the successful design of such an environment is its (cyber)-security, as  these labs require non-trivial compute resources open to a vast audience. Benefits as well as implied costs of different hosting options are discussed, comparing public cloud, hybrid, private cloud and even large desktops.
We encourage HPC centers to test our method using our fully open source blueprints. We hope to thus unburden the research staff and scientists to follow FAIR principles and support open source goals without needing a deep knowledge of cloud computing.

How to cite: Roedig, C. and Sorathia, K.: Cloud native OpenScienceLabs for HPC : Easing the road to FAIR collaboration and OpenSource, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7703, https://doi.org/10.5194/egusphere-egu23-7703, 2023.

Established in 2017 as a pilot project, the NASA Commercial Smallsat Data Acquisition (CSDA) Program evaluates and acquires commercial datasets that compliment NASA Earth Science research and application goals. The success of the pilot and recognition of the value commercial data provide to the scientific community led to establishment of a sustained program within NASA’s Earth Science Division (ESD) with objectives of providing continuous on-ramp of new commercial vendors to evaluate the potential to advance NASA’s Earth science research and application activities, enable sustained use of the purchased data by the scientific community, ensure long-term preservation of purchased data for scientific reproducibility, and coordinate with other U.S. Government agencies and international partners on the evaluation and use of commercial data. This presentation will focus on data made available for scientific use through the CSDA Program, especially those datasets added since the conclusion of the original pilot project, describe the process for end users to access of CSDA managed datasets, and provide a status overview of ongoing and upcoming vendor evaluation activities will be given. Recent scientific research results from CSDA subject matter experts utilizing commercial data will also be provided.

How to cite: Kaulfus, A., Hall, A., Maskey, M., McCarty, W., and Policelli, F.: Programmatic Update for NASA’s Commercial Smallsat Data Acquisition (CSDA) Program, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8585, https://doi.org/10.5194/egusphere-egu23-8585, 2023.

Material samples are a vital output of the scientific endeavour. They underpin research in the Earth, Space, and Environmental Sciences, and are a necessary component of ensuring the transparency and reproducibility of such research. While there has been a lot of discussion in recent years about the openness and FAIRness of data, code, methods, and so on, material samples have been much less under the spotlight.

The lack of focus on material samples is in part due to them being unique as a research output, in the sense that they are inherently physical and thus they are mostly transported and managed by human beings rather than machines; it is rather more straightforward to archive and share both information about an output—and the output itself—for something that is already a digital object. However, it is for this reason that materials samples must be made more FAIR and treated as first-class citizens of Open Science. To do this, one needs to connect the physical and digital worlds. IGSN IDs enable these connections to be made.

The IGSN ID is a globally unique and persistent identifier (PID) specifically for labelling material samples themselves (i.e., they are for neither images nor data about a sample). Functionally a Digital Object Identifier (DOI) registered under DataCite services, the IGSN ID can be applied to all types of material samples coming from any discipline. Not only can IGSN IDs be used to identify individual material samples that currently exist in a repository, museum, or otherwise, but they can also be registered

• At the aggregate level for sample collections.
• For the sites from which the samples are taken.
• For ephemeral samples.

Importantly, in all cases, when registering an IGSN IDs, one must supply metadata in the DataCite Metadata Schema, as well as create landing pages that supply additional, disciplinary, user-focussed information about the collection, site, or (sub)sample. Hence, by registering a PID for a physical object, it is given a permanently resolvable URI to a findable and accessible digital footprint, and through the provision of rich metadata, enables its interoperability and reusability. Sharing of associated data is also possible within the metadata, and one may even include the potential for relocation of a sample itself for reuse.

This presentation will briefly introduce the IGSN ID and the partnership between DataCite and the IGSN e.V. to transfer the IGSN PID infrastructure under DataCite DOI services. It will mainly highlight practical use cases of IGSN IDs, including what is needed to include them in the sample workflow. It will also talk about efforts to better support IGSN IDs and sample metadata within the DataCite Metadata Schema.

How to cite: Edmunds, R.: FAIR & Open Material Samples: The IGSN ID, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12144, https://doi.org/10.5194/egusphere-egu23-12144, 2023.

Many Earth systems cannot be observed directly, or in isolation. Instead, we must infer their properties and characteristics from their signature in one or more datasets, using a variety of techniques (including those based on optimization, statistical methods, or machine learning). Development of these techniques is an area of focus for many geoscience researchers, and methodological advances can be instrumental in enhancing our understanding of the Earth.         

In our experience, progress is substantially hindered by the absence of infrastructure facilitating communication between sub-disciplines. Researchers tend to focus on one area of the earth sciences — such as seismology, hydrology or oceanography — with only slow percolation of ideas and innovations from one area to another. Indeed, silos often exist even within these subfields. Testing new ideas on new problems is challenging as it requires the acquisition of domain knowledge, an often difficult and time-consuming endeavour with uncertain returns. Key questions that arise include: What is a relevant field data set, and how has it been processed? Which simulation package is most appropriate to predict the data? What would a 'good' model look like and what should it be able to resolve? What is the current best practice?

To address this, we introduce the ESPRESSO project — a collection of Earth Science Problems for the Evaluation of Strategies, Solvers and Optimisers. It aims to provide  access to a suite of ‘test problems’, spanning a wide range of inference and inversion scenarios. Each test problem defines appropriate dataset(s) and simulation routines, accessible within a standardised Python interface. This will allow researchers to rapidly test new techniques across a spectrum of problems, share domain-specific inference problems and ultimately identify areas where there may be potential for fruitful collaboration and development. ESPRESSO is envisaged as an open, community-sourced project, and we invite contributions from across the geosciences.

How to cite: Valentine, A., He, J., Hauser, J., and Sambridge, M.: ESPRESSO: Earth Science Problems for the Evaluation of Strategies, Solvers and Optimizers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12173, https://doi.org/10.5194/egusphere-egu23-12173, 2023.

This paper identifies the aspects of living environment elements (PM^2.5, PM¹⁰, Noise) throughout Seoul and the urban planning characteristics that affect them by utilizing the big data of the S-Dot sensor in Seoul, which has recently become a hot topic. In other words, it proposes a big data-based research methodology and research direction to confirm the relationship between urban characteristics and environmental sectors that directly affect citizens.  The temporal range is from 2020 to 2022, which is the available range of time series data for S-Dot sensors, and the spatial range is throughout Seoul by 500m*500m GRID. First of all, as part of analyzing specific living environment patterns, simple trends through EDA are identified, and cluster analysis is conducted based on the trends. After that, in order to derive specific urban planning characteristics of each cluster, basic statistical analysis such as ANOA and OLS, and MNL analysis were conducted to confirm more specific characteristics. As a result of this study, cluster patterns of PM^2.5, PM¹⁰, noise and urban planning characteristics that affect them are identified, and there are areas with relatively high or low long-term living environment values compared to other regions. The results of this study are believed to be a reference for urban planning management measures for vulnerable areas of living environment, and it is expected to be an exploratory study that can provide directions to studies related to data in various fields related to environmental data in the future.

How to cite: Kim, D., Kim, K., Lee, D., Yang, J.-H., Kim, S., and Park, J.-M.: An Exploratory Study on the Methodology for the Analysis of Urban Environmental Characteristics in Seoul City based on S-Dot Sensor Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12381, https://doi.org/10.5194/egusphere-egu23-12381, 2023.

Studying how the Earth’s climate changed in the past requires a joint interdisciplinary effort of scientists from different scientific domains. Paleoclimatic records are increasingly obtained on multiple archives (e.g. marine and terrestrial sediments, ice cores, speleothems, corals) and they document past changes in various climatic variables of the different components of the climatic system (e.g. ocean, atmosphere, vegetation, ice). 

Most paleoclimatic records still rely on independent observations with no standard format describing their data or metadata, resulting in a progressive increase of variables and taxonomies. Therefore, despite the achievements of the last decades (e.g. NOAA, NEOTOMA and PANGAEA databases), the lack of a common language strongly limits the systematic reusability of paleoclimate data, for example for the construction of paleoclimatic data syntheses or the evaluation of climate model simulations.

The international project “Abrupt Change in Climate and Ecosystems: Data and e-infrastructure” (ACCEDE, funded by the Belmont Forum) aims at creating an ecosystem for paleoclimatic data in order to investigate the tipping points of past climatic changes. In this context, the recently formalized Linked PaleoData (LiPD) format is the core for the standardization of paleoclimate data and metadata, and it is acting as communication protocol between the different databases that compose the e-infrastructure.

Here we show two web-based solutions that are part of this effort and that take advantage of the LiPD ecosystem. The African Pollen Database, and the IPSL Paleoclimate Database, both hosted and developed by Institut Pierre-Simon Laplace, France, have the objectives (1) to give open access, while respecting the FAIR principles, to a variety of paleoclimate datasets - from pollen fossils to various tracers measured on marine sediments, ice cores or tree rings -, and (2) to combine and compare, using visualization tools, carefully selected and well dated paleoclimatic records from different disciplines to address specific research questions. 

The two databases are the result of data recovery from pre-existing and obsolete archives that followed a process of data (and metadata) consolidation, enrichment and formatting, in order to respect the LiPD specification and ensure the interoperability between them and the already existing databases. We designed harmonised web interfaces and REST APIs to explore and export existing datasets with the help of filtering tools. Datasets are published with DOI under an open license, allowing free access to the completeness of information. A LiPD upload form is embedded to the websites, in order to encourage both users and data stewards to propose, edit, add new records, and to bring the community into the use of LiPD format. We are currently working on finalizing visualization tools to evaluate aggregate data for research and education purposes.

With this effort we are developing a framework in which heterogeneous paleoclimatic records are fully interoperable, allowing scientists from the whole community to take advantage of the completeness of the available data, and to reuse them for very different research applications.

How to cite: Morichetta, A., Lézine, A.-M., Govin, A., and Douet, V.: Development of interoperable web applications for paleoclimate research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13420, https://doi.org/10.5194/egusphere-egu23-13420, 2023.

The Geo-INQUIRE (Geosphere INfrastructure for QUestions into Integrated REsearch) project, supported by the Horizon Europe Programme, is aimed at enhancing services to make data and high-level products accessible to the broad Geoscience scientific community. Geo-INQUIRE’s goal is to encourage curiosity-driven studies into understanding the geosphere dynamics at the interface between the solid Earth, the oceans and the atmosphere using long data streams, high-performance computing and cutting-edge facilities.

In the framework of Geo-INQUIRE, Transnational Access (TA, both virtual and on-site) will be provided at six test beds across Europe: the Bedretto Laboratory, Switzerland; the Ella-Link Geolab, Portugal; the Liguria-Nice-Monaco submarine infrastructure, Italy/France; the Irpinia Near-Fault Observatory, Italy; the Eastern Sicily facility, Italy; and the Corinth Rift Laboratory, Greece. These test beds are state-of-the-art research infrastructures, covering the Earth’s surface, subsurface, and marine environments over different spatial scales, from small-scale experiments in laboratories to kilometric submarine fibre cables. The TA will revolve around answering scientific key-questions on the comprehension of fundamental processes associated with geohazards and georesources such as: the preparatory phases of earthquakes, the role of the fluids within the Earth crust, the fluid-solid interaction at the seabed, and the impact of geothermal exploitation. TA will be also offered for software and workflows belonging to the EPOS-ERIC and the ChEESE Centre of Excellence for Exascale in Solid Earth, to develop awarded user’s projects. These are grounded on simulation of seismic waves and rupture dynamics in complex media, tsunamis, subaerial and submarine landslides. HPC-based Probabilistic Tsunami, Seismic and Volcanic Hazard workflows are offered to assess hazard at high-resolution with extensive uncertainty exploration. Support and collaboration will be offered to the awardees to facilitate the access and usage of HPC resources for tackling geoscience problems. Geo-INQUIRE will grant TA to researchers to develop their own lab or numerical experiments with the aim of advancing scientific knowledge of Earth processes while fostering cross-disciplinary research across Europe. To be granted, researchers submit a proposal to the yearly TA calls that will be issued three times during the project life. Calls will be advertised at the Geo-INQUIRE web page https://www.geo-inquire.eu/ and through the existing community channels.

To encourage the cross-disciplinary research, Geo-INQUIRE will also organize a series of training and workshops, focused on data, data products and software delivered by research infrastructures, and useful for researchers. In addition, two summer schools will be organized, dedicated to cross-disciplinary interactions of solid earth and marine science.

The proposals, for both transnational access and training, will be evaluated by a panel that reviews the technical and scientific feasibility of the project, ensuring equal opportunities and diversity in terms of gender, geographical distribution and career stage. The first call is expected to be issued by the end of Summer 2023. The data and products generated during the TAs will be made available to the scientific community via the project’s strict adherence to FAIR principles.

How to cite: Festa, G., Murphy, S., Majdanski, M., Christadler, I., Cotton, F., Strollo, A., Urvois, M., Röhling, V., Lorito, S., Babeyko, A., Bailo, D., Michalek, J., Lange, O., Quinteros, J., Prestes, M., and Weege, S.: The Transnational access and training in the Geo-INQUIRE EU-project, an opportunity for researchers to develop leading-edge science at selected facilities and test-beds across Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13455, https://doi.org/10.5194/egusphere-egu23-13455, 2023.

EPOS-GNSS is the Thematic Core Service dedicated to GNSS data and products for the European Plate Observing System.
EPOS-GNSS provides a service to explore and download validated and quality controlled data and metadata. This service is based on a network of 10 data nodes connected to a centralized portal, called "EPOS-GNSS Data Gateway". The service aims to follow the FAIR principles and continues to evolve to better meet them. It currently provides more than 4 millions of daily files in the RINEX standardized format for 1670 European GNSS stations and their associated metadata.
In addition to the integration into the multi-disciplinary EPOS data portal, the service proposes a direct access to the data and metadata for users with a need for more complex or more specific queries and filtering. A GUI (web client) and a specialized command line client are provided to facilitate the exploration and download of the data and metadata.
The presentation introduces the EPOS GNSS-Data Gateway (https://gnssdata-epos.oca.eu), its clients, and its use.

How to cite: Vergnolle, M. and Menut, J.-L.: EPOS-GNSS DATA GATEWAY: a portal to European GNSS Data and Metadata, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14423, https://doi.org/10.5194/egusphere-egu23-14423, 2023.

Earth System Science (ESS) relies on the availability of data from varying resources and ranging over different disciplines. Hence, data sources are rich and diverse, including observatories, satellites, measuring campaigns, model simulations, case studies, laboratory experiments as well as citizen science etc. At the same time, practices of professional research data management (RDM) are differing significantly among various disciplines. There are many well-known challenges in enabling a free flow of data in the sense of the FAIR criteria. Such are data quality assurance, unique digital identifiers, access to and integration of data repositories, just to mention a few. 

The Helmholtz DataHub Earth&Environment is addressing digitalization in ESS by developing a federated data infrastructure. Existing RDM practices at seven centers of the Helmholtz Association working together in a joint research program within the Research Field Earth and Environment (RF E&E) are harmonized and integrated in a comprehensive way. The vision is to establish a digital research ecosystem fostering digitalization in geosciences and environmental sciences. Hereby, issues of common metadata standards, digital object identifiers for samples, instruments and datasets, defined role models for data sharing certainly play a central role. The various data generating infrastructures are registered digitally in order to collect metadata as early as possible and enrich them along the flow of the research cycle.

Joint RDM bridging several institutions relies on professional practices of distributed software development. Apart from operating cross-center software development teams, the solutions rely on concepts of modular software design. For example, a generic framework has been developed to allow for quick development of tools for domain specific data exploration in a distributed manner. Other tools incorporate automated quality control in data streams. Software is being developed following guiding principles of open and reusable research software development.

A suite of views is being provided, allowing for varying user perspectives, monitoring data flows from sensor to archive, or publishing data in quality assured repositories. Furthermore, high-level data products are being provided for stakeholders and knowledge transfer (for examples see https://datahub.erde-und-umwelt.de). Furthermore, tools for integrated data analysis, e.g. using AI approaches for marine litter detection can be implemented on top of the existing software stack.

Of course, this initiative does not exist in isolation. It is part of a long-term strategy being embedded within national (e.g. NFDI) and international (e.g. EOSC, RDA) initiatives.

How to cite: zu Castell, W., Bumberger, J., Braesicke, P., Frickenhaus, S., Kleeberg, U., Kunkel, R., and Lorenz, S.: Towards an interoperable digital ecosystem in Earth System Science research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14605, https://doi.org/10.5194/egusphere-egu23-14605, 2023.

Since the development of the original specifications in the '90s the PDF document format has become the de-facto standard for the distribution and archival of documents in electronic form because of its ability to preserve the original layout of the documents, independently of the hardware, operating system and application software used to visualize them.

Unfortunately the PDF format does not contain explicit structural and semantic information, making it very difficult to extract structured information from them, in particular data presented in tabular form. 
The automatic extraction of tabular data is a difficult and challenging task because tables can have extremely different formats and layouts, and involves several complex steps, from the proper recognition and conversion of printed text into machine-encoded characters, to the identification of logically coherent table constructs (headers, columns, rows, spanning elements), and to the breaking down of the data constructs into elemental objects.

Several tools have been developed to support the extraction process. In this work we survey the most interesting tools for the automatic detection and extraction of tabular data, analyzing their respective advantages and limitations. A particular emphasis is given on programmable open source tools because of their flexibility and long-term availability, together with the possibility to easily tweak them to meet the peculiar needs of the problem at hand.

As a practical application, we also present a workflow based on a set of R and AWK scripts that can automatically extract daily temperature and precipitation data from the official PDF documents made available each year by Regione Puglia, in Italy. The lessons learned from the development of this workflow and the possibility to generalize the approach to different kinds of PDF documents are also discussed.

How to cite: Maggi, S., Fuina, S., and Vicario, S.: Automated Extraction of Bioclimatic Time Series from PDF Tables, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15072, https://doi.org/10.5194/egusphere-egu23-15072, 2023.

The transmission of West Nile Virus is known to be affected by multiple factors related to the behavior and interactions between reservoir (birds), vector (Culex-mosquitos), and hosts (humans). Environmental parameters can play a critical role in understanding WNV epidemiology. The aim of this research was to determine the association of various climatic factors with the Culex mosquito abundance in Greece during the period 2011-2022. Climate data were acquired from ERA5 (European Centre for Medium-Range Weather Forecasts), while Culex abundance data were obtained through the mosquito surveillance network of ECODEVELOPMENT S.A, who hold the biggest mosquito surveillance network in Greece. The research was conducted at the municipality level. Culex abundance depends in a nonlinear fashion from temperature (Figure 1). The spread of the measurements indicates however there are other factors that affect the abundance of mosquitoes.

Figure 1 Scatter plot of air temperature VS Culex abundance in a municipality (Delta) with relatively sizeable mosquito population.

Correlation heatmaps were used as a tool to visualize the correlation of vector abundance and average monthly temperature up to 2 months before at several municipalities in the Region of Central Macedonia. The correlations decrease with increasing the lag in temperature (Figure 2). Moreover, there are some municipalities in which the correlation coefficient is considerably greater than others. Those correlations cannot be explained without considering the mosquito breeding sites found in these municipalities. In these municipalities there is a presence of important water resources, such as rice paddies, drainage canals, wetland systems or a combination of all the above. When surface waters warm and the outside temperature rises, the mosquito life cycle is completed more quickly, resulting in more generations being produced in a shorter period of time.

Figure 2 Correlation heatmap of the correlation coefficient between the mosquito abundance (municipality scale) and the average monthly temperature up to 2 months before.

Scatterplots and correlation heatmaps calculated with the Culex abundance and total precipitation, relative humidity or wind speed did not reveal similar patterns. Ongoing analysis focuses in more factors, environmental and not, which affect the abundance of mosquitoes that transmit WNV.

Acknowledgments 
This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Τ2ΕΔΚ-02070). 

How to cite: Angelou, A., Gewehr, S., Mourelatos, S., and Kioutsioukis, I.: Environmental parameters as a critical factor in understanding mosquito population, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15293, https://doi.org/10.5194/egusphere-egu23-15293, 2023.

Climate Services (CS) are crucial in empowering citizens, stakeholders and decision-makers in defining resilient pathways to adapt to climate change and extreme weather events. Despite advances in scientific data and knowledge (e.g. Copernicus, GEOSS), current CS fail to achieve their full value proposition to end users. Challenges include incorporation of social and behavioral factors, local needs, knowledge and the customs of end users. In I-CISK, we put forward a co-design based requirement analysis to develop a Spatial Data Infrastructure and Platform that empowers a next generation of end user CS, which follow a social and behaviorally informed approach to co-producing services that meet climate information needs of the Living Labs of the European I-CISK project. Core to the project are climate extremes such as droughts, floods and heatwaves. The use-cases touch upon agriculture, forestry, tourism, energy, health, and the humanitarian sectors. We will present the summarized stakeholders' requirements regarding the new climate-service platform and their technical implications for the open source spatial infrastructure. The design also includes assessing, managing and presenting uncertainties that are an inherent component of climate models.

How to cite: Gräler, B., Demmich, K., Schnell, J., Vogel, M., Bagli, S., and Mazzoli, P.: Building an Open Source Infrastructure for Next Generation End User Climate Services, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15863, https://doi.org/10.5194/egusphere-egu23-15863, 2023.

Active volcanoes are complex, poorly predictable systems that can pose a threat to humans and their infrastructures. As such, it is important to improve as much as possible the understanding of their behavior. The Stromboli volcano, in Italy, is one of the most active volcanoes in the world, and its almost persistent activity is documented since centuries. The persistent background activity is sometimes interrupted by much more energetic, dangerous episodes. The Istituto Nazionale di Geofisica e Vulcanologia (Italy) set up the interdisciplinary “UNO” project, aimed to understand when the Stromboli volcano is about to switch from the ordinary to the extraordinary activity. The UNO project includes an outstanding variety of research activities, such as sampling in the field, the modeling of Stromboli topography from ALS technique and satellite data, the 3D numerical simulations of ballistic trajectories, or the set up of an ultrasonic microphones system. Key to the success of the project is the collection of integrated high spatial and temporal resolution data and their joint analyses in a shared relational database. We present here the simplified logical model of such database, focusing on the identification of entities and their relationships.

How to cite: Tarquini, S., Martinelli, F., Bisson, M., De Beni, E., Spinetti, C., and Tarabusi, G.: The set up of the “UNO” project relational database for Stromboli volcano, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16416, https://doi.org/10.5194/egusphere-egu23-16416, 2023.

NASA’s Science Plan includes a strategy to advance discovery by leveraging cross-disciplinary opportunities between scientific disciplines. In addition, NASA is committed to building an inclusive, open science community over the next decade and is championing the new Open-Source Science Initiative (OSSI) to foster that community. The OSSI supports many activities to promote open science including the development of an empowering cyberinfrastructure to accelerate the time to actionable science. One component of the OSSI cyberinfrastructure is the Science Discovery Engine (SDE). The goal of the SDE is to enable the discovery of data, software and documentation across the five SMD divisions including Astrophysics, Biological and Physical Sciences, Earth Science, Heliophysics and Planetary Science. The SDE increases accessibility to NASA’s open science data and information and promotes interdisciplinary scientific discovery. In this presentation, we describe our work to develop the Science Discovery Engine in Sinequa, a Cognitive Search capability. We also share lessons learned about data governance, curation and information access.

How to cite: Bugbee, K., Acharya, A., Davis, C., Foshee, E., Ramachandran, R., Li, X., and Ramasubramanian, M.: NASA’s Science Discovery Engine: An Interdisciplinary, Open Science Data and Information Discovery Service, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16605, https://doi.org/10.5194/egusphere-egu23-16605, 2023.

Increasing coverage of commercial and public satellites allows us to monitor the pulse of the Earth in ever-shorter frequency (Zhu et al., 2017). Together with the rise of deep learning in artificial intelligence (AI) (LeCun et al., 2015), the field of AI for Earth Observation (AI4EO) is growing rapidly. However, many supervised deep learning techniques are data-hungry, which means that annotated data in large quantities are necessary to help these algorithms reach their full potential. In many Earth Observation applications such as change detection, this is often infeasible because high-quality annotations require manual labeling which is time-consuming and costly.  

Self-supervised learning (SSL) can help tackle the issue of limited label availability in AI4EO. In SSL, an algorithm is pretrained with tasks that only require the input data without annotation. Notably, Masked Autoencoders (MAE) have shown promising performances recently where a Vision Transformer learns to reconstruct a full image with only 25% of it as input. We hypothesize that the success of MAEs also extends to satellite imagery and evaluate this with a change detection downstream task. In addition, we provide a multitemporal DINO baseline which is another widely successful SSL method. Further, we test a second version of MAEs, which we call GeoMAE. GeoMAE incorporates the location and date of the satellite image as auxiliary information in self-supervised pretraining. The coordinates and date information are passed as additional tokens to the MAE model similar to the positional encoding. 
The pretraining dataset used is the RapidAI4EO corpus which contains multi-temporal Planet Fusion imagery for a variety of locations across Europe. The dataset for the downstream task also uses Planet Fusion in pairs as input data. These are provided on a 600m * 600m patch level three months apart together with a classification if the respective patch has changed in this period. Self-supervised pretraining is done for up to 150 epochs where we take the model with the best validation performance on the downstream task as a starting point for the test set. 

We find that the regular MAE model scores the best on the test set with an accuracy of 81.54% followed by DINO with 80.63% and GeoMAE with 80.02%. Pretraining MAE with ImageNet data instead of satellite images results in a notable performance loss down to 71.36%. Overall, our current pretraining experiments can not yet confirm our hypothesis that GeoMAE is advantageous compared to regular MAE. However, in similar spirit, Cong et al. (2022) recently introduced SatMAE which outlines that for other remote sensing applications, the combination of auxiliary information and novel masking strategies is a key factor. Therefore, it seems that a combination of location and time inputs together with adapted masking may also hold the most potential for change detection. There is ample potential for future research in geo-specific applications of MAEs and we provide a starting point for this with our experimental results for change detection. 

How to cite: Kondmann, L., Senaras, C., Asano, Y. M., Rana, A. S., Wania, A., and Zhu, X. X.: Geography-Aware Masked Autoencoders for Change Detection in Remote Sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2843, https://doi.org/10.5194/egusphere-egu23-2843, 2023.

New models are emerging from Artificial Intelligence (AI) and its sub-fields, in particular, Machine Learning and Deep Learning that are being applied in different application areas including geography (e.g., land cover identification and traffic volume forecasting based on spatial data). Different from well-known datasets often used to develop AI models (e.g., ImageNet for image classification), spatial data has an intrinsic feature, i.e., spatial heterogeneity, which leads to varying relationships across different regions between the independent (i.e., the model input X) and dependent variables (i.e., the model output Y). This makes it difficult to conduct large-scale studies with a single robust AI model. In this study, we draw on the idea of modular learning, i.e., to decompose large-scale tasks into sub-tasks for specific sub-regions and use multiple AI models to achieve these sub-tasks. The decomposition is based on the spatial characteristics to ensure that the relationship between independent and dependent variables is similar in each sub-region. We explore this approach for forecasting COVID-19 cases in Germany using spatiotemporal data (e.g., weather data and human mobility data) as an example and compare the prediction tasks with a single model to the proposed decomposition learning procedure in terms of accuracy and efficiency. This study is part of the project DAKI-FWS which is funded by the Federal Ministry of Economic Affairs and Climate Action in Germany to develop an early warning system to stabilize the German economy.

How to cite: Cheng, X., Arndt, J., Marquez, E., and Ma, J.: Decomposition learning based on spatial heterogeneity: A case study of COVID-19 infection forecasting in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3267, https://doi.org/10.5194/egusphere-egu23-3267, 2023.

The iMagine project is devoted to developing and delivering imaging data and services for aquatic science. Started in September 2022, the project will provide a portfolio of image data collections, high-performance image analysis tools empowered with Artificial Intelligence, and best practice documents for scientific image analysis. These services and documentation will enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant to healthy oceans, seas, and coastal and inland waters. By building on the European Open Science Cloud compute platform, iMagine delivers a generic framework for AI model development, training, and deployment, which researchers can adopt for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field. The iMagine AI development and testing framework offers neural networks, parallel post-processing of extensive data, and analysis of massive online data streams in distributed environments. The synergies among the eight aquatic use cases in the project will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness and contribute to harmonisation across RIs. The resulting iMagine AI development and testing platform and the iMagine use case applications will provide another component to the European marine data management landscape, valid for the Digital Twin of the Ocean, EMODnet, Copernicus, and international initiatives. 

How to cite: Fava, I., Thijsse, P., Sipos, G., and Schaap, D.: Using AI and ML to support marine science research, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4929, https://doi.org/10.5194/egusphere-egu23-4929, 2023.

Eddies are circular rotating water masses, which are usually generated near the large ocean currents, e.g., Gulf Stream. Monitoring eddies and gaining knowledge on eddy statistics over a large region are important for fishery, marine biology studies, and testing ocean models.

At mesoscale, eddies are observed in radar altimetry, and methods have been developed to identify, track and classify them in gridded maps of sea surface height derived from multi-mission data sets. However, this procedure has drawbacks since much information is lost in the gridded maps. Inevitably, the spatial and temporal resolution of the original altimetry data degrades during the gridding process. On the other hand, the task of identifying eddies has been a post-analysis process on the gridded dataset, which is, by far, not meaningful for near-real time applications or forecasts. In the EDDY project at the University of Bonn, we aim to develop methods for identifying eddies directly from along track altimetry data via a machine (deep) learning approach.

Since eddy signatures (eddy boundary and highs and lows on sea level anomaly, SLA) are not possible to extract directly from along track altimetry data, the gridded altimetry maps from AVISO are used to detect eddies. These will serve as the reference data for Machine Learning. The eddy detection on 2D grid maps is produced by open-source geometry-based approach (e.g., py-eddy-tracker, Mason et al., 2014) with additional constraints like Okubo-Weiss parameter. Later, Sea Surface Temperature (SST) maps of the same region and date (also available from AVISO) are used for manually cleaning the reference data. Noting that altimetry grid maps and SST maps have different temporal and spatial resolution, we also use the high resolution (~6 km) ocean modeling simulation dataset (e.g., FESOM, Finite Element Sea ice Ocean Model). In this case, the FESOM dataset provides a coherent, high-resolution SLA and SST, salinity maps for the study area and is a potential test basis to develop the deep learning network.

The single modal training via a Conventional Neural Network (CNN) for the 2D altimetry grid maps produced excellent dice score of 86%, meaning the network almost detects all eddies in the Gulf Stream, which are consistent with reference data. For the multi-modal training, two different training networks are developed for 1D along-track altimetry data and 2D grid maps from SLA and SST, respectively, and then they are combined to give the final classification output. A transformer model is deemed to be efficient for encoding the spatiotemporal information from 1D along track altimetry data, while CNN is sufficient for 2D grid maps from multi-sensors.

In this presentation, we show the eddy classification results from the multi-modal deep learning approach based on along track and gridded multi-source datasets for the Gulf stream area for the period between 2017 and 2019. Results show that multi-modal deep learning improve the classification by more than 20% compared to transformer model training on along-track data alone.

How to cite: Abulaitijiang, A., Bolmer, E., Roscher, R., Kusche, J., and Fenoglio-Marc, L.: Eddy identification from along-track altimeter data with multi-modal deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6818, https://doi.org/10.5194/egusphere-egu23-6818, 2023.

Machine learning models are able to capture highly complex, nonlinear relationships, and have been used in recent years to accurately predict crop yields at regional and national scales. This success suggests that the use of ‘interpretable’ or ‘explainable’ machine learning (XAI) methods may facilitate improved scientific understanding of the compounding interactions between climate, crop physiology and yields. However, studies have identified implausible, contradicting or ambiguous results from the use of these methods. At the same time, researchers in fields such as ecology and remote sensing have called attention to issues with robust model evaluation on spatiotemporal datasets. This suggests that XAI methods may produce misleading results when applied to spatiotemporal datasets, but the impact of model evaluation strategy on the results of such methods has not yet been examined.

In this study, machine learning models are trained to predict simulated crop yield, and the impact of model evaluation strategy on the interpretation and performance of the resulting models is assessed. Using data from a process-based crop model allows us to then comment on the plausibility of the explanations provided by common XAI methods. Our results show that the choice of evaluation strategy has an impact on (i) the interpretations of the model using common XAI methods such as permutation feature importance and (ii) the resulting model skill on unseen years and regions. We find that use of a novel cross-validation strategy based on clustering in feature-space results in the most plausible interpretations. Additionally, we find that the use of this strategy during hyperparameter tuning and feature selection results in improved model performance on unseen years and regions. Our results provide a first step towards the establishment of best practices for model evaluation strategy in similar future studies.

How to cite: Sweet, L., Müller, C., Anand, M., and Zscheischler, J.: Model evaluation strategy impacts the interpretation and performance of machine learning models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8479, https://doi.org/10.5194/egusphere-egu23-8479, 2023.

Predictive learning from data usually is formulated as a problem of finding the best connection between input and output spaces by optimizing well-defined cost or risk functions.

In geo-environmental studies input space is usually constructed from the geographical coordinates and features generated from different sources of available information (feature engineering), by applying expert knowledge, using deep learning technologies and taking into account the objectives of the study. Often, it is not known in advance if the input space is complete or contains redundant features. Therefore, unsupervised learning (UL) is essential in environmental data analysis, modelling, prediction and visualization. UL also helps better understand the data and phenomena they describe as well as in interpreting/communicating modelling strategies and the results in the decision-making process.

The main objective of the present investigation is to review some important topics in unsupervised learning from environmental data: 1) quantitative description of the input space (“monitoring network”) structure using global and local topological and fractal measures, 2) dimensionality reduction, 3) unsupervised feature selection and clustering by applying a variety of machine learning algorithms (kernel-based, ensemble learning, self-organizing maps) and visualization tools.

Major attention is paid to the simulated and real spatial data (pollution, permafrost, geomorphological and wind fields data).  Considered case studies have different input space dimensionality/topology and number of measurements. It is confirmed that UL should be considered an integral part of a generic methodology of environmental data analysis. Comprehensive comparisons and discussions of the results conclude the research.

How to cite: Kanevski, M.: On Unsupervised Learning from Environmental Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9437, https://doi.org/10.5194/egusphere-egu23-9437, 2023.

Unsupervised classification techniques are becoming essential to extract information from the wealth of data that Earth observation satellites and other sensors currently provide. These datasets are inherently complex to analyze due to the extent across multiple dimensions - spatial, temporal, and often spectral or band dimension – their size, and the high resolution of current sensors. Traditional one-dimensional cluster analysis approaches, which are designed to find groups of similar elements in datasets such as rasters or time series, may come short of identifying patterns in these higher-dimensional datasets, often referred to as data cubes. In this context, we present our Clustering Geodata Cubes (CGC) software, an open-source Python package that implements a set of co- and tri-clustering algorithms to simultaneously group elements across two and three dimensions, respectively. The package includes different implementations to most efficiently tackle datasets that fit into the memory of a single machine as well as very large datasets that require cluster computing. A refining strategy to facilitate data pattern identification is also provided. We apply CGC to investigate gridded datasets representing the predicted day of the year when spring onset events (first leaf, first bloom) occur according to a well-established phenological model. Specifically, we consider spring indices computed at high spatial resolution (1 km) and continental scale (conterminous United States) for the last 40+ years and extract the main spatiotemporal patterns present in the data via CGC co-clustering functionality.  

How to cite: Nattino, F., Ku, O., Grootes, M. W., Izquierdo-Verdiguier, E., Girgin, S., and Zurita-Milla, R.: Clustering Geodata Cubes (CGC) and Its Application to Phenological Datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11601, https://doi.org/10.5194/egusphere-egu23-11601, 2023.

Industrial pollution remains a major challenge in spite of recent technological developments and purification procedures. To effectively monitor atmosphere contamination, data from air quality networks should be coupled with advanced spatiotemporal statistical methods.

Our previous studies showed that standard interpolation techniques (like inverse distance weighting, linear or spline interpolation, kernel-based Gaussian Process Regression, GPR) are quite limited for the simulation of a smoke-like narrow-directed industrial pollution in the vicinity of the source (a few tenths of kilometers). In this work, we try to apply GPR, based on statistically estimated covariances. These covariances are calculated using СALPUFF atmospheric pollution dispersion model for a one-year simulation in the Kryvyi Rih region. The application of GPR permits taking into account high correlations between pollution values in neighboring points revealed by modeling. The result of the GPR covariance-based technique is compared with other interpolation techniques. It can be used then in the estimation and optimization of air quality networks.

How to cite: Sokolov, A., Delbarre, H., Boldyriev, D., Bulana, T., Molodets, B., and Grabovets, D.: Industrial Atmospheric Pollution Estimation Using Gaussian Process Regression, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12773, https://doi.org/10.5194/egusphere-egu23-12773, 2023.