EGU General Assembly 2022  

There is serious concern that the hazard, or probability, of river floods is increasing over time. Anticipating any change in flood hazard is extremely important for adapting flood management strategies and thereby reducing potential damage and loss of life. However, floods are the result of complex interactions of runoff generation processes within the catchment area not easy to quantify. This presentation will review recent advances in understanding how and why river floods, and their probabilities, are changing over time.

Land use change, such as deforestation, urbanisation and soil compaction resulting from more intense agriculture, modify river floods by altering the infiltration capacity and soil moisture. Locally, these processes are well understood but less so at the catchment scale. The effect of land use on floods is particularly pronounced for flash floods in small catchments because of the role of soil permeability in infiltration at this scale. For regional floods, and for the most extreme events, land use is usually not the most important control, because areas of soil saturation are more relevant in runoff generation, which are less driven by soil permeability.

Instead, hydraulic engineering works, such as river training, reservoirs and levees, are more relevant. The effect of individual hydraulic structures can be captured well by hydraulic  modelling based on mass and momentum balance, and their role depends on the event magnitude. There is a tendency for all of these engineering works to exert the greatest effect on floods for events of intermediate magnitude, e.g. associated with return periods of the order of ten to one hundred years. Regional effects of engineering works are an active research topic.

Climate change can affect river floods at all catchment scales, from a few hectares to hundreds of thousands of square kilometres. Observed changes in river floods, e.g. in Europe, suggest that climate change is indeed modifying the river flood hazard, but the changes are not necessarily directly linked to precipitation, nor are they directly linked to rising air temperatures. The key to understanding climate change effects on floods is therefore the seasonal interaction between soil moisture (influenced by precipitation and evaporation), snow processes, extreme precipitation and runoff generation. In Europe, there have been a number of flood-rich periods in the past 500 yrs and we are currently in one of them. A trend of storm tracks to move further north in Europe has increased both average and extreme precipitation and thus river flood hazard in the Northwest of Europe, but floods are decreasing where snowmelt is relevant due to shallower snowpacks. There is a tendency for climate change to have the greatest effect on floods of large event magnitudes.

It is concluded that substantial progress has been made in recent years in understanding the role of land use, river works and climate in changing river flood hazards, both through data based and modelling approaches. Considering all three controls of change is essential in reliable flood risk assessment and management in order to maximise protection levels at an affordable cost.

Publications: https://hydro.tuwien.ac.at/forschung/publikationen/download-journal-publications/

How to cite: Blöschl, G.: Understanding changing river flood hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1892, https://doi.org/10.5194/egusphere-egu22-1892, 2022.

Over recent years, research on compound weather and climate event has emerged as a new research frontier at the interface of climate science, climate impact research, engineering and statistics. Compound weather and climate events refer to the combination of multiple drivers and/or hazards that contribute to environmental or societal risk. Compound event analysis combines traditional research on climate extremes with impact-focused bottom-up assessments, thereby providing new insights on present-day and future climate risk. In this talk, I will illustrate my own trajectory into compound event analysis and highlight current and future challenges in this novel and exciting field of research. 

How to cite: Zscheischler, J.: The emergence of compound event analysis as a new research frontier, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7784, https://doi.org/10.5194/egusphere-egu22-7784, 2022.

Mineral dust, one of the most abundant aerosols in the atmosphere, can be transported by winds thousands of kilometers away from its source and deposited on land and sea. In response to growing societal interest in reducing risks from various hazardous dust effects, the World Meteorological Organization has established a long-lasting Sand and Dust Storm Warning Advisory and Assessment System, while the UN formed in 2018 the Coalition to Combat Sand and Dust Storms. Over the last twenty years, thanks to dust-related research, numerical models have been developed which today successfully predict the atmospheric dust process. Such models include dust concentration as a prognostic parameter and can now successfully assess the occurrence of most dust storms. There are numerous impacts of dust on weather, climate, marine and terrestrial ecosystems, many of them with detrimental effects, such as: adverse influence on human health; disruption of ground and aviation transport; reduced agricultural growth; disfunctioning of solar energy panels; and acceleration of snow and ice melting in high-latitudes. This study will present examples illustrating recent efforts to develop user-oriented applications based on the use of dust forecasting products to mitigate the negative impacts of this aerosol.

How to cite: Nickovic, S.: Numerical prediction of the atmospheric dust process: the way to reduce risks from its adverse effect, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13258, https://doi.org/10.5194/egusphere-egu22-13258, 2022.

One of the great challenges facing our society is to cope with the increase in natural risks induced by climate change and human activity. The frequency of heavy rains and changes in vegetation cover have intensified over most areas, leading to enhanced risks of landslides and the tsunamis they can generate. Rising sea levels, partly induced by polar ice mass loss due to ice sheet melting and iceberg calving, make the increasing coastal population and infrastructures even more vulnerable to tsunamis. This creates an urgent need for precise quantification of landslides, tsunamis, and sea level rise impact to build reliable hazard maps for early warning systems and evacuation plans.

Accurate prediction of landslides and ice sheet mass loss is usually unreachable despite a tremendous amount of high quality data from imagery, GPS and dense arrays of seismic and oceanic sensors that record the seismic and water waves generated by landslides and iceberg calving at distances of more than 1000 km from the source, depending on the event volume (m³ to km³). These waves carry key information on the source such as mobilized mass, friction of the sliding material, and interaction with water. Therefore, beyond mere detection and localization of landslides and iceberg calving, full exploitation of these wave data should provide unprecedented clues to the complex characteristics and dynamics of these sources. Despite increasing research in environmental seismology this last decade, this is still a highly challenging issue because of the complexity of natural processes and their intricate imprint on wave characteristics. Until recently, only very simplified models have been used to simulate the generated seismic signal, making it difficult to separate the effects of model uncertainties or other parameters such as topography, flow dynamics, and wave propagation on the recorded signal. In parallel to environmental seismology, key advances are being made in the mechanics of granular materials, mathematics, and computing capacity.

By bridging geophysics, mathematics, and mechanics, we have developed sophisticated source models describing granular flows over complex topography.  By coupling them with seismic wave propagation models, we have shown that the low frequency seismic signal can be simulated and inverted to constrain the flow dynamics, rheological properties, and physical processes involved. In a similar way, we have quantified ice mass loss due to calving in Greenland over the last twenty years by coupling the inversion of seismic waves with advanced modeling of iceberg calving. To illustrate this multidisciplinary approach, I will present recent laboratory experiments and numerical modeling of granular flows, iceberg calving, and emitted seismic waves. In particular, I will demonstrate the key role of topography, rheology, erosion, and solid/fluid interaction in these phenomena and generated waves, as well as the challenges in their accurate description in numerical models applicable at the field scale at tractable computational costs.  Addressing these issues in the future will break new ground in the detection and modeling of landslides, tsunamis, and glaciers, leading to improved assessment of related hazards and the quantification of their link with climatic, seismic, and volcanic activity.

How to cite: Mangeney, A. and the multidisciplinary group of her collaborators: Challenges in physical modeling of landslides, glaciers, and generated seismic and tsunami waves for hazard assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4490, https://doi.org/10.5194/egusphere-egu22-4490, 2022.

Most of the largest volcanic activity in the world occurs in remote places as deep oceans or poorly monitored oceanic islands. Thus, our capacity of monitoring volcanoes is limited to remote sensing and global geophysical observations. However, the rapid estimation of volcanic eruption parameters is needed for scientific understanding of the eruptive process and rapid hazard estimation. We first a method to rapidly identify large volcanic explosions, based on analysis of seismic data. The method automatically detects and locate long period (0.01-0.03Hz) signals associated with physical processes close to the Earth surface, by analyzing surface waves recorded at global seismic stations. With this methodology, we promptly detect the January 15, 2022 Hunga Tonga eruption, among many other signals associated with known and unknown processes. We further use the waves generate by the Hunga Tonga volcanic explosion and estimate important first-order parameters of the eruption (Force spectrum, impulse). We then relate the estimated parameters with the volcanic explosivity index (VEI). Our estimate of VEI~6, indicate how the Hunga Tonga eruption is among the largest volcanic activity ever recorded with modern geophysical instrumentation, and can provide new insights about the physics of large volcanoes.

How to cite: Poli, P. and Shapiro, N.: Seismological characterization of dynamics parameter of the Hunga Tonga explosion from teleseismic waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13572, https://doi.org/10.5194/egusphere-egu22-13572, 2022.

The Hunga Tonga volcanic eruption on 15 January 2022 created a tsunami affecting the entire Pacific Ocean. The observed tsunami was found to have a dual mechanism and was caused both by the wave incoming from the source area and by an atmospheric wave propagating with the speed of sound. The tsunami was clearly recorded in the marginal seas of the northwestern Pacific, including the Sea of Japan, the Sea of Okhotsk and the Bering Sea, in particular on the coasts of Kamchatka, the Kuril Islands and the Aleutian Islands. We examined high-resolution records (1-min sampling) of about 50 tide gauges and 15 air pressure stations in these seas for the period of 14-17 January 2022. On the Russian coast, the highest wave with a trough-to-crest wave height of 1.4 m was recorded at Vodopadnaya, on the southeastern Kamchatka Peninsula; on the coasts of the Aleutian Islands the tsunami waves were even higher, up to 2 m. Based on numerical modelling we estimated the arrival time of the gravitational tsunami waves from the source. We revealed that the character of sea level oscillations for most of the stations evidently changed before these waves arrived. A comparative analysis of sea level and atmospheric data indicated that these changes were probably caused by the atmospheric waves generated by the volcanic eruption.

How to cite: Tsukanova, E., Medvedeva, A., Medvedev, I., and Ivelskaya, T.: The 2022 Tonga tsunami in the marginal seas of the northwestern Pacific Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13576, https://doi.org/10.5194/egusphere-egu22-13576, 2022.

The Hunga Tonga volcanic eruption on Jan. 15, 2022 released a highly energetic atmospheric pressure wave that was observed all around the globe in different types of measurements (e.g., barometers and infrasound sensors, satellites images, ionospheric measurements, etc.). In addition, the eruption triggered a meteo-tsunami followed by a series of tsunami waves. Tide gauges across the Pacific Ocean, the Atlantic and the Indian oceans recorded significant sea-level changes related to the primary eruption.

We focus our presentation on the imprint of tsunami waves on the ionosphere. We make use of an extensive collection of Global Navigation Satellites Systems (GNSS) data recorded by multi-constellation GNSS receivers across the Pacific Ocean and beyond. The observation of tsunami-induced ionospheric signatures is made possible by the efficient coupling of tsunami waves with the surrounding atmosphere and the generation of internal gravity waves (IGWs). With the help of GNSS systems (Beidou, GPS, Galileo, GLONASS, QZSS), ionospheric disturbances can be monitored and observed by utilizing the Total Electron Content (TEC) derived from the delay that the ionosphere imposes in the electromagnetic signals transmitted by the GNSS satellites. We identify and characterize the ionospheric TEC signatures following the passage of the Tonga tsunami. We investigate the influence of known key ambient parameters such as the local geomagnetic field, the tsunami propagation direction, and the distance to the tsunami source on the amplitude of the observed signatures. Moreover, we correlate the detected tsunami-induced TEC signatures with sea level measurements to assess their tsunami origins. And we contrast the identified TEC signatures in the Pacific Ocean with their analogs induced by the tsunami triggered by the Mar. 4, 2021 8.1 Mw Kermadec Islands earthquake. Both events took place relatively in the same geographical region, with the former being less complex (no meteo-tsunami, shorter duration, and about one order of magnitude smaller in amplitude). Finally, we provide estimations of the tsunami amplitude at the ocean level in the areas crossed by GNSS radio signals, some of them not covered by open ocean sea-level sensors (DART buoys).

How to cite: Munaibari, E., Rolland, L., Sladen, A., and Delouis, B.: Global ionospheric signature of the tsunami triggered by the 2022 Hunga Tonga volcanic eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13578, https://doi.org/10.5194/egusphere-egu22-13578, 2022.

Low-frequency seismic energy whose spectrum is centered at certain narrow bands has been detected after violent volcano eruptions. Normal-mode analysis related this signal to the resonances between the atmosphere and the solid earth.
After the powerful eruption of Hunga Tonga-Hunga Ha’apai volcano on Jan. 15, 2022, this low-frequency signal is found on long period and very long period seismometers worldwide. The amplitude spectrum of the signal for this eruption consists of three clear peaks locating at 3.72, 4.61 and 6.07 mHz, instead of two distinct bands for previous cases. The spectrogram analysis shows that this low-frequency energy lasts for several hour and is independent of air wave arrival, while the cross-correlation result confirms that the signal travels as Rayleigh waves with a speed of 3.68 km/s. In this study, we summarize our findings on the observation, and show our synthetic waveforms to provide a possible explanation for the source of this signal. We suggest that the atmospheric oscillations near the volcano excited by the eruption act as an enduring external force on the surface of the solid earth, and produce Rayleigh waves propagating all over the world.

How to cite: Zhang, S., Wang, R., and Dahm, T.: Modeling low-frequency Rayleigh waves excited by the Jan. 15, 2022 eruption of Hunga Tonga-Hunga Ha’apai volcano, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13579, https://doi.org/10.5194/egusphere-egu22-13579, 2022.

The population and built infrastructure of the Kingdom of Tonga are highly exposed to ocean- and climate-related coastal hazards. The archipelago was impacted on January 15, 2022, by a destructive tsunami caused by the Hunga Tonga-Hunga Ha'apai submarine volcanic eruption. Weeks later, several islands were still cut off from the world, this situation was made worse by covid-19-related international lockdowns and no precise idea of the magnitude and pattern of destruction. Like in most Pacific islands, the Kingdom of Tonga lacks an accurate population and infrastructure database. The occurrence of events such as this in remote island communities highlights the need for (1) precisely knowing the distribution of residential and public buildings, (2) evaluating what proportion of those would be vulnerable to a tsunami depending on various run-up scenarios, (3) providing tools to the local authorities for elaborating efficient evacuation plans and securing essential services outside the hazard zones. Using a GIS-based dasymetric mapping method previously tested in New Caledonia for assessing, calibrating, and mapping population distribution at high resolution, we produce maps that combine population clusters, critical elevation contours, and the precise location of essential services (hospitals, airports, shopping centers, etc.), backed up by before–after imagery accessible online. Results show that 62% of the population on the main island of Tonga lives in well-defined clusters between sea level and the 15 m elevation contour, which is also the value of the maximum tsunami run-up reported on this occasion. The patterns of vulnerability thus obtained for each island in the archipelago, are further compared to the destruction patterns recorded after the earthquake-related 2009 tsunami in Tonga, thereby also allowing us to rank exposure and potential for cumulative damage as a function of tsunami cause and source-area. By relying on low-cost tools and incomplete datasets for rapid implementation in the context of natural disasters, this approach can assist in (1) guiding emergency rescue targets, and (2) elaborating future land-use planning priorities for disaster risk-reduction purposes. By involving an interactive mapping tool to be shared with the resident population, the approach aims to enhance disaster-preparedness and resilience. It works for all types of natural hazards and is easily transferable to other insular settings.

How to cite: Thomas, B. E. O., Roger, J., and Gunnell, Y.: A rapid, low-cost, high-resolution, map-based assessment of the January 15, 2022 tsunami impact on population and buildings in the Kingdom of Tonga, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13580, https://doi.org/10.5194/egusphere-egu22-13580, 2022.

The phreatic eruption of Hunga-Tonga on January 15, 2022 was so energetic that it excited globe circling air-waves. These wave packets with a dominant period of 30 minutes have been observed in single barograms even after completing at least  four orbits or 6 days after the eruption. Constructive and destructive interference between waves that have left the source region in opposite direction lead to the emergence of standing pressure waves: normal modes of the atmosphere.

We report on individual modes of spherical harmonic degree between 30 and 80 covering the frequency bend from 0.2 mHz to 0.8 mHz. These modes belong to the Lamb wave equivalent modes with a phase velocity of 313 m/s.  They are trapped to the Earth’s surface, decay exponentially with altitude and their particle motion is longitudinal and horizontal. The restoring force is dominated by incompressibility. 

In the frequency band where we observe these modes the mode branches do not cross with mode branches of the solid Earth. Hence we do not expect any significant coupling with seismic normal modes of the solid Earth. Such a crossing occurs at 3.7mHz and aboce.

How to cite: Widmer-Schnidrig, R.: Observation of acoustic normal modes of the atmosphere after the 2022 Hunga-Tonga eruption., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13581, https://doi.org/10.5194/egusphere-egu22-13581, 2022.

The explosive eruption of the Hunga Tonga-Hunga Ha’apai volcano on 15^th of January 2022 impacted the Earth, its oceans and atmosphere on a global scale. Witnesses report an audible “bang” as a result of the event in distances of up to several thousand kilometers. With infrasound sensors this sound wave can be detected where the frequency content or the amplitude of the signal renders the event inaudible to the human ear. Infrasound sensors are distributed globally, a selection of these stations upload their data in real time to publicly available servers. In combination with Open Source libraries such as obspy or scipy it is possible to use these data sources to observe the atmospheric disturbances caused by the eruption on a global scale in near real time. With a minimum of data processing not only the first arrival peak of the atmospheric lamb wave can be identified at most stations but also further passes of the wave as it propagates around the planet several times. Having large amounts of publicly available data is crucial in that process. New data chunks can be analyzed and displayed immediately while the signal is still ongoing because data access requests are not required. Additionally, having immediate access to a large dataset allows for big data analysis and reduces the necessity to consider outliers at individual stations and increases the chance to identify the signal after multiple days when overall signal to noise ratios have decreased.

How to cite: Eckel, F., Garcés, M., and Colet, M.: The 15 January 2022 Hunga Tonga event: Using Open Source to observe a volcanic eruption on a global scale in near real time, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13582, https://doi.org/10.5194/egusphere-egu22-13582, 2022.

The 15 January 2022 eruption of the submarine Hunga Tonga-Hunga Ha'apai (HTHH) volcano (Tonga) ranks among the largest volcanic explosions of the satellite remote sensing era, and perhaps the last century. It shares many characteristics with the 1883 Krakatau eruption (Indonesia), including atmospheric pressure waves and tsunamis, and the phreatomagmatic interaction of magma and seawater likely played a major role in the dynamics of both events. A portion of the HTHH eruption column rose to lower mesospheric altitudes (~55 km) and the umbrella cloud extent (~500 km diameter at ~30-35 km altitude) rivalled that of the 1991 Pinatubo eruption, indicative of very high mass eruption rates. However, sulfur dioxide (SO₂) emissions measured in the HTHH volcanic cloud (~0.4 Tg) were significantly lower than the post-Pinatubo SO₂ loading (~10–15 Tg SO₂), and on this basis we would expect minimal climate impacts from the HTHH event. Yet, in the aftermath of the eruption satellite observations show a persistent stratospheric aerosol layer with the characteristics of sulfate aerosol, along with a large stratospheric water vapor anomaly. At the time of writing, the origin, composition and eventual impacts of this stratospheric gas and aerosol veil are unclear. We present the preliminary results of a multi-disciplinary approach to understanding the HTHH eruption, including 1D- and 3D-modeling of the eruption column coupled to a 3D atmospheric general circulation model (NASA’s GEOS-5 model), volatile mass balance considerations involving potential magmatic, seawater and atmospheric volatile and aerosol sources, and an extensive suite of satellite observations. Analysis of the HTHH eruption will provide new insight into the dynamics and atmospheric impacts of large, shallow submarine eruptions. Such eruptions have likely occurred throughout Earth’s history but have never been observed with modern instrumentation.

How to cite: Carn, S., Andrews, B., Aquila, V., Cauley, C., Colarco, P., Dufek, J., Fischer, T., Kenis, L., Krotkov, N., Li, C., Mastin, L., Newman, P., and Wallace, P.: Satellite observations and modeling of the 2022 Hunga Tonga-Hunga Ha'apai eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13583, https://doi.org/10.5194/egusphere-egu22-13583, 2022.

The phreatoplinan eruption of the shallow submarine Hunga Volcano Tonga formed global air-pressure waves, regional tsunami and an up to 55 km-high eruption column. Despite its large explosive magnitude, the magma erupted were similar to past compositions, and comprised crystal poor (<8 wt% total; plag>cpx>opx) andesite with ~57-63 wt% silica glass. Low magnitude Surtseyan eruptions in 2009-2015 formed from small pockets of andesite that ascended slowly, resulting in high microphenocryst and microlite contents. Large eruptions, including events in ~AD200 and AD1100 and the 2022 event drew magma rapidly from a ~5-7 km deep mid-crustal reservoir. Rapid decompression and quenching (augmented by magma-water interaction) records the heterogeneity of the reservoir, with mingled glass textures and cryptic mixing of subtly different melts. The 2022 feldspar phenocrysts show more mafic melt inclusion compositions than host glass, clear uniform cores and thin rims evidencing ~1 month-long changes caused by decompression, rise and internal mingling of subtlety different melts. CPX phenocrysts show uniform cores a variety of more mafic and similar melt inclusions to the bulk glass, and thin overgrowth rims reflecting only decompression and mingling. Lithic fragments (<8wt%) include common hydrothermal minerals (sulphides, quartz etc). Without evidence of a mafic trigger, or crystalisation induced overpressures, this extremely violent eruption was triggered by top-down processes that led to rapid exhumation/decompression of magma and very efficient explosive magma-water interaction. This could include any, or all of: flank collapse; hydrothermal seal fracturing and ingress of water into the upper magma system and caldera collapse. Subsequent earthquakes suggest that the crustal magma system was rapidly recharged in the days following the eruption.

How to cite: Cronin, S., Brenna, M., Kula, T., Ukstins, I., Adams, D., Wu, J., Paredes Marino, J., Kilgour, G., Leonard, G., White, J., Barker, S., and Gravley, D.: The 15 January 2022 Hunga eruption, Tonga – first petrographic and geochemical results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13584, https://doi.org/10.5194/egusphere-egu22-13584, 2022.

The 15 Jan 2022 eruption of Hunga-Tonga-Hunga-Ha’apai was the largest explosive volcanic event in the last 30 years. These islands represent the subaerially exposed summit of the Hunga Volcano, merged into a single land mass during the most recent eruption in 2014-2015. The 2022 eruption likely represents a 1-in-1000-year event for the Hunga Volcano, with the previous large-magnitude eruption occurring in ~1100 CE during a series of caldera-forming events. The 2022 erupted magma is plagioclase-, orthopyroxene- and clinopyroxene-bearing basaltic andesite to andesite dominated by blocky, poorly vesicular glassy ash with lesser amounts of vesicular pumiceous ash and fine lapilli. Melt Inclusions (MIs) hosted in plagioclase, clinopyroxene and orthopyroxene are abundant and glassy, some displaying shrinkage bubbles, with no evidence of secondary crystallization along the walls or within the MI glass. The groundmass glass and MI in the three main phenocryst phases were analysed for major, trace and volatile element concentrations to enable identification of magmatic sources and to better constrain processes happening at depth. Preliminary data indicate that plagioclase phenocrysts range from An₉₃ to An₇₈, and MI range from 54.1 to 58.7 wt % SiO₂, with MgO from 2.5 to 5.3 wt %. Clinopyroxene phenocrysts range from En₄₂ to En₅₀, and MI range from 51.6 to 65.1 wt % SiO₂, with MgO from 1.1 to 5.7 wt %. Orthopyroxene phenocrysts range from En₆₈ to En₇₇, and MI range from 55.7 to 59.6 wt % SiO₂, with MgO from 2.5 to 5.3 wt %. Clinopyroxene MI span the full range of SiO₂ compositions observed from the Hunga Volcano, from the host 2022 event (SiO₂: ~57.5 wt %), the 1100 CE event (SiO₂: ~60 wt %), the 2014-2015 event (SiO₂: ~60.5 wt %), and the most evolved 2009 event (SiO₂: ~63 wt %) and extend an additional ~4 wt % SiO₂ to more mafic compositions. Orthopyroxene MI most closely resemble the 1100 CE event and the average groundmass glass compositions of the 2022 event. Plagioclase MI overlap the least silicic compositions observed in the 2022 groundmass glass (58.6 wt% SiO₂) and extend down to 54 wt % SiO₂, overlapping the main field of clinopyroxene MI. Both plagioclase and clinopyroxene MI tend to show higher MgO as compared to the 2022 groundmass glass at the same SiO₂ concentration, whereas orthopyroxene shows lower MgO than the groundmass glass. SO₃ in MI ranges up to 1600 ppm, significantly higher than the 2022 groundmass glass which averages 200 ppm, with both plagioclase and clinopyroxene MI preserving the highest observed concentrations. In contrast, Cl concentrations in MI extend to 2000 ppm, with the highest values in orthopyroxene and clinopyroxene, and plagioclase MI are lower and generally overlie the main groundmass glass concentrations (~1300 ppm). F was below detection limits. We postulate that clinopyroxene crystals reflect a more primitive basaltic andesite magma, whereas orthopyroxene crystals were likely derived from the magmatic remnants of the 2009 and 2014/2015 events in the upper magma system, and plagioclase crystals were sourced from the full range of magma sources.

How to cite: Ukstins, I., Cronin, S., Adams, D., Wu, J., Paredes Marino, J., Brenna, M., Smith, I., and Brooks-Clarke, I.: Hunga-Tonga-Hunga-Ha’apai Jan 15, 2022 eruption: Assembly of heterogeneous magma sources recorded in melt inclusions from plagioclase, clinopyroxene and orthopyroxene., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13585, https://doi.org/10.5194/egusphere-egu22-13585, 2022.

In December 2014, eruptions began from a submarine vent between the islands of Hunga Tonga and Hunga Ha’apai, 65 km north of Tongatapu, Tonga. The “Hungas” represent small NW and NE remnants of the flanks of a larger edifice, with a ~5 km-diameter collapse caldera south of them. The 2014/15 Surtseyan explosive eruptions lasted for 5 weeks, building a 140 m-high tuff ring.

Deposits on Hunga Ha’apai and tephra fall on Tongatapu record two very large magnitude eruptions producing local pyroclastic density currents and tephra falls of >10 cm-thick >65 km away. These likely derive from the central edifice/caldera. The 2022 eruption produced slightly less tephra fall, but an extremely large explosive event, with regional tsunami indicating substantive topographic change.

Here we report the bathymetric details of the caldera as of November 2015. A multibeam sounder (WASSP) was used to mapping the shallow (<250 m) seafloor concentrating on the edges of the Hunga caldera. These results were combined with an aerial survey of the 2015 tuff cone, using a combination of drone photogrammetry and real-time kinematic GPS surveys. The bathymetry reveals that previous historical eruptions, including 1988 and 2009, and likely many other recent unknown produced a series of well-preserved cones around the rim of the caldera. Aside from the raised ground in the northern caldera produced by the 2009 and 2014/15 eruptions, the southern portion is also elevated to within a few m below sea level, with reefs present. During the 2015 visit, uplifted fresh coral showed that inflation was ongoing and that the caldera was likely in the process of resurgence.

Much of Hunga Tonga and the 2014/2015 cone was destroyed in the 2022 eruptions, with Hunga Ha’apai intact, but dropping vertically by ~10-15 m. The violence of the 2022 eruption was likely augmented by either caldera collapse or flank collapse from the upper edifice, rapidly unroofing the andesitic magma system and enabling efficient water ingress.

This data provides an essential base layer for assessing changes on the ocean floor, especially to determine any caldera or upper-flank changes. Understanding these changes is crucial for future forecasting future volcanic hazards at Hunga and other nearby large submarine volcanoes.

How to cite: Stern, S., Cronin, S., Ribo, M., Barker, S., Brenna, M., Smith, I. E. M., Ford, M., Kula, T., and Vaiomounga, R.: Post-2015 caldera morphology of the Hunga Tonga-Hunga Ha’apai caldera, Tonga, through drone photogrammetry and summit area bathymetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13586, https://doi.org/10.5194/egusphere-egu22-13586, 2022.

The January 2022 eruption of Hunga Volcano, Tonga is likely the most explosive mafic eruption yet documented. It exhibited dynamics of ash plume expansion and atmospheric pressure waves unlike anything seen before. This is remarkable considering that it erupted crystal-poor and microlite-poor andesitic magma (57-63 wt% silica glass). The climactic phase produced an eruptive column of at least 39 km in height, however, the ash volume appears anomalously small for the explosive magnitude. Ash from nine different sites across the Kingdom of Tonga were analyzed for textural and morphological properties and grain size distribution. The tephra comprises light pumice (16%), dark pumice (44%), glassy microlite-rich grains (25%), lithics (7%) and free-crystals (Pl, Cpx, Opx) (8%). Specific gravity of particles range from 0.4 to ~2.5. Secondary electron images show that pumices have a variable vesicularity, from dense glassy blocky particles; glassy particles with isolated vesicles and weakly deformed, thick vesicle walls; and a smaller percentage of microvesicular pumices, coated in finer particles. The general characteristics imply a rapid decompression, fragmentation and chilling. This implies some form of phreatomagmatism but with high-efficiency to generate such a large blast – e.g., via propagation of stress waves and thermal contraction rapidly increasing a magma surface area for interaction. The ash is fine-grained and poorly sorted overall. Less than 20 wt.% of ash particles are >1 mm at 80 km SE of the volcano on the main island of Tongatapu, while 70 km NE of the volcano (Nomuka Island) has finer ash, with only 2% of particles >1 mm. It appears that the dispersion axis for the event was directed toward the E or ESE, across the main population centre of Nuku’alofa on Tongatapu. Of the fine fraction 20 wt.% is < 30 micron, 8 wt.% <10 micron but unusually few particles of very fine range (<0.05 wt.% finer than 1 micron). Variations in the mode and sorting of ash fall at different locations and angles from the vent show that there was potentially complex dispersal of ash from different phases of the 11-hour long eruption, and or different plume heights and fragmentation processes involved. Plume observations suggest at least two different plume levels during main phases of the eruption and the fragmentation mechanisms likely varied from the blast-generating phase and the lesser-explosive phases leading up to and following this.

How to cite: Paredes-Mariño, J., White, J., Dürig, T., Baxter, R., Kula, T., Cronin, S., Ukstins, I., Wu, J., Adams, D., Brenna, M., and Brooks-Clarke, I.: Understanding fragmentation mechanism(s) during the 15 January 2022 Hunga Volcano (Tonga) eruption through particle characteristics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13587, https://doi.org/10.5194/egusphere-egu22-13587, 2022.

The Tonga volcanic eruption of 15 January 2022 generated tsunami waves that impacted the entire Global Ocean as far away as 18,000 km from the source in the tropical Pacific Ocean. A defining characteristic of the tsunami was the dual forcing mechanism that sent oceanic waves radiating outward from the source at the longwave speed and atmospheric pressure Lamb waves radiating around the globe at the speed of sound (i.e. roughly 1.5 times faster than the longwave phase speed). Based on time series from several hundred high-resolution observational sites, we constructed global maps of the oceanic tsunami waves and the atmospheric Lamb waves. In some areas of the Pacific Ocean, we were able to distinguish between the two types of motions and estimate their relative contribution. A global numerical model of tsunami waves was constructed and results from the model compared with the observations. The modeled and observed tsunami wave heights were in good agreement. The global maps also enabled us to identify regional “hot spots” where the tsunami heights were highest. In addition to areas in the Pacific Ocean (Chile, New Zealand, Japan, the U.S. West Coast, and the Alaska/Aleutian Islands), “hot regions” included the Western Mediterranean and the Atlantic coasts of Europe and northern Africa.

How to cite: Sepic, J., Medvedev, I., Fine, I., Thomson, R., and Rabinovich, A.: The global reach of the 2022 Tonga volcanic eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13588, https://doi.org/10.5194/egusphere-egu22-13588, 2022.

The Hunga Tonga-Hunga Ha’apai eruption of January 15 2022 was the culminating event of a sequence of seismic and volcanic events starting back in December 2021. The January 15 eruption manifested itself above the sea level with a number of phenomena, including the generation of a convective column ascending well into the stratosphere, pyroclastic flows travelling over the sea surface, an atmospheric pressure wave recorded by several instruments around the globe, and a tsunami, that represents the main focus of this study.

The tsunami that followed the eruption was observed both in the near-field and in the far-field, propagating across the entire Pacific Ocean and causing damage and loss of lives as far as Peru. In the near-field (Tonga archipelago) it is trickier to distinguish the damage induced by the impact of the eruption and the tsunami waves.

It is still not clear what the main generating mechanism for the ensuing tsunami was. In this contribution, several different hypotheses are investigated, adopting simplified models ranging from the submerged volcanic edifice collapse to the phreatomagmatic explosion and to the atmospheric pressure wave that was recorded across the entire globe. The propagation of the tsunami is simulated numerically with both non-dispersive and dispersive codes. Different spatial scales and resolutions are adopted to check the relative weight of the different generating mechanisms in the near- and in the far-field. Tentative conclusions are drawn by comparing the simulated results with the available experimental data in terms of tide-gauge records and near-field coastal impact.

How to cite: Armigliato, A., Angeli, C., Gallotti, G., Tinti, S., Zanetti, M., and Zaniboni, F.: Numerical investigations on different possible generating mechanisms for the tsunami following the January 15 2022 Hunga Tonga-Hunga Ha’apai eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13589, https://doi.org/10.5194/egusphere-egu22-13589, 2022.

The Hunga-Tonga eruption culminated on January 15, 2022, with a high-intensity Plinian eruption exceeding 20 km height, tsunamis affecting local islands and the circumpacific region, locally air-coupled seismic surface waves recorded at teleseismic distances, and explosive shock waves that repeatedly travelled around the world. Hunga-Tonga is a flat-topped volcano that rises about 1700 m above the seafloor, hosting a submarine 3-4 km diameter caldera floor that lies at less than 200 m water depth and is surrounded by an elevated, approx. 100-200 m high caldera wall. Only small parts of the volcano are rising at the caldera wall above the sea level, such as the islands Hunga Tonga Hunga Ha'apai in the north and small unnamed rocks in the south. Satellite imagery acquired by Pleiades and Sentinel 1A suggests that during the January 15, 2022 eruption, the central part of the Hunga Tonga Hunga Ha'apai as well as the small rocks in the south disappeared. By analysing satellite radar and imagery, we constrain island perimeters and morphologies before and after the eruption, to find evidence for island subsidence and erosion. In addition, seismic data recorded during the January 15, 2022 eruption was analysed in the time and frequency domains, revealing high amplitude activity over ~1 hr. The comparison of seismic, GNSS and local tsunami recordings gives insights into the time-succession of the eruption. For instance, moment tensor inversion suggests that the largest amplitude seismic signal was produced by a dominant tensile non-double component, characteristic of volcanic explosions. Furthermore, we also found evidence for reverse polarity mechanisms in agreement with subsidence of a caldera, possibly indicating incremental activity of a ring fault. We discuss the possible contribution of a caldera to the evolving eruption dynamics and the need to improve geophysical monitoring of this island arc in general and acquire high-resolution submarine data Hunga Tonga Hunga Ha'apai in specific.

How to cite: Walter, T. R. and Cesca, S. and the GFZ-DLR-Geomar Task Force Team: Caldera subsidence during the Hunga-Tonga explosive eruption?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13590, https://doi.org/10.5194/egusphere-egu22-13590, 2022.

The explosive eruption of the Hunga Tonga-Hunga Ha'apai volcano on January 15, 2022 triggered tsunami waves that were observed throughout the Pacific Ocean. In particular, the waves were recorded by several dozen deep-ocean DART stations located at source distances from hundreds to more than 10 thousand kilometers. Our study is aimed at analyzing tsunami waveforms recorded by DART stations in order to identify the formation mechanisms of this volcanogenic tsunami. Waveforms are processed using wavelet analysis. The arrival times of signals of different genesis are estimated making use robust physical assumptions, numerical modeling and satellite images. It has been found that in all records the tsunami signal is clearly observed long before the calculated moment of arrival of gravity surface waves caused by sources localized in the immediate vicinity of the volcano. On the records obtained by distant stations (~10000 km) dispersive gravity waves arrive with a delay of several hours after the signals following the passage of acoustic wave in the atmosphere. In addition to the analysis of waveforms, theoretical estimates of the amplitude of gravity waves in the ocean, caused by an acoustic wave in the atmosphere, will be presented. We also provide a theoretical estimate on how acoustic waves in the atmosphere manifest in pressure variations recorded by an ocean-bottom sensor.

This study was funded by a grant of the Russian Science Foundation № 22-27-00415, https://rscf.ru/en/project/22-27-00415/.

How to cite: Nosov, M., Sementsov, K., Kolesov, S., and Pryadun, V.: Volcanogenic tsunami on January 15, 2022: insights from deep-ocean measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13591, https://doi.org/10.5194/egusphere-egu22-13591, 2022.

The 15th January 2022 Hunga Tonga- Hunga Ha’apai (HTHH) volcano explosion is one of the most powerful eruptive events over the last 30 years. Based on early computations, its VEI was at least 5. The explosion caused atmospheric air shock waves that propagated around the globe, and also generated a tsunami. All these effects seemed to have produced quite a significant response in the ionosphere.

In this contribution, we analyze the ionospheric disturbances generated by the HTHH volcano eruption by using ground-based 8 GNSS receivers located in the near-field of the volcano (i.e., less than 2000 km). We test our previously developed methods to detect and locate the explosive event and its ionospheric signatures in a near-real-time (NRT) scenario. 

To detect co-volcanic ionospheric disturbances (co-VID), we use the TEC time derivative approach that was previously used for detection of ionospheric disturbances generated by large earthquakes. For this event, we modified the previously developed method to proceed not only 1-second but also 30 sec data. This approach detects the first perturbations ~12-15 minutes after the eruption onset. Further, it estimates the instantaneous velocities in a near field to be about ~500-800 m/s. Finally, from the obtained velocity vectors and the azimuths of co-VID propagation we calculate the position of the source in the ionosphere. 

Besides, we used the same TEC time derivative approach to produce NRT Travel Time Diagrams. The NRT TTD additionally verify the correlation with the source and velocities’ values.

How to cite: Maletckii, B. and Astafyeva, E.: The Near Real time analysis of Hunga Tonga-Hunga Ha’apai eruption in the ionosphere by GNSS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13592, https://doi.org/10.5194/egusphere-egu22-13592, 2022.

In the frame of the Strateole 2 balloon project, 17 long-duration stratospheric balloons were launched from Seychelles in fall 2021. At the time of the main eruption of Hunga-Tonga on January 15 2022, two balloons were still in flight over the tropical Pacific, respectively at altitudes of 20 and 18.5 km, and distances of 2,200 and 7,600 km from the volcano. The balloon measurements include wind, temperature and pressure at a sampling rate of 1 Hz. Those observations of this extreme event at that altitude are unique.

In this presentation, we will describe the observations of multiple wave trains by the balloons. The signature of the Lamb wave and infrasounds are particularly striking. The characteristics of the eruption and its scenario will be examined using a synergy of stratospheric in situ observations, ground observations and geostationary satellite images. Finally, we will discuss the complementarity of balloon observations with respect to the ground network due to their altitude and geographic location with respect to the source.

How to cite: Podglajen, A., Garcia, R., Gerier, S., Hauchecorne, A., Hertzog, A., Le Pichon, A., Lott, F., and Millet, C.: Stratospheric observations of acoustic-gravity waves from the Hunga-Tonga eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13593, https://doi.org/10.5194/egusphere-egu22-13593, 2022.

Meteotsunamis are long ocean waves generated by atmospheric disturbances. The Tonga volcano eruption on 15 January 2022 generated a Lamb pressure wave propagating all over the globe and generating a tsunami observed at most tide gauges in the world. A first atmospheric wave arrived 20 hours after the eruption on the French Mediterranean coasts and propagated southward. This abrupt atmospheric pressure change was recorded by hundreds of barometers of weather stations around Europe. A second one originating from Africa was observed four hours later with an attenuated amplitude. The first wave can be roughly defined by a sinusoid signal with a period close to one hour and an amplitude of 150 Pa. The associated tsunami was observed by the French stations of the HTM-NET network (https://htmnet.mio.osupytheas.fr/) [1]. Amplitudes range from a few cm to 15 cm and periods range from 20 min to 1 hour.

Numerical simulation of the tsunami is performed by the operational code Taitoko developed at CEA [2]. The nested multigrid approach is used to simulate the water waves propagating in the bay of Toulon. The meteotsunami is generated by calculating analytically the atmospheric pressure gradient in the momentum equation. Comparisons of time series between numerical solutions and records are very satisfactory in regions defined by a high resolution topo-bathymetry. A second tsunami simulation is performed by introducing a second pressure wave propagating in the North direction and reaching the HTM-NET stations 4 hours after the first arrival. This second pressure wave results in additional and higher tsunami water waves in agreement with records.

[1] Rey, V., Dufresne, C., Fuda, J. L., Mallarino, D., Missamou, T., Paugam, C., Rougier, G., Taupier-Letage, I., On the use of long term observation of water level and temperature along the shore for a better understanding of the dynamics: Example of Toulon area, France Ocean Dyn., 2020, https://doi.org/10.1007/s10236-020-01363-7.

[2] Heinrich, P, Jamelot, A., Cauquis, A., Gailler A., 2021. Taitoko, an advanced code for tsunami propagation, developed at the French Tsunami Warning Centers. European Journal of Mechanics - B/Fluids 88(84) . DOI: 10.1016/j.euromechflu.2021.03.001.

How to cite: Gailler, A., Heinrich, P., Rey, V., Hébert, H., Dupont, A., Listowski, C., Forestier, E., and Ntafis, S.: Observation and simulation of the meteotsunami generated in the Mediterranean Sea by the Tonga eruption on 15 January 2022, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13594, https://doi.org/10.5194/egusphere-egu22-13594, 2022.

The Hunga Tonga eruption has generated an atmospheric plume rising above 40 km,  establishing an observational record. Due to the explosive nature of the eruption with a lot of water, the plume carried an unprecedented amount of water and a cloud of sulfated aerosols and possibly ultra-thin ashes was released. The aerosols have already persisted for four weeks with peak scatterring ratio initially above 200 that are still above 30 on many patches, as seen from CALIOP. These high values combined with low depolarization suggest a large amount of small sub-micronic spherical particles, confirmed by in situ balloon measurements. This is compatible with dominance of sulfated aerosols.

As the stratospheric flow has been mostly zonal with no breaking wave during the period and region of interest, and the horizontal shear dominates, the plume has been mostly dispersed in longitude keeping a similar latitudinal vertical pattern from the early days. A part has migrated to the tropical band reaching 10°N. Several concentrated patches have been preserved in particular a "mushroom" like pattern at 20S which has already circulated once around the Earth. . We will discuss the stability of this pattern in relation with vortical and thermal structures that are detected from several instruments and the meteorological analysis.

We will also discuss the likely impact on the stratospheric composition and the radiative effect on the yearly basis.  

How to cite: Legras, B., Khaykin, S., Podglajen, A., and Sellitto, P. and the ASTuS: Persistence Hunga Tonga plume in the stratosphere and its journey around the Earth., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13595, https://doi.org/10.5194/egusphere-egu22-13595, 2022.

The eruption of Hunga volcano, Tonga is the most energetic event recorded by the infrasound component of the global International Monitoring System (IMS). Infrasound, acoustic-gravity and Lamb waves were recorded by all 53 operational stations after circling four times the globe. The atmospheric waves recorded globally exhibit amplitude and period comparable to the ones observed following the 1883 Krakatoa eruptions. In the context of the future verification of the Comprehensive Nuclear-Test-Ban Treaty, this event provides a prominent milestone for studying in detail infrasound propagation around the globe for almost one week as well as for calibrating the performance of the IMS network in a broad frequency band.

How to cite: Vergoz, J., Le Pichon, A., Listowski, C., Hupe, P., Pilger, C., Gaebler, P., Ceranna, L., Garcés, M., Marchetti, E., Labazuy, P., Mialle, P., Brissaud, Q., Näsholm, P., Shapiro, N., and Poli, P.: A global analysis of deep infrasound produced by the January 2022 eruption of Hunga volcano, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13598, https://doi.org/10.5194/egusphere-egu22-13598, 2022.

Explosive volcanism periodically induces disturbances of the upper troposphere and low stratosphere. These injections of massive amount of aerosols, ash and gases perturb locally the physico-chemical balance of the impacted atmospheric layers, in particular the ozone concentration via heterogeneous chemistry on particles. On a larger scale some exceptional eruption can have a significant influence on the Earth radiative budget as it was the case following eruptions of El Chichon in 1982 and Mount Pinatubo in 1991.

On January 15, 2022, the Hunga-Tonga volcano erupted in the Tonga archipelago (20.5°S, 175.4°W). The Plinian eruption was of a rare intensity, especially because of the depth of the underwater caldera. The first estimates indicate a power between 10 and 15 Mt TNT, probably the most powerful since the eruption of Krakatoa in 1883. This short (~ 8min) but intense explosion whose pressure wave was observed all around the globe injected about 400 kt of material into the atmosphere (to be compared to the 20 Mt injected during the Mount Pinatubo eruption). The Volcano Stratospheric Plume (VSP) quickly moved westwards and then overflew the island of La Réunion (21°S, 55°E), located at ~12000 km away from Tonga.

In order to monitor the evolution of the VSP, lidar observations were performed at the Observatoire de Physique de l’Atmosphère de La Réunion (OPAR). This observatory is equipped with three lidars capable of stratospheric aerosols measurements at two wavelengths (355 nm and 532 nm). First observations were performed every night from 19 to 27 January 2022 when the first passage of the VSP occurred. The plume structures appeared to be highly variable along time, with altitudes ranging from 19 km to 36 km above the mean sea level while plume thicknesses were ranging from ~1 km to more than 3 km. Remarkable aerosol optical depth were associated with these stratospheric aerosol layers, up to 0.8 at 532 nm on January 21.

The temporal evolution of the VSP structure and optical properties will be presented and discussed.

How to cite: Baron, A., Payen, G., Duflot, V., Chazette, P., Khaykin, S., Hello, Y., Marquestaut, N., Ranaivombola, M., Bègue, N., Portafaix, T., and Cammas, J.-P.: Early evolution of the Hunga – Tonga Volcanic Plume from Lidar Observations at Reunion Island (Indian Ocean, 21°S, 55°E), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13599, https://doi.org/10.5194/egusphere-egu22-13599, 2022.

Hunga Tonga-Hunga Haʻapai, a submarine volcano in the South Pacific, reached an eruption climax on 15 January 2022. The blast sent a plume of ash well into the stratosphere, triggered tsunami alerts across the world, and caused ionospheric disturbances. A few hours after the violent eruption, the Microwave Limb Sounder (MLS) measured enhanced values of water vapor at altitudes as high as 50 km - near the stratopause.
On the following days, as the plume dispersed, several MLS chemical species, including H₂O and SO₂, displayed elevated values, far exceeding any previous values in the 18-year record. In this presentation we discuss the validity of these measurements, the stratospheric evolution of the SO₂ and H₂O plumes, and, lastly, the implications of the large-scale hydration of the stratosphere by the eruption.

How to cite: Millán, L., Froidevaux, L., Manney, G., Lambert, A., Livesey, N., Pumphrey, H., Read, W., Santee, M., Schwartz, M., Su, H., Werner, F., and Wu, L.: The Hunga Tonga-Hunga Haʻapai hydration of the stratosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13601, https://doi.org/10.5194/egusphere-egu22-13601, 2022.

India witnessed the second longest recorded heat wave during May-June 2019 causing more human deaths with the maximum temperature recorded was about 51.8^oC in a place called Churu in the state of Rajasthan. The present study investigated the spatio-temporal pattern of the maximum temperature and the associate heat waves in the country. The relationship of the heat wave spread and the variables like temperature, humidity, soil moisture as well as the land use and land cover is explored. The dynamics of large scale oceanic and atmospheric features resulting advection and local heating mechanism is found to be the reason of such high intense heat wave in 2019 summer season. The anomaly of all the related weather parameters are linked with the intense maximum temperature and resultant heat wave and the hot spots are identified. The impacts of ENSO (including 'El Niño Modoki') and MJO on the longest and highest heat wave phenomena are also quantified for the year 2019. The role of soil moisture and the evapotranspiration also observed in the analysis which clearly shows lack of these parameters also triggers the intense heat wave events. This study will help in better understanding of the local heat wave dynamics and these informations can be useful for the public health interventions against the intense heat wave situations.

How to cite: Devi, R. and Gouda, K. C.: Investigating the associated dynamics of 2019 Heat wave over India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-237, https://doi.org/10.5194/egusphere-egu22-237, 2022.

Global warming increases the number and severity of deadly heatwaves. Recent heatwaves often coincided with soil droughts that acted to intensify air temperature but lower air humidity. Since lowering air humidity may reduce human heat stress, the net impact of soil desiccation on the morbidity and mortality of heatwaves remains unclear. Combining weather balloon and satellite observations, atmospheric modelling, and meta-analyses of heatwave mortality, we find that soil droughts—despite their warming effect—lead to a mild reduction in heatwave lethality. More specifically, morning dry soils attenuate the afternoon heat stress anomaly by ~5%. This occurs due to reduced surface evaporation and increased entrainment of dry air from aloft. The benefit appears more pronounced during specific events, such as the Chicago 1995 and Northern U.S. 2006 and 2012 heatwaves. Likewise, our findings suggest that irrigated agriculture may intensify lethal heat stress, and question recently proposed heatwave mitigation measures involving surface moistening to increase evaporative cooling.

The manuscript of the findings is in press for Science Advances.

How to cite: Wouters, H., Keune, J., Petrova, I. Y., van Heerwaarden, C. C., Teuling, A. J., Pal, J. S., Vilà-Guerau de Arellano, J., and Miralles, D. G.: Less-deadly heatwaves due to soil drought, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1204, https://doi.org/10.5194/egusphere-egu22-1204, 2022.

Past case studies have proposed many different spring and early summer sea surface temperature anomalies (SSTA) over the North Atlantic as precursors of European summer heat waves. Negative SSTAs in the Subpolar Gyre and western tropical Atlantic, and positive SSTAs in North Sea and Mediterranean Sea are few of the examples suggested to precede different European summer heat waves. Any robust description of North Atlantic spring SSTA precursors is further complicated by the large spatial heterogeneity of European summer heat waves and the limited number of observed events. Here, we combine the MPI-Grand Ensemble dataset with its 100 historical simulations (1850-2006) with a Neural-Network-based Explainable Artificial Intelligence method. In this unique data set, we systematically investigate the relevance of the North Atlantic spring SSTAs in preceding different types of European summer heat waves. We find that spring European regional seas provide useful information to differentiate and anticipate different types of European summer heat waves. While positive SSTAs in western Iberian Peninsula precede western European summer heat waves, positive SSTAs in the North Sea or Mediterranean Sea precede eastern European summer heat waves. The regional spring SSTAs relate to distinct soil moisture anomaly patterns in June, which resemble the location of the heat waves. These results could potentially improve seasonal prediction of European summer heat waves.

How to cite: Beobide-Arsuaga, G., Düsterhus, A., Müller, W. A., Barnes, E. A., and Baehr, J.: Spring regional sea surface temperature precursors of European summer heat waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1538, https://doi.org/10.5194/egusphere-egu22-1538, 2022.

It is well-established that exposure to extreme ambient temperatures is linked to adverse health effects associated with cardiovascular and respiratory diseases. Epidemiological studies demonstrate that the relationship between air temperature and mortality is depicted as a “U”, “V” or “J” shaped curve where the lower extrema reflect the comfort zone and mortality rises beyond a temperature threshold that is region- and population-specific and depends on various socioeconomic factors. However, temperature is not the only parameter determining thermal stress, as relative humidity, wind speed and other meteorological parameters are also known to play an important role which is often ignored. This study investigated the relationship between mortality and thermal conditions in the region of Northern Greece, using several bioclimatic indices as indicators. The data used included mean daily values of air temperature, relative humidity and wind speed and daily mortality counts due to cardiovascular diseases for the time-period 2010-2018. The following 3 thermal indices were estimated: (a) Effective Temperature (ET), (b) Normal Effective Temperature (NET) and (c) Apparent Temperature (AT). These indices were selected as they depend on typically measured variables and they can describe thermal comfort in both warm and cold environments. The association between each thermal index and mortality was studied by fitting a Poisson regression model for over-dispersed data, combined with a distributed lag non-linear model. In order to detect delayed adverse effects of low temperatures, the lag period was extended to 21 days. A “U” shape curve was found to describe the relationship between each thermal index examined and mortality, indicating the existence of a cold and a hot threshold. Thresholds were identified at 16.6^oC and 31.3^oC for AT, at 16.1^oC and 25.5^oC for ET and at 13.7^oC and 24.3^oC for NET. Exposure to high temperatures was found to be more hazardous compared to low temperatures. The cardiovascular mortality risk increased by 8%, 14% and 10% for each additional degree above the AT, NET and ET hot threshold, respectively. On the other hand, a degree below the AT cold threshold resulted in 1% rise in the mortality risk and 2% rise for the case of ET and NET. Furthermore, the thresholds identified for the bioclimatic indices were used to identify temperature thresholds. In all cases the cold temperature threshold lied between 18.1^oC and 20.7^oC, confirming that cold-mortality is not necessarily linked to the lowest temperatures. The hot temperature threshold was almost the same in all cases; 27.6^oC for AT and ΝET and 27.7 for ET. On the whole, this study confirms the complexity of climate-health associations and highlights the importance of bioclimatic indices as tools to evaluate thermal stress and to feed adverse health effect prevention strategies.

ACKNOWLEDGEMENT: We acknowledge support of this work by the project “Risk and Resilience Assessment Center –Prefecture of East Macedonia and Thrace -Greece.” (MIS 5047293) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). 

How to cite: Paschalidou, A., Psistaki, K., Begou, P., Petrou, I., and Dokas, I. M.: Exploring the association between bioclimatic indices and cardiovascular mortality: Preliminary results from Northern Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2046, https://doi.org/10.5194/egusphere-egu22-2046, 2022.

Research on heatwave-related impacts typically focusses on risks to health or critical infrastructure. However, since high temperatures are an important element of convection-driven extreme rainfall events that can trigger flash floods, heatwave-induced extreme rainfall events are also important when considering heatwave impacts. Heavy rainfall events following heatwaves might alleviate the direct impacts of the heat but introduce other risks related to flash floods.

Using sub-daily rainfall observations on a global scale, we show that short duration rainfall extremes are indeed more likely to occur if preceded by a heatwave than compared to non-heatwave events. In addition, these rainfall events are more intense as well. However, this link is dependent on the region, with some locations, especially arid regions, showing no relationship between the two phenomena at all. We also investigate if hotter heatwaves are more likely to be followed by rainfall extremes. This could have implications for future heatwaves which are projected to become more intense.

How to cite: Sauter, C., White, C., Fowler, H., and Westra, S.: Heatwave-related extreme rainfall events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2397, https://doi.org/10.5194/egusphere-egu22-2397, 2022.

Introduction: Last IPCC AR6 reported with very high confidence that more frequent hot extremes will increase for the severity of heatwaves all-round the globe. It is known that heat and hot weather that can last for several days (so called heatwaves) can significantly influence human health as well as rise in heat-related deaths.

Design and methods: In this work, climate simulations obtained by regional climate model RegCM4 over Croatia are used. RegCM4 was forced by four different global climate models on 12.5 km horizontal resolution. Historical climate simulated by model is compared with observed daily data measured at Croatian meteorological stations in order to evaluate simulations. Future climate is considered by three different IPCC scenarios: the lowest RCP2.6, the middle RCP4.5 and the highest RCP8.5 emission scenario. We considered three future time slices: 2021-2050 (P1), 2031-2060 (P2) and 2041-2070 (P3).

Results: The range of climate change for maximum temperature during summer will be examined in the future time slices. We will also look into duration and number of heat waves in different parts of Croatia. Knowledge of the current situation as well as possible change in the future can help in the planning future adaptation and mitigation measures.

How to cite: Srnec, L., Magjarević, V., and Güttler, I.: Historical and projected heat waves in Croatia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2473, https://doi.org/10.5194/egusphere-egu22-2473, 2022.

The future change in dry and humid heatwaves is assessed in 10 year pan-African convective scale (4.5km) and parameterised convection (25km) climate model simulations. Compared to reanalysis, the convective scale simulation is better able to represent humid heatwaves than the parameterised simulation. Model performance for dry heatwaves is much more similar. Both model configurations simulate large increases in the intensity, duration and frequency of heatwaves by 2100 under RCP8.5. Present day conditions that occur on 3 to 6 heatwave days per year will be normal by 2100, occurring on 150-180 days per year. The future change in dry heatwaves is similar in both climate model configurations, whereas the future change in humid heatwaves is 56% higher in intensity and 20% higher in frequency in the convective scale model. Dry heatwaves are associated with low rainfall, reduced cloud, increased surface shortwave heating and increased sensible heat flux. In contrast, humid heatwaves are predominately controlled by increased humidity, which is associated with increased rainfall, cloud, longwave heating and evaporation, with dry bulb temperature gaining more significance in the most humid regions. Approximately one third (32%) of present day humid heatwaves commence on wet days, suggesting the potential for compound flood-humid heat climate extremes. Moist processes are known to be better represented in convective scale models. Climate models with parameterised convection, such as those in CMIP, may underestimate the future change in humid heatwaves, which heightens the need for mitigation and adaptation strategies and indicates there may be less time available to implement them to avoid future catastrophic heat stress conditions than previously thought.

How to cite: Birch, C., Jackson, L., Finney, D., Marsham, J., Stratton, R., Tucker, S., Chapman, S., Senior, C., Keane, R., Guichard, F., and Kendon, E.: Future changes in African heatwaves and their drivers at the convective scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2671, https://doi.org/10.5194/egusphere-egu22-2671, 2022.

Over the past century there was a significant increase in heatwaves in several regions around the globe. This increase is projected to continue with ongoing global warming and forms a serious risk for various ecosystems as well as human health. Changes in the occurrence and the characteristics of heatwaves since the middle of the 20^th century are extensively studied in observational datasets and model simulations. However, there is a research gap concerning preindustrial (1850-1900) heatwaves and heatwaves in the early 20^th century and their relation to forcings and large-scale variability modes.

In this study we analyse the occurrence of heatwaves and the spatial and temporal distribution of different heatwave characteristics since 1850 using different observational datasets (20CRv3 reanalysis, EUSTACE gridded temperature, HadEX3 and station data) and a 36-member ensemble of atmospheric model simulations. We compare preindustrial heatwaves to recent and projected heatwaves and analyse how global or local heatwave hotspots change over time.

We use a new approach, a 30-year running baseline climatology, which allows us to analyse heatwave characteristics across different centuries. Our analysis shows that the different observational datasets show a comparable distribution of heatwave characteristics. Furthermore, the atmospheric model based on observed volcanic forcings can also be used to analyse preindustrial and early 20^th century heatwaves.  The agreement of the model simulations with the observational datasets allows to use the atmospheric model to analyse earlier preindustrial time periods that are not covered by observations. With our on-going analysis of preindustrial heatwaves, we consequently contribute to a better understanding of past climate extremes.

How to cite: Hövel, L., Hand, R., and Brönnimann, S.: A global assessment of heatwaves since 1850 in different datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2728, https://doi.org/10.5194/egusphere-egu22-2728, 2022.

Climate change-induced rise in global temperatures is linked to changes in hot extremes. The recent summer of 2021 was marked by extremely high temperatures over the Mediterranean, which together with numerous wildfires considerably affected human society and natural environment. Using daily maximum temperatures from the ERA-5 reanalysis, we aim to assess the severity of heat waves in 2021 in the context of past major European heat waves (since 1950) through analysing their length, spatial extent, intensity, and overall magnitude. We show that the summer of 2021 was record-breaking in terms of total duration of heat waves and their magnitude was comparable to those in 2003 and 2010. The past two decades (2002–2021) almost completely redraw the spatial pattern of the occurrence of the historically most severe heat wave in European regions. Before 2002, heat waves of 1955, 1972, and 1994 were the most severe in many parts of Europe. Considering the whole 1950–2021 period, however, those heat waves remain as historically the most severe only over a small portion of their original area, and the map is dominated by the 2003, 2010, 2018, and 2021 events. This documents a rapid change in heat wave characteristics in Europe over the last two decades.

How to cite: Lhotka, O. and Kyselý, J.: European heat waves of summer 2021 in the context of past major heat waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3666, https://doi.org/10.5194/egusphere-egu22-3666, 2022.

The driving mechanisms of extreme heat events are known to live on a range of spatio-temporal scales. The occurrence and severity of a heatwave can be influenced by (a) slow variations in the ocean and sub-surface, (b) planetary tele-connections, (c) variations in the jet-stream and synoptic weather systems, as well as (d) local-scale feedbacks.

While important progress has been made on each of these individual contributions, fewer studies have attempted to draw a unified picture including them all. We approach this task with tools from classic statistical modeling, as well as image processing machine learning. With the help of wavelet-transforms, predictor variables can be separated into individual scales. Together with local variables and global principal component time-series, these potential drivers are supplied to a statistical learner with the task of reconstructing the field of heatwave occurrences. Contributions from individual scales can then directly be identified, either via variable selection before or during learning, or by measures of feature importance applied to the trained models.

We demonstrate this approach for the case of summer heatwaves in the ERA5 reanalysis. If successful, our  framework can also be transferred to other extreme events such as droughts, cold spells or wind storms.

How to cite: Buschow, S., Keller, J., and Wahl, S.: Identifying drivers for heat waves using wavelets and machine learning approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3750, https://doi.org/10.5194/egusphere-egu22-3750, 2022.

Extreme heat is a growing risk to both human and planetary health. It is an area of research with many mathematical models that attempt to capture mostly human responses to thermal conditions. However, like many science fields software is often not developed in a reproducible manner, which adheres to the shared principles of open science, software and research. Here, we present thermofeel which is a python thermal comfort library that was developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) with the dual purpose of being able to be integrated into their operational forecasting systems and allowing users of ECMWF products to be able to use the same methods with their data. In addition, hosting thermofeel on GitHub allows for future growth through open research software process in line with the fast-moving extreme heat field and gives the potential for collaboration between the ECMWF with many other user groups. Further, the development here could lead to a global heat hazard early warning system and the first forecasting results will be presented demonstrating the skill of thermal indices. Finally, thermofeel is currently in pre-operational forecasting at ECMWF and is available for everybody through pip and GitHub. This work has been funded by the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no 824115. 

How to cite: Brimicombe, C., Quintino, T., Di Napoli, C., Pappenberger, F., Cornforth, R., and Cloke, H.: thermofeel: developing an open research software project for heat stress and thermal comfort., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4042, https://doi.org/10.5194/egusphere-egu22-4042, 2022.

Extreme high temperatures are associated with elevated human mortality risks. This is evidenced by a typically U- or J-shaped relationship between daily temperature and mortality found for most places in the world where data exist. However, high temperature is not the only contributor to heat stress. Humidity is also an important factor because it affects evaporation of sweat, which is crucial for cooling the human body in hot environments. Although various heat stress metrics, many of which are a combination of atmospheric temperature and humidity based on different physiological assumptions, have been developed to estimate heat stress, the relationship between these metrics and mortality remains unclear.

In this study, the relationships between seven heat stress metrics — wet bulb temperature, apparent temperature, discomfort index and swamp cooler temperatures at four different efficiencies [1] — and mortality are systematically assessed using well-established Distributed Lag Non-linear Models (DLNMs) [2]. The predictive powers of these metrics, as well as that of daily mean temperature, are compared for the summer season at global locations in 39 countries, where sufficient meteorological and health data are available [3]. The results of this study provide new information as to which of these metrics are most indicative of summer mortality in different locations, and whether the ‘best-fit’ heat stress metric for a location gives a substantially different mortality estimate compared to the commonly used daily mean temperature. These results have important implications for heat-health impact monitoring, developing national and international heat-health action plans, as well as for projecting future heat-related mortality under different climate change scenarios.

References:

[1] Buzan, J. R. et al.: Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5. Geosci. Model Dev., 8, 151–170, 2015.

[2] Gasparrini and Armstrong: Reducing and meta-analysing estimates from distributed lag non-linear models. BMC Medical Research Methodology, 13:1, 2013.

[3] Vicedo-Cabrera, A. M. et al.: The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate Change, 11, 492–500, 2021.

How to cite: Lo, E., Vicedo-Cabrera, A. M., Mitchell, D., Buzan, J., and Zscheischler, J.: Is heat stress more indicative of summer mortality than temperature alone?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4129, https://doi.org/10.5194/egusphere-egu22-4129, 2022.

Extreme heat is an increasing climate threat, most pronounced in urban areaswhere poor populations are at particular risk.Weanalyzed heat impacts and vulnerabilities of 1027 outdoorworkerswho participated in a KAP survey in Hanoi, Vietnam in 2018, and the influence of their mitigation actions, their knowledge of heat-risks, and access to early warnings.
We grouped respondents by their main income (vendors, builders, shippers, others, multiple jobs, and nonworking) and analyzed their reported heat-health impacts, taking into consideration socioeconomics, knowledge of heat impacts and preventive measures, actions taken, access to air-conditioning, drinking amounts and use of weather forecasts. We applied linear and logistic regression analyses using R.
Construction workers were younger and had less knowledge of heat-health impacts, but also reported fewer symptoms. Older females were more likely to report symptoms and visit a doctor. Access to air-conditioning in the bedroom depended on age and house ownership, but did not influence heat impacts as cooling was too expensive. Respondents who knew more heat exhaustion symptomswere more likely to report impacts (p< 0.01) or consult a doctor (p<0.05). Similarly, thosewho checkedweather updateswere more likely to report heat impacts (p< 0.01) and experienced about 0.6 more symptoms (p< 0.01). Even though occupation type did not explain heat illness, builders knewconsiderably less (40%; p<0.05) about heat than other groups butwere twice as likely to consult a doctor than street vendors (p < 0.01). Knowledge of preventive actions and taking these actions both correlated positively with reporting of heat-health symptoms, while drinking water did not reduce these symptoms (p < 0.01). Child carers and homeowners experienced income losses in heatwaves (p < 0.01). The differences support directed actions, such as dissemination of educational materials and weather forecasts for construction workers. The Red Cross assisted all groups with cooling tents, provision of drinks and health advice.

How to cite: Lohrey, S., Chua, M., Gros, C., Faucet, J., and Lee, J. K. W.: Perceptions of heat-health impacts and the effects of knowledge andpreventive actions by outdoor workers in Hanoi, Vietnam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4219, https://doi.org/10.5194/egusphere-egu22-4219, 2022.

There is high confidence that heatwaves will become more frequent and more intense under the influence of climate change. Different definitions of heatwaves exist based on the statistical distribution of temperature, in general using thresholds and duration and extension criteria.
If one observes the overlap between these definitions and the actual human and material damage produced by heatwaves, it appears that there is low consistency between the two. In other terms, a large amplitude heatwave in the physical climatological sense may not be equivalently as large in terms of impacts.
By crossing meteorological (E-OBS), demographic (WorldPop, GHS-POP), and impact (EM-DAT) databases at the European scale, we developed indices to classify heatwaves and select extreme ones in terms of impacts. We also proposed a method to evaluate the classification abilities of these indices. Including demographic data in the indices seems central to understand the links between meteorological conditions and observed impacts.

How to cite: Mandonnet, T., Jézéquel, A., D'Andrea, F., Vallet, A., and Guivarch, C.: Extreme heatwaves in Europe 1950-2020: analysis of the links between meteorology, population, and impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4390, https://doi.org/10.5194/egusphere-egu22-4390, 2022.

Climate change and increasing heat stress reduces labour productivity and supply all across the globe. In a global warming scenario of 3°C, effective labour (i.e., the combination of productivity and supply) is expected to decrease by up to 50 percentage points relative to the period 1986-2005. Central Africa, Southeast Asia and Latin America will be most affected. In these regions, the agricultural sector is still of paramount importance for livelihoods and food security and outdoor work is more common. When heat stress further increases, the capability for physical activity will reduce across a wide range of working places, primarily outdoors. Especially in low- and middle-income countries the effects of climate change will lead to a reduction in economic activity and decrease the capacity for economic growth.

Automation and mechanization of outdoor work could greatly reduce the economic costs of heat stress and counts as the most effective adaptation strategy in the agricultural- and construction sectors to climate change, but scenarios of potential future deployment of mechanization are in their infancy. Here we propose a Mechanization Deployment Index (MDI), which builds on the concept of constrained adaptative capacity reflecting a level of mechanization under the presence of socio-economic constraints compared to the maximum mechanization potential in the absence of constraints to adaptive capacity. By identifying socioeconomic variables within the framework of the Shared Socioeconomic Pathways (SSPs) that correlate with the current level of mechanization deployment, we are able to project five scenarios for future mechanization implementation alongside the SSPs. For the first time, we will be able to show how different socio-economic trajectories strongly modulate future heat stress impacts in the agriculture sector. These scenarios can be included in integrated assessments of climate change and improve the economic risk assessment in the 21^st century.

How to cite: van Maanen, N., Orlov, A., and Schleussner, C.-F.: Adaptation to extreme heat in the agricultural sector – SSP-dependent scenarios for mechanization deployment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4753, https://doi.org/10.5194/egusphere-egu22-4753, 2022.

Heatwaves are meteorological disasters that can damage human health and reduce agricultural production when extremely high temperatures are involved. A heatwave over the Korean Peninsula in 2018 broke the temperature and duration records kept since observations began. This event caused significant socio-economic damage. High pressure in the upper atmosphere over Eastern Europe and strong convection over the western North Pacific subtropical region are major fluctuations known to strengthen heatwaves over the Korean Peninsula. This study analyzed how these factors affected predictions of the 2018 heatwave over the Korean Peninsula using the sub-seasonal to seasonal (S2S) prediction model. Of the 11 models used in the S2S prediction project, 6 were selected: CMA, ECCC, ECMWF, KMA, NCEP, and UKMO. These models underestimated the daily surface temperature from July to August 2018 compared with observations, and the prediction errors gradually increased as lead-time increased. The model that simulated significant upper-level high pressure events in Eastern Europe and convection activities in the western North Pacific subtropical region predicted surface temperatures for the Korean Peninsula that were similar to the observed values. The increase in air pressure in the upper atmosphere over Eastern Europe is related to the recent expansion of areas affected by heatwaves in Europe. Even in the S2S models, the model that accurately predicted the characteristics of the heatwave showed excellent prediction performance for the Korean Peninsula. The increase in convection activities in the western North Pacific subtropical region increased when the amplitude of phases 4–6 of the Madden–Julian Oscillation (MJO) was large and they included many days. If the S2S model simulates the characteristics of the MJO accurately, the surface temperature prediction performance for the Korean Peninsula will increase. Therefore, it is very important for the S2S model to predict these two factors accurately, particularly when predicting heatwaves similar to that which occurred over the Korean Peninsula in 2018.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2020-01212.

How to cite: Wie, J. and Moon, B.-K.: Effect of Upper-Level High Pressure in Eastern Europe and Convection Activities in the Western North Pacific Subtropical Region on the Prediction of Heatwaves over the Korean Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6693, https://doi.org/10.5194/egusphere-egu22-6693, 2022.

Summer heatwave events have exhibited increasing trends, with sudden increases occurring since the early 2000s over northeastern China and along the northern boundary of Mongolia. However, the mechanism behind heatwaves remains unexplored. To quantitatively examine the feedback attribution of concurrent events related to surface temperature anomalies, the coupled atmosphere–surface climate feedback-response analysis method based on the total energy balance within the atmosphere–surface column was applied. The results demonstrate that the contributions of the latent heat flux and surface dynamic processes served as positive feedback for surface warming by reducing the heat release from the surface to the atmosphere because of deficient soil moisture based on dry conditions. Cloud feedback also led to warm temperature anomalies through increasing solar insolation caused by decreasing cloud amounts associated with anomalous high-pressure systems. In contrast, the sensible heat flux played a role in reducing the warm temperature anomalies by the emission of heat from the surface. Atmospheric dynamic feedback led to cold anomalies. The influence of ozone, surface albedo, and water vapor processes is very weak. This study provides a better understanding of combined extreme climate events in the context of radiative and dynamic feedback processes.

How to cite: Seo, Y.-W., Ha, K.-J., and Park, T.-W.: Feedback attribution to dry heatwaves over East Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6827, https://doi.org/10.5194/egusphere-egu22-6827, 2022.

 Extreme temperatures have a wide societal impact yet remain a major uncertainty in climate projections. Past studies have identified several drivers of heatwaves, including atmospheric blocking and soil moisture-atmosphere feedback. However, it remains unknown what limits the magnitude of extreme temperatures, and a quantitative understanding of heatwaves is lacking. Here we provide a theory of mid-latitude extreme temperatures based on a convective-instability mechanism. We formulate the upper bound of the surface temperature as a function of the temperature at the 500-hPa pressure level (T500), which is supported by observations and reanalysis data. Based on this theory, we project that the annual hottest daily maximum temperature (TXx) should increase by 1.9 K for each 1 K of increase in T500 over mid-latitude land if there is no evident drying or moistening of surface air on the annual hottest days. The observed TXx trend over the past four decades between 40°N-65°N is consistent with our projection. With T500 within 40°N-65°N increasing slightly faster than the global warming, the warming rate of TXx of this region will be on average around twice of the global warming if specific humidity does not change on the hottest days. However, TXx will increase at a faster rate over regions with a decrease in specific humidity on the hottest days, and vice versa.

How to cite: Zhang, Y. and Boos, W.: The upper bound of mid-latitude extreme temperatures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7118, https://doi.org/10.5194/egusphere-egu22-7118, 2022.

Extremes in temperatures not only directly affect the marine environment and ecosystems but also have indirect impacts on hydrodynamics and marine life. The role of heatwave events responsible for the occurrence and persistence of thermal stratification was analysed using a fully coupled hydrodynamic and wave model within the framework of the Geesthacht Coupled cOAstal model SysTem (GCOAST) for the North Sea. The model results were assessed against satellite reprocessed data and in situ observations from field campaigns and fixed MARNET stations. To quantify the degree of stratification, a potential energy anomaly over the water column was calculated. A linear correlation existed between the air temperatures and the potential energy anomaly in the North Sea excluding the Norwegian Trench and the area south of 54◦N latitude. Contrary to the northern part of the North Sea, where the water column is stratified in the warming season each year, the southern North Sea is seasonally stratified in years when a heatwave occurs. The influences of heatwaves on the occurrence of summer stratification in the southern North Sea are mainly in the form of two aspects, i.e., a rapid rise in sea surface temperature at the early stage of the heatwave period and a relatively higher water temperature during summer than the multiyear mean. Another factor that enhances the thermal stratification in summer is the memory of the water column to cold spells earlier in the year. Differences between the seasonally stratified northern North Sea and the heatwave-induced stratified southern North Sea were attributed to changes in water depth.

How to cite: Chen, W., Staneva, J., Grayek, S., Schulz-Stellenfleth, J., and Greinert, J.: The role of heatwave events on the occurrence and persistence of thermal stratification in the southern North Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7778, https://doi.org/10.5194/egusphere-egu22-7778, 2022.

We evaluate the contribution of the decadal to multidecadal variability in the North Atlantic climate system to impact-relevant extreme heat metrics over Europe, and how this contribution evolves in a warming world. To do this, we use the largest existing ensemble of a comprehensive, fully-coupled climate model: the 100-member Max Planck Institute Grand Ensemble (MPI-GE). MPI-GE has been shown to have one of the most adequate representations of the variability and forced response in observed temperatures in the historical record. Furthermore, the large ensemble size of MPI-GE provides the robust sampling of internal variability that is required to evaluate the contribution of variability on decadal to multidecadal timescales to low-probability, high-impact extreme events.

In our evaluation, we go beyond common metrics defining heatwave intensity or duration, and employ heat excess metrics that account for the cumulative intensity and persistence of heat per Summer beyond given thresholds. We use these cumulative heat metrics to assess excess dry heat as well as other impact-relevant aspects of heatwaves, such as hot and humid conditions and lack of night time cooling. Our preliminary results indicate that the contribution of the decadal variability in the North Atlantic, represented by the Atlantic Multidecadal Variability (AMV), contributes to differences in these metrics between positive versus negative AMV phases that are comparable to the forced changes due to anthropogenic global warming in parts of Europe. This potential for the exacerbation of such extreme conditions under positive AMV phases highlights the necessity for considering these decadal variations both in the attribution of past events as well as in our projections of future extreme heat.

How to cite: Suarez-Gutierrez, L., Müller, W. A., and Marotzke, J.: The Decadal Variability of Extreme European Heat, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10558, https://doi.org/10.5194/egusphere-egu22-10558, 2022.

Mainly caused by anthropogenic climate change occurring heatwaves have become more frequent and extreme throughout the 21th century. Summer heatwaves over Europe are mainly caused by positive phases of the North Atlantic Oscillation (NAO) and jet stream anomalies, subsequently causing atmospheric blocking over different parts of Europe. With this work we aim to define families of European heatwaves caused by different atmospheric regimes. In the long run this could help predicting European heatwaves and their length, intensity and spatial extend. To identify European heatwaves and their spatial extend we use the graph framework DeepGraphs. Within this framework every extreme heat day isconsidered a node and a heatwave is defined as the union of all nearest neighbour nodes (which are connected by edges). 

Two clustering steps are applied to cluster the heatwave into families depending on their length, season and spatial extend. 

Our results reveal a promising way to classify European heatwaves based on their atmospheric cause which could help forecasting heatwaves in the future.

How to cite: Hellmig, J., Strnad, F., and Goswami, B.: Identification of European heatwave families, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10642, https://doi.org/10.5194/egusphere-egu22-10642, 2022.

The Pacific Northwest heat wave is one of a series of record-shattering heat extremes that, based on the previous observational record, may have been deemed impossible. Here we address the question of whether the potential for such an extreme heat wave could have been foreseen using simulated physical climate storylines.

We use a novel approach, called ensemble boosting, in which a fully-coupled free-running climate model (CESM2) is used to develop physical storylines of very rare heat extremes under present-day conditions. In ensemble boosting, the most extreme events in an initial-condition large ensemble for the near future are re-initialized with slightly perturbed atmospheric initial conditions to efficiently generate events that are even more extreme, with the goal of sampling events with magnitudes that have not been seen before.

We demonstrate that, with this approach, CESM2 can efficiently simulate events that reach or even exceed the magnitude and duration of the 2021 Pacific Northwest heatwave anomaly. The atmospheric circulation anomalies associated with the most extreme simulated heat waves in the boosted ensemble are remarkably similar to the observed event. We further evaluate the anomalies in the surface energy and water budgets that contribute to the most intense simulated events. We conclude that based on this approach, heat waves unseen in the observational record can be simulated in models, at least in some regions. After probing this approach for the Pacific Northwest heatwave, we apply it to other mid-latitude regions where extreme heat events of much higher magnitude than has been observed are plausible in the near future.

The ensemble boosting approach is computationally efficient, and it preserves physical consistency both in time, in space and across variables. This has the major advantages that the drivers can be directly evaluated against observed events and the generated storylines can be used in impact studies that require physical consistency, e.g. for the evaluation of humid heatwaves or compound events, for assessing wildfire risks or for ecosystem modelling.

How to cite: Fischer, E., Beyerle, U., Gessner, C., Lehner, F., Pendergrass, A., Sippel, S., Zeder, J., and Knutti, R.: Probing the unfathomable: ensemble boosting for physical climate storylines of unseen heat extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11189, https://doi.org/10.5194/egusphere-egu22-11189, 2022.

With ongoing climate change, the number, frequency, and intensity of events of extremely hot days during summers called heatwaves are progressing. Vulnerability of the population is one aspect responsible for the risk induced by such heatwaves. In society, certain characteristics make one group of people more vulnerable to heatwaves than others, such as poverty, access to cooling facilities, age, gender etc. The current research identifies such vulnerability factors or indicators of population to help in devising heat management strategies. This research focuses on a small bucolic region (Guildford) in Surrey county of the United Kingdom as mostly risk or vulnerability factors are underestimated and ignored in such regions compared to city population. Twelve heat vulnerability factors or indicators (house type, sex, age, ethnicity, place density, access to central heating, residence type: communal, health condition, household composition, disability, accommodation tenure i.e. rented or owned, and education level) were selected after reviewing several literatures to include in the study based on their data availability. Census data on such vulnerability indicators at lower output scale were collected. Principal component analysis was performed, and four major principal components were identified from these 12 factors which explained most of the variance (82 %) in the data. The corresponding loading value of each of these factors were utilised to find heat vulnerability indices for each lower output area and these indices were mapped using QGIS. It was noted that not only people living in town centre which is generally considered hotter and so are highly vulnerable, but outskirt regions were also significantly vulnerable compared to other lesser vulnerable regions. Such a vulnerability map can help authorities for site focused heat mitigation strategies application, early warnings, and preparation during summers, particularly during excessively hot days i.e., heatwaves. Nature-based permanent solution can be encouraged in regions of such highly vulnerable identified regions. 

How to cite: Sahani, J., Debele, S. E., and Kumar, P.: Heat vulnerability assessment and mapping for a bucolic town in the UK, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13479, https://doi.org/10.5194/egusphere-egu22-13479, 2022.

Gode Bola^1,4, Raphael M. Tshimanga¹, Jeff Neal²,  Laurence Hawker², Mark A. Trigg³, Lukanda Mwamba⁴ , Paul Bates²

¹ Congo Basin Water Resources Research Center (CRREBaC) & Department of Natural Resources Management, University of Kinshasa, DR Congo

²School of Geographical Sciences, University of Bristol, United Kingdom

³School of Civil Engineering, University of Leeds, United Kingdom

⁴General Commission of Atomic Energy, Regional Center for Nuclear Study, Kinshasa, DR Congo

Flood disasters have always been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where the availability of in-situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc-seconds (~ 90 m), gridded population density data of 1 arc-second (~ 30 m) spatial resolution, and a spatial layer of infrastructure dataset are used to address flood risk at the scale of the Congo Basin. The global flood data provide different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75 and 100 years). Of the 525 administrative territories, 374 are exposed to fluvial floods, and 38 (10 %) of them are categorised as risk hotspots. Analysis shows that the most exposed territories represent 1% of total exposure which is estimated at 2.65% of the basin’s population. This study demonstrates the first and potentially most important stage in developing flood responses by determining the flood hazards areas and the population/infrastructures that would be exposed. The flood risk maps produced in this study provide information necessary to support policy decisions of flood disasters prevention, including prioritisation of interventions to reduce flood risk in the CRB.

Keywords: Flood hazard, Risk assessment, Return period, Congo River Basin

How to cite: Gode, B. B.: Multi return periods flood hazards and risks assessment in the Congo River Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-248, https://doi.org/10.5194/egusphere-egu22-248, 2022.

Flooding is the most damaging natural hazard in terms of economic and population affected. Hydrological-hydraulic models are essential tools for evaluating the risks associated with flooding since they provide a physically based approach. In this work, we propose a novel approach that takes advantage of the coverage advantages of large-scale modeling and the accurate representation of local modeling, where high-resolution data are available. A dynamic downscaling framework, so-called hydraulic zoom, has been created by coupling the local relevant discharge estimation of the large-scale models with the detailed local representation of the reach-scale models. The large-scale hydrological model (MGB) is employed for estimating the inflow, rainfall excesses, infiltration, and evaporation from open water in order to use as input into an area in which the flow is solved through the full shallow waters formulation. The HEC-RAS 2D 6.1 is applied for solving the 2D dynamic equations. Besides, HEC-RAS enables forcing rainfall excess distributed inside the 2D area by the rain-on-grid approach while also allowing incorporate evaporation and infiltration. 

The hydraulic zoom is applied in the Itajai-Açu river basin of 15000 km² in Southern Brazil in the Santa Catarina State. The 2D area is about 833.6 km², considering  95 km of the main river until the outlet into the sea. The 2D area modeled is highly prone to floods, recording flood events with more than 53 deaths and more than 1 million affected people only between 1983 and 2011.

Estimations from MGB and from HEC-RAS 2D (fed with the MGB outputs) are compared against observed water surface level (WSE), WSE anomalies, and flood extent. The results reveal that streamflows estimated by a regional hydrological model can be incorporated into a local model improving in mean the estimations in about 41% (0.8 m) for WSE, 29% (0.35m) for WSE anomalies, and 10% of the Fit metric for flood extent. This hydraulic zoom framework reveals greate potential of producing high-resolution flood hazard maps allowing also representing pluvial floods, with regional distribution but local resolution. 

How to cite: Narváez, G. and Paiva, R.: Hydraulic zoom: a hydrological/hydrodynamic downscaling framework from regional to local scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-389, https://doi.org/10.5194/egusphere-egu22-389, 2022.

Abstract

Flood inundation and hazard maps have played various crucial roles in terms of municipal hazard planning, timely flood control countermeasure operation, economic levee design, and developing flood forecasting or nowcasting systems. Given that the riparian areas prone to flood conventionally imposed special cares to justify applications of recently available flood inundation or hazard assessment numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. However, laborious and time & cost-consuming processes were required to proficiently produce inundation and hazard maps, which includes preparation of geometric and hydrologic data as input for the targeted numerical model, executing the model and post-processing, and inundation and subsequent hazard mapping. For example in Korea, field surveyed geometric dataset are provided in CAD format and should have to be manually converted into cross-sectional information compatible with HEC-RAS as a numerical model, where such dataset are not managed in centralized and standardized database. Then, flood inundation and hazard maps are generated one by one based on flood stage heights simulated from the HEC-RAS, where additional tools such as HEC-GeoRAS or manual drawing against DEM are usually applied. In order to efficiently and cost-effectively provide a series of flood inundation and hazard maps automatically with minimum practitioner involvement, this study demonstrates a set of open-source based tools that automated flood and hazard mapping processes as follows: a) parse CAD files containing geometric surveys like cross-sections and store them into server-based Arc River database approachable through website; b) retrieve geometric information using RiverML from Arc River and implicitly make them compatible with HEC-RAS input format; c) execute the HEC-RAS with some designated boundary conditions and various flood discharge; d) parse HEC-RAS output in binary format and draw flood inundation and hazard map on a given DEM through a developed add-on in QGIS using Python. We found that the proposed entire autonomous processes substantially enhanced efficiency and accuracy for flood mapping. The spatial accuracy of flood inundation and hazard map after applying above processes were validated throughout comparison with an inundation trace map acquired from typhoon Nari, 2007, in Hancheon basin located in Jeju Island, Korea, where a series of inundation and hazard maps were comprehensively investigated to track the burst of flood in the extreme flood events.

Acknowledgment

This work was supported by the US Geological Survey Cooperative Grant Agreement #G19AC00257 and by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (21AWMP- B121092-06).

How to cite: Kim, K., You, H., Kim, D., and Gwon, Y.: QGIS-based Autonomous Process and Arc River Data Repository for Efficient Flood Inundation and Hazard Mapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-687, https://doi.org/10.5194/egusphere-egu22-687, 2022.

This study proposes an innovative idea to reduce scour in river structures via air-bubble screens, which does not provoke a significant impact on the ecological environment. Check dam is one of the most popular river facilities and is selected as the research target of this study. The scouring problem on the downstream side of check dam may damage its own safety and therefore, preventing the check dam from souring has been a challenge task for years. To lessen the safety impact from scouring, the existing methods often rely on using reinforced concrete structures that often, does not solve the problem but induces a series of scouring problem. Further, reinforced concrete structure may damage the river ecological environment during and after the construction. On the other hand, air-bubble screen may provide an alternative solution in solving the scouring problem without interrupting the environment. A scaled-check dam model using flume channel at Hydrotech Research Institute in NTU is conducted, and then the FLOW-3D is used to carry out numerical simulation to evaluate the effectiveness of the air-bubble screen in reducing the depth and range (or volume) of the scours. Results shown that air-bubble screen is able to effectively reduce the check dam scours. Based on results from experiments and simulations, the design principles for air-bubble screen are provided as a reference for future practice. 

How to cite: Liao, K.-W. and Wang, Z.-Z.: Investigation of Air-Bubble Screen on Reducing Scour in River Facility, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1137, https://doi.org/10.5194/egusphere-egu22-1137, 2022.

The disastrous flood of July 2021 in Germany has shown that forecasts of river discharge or water levels at selected gauges do not provide sufficient information for timely and location specific warning of the population and targeted disaster management actions. The gauge forecasts as well as the available flood hazard maps were insufficient to assess the flood severity in downstream areas. In order to provide more actionable flood forecasts, the hydraulic model RIM2D was developed and setup for the Ahr river. It solves the inertial formulation of the shallow water equations on a regular grid, and is highly parallelized on Graphical Processor Units (GPUs). Moreover, the modelling concept is parsimonious and allows for fast model setup. We show that hydraulic simulations driven by the available hydrological gauge forecasts would have been feasible with short simulation duration. It would be possible to provide spatially explicit forecasts of inundation depths and flow velocities with sufficient lead time. Moreover, we also show that impact forecasts indicating human instability in water and building failure hazard can be additionally provided in operational mode. We argue that using these hydraulic and impact forecasts would have had a substantial impact on the flood alertness of the population and responsible authorities, enabling a better early warning and disaster management. This could eventually save lives during future extreme flash floods.

How to cite: Apel, H., Vorogushyn, S., and Merz, B.: Operational hydraulic flood impact forecasting with RIM2D for improved disaster management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1424, https://doi.org/10.5194/egusphere-egu22-1424, 2022.

With the acceleration of urbanization, urban pluvial flooding seriously threatens urban sustainable development and human life. It is widely accepted that various landscape elements contribute to the magnitude of urban pluvial flooding. Considerable efforts investigated the universal mechanism of urban pluvial flooding by regarding the whole study area as spatial homogeneous while ignoring its local specific mechanism. The spatially heterogeneous effects of landscape elements on urban pluvial flooding remain poorly understood. Additionally, it is still unclear how the interactive effects of landscape elements affect urban pluvial flooding. In most practical situations, urban pluvial flooding is affected by multiple factors, rather than by a single factor alone. These shortcomings make it impossible to formulate urban pluvial flooding mitigation measures based on the relative contribution of various landscape elements on urban pluvial flooding. To shed some light on this topic, an innovative method that integrated the all subsect regression model, cubist regression tree, and geographical detector model is presented to spatially explicit the heterogeneous forces driving urban pluvial flooding variation and identify the pluvial flooding dominant driving forces with different local conditions. By comparing with two other commonly used regression methods (global regression model, spatial lag model), the proposed method can fully quantify this spatial non-stationarity mechanism and spatially explicit the local driving forces. Urban pluvial flooding dominant driving factors and their contribution vary with the local site conditions. Even for the same dominant factor, its contribution to pluvial flooding varies considerably in different watersheds. Based on this, local authorities can develop site-specific urban pluvial flooding mitigation strategies according to the dominant factors in different areas. The results of this study extend our scientific understanding of the site-specific mechanism of urban pluvial flooding, providing useful information for formulating more targeted and effective urban pluvial flooding mitigation strategies with different local conditions, rather than a “one-size-fits-all” policy.

How to cite: Zhang, Q., Wu, Z., Guo, G., and Tarolli, P.: How to develop site-specific urban pluvial flooding mitigation strategies? A new approach to investigating the spatial heterogeneous driving forces of urban pluvial flooding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1573, https://doi.org/10.5194/egusphere-egu22-1573, 2022.

The monitoring of river water-levels is essential to study floods and mitigate their risks. However, it is difficult to obtain accurate measurements of river water-levels: indeed, the river gauges commonly used to measure these levels can be overwhelmed during flood events, and their number is declining globally [1,2]. This means that the monitoring and study of floods relying on gauge station measurements can only be based on sparse and possibly inaccurate river water-level data distributed unevenly along the rivers, sometimes several kilometres away from the location of interest.

We investigate if deep learning can be used to monitor river water-levels in a more flexible and efficient way. More specifically, we apply two deep learning approaches on river cameras, which are CCTV cameras commonly used to monitor the surroundings of rivers and could be easily installed at new locations. The first approach [3,4] relies on transfer learning to train water segmentation networks able to find the water pixels within the camera images and use the number of water pixels within (regions of) the images to monitor the relative evolution of the river water-level. The second approach is based on the creation of a large dataset of 32,715 images annotated with distant gauge water-level data in order to accurately train networks able to produce river water-level indexes independent from the field of view of the cameras. 

We show that both approaches can be used as sources of river water-level data. The first approach is able to produce river water-level indexes highly correlated with ground truth river water-levels (Pearson correlation coefficient >0.94). While the second approach is less accurate (Pearson correlation coefficients between 0.8 and 0.94), it is able to produce calibrated indexes independent from the field of view of the camera. 

[1] Mishra, A. K., and Coulibaly, P. (2009), Developments in hydrometric network design: A review, Rev. Geophys., 47, RG2001, doi:10.1029/2007RG000243.

[2] Global Runoff Data Center (2016).  Global runoff data base, temporal distribution of available discharge data.  https://www.bafg.de/SharedDocs/Bilder/Bilder_GRDC/grdcStations_tornadoChart.jpg. Last visited:2021-04-26.

[3] Vandaele, R., Dance, S. L., & Ojha, V. (2020, September). Automated water segmentation and river level detection on camera images using transfer learning. In DAGM German Conference on Pattern Recognition (pp. 232-245). Springer, Cham.

[4] Vandaele, R., Dance, S. L., & Ojha, V. (2021). Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning. Hydrology and Earth System Sciences, 25(8), 4435-4453.

How to cite: Vandaele, R., Dance, S. L., and Ojha, V.: Deep learning approaches to study floods through river cameras, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2344, https://doi.org/10.5194/egusphere-egu22-2344, 2022.

Flood is one of the most devastating natural disasters that cause enormous socioeconomic and environmental destruction. The severity of flood losses has evoked emphasis on more comprehensive and vigorous flood modeling techniques for alleviating flood damages. Flood vulnerability in Navsari is intensifying due to urbanization, industrialization, and population growth. Although there has been a significant increase in research on flood assessment at a local scale in Navsari, there remains a lack of tools developed which utilize the risk map of the city. In response to this prerequisite, in this study we have employed a GIS-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis model to develop a flood risk map for Navsari city in Gujarat, India, to determine the vulnerable areas that are more susceptible to flooding. To estimate the extent of flood hazard, vulnerability, and risk intensities in terms of area covered, the city was divided into ten zones (i.e. NC1 to NC10) and classified into five classes: very high, high, moderate, low, and very low. A total of seven hazard forming spatial layers (i.e. slope, elevation, soil, rainfall, flow accumulation, distance to a river, and drainage density) and seven vulnerability forming spatial layers (i.e. female population, population density, land use, household, distance to hospital, road network density, and literacy rate) were appraised for evaluating the risk of flooding. The generated flood risk map has been compared with the extent of flood calculated based on field data collected from thirty-six random places. The outcome of the model unveiled the capability of the TOPSIS model since it capitulate low RMSE value varied between 0.95 to 0.43 and high R square value ranged from 0.78 to 0.95. The zones indicated under ‘high’ and ‘very high’ categories (i.e. NC8, NC6, NC4, NC1, NC7, and NC10) demand abrupt flood control action to alleviate the severity of flood risk and subsequent damages. The approach implemented in the study can be applied to any flood-sensitive region around the globe to accurately evaluate the risk of flood. Lastly, flood risk mapping using TOPSIS based geospatial techniques divulge the novel and efficacious approach, especially for data-sparse regions.

How to cite: Pathan, A. I., Agnihotri, Dr. P. G., Said, Dr. S., Patel, Dr. D., Prieto, Dr. C., Mohsini, U., Patidar, N., Gandhi, Dr. P., Jariwala, K., Đurin, B., Azimi, M. Y., Rasuli, J., Dummu, K., Raaj, S., Shaikh, A. A., and Salihi, M.: Flood risk mapping using multi-criteria analysis (TOPSIS) model through geospatial techniques- A case study of the Navsari city, Gujarat, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2418, https://doi.org/10.5194/egusphere-egu22-2418, 2022.

An intense Mediterranean hurricane (medicane Apollo) hit many countries during the last week of October 2021. Up to 7 people died because of the floods caused by the cyclone in Tunisia, Algeria, Malta and Italy. Apollo persisted over the same Mediterranean area from 24 October to 1 November 2021 producing flash-flood and flood episodes with very intense rainfall events, especially over eastern Sicily and Calabria on 25-26 October 2021. CIMA Foundation operated in real-time with a complete forecasting chain to predict both the Apollo medicane weather evolution and its hydrological and hydraulic impacts. The work provides support to the Italian Civil Protection Department early warning activities and in the framework of the H2020 LEXIS and E-SHAPE projects. The complete meteo-hydrological forecasting chain is composed by the cloud-resolving WRF model assimilating radar data and in situ weather stations (WRF-3DVAR), the fully distributed hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D. This work presents the forecasting performances of each model involved in the CIMA meteo-hydrological chain, with focus on both very short-range temporal scales (up to 6 hours ahead) and short-range forecasts (up to 48 hours ahead). The WRF-3DVAR model results showed very good predictive capability of the most intense rainfall events in terms of timing and location over Catania and Siracusa provinces in Sicily. Thus, enabling also very accurate discharge peaks and timing predictions for the creeks hydrological network peculiar of eastern Sicily. Starting from the WRF-3DVAR model predictions, the daily AUTOWADE tool run using Sentnel-1 (S1) data, was anticipated with respect to the scheduled timing to quickly produce a flood map (S1 acquisition performed on 25 October 2021 at 05UTC, flood map produced on the same day at 13UTC). Furthermore, an ad hoc tasking of the COSMO-SkyMed satellite constellation was performed, again based on the on the WRF-3DVAR predictions, to overcome the S1 data latency on eastern Sicily during the period 26-30 October 2021. Finally, the resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC. Due to the probable frequency increase of such extreme events (in light of the ongoing climate change), the application of a complete meteo-hydrological chain presented in this work can pave the way for future applications in early warning activities in the Mediterranean areas.

How to cite: Lagasio, M., Fagugli, G., Ferraris, L., Fiori, E., Gabellani, S., Masi, R., Mazzarella, V., Milelli, M., Parodi, A., Pignone, F., Puca, S., Pulvirenti, L., Silvestro, F., Squicciarino, G., and Parodi, A.: A complete meteo-hydrological chain to support early warning systems from weather scenarios to flooded areas: the Apollo medicane use case, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2622, https://doi.org/10.5194/egusphere-egu22-2622, 2022.

Floods are natural hazards with severe socio-economic and environmental impacts on affected areas and societies every year. A chain of different processes being involved in a flooding - characterized by precipitation, topography, land use etc. - complicates the understanding of the dynamics of a flood. However, the prediction of probabilities, flood hazards, flooding extents, dike failure, consequences and understanding the ongoing processes during a flood event are important issues in flood risk management. Computational modelling is a key method in supporting flood risk management and tackling the mentioned challenges.

While several computer-based models for assisting flood risk management exist, typically they concentrate on only one component of the flood risk analysis chain such as rainfall generation, hydrological/hydraulic modelling or damage analysis. They do not merge the other components on one platform which may result in encapsulated conclusions. In recent years the availability of higher detailed data, larger study domains, more computational power and more innovative models paved the way for more effective solutions.

In this work we present ProMaIDes (Protection Measures against Inundation Decision support), an open-source, free software package for risk-based evaluation of flood risk mitigation measures¹. The software package consists of numerous relevant modules for a flood risk analysis in riverine and coastal regions: the HYD-module for a hydrodynamic analysis, the DAM-module for an analysis of consequences (including economical damage, consequences to people and the disruption of critical infrastructure services), the FPL-module for the reliability analysis of dikes and dunes as well as a combining RISK-module and the decision support MADM-module. To support a user-friendly model setup, visualization of input and data results, a connection with the free QGIS-system is established by QGIS-plugins and a PostgreSQL-database as data-management system. A detailed online documentation featuring theory, application and programming is available². A community of users is currently set-up.

In order to give a better understanding and to demonstrate the capabilities of ProMaIDes, the tool itself, but also the modules combined with case studies are shortly presented.

¹ https://promaides.h2.de

² https://promaides.myjetbrains.com/youtrack/articles/PMID-A-7/General

How to cite: Bachmann, D., Schotten, R., and Khosh Bin Ghomash, S.: Introducing ProMaIDes: A State-of-the Science Flood Risk Management Tool, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2696, https://doi.org/10.5194/egusphere-egu22-2696, 2022.

Slope instability of river dikes during floods is often driven by the evolution of groundwater pressures. Despite the temporal nature of high river water levels, pressure heads during floods are often assumed to reflect steady-state seepage conditions, leading to conservative estimates of dike slope safety. Here, we investigate the influence of transient groundwater conditions that result from variable flood wave shapes on probabilistic safety estimates of slope stability. We have sampled a large number of flood waves, aiming to maximize the variability in the flood wave shapes, and used them in a modeling chain consisting of a hydrological model (MODFLOW) and a probabilistic dike slope safety assessment (FORM). We compared the resulting time-dependent probabilistic dike safety for inner (landward) slope and outer (riverward) slope stability with the current flood safety assessment in the Netherlands. This comparison showed that current methods based on steady-state and analytical solutions seem to underestimate dike safety. Other methods, based on a design discharge wave, are more consistent with the multi-flood wave dike reliability, but their error increases at extreme water levels. In line with the temporal component of variable flood water levels, the failure probability also has a strong temporal component. Our results indicate that the highest failure probability always occurs after the river water level peak, with a delay of up to 15 days for both inner slope and outer slope stability. In addition, the uncertainty in the shape of the flood wave can be as important as the uncertainty in the geomechanical material properties for explaining the variation in dike failure probabilities. Therefore, this research strongly suggests that transient-groundwater conditions as a function of variable flood wave shapes should be incorporated in dike safety assessment. As a first step, we recommend further research on the occurrence probability of the most influential waveform characteristics, being the total flood wave volume (for the inner slope) and the total water level decrease after the peak (for the outer slope).

How to cite: van Woerkom, T., van der Krogt, M., and Bierkens, M.: On the incorporation of transient groundwater conditions resulting from variable flood wave shapes in probabilistic slope stability assessments of dikes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3099, https://doi.org/10.5194/egusphere-egu22-3099, 2022.

Flood is widely recognized as the most common and frequent natural phenomenon which currently threatens huge damage worldwide. The Prek Thnot River (PTR) in Cambodia is one of the flood-prone areas where severe floods occur every year and cause damage to residents downstream. This study aims to evaluate the forecasting performance of flooding in the PTR using near real-time datasets from satellite observation (i.e., GSMaP and GPM) and forecasted rainfall from NICAM-LETKF numerical weather prediction (so called GSMaPxNEXRA) dataset. GSMaPxNEXRA data is produced by Global Cloud Resolving Model with Data Assimilation. This study used a fully distributed rainfall-runoff-inundation (RRI) model for river discharge and water level simulations. The RRI model was calibrated and validated with gauged observed rainfall during flood events in 2000, 2001, 2007, 2010, and 2020 with satisfactory and acceptable results. The most recent flood event in 2020 was considered to evaluate real-time flood forecasting. The near real-time simulation indicated the results discharge and water level with statistical indicators KGE = 0.80 and 0.07 and r² = 0.83 and 0.87 for GPM and KGE = 0.48 and -0.12 and r² = 0.54 and 0.67 for GSMaP. The GPM rainfall product outperforms GSMaP rainfall in the PTR. Flood forecast from the GSMaPxNEXRA showed an accuracy with KGE = 0.79 and r² = 0.89 (1-day forecast) to KGE = 0.66 and r² = 0.76 (5-day forecast). On the other hand, the performance of 1-day to 5-day forecast indicated with coefficient of extrapolation (CE) and coefficient of persistence (CP) between CE = -2.62 and CP = -2.65 for 1-day forecast to CE = 0.71 and CP = -0.06 for 5-day forecast. To conclude, real-time flood forecasting in the PTR was successfully assessed and evaluated in this study; however, the accuracy of flood prediction should be further improved in the future by considering data assimilation and machine learning.

How to cite: Try, S., Sayama, T., Sok, T., Phy, S. R., and Oeurng, C.: Real-time Flood Forecasting Using Numerical Weather Prediction System Through NICAM-LETKF Data Assimilation in the Prek Thnot River, Cambodia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3374, https://doi.org/10.5194/egusphere-egu22-3374, 2022.

This study presents a forecasting model for pluvial flooding in the city of Zadar, Croatia, where a huge mesoscale convective system recently caused massive pluvial flooding and widespread property damage. Flood forecasting approaches based on hydrologic-hydraulic models require a large set of accurate data to provide reliable simulations. They also require many simulations, which can be computationally expensive and time consuming. Therefore, we are investigating the possibility of using a data-driven approach based on local news reports of pluvial flooding combined with a local high-resolution rain gauge. To this end, we considered two different computational approaches. The first - a conventional one - is based on rainfall threshold curves that define the critical rainfall depth for different time periods above which flooding is likely to occur. The second approach is based on machine learning and a classification problem - predicting whether accumulated rainfall depths over different time periods will lead to pluvial flooding. For the second approach, we considered 10 different methods that belong to five categories of machine learning typically used for classification problems. They are logistic regression, support vector machine, discriminant analysis, decision trees, and nearest neighbours. After a careful analysis, we defined rainfall threshold curves for Zadar that can be used for an early warning system and flood forecasting. We show that some machine learning models can provide slightly more accurate predictions than the threshold curve, with quadratic discriminant analysis being the most successful method for this purpose. Overall, this study shows that flood forecasting based on news reports in the city of Zadar can be a reliable approach. The analysis conducted in this study has laid the foundation for the implementation of an early warning system and pluvial flood forecasting in the Croatian coastal area.

How to cite: Krvavica, N., Horvat, B., Marinović, I., and Šiljeg, A.: Rainfall threshold curves and machine learning approaches for pluvial flood forecasting based on local news reports in Croatia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4201, https://doi.org/10.5194/egusphere-egu22-4201, 2022.

Surat is a district that has seen numerous floods and high rainfall over the last two decades. The solution to the problem, and the primary aim of this study, is to construct a storage facility, such as a dam, as part of flood prevention measures. The concept of multi-criteria decision making (MCDM) is now widely employed for everyday real-life challenges. Recent advancements and diverse approaches in geographic information systems (GIS) and remote sensing, along with the MCDM technique, will enable us to make an informed decision about where to build a dam site location model (DSLM). The Analytic Hierarchy Process (AHP) is the most frequently utilised MCDM technique for resolving water-related issues. To produce DSLM, ten thematic layers were considered: precipitation, stream order, geomorphology, geology, LULC, soil, distance to road, elevation, slope, and major fault fracture. Precipitation and stream order were the two most important elements affecting the DSLM. The weights of the thematic map layers were determined using the analytical hierarchy process (AHP) technique. These thematic maps and weights are used to perform overlay analysis, resulting in a suitability map with five classes ranging from high to low suitability. Three main sites have been selected as the best candidates for the construction of a new dam. By implementing this low-cost strategy, we may be able to reduce the amount of effort required in the traditional method of dam site selection while increasing decision-makers' accuracy. Approximately 14% of the Surat district is classified as a very high adaptability area, while 27.2 percent is classified as a high suitability area. This method can be applied all over the world to locate possible dam sites, which can be helpful for flood mitigation measures. In addition to that, the presented approach unveiled the scientific method for flood mitigation measures, which are in immediate demand all over the globe, especially in data-scarce regions.

How to cite: Raaj, S., Pathan, A., Mohseni, U., Patidar, N., jariwala, K., Kachhawa, N., Agnihotri, Dr. P. G., Patel, Dr. D., Prieto, Dr. C., Gandhi, Dr. P., and Đurin, Dr. B.: An Integrated Approach of AHP-GIS Based Dam Site Suitability Mapping - A Noval Approach for Flood Alleviating Measures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4210, https://doi.org/10.5194/egusphere-egu22-4210, 2022.

Both frequency and severity of urban pluvial floods have been increasing due to rapid urbanization and climate change. Hydrological and two dimensional (2D) hydrodynamic models are still too computationally demanding to be used for real-time applications for large urban areas (i.e. flood management scale). As an alternative, data-driven models could be used for flood susceptibility mapping. This study evaluated and compared the performance of image-based models represented by a convolutional neural network (CNN) and point-based models represented by an artificial neural network (ANN), a random forest (RF) and a support vector machine (SVM) with regard to the spatial resolution of the input data. We also examined model transferability. Eleven variables representing topography, anthropogenic aspects and precipitation were selected to predict flood susceptibility mapping. The results showed that: (1) all models were skilful with a minimum area under the curve AUC = 0.87. (2) The RF models outperformed the other models for all spatial resolutions. (3) The CNN models were superior in terms of transferability. (4) Aspect and elevation were the most important factors for flood susceptibility mapping for image-based and point-based models respectively.

How to cite: Seleem, O., Ayzel, G., Costa Tomaz de Souza, A., Bronstert, A., and Heistermann, M.: Towards Urban Flood Susceptibility Mapping Using Data-Driven Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4345, https://doi.org/10.5194/egusphere-egu22-4345, 2022.

Coastal areas are directly vulnerable to natural disasters like floods, which causes massive damages to natural resources and human resources. Dam induces floods can be devastating for surrounding low lying areas. Bharuch is a district with substantial industrial growth, and intended human activities were causing an imbalance in natural resources for planning and fulfilling other demands. Floods can be devastating concerning the Bharuch district's social, economic, and environmental perspectives. The proper analysis becomes very important to reduce the impact and find mitigation measuring techniques. I did flood susceptibility mapping using the frequency ratio model for the six sub-districts of the area. The susceptibility of a flood was analysed using the frequency ratio model by considering nine different independent variables (land use/land cover, elevation, slope, topographic wetness index, surface runoff, lithology, distance from the main river, soil texture, river network) through weighted-based bivariate probability values. In total, 151 historical floods were reported. I took locations for this study, from which I used 72 locations for susceptibility mapping. I combined the independent variables and historic flood locations to prepare a frequency ratio database for flood susceptibility mapping. The developed frequency ratio was varied from 0 to 13.2 and reclassified into five flood vulnerability zones, namely, very low (less than 0.99), low (0.99-2.04), moderate (2.04-5.58), high (5.58-13.2) and very high susceptibility (more than 13.2). The flood susceptibility analysis will be a valuable and efficient tool for local government administrators, researchers, and planners to devise flood mitigation plans.

Keywords: Flood Susceptibility · Flood · Frequency Ratio · Vulnerability · Bharuch

How to cite: Jariwala, K., Agnihotri, P., Patel, D., Pathan, A., Mohseni, U., and Patidar, N.: Application of frequency ratio modelling technique for predictive flooded area susceptibility mapping using remote sensing and GIS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4771, https://doi.org/10.5194/egusphere-egu22-4771, 2022.

Flood is one of the most devastating natural disasters. The damages of flood usually vary with the consideration of different factors (depth, duration, velocity, materials of infrastructures) of flooding. Therefore, flood damage estimation is a complex process. Most of previous studies considered only flood depth in developing flood damage functions for residential houses. However, the consideration of other flood parameters such as flood duration and flood velocity are also crucial to estimate flood damage more reliably. Therefore, this study aimed to consider various flood parameters such as flood depth, flood duration, and flood velocity in development of flood damage functions for residential houses.  In this study, the Teesta River Basin in Bangladesh was chosen as the study area. A detailed household questionnaire survey was conducted in flood-affected areas of Lalmonirhat and Rangpur districts (administrative unit of Bangladesh) to collect data of 2017 and 2019 flood events.  Most of the houses in the surveyed flood-affected areas are composed of mud base and side wall of corrugated iron sheets (called “MC type”). For each house, the questionnaire aimed to identify the flood information (flood depth, flood duration, the qualitative representation of flood velocity), household structure information (area, plinth height, ceiling height), structural damage mechanism and the required amount of material with labor work to repair the damage after each flood event. Using the survey data, we have developed depth-damage functions for MC type of house by considering different flood velocity and flood duration combinations. The newly developed depth-damage functions can generalize thresholds of flood depth, flood velocity and flood duration that are responsible for specific type of structural damages (mud removal from the base, mud removal from the base together with side wall instability, full structure instability) of MC type house. Finally, a grid-based approach through the integration of new depth-damage functions with hydrologic-hydraulic model (RRI) and Nays2DFlood Solver (iRIC software) simulation results has been developed to estimate the total flood damage for MC type houses in flood-affected areas of the Teesta River Basin. This comprehensive method can be easily used to derive the depth-damage functions and estimation of total damage for other types of houses if enough surveyed data can be obtained from the field.

Keywords: Flood damage estimation, Depth-damage function, MC type house, Hydrologic-hydraulic model

How to cite: Haque, S., Ikeuchi, K., Shrestha, B. B., and Minamide, M.: Generalizing flood damage mechanism processes of MC Type houses by developing comprehensive flood damage estimation method for Teesta River Basin, Bangladesh, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5110, https://doi.org/10.5194/egusphere-egu22-5110, 2022.

In Québec, Canada, extraordinary spring conditions in 2017 and 2019 have provided major incentives for the provincial government to commission the updating of current flood inundation maps. Indeed, some of these maps, dating back as far as the 1980’s, do not adequately reflect actual flood risks. Classical hydrodynamic models, such as HEC-RAS (1D, mixed, or full 2D), are generally used to perform the mapping, but they do require significant expertise, hydrometric data, and high-resolution bathymetric surveys. Given the need for updating flood inundation maps and reducing the associated financial costs (data collection and human resources), there is an emerging demand for simplified conceptual methods. In recent years, several models have been developed to fulfill this need, including the geomatic Height Above the Nearest Drainage (HAND) method which solely relies on a the digital elevation model (DEM).

This project aims at expanding upon earlier work carried out with HAND which was designed to compute the required water height to flood any DEM pixel of a watershed. The information provided by HAND along with the application of the Manning equation allow for the construction of a synthetic rating curve for any homogeneous river reach. This methodological approach has been used to come up with first-instance flood inundation mapping of large rivers in conterminous United States with a matching rate reaching 90% when compared to the use of HEC-RAS. However, to our knowledge, this has not been assessed for small rivers, and our goal here is to validate this simplified conceptual approach using two small watersheds (less than 200 km²) in Quebec.

The results of this study show that the ensuing synthetic rating curves for small rivers are consistent with river hydraulics (Froude numbers meeting the subcritical flow requirement behind the use of Manning equation) and in-situ derived rating curves of six hydrometric stations. The results also demonstrate the relevance of this approach when comparing the use of HAND with HEC-RAS 2D for the hydrographic networks of the two watersheds given flows simulated by a semi-distributed hydrological model (i.e., HYDROTEL). For this demonstration, the forcing data include the precipitation and temperature time series of the Canadian precipitation analysis system. Preliminary results indicate good performances (hitting rate above 60%) for the pilot river watersheds which are located in a data-sparse region.

While the preliminary results illustrate the potential to produce first-instance flood inundation mapping solely based on a DEM and simulated streamflows, future work will contribute to the advancement of our understanding of flood risks in poorly-gauged watersheds. HAND-derived inundation mapping will be further analyzed and compared to HEC-RAS-2D applications (i.e., the diffusion-wave equations), although the presence of complex urban infrastructures such as culverts, pipes, or bridges may represent a major challenge for the proposed approach. We believe a modeling continuum based on hydrological modeling and HAND-derived flood inundation mapping will inform and strengthen land management planning and contribute to the elaboration of public safety protocols.

How to cite: El Batti, Z., Foulon, E., Gordon, C., and Rousseau, A.: Development of a loosely coupled geomatic-hydrological modeling approach for flood inundation mapping in small watersheds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6322, https://doi.org/10.5194/egusphere-egu22-6322, 2022.

Floods, the most frequent and severe of natural disasters worldwide, inflict significant social, environmental and fiscal impacts, including: loss of human life, damage to natural habitats and damage to infrastructure. Flood risk mapping can be used to mitigate these impacts as it provides a holistic approach to identifying flood prone areas by simultaneously considering socioeconomic and environmental indicators. This research compares the performance of two multi-criteria decision making methods, and one Machine Learning (ML) method in the development of flood risk mapping. This approach was first developed and validated for the Don River watershed in the Greater Toronto Area and subsequently extended to several other watersheds across Southern Ontario. Remote sensing data such as Digital Elevation Models and landuse and lancover datasets were used to develop the environmental flood hazard extent, and combined together with socioeconomic indicators, flood risk maps were developed using subjective and objective weighting schemes in a GIS analysis. The subjective maps were produced using the Analytical Hierarchy Process (AHP), the objective maps were produced using the Shannon Entropy method and the ML maps were produced using Artificial Neural Networks. The accuracy of these maps was compared against the floodplain map of the Don River. For a range of flood risk severity, where 1 was very low risk and 5 was very high risk, the AHP maps were superior in identifying areas where flood risk severity was 4 or greater. Conversely, the Entropy maps were superior in identifying areas where flood hazard risk was 5, however the difference in accuracy for both scenarios was marginal between the two methods. The accuracy of the ML maps showed marginal superior performance under both scenarios in comparison to the multi-criteria maps. Additionally, the uncertainty in the combination of flood risk indicators was quantified through a sensitivity analysis focusing on the discretization of the number of classes in each indicator dataset. The outcome of this research provides an accurate and simplified alternative to using hydrological and hydraulic models, especially when insufficient data limits the use of hydrological and hydraulic models. Future research should focus on an optimisation approach to the discretization of classes in indicator datasets.

How to cite: Khalid, R. and Khan, U. T.: A comparison of multi-criteria and machine learning weighting for flood risk assessment in the Southern Ontario, Canada, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6574, https://doi.org/10.5194/egusphere-egu22-6574, 2022.

This research examined a new method for coupling flood flow modelling with the machine learning (ML)-based land cover detection from the Unmanned Aerial Vehicle (UAV) and satellite river imagery. We examined a 2 km river channel section with a gravel bed in the Kurobe River, Japan. The method used Random Forests (RF) for riverine land cover detection with the satellite images' RGBs and Near InfraRed (NIRs). In the process, the UAV images were used effectively to train the RF in several small portions of the river channel where the types of riverine land cover were precise. Using these UAV images with the corresponding feature values (i.e., RGBs and NIRs) of the satellite images made it possible to create the training data with high accuracy for land cover detection. The results indicated that combining the high- and low-resolution images in the RF could effectively detect waters, gravel/sand, trees, and grasses from the satellite images with a certain degree of accuracy. Its F-measure, consisting of precision and recall rates, had high enough with 0.8. Then, the ML-based land covers were coupled with a flood flow model. In the coupling, the results of the detected riverine land covers were converted into the roughness coefficients of the two-dimensional flood flow analysis. The flood flow simulation could reproduce the velocity field and water surface profile of flood flows with high accuracy. These results strongly suggest the effectiveness of coupling the current flood flow modelling with the ML-based land cover detection for grasping the most vulnerable portions in river flood management.

How to cite: Sato, T., Iwami, S., and Miyamoto, H.: Flood flow modelling coupled with ML-based land cover detection from UAV and satellite river imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6636, https://doi.org/10.5194/egusphere-egu22-6636, 2022.

Flood warning systems have a long track record of protecting human lives, but monetary damage continue to increase. Knowledge about the effectiveness of early flood warnings in reducing monetary damage is sparse, especially at the individual level. To gain more knowledge in this area, we analyse a dataset that is unique in terms of detailed information on warning reception and monetary damage at the property level. The dataset contains 4,468 damage cases from six flood events in Germany. We show quantitatively that early flood warnings are only effective in reducing monetary damage if people know what to do when they receive the warning (with at least one hour's notice). The average reduction in contents damage is 4 percentage points, which corresponds to a reduction of EUR 3,800 for the average warning recipient. This is substantial compared to the mean contents damage ratio of 21% and an absolute contents damage of 17,000 EUR. For the building damage ratio, the average reduction is 2 percentage points, which corresponds to a damage reduction of EUR 10,000. This is a remarkable reduction compared to the mean building damage ratio of 11% and a mean absolute building damage of 48,000 EUR. We also show that particularly long-term preparedness is related to people knowing what to do when they receive a warning. Risk communication, training and (financial) support for private preparedness are thus effective in mitigating flood damage in two ways: through precautionary measures and more effective emergency measures.

How to cite: Kreibich, H., Hudson, P., and Merz, B.: Flood early warning can significantly mitigate monetary damage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7247, https://doi.org/10.5194/egusphere-egu22-7247, 2022.

Flood Foresight is JBA’s strategic flood monitoring and forecasting system, providing flood inundation and depth estimates across the UK and Ireland at 30m resolution up to 10-days ahead of fluvial flood events. It consists of Flood Monitoring (based on observed discharges from river gauge telemetry) and Flood Forecasting (based on simulated discharge from a rainfall-runoff model) modules.

Recently, Flood Foresight has been expanded to provide asset alerting around heavy rainfall and surface water (pluvial) flooding, demonstrated in a proof-of-concept system on behalf of Network Rail during a Small Business Research Initiative project funded by Department for Transport and delivered by InnovateUK.

The surface water flood forecasting system is now running in real time using high resolution ensemble rainfall forecasts from Met Eireann (IREPS).  This system represents a major advance in the availability of information indicating the risk to rail infrastructure across Great Britain.  Taking advantage of ensemble rainfall forecasts, it is possible to give an indication of where rain might happen and the severity of that rain (in comparison to historical rainfall amounts), and also to provide an indication of the confidence in that forecast.  This concept is crucial to the handling of intense rainfall events, due to their inherent lack of predictability.  The presentation of mapped likelihood information for both rainfall and surface water flooding forecasts provides users with spatial context for the asset alerts.  It allows them to see the extent and uncertainty in the location of the intense rainfall event. 

The system has been developed to run autonomously using rainfall forecasts as they are provided by Met Eireann, via FTP.  Therefore the resulting asset alert information is always available, and always presents the most up-to-date information.  This gives asset managers the ability to access the information at a time that is convenient to them, but also the system can provide alerts when assets are identified as at risk as the information becomes available. 

The forecast data is available beyond 36 hours into the future, providing sufficient lead time for asset managers to coordinate responses and mobilise staff and equipment, if needed.  The temporal resolution of the forecast information is high at short lead times (i.e.  hourly for the first 6 hours), decreasing as lead time increases (after 24 hours the information is 6 hourly, further reducing to 12 hourly when longer lead time forecasts are available).  This decreasing temporal resolution with longer lead times allows for increased uncertainty in the timing of events further in the future to be obscured to the user, reducing confusion if the timing changes with subsequent forecasts. 

The proof-of-concept system focuses on the rail industry, however it is extensible to other sectors where population, assets or infrastructure are vulnerable to surface water flooding. Flood impact data and associated alerts can be customised based on a client’s asset portfolio and their incident management needs.

The presentation will explore heavy rainfall events evaluated during the proof-of-concept demonstrations, describing the information the Flood Foresight system could have provided ahead of, and during the event.

How to cite: Forbes, H., Bevington, J., Evans, A., Gubbin, A., Shelton, K., Smith, R., and Wood, E.: Improving resilience through a surface water flooding decision support system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8450, https://doi.org/10.5194/egusphere-egu22-8450, 2022.

Flood warning systems are needed to plan mitigation measures and inform response strategies. The extent and dynamics of floods are typically predicted using physics-based hydrological models, which are computationally expensive and data assimilation is difficult. Deep-learning models can overcome these limitations, enabling fast predictions informed by multiple sources of data. Studies show this can be achieved while retaining or improving the level of detail and accuracy previously attainable. We, therefore, propose a deep-learning flood forecasting tool that combines multiple sources of readily available data to quickly generate flood extent maps, which can inform warnings.

We train a neural network with U-NET architecture consisting of encoder and decoder convolutional modules. In the encoder module, features are extracted from the input and the data is downsampled to reduce complexity. Subsequently, the data is upsampled back to the original dimension in the decoder module and each 10 by 10 m pixel of the output image represents a flood prediction. The input to the neural network includes radar rainfall observations, LIDAR topographic scans, soil type and land use maps, groundwater depth simulations and previous inundation maps. All inputs are individually normalized and pre-processed. The rainfall observations are temporally aggregated to various intervals, hydrological features are highlighted in the topographic scans, and soil types and uses are grouped into categories.

The model is trained and evaluated against a set of maps of surface water extent derived from Synthetic Aperture Radar (SAR) satellite observations. The predictions are scored against the target images by computing the critical success index (CSI), which measures the percentage of correct predictions among the total predicted of observed flooded areas. Permanent water bodies and areas where flooding is not captured by the satellite images (e.g. in forests) are masked out during both training and evaluation. The model is trained on a set of flooding events that occurred between 2018 and 2020 within the Jammerbugt Municipality in northern Denmark, which extends for about 850 km². The model is validated on spatially independent data and tested on temporally independent events from the same study area.

The proposed model yielded up to ~60% CSI with the test dataset, which is comparable to existing flood screening approaches. The test data included both fluvial and pluvial flooding as well as observed surface water in coastal areas. Large flooded areas were correctly predicted, while false negatives were frequently obtained for smaller areas. The overall performance of the proposed method is expected to improve by further tuning the model hyperparameters and by treating separately flood processes with different dynamics (e.g. pluvial vs. fluvial vs. coastal). These tradeoffs are compensated by the minimal computational time required to generate predictions once the model has been trained. Also, it is expected that the model can easily be transferred to other locations since it relies on local topographic information. The additional advantage of using a deep-learning approach is the ability to easily integrate alternative and additional data sources, which enables, for example, longer-term flood warnings driven by rainfall forecasts instead of observations.

How to cite: Palmitessa, R., Hjermitslev, O. G., Johansen, H. E., Arnbjerg-Nielsen, K., Bauer‐Gottwein, P., Mikkelsen, P. S., and Löwe, R.: Improved flood predictions by combining satellite observations, topographic information and rainfall spatial data using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8823, https://doi.org/10.5194/egusphere-egu22-8823, 2022.

Japan has a history of major natural disasters, mostly due to its geographical characteristics and topographic features. Major typhoons and floods cause severe damages to lives, properties and important infrastructure, which may increase in future due to climate change. Therefore, sustainable and cost-effective disaster management strategies are of timely requirement, and paddy fields in the river flood plain areas of Japan can be effectively utilized in this regard. After the paddy harvest season, most paddy fields remain unused for a few months and during this time it can work as storage reservoir with minor interventions. During intense rainfall, water can be stored within the paddy field bunds if the drainage outlets are kept closed for some time. Thus, contribution of rainwater to the river can be lessened, resulting river discharge reduction to some extent and protecting important areas from flood damages. The potential of paddy fields in Japan as storage reservoir is not well represented in any research that involves hydrological modelling. This study is performed to assess the impact of using paddy fields for river discharge and inundation reduction, through hydrological model simulation. Two major river basins in Japan, Abukuma river in Fukushima prefecture and Chikuma river in Nagano prefecture are selected as study areas. Paddy field covers 15-20% of watershed areas of these rivers and most of these fields are very close to the main river stream, which indicates their fair potential to store rainwater and contribute to discharge reduction. A global hydrological and water resources model named ‘H08’ is used in this study to simulate river discharge for two scenarios, where one is the control scenario with no storage of water within the paddy field and another is storing rainwater within the exiting or extended paddy bunds. Simulations are performed for 2018 and 2019 to compare the normal flood year and extreme event (a super typhoon occurred in Japan in 2019). Observed and simulated discharge is compared for model calibration and results show better correlation in the upstream section of the rivers. More adjustment of model parameters is still necessary for better calibration. Simulation results show that for 2018, Abukuma river experienced 21-25% decrease in river discharge when water is stored within the conventional 25cm paddy bund. The reduction increased up to 35% when the paddy bunds are assumed to be extended up to 50cm in height. Similar results are observed for Chikuma river basin. For 2019, discharge shows 10-15% decrease for 25cm paddy bunds and around 20% reduction for proposed 50cm bund. With this discharge reduction potential, if paddy field bunds can be extended up to 50cm with a working public-private partnership, where farmers are aware of the advantages of utilizing unused paddy fields as such an effective means of flood management, then this strategy can be considered a sustainable and cost-effective way of disaster management, where the existing land-cover will act as a natural means of storage reservoir. Moreover, this sustainable strategy can be adopted in other countries having similar geographical features as Japan.

How to cite: Saha, D., Oki, K., Yoshida, K., and Kamiya, H.: Improvement of Disaster Management Approaches in Japan Using Paddy Field, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9037, https://doi.org/10.5194/egusphere-egu22-9037, 2022.

Floods in Australia are among the most costly and deadly natural disasters causing significant material damage, injury, and death. Effective emergency management to reduce the devastating consequences of flooding depends on the accuracy and reliability of forecasts. Effective infrastructure planning for flood mitigation depends on the accuracy and reliability of future projections. Flood inundation mapping is a tool widely used for flood mitigation purposes by providing information on flood event characteristics such as occurrence, magnitude, timing, and spatial extent. However, information derived from flood inundation maps is subject to uncertainties in each step of a complex modelling chain, including uncertainties in hydro-meteorological and observational datasets, digital elevation models and representation of rivers, as well as over-simplification of hydrological and hydraulic processes. Therefore, relying on a purely deterministic representation of flood characteristics may lead to poor decision making. Probabilistic flood maps are capable of accounting for uncertainty by estimating the probability of a certain area being flooded, which is a recommended approach for risk-based decision making. In addition, providing probabilistic flood map information encompassing past, present, and future, will improve Australia’s resilience to flood events and target infrastructure spending. Generation of seamless probabilistic flood maps in an operational setting, particularly at a continental scale, needs to be supported with an integrated and consistent set of hydro-meteorological datasets across timescales and catchments.  

The aim of this study is to develop a seamless probabilistic flood inundation mapping framework for near-future to far future floods across flood-prone Australian catchments. We take advantage of products from the Australian Water Outlook (AWO: awo.bom.gov.au), a water service that provides nationally consistent water information since 1911 until the present as well as long-term projections out to 2100. In this framework, large rainfall events are detected based on ensemble forecasts or projections from AWO using a threshold analysis. After detection of a potential flood, an event-based hydrological model (URBS) is initialised to generate an ensemble of river reach hydrographs in a Monte Carlo framework where the parameterisation of the catchment wetness is informed by historical flood events for the catchment. This enables uncertainty from ensemble rainfall and catchment losses to be quantified and incorporated within the hydrograph generation step. Lastly, we combine remotely sensed data with topographic and river network information to map the flood extent, using the height above nearest drainage (HAND) method. This framework will be tested for two major flood events in February 2020 and March 2021 at Hawkesbury Nepean Valley catchment located in New South Wales, Australia, which, due to significantly different antecedent conditions, had dissimilar flood characteristics, thereby demonstrating the suitability of the framework.

How to cite: Bahramian, K., Sharples, W., Rudiger, C., Unnithan, S. L. K., Biswal, B., Carrara, E., and Khan, Z.: Towards development of a seamless probabilistic flood inundation map for extreme flood events across Australian catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10674, https://doi.org/10.5194/egusphere-egu22-10674, 2022.

Streamflow data based on the observation may be partially missing due to flood or malfunction of the measuring equipment. Here, it is important to complement the missing flow rate with high accuracy for water resource management and flood risk management. Various statistical approaches such as linear regression and multiple regression models have been proposed as methods for complementing missing flow rates. Among the statistical methods, deep learning has been rapidly evolved with the improvement of computational equipment. Then, deep learning methods have achieved remarkable success in various fields. It may indicate that there is a possibility that the missing flow rate can be complemented with high accuracy by using the deep learning method. Therefore, this study implemented deep learning for missing streamflow complementation. In addition, because the network structure of deep learning may have a great influence on estimation accuracy, this study conducted a sensitivity analysis of the network structure. Among the deep learning methods, Bidirectional LSTM (Bi-LSTM) was implemented in this study. Bi-LSTM is a kind of LSTM that can learn long-term dependence of time series data. Bi-LSTM learns data in both forward and backward directions, compared to Unidirectional LSTM which learns data forward directions. As for the input data, both hourly streamflow and precipitation data were used. For model learning and evaluation, missing streamflow data were artificially generated. The results show that Bi-LSTM can complement the flow rate with high accuracy. It also showed the importance of optimizing the network structure.

How to cite: Nagasato, T., Ishida, K., Sakaguchi, D., Amagasaki, M., and Kiyama, M.: Sensitivity analysis of network structure in missing streamflow data complementation using Bidirectional Short-Term Memory network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10803, https://doi.org/10.5194/egusphere-egu22-10803, 2022.

Evaluating average annual loss (AAL) is an essential component of assessing and minimizing future flood risk. A robust method for quantifying flood AAL is needed to provide valuable information for stakeholder decision-making. Several recent studies suggest that the numerical integration method can provide meaningful AAL estimates since this technique includes the full loss‐exceedance probability of flood. While past research focuses on applying the numerical integration method on a single, one-family residential house, calculations across space are necessary for assessing community vulnerability. This research develops a computational framework in MATLAB for integrating across the full loss-exceedance probability curve through space to evaluate flood AAL for multiple single-family homes, including loss to the structure, content, and time spent in refurbishing it (i.e., use), over a case-study census block in Jefferson Parish, Louisiana, USA. To further inform flood mitigation planning, the AAL is also calculated for one, two, three, and four feet of freeboard and separately for each owner-occupant type of residence (i.e., homeowner, landlord, and tenant). Although previous studies provided essential information related to the structure and content loss for one type for ownership-occupant type (homeowner), the wider scope of this study allows for consideration of the use loss and the remaining ownership-occupant types. Results show that individual building AAL varies within a community because of its building attributes. Besides, results highlight the difference of AALs by owner-occupant type, with homeowners generally bearing the highest total AAL and tenants incurring the lowest total AALs. A simple elevation of only one foot can decrease the AAL by as much as 90 percent. A sensitivity analysis underscores the importance of using the exact values of the base flood elevation (BFE) compared to rounding to the nearest integer and excluding damage lower than first flood height (FFH) in the depth-damage functions (DDFs). Expanding the application of the numerical integration method to a broad-scale study area may enhance validity and accuracy as compensating errors are likely to make bulk estimates more reasonable, which might augment its utility at the community level. In general, such techniques improve resilience to flood, the costliest natural hazard, by assisting in better understanding of risk with and without mitigation efforts. 

How to cite: Mostafiz, R. B., Assi, A. A., Friedland, C., Rohli, R., and Rahim, M. A.: A Numerically-integrated Approach for Residential Flood Loss Estimation at the Community Level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10827, https://doi.org/10.5194/egusphere-egu22-10827, 2022.

In recent years, disasters are more frequent and enormous due to global warming. In the field of hydrology, high-precision rainfall-runoff modeling is required. Recently, deep learning has been applied to rainfall-runoff modeling and shows high accuracy. It is also expected that the accuracy will be improved by using ensemble learning for deep learning. This study tried to improve the accuracy of river flow estimation by performing ensemble learning for deep learning. Stacking was used as the ensemble learning method. For deep learning, LSTM, CNN, and MLP was used and compared. XGBoost was used as the learning device used for ensemble learning. The target area was the Tedori River basin in Ishikawa Prefecture, Japan. In deep learning, the input data were daily average precipitation and temperature. In deep learning and ensemble learning, the target data was the daily average river flow. RMSE was used as the evaluation index. As a result, the accuracy was the highest after ensemble learning when using LSTM. It shows that the selection of the learning device is important for ensemble learning.

How to cite: Sakaguchi, D., Ishida, K., Nagasato, T., Amagasaki, M., and Kiyama, M.: Improvement of river flow estimation accuracy using ensemble learning stacking, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10891, https://doi.org/10.5194/egusphere-egu22-10891, 2022.

In recent years, climate change intensifies heavy rainfall, resulting in annual flood damage. Population is increasing worldwide, and urbanization is expected to continue expanding. Under these circumstances, once an inundation occurs, the damage is expected to be more extensive than ever before. Therefore, in this study, we are analyzing the effects of DEM resolution and land use data, which are the calculation conditions for inundation calculations in flood forecasting, on inundation characteristics such as inundation magnitude and duration during large-scale inundation.

　In this paper, the target watershed was the Tone River in Japan, where major floods have occurred in the past, and the analysis was conducted in the plain area. DEM data and land use data are important factors in determining inundation characteristics; The higher the resolution of the DEM data, the better it can represent the microtopography, which in turn affects the inundation flow. Also, land use data determines the roughness coefficient, which affects the velocity of floodwaters, and the infiltration capacity and initial loss into the ground. In this paper, The DEM data were analyzed with resolutions of 5m, 25m, 50, 100m, and 250m. The land use data for the years 1978, 1987, 1997, 2006 and 2016 were used to analyze the inundation characteristics due to increasing urbanization.

The results of inundation analysis with different resolutions of DEM data show that the resolution has no significant effect on the inundation rate. However, as for the inundation area, the larger the mesh size, the larger the inundation area, which is expected to be caused by the homogenization of DEM data. It was also found that as urbanization progresses, the inundation area spreads faster. In addition, the urbanization process affects the diminishing period of inundation rather than the expansion process, because it loses the function of infiltration capacity, and the inundation depth is less likely to decrease.

How to cite: Koyama, N. and Yamada, T.: Analysis of inundation characteristics under various computational conditions for large-scale flood forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10930, https://doi.org/10.5194/egusphere-egu22-10930, 2022.

Average annual loss (AAL) is used as the basis for the evaluation of risk mitigation measures.  However, the current AAL implementations in flood risk assessment have several shortcomings. For instance, results generated using Riemann trapezoids for the available return periods of a site are typically gross approximations, especially when damage changes rapidly with depth. Monte Carlo simulations offer improvements in precision but at the expense of being computationally intensive. The log-linear method that extrapolates losses to higher return periods and performs piece-wise Riemann sum with these limited return periods can fail to capture the non-linear flood behavior. This paper presents an improved implementation that quantifies AAL at the micro-scale level including the full range of loss‐exceedance probabilities. To demonstrate the methodology, the financial benefit of increasing the lowest floor elevation for a one-story single-family residence is assessed. Several depth-damage functions (DDFs) are selected and compared to examine the variability in AAL results related to the DDF choice. Results demonstrate the need for an AAL estimate that includes the full loss‐exceedance probabilities. Results also highlight the need to assess flood risk at the micro-scale level for a more localized and accurate assessment, whereupon the estimate can be expanded to broader-scale risk estimations with a higher degree of accuracy. The more realistic AAL estimates results could encourage homeowners and communities to take action and support government decision-makers by investing in flood mitigation and considering building code changes.

How to cite: Rahim, M. A., Gnan, E. S., Friedland, C. J., Mostafiz, R. B., and Rohli, R. V.: An Improved Micro Scale Average Annual Flood Loss Implementation Approach , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10940, https://doi.org/10.5194/egusphere-egu22-10940, 2022.

Natural flood management (NFM) is widely promoted and adopted as an effective way of managing flood risks. However, there remain many unknowns especially on its effectiveness at medium and large scales. This study has first analysed the consistency of a modelling framework that integrates the Soil and Water Assessment Tool (SWAT) model for simulating the land based NFM in two medium scale lowland catchments within the Thames River basin (UK). Afterwards, it has assessed the effectiveness of NFM in these catchments using broadscale hypothetical scenarios. The results show that it is possible to model land-based NFM in medium scale catchments but this is highly dependent on the one hand on catchment landscape characteristics and on the other hand on the availability and quality of necessary input datasets, model choice, configuration, parametrisation and calibration and uncertainty analysis techniques. Furthermore, the NFM effects vary across the catchments and landscapes characteristics. Afforestation seems to provide less effect on large flood events in terms of reducing the peak flows compared to small events. The implementation of crop rotation scenarios, depending on the crop choice and tillage practice may lead to the increase of the peak flows. Overall, this study showed that NFM modelling in medium catchments is not straightforward and prior to any task, an extensive analysis needs to be carried out to understand the datasets, the model, and processes configuration as well as different calibration and uncertainties analysis techniques. Moreover, the choice of woodland planting only as NFM measure will require an extensive work within the catchment to produce an effect which suggests that to better minimise the flood risk, the combination with other measures that can reduce the amount of flow reaching the river channel or delay the timing of the peak flow (eg. leaky barriers) would be necessary.

How to cite: Badjana, H. M., Verhoef, A., Cloke, H., Julich, S., McGuire, P., Camargos, C., and Clark, J.: Modelling the natural flood management in medium scale lowland catchments in Thames Basin (UK), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12430, https://doi.org/10.5194/egusphere-egu22-12430, 2022.

Human mortality and economic losses due to climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. This study will assess the attribution of rising climatic disaster mortality to indicators of climatic hazards, exposure, and vulnerability at the subnational scale in a low-income country, using Nepal as a case study. 
This empirical study at the scale of 753 local administrative units of Nepal will follow a regression-based approach that will overcome the limitations of the commonly used loss normalization approach in studying the attribution of disaster-induced loss and damage.

In Nepal, landslides and floods account for more than two-thirds of the total climatic disaster mortality. Hence, we will use the past 30 years (1991-2020) landslides and floods mortality data from DesInventar and Nepal's Disaster Risk Reduction portal as the dependent variable. As explanatory variables to represent climatic hazards, we will estimate and use mean and extreme precipitation indices from observational data by the Department of Hydrology and Meteorology Nepal. We will use the local unit’s population as a proxy of disaster exposure. Socio-economic and environmental indicators such as annual per capita income, percentage of people with access to mobile phones and internet, land cover distribution, and slope will be used as indicators of vulnerability. Exposure and vulnerability indicators data will be accessed from Nepal’s Central Bureau of Statistics and other sources. This study is expected to identify indicators of climatic hazards, exposure, and vulnerability that could explain the spatial and temporal variability of climatic disaster mortality in Nepal. Similarly, it will provide new insights on the role of climate change on rising climatic disaster mortality from the low-income countries’ context.

How to cite: Chapagain, D., Bharati, L., and Borgemeister, C.: Is climate change to blame for rising climatic disasters mortality in Nepal?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-359, https://doi.org/10.5194/egusphere-egu22-359, 2022.

Motivation: The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of processes on many scales, as many of these processes as possible must be considered for a comprehensive answer.

Objectives: Our aim is a probabilistic reconstruction of summer lightning over the European Eastern Alps down to its seasonally varying diurnal cycle. This necessitates consideration of many processes which becomes feasible by combining a statistical learning approach with several recent scientific achievements: Decade-long seamless lightning measurements by the Austrian Lightning Detection & Information System (ALDIS) and hourly reanalyses of atmospheric conditions including cloud micro-physics within the fifth generation ECMWF atmospheric reanalysis (ERA5).

Methods: These two data sets have been linked by the statistical learning approach called generalized additive model (GAM). GAMs are capable to identify nonlinear relationships between the target variable (lightning yes/no) and explanatory variables (ERA5). The most important explanatory variables have been selected objectively using a combination of stability selection and gradient boosting. This objective selection has reduced the pool of 85 potential ERA5 variables to the 9 most important ones. This reduced set still represents a large variety of processes including favorable environments for thunderstorms, charge separation and trigger mechanisms. The performance of the resulting GAM has been tested using cross-validation over the period of 2010-2019. 

Results: With the resulting GAM lightning for the Eastern Alps and their surroundings has been reconstructed over a period of four decades (1979-2019). The most intense changes occurred over the high Alps where lightning activity doubled in the past decade compared to the 1980s. There, the lightning season reaches a higher maximum and starts one month earlier. Diurnally, the peak is up to 50% stronger with more lightning strikes in the afternoon and evening hours. Signals along the southern and northern alpine rim are similar but weaker whereas the flatlands north of the Alps have no significant trend.

How to cite: Simon, T., Mayr, G. J., Morgenstern, D., Umlauf, N., and Zeileis, A.: Amplification of annual and diurnal cycles of alpine lightning over the past four decades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1314, https://doi.org/10.5194/egusphere-egu22-1314, 2022.

The synoptic dynamics leading to the longest wet spells in Europe are so far poorly investigated, despite these events’ potentially large societal impacts. In this study we examine the role of cyclones and PV cutoffs for unusually long wet spells in Europe, defined as the 20 longest uninterrupted periods with at least 5 mm daily accumulated precipitation at each ERA-Interim grid point in Europe (this set of spells is hereafter referred to as S₂₀). The S₂₀ occur predominantly in summer over the eastern continent, in winter over the North Atlantic, in winter or fall over the Atlantic, and in fall over the Mediterranean and European inland seas. Four case studies reveal archetypal synoptic storylines for long wet spells: (a) A seven-day wet spell near Moscow, Russia, is associated with a single slow-moving cutoff-cyclone couple; (b) a 15-day wet spell in Norway features a total of nine rapidly passing extratropical cyclones and illustrates serial cyclone clustering as a second storyline; (c) a 12-day wet spell in Tuscany, Italy, is associated with a single but very large cutoff-complex, which is replenished multiple times by a sequence of recurrent anticyclonic wave breaking events over the North Atlantic and western Europe; and (d) a 17-day wet spell in the Balkans features intermittent periods of diurnal convective precipitation in an environment of weak synoptic forcing and recurrent passages of upper-level troughs and PV cutoffs and thus also highlights the role of diurnal convection for long wet spells over land. A systematic analysis of cyclone and cutoff occurrences during the S₂₀ reveals considerable spatial variability in their respective role for the S₂₀. For instance, cyclones and cutoffs are present anywhere between 10% and 90%, and 20% and 70% of the S₂₀ time steps, respectively, depending on the geographical region. However, overall both cyclones and cutoffs, appear in larger number and at a higher rate during the S₂₀ compared to climatology. Furthermore, in the Mediterranean, the PV cutoffs and cyclones are significantly slower moving and/or longer-lived during the S₂₀ compared to climatology. Our study thus documents for the first time the palette of synoptic storylines accompanying unusually long wet spells across Europe, which is a prerequisite for developing an understanding of how these events might change in a warming climate and for evaluating the ability of climate models to realistically simulate the synoptic processes relevant to these events.

How to cite: Röthlisberger, M., Scherrer, B., de Vries, A. J., and Portmann, R.: The role of cyclones and PV cutoffs for the occurrence of unusually long wet spells in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1758, https://doi.org/10.5194/egusphere-egu22-1758, 2022.

The year of 2020 was characterised by an unprecedented fire season in Pantanal, the largest continuous tropical wetland, located in south-western Brazil. This event was the largest ever recorded over, at least, the last two decades, reaching an amount of 3.9 million ha and affecting 17 million vertebrates^1,2. Recent evidence points out that this event resulted from a complex interplay between human, landscape, and meteorological factors^3,4. Indeed, much of the Pantanal has been affected by severe dry conditions since 2019, with 2020’s drought being the most extreme and widespread ever recorded in the last 70 years^5,6. The drought condition was maintained at record levels during most of the year of 2021, following the climate change scenarios expected for this region⁷. Prior to this comprehensive assessment, the 2020’s fire season has been analyzed at the univariate level of a single climate event, not considering the co-occurrence of extreme and persistent temperatures with soil dryness conditions. Here, we show that the influence of land–atmosphere feedbacks contributed decisively to the simultaneous occurrence of dry and hot spells, exacerbating fire risk. These hot spells, with maximum temperatures 6 ºC above-average were associated with the prevalence of the ideal synoptic conditions for strong atmospheric heating, large evaporation rates and precipitation deficits⁴. We stress that more than half of the burned area during the fire season occurred during compound drought-heatwave conditions. The synergistic effect between fuel availability and weather-hydrological conditions was particularly acute in the vulnerable northern forested areas. These findings are relevant for integrated fire management in the Pantanal as well as within a broader context, as the driving mechanisms apply across other ecosystems, implying further efforts for monitoring and predicting such extreme events.

References

[1] Garcia, L.C, et al.. Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. J. Environ. Manage. 2021, 293, 112870.

[2] Tomas, W. M., et al. Counting the dead: 17 million vertebrates directly killed by the 2020’s wildfires in the Pantanal wetland, Brazil. Sci. Rep. accepted.

[3] Libonati, R.; et al. Rescue Brazil’s burning Pantanal wetlands. Nature. 2020, 588, 217–219.

[4] Libonati, R., et al. Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. Environmental Research Letters. 2022, 17, 1.

[5] Thielen, D., et al. The Pantanal under Siege—On the Origin, Dynamics and Forecast of the Megadrought Severely Affecting the Largest Wetland in the World. Water. 2021, 13(21), 3034.

[6] Marengo, J.A., et al. Extreme Drought in the Brazilian Pantanal in 2019–2020: Characterization, Causes, and Impacts. Front. Water. 2021, 0, 13.

[7] Gomes, G.D.; et al.. Projections of subcontinental changes in seasonal precipitation over the two major river basins in South America under an extreme climate scenario. Clim. Dyn. 2021, 1-23.

This work was supported by Project Rede Pantanal from the Ministry of Science, Technology and Innovations of Brazil (FINEP grant 01.20.0201.00). R.L. was supported by CNPq [grant 305159/2018–6] and FAPERJ [grant E26/202.714/2019]

How to cite: Libonati, R., Geirinhas, J. L., Silva, P. S., Russo, A., Rodrigues, J. A., Belem, L. B. C., Nogueira, J., Roque, F. O., DaCamara, C. C., Nunes, A. M. B., Marengo, J. A., and Trigo, R. M.: The impact of compound drought and heatwave events on the unprecedented 2020 fire season in the Pantanal, Brazil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1843, https://doi.org/10.5194/egusphere-egu22-1843, 2022.

Although fluvial erosion is predominantly governed by surface driven fluvial incision, more exotic erosional processes can significantly contribute to the fluvial shaping of landscapes. To this group belongs sapping caused by concentrated groundwater discharge, which can form a very distinct type of topography (characterised e.g. by the development of theatre-shaped channel heads). Fluvial erosion through sapping occurs where groundwater encounters a rapid change in elevation (i.e. across scarps, cliffs), and it is highly modulated by the physical properties of the solid. Groundwater sapping is, for example, promoted by inhomogeneities of permeability and/or lithological composition of the subsurface, which is often prevalent in sedimentary deposits and along contact boundaries between different lithological units. Consequently, topography shaped by groundwater sapping can be found in many places on Earth and even on Mars, and the formation of these landscapes can integrate over thousands to millions of years. However, in some regions, such as coastal areas, groundwater sapping has been reported to be associated with severe soil loss and high erosion rates on the order of tens of metres per day.

A similar magnitude of soil loss could be observed close to the village of Erftstadt-Blessem, Germany, as caused by severe flooding, peaking the 15^th of July 2021. Here, intense rain events caused the formation of local drainage networks towards a gravel pit located to the north of the village. As a consequence, adjacent arable land was subject to intense backward incision, thereby eroding the underlying Quaternary sediments. The erosion formed drainage networks that appear to resemble characteristic groundwater sapping. This fluvial topography was largely preserved after the flooding, thus providing the opportunity to decipher the processes involved in the formation of these features. We use Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry to reconstruct the drainage geometry based on drone imagery (provided by the Kreisverbindungskommando Köln, M. Wiese; additional SfM-MVS photogrammetry data provided by ESRI Deutschland GmbH, T. Gersthofer) and photographs taken in the field using a handheld camera. The data is subsequently used to characterise the drainage networks and to compare the topography to other groundwater sapping landscapes on Earth and on Mars. Additionally, we intend to perform grain size analyses of the different sediment layers and to quantify fallout ^239+240Pu in selected samples to asses the physical properties of the substratum and to trace the fate of the radionuclides during the flood event. Our aim is that our data will contribute to a better understanding of how groundwater sapping processes operate over time and to assess the importance of individual factors (e.g. substrate properties, vegetation cover and -type) on the severity of erosion. The outcome could thus not only be important for modelling terrestrial and extra-terrestrial processes but has also practical applications to the loss of arable land and the effects of outburst flooding.

How to cite: Mohren, J., Ritter, M., Binnie, S. A., and Dunai, T. J.: Erosion of arable land during the July 2021 flood event in Erftstadt-Blessem, Germany: understanding groundwater sapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2312, https://doi.org/10.5194/egusphere-egu22-2312, 2022.

This contribution has the main goal of identifying, characterizing, tracking and nowcasting severe thunderstorms using the Density of the Vertical Integrated Liquid (DVIL). The DVIL can synthesize all the volumetric information of a column of the weather radar in a 2D plane. This is, it estimates the quantity of precipitable liquid water in the column but, besides, it reduces the dependency on the height of the column. This point becomes crucial to give an appropriate weight of potential danger to thunderstorms that occurred out of the typical convective season. . This is particularly useful to improve the decision-making and early warning in critical environments and infrastructures, like airports and air traffic management (ATM). The usage of DVIL has multiple advantages, for instance, reducing the computational time consumed on the analysis of large areas. Also, to obtain a good and simple description of the potentially dangerous thunderstorms, and to have an easily integrating into other systems for ATM decision making. The main disadvantage is a less precise characterization of the atmospheric objects than with the whole radar volumetric data. Nevertheless, the differences are scarce and do not produce any significant inconvenience in the procedure. The algorithm first identifies those areas exceeding a DVIL threshold, which is established for thunderstorms with a certain probability of producing severe weather. The characterization module turns out simpler than in other methodologies because of the data type (2D instead of 3D reflectivity fields), but it can be combined with other data types if needed. The tracking and nowcasting procedure obtain the past trajectory of the thunderstorm and then use it to weather forecast from 5 to the next 60 minutes, with 5 minutes steps. Different convective episodes that have affected the proximity of Italian and Spanish airports have been analysed to evaluate the following points: (1) the performance of the correct identification of potentially dangerous thunderstorms, (2) the capability of tracking the path and characterizing the life cycle of those storms, and (3) the ability of the nowcasting to correctly forecast the time and the most dangerous area.

This project has received funding from the SESAR Joint Undertaking under grant agreement No 892362, SINOPTICA-H2020 (Satellite-borne and IN-situ Observations to Predict the Initiation of Convection for Air traffic management) project.

How to cite: Esbrí, L., Rigo, T., Llasat, M. C., and Parodi, A.: Using Vertical Integrated Liquid Density from a Weather Radar Network to Nowcast Severe Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2532, https://doi.org/10.5194/egusphere-egu22-2532, 2022.

Flooding is a natural disaster with extremely wide-reaching impacts and is a recurring problem in Indonesia. Whilst possible impacts of climate change are expected to aggravate flood risk in already flood-vulnerable areas, many countries struggle to achieve the United Nations’ (UN) 2030 Sustainable Development Goals (SDGs) to achieve a better and more sustainable future for all. Using the case study of Majalaya, Indonesia, the authors investigated the impact of climate change and climate variability on urban flood risk through science-based spatio-temporal flood simulations. Based on the ensemble of 17 General Circulation Models (GCMs) CMIP6, the near-future (2021 to 2050) flood projection under Shared Socioeconomic Pathways (SSPs) 2.6 (low forcing), 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1981 to 2014) was simulated. The area’s future risk of flooding was then investigated and adaptation measures were suggested for reducing and mitigating worsening flood conditions. A numerical model was developed in HEC-RAS that represented the city of Majalaya and the results were combined with the ensemble of climate projections to enable the assessment of the effects of flooding due to the combined effect of climate change and urbanisation. The model was calibrated using historical stream gauge records and past extreme flood inundation boundaries. Using the model’s output metrics (e.g. flood depth, velocity) and local demographic data, the project aims then to use a vulnerability assessment framework to quantify the impact of climate change on flood risk. The modelling results will allow for spatio-temporal mapping of the flood-prone areas in Majalaya, which will help reduce risk and vulnerability for disadvantaged populations. The development of flood vulnerability maps and future flood risk projections will assist the government in developing land-use and flood prevention management policies. This research area, drawing from the combination of flood modelling and the use of climate projections, allows for an assessment of future flood risk scenarios of the city of Majalaya and paves new avenues towards future research.

How to cite: Rohmat, F. I. W., Stamataki, I., Sa'adi, Z., and Fitriani, D.: Flood analysis using HEC-RAS: The case study of Majalaya, Indonesia under the CMIP6 projection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3090, https://doi.org/10.5194/egusphere-egu22-3090, 2022.

Hail is a meteorological phenomenon with adverse impacts affecting multiple socio-economic sectors such as agriculture, renewable energy and insurance (e.g. Púčik et al., 2019; Martius et al., 2018; Macdonald et al., 2016). The mitigation of the hail-related risk in particularly sensitive regions such as Italy has fostered hail research, aiming at a deeper understanding of the favorable environmental conditions for hail formation and the improvement of hail forecasting skills (Mohr and Kunz, 2013). Nevertheless, one of the major limitations for the study of long-term hail variability is the inherent difficulty in measuring all the hail occurrences and the consequent scarce temporal and spatial coverage of hail observations (Mohr et al., 2015). Therefore, in this study, the Probability Density Functions (PDFs) of several large-scale meteorological variables and convective indices from the ERA5 reanalysis are considered instead, with the aim of describing a conditioned hail probability, following the statistical method by Prein and Holland (2018). Then, the best set of variables to be used as predictors in the hail model are selected with a machine learning approach, based on a genetic algorithm. The model output is an estimation of the hail probability over Italy in the 1979-2020 period, on a 30x30 km grid. The model is validated over the Friuli-Venezia-Giulia region through an independent dataset based on hail pads. The estimated hail probability has been used to characterize the seasonality, long-term variability and trends of the hail frequency and to investigate the potential large-scale drivers of hailstorms over Italy. 

REFERENCES:

Púčik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T., Antonescu, B., & Faust, E. (2019). Large hail incidence and its economic and societal impacts across Europe. Monthly Weather Review, 147(11), 3901-3916. doi: 10.1175/MWR-D-19-0204.1.

Martius, O., Hering, A., Kunz, M., Manzato, A., Mohr, S., Nisi, L., & Trefalt, S. (2018). Challenges and recent advances in hail research. Bulletin of the American Meteorological Society, 99(3), ES51-ES54. doi: 10.1175/BAMS-D-17-0207.1.

Macdonald, H., Infield, D., Nash, D. H., & Stack, M. M. (2016). Mapping hail meteorological observations for prediction of erosion in wind turbines. Wind Energy, 19(4), 777-784. doi: 10.1002/we.1854.

Mohr, S., Kunz, M., & Geyer, B. (2015). Hail potential in Europe based on a regional climate model hindcast. Geophysical Research Letters, 42(24), 10-904. doi:10.1002/2015GL067118.

Prein, A. F., & Holland, G. J. (2018). Global estimates of damaging hail hazard. Weather and Climate Extremes, 22, 10-23. doi: 10.1016/j.wace.2018.10.004.

How to cite: Hénin, R., Torralba, V., Cantelli, A., Scoccimarro, E., Materia, S., and Gualdi, S.: Modelling hail probability over Italy using a machine learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4102, https://doi.org/10.5194/egusphere-egu22-4102, 2022.

Extreme, large-scale precipitation events can lead to extreme river floodings which are one of the most dangerous weather events for society when occurring in highly populated areas. However, the largest impacts are caused by very rare events with return periods on the order of 100 years. To do a quantitative and robust analysis of daily 100-year precipitation events, observational time series are typically too short. Therefore, an approach is applied here in which operational ensemble prediction data from the ECMWF are used to generate a large pool of simulated, but realistic daily precipitation events (corresponding to 1200 years of data) from which several 100-year events can be analysed. For five different major Central European river catchments, composite analyses show that 100-year precipitation events in all catchments are typically associated with an upper-level trough moving into Central Europe 24h to 48h before the occurrence of the events. During the 24h before the events, details in the progression of the trough and the location of the associated surface cyclone determine in which catchment extreme precipitation occurs. A comparison to composite analyses of less extreme precipitation events shows that dynamical mechanisms such as an amplified mid-tropospheric trough/cut off are more important for an intensification of precipitation events in the Danube and Oder catchments while in the Elbe, Rhine and Weser/Ems catchments thermodynamical mechanisms such as a larger moisture flux are more important. The question how a warmer climate will affect the dynamical processes of such extreme precipitation events will be investigated in a follow-up study.

How to cite: Ruff, F. and Pfahl, S.: Dynamical analysis of large-scale 100-year precipitation events over Central European river catchments and their differences to less extreme events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4205, https://doi.org/10.5194/egusphere-egu22-4205, 2022.

Flash droughts are distinguished by a rapid development and intensification, which increase the potential drought impacts on natural and socio-economic systems. In recent years, a great effort has been made to identify and quantify this type of events in different regions of the world using different metrics. We developed a methodology to analyze the flash droughts based on SPEI at short-time scale (1-month) and high-frequency data (weekly). Thus, we characterized the occurrence of flash drought in Spain over the period 1961-2018 and showed that flash drought is a frequent phenomenon (40% of all droughts were characterized by rapid development), which exhibit a great spatiotemporal variability. The northern regions, where a higher frequency of flash droughts was found, showed negative trends in the frequency of flash droughts, while the central and southern regions subject to fewer flash drought events showed generally positive trends. Usually, the flash drought is associated with severe precipitation deficits and/or anomalous increases in atmospheric evaporative demand (AED), but while the role of precipitation seems obvious and essential, the role played by AED in triggering or reinforcing flash drought episodes is much more complex and exhibits important spatial and temporal contrasts. In Spain, the effect of AED is mainly restricted to water-limited regions and the warm season, but its role is minimal in energy-limited regions and in cold periods in which precipitation deficits are the main cause of flash drought development. However, the contribution of the AED on the development of flash droughts has increased notably over the last six decades, thus becoming a decisive driver in explaining the occurrence of the latest flash droughts in some regions of Spain. These findings have strong implications for proper understanding of the recent spatiotemporal behavior of flash droughts in Spain and illustrate how this type of event can be related to global warming processes.

How to cite: Noguera, I., Domínguez-Castro, F., and Vicente-Serrano, S. M.: How does the rise of atmospheric water demand affect flash drought development in Spain?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4353, https://doi.org/10.5194/egusphere-egu22-4353, 2022.

Over the past few decades, while several advancements in improving the performance of global climate models (GCMs), such as predicting mean climate,  have been made, predicting extreme rainfall events related to Mei-yu fronts (MYFs) and tropical cyclones (TCs) remains an open challenge. This is partially due the coarse spatial resolution of the GCMs that restricts their ability to represent extreme events and the associated processes on relevant spatial scales. This poses a problem for stakeholders as a failure to take appropriate precautionary action before the occurrence of extreme events can have disastrous consequences. Although the spatial resolutions of typical GCMs are too coarse to simulate extreme precipitation accurately, they are more likely to be able to simulate large-scale climate modes (LSCMs) better. Given that the activities of MYFs and TCs are linked to LSCMs, we can make use of these causal connections between LSCMs and extreme rainfall associated with MYFs/TCs to construct useful prediction models. This can then be applied to the outputs of climate GCM simulations to increase our capability in predicting extreme rainfall in the future.

In this presentation, we demonstrate a novel technique based on causality-guided statistical models (CGSMs) to assess the projected future changes of extreme rainfall associated with MYFs and TCs over China using the CMIP6 historical and SSP585 scenario simulations for four selected models. First, we show that CGSMs, which are constructed using historical observations and reanalysis, have good performance in modelling historical observations. Then we compare extreme rainfall related to MYFs/TCs from the CMIP6 historical direct output of the selected models with the CGSMs predictions. Our results show that the climatological patterns of CMIP6 direct historical outputs are different to the observed climatological patterns. Yet, CGSMs driven by CMIP6 LSCMs can produce similar patterns as the observed climatology. For the projected change under the SSP585 scenario, projections based on CGSMs provide a more coherent picture than CMIP6 direct model outputs. This shows the potential of causality-guided approach in coarse resolution climate model outputs. The implication and potential use of this approach is also discussed.

How to cite: Ng, K. S., Leckebusch, G. C., and Hodges, K.: A Causality-guided Approach for Predicting Future Changes in Extreme Rainfall over China Using Known Large-scale Modes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4588, https://doi.org/10.5194/egusphere-egu22-4588, 2022.

Extreme precipitation and discharge between July 13^th and 16^th 2021 caused serious flooding with bank erosion, including damages to infrastructure and buildings nearby the Eifel mountain region. Especially the small town of Stolberg and Eschweiler in the Inde River catchment were heavily affected. On-site investigation along the Inde River and its tributary, the Vichtbach creek, after the flood event show that mainly coarse sediments were remobilized and accumulated in the upper and middle reaches. The water masses mobilized not only sediments including gravel but also large objects like broken down trees and cars. In contrast, silty sediments were deposited in the lower reaches.

The Stolberg region is a former mining area with related industries resulting in contaminated soils and tailings close to the floodplains (Esser et al. 2020). Therefore, our investigations also focus on pollution by sediment-bound heavy metals and their distribution in the floodplains before and after this event. Flood sediment samples were taken immediately after the extreme flood event. Based on the results of flood-related pollution monitoring, conducted between 2016 and 2019 (Esser, 2020), the impact of the extreme event in July can be evaluated. During the July flood event, an exceptional amount of pollutants was remobilized. In addition to an increase in pollutants on the modern floodplain, wider areas of older and higher floodplains (Altauen) were also affected.

Esser, V. (2020): Untersuchungen zur fluvialen Morphodynamik und zur rezenten Schadstoffausbreitung in Flusssystemen - Beispiele aus der Grenzregion Belgien, Niederlande und Deutschland. PhD-Thesis, RWTH Aachen University.

Esser, V., Buchty-Lemke, M., Schulte, P., Podzun, L.S., Lehmkuhl, F. (2020): Signatures of recent pollution profiles in comparable Central European rivers - Examples from the International River Basin District Meuse. Catena 193: 104646. https://doi.org/10.1016/j.catena.2020.104646

How to cite: Lehmkuhl, F., Esser, V., Schulte, P., Weber, A., Wolf, S., and Schüttrumpf, H.: Sediment pollution and morphodynamics of an extreme event: Examples from the July 2021 flood event from the Inde River catchment in North Rhine-Westphalia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5114, https://doi.org/10.5194/egusphere-egu22-5114, 2022.

Extreme rainfall events are increasing the frequency of hydrological hazards such as landslides, debris flow, and erosion processes. Understanding the coupling of these hazards is still a challenging task, current methodologies often take a single hazard approach without integrating the mechanisms that describe the influence of one hazard on another under the same rainfall event. Physically-based distributed models have overcome these limitations incorporating the coupling of hillslope-hydrological processes that influence the interactions of hydrological hazards at the catchment scale. Nonetheless, within these models, the physical characteristics of the catchment domain are subject to a large spatial variability increasing the uncertainty in the parameters that influence the interaction of these hazards, hindering their representation in data-scarce catchments. The aim of this study is to elaborate an experimental design to parameterize a physically-distributed model to identify the parameters that have an acceptable influence in representing and describing hydrological hazard interactions under a data-scarce environment.

The study area is set in the Soufriere catchment in Saint Lucia, which recorded multiple landslides and debris flows with impacts on catchment erosion triggered by Hurricane Tomas in October 2010. The OpenLISEM model was used to estimate the parameters that influenced the triggering of hydrological hazards that occurred during Hurricane Tomas. The parameter estimation was performed through a Global Sensitivity Analysis (GSA) All-At-a-Time (ATT) to assess simultaneously under 144 simulations the estimation of hydrological and geotechnical parameters. The parameters subject to Sensitivity Analysis were saturated moisture content, saturated hydraulic conductivity, soil cohesion, and internal friction angle. The results were verified through the Sorensen-Dice coefficient. The coefficient was calculated through a spatial overlapping method between landslide simulated areas and landslide inventory areas corresponding to the Hurricane Tomas triggered landslides obtained from the British Geological Survey (2014). The results indicated that the representation of landslides, debris flows, and erosion processes on the OpenLISEM model highly depend on the quality of the input data. The latter was confirmed by the Sorensen-Dice coefficient indicated low spatial overlap values between the simulations performed. Nevertheless, the response of the OpenLISEM model to an acceptable landslide representation similar to the landslides triggered by Hurricane Tomas was influenced in the first place by the soil cohesion and internal friction angle and in the second place by the saturated moisture content and saturated hydraulic conductivity. The identification of these parameters introduces an improvement to provide an acceptable representation of hydrological hazards interactions given the data available in a data-scarce environment.

How to cite: López, P., Holcombe, L., Michaelides, K., and Phillips, J.: Parameter exploration for hydrological hazard interactions in a data-scarce catchment., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5815, https://doi.org/10.5194/egusphere-egu22-5815, 2022.

The evaluation of the resilience of flood protection systems requires the assessment of the impact of climate change scenarios on future flood regimes. Due to the high computational effort and to the scarcity of hourly climate modelling chains, expected changes in future floods are often simulated by hydrological models on a daily basis, even for basins with short response times, where hourly simulations would be needed.

In this work, the expected occurrence and magnitude of future flood events is modelled through the coupling of bias-corrected local climate scenarios at hourly time scale and continuous rainfall-runoff modelling, in reference to the Panaro river (one of the OpenAir Laboratories in the OPERANDUM H2020 project), a tributary of the Po River in the Apennines.

The investigation exploits hourly precipitation and daily max/min temperature (used for interpolation at hourly scale) timeseries for a subset of climate modelling chains included in the EURO-CORDEX initiative through the dynamical downscaling of Global Climate Models under the RCP 8.5 concentration scenario. The comparison with observed spatial fields obtained from weather stations and from gridded E-OBS products allows to assess the biases affecting the climate raw data.

The Scaled Distribution Mapping (SDM) bias correction procedure (Switanek et al. 2017), that preserves raw climate model projected changes in the bias-corrected series, is then applied to adjust the raw model output towards observations.

A semi-distributed, continuously simulating rainfall-runoff model is parameterised on the basis of the observed meteorological and streamflow time-series, especially focusing on the reproduction of past flood events. The model is then run to reproduce the continuous hourly streamflow time-series in the Panaro river over past and future decades, providing in input i) observed meteorological forcing based on ground stations, ii) raw and bias-corrected climate scenarios over the control period, iii) bias-corrected climate scenarios for the future decades. Finally, the flood events are extracted from the continuous streamflow simulations and the changes in the flood signals expected over the future decades are analysed, in terms of both peaks and volumes.

References

Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R., and Demaria, E. M. C.: Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes, Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, 2017.

How to cite: Toth, E., Neri, M., Reder, A., and Rianna, G.: Future occurrence and magnitude of flood events in the Panaro River (Northern Italy): coupling bias-corrected hourly climate scenarios and semi-distributed rainfall-runoff modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6503, https://doi.org/10.5194/egusphere-egu22-6503, 2022.

Storm events lead to a mobilization of dissolved and particle-associated organic pollutants that pose a risk to river ecosystems. Target screening can hardly capture the broad range of compounds present in stormwater and receiving streams. Thus, an additional monitoring proxy that describes the overall chemical load in stormwater is needed. Each chemical in a mixture contributes, albeit with different potency, to cytotoxicity measured by reduction of cell viability after 24h in four human cell lines. Thus, the aim of this study was to investigate the applicability of cytotoxicity as a proxy for the organic contaminant load of rivers during storm events. Field investigations took place in the Ammer River (annual average discharge 0.87 m³ s^-1) close to Tübingen, Germany, during intense precipitation events in June 2021. The sampling site was located at the outlet of the gauged catchment (134 km²), thus, integrating inflowing water from all upstream tributaries and sewer overflows. During storm events, high-resolution temporal monitoring of discharge, suspended particles, particle characteristics, as well as dissolved and particle-associated organic contaminants was conducted using both chemical analyses and cell-based in vitro bioassays. The cytotoxicity in the water phase (expressed as toxic units, TU), was similar among the cell lines. The TU flux followed the course of the hydrograph with highest values at the maximum or slightly after the discharge peak. This finding suggests that the chemical load is controlled by the transported volume of water despite the fact that different contaminant sources are likely to contribute to the water flux and pollutant load in the river at different time points of the hydrograph. For the particle-associated cytotoxicity, the TU flux also followed the course of the events suggesting that the particle-associated cytotoxicity in the river is, similar to the water cytotoxicity, controlled by the particle load in the river. This highlights that the cytotoxicity is a suitable proxy to detect mixtures of organic compounds and, thus, assess the chemical load in rivers during storm events.

How to cite: Glaser, C., Engelhardt, M., Escher, B., Gärtner, A., Krauss, M., König, M., Schlichting, R., Zarfl, C., and Spahr, S.: Cytotoxicity as a proxy for particle-associated and dissolved organic contaminant loads in rivers during floods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6925, https://doi.org/10.5194/egusphere-egu22-6925, 2022.

In East Asia, unusually long-term and heavy rainfall in 2020 resulted in concentrated socio-economic damage and flooding. In this study, the characteristics of the Madden–Julian Oscillation (MJO) related to the prediction of heavy rainfall in East Asia were analyzed using the sub-seasonal to seasonal (S2S) prediction model. In 2020, unusually high precipitation fell in East Asia, compared to an average year, for an extended time. Precipitation was concentrated from the end of June to the middle of August; therefore, the analysis was carried out with an initial model date of July 2, 2020, while the lead-time was selected 1–31 day (July 3 to August 1). The model underestimated cumulative precipitation compared to observations, with KMA and UKMO having the lowest errors and ECMWF and CMA having the largest errors. The 850-hPa position altitude and wind field anomaly was analyzed and averaged over the prediction period. The results revealed that models with large errors showed different locations for the western and northern boundaries of the high pressure in the western North Pacific region, relative to observations, or else underestimated the size of the high-pressure zone. Based on the MJO prediction phases for July in the S2S models, models with good precipitation prediction performance in East Asia mainly showed phases 1–3 that were similar to observations and their amplitudes were also large. In contrast, models with poor prediction performance exhibited fewer instances of phases 1–3 on strong days or their amplitudes were small. This suggests that if an S2S model predicts the characteristics of the MJO accurately, similar to observations, it could improve predictions of summer precipitation in East Asia.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2020-01212.

How to cite: Moon, B.-K., Wie, J., and Kang, J.: Madden–Julian Oscillation related to the prolonged heavy rainfall in East Asia in 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7030, https://doi.org/10.5194/egusphere-egu22-7030, 2022.

Heavy precipitation is a major natural hazard that can have severe impacts.  In response to global warming, the character of heavy precipitation is expected to change. Projections of the future hydrologic cycle, especially of heavy precipitation, are uncertain. Especially at the regional scale, different data sources, such as different ensembles of global and regional climate models (GCMs and RCMs), provide sometimes conflicting conclusions. Therefore, it is even more important to investigate where differences between ensembles lie and to which processes they can be attributed.

A precipitation scaling (introduced by Paul O’Gorman) is used to disentangle thermodynamic and dynamic contributions in extreme precipitation. In this work, we compare the results of CMIP5 and CMIP6 and focus on climate change signals between the periods 1971-2000 and 2071-2100 over Europe. The thermodynamic component provides homogeneous signals across Europe with a rise in extreme precipitation of about 7 %/K. In contrast, the dynamic component shows no spatial homogeneous results where the dynamic contribution can even modify the thermodynamic signal. The spread between the models within one ensemble is much larger. However, based on initial analyses, the spread in the CMIP6 models appears to have become smaller compared to CMIP5. This means, understanding the dynamic changes is the key to understanding the differences between the ensembles.

As a next step, to analyze the discrepancy between CMIP5 and CMIP6 in terms of atmospheric circulation changes, we look into three atmospheric drivers: tropical and polar amplification of global warming and changes in stratospheric vortex strength.  

How to cite: Ritzhaupt, N. and Maraun, D.: Differences in the regional pattern of projected future changes in extreme precipitation over Europe are driven by the dynamic contribution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7209, https://doi.org/10.5194/egusphere-egu22-7209, 2022.

An additive logistic regression model for large hail was developed based on convective parameters from ERA5 reanalysis, severe weather reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). This model was shown to accurately reproduce the spatial distribution and the seasonal cycle of observed hail events in Europe. A spatial map of the modelled mean distribution for hail > 2 cm will be presented.

To explore the value of this approach to medium-range forecasting, a similar statistical model was developed using four predictor parameters available from the ECMWF Ensemble Prediction System (EPS) reforecasts: Mixed Layer CAPE, Deep Layer Shear, Mixed Layer Mixing Ratio and the Wet Bulb Zero Height. Probabilistic large hail predictions were created for all available 11-member ensemble forecasts (2008 to 2019), for lead times from 12 to 228 hours.

First, we evaluated the model’s predictive skill depending on the forecast lead time using the Area Under the ROC Curve (AUC) as a validation score. For forecasts up to two to three days, the model highlights a very high predictive skill (AUC > 0.95). Furthermore, the model retains a high predictive skill even for extended forecasts (AUC = 0.85 at 180 hours lead time) showing that it can identify regions with hail potential well in advance. Second, we compared the forecast spatial probabilities at various lead times with observed hail occurrence focusing on a few recent hail outbreaks. Finally, our four-dimensional model was compared with logistic models based on composite parameters such as the Significant Hail Parameter (SHP) and the product of CAPE and Deep Layer Shear (CAPESHEAR). The four-dimensional model outperformed these composite-based ones at lead times up to four days. The high AUC scores show that this model could improve short-medium range hail forecasts. Preliminary application of this approach to other convective hazards such as convective wind gusts will be presented as well.

How to cite: Battaglioli, F., Groenemeijer, P., and Tsonesvky, I.: Forecasting Large Hail Using Logistic Models and the ECMWF Ensemble Prediction System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7214, https://doi.org/10.5194/egusphere-egu22-7214, 2022.

Humankind is currently living in an era governed by continuous climate warm-up and unstoppable urbanization, in which the ongoing climate change is leading to an exacerbation of hydrometeorological events. With an intensification of magnitude and frequency of extreme rainfall events, engineers and scientists are striving to develop methodologies and strategies to effectively defend people and assets from pluvial flooding. Pluvial floods produced by local, intense, and fast rainstorms cause the surcharge of urban drainage systems inducing the inundation of streets and buildings before the runoff reaches the receptor watercourse. Pluvial flood damage has been defined as an ‘invisible hazard’ but it increasingly weighs on the budget of direct flood losses, raising the costs incurred by flood damages. Besides the tangible losses, the costs may be even higher when the intangible share is considered, such as the potential loss of heritage held in ancient towns. For this reason, the inestimable cultural and artistic heritage preserved in historical buildings require a high-level of protection against hazards induced by natural calamities. The present study investigates extreme rainfall-related impacts and hazards threatening the cultural heritage situated in the most vulnerable areas of the Santa Croce district (Florence, Italy). The district hosts some of the most important buildings of the city: the National Central Library of Florence and the Opera di Santa Croce. The geographical location of this monumental complex makes the cultural heritage guarded inside of it dangerously exposed to multiple sources of flood hazard. Firstly, river flooding due to the proximity to the Arno River (this area has been already harshly damaged by the catastrophic flood in 1966). Secondly, flooding by sewage since that the internal drainage network is linked with one of the main sewer conduits of the city. Then, surface runoff flowing down from the headwater. Considering this framework, the pluvial flood hazard assessment is performed using a 1D/2D dual drainage model specifically implemented to simulate all hydraulic phenomena occurring both on the surface and through the sewer network. The analysis comprehends a series of scenarios designed to simulate the impact of hydrometeorological extremes on the study area and each possible concatenation of consequences or failures. The hydraulic model incorporates different layers of information: the high-resolution digital surface model of the area and buildings, the public sewer network, and the internal rainfall collection system of the district. Geometrical features and technical specifications of the sewer network have been retrieved from detailed field surveys and research in historical archives. Model’s outcomes allow identifying the critical nodes within the drainage network, delineating the most vulnerable areas, and prioritizing the rescue efforts in case of severe cloudbursts. Results may help site managers to improve the efficiency of their hazard management and emergency plans. Furthermore, the study intends to propose suitable technical solutions for safeguarding the cultural heritage where designing intrusive engineering works hardly fits within the historical urban context.

How to cite: Tamagnone, P., Caporali, E., and Sidoti, A.: Safeguarding heritage sites from hydrometeorological extremes: the Santa Croce district in Florence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7449, https://doi.org/10.5194/egusphere-egu22-7449, 2022.

The 2021 European floods (July 13^th–16^th, 2021) marked Germany’s deadliest (>180 fatalities) and most costly (>€ 30 billion) natural disaster of the 21^st century. In North Rhine-Westphalia (Germany) the floods have caused drastic scenes of destruction along small mountainous river systems, such as the Vicht and Inde rivers. Alongside this destruction stands the release of organic pollutants and the remobilization of sediment-associated old burdens in the former mining area of Stolberg. In a preliminary study 10 samples along the floodplains and urban areas of Vicht and the successive Inde rivers have been collected directly after the flood to determine the pollution concentration, dispersion and potential sources. With this information an assessment of the short-term and long-term environmental risks can be evaluated.

First results show acute enrichment of organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs – petrogenic pollutants), polychlorinated biphenlys (PCBs – old burdens/plasticizers) and linear alkylbenzenes (LABs – sewage). The sewage indicators show their highest release and accumulation in samples taken in the urban areas, and subsequently dilute along the natural floodplain segments. This repeats for at least for Stolberg and Eschweiler, which were flooded by the Vicht and Inde, respectively. Old burdens, such as represented by PCBs, related to historical and present heavy industry in the vicinity to the rivers. The flood caused the remobilization of respective old burdens from contaminated plains and urban sources. Petrogenic markers, especially those of PAHs, have been measured in concentrations of mg/kg, vastly exceeding all environmental guidelines and restrictions. These can also be linked to the flooding of industrial and urban sites (e.g., household oil heating tanks, vehicles).

The wide range of observed pollution and fast dispersion of sediment-associated pollutants can be linked to the highly dynamic nature of this flood. In addition, the multitude of historical (mining, heavy industry) and present sources (e.g., fuels, oil, factory effluents, wastewater), sediment-associated pollutants have been remobilized or acutely released with the flood. This unprecedented 2021 European floods may allow insights into the relationships and interactions between hydrodynamics, sedimentology and pollution during such events.

How to cite: Bellanova, P., Schwarzbauer, J., and Reicherter, K.: Toxic European Summer Flood – Dispersion of organic pollutants along the Vicht and Inde rivers, Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8550, https://doi.org/10.5194/egusphere-egu22-8550, 2022.

Sub-daily precipitation extremes are expected to increase in intensity in a warming climate, at a rate higher than that expected from the Clausius Clapeyron scaling. Depending on the region, these precipitation extremes can be caused by different weather system types, such as extratropical or tropical cyclones, fronts, and thunderstorms. In this study we use a storm typology, based on the objective identification of cyclone, fronts and thunderstorms, to add insight to the scaling relationship between temperature and extreme precipitation.

We use 6-hourly information on the type of weather system present at each grid box over the North Atlantic and European region from ERA5 (1981-2000) during boreal winter (DJF). The mean hourly 2-m dew-point temperature over the 6 hours closest to the weather system type, and the maximum of the hourly precipitation over the same period are then used to estimate the scaling of the precipitation extremes with temperature for each storm type. Preliminary results using quantile regression we find significantly larger scaling for weather systems including thunderstorms (greater than CC scaling) than for those that do not. We also find that for the most common weather systems over Northern Europe (front only and cyclone and front together), the scaling of extreme precipitation with temperature is below CC scaling. The future impacts of the extreme precipitation events will depend on the future changes in the frequency of different weather system types as well as the temperature scaling.

How to cite: Catto, J., Sansom, P., and Stephenson, D.: Storm-type specific scaling of sub-daily precipitation with temperature over the North Atlantic and Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8566, https://doi.org/10.5194/egusphere-egu22-8566, 2022.

Extreme weather events in Europe have increased in frequency and intensity in the last decades, especially in some areas like Alpes and Balkans, and is expected to increase even more in the upcoming years due to the climate change. Monitoring and forecasting the severe weather events locally developed and in a short time range is very challenging but also very important for aviation safety. Several studies have been made for studying the pre-convective environment, however there are still gaps in the knowledge of the dynamical processes of regional and short duration deep convective systems.

This study is implemented within the SESAR ALARM project and focuses on the analysis of the pre-convective and convective environment in support to the air traffic management and air traffic control. The work focuses in the detection, analysis and nowcasting of severe weather events in a selected hotspot: the area of Milano Malpensa airport in Italy. We have used the data from 28 weather stations, 8 GNSS stations, radar and lightning detectors, in the period 2010-2020 to train a nowcasting algorithm and to characterize the pre-convective environment.

Our first results for different locations in the area of interest, show on average that the root mean square error of the rainfall prediction lie in the range 0.1029-0.2838 mm and 0.2720-0.7815 m/s for the wind speed prediction. Our algorithm shows the best rain predictive performance in the next 10 minutes higher than 90%, and higher than 80% in the next 30 minutes. Moreover, the pre-convective environment analysis shows that all the cases with wind field divergence never show an increasing trend of GNSS Zenith Total Delay before the event.

How to cite: Anesiadou, A., Chkeir, S., and Biondi, R.: Characterization and nowcasting of severe weather events over Milano Malpensa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9357, https://doi.org/10.5194/egusphere-egu22-9357, 2022.

Valley confined floods are a major hazard. In contrast to large river floods with day long warning time, they can evolve within minutes to hours, exhibit higher flow velocities and drive large amounts of debris into populated places. While many Alpine communities have developed mitigation, early warning and rapid response schemes for this natural hazard type, these measures are virtually unknown in Central European upland regions. Beyond flood protection, lacking measurement infrastructure also prevents retrospective collection of event anatomy data, which would be key to understand the evolution of an event and, hence to improve our response to future hazards.

The 14–15 July 2021 flood that hit the Ahr valley in the Eifel mountains, west Germany, was a drastic example of the potential of such valley confined floods. A wall of water flushed through the deeply incised valley, flooding more than 15 towns and affecting 42,000 people, resulting in the highest number of casualties in Germany since 1962. All gauges along the main channel were destroyed while the flood hydrograph was still on the rising limb and grid power loss interrupted collection and transmission of data from other potential sensors.

Here, we use data from a single seismic station near the town of Ahrweiler, originally deployed for earthquake seismology. Despite grid power cutoff around 23:19 CEST, the station recorded the arrival of the fast rising limb of the flood. We show how even an incomplete record of a single station not set up for flood early warning can be used to infer crucial and timely information about the flood: propagation velocity, water level and debris transport rate. We argue that installing a network of a few distributed low cost seismic sensors could have improved flood early warning and near real time provision of kinetic flood data. More importantly, such a network would be the key for improved response actions for future floods, deemed more likely in Central Europe under the currently changing climate conditions.

How to cite: Dietze, M., Bell, R., Hoffmann, T., and Schrott, L.: The seismic footprint of the devastating July 2021 Ahr Valley flood, Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9520, https://doi.org/10.5194/egusphere-egu22-9520, 2022.

The summer of 2018 was an extraordinary extreme season in Europe bringing simultaneous, widespread and coherent extremes of heat and drought in large parts of the continent with extensive impacts on agriculture, forests, water supply, and large financial losses. Joining different areas of expertise available within the German ClimXtreme project (https://www.climxtreme.net/index.php/en/), we present a comprehensive analysis of the 2018 extreme European summer in terms of heat and drought.

First, we define the events using different traditional, as well as, novel metrics. Then, we present a comprehensive dynamical analysis of the background atmospheric state, in order to better understand the events by bringing together different approaches. First results indicate that the summer of 2018 was characterized by persistent NAO+ conditions, which favored the occurrence and persistence of a Eurasian double jet stream structure. Both of those features contribute to the occurrence of heat extremes in western and central Europe. Additionally, positive blocking frequency anomalies were present over Scandinavia, which favored the intense heatwave in the region. An analysis of Rossby wave activity during the 2018 summer shows an eastward propagation of Rossby wave packets from the Pacific towards the Atlantic and the European continent already at the end of June and before the initiation of the heatwave over Scandinavia. When the peak over the Iberia occurs, there is no pronounced Rossby wave activity, which highlights the different mechanisms involved, i.e., subtropical ridges and Saharan air intrusions.

Low-frequency precursors, such as SSTs and soil moisture in spring, and their role in shaping those extreme events are also analyzed. A conspicuous tripolar SST anomaly pattern over the N. Atlantic, consisting of a cold blob south of Greenland and Iceland, was prominent starting in early spring. At the same time, a severe soil moisture depletion over Germany between April and July reflects the persistently warm and dry conditions in spring 2018 that caused anomalously dry soils in summer.

Last but not least, a tailored attribution study is presented, comparing the 2018 central European heatwave with similar events in the MPI Grande Ensemble and in CMIP6 models. To provide tailored information for this study, the event was defined as the maximum daily temperature in Germany averaged over different lengths of periods of consecutive days to account for the prolonged heat that characterized the summer of 2018. According to the MPI-GE almost every summer will be more extreme than 2018 under a 2˚C warmer world.

As heat and drought conditions are likely to become more frequent and intense under anthropogenic climate change, we argue that the scientific community can benefit from such comprehensive and transdisciplinary studies.

How to cite: Rousi, E., Fink, A., and Suarez-Gutierrez, L. and the ClimXtreme project: A comprehensive study of the extreme heat and drought of the 2018 European summer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9831, https://doi.org/10.5194/egusphere-egu22-9831, 2022.

In the present climate change scenario, the perception regarding the frequency and magnitude of flood events is changing. Nevertheless, to establish return periods and flooding patterns it is important to expand the time-window of observation beyond the historical period. To achieve this purpose, it is crucial to use the sedimentological record of alluvial plains and river banks. However, anthropogenic activities have disrupted the sedimentary dynamics thus interfering with the geomorphological settings and their stratigraphy’s. An alternative setting is the shallow nearshore, below storm wave base, where potentially stratigraphy is better preserved.

After a campaign on board RV Meteor, a group of sediment cores were collected offshore the south Portuguese coast. These cores cover the Holocene Epoch and consist essentially on alternations of silty bioclastic layers with some sandy units rich in quartz and bioclasts. The vertical variation of several sedimentological proxies allowed the differentiation of disruptive events, mostly related with extreme marine inundations or possibly linked with abrupt fluvial discharges.

Here we present some preliminary results based on grain-size and compositional analysis (XRD) and attempt to establish a chronology of those events. The preliminary data interpretation seems to suggest an increase in the flood record over the last 1000 years. However, this observation needs further support from other locations in the area and also requires a better understanding of post-depositional processes that affect the record of thin muddy layers on the nearshore stratigraphy.

This work was supported by projects OnOff - PTDC/CTAGEO/28941/2017 – financed by FCT. and FCT/UIDB/50019/2020 - IDL, also funded by FCT.

How to cite: Costa, P. and the RV Meteor M-152 scientific team: Flood sedimentological records off the south Portuguese coasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9985, https://doi.org/10.5194/egusphere-egu22-9985, 2022.

On October 4, 2019, giant hailstones of 11 cm were reported in northern parts of Attica in southern Greece. During the same day, multiple large hail reports of hailstones larger than 3 cm as well as 5 tornadoes were reported in the European Severe Weather Database along the track of a long lived supercell thunderstorm that formed over northeastern Peloponnese and moved northeastwards to Attica and Euboea. In this study, we investigate the synoptic and mesoscale weather conditions that led to this rare event by using upper-air measurements from the Athens International Airport, satellite retrievals from METEOSAT, and reanalysis data from ERA5. 

Furthermore, the predictability of this rare event is studied through high-resolution simulations performed with BOLAM, MOLOCH and WRF-ARW models, which are used operationally by the METEO unit at the National Observatory of Athens. The models were able to reproduce the mesoscale environment associated with these severe weather events, showing a highly unstable environment in Saronic gulf with more than 3000 J kg^-1 MLCAPE overlapped by more than 25 m s^-1 0–6 km Bulk Shear. However, the models were not able to fairly reproduce the triggering, track and timing of the supercell formation highlighting the great uncertainties associated with the initiation of deep moist convection over areas with complex terrain. Here, we attempt to constrain these uncertainties by applying a diagnostic tool for predicting hail size using an ensemble of high resolution simulations and we discuss its operational usage. 

How to cite: Papavasileiou, G., Kotroni, V., Lagouvardos, K., and Giannaros, T. M.: Observational and numerical study of a giant hailstorm in Attica, Greece, on October 4, 2019, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10975, https://doi.org/10.5194/egusphere-egu22-10975, 2022.

The serious hydrological consequences of climate change faced by developing countries like India show regional variability. Understanding these regional hydrologic impacts has a crucial role in the management of water resources. Mahanadi river basin (MRB) is a major large-scale river basin in India that is predicted to face severe floods under future climate change scenarios. Commonly, climate change impacts are simulated for a specific decade, specific scenario, or specific climate model in the future. We, however, employed an arguably more objective, approach that would identify the impacts of all possible combinations of specific change within the possible mean annual temperature and precipitation 2-dimensional scenario space (derived from thirteen CMIP6 models) on the hydrological responses. CMIP6 is the recent generation of climate models, released to overcome the drawbacks of the previous generation CMIP5 models such as under/overestimating the monsoon characteristics over the Indian subcontinent. Our methodological approach also involves using an ensemble of VIC models, representing the overall model uncertainty due to parameter value choices, in conjunction with these climate projections, instead of using a single calibrated model to predict the hydrological responses. The climate projections show an overall change in mean annual precipitation and mean annual average temperature that ranges from -5 to +105% and 0-7◦C respectively. This has resulted in significant changes in both mean annual flows and peak flows of up to 2849 and 29,776 m³s^-1 respectively. Uncertainties associated with the model parameters, of up to 1211 m³s^-1 are observed in the predicted peak flow magnitudes, which is considerably higher than in predicted annual flow magnitudes. Our findings indicate that precipitation mainly controls the future predicted flows in the basin. This study has provided a set of results on the likely future behavior of the MRB mean annual and peak flows under the CMIP6 climate projections. Future projections of hydrologic variables, along with the associated model parameter uncertainties can help with better hydrologic impact assessment and developing adaptation strategies for MRB in India.

Keywords: Climate change, CMIP6, VIC, Mahanadi river basin, flows

How to cite: Naha, S., Rico Ramirez, M. A., and Rosolem, R.: Quantifying the hydrological responses of future climate changes on a large scale river basin in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11093, https://doi.org/10.5194/egusphere-egu22-11093, 2022.

From the 12^th to the 15^th of July 2021, Western Europe was confronted with an abnormal amount of precipitation leading to extreme floods and enormous damage in western Germany, Belgium, Luxembourg and the Netherlands. Locally, almost thrice as much as the monthly precipitation amount was observed, up to 175 mm in two days. The large-scale weather pattern in Western Europe was characterised by an intense and stationary upper-level cut-off low.

In this study the atmospheric conditions resulting in this extreme precipitation are investigated, with a focus on understanding the enhanced moisture supply leading to the extreme precipitation amounts. Previous to the event, the Baltic area experienced a significant heatwave, and it was hypothesized that due to high evaporation rates more humid air over this region would be transported towards western Europe to result in these enormous amounts of rain.

We analysed the moisture origin of the extreme precipitation with the Lagrangian trajectory diagnostic LAGRANTO applied to both re-analysis data (ERA5) and simulations with the non-hydrostatic weather research and forecasting model (WRF). Both models represent the case rather well. In addition, the impact on precipitation by adapting the sea surface temperature (SST) of both the Baltic and the Mediterranean Sea was studied using WRF. This analysis showed that SST changes in the Mediterranean had the largest impact on precipitation in western Europe. Furthermore, first results indicate that the Mediterranean Sea, which had a positive SST anomaly of 2˚C, was the main moisture source preceding the precipitation event, contrasting our initial hypothesis.

How to cite: Benedict, I., Polak, F., Vermeulen, T., and Weijenborg, C.: Moisture origin of the extreme precipitation event in Western Europe in July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11221, https://doi.org/10.5194/egusphere-egu22-11221, 2022.

The dynamics of tornadoes include large vorticity in the lower troposphere and an intense updraft, whose combination may result in their formation. In this study we investigate the possibility of using a statistical relation for their description. In fact, the nonlinearity, complexity and fine scale of these processes presently prevents their simulation in the atmospheric circulation models currently used for weather forecasts and climate projections. Here we use a large dataset of tornadoes observed in the USA and Europe and the data of ERA5 (ECMWF ReAnalysis 5) to establish a statistical link between the occurrence of tornadoes and factors whose values can be extracted from atmospheric circulation models. The values of CAPE (convective available potential energy), WS (wind shear in the lower troposphere), SRH (storm relative helicity) and LCL (lifting condensation level) of the high resolution (about 30km) ERA5 data have been considered. The analysis shows all these variables are significantly linked to the formation of tornadoes with WS and CAPE being the most relevant ones. The analysis is an extension of a former study (Ingrosso et al., 2020, 10.3390/atmos11030301) based on a dataset of tornadoes events much larger than previously, on higher resolution atmospheric data, and more prognostic variables. The results provide a new expression for the probability of occurrence of tornadoes that can be used for forecasting their likelihood with potential applications to their predictions and future changes of their frequency.

How to cite: Lionello, P., Ingrosso, R., Miglietta, M. M., and Salvadori, G.: Factors leading to the formation of tornadoes: statistical links emerging from a large dataset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11732, https://doi.org/10.5194/egusphere-egu22-11732, 2022.

In the last years, many countries in Europe have been experiencing an increased frequency of extreme precipitation leading to natural disasters like floods and landslides. In Italy, the majority of the country’s natural disasters have been related to extreme precipitation. Floods and landslides have led to the country experiencing great loss in its social and economic structure. Early warning systems are important to stakeholders such as Disaster Risk Managers to make informed decisions in relation to a forecasted disaster.

Extreme precipitation is often associated with specific circulation patterns. Precursor information about atmospheric circulation patterns can therefore act as an indicator of an oncoming extreme precipitation event. The objective of this work is to identify the weather circulation patterns associated with extreme precipitation events over Italy.

E-OBS precipitation datasets were used to identify the most intense extreme precipitation events for each season for the period 1990-2020 across Italy. Mean sea level pressure and 500 hPa geopotential height from the ERA5 dataset were used to identify circulation anomalies associated with the extreme events. The analysis is performed by clustering extreme precipitation events into three homogeneous climatic zones in Italy defined following the Köppen-Geiger classification.

Results show that extreme precipitation events are always associated with an intense low pressure system located within the Euro-Mediterranean region. Depending on the location of precipitation extremes across different climatic zones, low pressure location changes, also modifying the atmospheric circulation and the associated moisture transport. Namely, for precipitation extremes occurring in the Italian peninsula, the low pressure is located in central-western Europe, while for extremes in Sardinia and Sicily, low pressure is in the Mediterranean. 

How to cite: Scott, W., Gaetani, M., and Fosser, G.: Weather circulation patterns associated with extreme precipitation events in Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11944, https://doi.org/10.5194/egusphere-egu22-11944, 2022.

Despite being major sources of hazards and having impacts on local and national populations, environment and economies, processes involved in extremes’ intensification and generation of disastrous impacts, such as extreme and widespread dry and wet events or flash flooding, are not fully understood yet. Therefore, the goal of WEx-Atlantic project is to perform research, to improve knowledge on weather extremes in the North Atlantic European sector and to communicate it to society. Considered extremes are strong winds and heavy hydrometeorological (HM – dry and wet) events associated with extratropical cyclones (EC), frontal systems and atmospheric rivers (AR).

WEx-Atlantic contributes to improve our understanding on the assessment of weather systems and the underlying physical mechanisms, variability and expected changes under global warming, as well as meteorological, environmental (e.g. forest) and socioeconomic (e.g. renewable wind energy and power grid) impacts on Portugal including the Macaronesia Islands.

WEx-Atlantic applies state-of-the-art techniques to detect and track weather systems, including AR, mid-latitude systems and weather types to reanalysis datasets as well as to GCMs. Here a review of WEx-Atlantic research and new contribution is presented.

This work was supported by project “Weather Extremes in the Euro Atlantic Region: Assessment and Impacts—WEx-Atlantic” (PTDC/CTA-MET/29233/2017; LISBOA-01-0145-FEDER-029233, NORTE-01-0145-FEDER-029233) funded by Fundação para a Ciência e a Tecnologia, Portugal (FCT). Alexandre. M. Ramos was supported by the FCT Scientific Employment Stimulus 2017 (CEECIND/00027/2017).

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How to cite: Liberato, M. L. R. and Ramos, A. M.: Weather Extremes in the Euro Atlantic Region: Assessment and Impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12077, https://doi.org/10.5194/egusphere-egu22-12077, 2022.

With a projected increase in frequency and magnitude of extreme weather events, the fast-growing coastal population centers of the Asian Global South experience a higher susceptibility to flood-related pollution. This is fueled by rapid land-use changes, urbanization, a multitude of emission sources, as well as anthropogenic- and flood-induced remobilization and relocation of pollutants. To yield a more comprehensive understanding of riverine and coastal floods in conjunction with these rapid urban and land-use changes, their impact on the environment and the health risks posed to local communities, sedimentary archives need to be studied.

Meandering through densely populated urban areas, Chennai’s rivers (Cooum and Adyar) and coastal systems have been affected by monsoon-induced floods (e.g., 2015 South Indian floods) and the 2004 Indian Ocean tsunami. Simultaneously, Chennai experienced an explosive population growth over the past 30 years, with the coinciding changes in land-use, urbanization, anthropogenic alterations to aquatic systems (e.g., damming, dredging), and (unregulated) environmental pollution. Especially the missing regulations, as well as growing volumes of sewage and physical waste have an enormous toll on the aquatic systems, but also pose threats by remobilization during floods.

To investigate potential flood-induced strata and chemostratigraphic changes over time, a total of nine sediment profiles along the Adyar and Cooum rivers are subject to GC-MS analyses of organic pollutants in correlation to stratigraphic changes in the obtained sediment profiles.

First results indicate that organic pollutants, such as petrogenic compounds (hopanes, PAHs), urban wastewater compounds (LABs, DEHA, methyl-triclosane), technical compounds (Mesamoll^®, DPE, NBFA) and pesticides (e.g., DDX) allow for the identification of past flooding events and their characterization in terms of release and distribution of pollution. These proxies are used to assess (chemo-)stratigraphical alterations preserved in these sedimentary archives. However, sedimentary archives in fast-growing, urbanized environments are influenced by physical anthropogenic alterations leading to superimpositions or a hiatus in the sedimentary archives, thus hampering with the (chemo-)stratigraphic reconstruction of past flooding events and environmental changes.

How to cite: Bellanova, L., Bellanova, P., Schwarzbauer, J., Lehmkuhl, F., Schulte, P., and Reicherter, K.: Chennai’s urban river systems – environmental changes, anthropogenic pollution and flood-induced remobilization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12481, https://doi.org/10.5194/egusphere-egu22-12481, 2022.

Due to extreme precipitation and runoff, severe flooding occurred in Germany in the summer of 2021 (July 13^th–16^th). In the catchment area of the Rur river, especially along its tributaries Inde and Wurm, but also along the Rur itself, this flood caused severe destruction and impacts on modern and older floodplains and anthropogenic utilized areas. This led to the acute and unusual input of harmful organic pollutants, as well as the remobilization and relocation of old burdens.

Particularly floodplains are of central importance during such flood events as their natural functions include water, sediment, and nutrient retention, as well as the self-purification of water bodies. The focus of this investigation was therefore on the importance and relevance of natural floodplains during and after the 2021 summer flood. For this purpose, 16 different floodplains distributed throughout the Rur’s course were sampled immediately after the flood. The objectives were to determine pollutant concentrations, distribution, and accumulation, as well as the identification of potential pollution sources. In this context, the results of previous floodplain sampling and regular monitoring of the river’s sediments are also considered.

Preliminary results indicate elevated concentrations of several organic pollutant groups, including PAHs (polycyclic aromatic hydrocarbons), PCBs (polychlorinated biphenyls), and LABs (linear alkylbenzenes). These substances are indicators of petrogenic pollution, historical (old burdens) and current heavy industry in the catchment area, and, of wastewater and urban pollution, respectively.

By considering these indicators and identifying emission sources (e.g., wastewater treatment plants, destructed infrastructure and industry along the main river and its tributaries) and accumulation areas that are relevant for remobilization, statements can be obtained about the high dynamics of the flood event. Furthermore, the importance of natural floodplains for the accumulation and remobilization of organic pollutants, but also the self-purification of water bodies is thus investigated and emphasized. This is of great importance for the holistic assessment of the fate and behaviour of organic pollutants as well as for the estimation of short- and long-term environmental risks and hazards related to (extreme) flood events.

How to cite: Schwanen, C., Bellanova, P., and Schwarzbauer, J.: The 2021 Flood Disaster in Germany – Distribution, remobilization and accumulation of organic pollutants along the natural floodplains of the Rur river, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12558, https://doi.org/10.5194/egusphere-egu22-12558, 2022.

Over the last few decades, anthropogenic greenhouse gas emissions have increased the frequency of climatological anomalies such as temperature, precipitation, and evapotranspiration. It is noticed that the frequency and severity of the intense precipitation signify a greater susceptibility to flash flooding. Flash flooding continues to be a major threat to European cities, with devastating mortality and considerable damage to urban infrastructure. As a result, accurate forecasting of future extreme precipitation events is critical for natural hazard mitigation. A network of ground-based GNSS receivers enables the measurement of integrated water vapour along slant pathways providing three-dimensional water vapour distributions. This study aims to demonstrate how GNSS sensing of the troposphere can be used to monitor the rapid and extreme weather events that occurred in central Europe in June 2013 and resulted in flash floods and property damage. We recovered one-way slant wet delay (SWD) by adding GNSS post-fit phase residuals, representing the troposphere's higher-order inhomogeneity. Nonetheless, noise in the GNSS phase observable caused by site-specific multipath can significantly affect the SWD from individual satellites. To overcome the problem, we employ a suitable averaging strategy for stacking post-fit phase residuals obtained from the PPP processing strategy to generate site-specific multipath corrections maps (MPS). The spatial stacking is carried out in congruent cells with an optimal resolution in elevation and azimuth at the local horizon but with decreasing azimuth resolution as the elevation angle increases. This permits an approximately equal number of observations allocated to each cell. The spatio-temporal fluctuations in the SWD as measured by GNSS closely mirrored the moisture field associated with severe weather events in central Europe, i.e., a brief rise prior to the main rain events, followed by a rapid decline once the storms passed. Furthermore, we validated the one-way SWD between ground-based water-vapour radiometry (WVR) and GNSS-derived SWD for different elevation angles.

How to cite: Hunegnaw, A., Duman, H., Elgered, G., Dousa, J., and Teferle, N.: Analysis of GNSS sensed slant wet delay during the severe weather events in central Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12589, https://doi.org/10.5194/egusphere-egu22-12589, 2022.

Climate change has a significant role in increasing extreme precipitation, including the intensity, frequency, and magnitude of events due to increases in atmospheric moisture and climate variability. This means that future increases in floods due to climate change must be considered in the construction of flood defenses, as well as the planning of new infrastructure and hydraulic structures. Previous approaches for stress testing the design of flood defenses have relied on the scenario neutral approach and the use of harmonic functions to represent changes in the seasonality and mean of precipitation. Such approaches may inadequately account for changes in extreme precipitation, especially in runoff dominated catchments. Here, we adapt the scenario neutral approach by integrating a discrete wavelet transform (DWT) to develop the flood response surface. Such an approach allows evaluation of flood sensitivity to high and low frequency components of precipitation. Using 39 catchments in Ireland, we examine the sensitivity of flooding (QT20) to changes in low and high frequency precipitation and air temperature. A sensitivity domain of 525 extreme precipitation scenarios is applied by combining 21 low frequency and 25 high frequency sets of precipitation and air temperature changes, with short duration frequency incorporated in each harmonic wavelet function. Clustering and discriminant analysis are used to create a typology of catchment sensitivity based on generated response surfaces, the mean of annual maximum precipitation, and the mean of annual maximum flows. Results allow characterization of catchment sensitivity in gauged and ungauged locations and the integration of a wider spectrum of precipitation changes when assessing sensitivity allowances for climate change.  

How to cite: Meresa, H. and Murphy, C.: Evaluating flood sensitivity to changes in high and low frequency precipitation using a discrete wavelet transform, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13038, https://doi.org/10.5194/egusphere-egu22-13038, 2022.

Analysis of the variation of the potential gradient (PG) at ground level is important to monitor the global electric circuit and the different solar and geophysical phenomena affecting it. However, this is challenging since several factors (e.g., meteorological) produce perturbations in the potential gradient. For this reason, timeseries and spectral analysis of PG at several stations are required. In this work, for the first time we performe the spectral analysis of the potential gradient recorded at several sites located at Vostok, Concordia, Halley and Casleo (South Hemisphere), and Sodankyla and Reading (North Hemisphere). In order to find the main periodicities and how the amplitude of those periods change as a function of time we use the Lomb-Scargle Periodogram and the Wavelet Transform, respectively. For all PG sites the periodicities of 0.5, 1, ~180 and 365-day were found. It was also found evidence of the ~27- and ~45-day periods. Further analysis using the cross-wavelet transform for PG versus cosmic rays, PG versus Madden-Julian Oscillation index, and PG versus meteorological parameters, shows that the 27- and 45-day periods are likely related to the solar rotation and Madden- Julian Oscillation, respectively. Moreover, for the 27-day period we found that the relationship is stronger during the occurrences of co-rotating interaction regions.

How to cite: Tacza, J., Nicoll, K., and Macotela, E.: Periodicities in fair weather potential gradient at ground level from different latitudes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-97, https://doi.org/10.5194/egusphere-egu22-97, 2022.

Thunderstorm ground enhancements (TGEs) registered on Aragats research station (530 events during  2008-2021) are frequently interrupted by the nearby lightning flashes. We are monitoring charged and neutral particle fluxes, near-surface electric field, distance to lightning flash, and numerous meteorological parameters 24/7. Our datasets [1,2] contains 165 TGEs interrupted mostly by the negative cloud-to-ground discharges (-CGs: 50%, inverted intracloud (IC) flashes followed by –CGs: 21%, inverted ICs: 18%, normal ICs:11%). The mean distance to the lightning flash estimated by EFM-100 electric mill is 5.8 +/- 3.1 km (based on 130 TGEs). Mean distance, estimated by a smaller subsample (18 TGEs) of this dataset, which contains also an estimate made by the worldwide lightning located network (WWLLN) is 6.6 +/- 5.6 km, by EFM – 4.5 +/- 2.6 km. The times of lightning occurrences measured by Aragats facilities and by the WWLLN coincide within a few microseconds. TGEs were interrupted by lightning during positive and negative near-surface electric fields. The TGEs which started at the negative (positive) near-surface electric field and terminated during the positive (negative) electric field were also observed.

How to cite: Chilingarian, A. and Soghomonyan, S.: Thunderstorm ground enhancements abruptly terminated by a Lightning flash, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-331, https://doi.org/10.5194/egusphere-egu22-331, 2022.

One of the possible sources of Terrestrial Gamma-ray Flashes (TGF) is Relativistic Runaway Electron Avalanches (RREA) accelerated in large-scale thunderstorm electric fields. In order to produce short and bright gamma-ray flash, a huge number of RREAs must exist simultaneously. This can be obtained through positive feedback mechanisms in RREA dynamics. At quasi-uniform thunderstorm electric fields, relativistic feedback provides RREAs multiplication via positrons and reversed gamma-rays. A significant disadvantage of relativistic feedback is that it requires high electric field strength in order to produce a TGF.

In complex thunderstorm electric structures, an additional feedback mechanism appears, the reactor feedback. Reactor feedback emerges if a thunderstorm consists of several RREA-producing regions, cells. A RREA developed in a cell radiates bremsstrahlung gamma-rays. Gamma-rays have high penetrative power and propagate through semi-critical electric field regions, where runaway electrons can stop, reaching other cells. There gamma-rays interact with air molecules, producing RREAs. Therefore, cells amplify each other by the gamma-ray exchange. The amplification rate can be strong enough to make RREAs self-sustainable, that is infinite reactor feedback. Infinite reactor feedback requires lower electric field strength compared to infinite relativistic feedback. Moreover, such RREA multiplication can cause a TGF.

In this report, a theoretical technique is developed to describe relativistic runaway electron avalanches dynamics in complex electric structures. Cells' interaction via high-energy particles exchange can be described with the Feedback Matrix, which is a matrix consisting of feedback operators. A feedback matrix action on RREA starting point distribution in i-th feedback generation creates RREAs starting point distribution in the next (i+1)-th generation. Matrix elements depend on thunderstorm electric field parameters and include RREA development physics and gamma-ray propagation physics. Diagonal matrix elements describe the self-action of cells, which is the relativistic feedback. The proposed approach includes all the feedback mechanisms and reduces the problem of avalanche dynamics to finding eigenvalues and eigenfunctions of the feedback matrix, as the eigenvalues are feedback coefficients.

With the feedback matrix solutions for RREA dynamics in different electric field geometries are obtained. Thunderstorm conditions required for TGF development are found. It is shown that the more complex the electric structure is the lower the electric field strength is required to produce a TGF.

How to cite: Stadnichuk, E.: Terrestrial Gamma-ray Flashes produced by complex thunderstorm electric structures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-496, https://doi.org/10.5194/egusphere-egu22-496, 2022.

Pre-monsoon thunderstorms are a common source of natural destruction over eastern India, commonly known as Nor'westors. Meteorologists studied these Nor'westers for more than a century over India. Various studies highlighted that the Chota Nagpur plateau, situated in Jharkhand state, acts as a triggering source for initiating these thunderstorms. The present study attempts to evaluate the topographical variations of the Chota Nagpur plateau for initiating the Nor'westors. The current research simulated ten thunderstorm events over the Kolkata region, West Bengal, by changing the Chota Nagpur plateau's topography (increasing and decreasing along with natural topography). The study uses the Weather Research and Forecasting model (WRF ARW 3.9.1) with a triple nested domain. The innermost domain has a resolution of 3 km across eastern India. The simulated model variables are validated against vertical profiles and surface observations of point locations obtained from the India Meteorological Department's radiosonde and automatic weather station data sets. The model simulations significantly capture the observational (surface and vertical profile) characteristics. Thermodynamic indices obtained from simulations revealed that the plateau's changed (increased/decreased) topography alters the values considerably below/above thresholds for thunderstorms over the region.

Keywords: Thunderstorms; Topography; Numerical Simulation; Thermodynamic Indices

How to cite: Sahu, R. K., Singh, K. S., Nayak, H. P., and Tyagi, B.: Evaluating the Impact of Topography on Initiation of Nor’westers over Eastern India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-558, https://doi.org/10.5194/egusphere-egu22-558, 2022.

Relativistic electrons in strong large-scale thunderstorm electric fields can obtain more energy from acceleration by the electric field than they on average lose on interactions with air molecules. Such accelerating electrons are called runaway electrons. Runaway electrons can produce additional runaway electrons by Moller scattering on air molecules. In this way, runaway electrons multiply and form a relativistic runaway electron avalanche (RREA).

In strong electric fields, RREA can multiply by relativistic feedback. Infinite relativistic feedback makes avalanches self-sustaining and could hypothetically trigger a terrestrial gamma-ray burst (TGF). This report presents the results of modeling the simplest reactor - the model of the appearance of a TGF, consisting of two cells looking at each other, their comparison with theoretical calculations and previous models.  It was found that the considered model predicts lower requirements for the electric field for the appearance of TGF than the others.

How to cite: Zemlianskaya, D. and Stadnichuk, E.: Simulation of the simplest reactor model of the dynamics of runaway electron avalanches in thunderclouds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-563, https://doi.org/10.5194/egusphere-egu22-563, 2022.

Lightning in winter (December, January, February, DJF) is rare compared to lightning in summer (June, July, August, JJA) in central Europe. The conventional explanation attributes the scarcity of winter lightning to seasonally low values of variables that create favorable conditions in summer. Here we systematically examine whether different meteorological processes are at play in winter. We use cluster analysis and principal component analysis and find physically meaningful groups in ERA5 atmospheric reanalysis data and lightning data for northern Germany. Two sets of conditions emerged: Wind-field dominated and mass-field (temperature) dominated lightning conditions. Wind-field type lightning is characterized by increased wind speeds, high cloud shear, large dissipation of kinetic energy in the boundary layer, and moderate temperatures. Clouds are close to the ground and a relatively large fraction of the clouds is warmer than −10 degree Celsius. Mass-field type lightning is characterized by increased convective available potential energy (CAPE), the presence of convective inhibition (CIN), high temperatures, and accompanying large amounts of water vapor. Large amounts of cloud-physics variables related to charge separation such as ice particles and solid hydrometeors further differentiate both mass-field and wind-field lightning. Winter lightning is wind-field driven whereas in summer lightning is mostly mass-field driven with a small fraction of cases being wind-field driven. Consequently, typical weather situations for wind-field lightning in the study area in northern Germany are strong westerlies with embedded cyclones. For mass-field lightning, the area is typically on the anticyclonic side of a southwesterly jet.

Keywords: ERA5, cold-season thunderstorm, k-means clustering, winter lightning.

How to cite: Morgenstern, D., Stucke, I., Simon, T., Mayr, G. J., and Zeileis, A.: Differentiating lightning in winter and summer with characteristics of wind field and mass field, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-824, https://doi.org/10.5194/egusphere-egu22-824, 2022.

Despite its scarcity, upward lightning initiated from tall structures causes more damage than common downward lightning. One particular subtype with a continuous current only is not detectable by conventional lightning location systems (LLS) causing a significantly reduced detection efficiency. Upward lightning has become a major concern due to the recent push in the field of renewable wind energy generation . The growing number of tall wind turbines increased lightning related damages. Upward lightning may be initiated by the tall structure triggering the flash itself (self-triggered) or by a flash striking close by (other-triggered).

The major objective of this study is to find the driving atmospheric conditions influencing whether an upward flash is self-triggered or other-triggered and whether it is of the undetectable subtype. We explore upward flashes directly measured at the Gaisberg Tower in Salzburg (Austria) between 2000 and 2015. These upward flashes are combined with atmospheric reanalysis data stratified into five main meteorological groups: cloud physics, mass field, moisture field, surface exchange and wind field. We use classification methods based on tree-structured ensembles in form of conditional random forests. From these random forests we assess the meteorological influence and find the most important atmospheric drivers for one event or the other, respectively.

Whether upward lightning is self-triggered or other-triggered can be reliably explained by meteorology. The closer the -10  °C isotherm is to the tall structure, the higher is the probability of self-triggered flashes. On the other hand, lower proportions of solid hydrometeors, supercooled liquid water and lower amounts of large scale precipitation increase the probability of an initial continuous current only flash type. However, the occurrence of nearby lightning discharges is about ten times more important for the type of upward flash. No nearby discharges (or them being further than 4 km away) considerably increases the probability of the initial continuous current only flash type.

How to cite: Stucke, I., Morgenstern, D., Simon, T., Mayr, G. J., Diendorfer, G., Schulz, W., Pichler, H., and Zeileis, A.: Upward lightning at tall structures: Atmospheric drivers for trigger mechanisms and flash type, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-958, https://doi.org/10.5194/egusphere-egu22-958, 2022.

We present an analysis of the evolution of PG during the course of rain, hail and snow events at the Xanthi site, N. Greece. In particular, using data from eight rain events in 2021, four hail events in the period 2018-2021 and four snow events during the same period, we examine how the PG frequency distribution changes during the progression of these events and discuss potential implications for the charge of the hydrometeors and the clouds that produce them. We also present some first results from recently started measurements of PG and lightning at the high altitude (2340 m ASL) site of Helmos Observatory, Peloponnese, Greece.

How to cite: Kourtidis, K., Misios, S., Karagioras, A., and Kosmadakis, I.: Measurements of PG during rain, hail, snow and lightning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1653, https://doi.org/10.5194/egusphere-egu22-1653, 2022.

The Rakia mission is a private space flight to the ISS, that was executed by the Axiom company in March 2022. The ILAN-ES (Imaging of Lightning And Nocturnal Emissions from Space) is heritage of the MEIDEX conducted on board the space shuttle Columbia in its final mission in January 2003 (Yair et al., 2004). We optimized the limited observation time (2 hours, 12x10 minutes) from the ISS such that only selected targets at prescribed times were imaged by the astronaut. We used an upgraded prediction procedure for potential TLE-producing thunderstorms, based on the verification scheme used during the THOR campaign (Chanrion et al., 2016), and by using the lightning location networks (WWLLN, ENTLN) data for selected active regions. In addition, we computed their magnetic conjugate points, so as to enlarge potential daily targets (geomagnetically conjugate sprites were never recorded from space; Marshal et al., 2005).

The camera used during ILAN-ES was a Nikon D6 set at 6400 ISO and recording 24 frames per second at 1920 x 1080 pixels. It was mounted with a 58mm/f1.2 lens, giving a 34.4^o x 19.75^o field of view corresponding to 1.07’/pixel. With these settings, the camera resolution was 130 m at nadir. The camera The astronauts operated the camera from the Copula window of the ISS while visually tracking lightning activity and directing the camera towards the bright flashes. When conjugate targets were allocated, the aim was to conduct nadir viewing.

To overcome the challenge of manual detection of lightning and transient luminous events in the video footage, we will harness several Machine Learning (ML) and Deep Learning (DL) techniques. A comprehensive ground-based campaign accompanied the ISS observations, most notably by the ground-based LEONA network in South America and by schools in Ghana, Rwanda and Zimbabwe.

This presentation will discuss mission operations and the results of the ISS and ground observations.

How to cite: Yair, Y., Price, C., Reuveni, Y., Yaniv, R., Sao Sabbas, E., and Rubanenko, L.: Results from targeted TLE and geomagnetically conjugate sprites observations from the International Space Station during the Rakia mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2485, https://doi.org/10.5194/egusphere-egu22-2485, 2022.

Lightning location information has a broad range of uses from Nowcasting through to aviation safety. Hence, the Met Office, based in the United Kingdom, has operated Lightning location systems since 1935. Here the Met Office’s next generation VLF lightning location system: Lightning Electromagnetic Emission Location using Arrival time differencing (LEELA) is described. It is set to replace ATDnet, the Met Office’s current operational system in 2022. LEELA features newly designed hardware and processing architecture, with a new novel technique to extract the sferics from the raw VLF data, and new fixing algorithms that improve location accuracy and detection efficiency over that of ATDnet. The night time issues from modal interference that ATDnet suffered from have been mitigated against and the night time performance of LEELA is improved. It will be shown that LEELA can provide lightning information over Europe, Africa, middle east and central America. In addition to this, the new processing architecture means that a near constant stream of VLF data is recorded and archived allowing investigations into sudden Ionospheric disturbances by observing changes in received power from VLF transmitters.

How to cite: Marlton, G., Potts, M., Twelves, S., Prust, S., Stone, E., and O'Sullivan, D.: LEELA: The Met Offices next generation lightning location system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2541, https://doi.org/10.5194/egusphere-egu22-2541, 2022.

On January 10th 2022 the ASIM mission to the ISS was moved to the SDN mount point on the Columbus module for observations towards the earth rim and the cosmos. This enabled the ASIM/MXGS imager spectrometer to observe TGFs in a small ISS nadir FOV covering 35-65 degrees off-axis, and GRBs in the cosmos which covers most of its FOV. We present here initial results of imaging locations for off-axis TGFs with a low spectral hardness ratio and a much larger event count due to the reduction in the "flux cosine effect", and how this might facilitate estimates of mean TGF altitude. We also present the first MXGS location images of second long GRBs and their associated lightcurves and spectra.

How to cite: Connell, P., Reglero, V., Navarro, J., and Eyles, C.: Imaging TGFs and GRBs from the earth rim mounting of the ASIM/MXGS imager-spectrometer on the ISS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3037, https://doi.org/10.5194/egusphere-egu22-3037, 2022.

Atmosphere Space Interaction Monitor (ASIM) has now observed more than 1000 Terrestrila Gamma-ray falshes (TGFs) since the launch in 2018. ASIM has two payloads, the Modular X- and Gamma-ray Sensor (MXGS) and the Modular Multi-Spectral Imaging Assembly (MMIA). MXGS consists of two detector layers, one pixelated detector in the low energy range (50 keV to 400 keV) and another in the high energy range (300 keV to >30 MeV), with temporal resolution of 1µs and 28 ns, respectively.  MMIA has three photometers (337 nm, 180-230 nm, 777 nm) and two cameras (337 nm and 777 nm). During nighttime we observe both the TGFs and the lightning that produced them. Multiple and double TGFs  separated by 1-2 ms have frequently been observed by ASIM. In this paper we present three events of double TGFs. All of them are associated with  optical pulses from a hot leader (777 nm), and the first and second pulses come from the same location, indicating that the double TGFs are produced by the same leader as it propagates upward. 

How to cite: Ostgaard, N., Mezentsev, A., Marisaldi, M., Sarria, D., Ullaland, K., Yang, S., Genov, G., Neubert, T., Chanrion, O., Christiansen, F., Cummer, S., Lu, G., Reglero, V., and Luque, A.: Double TGFs and optical pulses observed by ASIM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3382, https://doi.org/10.5194/egusphere-egu22-3382, 2022.

Our study aims at initial stage of energetic negative cloud-to-ground (CG) winter lightning flashes. We analyze broadband magnetic-field measurements recorded in the West Mediterranean region in winter 2014/2015. By combining our data with information provided by the French national lightning locating system MÉTÉORAGE, we were able to select 200 waveform captures, which contained return stroke (RS) pulses emitted by negative CG discharges with peak currents exceeding 100 kA. The frequency band of our instrumentation (5 kHz-90 MHz) allowed us to investigate fine details of recorded waveforms. We found that the winter pre-stroke processes were very short, lasting on average only 1.7 ms from the first bipolar preliminary breakdown (PB) pulse to the following return stroke pulse.  The amplitudes of the strongest PB pulses reached on average only 25 % of the corresponding RS pulse. We investigate the evolution of peak amplitudes and inter-pulse intervals of PB pulses within individual PB trains. We found that in some trains the amplitudes of pulses were nearly monotonically increasing with time, they reached a maximum in a few hundreds of microseconds, and then decreased again being relatively regularly distributed in time. Within other PB trains, the pulses were chaotically spaced and their peak amplitudes did not show any trend. We assume that the short duration of the pre-stroke process indicate strong electric fields inside winter thunderclouds and hypothesize that the time evolution of PB pulse amplitudes and interpulse intervals reflect the spatial arrangement of the negative charge region.

How to cite: Kolmašová, I., Santolík, O., Pedeboy, S., Kolínská, A., Amrich, S., Lán, R., and Uhlíř, L.: Characteristics of pulse trains observed during the initial stage of high peak current winter flashes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3752, https://doi.org/10.5194/egusphere-egu22-3752, 2022.

The north-western Mediterranean basin often experiences thunderstorms with heavy precipitation and intense lightning activity causing damages to this densely populated area. This study is conducted within the framework of the EXAEDRE (EXploiting new Atmospheric Electricity Data for Research and the Environment) project that aims to better monitor the thunderstorms in this area through a better understanding of the physical processes that drive the dynamics, the microphysics and the electrical activity of the convective systems. These thunderstorms can exhibit distinct vertical charge structures (normal and anomalous) that produce lightning flashes with different properties. The goal of this study is to compare these characteristics (CG production, flash polarity...) according to both charge structures as measured in Corsica.

The study evaluates the properties of both types of Corsican storms at the electrical cell scale. Hence, observations of the LMA (Lightning Mapping Array) SAETTA network, deployed in Corsica, are used to document in 3D the total lightning activity. Complementary 2D lightning observations recorded by the French LLS (Lightning Locating System) METEORAGE are also used. We also add Météo France weather radar data to document the cumulative rainfall associated to each electrical cell. A clustering algorithm is applied on the lightning data to identify and track the cells. Then we extract lightning and radar data for each cell to document the evolution of several lightning-related parameters during their lifetime. We also apply a recently published method to automatically infer the vertical structure of the electrical charge regions within each cell. These algorithms allow us to create a database of hundreds of electrical cells in Corsica for the period of study (June – October 2018).

We first introduce the different observations and methodologies applied here. Then we present the geographical and temporal distribution of the normal and anomalous cells over the study period. Finally we compare the electrical properties associated to these different vertical charge structure configuration. Overall, anomalous cells represented around 15% of the cells population in Corsica over the study period. Anomalous storms produced less lightning jumps per cell but produced more CGs relative to the total number of flashes per cell. We also show that anomalous cells tend to form shorter flashes. The relationship between number of CGs and cumulative rainfall in Corsica for both charge structure is linear and in accordance with previous results.

How to cite: Houel, R., Defer, E., Lambert, D., Prieur, S., Pédeboy, S., Gaussiat, N., and Radojevic, M.: Normal versus anomalous thunderstorms, a comparison of electrical cells properties observed with the SAETTA LMA over the Corsican island, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4025, https://doi.org/10.5194/egusphere-egu22-4025, 2022.

We have developed a new electrodynamic return stroke (RS) model, which is based on solving the full set of Maxwell’s equation together with the electrostatic Poission’s equation for a realistic thundercloud charge structure. The evolution of the line conductivity of the RS channel is characterized by a nonlinear resistance model. The RS channel consists of a vertical channel connecting the ground with the thundercloud and a horizontal in-cloud channel. The RS processes are initiated by adding a zero potential element into the bottom end of the vertical channel. The channel-base current does not have a predefined form, but results from our model. We show the comparison of the modeled magnetic field waveforms with the observations at distances of tens of kilometers from their source lightning discharge. We also verify the simulated electric and magnetic field RS waveforms at shorter distances and compare them with their typical shapes found in the literature. The line charge density and the electric potential prior to and after the RS initiation are also investigated from the point of view of the bidirectional leader concept.

How to cite: Kaspar, P., Kolmasova, I., and Santolik, O.: The electrodynamic model of the return stroke processes involving a bipolar leader scheme, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4157, https://doi.org/10.5194/egusphere-egu22-4157, 2022.

We used the World Wide Lightning Location Network and cyclones tracks from the International Best Track Archive for Climate Stewardship to study properties of lightning strokes occurring in tropical cyclones. We studied 429 cyclones occurring from 2012 to 2017 in both hemispheres with more than 11 million lightning strokes found within a distance of 600 km from the cyclone eye. For purposes of our study, we divided the cyclones into 6 basins: Indian Ocean, North Atlantic, Northeast Pacific and Northwest Pacific in the northern hemisphere and Indian Ocean and Southern Pacific in the southern hemisphere. We found differences in the numbers, energies and multiplicities of lightning strokes occurring in the cyclones in the northern and southern hemispheres. We calculated the median stroke energy for each cyclone. We used Saffir-Simpson scale for classifying the intensity of tropical cyclones and found a tendency of decreasing median stroke energies with an increasing cyclone intensity. We compared the evolution of lightning activity accompanying the cyclones with the evolution of their central pressure and wind speed to examine the possibility of using the lightning activity for prediction of cyclone intensity changes. In the northern hemisphere, there was on average about 28 thousands of strokes per cyclone with a median energy of 1.7 kJ, while in the southern hemisphere, there was on average 24 thousands of strokes per cyclone with a median energy of 2.7 kJ. The difference in multiplicity is not really noticeable with an average of 1.39 strokes per flash in the northern hemisphere and 1.34 strokes per flash in the southern hemisphere. In our dataset, we found 28 strokes with an energy over 1 MJ (superbolts), which occurred in a short period during the winter 2013-14, which was the winter exhibiting the largest SOI (Southern oscillation index).

How to cite: Rosická, K., Kolmašová, I., and Santolík, O.: Lightning activity accompanying tropical cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4164, https://doi.org/10.5194/egusphere-egu22-4164, 2022.

TGFs are short-duration bursts of high-energy photons shot from Earth’s atmosphere to space. They are produced during the initial upward propagation of +IC lightning leaders and are often associated with LF radio sferics. The Atmosphere-Space Interactions Monitor (ASIM) instrument provides X- and gamma-ray measurements synchronous with optical recordings in 180-240 nm, 337 nm and 777.4 nm wavelengths, allowing simultaneous detection of TGFs and the lightning processes associated with them.

ASIM observations show that TGFs are accompanied by a prominent optical pulse that marks the beginning of a lightning flash. TGFs tend to precede the pulse slightly, but the short duration of TGFs, together with the delay of the optical pulses from photon scattering in cloud particles, does not allow to resolve the correct sequence of events with confidence.

The same problem is present in measurements of radio waves, where the waves emitted by the TGF currents usually are mixed with those of the lightning currents because of the temporal proximity of the processes.

Here we report a remarkable TGF, with a high fluence of 360 counts in the energy range 0.4 - 20 MeV and a relatively long duration of 580 µs. The associated optical pulse is clearly following the TGF, which leads us to conclude that the current surge inside the leader channel is not generating the TGF, as has been proposed by models, but instead that the TGF process conditions the current surge that follows.

How to cite: Mezentsev, A., Østgaard, N., Marisaldi, M., Neubert, T., Chanrion, O., and Reglero, V.: Discerning TGF and leader current pulse in ASIM observation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4240, https://doi.org/10.5194/egusphere-egu22-4240, 2022.

Terrestrial Gamma-ray Flashes (TGFs) are short emissions of high-energy photons associated with thunderstorms. Since their discovery, it has been clear that they are associated with lightning, and several case studies have shown that the TGFs are produced in the initial phase of the lightning flash. However, it has not been tested whether this is true in general. Here we present such study using the largest TGF sample available to date from the RHESSI, Fermi, AGILE, ASIM catalogs, combined with ground-based radio lightning detection data. Based on stacking analysis of the TGFs and associated lightning activity, together with the high temporal resolution of the optical data from the ASIM photometers, we show that, indeed, TGFs are produced at the beginning of lightning flashes. We also find that the detected sferic activity from the source locations in many cases is enhanced during ~150 - 750 ms following the TGFs, as also reported in Omar et al. (2014) and Smith et al. (2016). This enhanced activity is not present in a randomly-selected sample of flashes, suggesting it is a characteristic property of a significant fraction of flashes that start with a TGF.

The study is submitted to JGR Atmospheres.

Omar et al. (2014), Characterizing the TGF-lightning relationship using ENTLN, AGU Fall Meeting 2014, Abstract AE31A-3388.

Smith et al. (2016), doi:10.1002/2016JD025395.

How to cite: Lindanger, A., Skeie, C. A., Marisaldi, M., Bjørge-Engeland, I., Østgaard, N., Mezentsev, A., Sarria, D., Lehtinen, N., Reglero, V., Chanrion, O., and Neubert, T.: Production of Terrestrial Gamma-ray Flashes During the Early Stages of Lightning Flashes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4386, https://doi.org/10.5194/egusphere-egu22-4386, 2022.

Lightning activity is predicted to increase with global warming, though estimates of lightning sensitivity to a change of temperature vary widely.  Since lightning is a small scale process, it must be represented by parameterizations in climate models. This paper uses large-scale meteorological parameters tied to thunderstorm generation to improve existing empirical models that simulate regional thunderstorm behavior. This study focuses on Tropical America, and uses the ERA5 higher resolution reanalysis data (ERA5) to develop our empirical model.  Thunderstorm data were taken from the World Wide Lightning Location Network (WWLLN) and processed using the clustering algorithm developed by Mezuman et al. (2014). The two meteorological parameters that correlated best with thunderstorm clusters in Tropical America were specific humidity (SH) and convective available potential energy (CAPE).  The resulting empirical model was run from 1979-2019 using ERA5 reanalysis data as input. This approach enables the observation of long-term trends in the behavior of thunderstorms in the regions, in the absence of a complete historical lightning record. To our surprise, Tropical American thunderstorms exhibited a negative trend over this period, with a ~8% decrease in thunderstorm clusters since the 1980s even with a rise of 1K in temperature over the same period. The regions of largest decreases in thunderstorm activity align well with estimates of deforestation.  We estimate that for every 1 Tg C lost due to deforestation, there is a 10% decrease in thunderstorm number.

How to cite: Price, C., Bekenstein, R., and Mareev, E.: Is Amazon deforestation decreasing the number of thunderstorms over Tropical America?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4393, https://doi.org/10.5194/egusphere-egu22-4393, 2022.

Nowadays, there is a great need for the preservation of historical data in earth sciences as time series covering a long time period are of extreme importance in studying long-term variations of the Earth’s environment. This is the case in the field of atmospheric electricity research, too. In this work, we focus on one of the most frequently recorded parameters of the discipline, the atmospheric electric potential gradient (PG).

The PG is the reverse of the vertical atmospheric electric field, a quasi-DC quantity measured in Vm-1 units usually near the ground most often at 1–3 m heights [1]. The PG has been measured quasi-continuously at the Széchenyi István Geophysical Observatory near Nagycenk, Hungary (NCK, 47°38’ N, 16°43’ E) since 1962 [2]. Between 1962 and 2011, the PG was recorded on photo papers which were evaluated manually and the hourly averaged PG values were archived. Nevertheless, the original photopapers, too, were kept.

In this contribution, we present a recently developed image processing algorithm to digitize the analogue PG records on the old photo papers semi-automatically. By means of this algorithm, PG averages can be obtained with a temporal resolution as high as 30 s. In order to validate the digitized data, they have been compared to the archived hourly PG averages between 1999 and 2009. The long-term, seasonal, and diurnal variations of the PG at NCK between 1999 and 2009 based on the digitized and the archived data are also presented.

[1] Rycroft, M. J., Israelsson, S., and Price, C.: The global atmospheric electric circuit, solar activity and climate change, J. Atmos. Sol.-Terr. Phy., 62, 1563–1576, 2000.

[2] Bór, J., Sátori, G., Barta, V., Szabóné-André, K., Szendrői, J., Wesztergom, V., Bozóki, T., Buzás, A., and Koronczay, D.: Measurements of atmospheric electricity in the Széchenyi István Geophysical Observatory, Hungary, Hist. Geo Space. Sci., 11, 53–70, https://doi.org/10.5194/hgss-11-53-2020, 2020.

How to cite: Magos, L., Bozóki, T., Bozsó, I., Bór, J., Horváth, A., Kuslits, L., Timkó, M., and Buzás, A.: Digitizing archive atmospheric electric potential gradient data for scientific research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5228, https://doi.org/10.5194/egusphere-egu22-5228, 2022.

We have developed a time-resolved interferometric imaging in 3D (TRI-D) method for LOFAR data where the signals of 400 individual antennas are added coherently. This allows us to reach an even better resolution in 3D than with our original impulsive imager (based on a time-of-arrival-difference method and reaching a meter scale resolution) and still have a time resolution close to the impulse-response time of our system (25 ns).

After a short outline of the TRI-D technique we show that with this new imaging technique we can resolve the fine dynamics in the different negative leader propagations modes, varying from normal negative leaders with a stepping distance of the order of a few tens of meters to negative leaders at altitudes above 7 km that propagate with steps of a few hundred meter to Intensely Radiating Negative Leaders that propagate as a broad front with an area of up to km^2  over distances of a few kilometers.  

How to cite: Scholten, O., Hare, B., Dwyer, J., Liu, N., Sterpka, C., Buitink, S., and ter Veen, S.: A range of different negative leader propagation modes as imaged with LOFAR, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5965, https://doi.org/10.5194/egusphere-egu22-5965, 2022.

How to cite: Bai, X., Fullekrug, M., Chanrion, O., Soula, S., Peverell, A., Mashao, D., Kosch, M., and Neubert, T.: Height Determination of a Blue Discharge Observed by ASIM/MMIA on the International Space Station, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5994, https://doi.org/10.5194/egusphere-egu22-5994, 2022.

In continuation of the study reported last year, we report additional results from imaging lightning initiation via interferometric beamforming of data collected by the Dutch LOw Frequency ARray (LOFAR).  Significant improvements have been incorporated into the analysis, including: more accurate antenna characterization, improvements to location accuracy, and the inclusion of a polarization model [Scholten, O., et al., PRD DD12993].  This project complements and enhances the previous work of the LOFAR lightning group of Groningen [Hare, B.M., et al., Nature 568, 360363 (2019)] and [Scholten, O., et al., ESSOAr 10503153] and elucidates regions in which there are a high number of sources within a short duration of time.  Interferometric beamforming techniques enhance both spatial and temporal resolution of lightning sources and as a result, locates and images the first non-impulsive sources in lightning flashes.  These sources are believed to be caused by a streamer-cascade-like initiation event which leads to the formation of the first leader.  Previously observed initiation events start from essentially background and within tens of microseconds ramp up a few orders of magnitude before the first impulsive sources connected with lightning leaders are observed.  The new techniques build upon those previously reported [Sterpka, C., et al., Geophysical Research Letters 48 (2021)] and [Sterpka, C., et al., EGU General Assembly EGU21-13711 (2021)], uncovering new detail in the lightning initiation region and characterization of additional flashes.  This new data includes a slow-propagating initiation discharge, starting 60 ms before the formation of the corresponding lightning leader.  The discharge is within 50 m of the initiation of the lightning leader and propagation speed of this discharge is about 700 ± 30  m/s, comparable to the ion drift speed.  This discharge continues for 30 ms before ceasing, and is likely a failed initiation attempt.

How to cite: Sterpka, C., Dwyer, J., Liu, N., Demers, N., Hare, B., and Scholten, O.: Uncharacteristically Slow Discharge Process Observed Preceding Lightning Initiation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6066, https://doi.org/10.5194/egusphere-egu22-6066, 2022.

Streamers are an important stage of lightning, taking place before the formation of a leader discharge. The goal of the novel Streamer Parameter Model (SPM) is to explain the mechanism that determines the parameters of a streamer, such as its radius and propagation velocity. We demonstrate that SPM predictions agree well with the published hydrodynamic simulation (HDS) results [1]. The discrepancies between SPM and HDS were of the same order of magnitude as the discrepancies between different HDS codes. The comparison was performed for two different streamer propagation mechanisms: photoionization and background ionization. The largest discrepancies were for the case of low background ionization, which was also challenging for HDS. Electron diffusion did not change the streamer parameters significantly. We propose that SPM, despite the crudeness of the model, provides a computationally simple way to reliably assess streamer properties.

How to cite: Lehtinen, N. and Marskar, R.: Verification of the Streamer Parameter Model by comparing to Hydrodynamic Simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6211, https://doi.org/10.5194/egusphere-egu22-6211, 2022.

Blue electric streamer discharges in the upper reaches of thunderclouds are observed as flashes in the second positive band of molecular nitrogen at 337.0 nm (blue) with faint emissions from atomic oxygen at 777.4 nm (red), a dominant line of lightning leaders. Using 2.5 years of measurements by the Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS), we find that their rise time distribution suggests two distinct categories. One includes those with fast rise times less than 30 mus that are relatively unaffected by cloud scattering and emanate from within ~2 km of the cloud tops, and the other those with longer rise times that come from deeper within the clouds. Satellite measurements show that the clouds with blue discharges have an average cloud top temperature ~200 K compared to ~210 K for those of normal lightning, suggesting that blue discharges occur in clouds that reach near the tropopause. The average convective available potential energy (CAPE) determined from ERA5 reanalysis data is ~1550 J/kg for the shallow events and ~1290 J/kg for the deeper events, compared to ~1010 J/kg for regular lightning, suggesting that the discharges favour strong convective environments. This is further indicated by the geographical distribution of blue discharges which show that they occur mainly near mountain ridges or coastlines known for their strongly convective environments.

How to cite: Husbjerg, L., Neubert, T., Chanrion, O., Dimitriadou, K., Stendel, M., Kaas, E., Østgaard, N., and Reglero, V.: Observations of Blue Corona Discharges in Thunderclouds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7225, https://doi.org/10.5194/egusphere-egu22-7225, 2022.

Terrestrial Gamma-ray Flashes (TGFs) are short flashes of high-energy photons produced by thunderstorms. They are intense phenomena that may have high photon fluxes with energies up to 40 MeV when observed from detectors in orbit. All instruments in space have suffered instrumental saturation during bright events, including CGRO-BATSE, RHESSI, Fermi-GBM, AGILE-MCAL and ASIM-MXGS. The effects include dead-time and pulse pile-up, which lead to an underestimation of the TGF fluences and, in some cases, incorrect photon energies. 
    A key asset of ASIM is that it has two detectors on the same platform: the High Energy Detector (HED, 300 keV to ~40 MeV) and the Low Energy Detector (LED, 50 keV to 400 keV). LED is only weakly affected, which makes it possible to estimate corrections to the HED measurements for even the brightest TGFs. With the method we propose, we estimate the loss of photons by combining the LED and HED measurements with GEANT4 Monte-Carlo simulations of the detector responses. 
    We applied the method to three TGF events. The first, TGF-200728, has about 0.15 counts per microsecond  per unit, and is not expected to experience saturation and is used as a sanity check for the method. The other events, TGF-181102 (1.5 counts per microsecond per unit) and TGF-181025 (2.8 counts per microsecond per unit), indicate that the HED misses at least 50% of the photon counts for the brightest TGF events.

How to cite: Sarria, D., Østgaard, N., Marisaldi, M., Lindanger, A., Mezentsev, A., Lehtinen, N., Neubert, T., Christiansen, F., and Reglero, V.: Investigating the fluence of bright TGF events detected by the Atmosphere-Space Interactions Monitor, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7408, https://doi.org/10.5194/egusphere-egu22-7408, 2022.

ELVES are being studied since 2013 with the twenty-four FD Telescopes of the Pierre Auger Observatory, the world’s largest facility for the study of ultra-high energy cosmic rays, in the province of Mendoza (Argentina) exploiting a dedicated trigger and extended readout. Since December 2020, this trigger has been extended to the three High Elevation Auger Telescopes (HEAT), which observe the night sky at elevation angles between 30 and 60 degrees, allowing to study ELVES from closer lightning. The high time resolution of the Auger telescopes allows to do detailed studies on multi-ELVES. The origin of multi-ELVES is not yet fully understood, and can be studied by analysing the time difference(s) and the amplitude ratio(s)  between flashes as a function of the radial distance of lightning emission from the causative lightning. At least two, if not three, distinct models are needed to explain the geometry of multi-ELVES events. This contribution will review the frequency of each class of multi-ELVES and correlate them to data from ENTLN, WWLLN, and GOES databases.

How to cite: Mussa, R. and the Pierre Auger Collaboration: Parametrization and Characterization of Multiple ELVES at the Pierre Auger Observatory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7542, https://doi.org/10.5194/egusphere-egu22-7542, 2022.

We have recently developed a new 3D beamforming algorithm, using data from the LOw Frequency ARray (LOFAR) radio telescope (in the 30-80 MHz band), that is capable of resolving even the most complex lightning phenomena with meter and nanosecond scale accuracy. Because it operates in full 3D, this algorithm inherently extracts and accounts for the 3D polarization of the VHF sources. Here we demonstrate the full power of this technique by extracting the full 3D polarization of multiple sections of recoil leaders. We confirm previous work that showed that recoil leaders have significant polarization perpendicular to the lightning channel, likely due to charge flow between the lightning channel core and corona sheath. However, we also show that recoil leaders can also have significant polarization parallel to the channel as well. In addition, we show that the ratio of parallel-to-perpendicular polarization is strongly correlated with faster and more intensely emitting recoil leaders. We will argue that this could be due to faster recoils causing the electric field parallel to the channel to change more rapidly, and if the electric field changes rapidly enough than the recoil leader can create streamers parallel to the lightning channel which are much more strongly emitting than streamers perpendicular to the lightning channel.

How to cite: Hare, B., Scholten, O., Dwyer, J., Liu, N., Strepka, C., Buitink, S., and ter Veen, S.: The 3D Polarization of Recoil Leaders, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7791, https://doi.org/10.5194/egusphere-egu22-7791, 2022.

SOFT-IO-LI is a new tool merging space and ground based lightning observations and aircraft NOx measurements to provide a database of new parameters characterizing lightning-NOx air masses observed at the global scale, to the scientific community.

The tool takes lightning-NOx air masses measured by IAGOS (In-service Aircraft for a Global Observing System) a European Research Infrastructure for global observations of atmospheric composition using commercial aircraft. FLEXPART the Lagrangian transport and dispersion model is used, together with ERA5 reanalysis weather data from ECMWF, to perform a backward trajectory of these air masses, resulting in hourly mass residence times for lightning-NOx particles for the days prior to the flight measurements.

We will present, for IAGOS transatlantic flights, the comparison of these mass residence times with ground based NLDN (National Lightning Detection Network) and space based GLM (Global Lightning Mapper) lightning observations, as well as with ABI (Advanced Baseline Imager) cloud data. We will show that SOFT-IO-LI is capable of relating lightning events and chemical species observed in situ, in order to determine characteristics of the different lightning-related air masses.

How to cite: Mackay, C., Sauvage, B., Wolff, P., Defer, E., Mahnke, C., Petzold, A., Bundke, U., and Kennert, M.: SOFT-IO-LI: a new tool merging space and ground based lightning observations and aircraft NOx measurements., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7794, https://doi.org/10.5194/egusphere-egu22-7794, 2022.

Negative lightning leaders, which are associated with the production of terrestrial gamma-ray flashes, prolonged X- and gamma-ray glows and neutron beams, move in a step-wise manner when the original leader channel connects to the hot, highly conductive space leader forming ahead of the leader tip. However, details about the formation and heating of the space leader, and thus of the leader stepping process, are still unknown. Here, we present a novel mechanism on the origin of space leaders: After streamer coronae have formed ahead of the leader tip, plasma chemistry and heating turn a selection of the corona streamers into a highly-conductive region. Further heating subsequently allows for the inception of secondary streamer coronae at the vertices of the conductive region, which continue to heat the already heated plasma filament, finally translating into the hot and conductive space leader. We simulate the evolution of the electric field and the associated plasma chemistry in single streamer channels and present the temporal evolution of the electron density and the electric field as well as of the temperature increase and the conductivity. We find that one streamer alone cannot be heated sufficiently towards a hot space leader, but that the inception and evolution of branching streamer coronae from this initial streamer are necessary for further heating.

How to cite: Köhn, C., Babich, L., Kutsyk, I., Bochkov, E., and Neubert, T.: The formation of space leaders in streamer coronae of negative leaders, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7799, https://doi.org/10.5194/egusphere-egu22-7799, 2022.

Elves are rapidly expanding rings of optical emissions in the lower ionosphere. Narrow bipolar events (NBEs) are signatures in radio signals from intra-cloud discharges. They are thought to be fast streamer breakdown that may trigger the onset of lightning and blue jets. However, there is a lack of experimental evidence on whether the streamer discharges of NBEs carry sufficient currents to generate elves in the lower ionosphere. Here, we report the first simultaneous observation of NBEs and elves that confirm this hypothesis. The NBEs are observed simultaneously from the ground by an array of wave receivers located in China and from space by spectral measurements by the Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS). We observe thirteen negative and six positive NBEs produced in four thunderclouds penetrating into the stratosphere. Five NBEs are accompanied by elve emissions observed in the near-ultraviolet of the Lyman–Birge–Hopfield (LBH) band.  They were at ~18 km altitude, and their peak currents, estimated by a ground-based lightning detection network, were larger than 135 kA. The observations show that the impulse currents of the streamers are of sufficient magnitude to power elves, thereby adding to the new pathways that thunderstorms affect the lower ionosphere. 

How to cite: Liu, F., Neubert, T., Chanrion, O., Zhu, B., Lu, G., Lyu, F., Dimitriadou, K., Lei, J., Østgaard, N., and Reglero, V.: Ionospheric elves powered by negative Narrow Bipolar Events in overshooting thunderclouds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7949, https://doi.org/10.5194/egusphere-egu22-7949, 2022.

ASIM TGF catalog presents more than one thousand TGF from June 2018 till the end of 2021. Using this dataset, the detection rate of TGF by ASIM was about one TGF per day. The TGF detection is a stochastic process (each TGF is not related with the next) assuming this, the time-difference distribution between one detection and the next should fit an exponential distribution. This time between TGF events distribution fits the exponential with a significant deviation. We see an excess in the number of TGF separated by less than 5 min. From the expected value of less than 1% of the events, we have an 8% of the events in this range. We call that TGF population with a time separation of fewer than 5 minutes “Brother TGF”. Large storm areas could explain this deviation, because of the storm size or also the propagation effects of the TGF inside the storm as an effective mechanism to increase the TGF production in this region. This work we present is a detailed study of the most relevant “Brothers” in the ASIM TGF imaging list. With this, we can locate where these Brothers TGF are located, and add some clues about this Brother TGF production mechanism.

How to cite: Navarro-González, J., Connell, P., Eyles, C., Reglero, V., López, J. A., Montanyà, J., Marisaldi, M., Mezentzev, A., Kochkin, P., Lindanger, A., Sarria, D., Østgaard, N., Chanrion, O., Christiansen, F., and Neubert, T.: Brother TGF, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8201, https://doi.org/10.5194/egusphere-egu22-8201, 2022.

Charge influences the properties of liquid droplets, such as their evaporation rates, hydrodynamic stability and sticking probabilities in droplet-droplet collisions. Introducing additional charge into an assembly of droplets therefore provides a possible method of influencing the droplet properties, and, in the case of a fog or cloud, a route to weather modification. The effect of charging on natural droplets has been investigated by releasing additional unipolar and bipolar ions into a natural surface fog, from both within the fog at the surface and above the fog, using an Uncrewed Aerial Vehicle (UAV). Droplet properties were monitored before and after the ion release using optical methods, together with the atmospheric electric field using a field mill. The atmospheric electric field measurements robustly demonstrate the present of the additional ions. Further, changes in the fog properties apparent when the additional ions are introduced are discussed.

How to cite: Harrison, G., Marlton, G., Ambaum, M., and Nicoll, K.: Releasing corona ions into natural fog, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8665, https://doi.org/10.5194/egusphere-egu22-8665, 2022.

Sulfur hexafluoride (SF₆) and carbon tetrafluoride (CF₄) are inert gases in the atmosphere that can absorb infrared radiation and affect the climate. They have lifetimes up to 1278 years for SF₆ and 50000 years for CF₄. The International Panel on Climate Change lists the two as part of the most influential long-lived, well-mixed greenhouse gases, with SF₆ having the highest identified global warming potential. Both gases have anthropogenic major sources. SF₆ is used as an insulating gas in the electrical power industry, and CF₄ is a by-product of aluminum manufacturing. In this study, we question whether atmospheric electricity significantly influences the atmospheric concentrations of these molecules. We aim to investigate SF₆ and CF₄ decomposition within streamers at different altitudes in the atmosphere, and then estimate the global occurrence rate of such streamers and their impact. To accomplish this, we simulate positive streamers in synthetic air that contains a small concentration of the two gases. From our simulations, we identify relations between streamer properties and the amounts of SF₆ and CF₄ destroyed, which can be used to estimate the rates of chemical processes in observed streamer events.

How to cite: Francisco, H., Ebert, U., Fullekrug, M., and Plane, J.: Decomposition of long-lived greenhouse gases by atmospheric streamers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8701, https://doi.org/10.5194/egusphere-egu22-8701, 2022.

All evolution stages of cloud-to-ground (CG) lightning flashes, both positive (+CG) and negative (-CG), generate electromagnetic radiation, which can be used for their investigation. We focus on the electromagnetic activity immediately following the first return stroke (RS). We combine measurements of the broadband receiver BLESKA and the lightning mapping array (LMA) network SAETTA, capable of detecting sources of narrowband very high frequency (VHF) radiation. The French lightning location system Météorage provided us with the information about 2D location, polarity and peak currents for studied CG and intracloud (IC) discharges. From our data collected in the northwestern Mediterranean region from September to December 2015, we have selected and investigated the electromagnetic activity following 16 +CG and 38 -CG flashes.

Using the data from individual SAETTA stations we found that 36 -CG flashes exhibited a fast decrease in the counts and in the power of VHF radiation sources immediately after the RS pulse. The maximum count of 2000 VHF radiation sources was detected by the closest SAETTA station at an average time delay of 66 μs after the RS pulse peak. At a delay of 1.85 ms after the RS pulse peak or sooner, the VHF radiation rate decreased below 1500 VHF radiation sources, with the median value of this time equal to 195 μs, and kept decreasing.

In the case of all inspected +CG flashes, we observed an unexpectedly fast increase in the counts of the VHF radiation sources and their power after the RS pulse. Up to 161.95 ms after the RS pulse, the VHF radiation rate decreased below 1500 VHF radiation sources, with the median value of this time equal to 34.53 ms, much longer than in case of –CGs. At the same time, we observed a visible sequence of bipolar pulses lasting up to 50 ms in the magnetic-field waveforms recorded by BLESKA, with the amplitude of the biggest pulse varying from 2 to 10 nT.

This observed longer presence of VHF radiation after +CG flashes may be caused by a potential difference between the end of neutralized RS channel and the positive charge layer in the thundercloud in case of +CGs, which might result in a new electrical breakdown. Then a stepwise propagation of a new negative leader inside the thundercloud is possible, emitting electromagnetic radiation in a wide range of frequencies. This radiation can be detected by narrowband LMA stations in the form of VHF radiation sources, same as by a broadband receiver in the form of pulses.

How to cite: Kolínská, A., Kolmašová, I., Santolík, O., Defer, E., Pedeboy, S., and Lán, R.: Electromagnetic radiation following the first return strokes of negative and positive cloud-to-ground lightning flashes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9076, https://doi.org/10.5194/egusphere-egu22-9076, 2022.

Terrestrial gamma-ray flashes (TGFs) are short and highly energetic bursts of photons, produced in association with lightning in thunderstorms. Elves are rapidly expanding rings of optical emissions, with radii of several hundred kilometers, produced when electromagnetic pulses from lightning hit the base of the ionosphere. The Atmosphere-Space Interactions Monitor (ASIM) detects both TGFs and Elves, sometimes simultaneously. Here, we present a study of observations where TGFs are accompanied by Elves. The optical signatures from Elves are identified from measurements by the ASIM UV photometer. Using ground-based lightning location networks, we find associated sferic detections to these events, placing them mainly over oceans and in coastal regions. Using sferic detections by GLD360, we compare the peak currents of the lightning associated with the TGF-Elve pairs to peak currents associated with other TGFs detected by ASIM, as well as with lightning in general. We show that the TGFs accompanied by Elves are among the shorter TGFs detected by ASIM, and they are associated with very high peak currents of typically several hundred kA.

How to cite: Bjørge-Engeland, I., Østgaard, N., Mezentsev, A., Skeie, C. A., Sarria, D., Lapierre, J., Lindanger, A., Neubert, T., Marisaldi, M., Lehtinen, N., Chanrion, O., Ullaland, K., Yang, S., Genov, G., Christiansen, F., and Reglero, V.: Observations by ASIM of Terrestrial Gamma-ray Flashes accompanied by Elves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9085, https://doi.org/10.5194/egusphere-egu22-9085, 2022.

Narrow Bipolar Events (NBEs) are powerful radio emissions from thunderstorms, which sometimes occur isolated from lightning and at other times appear to initiate lightning. They are recently associated with Blue LUminous Events (BLUEs) on cloud tops and attributed to extensive streamer electrical discharges named fast breakdown, but their physics is not fully understood. Here, we analyse simultaneous observations of NBEs detected by radio receivers on the ground with their optical emissions observed by the Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS). In this study, we focus on the multiple-pulse BLUEs that include one primary BLUE pulse and one or several subsequent BLUE pulses with a few millisecond intervals, as detected by a photometer at 337 nm. The observations indicate that the initial streamer discharge of an NBE is followed within a few milliseconds of horizontally oriented secondary streamer discharges at similar or higher altitudes but without triggering a leader process.

How to cite: Li, D., Luque, A., Lehtinen, N. G., Gordillo-Vázquez, F. J., Neubert, T., Lu, G., Chanrion, O., Zhang, H., Østgaard, N., and Reglero, V.: Multiple-pulse blue luminous events detected by ASIM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9138, https://doi.org/10.5194/egusphere-egu22-9138, 2022.

Lightning is inherently a spatio-temporal process with individual lightning strikes represented by their time of occurrence and spatial coordinates. In this paper, we characterise and model lightning strikes in time and space from single thunderstorms, considering each set of lightning strikes to be a set of point events. This allows for real-world datasets to characterise lightning strikes and their physical properties. We select two case studies of severe thunderstorm systems over the UK, based on their synoptic analysis information as available in the published literature. This information allows us to separate the lightning strike dataset into subsets representing individual thunderstorms producing these strikes. We first identify three supercell thunderstorms with 7955, 11988 and 5655 lightning strikes from the larger storm system that crossed the English Midlands on 28 June 2012. A second set of three structurally different severe thunderstorms with 4218, 455 and 1926 lightning strikes was selected from a severe storm system across northern England on 1 to 2 July 2015. The six lightning strike datasets are representative of individual thunderstorms and each examined with regards to three physical properties: storm movement speed, lightning inter-event time distribution and lightning spatial spread distribution about the storm track. We use a least-squares plane fit in the spatio-temporal domain to estimate a range of representative movement speed values, finding 46-52 km/h for the first storm system and 67-105 km/h for the second. For inter-event time distribution, we find that values range from 0.01 to 100 s with all thunderstorms showing two peaks in density values around 0.1 s and between 1 and 10 s. To identify temporal structure in the inter-event time series, we perform autocorrelation analysis in natural time, which returns statistically significant autocorrelation values for all thunderstorms with some storms exhibiting short-range and others long-range autocorrelation. For estimating the storm track about which the orthogonal distances are calculated between the storm track and the lightning strikes, we consider orthogonal distance regression in the two-dimensional space domain. The analysis is done similarly to inter-event times for these orthogonal distances. We find a typical range of spatial spread values to be up to 50 km in magnitude, with one thunderstorm having exceptionally high values of up to 150 km. Autocorrelation analysis of these orthogonal distance values in natural time also return significant results that vary between individual thunderstorms. Finally, we present a synthetic lightning strike model where we can freely select the number of individual storms, their starting points, direction and movement speeds. For each storm, the point events after the starting point are produced about the storm track with inter-event times and orthogonal distance values taken from synthetic time series based on the analysis done during the characterisation. The characterisation in this paper of lightning strikes in time and space is representative of real-world severe thunderstorms and can inform statistical models to simulate lightning strike events.

How to cite: Zandovskis, U., Malamud, B. D., and Pigoli, D.: Characterisation and modelling of lightning strikes in time and space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10295, https://doi.org/10.5194/egusphere-egu22-10295, 2022.

Streamer discharges are often seen as the building blocks of sparks by playing a major role in their initiation and propagation. The stagnation of streamers is of great interest from the scientific point of view and for industrial applications since it helps defining a maximal length over which a streamer can propagate. Therefore, understanding the stagnation helps the design of high voltage equipment like circuit breakers and gas insulated systems.

In this presentation, we study the stagnation of positive streamers by means of numerical modelling. For negative streamers, the modelling of the stagnation mechanism is relatively straight forward, since the streamer head enlarges, and the tip electric field vanishes smoothly. For positive streamers, the modelling is more challenging since a classical drift-diffusion model with the local field approximation usually leads to an unstable increase of the streamer tip electric field.

In our recent results published in [1] and [2], we show that the instability originates mostly from the local field approximation for the calculation of the ionization source term, and we show that the non-local treatment of the ionization leads to a successful simulation of stagnation. We use 2 different models for the treatment of ionization; the first is a classical model in which the ionization source term in the streamer tip is slightly smoothed [1] and the second, which is based on an extended model [3,4].

The successful simulation allows to observe the physical mechanisms behind the stagnation of streamer discharges by showing the role of positive ions and makes it possible to determine the maximal length a streamer can reach.

[1] Niknezhad M, Chanrion O, Köhn C, Holbøll J & Neubert T 2021, 'A three-dimensional model of streamer discharges in unsteady airflow: Paper', Plasma Sources Science and Technology, vol. 30, no. 4, 045012. 

[2] Niknezhad M, Chanrion O, Holbøll J & Neubert T 2021, 'Underlying mechanism of the stagnation of positive streamers', Plasma Sources Science and Technology, vol. 30, no. 11, 115014.

[3] Aleksandrov N L and Kochetov I V 1996, ’Electron rate coefficients in gases under non-uniform field and electron density conditions’, Journal of Physics D: Applied Physics, vol. 29, no. 6, 1476—1483.

[4] Li C, Ebert U, Hundsdorfer W 2010, , Spatially hybrid computations for streamer discharges with generic features of pulled fronts: I. Planar fronts', Journal of Computational Physics, vol. 229, 200-220.

How to cite: Chanrion, O., Niknezhad, M., Holbøll, J., and Neubert, T.: Some results on streamer stagnation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10606, https://doi.org/10.5194/egusphere-egu22-10606, 2022.

X-rays have been observed in downward cloud-to-ground lightning and rocket-triggered lightning for the case of negative leaders both during the leader stepping and the dart leader phase [1-3]. X-rays have also been observed during the stepping of dart leaders in upward negative lightning flashes [4,5].      

In this study, we present results from three positive upward flashes. The observations consist of the simultaneous records of X-rays, electric current and electric field at 23 m. Observations from a 2D interferometer system are available for two of the flashes while the third flash was captured by a high-speed camera operating at 24 000 frames per second.

We report X-rays recorded during the initial phase of upward negative leader propagation. To the best of our knowledge, this is the first time that such observations are reported in the literature. The observed X-rays are associated with the stepping of upward negative leaders that can be observed both in the electric field and current waveforms. X-rays associated with the very first step of the upward negative leader were observed in one of the three flashes.

These observations are important to understand the initiation of upward lightning and the mechanisms involved in the initial breakdown.          

[1] Moore, C. B., Eack, K. B., Aulich, G. D., & Rison, W. (2001). Energetic radiation associated with lightning stepped-leaders. Geophysical Research Letters, 28(11), 2141–2144. https://doi.org/10.1029/2001gl013140

[2] Dwyer, J. R. (2003). Energetic Radiation Produced During Rocket-Triggered Lightning. Science, 299(5607), 694–697. https://doi.org/10.1126/science.1078940

[3] Bowers, G. S., Smith, D. M., Martinez‐McKinney, G. F., Kamogawa, M., Cummer, S. A., Dwyer, J. R., Wang, D., Stock, M., & Kawasaki, Z. (2017). Gamma Ray Signatures of Neutrons From a Terrestrial Gamma Ray Flash. Geophysical Research Letters, 44(19). https://doi.org/10.1002/2017gl075071

[4] Hettiarachchi, P., Cooray, V., Diendorfer, G., Pichler, H., Dwyer, J., & Rahman, M. (2018). X-ray Observations at Gaisberg Tower. Atmosphere, 9(1), 20. https://doi.org/10.3390/atmos9010020

[5] Sunjerga, A., Hettiarachchi, P., Smith, D., Rubinstein, M., Cooray, V., Azadifar, M., Mostajabi, A., & Rachidi, F. (2021). X-rays observations at the Santis Tower: Preliminary results. Copernicus GmbH. https://doi.org/10.5194/egusphere-egu21-9586

How to cite: Sunjerga, A., Hettiarachchi, P., Stanley, M., Smith, D., Chaffin, J., John, O., Cooray, V., Rubinstein, M., and Rachidi, F.: X-rays associated with the stepping of upward negative leaders at the Säntis Tower: Preliminary results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11214, https://doi.org/10.5194/egusphere-egu22-11214, 2022.

The Pierre Auger Observatory, designed to study ultra-high energy cosmic rays, has accidentally observed, with its 3000 km² surface array of water-Cherenkov detectors, several events that are very likely downward TGFs. Their morphology, as well as the signals observed in the detectors, are totally different from what observed when an extensive air shower strikes the array. The TGF-like events are characterized by large footprints (~200 km²) and long signals (~ 10 µs), if compared to cosmic-ray showers. They happen in coincidence with lightning as demonstrated by the correlation with WWLLN data and with strong variations observed in the electric fields measured by the E-mills available at the Observatory. Other events within 1 ms of these peculiar events were observed in the same zone of the array. This time interval is about the time taken by the steeped leader to reach its full length. Finally, from a first reconstruction, the source altitude of this events is estimated to be very close to ground, at about 1-2 km. From lidar measurements, we know that there were low clouds at altitudes compatible with the estimated source location at the time of many events. The source altitudes can be used as input parameter for the REAM simulation to start a campaign to compare our experimental results with TGF models. The rate of TGF-like events per year is very low, less than 2 events per year. To increase the statistics, a modification to the read-out logic to give priority to events which contain long signals was implemented ans is under test.

How to cite: Colalillo, R. and Dwyer, J. and the Pierre Auger Collaboration: The hidden power of lightning: studying the most explosive events in thunderstorms with the world’s largest cosmic-ray observatory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12119, https://doi.org/10.5194/egusphere-egu22-12119, 2022.

Gamma-Flash is an Italian program devoted to the investigation of radiation and particles produced during lightning and thunderstorms. The project is funded by the Italian Space Agency (ASI) and led by the National Institute for Astrophysics (INAF), with the collaboration of numerous institutions and universities. The aim of the project is the study and development of an innovative gamma-ray and neutron detector, and correlative instruments, to be placed onground, at the Climatic Observatory "O. Vittori" on Mt. Cimone (2165 m a.s.l., Northern-Central Italy). In a second phase, the program foresees the development of another payload, to be placed on aircraft for observations of thunderstorms in the air. Gamma-Flash is designed to detect both short-duration transients, such as terrestrial gamma-ray flashes (TGFs), as well as longer-lasting gamma-ray emissions, such as gamma-ray glows, and associated high-energy particle emissions. Main targets of the program are the study of high-energy emissions in thunderstorms, which can have substantial impact in many fields, such as local/global climate change, environmental studies, and atmospheric plasma physics. In addition, the experiment is aimed at the estimate of the susceptibility of electronic systems and devices to TGF-induced ionizing radiation and particles. The investigation of thunderstorm-related high-energy emissions will be supported by a continuous monitoring of the correlated atmospheric scenario, by means of meteorological data analysis on a local scale. The Gamma-Flash group shares cutting-edge expertise in the field of atmospheric physics, high-energy particle and radiation instruments, radiation damage, data analysis, and simulations, taking advantage of more than ten years experience of the ASI AGILE satellite in the field of TGF studies. Gamma-Flash is currently in its design and development phase. The ground-based detector is on its way to the final implementation it will be operative starting from spring 2022. We present an overall description of the Gamma-Flash program, of its detectors, payload, and system design, and of its main scientific objectives.

How to cite: Ursi, A. and the Gamma-Flash Team: Gamma-Flash: an Experiment to Detect Radiation and Particles in Thunderstorms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12378, https://doi.org/10.5194/egusphere-egu22-12378, 2022.

Shallow landslides susceptibility assessment by physically based methods relies on the parametrization of both hydraulic and geotechnical properties of soils, which in turn depend on the conditions of root structures and vegetation cover. Vegetation roots contribute to the shear strength of soils, but their quantitative contribution is currently uncertain. Saturated hydraulic conductivity (Ks) is also relevant for slope stability as it influences infiltration rates and runoff. While the literature clearly shows the dependence of Ks on soil texture, there is a general understatement of the role of root structures on this parameter. Moreover, the distribution patterns of vegetation follow relations with surface morphologies which are not fully understood and therefore, are worthy of further investigations. For these reasons, this work focuses on the quantitative assessment of the influence of vegetation on shear strength for shallow landsliding and the investigation of the relationships between vegetation characters, saturated hydraulic conductivity and topographic parameters. Study areas affected by shallow landslides are chosen in the Garfagnana and Alpi Apuane regions (Northern Apennines, Italy), as well as in the Mt. Amiata volcano area (Southern Tuscany, Italy), where field measurements of below-ground vegetation (Root Area Ratio - RAR), above-ground vegetation (Leaf Area Index - LAI and vegetation load) and Ks are acquired inside, in the neighbour and far from shallow landslide sites. To this aim, a multi-temporal landslide inventory is already available for the study area. Below-ground data are collected in trench profiles, while above-ground data are acquired by using a digital relascope as well as implementing vegetation cover photography methods. Measurements of Ks are carried out by means of both constant and falling head approaches. The morphometric analysis is performed by using some morphometric variables (eg. slope and hillslope curvatures) derived from a digital elevation model with cell size of 10 m. Morphometric clustering of these variables allows us to extract a set of land units where the distribution of vegetation characters and Ks are assessed. First results show that: a) root reinforcement to soil in terms of root-related cohesion plays a relevant role within the soil depths involved in shallow landslides; b) the weight of above-ground vegetation plays a “mild” negative role on slope stability; c) Ks is correlated with both RAR and soil depth, suggesting possible criteria for the straightforward parametrization of input parameters.

How to cite: Marzini, L., D'Addario, E., Papasidero, M. P., Amaddii, M., Disperati, L., and Chianucci, F.: Investigating the relationships among vegetation characters, saturated hydraulic conductivity and surface morphology at catchment scale by integrating new field data and morphometric analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2641, https://doi.org/10.5194/egusphere-egu22-2641, 2022.

The Mera River watershed in the Rhaetian Alps, between Italy and Switzerland, is subject to distributed erosion, and soil degradation, affecting slope stability, and sediment transport in the river. In the future under climate change, erosion is projected to increase especially in winter, as due to larger rainfall share, and smaller snow accumulation. It is therefore necessary to develop best practices for the maintenance of slopes, such as terracing, to reduce erosion and soil loss in the area. We present the results of the recent GE.RI.KO Mera Interreg, and IPCC MOUPA projects.

We first calibrate a hydrological model Poli-Hydro in the study area during 2010-2019, against discharge data, and snow cover area from satellite. Then a Dynamic-RUSLE (D-RUSLE) model is used to simulate spatially distributed soil erosion. The model considers snow melt/accumulation, and the year round dynamics of vegetation. Potential soil erosion is validated against sediment transport data taken in a sample station in the Mera River.

The dynamics of snow cover is simulated using Poli-Hydro, while the C-factor of land cover is corrected using NDVI (Normalized Difference Vegetation Index) from satellite images, accounting for variable vegetation stages, and larger leaf cover (LAI) in summer. The C-factor is further corrected for pasture areas, using productivity data as calculated using the Poli-Pasture model, mimicking pasture growth and biomass productivity. We considered two index species for high/low altitudes, and inter-specific competition.

We then project future scenarios of climate change, and impacts thereby. Six GCMs and four SSPs of the IPCC AR6 are used, to develop 24 climate change scenarios for precipitation and temperature. We also consider changes in CO₂ concentration, and temperature increase, upon land cover, through variation of timberline and growing season. Based upon our results, conservative practices may be devised, to help improvement of pasture productivity, and reduce soil erosion.

How to cite: Bocchiola, D., Casale, F., and Stucchi, L.: Changes in pasture productivity may affect potential soil erosion under climate change. The case study of Mera watershed., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2953, https://doi.org/10.5194/egusphere-egu22-2953, 2022.

Climate change has a significant impact on slope stability through atmospheric perturbations, water infiltration and soil erosion, which is often accompanied by local or shallow sliding of the slopes. Usually, the erosion is not seen as a stability-treating occurrence, but with time it can develop to a reduction of the shear soil strength and raise in the pore water pressure that can disturb the slope stability.

In order to overcome these problems, it is necessary to introduce techniques for surface stabilization of soil slopes that increase erosion resistance and reduce surface water infiltration. Moreover, they have to be environmentally friendly, thus recommendations refer to the application of natural polymer compounds that do not pollute the environment, and at the same time represent an effective and economical measure for slope stabilization. Very often, as an additional measure in the application of these biopolymer solutions on the surfaces of the slopes, at the same time, the application of seeds from low and medium vegetation is performed. In the first months, the biopolymers form a bond between the solid soil particles, which increases the erosion resistance and reduces the ability to infiltrate and absorb surface water. In parallel, the biopolymer helps and accelerates the growth of vegetation to ensure long-term erosion and slope stability.

The aim of the presented study was to investigate the effects of the xanthan gum as a compound and to develop an original biopolymer solution which will be later tested. The testing methodology was organized in two phases: laboratory tests on natural and biopolymer treated soil in the first phase, and experimental testing of biopolymer treated slope in the second phase.

In the first phase, the classification and strength parameters of treated and untreated soil were determined through standard laboratory tests. The tests were performed on specimens with various percentages of the xanthan gum additive, moreover, specimens were tested on days 1, 7, and 14 to examine the curing effects. From the results, it was observed that Xanthan gum has significantly increased the strength of the soil, up to 50% after the 14 days of curing time.

In the second phase, the erosion of treated and untreated soil was experimentally tested on the 1:1.5 slope during a rainfall of 10 liters per hour which was simulated for 180 minutes. The obtained results were better than expected showing a significant erosion resistance on the treated slope. During the 180 minutes of rainfall on the treated slope, there was no eroded soil registered. The Xanthan gum binder with a content of 1.0% filling the pores was able to limit the water infiltration into the soil, which improves interparticle cohesion and shows increased erosion resistance. In contrast, the amount of eroded soil on the untreated slope with an area of 1.0m² was about 1900gr or soil erosion of 9.5%.

Finally, from the study can be concluded that the proposed biopolymer is a natural-based solution for erosion control which has major potential because they represent efficient, economic and environmentally sustainable engineering solutions.

How to cite: Josifovski, J. and Nikolovska Atanasovska, A.: Biopolymer soil stabilization as protection from slope erosion and shallow sliding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4236, https://doi.org/10.5194/egusphere-egu22-4236, 2022.

Soil and Water Bioengineering (SWB) is a discipline established in the second half of XX century, finding its roots in ancient practices, which implies the use of vegetation and natural materials for natural hazards mitigation and ecosystem restoration. Nature-based solutions (NBS) is a recent collective term for solutions supported and/or inspired by nature to address climate-related challenges.

Although NBS cover a wide range of approaches based or inspired by natural processes and have many objectives in common with SWB, almost no attempts have been done so far to find overlaps and differences, which is needed especially when definitions are linked to legislations and funding mechanisms.

We present the results of a systematic comparison of NBS definitions, and other terminologies that fall under the NBS concept, with the definition of SWB. First, we identified applications that are related to the NBS umbrella concept, with their relative definitions, with a special focus on flood risk mitigation, ecosystem restoration, landslide and erosion mitigation. The applications analysed include: Watershed Management or hydraulic-forestry arrangements (WM), Nature-based Solutions (NBS), Green/blue Infrastructure (GI), Urban Forestry (UF), Ecological Engineering (EE), as well as Ecosystem-based Disaster Risk Reduction (Eco-DRR).

Secondly, a comparison matrix was proposed and developed. The matrix was developed by comparing the main aspects of SWB practice with the aims of the other NBS-related applications.

The structure of the matrix was the following:

• each row represents each of the 3 main aspects of SWB practices: namely "main aims", "fields of application" and "other objectives";
• the matrix columns designate all the other NBS-related terminologies, named above.

The three main aspects of the SWB discipline cover the following:

• main aims: the four main objectives of SWB; namely: technical, ecological, landscape and socio-economic objectives.
• fields of application: main domains of applications and fields of interventions;
• other objectives: the multi-purpose functions exerted by SWB.

Excerpts from relevant peer-review and grey literature on NBS were included in the matrix to cross-check the 3 main aspects of the SWB practice. We observed that SWB approaches have at least 2 "aims" in common with all the terms, particularly that all 3 main aspects are covered by the NBS definitions. In terms of "fields of application", the highest number of similarities are found between SWB and EE, and, to a smaller extent, WM, GBI and Eco-DRR.

In this work we conclude that SWB discipline can be recognized as a concept falling under the NBS unifying concept to prioritise nature to integrate climate change adaptation, mitigation and disaster reduction efforts. SWB overlaps and, in some cases, compliments many NBS-related terminologies. Thus, SWB can and should be recognized as having always been an NBS.

How to cite: Preti, F., Capobianco, V., and Sangalli, P.: Systematic comparison of definitions and aims between Soil and Water Bioengineering (SWB) and Nature-Based Solutions (NBS), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4499, https://doi.org/10.5194/egusphere-egu22-4499, 2022.

Abstract:

Vegetative barriers are narrow strips of plants or plant residues that are increasingly being used as measures to reduce the connectivity of catchments in terms of water and sediment fluxes (Frankl et al., 2021a). They can mostly be found at plot edges where they do not hinder farming activities too much. Their principal function is to reduce sediment export from cropland and thus mitigate negative off-site effects of erosion (e.g. muddy floods, pollution of rivers). Being implemented in concentrated flow zones where ephemeral gullying is recurrent, they also prevent their development (Frankl et al., 2018). Although vegetative barriers are increasingly being implemented in open agricultural areas, little information is available on the effectiveness of vegetation barriers at buffering the flows of water and sediment. Here, we focus on vegetative barriers that are widely implemented in Flanders (Belgium) and which are made of straw bales, wood chips or bales of coconut fibre. Based on three simulated runoff experiments performed in the field, we calculated the hydraulic roughness and sediment deposition ratio. Our experiments show that the barriers made of coconut-fibre bales performed markedly better than those of straw bales or wood chips (Frankl et al., 2021b). However, as vegetative barriers have to be renewed every few years because of the decomposition of organic material, barriers made of locally available materials are more sustainable as a nature-based solution to erosion. We conclude that the vegetative barriers are an effective way of mitigating the negative effects of soil erosion. While barriers made of coconut-fibre bales are superior in their regulation of flows of runoff and sediment, barriers made of locally sourced materials are more sustainable.

Keywords: agriculture, erosion control, hydrological connectivity, runoff, sediment

References:

Frankl, et al. (2021a) Gully prevention and control: Techniques, failures and effectiveness. Earth Surf. Process. Landforms: 46: 220– 238. https://doi.org/10.1002/esp.5033.

Frankl, A., et al. (2021b). Report on the effectiveness of vegetative barriers to regulate simulated fluxes of runoff and sediment in open agricultural landscapes (Flanders, Belgium). Land Degrad. Dev. 32: 4445– 4449. https://doi.org/10.1002/ldr.4048

Frankl, A. et al. (2018). The success of recent land management efforts to reduce soil erosion in northern France. Geomorphology 303: 84–93. doi:10.1016/j.geomorph.2017.11.018

How to cite: Stokes, A., De Boever, M., Bodyn, J., Buysens, S., Rosseel, L., Deprez, S., Bielders, C., Degré, A., and Frankl, A.: The implementation and effectiveness of vegetative barriers to regulate fluxes of runoff and sediment in open agricultural landscapes (Flanders, Belgium), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4768, https://doi.org/10.5194/egusphere-egu22-4768, 2022.

Vegetation is an important tool for managing urban surface water and shallow geotechnical assets. However, root water uptake driven changes in slope hydrology (soil water content, matric suction, and hydraulic conductivity) are poorly understood in heterogeneous soils and under extreme climatic conditions. Slope stability is affected by intrinsic factors, including geometry, soil properties, groundwater and vegetation driven matric suction. Field evidence indicates that engineered slopes are susceptible to hydrometeorological slope instability mechanisms and that these pose a potential failure hazard to asset operation and public safety. The UK hosts 15,800 km of railway network and 7100 km of strategic road network, accounting for 49,000 slopes. This is a significant portfolio of slopes that must be managed and maintained at considerable expense.

To better understand the influence of vegetation on soil water dynamics in geotechnical infrastructure, Electrical Resistivity Tomography (ERT) is being used. ERT is a non-invasive tool for measuring and imaging subsurface soil moisture dynamics volumetrically. ERT can be used to quantitatively establish how the presence of roots influences transient soil moisture content and suction to assess the effectiveness of vegetation in managing slope hydrology and excess surface water issues in the built environment. This research aims to use 4-D ERT to determine the impact of vegetation on the hydrological behaviour of a high plasticity clay derived sub-soil used in the construction of infrastructure slopes in the southern half of the UK. Laboratory-scale experiments are underway at the UK National Green Infrastructure Facility, Newcastle, using a controlled environment chamber. A suite of soil columns is planted with vegetation, False Oat Grass (Arrhenatherum elatius) and Common Bent (Agrostis capillaris) and feature a 3D ERT electrode array and point sensors for measurement of volumetric water content, matric suction, and electrical conductivity throughout the profile. Through frequent imaging of soil-water-plant interactions and correlation with destructive root architecture imaging, this research aims to highlight how these relationships change over time and respond to extreme weather conditions (drought/inundation) to better predict, manage, and mitigate the occurrence of slope failure. Furthermore, the work aims to improve understanding of vegetation-driven soil moisture movement in the near-surface to better assess seasonal and longer-term slope stability to inform asset management strategies.

How to cite: Thaman, N., Stirling, R., and Chambers, J. E.: Developing Novel Geophysical Tools to Investigate Urban Vegetated Soil Moisture Dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7386, https://doi.org/10.5194/egusphere-egu22-7386, 2022.

Many types of Nature-Based Solutions (NBSs) have been applied worldwide to mitigate impacts of hydro-meteorological hazards produced by anthropic activities such as grazing and agriculture. Among them, vegetated buffer strips (VBSs) and winter cover crops (WCCs) are suitable solutions for reducing runoff and soil erosion rates from cultivated fields. However, their mitigating effects depends largely on local conditions such as morphology and soil nature.

This study investigated these aspects by modelling the NBS effects on soil and water dynamics in two test sites located within the Massaciuccoli agricultural plain (Vecchiano, Pisa, Central Italy) and characterised by different soil types (peaty and silty soils). The SWAT+ model has been chosen to simulate hydraulic and hydrological phenomena using high-resolution data such as digital terrain models (DTMs) from close-range photogrammetry, detailed land cover mapping, actual crop rotations, and detailed calendars of agronomic operations. We considered two types of NBSs: i) 3 m wide VBSs planted along both sides of field ditches, covering about 10% of the agricultural land, and ii) WCCs sowed after harvesting summer cash crops. Both NBSs exert their action on 30% of the experimental area. The mitigating effect was tested by comparing simulation results from NBS and control (conventional agriculture) scenarios under ongoing climatic conditions and future climate changes.

Results indicated that VBSs and WCCs showed different capabilities to reduce runoff and sediment losses, and the adoption of both can enhance the mitigation effect significantly. NBSs resulted effective also in completely flat areas since slight topographic irregularities can cause local preferential flows resulting in high runoff rate and sediment losses. Furthermore, it is demonstrated how the soil variability in texture and organic matter content can affect the amount of runoff and sediment loss on a local scale. Consequently, the mitigating effects of NBS can be closely driven by the soil nature and heterogeneity. This influence is even more significant under extreme climatic conditions such as higher temperatures and more aggressive rainfall events. In these cases, NBSs can play an essential role in mitigating runoff and soil erosion phenomena on fine-textured mineral soils. In contrast, they lose much of their effectiveness on peat soils.

How to cite: Pignalosa, A., Silvestri, N., Pugliese, F., Gerundo, C., Corniello, A., Del Seppia, N., Lucchesi, M., Coscini, N., and De Paola, F.: Modelling the effects of NBS adoption in mitigating soil losses of a land reclamation area in the Massaciuccoli lake catchment (Central Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7965, https://doi.org/10.5194/egusphere-egu22-7965, 2022.

Plant roots increase soil shear strength. The increase primarily depends on the relative direction of the root orientation and the principal strains/stresses of the rooted soils. Most of the published work focused on the direct-shear behaviour of rooted soil, of which both the magnitude and direction of the principal stresses could not be controlled nor measured. Indeed, in the scenario of slopes, the stress path experienced by direct-shear soil samples and the associated shear strength parameters (e.g. cohesion and friction angle) derived are only relevant to the soil elements that are sheared in the direction parallel to the slope. The shearing behaviour of rooted soil following other stress paths, such as triaxial compression (near slope crest) and triaxial extension (near slope toe), have rarely been investigated. In this study, we conduct a comprehensive laboratory test campaign to study the effects of stress paths on the shearing behaviour including stress-strain (hardening and softening) on coarse-grained soils reinforced by the roots of vetiver grass (Chrysopogon zizanioides). Root-reinforced soil samples prepared to different root volume ratios (RVR; defined as the ratio of total root volume to total specimen volume) were subjected to undrained triaxial compression and extension stress paths at different confining stresses. We will present key experimental evidence to demonstrate how the different stress paths and RVRs affect the stress–strain behaviour of the soil. We will also present the effects of stress path on cohesion and friction angle and discuss the strength anisotropy of the rooted soils. The new test results will shed light on the selection of plants of desirable root architecture at different slope locations (i.e. crest, mid-slope, toe) to maximise the root reinforcement effects to shallow soils.

How to cite: Leung, A. and Karimzadeh, A. A.: Stress path effects on the shearing behaviour of root-reinforced soils, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8523, https://doi.org/10.5194/egusphere-egu22-8523, 2022.

Landslides are common natural hazards that greatly impact lives and property worldwide. The magnitude of landslide impacts depends strongly on how far landslide sediments travel, widely known as landslide mobility. Numerous studies showed that landslide mobility is complex, but largely affected by initial water content during landslide initiation. Here, water acts as a medium that carries the collapsed landslide mass downslope. Vegetation root systems may alter the initial water content by modifying the flow path within the soil. The mechanical reinforcement of root systems may also limit the spatial propagation of the landslide mass. Thus, vegetation root systems may exert significant effects on landslide mobility. Nevertheless, effects of root systems on landslide mobility have rarely been discussed in landslide studies. The objective of this study is to evaluate the effect of rooting systems on landslide mobility.

A flume constructed at a 1:70 scale was used to evaluate the effect of root systems on landslide mobility. The flume consisted of two segments representing landslide initiation (120 cm long, 35° inclination) and deposition (150 cm long, 35° inclination). All segments were 80 cm wide, 15 cm high, and constructed with 1-cm thick acrylic material. Sand (density=1.4 g/cm³, D₅₀=0.23 mm) was placed in the initiation segment to a depth of 10 cm. For conditions with vegetation (V), we grew pea (Pisum sativum L.) bean sprouts in the sand to simulate the root system. Sprouts were grown at 3 cm intervals for two weeks to simulate the root system on 2200 stem/ha of Japanese cedar forest. To initiate landslides, 90 mm/h of rainfall was applied via nozzles installed at 2 m above the flume. Timing of landslide initiation was then measured. Water content was also measured by TDR sensors installed at 3 and 7 cm depths below the soil surface. The L/H ratio was estimated based on total travel distance and total descent height of the landslide mass.

Vegetated conditions (V; n=3) were more stable than non-vegetated conditions (NV; n=3). Indeed, landslides initiated at 889-959 s (SD=41 s) on V, while on NV was 510-519 s (SD=5 s). Mean volumetric water content during landslide initiation was 0.2-0.22 (SD=0.01) on V, while on NV was 0.16-0.2 (SD=0.02). Because V had higher water content than NV, V was 1.2-1.4 times more mobile than NV. The L/H was 2.2-2.4 (SD=0.09) on V, while on NV it was 1.7-1.8 (SD=0.06). In general, vegetation root systems maintain slope stability by adding more cohesion to soils. Due to this reinforcement, greater gravitational forces and pore water pressure are needed to destabilize the slope. This consequently elevates the threshold of water content for landslide initiation. Since water content greatly influences mobility, wetter conditions enhance the mobility of the collapsed landslide mass. Our findings concur with previous studies that root reinforcement can mitigate slope instability. However, we highlight that such reinforcement can also enhance the mobility, which may elevate the potential impacts of landslides. We further investigate the effect of various stem densities on landslide mobility.

How to cite: Noviandi, R., Gomi, T., Sidle, R. C., Ritonga, R. P., and Hasunuma, Y.: Do Vegetation Root Systems Affect Landslide Mobility? A Flume Experiment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9004, https://doi.org/10.5194/egusphere-egu22-9004, 2022.

Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences, COSCI, Hydraulic Platform LCH, Institute of Civil Engineering, EPFL-ENAC, Lausanne, CH and University of Lausanne, Institute of Earth Surface Dynamics (marceline.vuaridel@unil.ch)

Floods and intense surface runoff are recurring hazards known for triggering erosion processes at the channel and the catchment slope scales, respectively. Whilst the firsts determine the removal of streambank material, also referred to as hydraulic streambank erosion (e.g., Ruiz-Villanueva et al., 2014), the seconds are typically responsible for destabilizing shallow landslides. Both processes are exacerbated by extreme precipitation events, and can cause important damages to forests, agriculture, civil structures, and settlements through the loss of land masses. Moreover, streambank erosion and shallow landslides can be responsible for the recruitment of large wood (LW), whose transport during floods may strongly impacts on downstream infrastructures of urbanized areas (e.g Ruiz-Villanueva et al., 2014).

Via augmented mechanical stabilization, plant roots may significantly decrease the susceptibility of riverbanks to hydraulic erosion as well as shallow landslides. Under certain conditions, plant roots can be considered an alternative protection against such processes with respect to other civil engineering measures (Stokes et al., 2014). However, root reinforcement effectiveness depends on many factors such as roots density, soil properties, and soil thickness (Cohen and Schwarz, 2017), which implies that some vegetated areas have a more significant effect than others. Most available models ignore the contribution of plant roots with acceptable spatial resolution.

In this work, we present BankforNET and SlideforNET, two physically-based modelling tools, which have been developed to take the different stabilizing effects of soil reinforcement mechanism by plant roots into account. This is important for proper modeling of bank erosion and landslides processes during extreme events, and to optimize forest protection strategies. BankforNET is a one-dimensional, probabilistic model which simulates expected hydraulic streambank erosion by considering channel morphology, bank sediment material, vegetation roots, and a certain discharge scenario. The SlideforNET is a probabilistic model based on the 3D analysis of slope stability and takes the lateral and basal root reinforcement into account. Ultimately, it gives an estimation of the degree of protection of a forest against landslides.

These tools are currently being tested in a catchment of 29 km² in NW Switzerland for the priorisation of protective forests against risks related to LW transport during floods. Based on the model results, the possible silvicultural measures are defined considering quantitative criteria such as the risk mitigation effect of the forest stands, or their risk increment due to LW recruitment and transport. This study is an example of how quantitative tools can be use by decision makers to priories the role of protection forest in a catchment and to support the definition of silvicultural measure to mitigate the risks due to LW transport.

How to cite: Vuaridel, M., Schwarz, M., Ruiz-Villanueva, V., Perona, P., and Cohen, D.: Roots mechanical effects on hydraulic riverbanks erosion and on shallow landslides: tools for the protection forest management along channels, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9254, https://doi.org/10.5194/egusphere-egu22-9254, 2022.

The Swedish Civil Contingencies Agency, the Swedish Geotechnical Institute, the Swedish Road Administration and the Swedish University of Agriculture have together been involved a project named “Vegetation as a mean for slope stabilisation”. The aim of the project was to introduce soil-bioengineering methods in Sweden through demonstration projects and to obtain experiences regarding the function and effect of plants on slope stability within Swedish conditions.

In three selected areas, the plant- and soil conditions were studied, with tests commencing in the spring of 2004 and in the beginning of 2005, respectively. The project ended in 2007 in a report containing recommendations, based on the experiences from the project, for the continued use of soil bioengineering methods.

In the test site Bispgården, a new road was built in 2004 through a gully area. The soil consists of highly erodible silt and sand material. Hedge- and brushlayers with grass seeding were selected to protect the soil from erosion in one slope. Equipment for measurements of pore pressure and precipitation were installed in the summer of 2004. Studies of the plant conditions were conducted several times during the first two years of the project.

In the test site Bydalen, a reconstruction of a country road was conducted in 2005, as the road was plagued with annually recurring erosion along it’s existing silty-till slopes. These slopes were to be restabilised during reconstruction. All together nine existing slopes were stabilised in early 2005 by different soil bioengineering methods proposed by the project group. The group analysed the function of the plants together with automated recordings of precipitation.

In the test site Näsåker, steep slopes of a country road were repaired in 2005-2008, due to continuing erosion and landslides in the silty soil slopes along the existing road. The slopes were stabilised with soil bioengineering and soil nailing.

Different soil bioengineering methods have been used in some new production sites, following this demonstration project. The methods may also be implemented in future projects.

The results from the demonstration in project sites, will be described in this presentation.

How to cite: Ånäs, M.: Vegetation as a remedial measure against erosion and shallow landslides in steep soil slopes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9829, https://doi.org/10.5194/egusphere-egu22-9829, 2022.

Root reinforcement is a variable factor that influences the disposition of shallow landslides over different time scales. Natural or anthropogenic forest disturbances, such as forest fires or clear cuts, may modify considerably the vegetation cover on a short time scale, with major consequences on several ecosystem services, including the mitigation of risks due to shallow landslides. After catastrophic forest disturbances, it is of primary importance for decision makers to assess how risks will change in order to evaluate the most appropriate protection measures. Therefore, the quantification of the effect of the temporal dynamic of root reinforcement is of fundamental importance to estimate the occurrence probability of shallow landslides.

Data on root distribution and pullout tests for spruce (Picea abies) and beech (Fagus silvatyca) trees are used to upscale the basal and lateral root reinforcement at the stand scale (Schwarz et al., 2012). The decay of root reinforcement is calculated based on data collected in a burnt (Vergani et al., 2017) and a clear-cut area (Vergani et al., 2016). The recovery of root reinforcement after disturbances is estimated considering the growing conditions of the stands (Flepp et al., 2021). The quantification of the dynamic of the forest stands and the derived root reinforcement at stand scale is based on the analysis of four Swiss National Forest Inventory (NFI 1-4). The estimated time-dependent variation of root reinforcement is implemented in the SlideforNET model (ecorisq.org) to calculate the occurrence probability of shallow landslide after disturbances.

The results show that the recovery of root reinforcement after disturbance is effective to reduce the hazards of shallow landslide only for a narrow range of disposition factors. Given a defined rainfall statistic, slope inclination is the factor that most influence the effectiveness of root reinforcement recovery, within a range of inclination variations of 4-8°. Further relevant factors are soil thickness and runoff contributing area.

The extended version of SlideforNET quantifies how effective is the recovery of root reinforcement in stabilizing shallow landslides after stand replacing forest disturbances. This information is fundamental to evaluate if additional temporal or permanent technical measures are needed to keep an acceptable level of risk after forest disturbances.

How to cite: Schwarz, M., Cohen, D., Giadrossich, F., May, D., Moos, C., and Dorren, L.: Influence of the temporal dynamic of root reinforcement on the disposition of shallow landslides, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11679, https://doi.org/10.5194/egusphere-egu22-11679, 2022.

Enhancing the overall resilience of vegetated slopes against shallow mass movement can be achieved by better understanding soil-root interaction.  To predict the behaviour of vegetated slopes during design, parameters representing the root system structure, such as root distribution, length, orientation and diameter, should be considered in slope stability models. Microscale quantifications of how root growth influences soil characteristics, able to inform computational models, are scarce in the literature, especially for stratified soils. This study quantifies the relationship between soil physical characteristics and root growth, emphasising particularly on (1) how roots influence the physical architecture of the surrounding soil structure and (2) how soil structure influences root growth. A systematic experimental study is carried out using high-resolution X-ray micro-computed tomography (µCT) to observe the root behaviour in layered soil. In total, 2 samples are scanned over 15 days of growth, enabling the acquisition of 10 sets of images. A machine learning algorithm for image segmentation is trained to act at 3 different training percentages, resulting in the processing of 30 sets of images, with the outcomes prompting a discussion on the size of the training data set. An automated in-house image processing algorithm is developed to provide values of void ratio and root volume ratio for Regions of Interest at varying distance from the root. This work investigates the effect of stratigraphy on root growth, along with the effect of image-segmentation parameters on soil constitutive properties.

How to cite: Nadimi, S., Kemp, N., Angelidakis, V., and Luli, S.: Observations of root growth in stratified soils at the microscopic scale: Insights from micro-computed tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12425, https://doi.org/10.5194/egusphere-egu22-12425, 2022.

The focus of geotechnical researchers and practitioners has recently been on the impact of vegetation on the mechanical behaviour of the soil as nature-based techniques against erosion and landslides. Although numerous laboratory studies have already been produced on this subject, there seems to be a lack of discussion on the significance of the results in relation to the representative elementary volume (REV) used. An excessive or scarce root/soil ratio can result in over- or underestimation of the strength of the soil specimen tested, respectively. In addition, a root/soil ratio very different from that which the plants have in-situ would risk making the laboratory results difficult to upscale to the slope or catchment level. To this end, the aim of this study is to present triaxial compression tests of large vegetated soil specimens (h = 400 mm Φ = 200 mm).

Silty sand was used and statically compacted at a dry density ρ_d = 1.60 Mg/m³ and at a water content w = 15%. Samples were then thoroughly poured with water up to a high degree of saturation (S_r ≈ 0.95). Eight of them were seeded with Cynodon dactilon, maintaining fixed seeding spacing and density. Samples were irrigated for eight months to induce sprouting and root growth: during this period, matric suction was monitored. The same procedure was followed to prepare ten fallow specimens.

Prior to testing, samples were sealed and left in the darkness in a temperature/relative humidity-controlled room for 24 hours, to equalise the desired value of initial suction. An isotropic consolidation stress between 10 and 50 kPa was imposed prior to shearing at a vertical displacement rate of 0.016 mm/min. Matric suction was measured by a tensiometer and the water content was checked at the beginning and at the end of each test. Finally, soil samples were washed to retrieve the entire root architecture, to assess root volume and tensile strength. The resulting values of the root volume ratio of Cynodon dactilon were in good agreement with those observed in-situ in literature studies.

Generally, the higher the initial soil matric suction, the higher the strength observed in the tests, with vegetated soil systematically showing greater strength than the bare one at similar hydro-mechanical states. In fact, at low values of suction, additional resistance in vegetated soil was observed once reaching large shear deformations, whereas, in drier soils, root reinforcement was activated at smaller strains. Indeed, soil hydraulic state affected the root failure mechanism. In nearly saturated soil, the roots subjected to shearing/tension are free to stretch and slip whereas in slightly saturated soil they are firmly bonded within the matrix and so they experience a more immediate breakage.  

Despite the root reinforcement, the vegetated samples exhibited larger volume deformations upon shearing, due to the changes generated by root growth on soil fabric (fissures).

A shear strength criterion for partially saturated soils was used to interpret successfully the results, considering suction, degree of saturation, and soil microstructure. Roots predominantly increased the apparent cohesion of the soil, with minor changes on the friction angle.

How to cite: Fraccica, A., Romero, E., and Fourcaud, T.: Large-scale triaxial tests of vegetated soil at low confining stresses, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12479, https://doi.org/10.5194/egusphere-egu22-12479, 2022.

The effect of a long-root grass on the shear strength response of a partially saturated pyroclastic soil was investigated through a field and laboratory experimental program. Field measurements of soil water content, suction, temperature, and laboratory tests aimed to estimate the shear strength of differently rooted soils were performed. The experimental investigation was carried out on a test site located in Nocera Inferiore, (Campania region, Italy), a few kilometers far from sites of past catastrophic flow-like landslides. The experimental program was carried out on three species of Perennial graminae grass species, characterized by fine and fasciculate long roots.  

In the field, soil temperature, pH, humidity, and suction were monitored from seeding. The trends were compared with those of air temperature and humidity. Moreover, soil suction and water content trends were related to daily rainfall.

Undisturbed pyroclastic soil specimens containing roots of perennial graminae grass species were collected after 5 months from seeding and tested at natural water content in standard and suction controlled direct shear equipment. The specimens exhibited different Root Volume ratio (RV) and suction. The shear envelopes were extrapolated using Bishop formulation of effective stress, which allows to consistently consider the partially saturated condition of the soils. The experimental results outlined that the shear strength envelope of vegetated specimens moves upwards in the τ-σ’ space, but also rotates counterclockwise. In general, the cohesion intercept increases, while the effective frictional angle reduces. Moreover, the RV influence on the magnitude of friction angle and cohesion has been assessed. Densely vegetated soils undergo larger modifications of the shear strength envelop than poorly vegetate specimens.   

The authors would like to acknowledge Prati Armati S.r.l. that provided the grass species used for the tests.

How to cite: Cuomo, S., Moscariello, M., and Foresta, V.: Shear strength of unsaturated soils artificially vegetated in a field test site, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12708, https://doi.org/10.5194/egusphere-egu22-12708, 2022.

Revealing the liquefaction mechanism and anisotropy behaviour of root-reinforced soils: an energy-based approach

Ali Akbar Karimzadeh, Anthony Kwan Leung

Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR

Abstract:

Recent physical modelling work has demonstrated that plant roots provide seismic resistance to geotechnical infrastructure such as slopes and pipelines against liquefaction. Indeed, there is evidence from published triaxial data that the presence of roots increased the liquefaction resistance of soil and changed the liquefaction failure mode from limited flow failure to cyclic mobility, depending on the amount of cyclic stress ratio applied and the available root volume. However, effects of root orientation on soil anisotropy and energy dissipation during the process of liquefaction, have not been adequately addressed in the literature. In this presentation, we will present a new energy-based framework and its application to reinterpret a set of published triaxial data concerning on the undrained strain-controlled cyclic behaviour of root-reinforced soils. Based on the framework, the changes in the amount of dissipated energy required to reach the liquefaction criteria (i.e. 5% double-amplitude axial strain) of the soil due to the presence of roots of different volume ratio will be determined. We will use this energy term and the strain values at the compressive and extensive sides of a cyclic loading at the liquefaction state to explore how root orientations would affect the soil anisotropy. A new correlation between normalised cumulative dissipated energy (∑ΔW/σ_c’, where σ_c’ is the effective confining pressure) and the cyclic resistance ratio at the cycle number of 15 (CRR₁₅) will be established. We will also present the correlation between the ∑ΔW/σ_c’ with the normalised cumulative strain energy (∑4W/σ_c’) which is representative to the the demand energy of an earthquake event. Finally, we will discuss any effects of the recycling and recovering of strain energy upon cyclic loading, and their importance in the energy interpretation to root-reinforced soils.

Keywords: Energy-based approach, Root-reinforced soil, Anisotropy, Liquefaction, Triaxial cyclic tests

How to cite: Karimzadeh, A. A. and Leung, A. K.: Revealing the liquefaction mechanism and anisotropy behaviour of root-reinforced soils: an energy-based approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12986, https://doi.org/10.5194/egusphere-egu22-12986, 2022.

Rainfall infiltration is the main inducing factor for the instability of unsaturated soil slopes, and root water uptake and reinforcement play an important role in preventing shallow landslide. In order to explore the influence of vegetation root on the soil hydraulic and mechanical properties under rainfall, a self-designed soil permeability coefficient measuring device considering the effects of vegetation was used to study the soil water characteristic curve (SWCC) and permeability coefficient of Festuca Arundinacea, Ophiopogon Japonicus, Ligustrum Vicaryi and bare soil under two different rainfall conditions (3.0mm/h and 5.0mm/h) were studied. Then, the direct shear tests of root-soil composite with different water contents and root contents were carried out. Finally, the slope stability under different rainfall and vegetation was simulated by GeoStudio. Results show that: root water uptake can effectively reduce soil water content and increase soil suction, and its influence range is about 2-3 times the length of the root system. Root water uptake can also significantly improve the soil water retention capacity. The air entry value of vegetation soil is larger than that of bare soil, and the permeability coefficient of vegetation soil is about one order of magnitude lower than that of bare soil. Among the three different types of vegetation, the effect of Festuca Arundinacea and Ophiopogon Japonicus on soil water content and suction is more significant than Ligustrum Vicaryi. Root reinforcement mainly increases the soil shear strength by improving the cohesion of the root-soil composite, but has little effect on the internal friction angle. The cohesion of the root-soil composite is affected by soil water content, root content and root distribution, which increases with the increase of root content, and decreases with the increase of water content. When the roots are vertically distributed, the cohesion of the root-soil composite is greater than when the roots are placed horizontally and inclined. Vegetation can effectively improve the stability of the shallow slope under various rainfall conditions, but has little effect on the stability of a deep slope. The safety factor of all three types of vegetated slopes is higher than that of bare soil slopes.

How to cite: Gao, X.-G., Wang, J.-P., Tan, Y.-R., Zhang, J., and François, B.: Effect of vegetation roots on soil hydraulic and mechanical characteristics under rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13252, https://doi.org/10.5194/egusphere-egu22-13252, 2022.

In New Zealand shallow landslides are a prominent contributor to soil erosion in unvegetated slopes (hill country) and to water quality degradation. Selective well-planned re-vegetation of steep slopes can reduce shallow landslide hazard with comparatively low economic consequences.

The main non-native planted tree species that contribute to slope stability are Poplar species (Populus sp.) and Pine (Pinus radiata). We will present field data quantifying the root distribution and root strength of poplar and pine trees from New Zealand. 4 poplar trees with a medium Diameter at Breast Height (DBH) of 0.48 m are included. Circular trenches have been dug at fixed distances from stem and the roots counted and their diameter measured systematically. 64 root pull-out tests over varying soil depth and root diameter provide calibration for lateral root reinforcement (Gehring et al., 2019; equation 3). The combination of root counts and root reinforcement calibration enables the parametrization of root reinforcement on a single tree scale. The Pinus radiata calibration is the adopted from Giandrossich et al., 2020 which applied a similar methodology.

Using the slope stability model SlideforMap, we assess and compare (re)vegetation scenarios and their effect on slope stability. In addition to a detailed inclusion of vegetation, SlideforMap takes local soil and hydrology into account in the parametrization. Scenarios without poplar/radiata stands, dispersed trees and plantations are run and compared under varying precipitation conditions.

We believe this approach enables regional decision makers to optimize tree planting to significantly reduce slope instability at minimal economic costs.

Literature:

Gehring, E., Conedera, M., Maringer, J., Giadrossich, F., Guastini, E., & Schwarz, M. (2019). Shallow landslide disposition in burnt European beech (Fagus sylvatica L.) forests. Scientific Reports, 9(1), 1–11. https://doi.org/10.1038/s41598-019-45073-7

Giadrossich, F., Schwarz, M., Marden, M., Marrosu, R., & Phillips, C. (2020). Minimum representative root distribution sampling for calculating slope stability in pinus radiata d.Don plantations in New Zealand. New Zealand Journal of Forestry Science, 50, 1–12. https://doi.org/10.33494/nzjfs502020x68x

How to cite: van Zadelhoff, F., Schwarz, M., Cohen, D., and Philips, C.: Quantifying the effect on shallow landslide activity of actual and potential poplar and pine stands in New Zealand hill country., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13261, https://doi.org/10.5194/egusphere-egu22-13261, 2022.

Plant roots can help to stabilise riverbanks and slopes by providing additional mechanical reinforcement through tensioning of root material. This problem has typically been studied at the ultimate limit state, focussing on quantifying the peak root-reinforced soil strength. Existing models however rarely account for the gradual mobilisation of root-reinforcement associated with increasing soil displacements. Understanding these deformations is important when deformation tolerances are low, for example when constructing infrastructure embankments, or when deformations may serve as an early warning signal for slope failure.

Several new models to quantify mechanical reinforcement were developed, with varying levels of complexity. At the most basic level, fibre bundle model theory was combined with early pioneering work by Wu and Waldron to form a new fibre bundle approach that remains simple to use yet respects the physics of soil and root deformation. A second and more comprehensive analytical model was developed that can calculate reinforcements as a function of increasing soil shear displacement. This model includes key parameters such as the elasto-plastic biomechanical root behaviour, three-dimensional root orientations, root slippage and changes in the geometry of the localised shear zone in the soil. A third model comprises a full set of constitutive stress-strain relationships for rooted soil that can be used in numerical finite-element simulations. In this framework, the rooted soil is treated as a single, composite material in which the soil and root phase can each be assigned their own unique material behaviour. The composite approach simplifies model parameterisation by using independently measurable root and soil parameters, and is also powerful enough to investigate the complicated interaction between stresses and deformations in the soil skeleton and in the roots.

These models all provided good predictions of experimentally measured root reinforcements in direct shear tests. They will be useful tools both for the engineering industry, in terms of rapid quantification of root reinforcement, as well as for directing future research into the drivers of mechanical root-reinforcement.

How to cite: Meijer, G., Knappett, J., Bengough, G., Muir Wood, D., and Liang, T.: New modelling tools for quantification of mechanical reinforcement of soil by plant roots, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13300, https://doi.org/10.5194/egusphere-egu22-13300, 2022.

Climate change is increasing the probability of extreme precipitation in many regions, which will lead to an increased risk of major flooding events. Recent years have seen an interest in the use of so-called nature-based solutions (NbS) to help respond and reduce the risk posed by such extreme events. This paper provides an analysis of the use of NbS to help reduce flood risk at the open-air laboratory Germany (OAL-Germany), which is part of the EU Horizon 2020 project OPERANDUM. OAL-Germany is located in the Biosphere Reserve Lower Saxony Elbe Valley. Following major flooding events which occurred in OAL-Germany in 2002 and 2013, a cooperative flood management NbS was implemented over the period 2014-2015 and has been in place since then. The NbS consisted of cutting back woody vegetation in certain locations along the riverbank which impeded overbanking during flood events, and the use of various grazing animals to try and prevent the regrowth of such woody vegetation. The objective of this study is to evaluate the efficiency of NBS against flood risk under present-day climate change scenarios and assess future flood inundations and velocities in OAL-Germany. The daily precipitation data obtained from the EURO-CORDEX project dataset for 1971–2000 and 2051–2080 represented historical and future simulations, respectively. The hydrologic model HEC-HMS was integrated with the hydraulic model HEC-RAS to simulate discharge, flood velocity, and water depth/inundations of past and future events. For HEC-RAS model boundary conditions, daily flow data with long-term quality-controlled data, obtained from the Global Runoff Data Centre were used. The model was simulated for two scenarios: (1) pre-NBS implementation, considering the landcover of mixed forest; and (2) post-NBS implementation using pastureland, which is the current NBS/landcover in place. The results of the simulation show that the pastureland released the floodwater from the main river system faster than the previous landcover. Overall, the floodwater velocity of pastureland increased by 21%, while flood depth showed a decrease of 2% compared with mixed forest. Therefore, if the modelled NBS had actually been in place in 2012, then it is reasonable to expect that they would have contributed to a reduction in flood risk further downstream from the modelled NBS areas, in the June 2013 flood event. This study can help to improve NBS uptake and upscaling, which is critical to improve NBS planning, implementation, and effectiveness assessment.

Keywords: Nature-based solutions; HEC-RAS Model; Flood depth; Flood Velocity; Roughness coefficients; Climate Change

Acknowledgments

This work has been carried out under the framework of OPERANDUM (OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks) project, which is funded by the European Union's Horizon 2020 research and innovation programme under the Grant Agreement No: 776848.

How to cite: Debele, S., Bowyer, P., Sahani, J., Alfieri, S. M., Menenti, M., Zieher, T., and Kumar, P.: Evaluating the Efficiency of a Nature-Based Solution on Flood Risk Reduction under climate change scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13319, https://doi.org/10.5194/egusphere-egu22-13319, 2022.

Climate change strongly affects the hydro-mechanical properties of soil. Due to drought and heavy rains
the soil is subjected to severe hydro-mechanical loads, that, in turn, alter the microstructure of the soil.
The most affected area is the so-called vadose zone, the layer of soil situated between the ground surface and
the water table. Here the presence of vegetation has a strong impact, related to the elongation/expansion
of the root architecture and the hydro-mechanical interactions with soil. Additionally, the presence of plant
roots facilitate the evapotranspiration process from deeper soil layers.
The research presents an experimental investigation, aimed to reproduce the typical hydro-mechanical
conditions as found in the vadose zone in controlled laboratory conditions. Drying-wetting cycles are induced
in soils samples, where maize plants are free to sprout and develop as well as in reference non-vegetated
samples. The water content and distribution within the soil matrix are studied through 4D (3D+time)
in-vivo x-ray computed tomography and effects on the soil-root microstructure are quantified with 3D
image analysis. Those are correlated with above ground measurements such as fluorescence (through a
spectroradiometer) that, in turn, provides leaf water potential, and the stomatal conductance that controls
the evapotranspiration.

How to cite: Anselmucci, F., Cheng, H., Zeng, Y., Fan, X., and Magnanimo, V.: Impact of wetting-drying cycles on the hydro-mechanical behaviour of vegetated soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13331, https://doi.org/10.5194/egusphere-egu22-13331, 2022.

LaRiMiT (Landslide Risk Mitigation Toolbox) is a web-based database and user portal for identifying and selecting mitigation measures for a specific landslide case, assisted by an embedded expert scoring system. The webtool, developed within KLIMA2050, contains more than 80 structural landslide mitigation measures, including active (aimed at reducing the likelihood of a landslide) and passive (aimed at reducing the consequences) measures. For each mitigation measure a description, examples of application and design methods are provided, as well as references from literature. An Analytic Hierarchy Process resident in the toolbox provides a ranked list of suitable mitigation measures for a specific case. The quantitative scores reflect the input relevance weights and option scores. Recently, the database has been expanded to include also Nature-based solutions (NBS). NBS applied to landslide hazard mitigation are mostly known as soil and water bio-engineering (SWB) and the main SWB techniques have been categorized and added to the database. For these measures, the period of installation, the materials involved, advantages, and disadvantages are also provided. The database containing all the mitigation measures has open access to all users at https://www.larimit.com/. 

A survey was sent to a group of experts in landslide management and SWB selected worldwide, with a focus on Europe, asking them to assign scores to each mitigation measure in the toolbox. The survey was made using Microsoft Forms. Each measure was linked to a dedicated response page through a hyperlink, and the experts could submit a response for the mitigation measures they felt more comfortable with giving scores. For each mitigation measure selected, the experts were asked to assess the measure by scoring 33 parameters, based on existing landslide classifications with regards to the type of movement, material type, rate of movement of the landslide (among others), as well as feasibility, economic suitability, and environmental suitability. A total of 153 experts, among landlide mitigation managers and experts of SWB practices, were asked to fill the survey. An innovative methodology for utilising experts' scoring directly within the decision support tool, was proposed and used to calculate the final scores for each parameter of the landslide mitigation measures. It consisted in 5 phases, namely Data analysis, Data filtering, data weighing, Data comparison, and Score selection.

A total of 38 out of the 153 invited experts (corresponding to just over 25%) contributed scores for at least one mitigation measure. In total, 296 responses were received of which 172 were for traditional mitigation measures, 111 for NBS, and 13 for hybrid solutions (combination of NBS and traditional engineering solutions). The results from this first pooling are discussed and analyzed, and the scores of 56 measures were updated on the basis of the pooling answers. All the NBS measures received between 3 and 9 responses, confirming that the NBS listed in the database were well known to most of the SWB experts who participated to the survey. 

The survey is still open and we encourage landslide mitigation experts that are willing to provide their contribution, to reach out the survey managers at vittoria.capobianco@ngi.no.

How to cite: Capobianco, V., Kalsnes, B., Strout, J., and Solheim, A.: Nature-based solutions for mitigating erosion and shallow landslides in LaRiMiT toolbox: use of expert scoring for evaluation of NBS measures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13423, https://doi.org/10.5194/egusphere-egu22-13423, 2022.

Rangelands ecosystems contain more than a third of the global land surface, sustaining key ecosystem services and livelihoods. Unfortunately, they suffer from severe degradation on a global scale. Normalized Differenced Vegetation Index (NDVI) has been used to monitor low ground cover vegetation, especially relevant for arid and semiarid regions.

MODIS data are commonly used to calculate NDVI to monitor rangelands. In this study, we used time series metrics and Hurst Exponent from multifractal detrended fluctuation analysis to cluster different rangeland types to monitor and classify temporally and spatially diverse rangelands.

The Northwest (Noroeste) agricultural region in the province of Murcia was selected from the Southeast of Spain. We selected approximately 20.000 pixels to cover different areas that include land uses that are utilized for grazing. The selection aimed to collect pixels where other land uses were kept to a minimum, given the great spatial variability in Spain. We collected the time series using satellite data of MODIS (MOD09Q1.006) from 2000 to 2020. The pixels have a spatial resolution of 250 x 250 m² and a temporal resolution of 8 days. This selected area represents a mix of cereal croplands, tree croplands, grasslands, scrublands, and forested areas; all of them with an arid climate.

We used unsupervised random forest and compared the produced clusters with the classification from the Spanish parcels classification systems to test our model. Our goal is to study the ability of unsupervised clustering using NDVI time series and their multifractal character to categorize and monitor their vegetation status, key information for farmers and managers to adapt to a changing situation due to climate change. This information can be used in other arid areas with similar geophysical conditions.

Acknowledgments: The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain, “Garantía Juvenil” scholarship from Comunidad de Madrid, and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.

References:

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study. Entropy, 23, 576.

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 13(5), 840.

Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1-4), 87-114.

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J. C., & Tarquis, A. M.  (2021). The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 23(5), 559.

Almeida-Ñauñay, A.F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. (2022). Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma, 406, 115488.

How to cite: Sanz Sancho, E., Almeida-Ñauñay, A., Díaz-Ambrona, C. G., Saa-Requejo, A., Ruiz-Ramos, M., Rodríguez, A., and Tarquis, A. M.: Clustering arid rangeland pixels using NDVI series and fractal analysis to classify land uses. Case in Southeastern Spain., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-287, https://doi.org/10.5194/egusphere-egu22-287, 2022.

Mediterranean agriculture faces drought as one of the most challenging obstacles to overcome. Especially, in semiarid grasslands, where every year the biomass production suffers severe damage due to several factors, being one of them the lack of precipitation. For this reason, semiarid vegetation monitoring allows us to improve the management and conservation of these essential ecosystems. Meteorological drought is commonly monitored using indices such as the Standard Precipitation Index (SPI) and the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agricultural drought is measured by the Vegetation Health Index (VHI). In this work, we present different methodologies to optimize the correlation between both droughts by standardizing the vegetation index and selecting the best time scale throughout the year.

First, we selected drought-vulnerable Mediterranean grasslands zones in the centre of Spain. By doing this, we pretend to evaluate the performance and the sensibility of the drought indices. MODIS data (MOD09Q1) was used to calculate the Normalized Difference Vegetation Index (NDVI), then it is standardised to define a standardized vegetation index (SVI). The meteorological indices SPI and SPEI were calculated using data collected from nearby weather stations. Overall, our results revealed that SPEI was better correlated with SVI and obtained better results in the critical seasons, in comparison to SPI. The quarterly scale was the most suitable, showing a higher relationship than the monthly scale. This fact suggest that vegetation growth phases should be considered in agricultural drought detection. The most sensitive time frame throughout the year was spring and autumn, implying that drought indices (SPI and SPEI) along with vegetation index (SVI) could offer an improvement in the monitoring during these periods.

References

Escribano Rodríguez, J.A., Díaz-Ambrona, C. H. y Tarquis Alfonso, A.M. (2014). Selección de índices de vegetación para la estimación de la producción herbácea en dehesas. Pastos, 44(2), 6-18.

Martín-Sotoca, J. J., Saa-Requejo, A., Moratiel, R., Dalezios, N., Faraslis, I., and Tarquis, A. M. (2019). Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds, Nat. Hazards Earth Syst. Sci., 19, 1685–1702, https://doi.org/10.5194/nhess-19-1685-2019

Sanz, Ernesto Antonio Saa-Requejo, Carlos H. Díaz-Ambrona, Margarita Ruiz-Ramos, Alfredo Rodríguez, Eva Iglesias, Paloma Esteve, Bárbara Soriano and Ana M. Tarquis (2021). Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 13(5), 840.

Andrés F. Almeida-Ñauñay, Rosa María Benito, Miguel Quemada, Juan Carlos Losada and Ana M. Tarquis (2021). The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 23(5), 559.

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study. Entropy, 23, 576.

Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. (2022). Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma, 406, 115488. https://doi.org/10.1016/j.geoderma.2021.115488

How to cite: Almeida Ñauñay, A. F., Sanz, E., Villeta, M., Quemada, M., and Tarquis, A. M.: Meteorological and agricultural drought indices in semiarid grasslands monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-360, https://doi.org/10.5194/egusphere-egu22-360, 2022.

Rainfed agriculture in Kenya is approximately 98% and highly susceptible to climate variability. Humid climatic regions of Kenya are key to sustainability of agricultural sector. This study focused on influence of coffee on real evapotranspiration in Nyeri and Embu Counties of humid Mount Kenya Region. This is because the economy of Kenya relies mostly on Coffee as the fourth largest export earner. Quality controlled 9-year long dataset was sought from Nyeri and Embu synoptic stations. Site specific soil parameters and coffee coefficient were used in computations of estimates. Penman-Monteith standard equation was used to estimate daily values of reference evapotranspiration. Average daily, monthly ET₀ and annual total estimates were computed. The ET₀ estimates were modelled using 1D Palmer-type soil model to estimate real evapotranspiration using soil parameters for the station at 1 m arable depth. Results showed a very slight variation among the average annual estimates of ET₀ between the two humid regions. For instance, in Nyeri the average annual estimate ET₀ was 1488±52 mm/year while in Embu it was 1488±48 mm/year. Average annual ET depicted slightly higher variation with estimates of 813±216 mm/year in Nyeri and 830±166 mm/year in Embu. Average monthly estimates of ET₀ and ET were almost the same with estimates of 124±21 mm/month and 68±30 mm/month in Nyeri and 124±23 mm/month and 71±37 mm/month in Embu respectively. Results also indicated that daily average,  ET₀ , ET and ET estimates with application of Kc  varied insignificantly with  4.1±1 mm/day, 2.2±1 mm/day and 2.2±1 mm/day in Nyeri respectively while the estimates were nearly the same in Embu. Coffee coefficient (Kc) had slight influence on real evapotranspiration in humid climatic regions under study. This is because the Kc values were almost 1 with a range of between 0.9 to 0.95. In addition, the study area receives adequate precipitation hence no soil water stress. Further, the slight differences among the ET with and without application of Kc were due to the linear function of available soil moisture used in the computation of ET from reference evapotranspiration (ET₀). The study is important in investigating the role of 1D Palmer type soil model on ET₀ and coffee coefficient influence on real evapotranspiration in Kenya in these regimes of climate extremes.

How to cite: Musyimi, P. K., Székely, B., and Weidinger, T.: Reference evapotranspiration estimation and influence of coffee on real evapotranspiration in humid climatic regions of Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-388, https://doi.org/10.5194/egusphere-egu22-388, 2022.

Recent adverse weather events in Europe have questioned the stability of crop production systems. We assessed the vulnerability of eleven major crops in France between 1959 and 2018 as a function of climate, crafting a novel hazard framework that combines exposure and sensitivity to weather-related hazards. Exposure was defined as the frequency of hazardous climate conditions, while sensitivity of crops was estimated by the yield response to single and compound hazards. We used reported yields available at departement (county) level. Vulnerability was computed as the exposure-weighted average of crop sensitivities. Our results do not reveal any historical evidence for an increased vulnerability of French crop production. Rather, the sensitivity to adverse weather events, and thus the overall vulnerability, has significantly decreased for six of the eleven crops between 1959 and 2018, and shown no significant decline or remained stable for the other five. Yet compound hazards can induce yield losses of 30% or more for several crops. Moreover, as heat-related hazards are projected to become more frequent with climate change, crop vulnerability may rise again in the future. Our results may support insurance design by identifying single and compound hazards that can severely affect yields.

How to cite: Schauberger, B., Makowski, D., Ben-Ari, T., Boé, J., and Ciais, P.: The decreasing vulnerability of French crop production to climatic hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1498, https://doi.org/10.5194/egusphere-egu22-1498, 2022.

Agriculture is facing the challenge of providing food for a growing world population in a context of climate change. The Mediterranean region is characterized for a semi-arid climate. Thus, water scarcity is coupled with the development of intensive crops that require irrigation, such as olive orchards. Recently, biochar –the solid aromatic carbonaceous product of the pyrolysis of residual biomasses– has been proposed as an amendment for reducing soil water loss [1] and increasing plant productivity [2].  The main objective of this study was to compare the effects of the application of biochar and green-compost (the organic amendment traditionally used) on soil properties and crop productivity at a super-intensive plantation of arbequina olive trees under deficit irrigation located at “La Hampa” field station (Coria del Río, Seville, Spain). Thus, soils were amended with 40 t ha^-1 of olive-waste biochar, green-compost or a biochar-compost mixture (50 % w/w). Un-amended plots were used as control. On a monthly basis, soil pH, water holding capacity, humidity and penetrability resistance, as well as TC and TN contents of soils were determined. Finally, the total weight of produced-olives per tree was measured.

Results showed that biochar application was the most effective amendment in increasing soil water holding capacity and moisture. All the organic amendments reduced the soil penetrability resistance. Olive production increased about 15 % at the biochar amended plots. Thus, the application of organic amendments, especially biochar, improved soil physical properties and led to a higher crop production.

Acknowledgements: The BBVA foundation is gratefully acknowledged for funding the scholarship Leonardo to “Investigadores y Creadores Culturales 2020”, what made this project possible.

References:

[1] Campos et al., 2021. Agronomy 11, 1394. https://doi.org/10.3390/agronomy11071394

[2]De la Rosa et al., 2014. Science of the Total Environment 499, 175-184. http://dx.doi.org/10.1016/j.scitotenv.2014.08.025

How to cite: Campos, P., Sánchez-Martín, Á., Santa-Olalla, A., Miller, A. Z., and de la Rosa, J. M.: Effects of biochar addition into intensive-olive orchard soils under deficit irrigation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1887, https://doi.org/10.5194/egusphere-egu22-1887, 2022.

Objective

Diverse cropping systems are associated with multiple ecosystem services and are suggested to alleviate the effects of drought and heat stress. The magnitude and frequency of extreme weather events are projected to increase in the future due to climate change, and diverse cropping systems might therefore become key for food security. However, there is still limited information on the spatiotemporal variation of crop species diversity and how it relates to differences in climate or soil type. We aimed at quantifying how crop diversity developed over time at the national and regional scale in Sweden between 1965 and 2019, and how crop species diversity is related to climatic factors and soil texture.

Methods

Sweden is an interesting case study due to the large range in latitude from north to south. The analyses were conducted using national databases containing historical records of crop production and climate data, as well information on soil texture. To quantify crop species diversity, species richness and a crop diversity index that reflects the area-weighted equivalent number of crops were used.

Results

Crop species richness and crop diversity index increased from north to south in Sweden, and our results showed a positive relationship between mean annual temperature and latitude to crop diversity at national level. The positive relationship shows how mean annual temperature and length of the vegetation period control crop diversity across the country. There were no significant relationships between crop diversity and mean annual precipitation and soil texture, respectively. Crop species richness did not change over time at national level while crop diversity index experienced a temporal decrease. At the county level, different temporal trends were observed among counties: in some counties an increase in crop diversity and species richness occurred, while other counties had no change or a temporal decrease.

Conclusions

The differences in the results between national and county level show the importance to include different scales in the examination of temporal developments of crop diversity. Although crop diversity index decreased over time at national level, the temporal increase observed in almost half of the counties suggest that it is possible to increase crop diversity in Sweden. The different temporal changes between counties imply that crop diversity is affected by an interplay between natural and socioeconomic factors. Natural factors constrain which crops can be grown, but to promote diversification of agricultural crops in the future, socioeconomic factors need to be considered.

How to cite: Sjulgård, H., Colombi, T., and Keller, T.: Spatiotemporal patterns of crop diversity reveal potential for diversification in Swedish agriculture, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2330, https://doi.org/10.5194/egusphere-egu22-2330, 2022.

Hailstorm damage in agriculture often results in considerable loss in harvestable product. Currently, crop damage is quantified by insurance companies through field inspectors’ assessment, a time-consuming activity that is potentially affected by estimation errors over large areas. Coupling remote sensing and crop modeling represents a promising solution for the crop insurance market for a reliable, objective, and less labor-intensive method to estimate hail damage. With the general aim of developing an automated platform that can identify crop yield failure at field level, the Ceres-Maize (DSSAT v 4.7.5) model was integrated with remote sensing data to reproduce maize growth and production dynamics. A two-year experiment was established at the Ca’ Tron farm on the low lying of Veneto Plain (NE Italy). The crop damage was performed using a custom-made machine at three intensities (low, medium, and high) depending on crop defoliation and four plant growing stages (early vegetative, flowering, early-milky, and dough stages). Each treatment was replicated three times on plots of 20x20 or 60x60m. Additional four subplots with no damage were used as control. Leaf area index (LAI) and biomass were measured after each damage event. LAI was estimated from both drone-borne multispectral sensors and satellites imageries (Sentinel-2), and ground-validated using a ceptometer. The Ceres-Maize model was used to predict obtainable and potential crop yields: 1. by embedding the estimated LAI reduction at the time of damage into the “PEST” sub-model; 2. by calibrating the model seasonal LAI dynamics using the drone- and Sentinel-based LAI observations over the cropping season. The first year of the experiment was used to calibrate DSSAT, the second one to validate its performance.

Results showed a satisfactory agreement between measured and simulated Ceres-Maize LAI dynamics. The final yields were also well reproduced among treatments. On the other hand, the model did not fully capture the residue biomass and the harvest index. Assimilating remote-sensing-based parameters in crop models appears to have promising benefits for the insurance market, providing more robust and less time-consuming methodologies.

How to cite: Longo, M., Furlanetto, J., Dal Ferro, N., Caceffo, D., and Morari, F.: Coupling process-based models and remote sensing data to predict yield loss by hail damage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4594, https://doi.org/10.5194/egusphere-egu22-4594, 2022.

Extreme weather events such as hailstorms represent a threat to crops, causing both economic and food supply losses. Hailstorm intensity is likely to increase in the future pushing more farmers to purchase crop insurances to prevent related economic losses. Currently, insurers mostly rely on field inspectors for crop damage assessments, which can build up limitations such as: (i) partial subjectivity in damage estimations; (ii) inaccuracies in wide-area assessments; (iii) difficulties in accounting for damage spatial variability. Sensors mounted on UAVs (Unmanned Aerial Vehicles) and satellites can fulfill these requirements when coupled with advanced spectral analysis techniques, such as spectral mixture analysis (SMA). In this experiment we applied SMA on UAV hyperspectral images to quantify dead-and-alive organs during the growth of winter wheat (Triticum aestivum L.) and estimate yield loss due to hail damage. The experiment was conducted on a 17-ha field located in the surroundings of Venice (NE Italy). The experiment involved four simulated hail treatments (null, low, medium and high damage) at three plant growing stages (flowering, milky and over-ripe). Treatments were in triplicate for a total of 30 plots, nine sized 60x60 m and 18, 20x20 m. Damages were inflicted using a prototype specifically designed at the University of Padova, consisting of a rotating pole with whips attached and positioned on the back of a tractor. Damage intensity was adjusted with the aid of insurance field inspectors. A UAV M600 Pro (DJI, Shenzhen, China) was equipped with a nanohyperspec (400-1000 nm) camera (Headwall, Boston, USA). Pixel ground resolution was about 0.04 m. UAV surveys were performed after each damage, leaving a period of 7-10 days to the crop for developing a detectable morphologic and physiologic response (e.g., leaf drying, development of necrosis). At each flight, crop samples were collected, and pure spectral signatures of dead and alive stems, leaves and spikes were analyzed using an ASD Fieldspec 4 (Malvern Panalytical Ltd, Malvern, UK) in proximal sensing configuration. SMA algorithm was run on UAV imagery by selecting endmembers composed of intact green plant organs, bare soil and dead spikes, thus allowing for differentiation between damaged and undamaged vegetation. Results showed that increasing yield loss due to hail damage intensity was associated with an increasing number of dead spikes. Proximal-sensed hyperspectral signatures highly differentiated between undamaged and damaged vegetation, especially in the red-edge and chlorophyll absorption (~ 680 nm) regions. In this context, the SMA technique was promising for disentangling dead spikes from alive organs, aiding the area-damaged classification and allowing hyperspectral imagery for a direct estimate of yield losses.

How to cite: Furlanetto, J., Longo, M., Nicoli, L., Caceffo, D., Persichetti, A., Morari, F., and Dal Ferro, N.: Spectral mixture analysis to quantity winter wheat (Triticum aestivum L.) damage caused by hailstorms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4868, https://doi.org/10.5194/egusphere-egu22-4868, 2022.

Managing weather related risks in cropping systems includes strategies to share the risk such as insurance schemes. Advances in satellite sensor technology and interpolated regional weather data enable insights in the relationship between extreme weather events and yields losses which in turn offers possibilities for area-yield insurance schemes.

Extreme weather events  during sensitive phenological stages of the growing season differed significantly (p < 0.05) between low and high crop yields (Gobin, 2012; Gobin, 2018). Spatial return levels confirmed the exceptionality of 2016 and 2018 as extreme wet and dry years with high return periods and yield impacts (Gobin and Van de Vyver, 2021). We investigated the importance of weather data, satellite sensor-derived vegetation indices and water balance simulations in estimating crop yields of winter wheat, potato and sugar beet for the period 2016-2018. The water balance simulations were performed with the Aquacrop model, while the yield simulations were realised with the machine learning technique random forest regression. Results for winter wheat showed that NDVI series did not respond to crop yield affecting weather conditions (Vannoppen and Gobin, 2021). Weather and/or soil water depletion during sensitive phenological stages in combination with the NDVI integral during the growing season explained up to 57 of late potato, 66% of winter wheat, 68% of early potato and 84% of sugar beet yield variability.

Machine learning techniques proved valuable in estimating crop yields thereby elucidating the importance of weather conditions during sensitive crop stages. The crop yield models developed make use of commonly available remote sensing indicators and weather data, and are commensurate with regional scale decision making.

Gobin, A., 2012. Impact of heat and drought stress on arable crop production in Belgium. Natural Hazards and Earth System Sciences 12: 1911–1922. https://doi.org/10.5194/nhess-12-1911-2012

Gobin, A., 2018. Weather related risks in Belgian arable agriculture. Agricultural Systems 159: 225-236. https://doi.org/10.1016/j.agsy.2017.06.009

Gobin, A., Van de Vyver, H., 2021. Spatio-temporal variability of dry and wet spells and their influence on crop yields. Agricultural And Forest Meteorology, 308-309, Art.No. 108565. https://doi.org/10.1016/j.agrformet.2021.108565

Vannoppen, A., Gobin, A., 2021. Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy-Basel, 11 (5), Art.No. 946. https://doi.org/10.3390/agronomy11050946

How to cite: Gobin, A. and Vannoppen, A.: Modelling the impacts of extreme weather events on crop yields using water balance and satellite sensor data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10664, https://doi.org/10.5194/egusphere-egu22-10664, 2022.

The agricultural expansion frontier in Ecuador is mainly attributed to the deforestation process. Exacerbated rates of forest loss have been motivated more significant impacts in the territory, even negatively affecting the new uses of land, agriculture, and livestock. Climatic conditions have been changing in larger deforested areas, increasing landslides, floods, water level rise, and drought. This work aims to go deeper in understanding the deforestation process patterns by analyzing the structure of the expansion at the patch level.

We used the concept of patch skeletons and the Local connected fractal analysis (LCFA) through its temporal dynamics to identify complex hotspots inside the new agricultural areas. The K-means algorithm was used to perform LCFA segmentation and colouring to identify the complex intensity of the deforestation structure. This may indicate active expansion areas associated with high risked areas to perform agriculture and livestock systems because of high ecosystem dynamics recovery.

Hot spotting derived from the fractal analysis and k-means clustering not only serves for reforestation but will also lead to decision-makers for monitoring other associated environmental impacts. Most of the deforested areas in Ecuador after 5 to 7 years in agriculture were abandoned because of the nutrient loss and agricultural failure activities because of feeble farming systems infrastructure. LCFA and colouring communicate in a straightforward spatially explicit visualization strategy the hot spot method to geographically allocate the complex points of the deforested structure of the patches.

Acknowledgements

The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.

References

Andrea Urgilez-Clavijo, J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, 2021, 72(2), 635-655. https://doi.org/10.1111/ejss.13032

Andrea Urgilez-Clavijo, D. Rivas-Tabares, J.J. Martín-Sotoca and Ana M. Tarquis. Local fractal connections to characterize the spatial processes of deforestation in Ecuadorian Amazon. Entropy, 23(6), 748, 2021, https://doi.org/10.3390/e23060748

How to cite: Urgilez-Clavijo, A. and Tarquis Alfonso, A. M.: Understanding the deforestation process may mitigate environmental risks in recent agricultural frontier expansion in Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10754, https://doi.org/10.5194/egusphere-egu22-10754, 2022.

Agricultural catchments are prone to high crop yield variability because of extreme weather events, and their impact destabilises agricultural income at different territorial scales. This study aims to use optimisation algorithms coupled with the Soil Water and Assessment Tool – SWAT to allocate specific agri-environmental measures to mitigate the impact of climate change in water fluxes availability at subbasin scale.

The SWAT tool as a semi-distributed model uses the hydrological response unit – HRU concept to split catchments into several territorial management units with similar soil properties, slopes, and land use. The HRUs were used as optimisation unit to change land use and crop management. The work was performed in a catchment located in the southern part of Cuenca city in Ecuador; the area delineates the Tarqui river. The primary land use of the area is grassland-livestock systems and seasonal cropping. Two steps were performed: first, a model run with calibration and validation was set as baseline model. A second step include an optimisation set of modeled scenarios derived from future stakeholder alternatives defined in a previous study as sustainable practices in the area.

Several SWAT model alternatives were optimised, changing crop sequences, fertilisation rates, and crop scheduling dates. As a result, stakeholders' perception majorly matches with scenarios results in optimising water availability during low flow periods increasing streamflow and soil water availability. However, several unexpected alternatives, coming from optimisation, hint at farmers and ranchers. These new options explore other uses and crop sequences that increase income and reduce fertilisation costs.

Acknowledgements

The authors acknowledge support from European Union NextGenerationEU and RD 289/2021 and the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain

References

• David Rivas-Tabares, Ana M. Tarquis, Ángel de Miguel, Anne Gobin, Bárbara Willaarts. Enhancing LULC scenarios impact assessment in hydrological dynamics using participatory mapping protocols in semiarid regions. Sci. Total Environ., 803, 149906, 2022. https://doi.org/10.1016/j.scitotenv.2021.149906
• Rivas-Tabares, A. de Miguel, B. Willarts and A.M. Tarquis. Self-organising map of soil properties in the context of hydrological modeling. Applied Mathematical Modelling, 88,175-189, 2020. https://doi.org/10.1016/j.apm.2020.06.044

How to cite: Rivas-Tabares, D., Tarquis Alfonso, A. M., and Célleri, R.: Optimising agri-environmental measures at catchment scale through specific allocation with the SWAT model – A case study in southern Andes of Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10790, https://doi.org/10.5194/egusphere-egu22-10790, 2022.

Frosts have a significant impact on agriculture in Spain, as in many other temperate countries. Their changing behaviour in the context of climate change is of great interest to public institutions, agricultural unions, and the agricultural insurance sector. For this reason, this study is focused on the evolution of days with temperatures below specific ranges in different projections (specifically RCP4.5 and RCP8.5) of climate change.

The wine grape crop was selected for this study, which is of great importance in the agriculture of our country and for which we have series (2005-2019) of frost event dates linked to crop damages. The values of local temperatures on those dates where not available, so this data were obtained from the observational EOBS gridded dataset. A range of relevant temperature was extracted for these data, and then the analysis of the temperatures within this range in the immediate future (2020-2050) with respect to 1990-2019 was performed.

The study of the present climate confirms that spring frosts appear in the three areas studied, with intrazonal differences being observed. In none of these zones do the most extreme winter frosts occur below -15ºC, but in all of them frosts below -10ºC do occur.

For a future with moderate warming (RCP4.5), the day of the last frost is expected to be up to 4 days earlier in most of the studied areas. The absolute minimum temperature in March, April and May is also expected to increase between 0.4 and 0.8 ºC with smaller increases as spring progress (minor increase in May than in March).

For a future with severe warming (RCP8.5), the date of the last frost is expected to be up to 8-11 days earlier. Other changes under this scenario are increasing in the absolute minimum temperature in March, April and May between 0.6 and 1.5ºC, with smaller increases in April.

Consequences for the wine grape management and varietal selection pursuing adaptation to reduce crop damages are discussed.

Acknowledgements

We are grateful for funding from the Entidad Estatal de Seguros Agrarios (www.mapa.gob.es/es/enesa/) under proyect P200220C321 titled “Accident rate in winemaking vineyards: retrospective evaluation taking into account the restructuring of the sector since 1995”. (Siniestralidad en viñedo de vinificación: evaluación retrospectiva teniendo en cuenta la reestructuración del sector desde 1995) 

How to cite: Saa-Requejo, A., Rodriguez, A., Ruiz-Ramos, M., Valencia, J. L., Tarquis, A. M., and Baeza, P.: Analysis of frost in vineyards in Spain in the context of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11843, https://doi.org/10.5194/egusphere-egu22-11843, 2022.

Water scarcity is increasingly recurring in irrigated agriculture in Mediterranean climate regions, and it is, therefore, necessary to establish alternatives to enable irrigators to deal with such problems, in a planned manner and in accordance with the technical and economic implications.  Although insurance schemes for droughts has long been standardized for rainfed crops, their application to irrigated crops is still under discussion. This study presents a new index-based drought insurance scheme, totally aligned with the river basin drought management procedures.

When considering a highly regulated water system, where natural water availability is altered by the operation of water infrastructure, traditional drought indicators (e.g., SPI, SPEI, SRI) lose significance, and ad-hoc index formulations tailored to the basin characteristics are required to reflect both regulation effects and natural fluctuations in the basin. Spain provides a paradigmatic example of a practical and systematic policy for the identification and mitigation of operational droughts: the river basin authorities are bound by law to design basin-specific state indexes. The state indexes are monthly monitored and used to trigger water demand and supply measures when entering a drought period, according to the specifications of the drought management plan. The study was carried out in an irrigation district (90% citrus fruits) in the Jucar river basin in Spain, a highly regulated water system.

Three insurance scheme options were evaluated: 1, a variable premium and/or variable franchise based on the forecast of water availability for the insured irrigation campaign, 2, a multi-annual insurance contract, and 3, an advance contract with a constant premium. In each of them, the values of the fair risk premiums, the maximum compensation, and the deductible franchise were established for different state indexes based on different combinations of system state variables (such as reservoir storages and inflows) and precipitation. The design of the insurances was done under the preexisting drought system operating rules to reduce the issue of the moral hazard, which is one of the main problems for this kind of insurance index. The selection of the insurance scheme is based on the gross margin of citrus crops with and without insurance contracts, including the value of additional premium loads, in addition to a basis risk analysis.

To evaluate the performance of the insurance, synthetic hydrological time series were generated using an ARMA model and implemented in the basin-wide water resource simulation management developed in DSS Shell AQUATOOL. The premium-claim ratio was used to assess the performance of the insurance company, finding stable values that can generate a balance of the long-term insurance scheme.

How to cite: Valenzuela Mahecha, M. A., Pulido-Velazquez, M., and Macian-Sorribes, H.: Hydrological drought index insurance in irrigated agriculture in a highly regulated system: an economic instrument for risk mitigation for the Jucar River Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11989, https://doi.org/10.5194/egusphere-egu22-11989, 2022.

Permanent grasslands are a very relevant cropping system in the North of Spain and support the main dairy farms in the country. Adaptation to climate change will be required given the projected changes of regional precipitation. To support such adaptation, modelling of these systems to generate high quality projections of the system performance is required. In the region to be simulated, grasslands are managed with a mixture of cuts and grazing. Several issues hinder the modelling of this type of systems: 1) the available data of grazing intensity presents large uncertainties; 2) there are few grassland models that allows flexibility to define a variable combination of cuts and grazing; 3) soil heterogeneity. It follows, and expands to grazing, the exercise performed by Gómara et al. (2020), who used the Pasture Simulation model (PaSim) to simulate a mown permanent grassland in the French Massif Central.
The model was calibrated using data from Villaviciosa (Asturias, Spain, 5º 26' 27" W, 43° 28' 50" N, 10 m a.s.l.), located at northern Spain with a temperate climate. This calibration was used to simulate several grassland locations distributed along the Cantabrian Sea. The soil information was obtained from Trueba et al. (2000). The model was configured for the optimum management for mowing and nitrogen fertilization. The 1976-2005 period and the 2030-2059 period were selected. For the future period two representative concentration pathway emission scenarios (RCP, van Vuuren et al., 2011) were selected (i.e. RCP4.5 and RCP8.5). An ensemble of climate models will be used from the Coordinated Regional Climate Downscaling Experiment (CORDEX, Giorgi and Gutowski, 2015) bias-adjusted by using the European observational database EOBS (Haylock et al., 2008) with the empirical quantile mapping method included in the climate4R R package (Iturbide et al., 2019). 
Modelling was challenging due to a combination of complexity (many processes involved) and uncertainty (observed data are difficult to generate). The results of the simulation exercise allow for assessing PaSim skill to reproduce the performance of these complex systems, as well as to determine the main weaknesses of the model and the observational/experimental required to improve the modelling work.

References
Giorgi, F. and Gutowski, W.J., 2015. Annual Review of Environment and Resources, 40(1): 467-490.
Gómara I, Bellocchi G, Martin R, Rodríguez-Fonseca B, Ruiz-Ramos M, 2020. Agricultural and Forest Meteorology, 280, 107768.
Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P. and New, M., 2008.  J. Geophys. Res., 113: D20119.
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández, J., Frías, M.D., Manzanas, R., San-Martín, D., Cimadevilla, E., Cofiño, A.S. and Gutiérrez, J.M., 2019. Environ. Modell. Softw., 111: 42-54.
Trueba, C., Millán, R., Schimd, T, Lago, (2000). CIEMAT. ISBN: 84-7834-370-9. Madrid.
van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J. and Rose, S.K., 2011. Clim. Change, 109: 5–31.

How to cite: Rodríguez, A., Gómara, I., Bellocchi, G., Martin, R., Martínez-Fernández, A., Carballal, A., Doltra, J., del Prado, A., and Ruiz-Ramos, M.: The challenges of modelling mixed management grasslands in North Spain under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12103, https://doi.org/10.5194/egusphere-egu22-12103, 2022.

Pastures are one of the most crucial land covers from the ecological and agricultural point of view. In Spain, pasture areas are being especially more vulnerable to the effects of climate change, which highlights the need to have tools for assessing and characterizing pastures, to understand the vegetation behaviour better, and anticipate potential risks such as events of drought or frosts mitigating the negative impacts that take place both in the crop and at an economic level for the farmers.

This work aims to establish an early warning system in pastures and evaluate the combined drought index by studying the behaviour of the NDVI (vegetation index) with the temporal dynamics of temperature and precipitation in two areas in north-central Spain (Ávila and Segovia). For this, the grass areas were selected, the behaviour of the climatic patterns and the vegetation index were studied, pastograms were analyzed to characterize and evaluate the amount of grass produced, and correlations were made to assess the behaviour of precipitation and the NDVI between development phases over 20 years.

The data analyzed and the methodologies followed for the study areas determine two highlighting points in the growth of the grasses, autumn and spring. There is also a linear relationship between cumulative precipitation and cumulative NDVI in both zones, which, together with the pastures model, allow obtaining the production estimate to characterize them. With this information obtained from the analysis for both Ávila Zone – ZAV and Segovia Zone – ZSE, a combined drought index and alarm system is proposed based on the values ​​of the standard curves and scores of the meteorological and physiological index for each zone during the period 2000-2020.

Acknowledgements

The second author acknowledges the Center for Studies and Research for the Management of Agricultural and Environmental Risks (CEIGRAM) funding through its 2020 call for grants to young researchers. The support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020 is highly appreciated.

References

• Ernesto Sanz, A. Saa-Requejo, C.H. Díaz-Ambrona, M. Ruiz-Ramos, A. Rodríguez, E. Iglesias, P. Esteve, B. Soriano and Ana M. Tarquis. Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland. Entropy, 2021, 23(5), 576. https://doi.org/10.3390/e23050576
• Andres Almeida-Ñauñay, Rosa M. Benito, Miguel Quemada, Juan C. Losada and Ana M. Tarquis. The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 2021, 23(5), 559; https://doi.org/10.3390/e23050559
• Ernesto Sanz, A. Saa-Requejo, C.H. Díaz-Ambrona, M. Ruiz-Ramos, A. Rodríguez, E. Iglesias, P. Esteve, B. Soriano and Ana M. Tarquis. Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 2021, 13(5), 840; https://doi.org/10.3390/rs13050840
• Eva Iglesias, K. Báez & C.H. Diaz-Ambrona. Assessing drought risk in Mediterranean Dehesa grazing lands. Agricultural Systems, 2026, 149, 65-74. https://doi.org/10.1016/j.agsy.2016.07.017

How to cite: Tarquis, A. M., Vargas, L., Rivas-Tabares, D., and Diaz-Ambrona, C. G. H.: Design of a Combined Drought Index for the Creation of an Early Warning System in Grasslands. Case Study in the Sierra de Guadarrama, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12213, https://doi.org/10.5194/egusphere-egu22-12213, 2022.

Olive trees play a vital role in Tunisia, a North African Mediterranean country. The Mediterranean basin region is experiencing severe climate change conditions. Indeed, traditional olive trees have the distinguishable ability to resist climatic conditions. However, warming trends and unusual both raining, and drought periods are threatening this crop by causing drying phenomenon and decreasing yield’s quality and quantity. Therefore, this culture is attracting great attention for effectively analyzing, and monitoring their changes to cope with the projected changes accurately and put the necessary adaptation strategies. Such work remains a challenge via field measurement approaches. Meanwhile, satellite imagery provides a wide range of data. In this work, we took advantage of MOD13Q1 products to analyze the relationships between vegetation indices and their hidden components and the land surface temperature (LST) for various reasons: Firstly, to assess the relationship between the LST and vegetation indices and their components. Secondly, to determine which temporal profiles are more closely related to each other. Thirdly, to quantify the impact of climate change on olive sites. To this aim, the wavelet transform is used to decompose the time series. Moreover, various similarity and statistical measures are calculated to better quantify these relationships. On one hand, no significant correlation is measured for the trend components. Moreover, the olive trend has shown a positive slope. In contrast, LST depicted negative dynamics. On the other hand, interestingly, the temporal profiles seemed similar. And the wavelet coherence showed a consistent relationship between them. Based on our findings, we remark the limitation of classical correlation measures in depicting the relationship between the discussed variables. Therefore, we conclude that a good causality study must rely on time point relation detection and not on the overall similarity between the environmental variables and the vegetation indices.

How to cite: Bounouh, O., Tarquis, A. M., and Riadh Farah, I.: Investigation of climate change impact on olive trees in Tunisia via MODIS LST and NDVI products and correlation measures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13255, https://doi.org/10.5194/egusphere-egu22-13255, 2022.

The study is geared towards the implementation of a workflow based on a Support Vector Regression Machine Learning (SVR-ML) approach which is conducive to estimates of flowrates across a given cross-section of a target stream in the presence of extreme precipitation events. The work is motivated by the observation that damages ensuing flash floods are a matter of global concern. A broad set of evidences suggests the ecosystem is experiencing changes of precipitation extremes, a causality relationship between increasing extreme floods and global climate dynamics being evidenced. In this context, practical tools associated with analyses of floods caused by extreme precipitation events can assist the design of early alert strategies across vulnerable regions. Physically and conceptually-based models have been extensively employed to link stream flowrates to precipitation events. These kinds of models are formulated and validated upon relying on continuous monitoring of flowrates as well as hydrometeorological variables associated with the area of the watershed related to a target stream. The typically high uncertainties underlying (a) the description of the physical processes governing the rainfall-runoff relationship as well as (b) monitoring and quantification of quantities and attributes characterizing the system behavior tend to propagate to outputs of interest of a given model. When considering well instrumented watersheds, data-driven modeling approaches grounded on machine learning (ML) algorithms can be an attractive alternative/complement to physically-based modeling approaches to analyze extreme flood events. Here, we rely on a Support Vector Regression ML (SVR-ML) algorithm that makes use of a linear kernel to provide estimates of hourly flowrate at a stream upon relying on observations of hydrometeorological variables across the watershed associated with the stream. The analysis encompasses three watersheds differing in size (ranging from about 25 to 250 km2) and located in the North of Italy and is structured across three steps: (i) identification of variables that are most informative to the target quantity (i.e., the flowrate in the stream), a step relying on cross-correlation and partial auto-correlation analyses; (ii) training of the SVR-ML algorithm, comprising the estimation of the optimal hyperparameters and parameters of trained models and the ensuing validation; and (iii) analysis of the anticipation time at which an early alert is effective, model performance being then quantified through the typical Mean Average Percentual Error (MAPE) metric. Our results suggest that, as expected, precipitation is the main driving force in a rainfall-runoff process, quantities such as temperature and relative humidity being least informative to the construction of the ML model considered. The predictive capability of the model (quantified through MAPE) is influenced by the desired anticipation time (i.e., the distance in time between the inputs and the output of the ML model). In general, one can note that (i) predictions of enhanced quality (MAPE smaller than 10%) are obtained for shorter anticipation times and (ii) models associated with low values of MAPE are obtained if the anticipation time is equal to or smaller than the time of concentration of the watershed.

How to cite: Sandoval, L., Riva, M., and Guadagnini, A.: Estimation of Floods Related to Extreme Precipitations through a Machine Learning Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1952, https://doi.org/10.5194/egusphere-egu22-1952, 2022.

Modelling river hydrodynamics in an efficient approach remains a technical challenge which limits our ability to assess river flood hazard or to use process-based erosion laws at a high-resolution in landscape evolution models. Here we present a fast iterative method, entitled FastFlood, to compute river depth and velocity in 2D on a digital elevation model (DEM). This new method solves for the 2D shallow water equation, without the inertia terms, by iteratively building the river water depth using classical flow routing algorithms based on directed acyclic graphs, including the classical single or multi-flow, applied to the water surface. At each iteration, the water depth of each cell of the DEM increases by an increment that is a function of water discharge, computed using a flow accumulation operation, and decreases based on a flow resistance equation, in a manner similar to the Floodos model (Davy et al., 2017). In the hydrostatic mode, this operation is repeated until reaching a near constant water depth over the entire DEM, which occurs after a few tens or hundreds of iterations. FastFlood can also solve for the dynamic propagation of a flood in the hydrodynamic mode. In this case, the water depth increment is only a function of the water discharge exiting the direct upstream neighbors and the iterations are replaced by a time evolution of the water depth. Water depths obtained with the hydrostatic solution were validated against an analytical solution in the case of a rectangular channel and with the Floodos model for natural DEMs. Compared to previous hydrodynamic models, the main benefits of FastFlood are its simplicity of implementation, which mainly requires a classical flow routing algorithm, and its efficiency. Indeed, for a DEM of 10⁶ cells, the algorithm takes about 2 minutes on a laptop to find the hydrostatic solution, about 10 times faster than using the Floodos model (Davy et al., 2017) that was already significantly faster than other hydrodynamic models. Moreover, the computational time scales a little more than linearly with the number of cells, which makes FastFlood a suitable solution even for DEMs larger than 10⁶ – 10⁷ cells. In the future, we expect to make progress on the numerical method by adapting graph-based solutions to the issue of flow water routing. Following Davy et al. (2017), we will also include FastFlood in a landscape evolution model to couple it to process-based laws for erosion, transport and deposition of sediments.

How to cite: Steer, P., Davy, P., Lague, D., Bernard, T., and Feliciano, H.: FastFlood: a fast and simple 2D hydrodynamic or hydrostatic numerical solution to river flow in landscape evolution models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3142, https://doi.org/10.5194/egusphere-egu22-3142, 2022.

Extreme precipitation events are expected to intensify with global warming, and naturally a widespread assumption is that the intensity and frequency of flooding will grow with the heavier downpours under climate change. However, flood magnitude is not only dependent on the spatial distribution, time evolution and rarity of precipitation, antecedent soil moisture and snowmelt are also the potential controls on flood hazard. Few studies have jointly quantified the influence of soil moisture dynamics and spatiotemporal distribution of precipitation on flood amplitude, though many research attempted to explain the elusive relationship between rainfall and flood conceptually. Here, the connections of changes in extreme precipitation and direct surface water flooding intensities in the periods of 1981-2000, 2021-2040 and 2061-2080 are quantified in 6 study areas in the UK, with high-resolution spatial and temporal characteristics of hourly rainfall data from UKCP Local 2.2 km. Dynamic soil moisture is modeled empirically and continuously to capture the moisture variation and infiltration loss, and distributed rainfall-runoff is calculated on the uneven terrain with the sub-grid river channel model in LISFLOOD-FP. Results indicate a strong correlation of the extreme rainfall and flood magnitude changes with the capacity of the soil moisture. Extreme precipitation can be magnified in rainy seasons due to amplified moisture convergence, while in dry periods limited moisture availability may offset extreme precipitation increases.

How to cite: Rong, Y., Bates, P., and Neal, J.: Quantifying the impact of soil moisture dynamics on UK flood hazard under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3213, https://doi.org/10.5194/egusphere-egu22-3213, 2022.

We investigate the 2021 summer flood in Ahr catchment in West Germany, with the return period estimated preliminarily as 1 in more than 500 years [1]. A recent study has indicated that science did not fail to predict the flood event [2]. Yet, several scientific and administrative challenges are still to be addressed to improve existing flood forecasting systems for supporting local authorities to manage such extreme events. We bring some examples of what science and technology gaps need to be filled to address these issues. To do this, uncertainties associated with near-real time precipitation products with hourly and daily resolutions provided by the German weather service (DWD) have been investigated. The hydrological response of the catchment is tested to several high-resolution gridded precipitation observations and reanalysis data for post-assessment of the event. A new feature to read hourly meteorological input data was added to the mesoscale Hydrologic Model (mHM- www.ufz.de/mhm) to forced it with Radar-Online-Adjustment of hourly values measured at the precipitation stations (RADOLAN-mHM). Comparing the flood peak from RADOLAN-mHM with REGNIE-mHM at daily time steps provided valuable insights on development-orientation of near-real time and high-resolution flash flood analysis and forecast applications for Germany. Last but not least, the variability of maximum streamflow in the Ahr catchment was evaluated for future periods under climate change to check if such megafloods can be considered as new norms.

Fig 1. Boxplots of the annual maximum streamflow in Ahr river simulated by the mesoscale Hydrologic Model (mHM)
  for three periods between 1971-2000, 2000-2050 and 2051-2100. Simulation is conducted based on 21 ensembles under RCP 2.6 and 49 ensembles under RCP 8.5

References

[1] L. Samaniego, H. Najafi, O. Rakovec, P. Shrestha, S. Thober. (2021) High-resolution hydrologic forecasts were able to predict the 2021 German Floods: what failed?. AGU 2021 Fall Meeting, New Orleans.
[2] World weather attribution report, (2021) Rapid attribution of heavy rainfall events leading to the severe flooding in Western Europe during July 2021. https://www.worldweatherattribution.org/wp-content/uploads/Scientific-report-Western-Europe-floods-2021-attribution.pdf

How to cite: Najafi, H., Thober, S., Rakovec, O., shrestha, P. K., and Samaniego, L.: New insights for real-time flood forecasting in Germany: Lessons learned from 2021 summer flood in Ahr river, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4952, https://doi.org/10.5194/egusphere-egu22-4952, 2022.

Extreme weather situations are becoming increasingly frequent with devastating consequences worldwide. Heavy rainfall events in July 2021 caused severe flash floods in western Germany, Belgium and the Netherlands, resulting in a high number of casualties and material damage. The high hazard potential combined with the low reaction times, associated with these events, make it necessary to develop efficient and reliable early warning systems (EWSs) to facilitate the preparation of response strategies. As nowcast precipitation forecasts are continuously improving in both quality and spatial resolution, they become an essential input for flash flood and landslide prediction models and therefore an important component in EWSs. However, the inherent uncertainty of radar-based nowcasting systems are carried over to the output of those prediction models. Therefore, this study aims to analyze the uncertainty sources of nowcasting products of the German weather service (DWD) using the July flood Event 2021 as a case study. More specifically, the objective is to determine whether the quality of precipitation nowcast products is sufficient for usage in physics-based flood or landslide prediction models. Due to the complex nature of weather and rainfall structures as well as their spatio-temporal variability, traditional cell-by-cell comparison of predictions and ground truth is insufficient to quantify forecast quality. To overcome this issue, uncertainties in magnitude, time and space and their respective sources are identified, using techniques from various fields of science. Subsequently, error propagation in flash flood prediction models is analyzed by applying the previously determined uncertainty ranges to a hydrological model.

How to cite: Winkels, B., Hofmann, J., Yildiz, A., Edrich, A.-K., Schüttrumpf, H., and Kowalski, J.: Quantifying rainfall forecast uncertainty and error propagation in flash flood and landslide prediction models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6170, https://doi.org/10.5194/egusphere-egu22-6170, 2022.

Flash floods are a rapid burst of flood water that can cause extreme damage to populated areas. The European floods in France, Belgium, Germany and the Netherlands in the summer of 2021 featured a wide range of flash floods with a large number of casualties and vast financial damage. Reflection on the risk reduction strategies have reemphasized the need for early warning systems in the upstream catchments of North-Western Europe. For applications such as this, the speed of flow simulations is critical, as the quality of real-time forecasting often depends on the frequency and amount of simulations that can be carried out as new weather forecasts come in. We present a new type of flood hazard model that, in many typical cases, solves flash flood hazard a 100 times faster with similar accuracy. The developed method employs steady-state solvers for diffusive wave water flow equations to skip the dynamical process and directly estimate relevant parameters such as maximum flow height, maximum flow velocity and relative arrival time of the flood water. These paramters are often the most important for warning systems and descision making in risk reduction. Our adapted algorithm improves upon traditional steady-state flow solvers by employing inversed flow accumulation results and compensation for partial steady-state flow. We show the accuracy of the method is similar to full dynamic water flow simulation in many types of events, such as the extreme 2003 floods in the Fella Basin (Italy), Hurricane-induced flooding on Dominica and the flood impact in Limburg in 2021 (The Netherlands). On average, with highly similar accuracy, calculation time was reduced from approximately 6 hours to 2.5 minutes. We further investigate the limits of the developed methods, in particular to practical applications in different type of flood events. While the sensitivity of the model to initial conditions is similar to that of regular flood models, the sensitivity of the hydraulic aspects is lower. Finally, we discuss potential usage for early-warning, spatial descision support systems and serious gaming approaches. While further investigation is required to fully validate the method, a break-through in flood hazard assessment could be on hand.

How to cite: van den Bout, B.: Super-fast flash flood simulation using steady-state flow solvers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6953, https://doi.org/10.5194/egusphere-egu22-6953, 2022.

Efficient pluvial flood mapping methods are needed to produce realistic flood scenarios in very small upstream catchments. The Cartino2D method was developed to launch automatically 2D models based on the Telemac2D hydraulic software. The principle is quite simple, (1) create automatically the mesh with a topography based on Lidar, (2) manage automatically the boundary conditions, (3) run the model based on rainfall input data, and (4) postprocess the results. The extent of each 2D model generally varies between 2 to 10 km² with a maximum of 20km². The only manual work consists in checking or modifying the limits of hydrological catchments.

We began to use this method on the Toulon metropole (South of France) with 66 complementary computation domains covering about 180km² and using eight statistical rainfalls. We also tested and evaluated this method on twenty other case studies in different regions of France. In this presentation, we focus on two evaluations (flood of June 2010 in Draguignan and flood in 2014/2015 around Montpellier) conducted within the ANR PICS project.

In this project, we improved the method to automatically integrate radar rainfall and to compare the results with local knowledge, observed historical floods and local hydraulic studies.

Cartino2D offers interesting results in areas with natural, rural land-use or few urban developments. The density of the mesh (less than 3m in the thalwegs) and the Telemac2D model quality are sufficient to obtain a good accuracy in these areas.  

In urban areas, the method provides a first knowledge, but more complex input data are needed to improve the accuracy of results.

We try in this presentation to describe which databases should be created to improve the accuracy of such automatic computations. At the scale of urban areas with results around buildings, the databases need to be spatially well defined. We propose some standard of databases to be integrated in computations: for example, the main underground channels or culverts, the main aerial channels (particularly very small channels not recognized by Lidar), a spatial distribution of the Curve Number and of the Manning’s coefficient.

This kind of databases, which cannot be deduced automatically from Lidar data, appears as essential to improve the results of Cartino2D automatic process. This kind of knowledge exists locally, but up to now it is not integrated in homogeneous national or regional databases.

In the same way, we need also to have well-defined databases to compare automatic results with historical floods as flood marks, gauge stations.

Automatic 2D mapping of flash floods seems to be a realizable goal at a scale of a region or country with standard 2D hydraulic models. But the current main limits appear to be a lack of good input database management, which limits the current accuracy of mapping results.

How to cite: Pons, F., Alquier, M., and Paya, E.: Automatic 2D mapping of flash floods: which possibilities and limits? An illustration based on the Cartino2D method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7212, https://doi.org/10.5194/egusphere-egu22-7212, 2022.

Mountain rivers experience channel widening as a response to high-magnitude hydrological events. Several studies indicate that the unit stream power (ω_pk) and the lateral confinement (CI) are among the most important constraints to explain channel modifications induced by a flood. That said, with the same controlling factors, a relatively broad spectrum of width ratios (WR = channel width after/before the flood) is commonly observed in real case studies. Sediment transport in mountain streams occurs via processes classified as debris flows (DFW), debris floods (DFD) and water flows (WF). This study aims to test if different flow-types are one of the drivers of channel response to floods, specifically investigating if there is a relationship between DFD (i.e. a transport condition characterized by extremely high bedload) and intense channel widening.

The case study is the Cordevole catchment (Dolomites, Italy; drainage area of 857 km²), which in October 2018 was affected by a severe hydrological event (Vaia storm). Besides the main stem of the Cordevole River, we considered four of its tributaries (Tegnas, Pettorina, Liera and Corpassa torrents). WR was determined at the sub-reach scale through aerial photographs analysis and ω_pkwas calculated considering the discharge at the flood peak provided by hydrological modelling. A post-flood survey allowed us to determine the flow-types that occurred at each sub-reach of the Tegnas Torrent during the event. The possible upheaval from WF to DFD along the other streams was determined considering the presence of conditions required for local occurrence of DFD (i.e. ω_pk exceeding 5000 Wm^-2 and/or DFW tributaries delivering large amount of sediment into a receiving stream).

DFD sub-reaches of the Tegnas Torrent experienced widenings that, at the same ω_pk, were 2-3 times larger than WR of WF sites. These processes-specific relationships were used to recognize sub-reaches of the other streams were an “anomalous widening” occurred during the Vaia event, i.e. sites where WR was significantly larger-than-expected for a specific ω_pkunder conditions of WF. Among 117 sub-reaches, anomalous widening was recognized at 13 and 6 sub-reaches of the Liera (WR up to 16) and Pettorina (WR up to 10) torrents, respectively. All these sub-reaches were characterized by the presence of conditions required for DFD occurrence during a high-magnitude flood, allowing us to infer that the process responsible for sediment transport during the Vaia event was likely DFD. Contrariwise, no sub-reaches of the Cordevole and Corpassa streams experienced anomalous widening, likely because WF occurred along their whole courses due to their morphological characteristics (e.g. wide channel before the flood) and/or lower magnitude of the flooding locally induced by the storm.  

These results suggest that an extraordinary-widening characterizes DFD channel sites, which, during a severe flood, can be affected by channel changes remarkably more intense than those occurring in response to WF. For this reason, in addition to hydraulic and morphological constraints, the different sediment–water flows possibly occurring at a sub-reach should be considered as a further controlling factor for channel modifications and, consequently, for prediction of geomorphic hazard at local scale.

How to cite: Brenna, A., Marchi, L., Borga, M., Zaramella, M., and Surian, N.: Debris floods and channel widening in mountain rivers: Examples from the Vaia Storm (October 2018) in the Cordevole River catchment (Dolomites, Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7768, https://doi.org/10.5194/egusphere-egu22-7768, 2022.

After having swept over western France, the ALEX storm led to an exceptional Mediterranean rainfall event which hit the “Alpes Maritimes” region during the night of the 2nd to 3rd October 2020. The rainfall accumulations observed on 12 to 24 hours durations were unique in this region, with a record of 663mm in 24h (EDF raingauge at Les Mesces).

Form West to East, several valleys, mainly those of Tinée, Vésubie, and Roya were affected by major floods, landslides, sediment transport and geomorphological changes. The hydrometric network was almost destroyed. The human and material damages were considerable, with many fatalities and missing people, several villages largely destroyed, and important destructions of communication and transport networks.

A lot of technical post-flood surveys were launched by national authorities to gather a detailed knowledge of the event characteristics, with regard to rainfall accumulations, water discharges, description the torrential phenomena, and inventory of damages. This communication is focused on the question of water discharges.

National and local authorities and organisms, universities and companies, were involved in different post-flood surveys aiming at gathering information on the peak discharges and the hydrographs of the floods, for their own needs and/or within structured programs (Administrative survey, HYMEX research project www.hymex.org).

Several kind of discharge field estimations were provided using field survey measurements, satellites images, post-event Lidar data, combined with hydraulic estimations based on hydraulic formulas, and 1D/2D hydraulic models. Several teams also applied hydrological models based on radar quantitative precipitation estimates, to calculate hydrographs at different basins outlets.

To combine and draw a uniform synthesis of all these results, a consensus exchange was launched to share the knowledge gathered by the different data providers. The objective was to compare, assess, and propose common intervals of peak discharges in the different impacted valleys. We also evaluated for each valley the return period of the final interval of discharge established by the consensus.

The final product is an official administrative document, established at the end of October 2021 by the French state authorities, providing the peak discharge values to be used for post flood studies, reconstruction, and prevention measures.

How to cite: Pons, F., Bonnifait, L., Criado, D., Payrastre, O., Billaud, F., Brigode, P., Fouchier, C., Gourbesville, P., Kuss, D., Le Nouveau, N., Martin, O., Nomis, S., Paquet, E., and Cardelli, B.: Towards a hydrological consensus about the 2nd – 3rd October 2020 ALEX storm event in the French “Alpes Maritimes” region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7913, https://doi.org/10.5194/egusphere-egu22-7913, 2022.

Best allocation of ressources from stakeholders that face fast-flooding implies a dynamic representation of the risk, exposure, and danger, in a situation where parameters (roughness, infiltration, drainage network etc.), and input (bathymetry and rainfall) can be both uncertain and volatile. Ensemble strategy simulation appears as a good approach to deal with these issues.
Fast-flood event are also typically events where meteorological predictions can underestimate the actual rainfalls until very late. Urban microcharacteristics can also make models sensitive to spatial resolution, and « events » such as log jam can even modify DEM. 
At Strane Innovation, we develop a decision-support tool called BlueMapping. To be operational, that is, fast to deploy and reliable, we use this ensemble strategy together with the fastests simulation models deployed on powerful computers. It also requires quick and robust routines for the setup of the model, with proxies when data is not available at the moment, and inputs that are easy to modify if necessary.
We will ilustrate this discussion through the test case of the Alex storm that hit la Vésubie and la Roya valleys. After a quick benchmark with a standard model, we will compare the outcome between the flooding predicted by different models and the actual outcome. 
Since BlueMapping can be integrated in an alarm system, it is important to assess the values of the confusion matrix, in particular the false alarm ratio, to make sure our tool keeps its value over time.

How to cite: Wacheux, C.: Ensemble strategy for decision-support tool : a case study of the Alex storm in 2020 in la Roya and la Vésubie valleys, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9023, https://doi.org/10.5194/egusphere-egu22-9023, 2022.

This contribution presents improvements of conceptual models in SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, underlying the French national flash flood forecasting system Vigicrues Flash [1], based on: (i) the 3-parameters model formulation and variational data assimilation algorithm of [2] that showed promising results (i) hypothesis testing on a large sample of catchments and flash floods; (ii) comparison of the SMASH model performances in uniform and distributed calibration to GR models; (iii) a new wrapped Python interface automatically generated by the f90wrap library [3]. Multiple tests have allowed us to converge on two parsimonious distributed model structures that have comparable performances to the GR models in spatially uniform calibration. These two structures, mainly based on GR operators at the pixel scale, differ in the production operator, with the 6-parameters structure being GR production and the 7-parameters structure being VIC production. Furthermore, the use of distributed calibration applied to these formulations via adjoint model resolution shows significantly better calibration performances without being less robust in spatio-temporal validation. Immediate work deals with improving the regional calibration scheme by tayloring the global search of semi-distributed prior parameter sets, with multi-gauge constrains, improving physiographic regularizations in the forward-inverse SMASH assimilation chain, using Python librairies.

References
[1] P. Javelle, et al. Flash flood warnings: Recent achievements in france with the national vigicrues flash system UNDRR GAR, 2019.
[2] M. Jay-Allemand, et al.. On the potential of variational calibration for a fully distributed hydrological model: application on a mediterranean catchment. HESS, 2020, https://doi.org/10.5194/hess-24-5519-2020
[3] J. R. Kermode. f90wrap: an automated tool for constructing deep python interfaces to modern fortran codes. 2020. https://doi.org/10.1088/1361-648X/ab82d2

How to cite: Colleoni, F., Garambois, P.-A., Jay-Allemand, M., Javelle, P., Arnaud, P., Fouchier, C., and Gejadze, I.: Assessing parsimonious hydrological model structures with distributed adjoint-based calibration in SMASH Python-Fortran platform on large sample of French catchments and flash floods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12136, https://doi.org/10.5194/egusphere-egu22-12136, 2022.

Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. Over the Sahel, intense rainfall from Mesoscale Convective Systems (MCSs) is the main driver of flash floods, with recent research showing that these have tripled in frequency over the last 35 years. This climate-change signal, combined with rapid urban expansion in the region, suggests that the socio-economic impacts of flash flooding will become more frequent and severe. Appropriate disaster preparedness, response, and resilience measures are required to manage this increasing risk.

The NFLICS (Nowcasting FLood Impacts of Convective storms in the Sahel) project has co-developed a prototype early warning system for Senegal, incorporating nowcasting of heavy rainfall likelihood and flood risk from MCSs at city and sub-national scales. This system uses remote sensed satellite data and has been developed in partnership with the national meteorological agency (ANACIM) to operate quickly in real-time. To identify convective activity, wavelet analysis is applied to Meteosat data on cloud-top temperature for historical periods (2004 to 2019) and for the start-time of a nowcast. Data on historical convective activity, conditioned on the present location and timing of observed convection, are used to produce probabilistic forecasts of convective activity out to six hours ahead. Verification against the convective activity analysis and the 24-hour raingauge accumulations over Dakar suggests that these probabilistic nowcasts provide useful information on the occurrence of convective activity. The highest skill (compared to nowcasts based solely on climatology) is obtained when the probability of convection is estimated over spatial scales between 100 and 200km, depending on the forecast lead-time considered. Furthermore, recent advances have included incorporation of land surface temperature anomalies to modify nowcast probabilities – this recognises that MCS evolution favour drier land.

A flood knowledge database, compiled with local partners, allows estimation of the flood risk over Dakar given the identified probability of convective activity. The flood hazard is estimated from the probabilistic convective-activity nowcast when combined with information on the historical relationship between convective activity and precipitation totals. Information on the antecedent conditions can also be included, with a higher level of hazard associated with recent rainfall and already-wet conditions. Flood vulnerability is estimated at the local scale from post-event analysis of the 2009 flood events along with information from recent modelling studies and flood-alleviation measures. The combined information from nowcasts of convective-activity and flood-risk is visualised through an interactive desktop GUI and an online portal. Operational trials over the 2020 and 2021 rainy seasons, and during intensive nowcasting testbeds with researchers and forecasters, has shown the utility of these new nowcast products to support Impact-based Forecasting.

How to cite: Cole, S. J., Anderson, S., Diop, A., Taylor, C., Klein, C., Wells, S., Nash, G., and Diagne, M.: Nowcasting Flood Impacts of Convective storms in the Sahel, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13445, https://doi.org/10.5194/egusphere-egu22-13445, 2022.

In this study, we investigated the chemical characteristics of rainwater and evaluated the correlation among rainwater quality factors for seven precipitation events from June 2020 to August 2020. Rainwater samples (n = 84) were collected every 50 mL at Pukyong National University, Busan, South Korea. Values of pH and electrical conductivity (EC) were measured in the field, and concentrations of water-soluble cations (Na⁺, Mg²⁺, K⁺, Ca²⁺, and NH₄⁺) and anions (Cl^-, NO₃^-, and SO₄^2-) were determined using ion chromatography. For all rainwater samples, the pH ranged from 3.63 to 5.59, with mean pH = 4.78, and the measured mean EC was 30.54 µS/cm, indicating that the precipitation was acidified in Busan, South Korea. A strong negative correlation (r = -0.83) was found between the pH and EC values. The major ionic components of rainwater were SO₄^2- > NH₄⁺ > NO₃^-, which are predominantly attributed to anthropogenic forces in the study area, such as emissions from vessels and fossil fuels. Anion concentrations of rainwater samples were SO₄^2- (average concentration: 2.15 mg/L) > NO₃^- (1.43 mg/L) > Cl^- (1.04 mg/L) and showed a strong positive correlation with EC values (r = 0.81) and a negative correlation with pH values (r = -0.72) of rainwater samples. The average concentrations of cations (NH₄⁺ (1.56 mg/L) > Ca²⁺ (1.31 mg/L) > Na⁺ (0.63 mg/L) > K⁺ (0.57 mg/L) > Mg²⁺(0.29 mg/L)) were relatively lower than those of anions. Cation concentrations showed no significant correlation with the values of EC (r = 0.29) and pH (r = -0.21). The result of this study indicates that acidic precipitation occurs even in summer with relatively low concentrations of air pollution and strong rainfall intensity.

How to cite: Park, H., Kim, T., and Yang, M.: Chemical characteristics of summer rainwater at an urban site in South Korea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1587, https://doi.org/10.5194/egusphere-egu22-1587, 2022.

The distribution functions for large rain events X_c in current climate is denoted F(x) = P{X_c ≤ x} and for large rain events X_f in a future climate G(x) = P{X_f ≤ x}. A climate factor k is introduced, and it is assumed that P{X_c ≤ x} = P{X_f ≤ k x} corresponding to G(k x) = F(x). If we further assume that the distribution functions F and G have exponential tails, the following simple transformation of the return period in current climate T_c to the corresponding return period in future climate T_f can be deduced

T_f = T_c^1/k

Applying a first order analysis on this equation with k as independent variable leads to a relation between the uncertainties of k and T_f. In terms of the coefficient of variations we get

CV{T_f} ≈ 1/ lnT_c CV{k}

This equation reveals that even with moderate uncertainty in k, the uncertainty in T_f is notably increased.

How to cite: Rosbjerg, D.: Return periods in current and future climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1730, https://doi.org/10.5194/egusphere-egu22-1730, 2022.

Disasters associated with heavy rainfall such as urban floods, riverine floods, and landslides often simultaneously occur while each of them sensitively reacts to rainfall variabilities at distinct ranges of time scales. Therefore, a stochastic rainfall model suitable for modeling compounding of disasters must be good at reproducing the rainfall variability across all timescales relevant to all types of disasters. This study proposes a point stochastic rainfall generator that can reproduce various rainfall characteristics at timescales between 5 minutes and one decade. The model generates the fine-scale rainfall time series using a randomized Bartlett-Lewis Rectangular Pulse (RBLRP) model. Then the rainstorms are shuffled such that the correlation structure between the consecutive storms is preserved. Finally, the time series is rearranged again at the monthly timescale based on the result of the separate coarse-scale monthly rainfall model. The method was tested using the 69 years of 5-minute rainfall data recorded at Bochum, Germany. The mean, variance, covariance, skewness, and rainfall intermittency were well reproduced at the timescales from 5 minutes to a decade without any systematic bias. The extreme values were also well reproduced at timescales from 5 minutes to 3 days. The past-7-day rainfall before an extreme rainfall event, which is highly associated with the extreme riverine flow and landslide was reproduced well too. Then, the model was extended to integrate the influence of climate change. For this, the model was re-parameterised in terms of parameters representing average magnitude and temporal structure of the rainfall time series. Then, the relationship between these new parameters and the covariates (e.g. monthly, weekly, daily temperature) were investigated. Lastly, the derived regression relationships were applied to adjust the duration and the magnitude of rain storms and cells that were generated by the stationary RBLRP model.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471).

How to cite: Kim, D., Onof, C., Park, J., and Wang, L.: A Stochastic Rainfall Generator Suitable for Modeling Future Compound Disasters Associated with Heavy Rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2595, https://doi.org/10.5194/egusphere-egu22-2595, 2022.

The representation of extreme precipitation at small spatio-temporal scales is of major importance in urban hydrology. The present study compares observations from tipping bucket gauges and a C-band radar to two sets of re-analysis climate model output data for a historic period of 14 years where there is full spatial and temporal overlap between datasets. The reanalysis data are based on models with different parametrizations and spatio-temporal resolutions, one being a “convective-permitting” model while the other uses a convective parametrization scheme to account for convective rainfall. The study focuses on an area of approximately 100 by 150 km.

The datasets are compared with respect to seasonality of occurrence, intensity levels and spatial structure of the extreme events. All datasets have similar seasonal distributions, and comparable intensity levels. There are, however, clear differences in the spatial correlation structure of the extremes. Seemingly, the radar data is best representation of a “real” spatial structure for extreme precipitation, even though challenges appear in data when moving far from the physical radar. The spatial correlation in point observations is a valid representation of the spatial structure of extreme precipitation. The convective-permitting climate model seem to represent the spatial structure of extreme precipitation much more realistically, compared to the coarser convective parameterized model. However, improvement could be made for the shortest durations and smallest spatial scales.

How to cite: Arnbjerg-Nielsen, K., Thomassen, E. D., Thorndahl, S. L., Andersen, C. B., Gregersen, I. B., and Sørup, H. J. D.: Comparing extreme precipitation between data from rain gauges, weather radar and high-resolution climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2624, https://doi.org/10.5194/egusphere-egu22-2624, 2022.

Rainfall data with high spatiotemporal resolutions are of great value in many research fields, such as meteorology, hydrology, global warming, and urban disaster monitoring. Current rainfall observation systems include ground-based rain gauges, remote sensing-based radar and satellites. However, there is an increasing demand of the spatiotemporal rainfall data with high resolution. Thanks to the advocacy from many research institutions and international organizations, several innovative crowdsourcing ideas including opportunistic sensing and citizen science initiatives have been followed in recent years. Commercial cellular communication networks, windshield wipers or optical sensors in moving vehicles, smart phones, social medias, and surveillance cameras/videos have been identified as alternative rain gauges. In particular environmental audio recordings are a rich and underexploited source to identified and even characterize rainfall events.
Widespread surveillance cameras can continuously record rainfall information, which even provides a basis for the possibility of rainfall monitoring. Comparing the aforementioned methods, surveillance audio-based rainfall estimation has been discussed in existing studies with advantages of high-spatiotemporal-resolution, low cost and all-weather. Therefore, this study focuses on mining the rainfall information from urban surveillance audio for quantitative inversion on precipitation. Rain sound is generated by the collision of rain particles with other underlying objects in the process of falling. In real applications, the complex subsurface structure and random background noises from human activity in urban areas make surveillance rainfall sound vulnerable and surveillance audio-based rainfall estimation more challenging. In our study, the rainfall acoustic indicators were selected for rainfall sound representation. Deep learning-based rainfall observation systems were built based on urban surveillance audio data. Experimental results show the efficiency of our system in rainfall estimation. Our research is a new attempt to develop crowdsourcing-based rainfall observations, which can also provide a beneficent supplement to the current rainfall observation networks.

How to cite: Wang, X., Liu, X., Glade, T., and Wang, M.: Surveillance audio-based rainfall observation: a crowdsourcing approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2799, https://doi.org/10.5194/egusphere-egu22-2799, 2022.

In the UK, decision-makers use hydraulic model outputs to inform funding, connection consent, adoption of new
drainage networks and planning application decisions. Current practice requires the application of design storms to
calculate sewer catchment performance metrics such as flood volume, discharge rate and flood count. With flooding
incidents occurring more frequently than their designs specify, hydraulic modelling outputs required by practice are
questionable. The main focus of this paper is the peakedness factor (ratio of maximum to average rainfall intensity)
of design storms, adjudging that this is a key contributor to model bias. Hydraulic models of two UK sewer
catchments were simulated under historical storms, design storms and design storms with modified peakedness to
test bias in modelling outputs and the effectiveness of peakedness modification in reducing bias. Sustainable
drainage systems (SuDS) were implemented at catchment scale and the betterment achieved in the modelling outputs
was tested. The proposed design storm modification reduced the bias that occurs when driving hydraulic models
using design storms in comparison with historical storms. It is concluded that SuDS benefits are underestimated when
using design rainfall because the synthetic rainfall shape prevents infiltration. Thus, SuDS interventions cannot
accurately be evaluated by design storms, modified or otherwise.

How to cite: Muhandes, S., Dobson, B., and Mijic, A.: A method for adjusting design stormpeakedness to reduce biasin hydraulic simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5356, https://doi.org/10.5194/egusphere-egu22-5356, 2022.

Urban drainage is subject to a variety of external influencing factors that can have a negative impact on hydraulic system performance. These include changing precipitation characteristics due to climate change [1], an increase in sealed surfaces due to advancing urbanisation [2], but also further failures and malfunctions [3] in the technical grey and green infrastructure. With an increasing share of decentralised urban stormwater measures and uncertainties regarding responsibility, care and maintenance of these facilities, an increase in malfunctions can be assumed [4]. In this work, we are investigating common malfunctions in urban drainage systems with 1D/2D urban flood model in a virtual urban study site. The goal is to highlight differences between the failures and malfunctions in both grey and blue-green infrastructures for different design rainfall events (Type Euler II) and compare them to other possible scenarios like climate change and urbanization.

For the research, a model of a small virtual urban study site (1.5ha) is developed with the commercial software PCSWMM2D [5], which represents a small part of an urban catchment. It includes the following sub-structures and assets: i) combined sewer system, ii) urban stream, iii) urban structures including buildings, marketplaces, streets, bridges, pathways, and underbridges and iv) four sustainable urban drainage system (SUDS) structures (green roofs, permeable pavements, swales and bioretention cell). Connected to the combined sewer system are three border areas (30ha, 10ha, 10ha), representing inflows from outside. The urban drainage infrastructures and SUDS were designed based on a design rainfall (Euler Typ II) event with a 5-year return period and 1-hour event duration.

In total 12 different scenarios were designed for the virtual urban study site i) the SUDS-base-scenario which includes four different green infrastructure assets, ii) three reference scenarios with climate change, urbanisation and wet preconditions and iii) the malfunction-scenarios with seven single malfunction scenarios and one worst case which is all of the single scenarios combined. Each scenario was run with design rainfall events with an Euler II distribution and interval lengths between 15 minutes and 24 hours as well as return periods between 1 and 100 years.

To compare the different scenarios and assess their severity for the urban area we used 3 different objective values. i) The maximum water depth in the vulnerable infrastructure (underbridge), ii) the flooded area with water depths > 10cm and iii) the total combined sewer overflow emissions released into the urban stream.

Results show a clear difference between the different malfunction scenarios, with a higher influence of malfunctions in grey than in green infrastructures. In most cases, the reference scenarios climate change, urbanisation and wet preconditions show higher values than the malfunctions scenario. All scenarios are highly dependent on the rainfall event characteristics, with no differences in the objective values compared to the base case for low return periods and rising differences for medium to high return periods.

How to cite: Funke, F., Reinstaller, S., and Kleidorfer, M.: Impact of malfunctions on urban drainage for different design rainfall events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6652, https://doi.org/10.5194/egusphere-egu22-6652, 2022.

Rainfall data collected by citizen scientists is typically regarded as low quality and therefore remains underused in hydrological applications. Conversely, official data collected by professional organisations is often treated as more reliable than it really is. Here we demonstrate that value can be extracted from citizen science rainfall data by applying automated statistical quality control combined with manual checks. We also consider the pros and cons of citizen science rain observations.

Carefully selected rain data from official and citizen science gauges have been blended with radar.  Examples of how rainfall depths vary depending on the data inputs are presented, highlighting the benefit of incorporating all available data sources. This approach is particularly important when determining rainfall during spatially and temporally variable convective storms. The research is concerned with convective storms that resulted in pluvial flooding in urban areas of the UK between 2014 – 2018, however, the methodology could be implemented in any location where hourly (or shorter interval) rain gauge data and radar are available.  

How to cite: O'Hara, T., Lewis, E., McClean, F., Fowler, H., and Parkin, G.: Selecting Good Quality Official and Citizen Science Rain Gauge Data and Blending with Radar for More Accurate Rainfall Representation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7879, https://doi.org/10.5194/egusphere-egu22-7879, 2022.

In response to recent major flood events in Ireland, the authorities have prioritised the development of a national flood forecasting model for use as a tool in flood risk management. Accurate flood predictions by this model require high resolution spatiotemporal rainfall data. One source for this type of data is the remote sensing estimated precipitation provided by the Global Precipitation Measurement (GPM) satellite. The GPM has ability to detect and estimate all forms of precipitation using a range of advanced instruments, including Microwave and Radar technologies. This study evaluates the accuracy of detecting the large rainfall events which occurred in Ireland during the period 2014-2021 by three Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products (i) early run ; (ii) late run; and (iii) final run. The satellite estimates of these events have been assessed using five statistical indices applied to various temporal scales; hourly, daily, and monthly. The results showed that, for satellite detection, all of the three IMERG products had an acceptable detection accuracy of the large rainfall events. In particular, the calibrated product – final run product – outperformed the other near-real-time products in terms of estimation error and bias. Overall, the results indicate that IMERG satellite precipitation products can be used with confidence to detect large events over high latitude areas such as Ireland. Besides, they have a high potential for coupling with in-situ data to improve the accuracy of the integrated flood forecasting model.

How to cite: Mohammed, S., Nasr, A., and Mahmoud, M.: Evaluation of the spatiotemporal representation of the GPM satellite precipitation products over diverse climatic regions in Ireland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8942, https://doi.org/10.5194/egusphere-egu22-8942, 2022.

Since Hinton et al. introduced Deep Learning (DL) in 2006 [1], DL methods have led to breakthroughs in various scientific fields, such as speech recognition, medical, materials, and many more. Various early attempts to apply DL to short-term rainfall forecasting (nowcasting) were also reported. However, these early models did not lead to significant improvements as compared to non-AI nowcasting models such as STEPS [2]. It was TrajGRU model [3] which first demonstrated the potential gains that may be achieved with DL-based nowcasting models. Since then, a variety of DL models have been proposed or applied to tackle rainfall nowcasting, with the most iconic ones including U-Net, MetNet and DGMR [4-6]. Similarly to the Trajectory GRU (TrajGRU) model, the U-Net and MetNet models show clear improvements in predicting the occurrence of rainfall at high spatial and temporal resolutions and with a longer lead time, as compared to non-AI models. However, the predicted rainfall images from these three models (and their variants) become overly smooth rather quickly (at lead times of 15-20 minutes); this is a common ‘feature’ of many other DL models [7]. This means that significant amount of spatial rainfall details is lost, which is undesirable for certain hydrological applications, such urban flow and flood forecasting where small-scale rainfall variability -in particular localised peaks- may have tangible impacts [8]. In 2016, DeepMind [6] proposed a new type of DL-based nowcasting model called the Deep Generative Model of Radar (DGMR), which is based upon a Generative Adversarial Network (GAN) framework. The DGMR successfully improves the aforementioned smoothing drawback of other DL-models by incorporating noise into the rainfall forecast generator such that small-scale rainfall details can be preserved and, consequently, localised peak intensities can be better predicted. DGMR thus shows great potential for hydrological applications.

In spite of the success, the model structure of DGMR is complex and hard to digest by someone without proper training in DL. Therefore, even though the model structure has been published, it remains a mystery for most hydrologists, thus hindering its application.

In this work, we explore the success of DGMR with an in-depth analysis of its model structure. More specifically, through the process of re-constructing the DGMR model, we have developed a short tutorial on the different model components, in plain language and with example images and intermediate analyses. This will enable better understanding of the features and behaviour of the DGMR model and of the implications for hydrological applications. Additionally, a better understanding of the DGMR model components may instigate further improvements.

References:

[1] Hinton, G.E., et al., Neural Comput., 18 (7), 1527-1554, 2006.

[2] Bowler, N.E., et al., Q. J. Roy. Meteor. Soc., 132(620), 2127-2155, 2006.

[3] Shi, X., arXiv preprint arXiv:1706.03458, 2017.

[4] Agrawal, S., et al., arXiv preprint arXiv:1912.12132, 2019.

[5] Sønderby, C.K., et al., arXiv preprint arXiv:2003.12140, 2020.

[6] Ravuri, S., et al., Nature, 597, 672-677, 2021.

[7] Ayzel, G., Geosci. Model Dev., 13(6), 2631-2644, 2020.

[8] Ochoa-Rodriguez, S., et al., J. Hydrol., 531, 389-407, 2015.

How to cite: Heh, Y.-T. and Wang, L.-P.: Unraveling the mystery of DeepMind’s rainfall nowcasting: a step-by-step tutorial for hydrologists, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9054, https://doi.org/10.5194/egusphere-egu22-9054, 2022.

Weather radars have become a valuable tool for urban hydrological studies because they capture the rainfall intensities at a high spatial and temporal resolution. However, radar products are affected by objects or phenomena not of meteorological interest, making it necessary to apply various algorithms to correct and improve their rainfall estimation. In addition, multiple methods for merging rain-gauge and radar data are presented in the literature, which combines the advantages of high spatial resolution of radar products with the measurement accuracy of rain gauge stations. While merging methods are commonly validated on rain-gauge measurements, little has been discussed in the literature about the influence of such techniques on urban hydrological models. Therefore, this study investigates the use and selection of gauge-radar merging methods as input for urban hydrological modeling.

This work studies the influence of different precipitation products (rain-gauge stations, radar, and radar-gauge merged products) on flow rates simulated with a hydrodynamic model in two cities: Hildesheim and Osnabrück, Germany. Sewer pipe measurements at least every 2 minutes for several discharge events within 2020-2021 are available and used to evaluate different rainfall products. The techniques to be assessed are temporal and spatial smoothing and radar merging methods such as external drift kriging, quantile mapping, and conditional merging. This study will allow identifying if, in general, there is a single product that presents the best results for urban flow simulations or if, on the contrary, it depends on the type of rainfall event. Additionally, since the study areas are located at different distances from the Hannover radar station, it will be possible to analyze the influence of the attenuation correction on the improvement of the radar product.

How to cite: Echeverri-Salazar, E., Shehu, B., Verworn, A., and Wallner, M.: Effect of spatially distributed radar-gauge rainfall products on simulated urban flows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10291, https://doi.org/10.5194/egusphere-egu22-10291, 2022.

The Computational Hydrometeorology Lab in National Taiwan University (NTU CompHydroMet Lab) recently launched a rainfall monitoring network, with a special focus on observing extreme storm events, such as typhoons and thunderstorms, over the south area of Taipei. Due to the topographic effect and the constant humidity brought by the sea breeze, together with the high temperature, south Taipei is a hotspot for the occurrence of thunderstorms in summer. The monitoring network constitutes a collocated pair of an OTT Pluvio S and an OTT Pluvio L weighing rain gauges, as well as two ‘unconventional’ rain sensors – an OTT Parsivel2 disdrometer and a Lufft WS100 radar precipitation sensor. These rain sensors are co-located within a 10 x 10 m2 area, providing rainfall estimates at high temporal resolutions, ranging from 10 seconds to 1 minute.

Since the launch of the monitoring network in March 2021, the monitoring network has collected rainfall data for two typhoons and a number of thunderstorms, with the highest peak intensity at 245.6 mm/h. The measurements are generally consistent between four sensors; in particular, those from two weighing gauges are of the highest consistency. In addition, a  preliminary comparison shows that the high-intensity rainfall measured by weighing gauges and disdrometer are in high agreement. This suggests that weighing gauges –which were widely used as a verification gauge for the tipping bucket gauges in the operational context–  can provide reliable rainfall measurements with high accuracy, including capturing extreme rainfall. 
 
As compared to other sensors, WS100 tends to underestimate rainfall at high intensities. However, it is more sensitive to low-intensity rainfall than others; and, similarly to the disdrometer, it provides reflectivity data and requires less maintenance. The cause of underestimation is currently under investigation, which could potentially be improved through the calibration of the current algorithm with weighing gauges’ measurements. 

At the next stage of the work, these ground measurements will be compared with the coincidental three-dimensional radar data product from the Central Weather Bureau (CWB), Taiwan. The radar data product from CWB is available at approximately 1.2 km spatial resolution and 10-min intervals. The comparison result will be presented, and the potential of using the monitoring network to support the correction of radar data will be discussed.

How to cite: Chou, C.-C. and Wang, L.-P.: Observing Extreme Rainfall Events at Fine Timescales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11015, https://doi.org/10.5194/egusphere-egu22-11015, 2022.

Accurate rainfall estimates in urban areas are vital for water management, pollution transport, and flood forecasts. To cover the high spatial (and temporal) variability of rainfall, uniformly distributed observation networks are required.

In many urban areas dedicated rainfall observations are limited because of low available budgets or unsuitable technology. Therefore, this study compared and assessed the accuracy of three “non-traditional” rainfall datasets in the Odaw (Accra, Ghana) river basin to help future modellers to decide which dataset is the best fit, for instance to predict floods. The Odaw river basin is one of the main drainage systems in Accra with a total catchment area of about 270 km². Over the past three decades, the Odaw basin has been challenged with floods, but due to the lack of a good representation of rainfall measurements, the ability to accurately simulate or forecast floods in the basin is limited.

Two rainfall datasets are derived from satellite observations and one from crowdsourced rain gauges. The three estimates were all available for the study period (2020) and were analysed and compared at a thirty-minute time-interval. The first space-based product is the most recent version (V06B) of IMERG, the gridded multisatellite precipitation product of the Global Precipitation Measurement (GPM) mission; the second space-based product is the MSG-SEVIRI infrared satellite imagery, an innovative rainfall dataset based on geostationary data available day and night. The ground-based data was retrieved from ten TAHMO rain gauges. Because the satellite products consists of pixels while the TAHMO observations are point measurements, the stations were assigned to the pixels of the satellite products.

Results show that all three rainfall datasets revealed a systematic spatial variation, with on average more rainfall observed upstream than downstream. Although all datasets reproduced a similar annual accumulation, the rainfall intensity observed by the TAHMO stations (point measurements) were much higher, sometimes even more than twice as high. Days with high rainfall amounts (when the daily average TAHMO rain rate exceeded 15 mm/hr) were used as case studies, as these days were hypothesised to be related to flooding. During these days space-borne radar overpasses were used to get some impression about the spatial characteristics of the rainfall events. With this presentation we aim to demonstrate the applicability of freely available data to estimate rainfall at various temporal and spatial scales in (formerly) ungauged urbanized river basins.

How to cite: Bogerd, L., Pinto, R. B., van Emmerik, T., and Uijlenhoet, R.: Gauging the ungauged: Estimating rainfall in urbanized river basins using ground-based and spaceborne sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11572, https://doi.org/10.5194/egusphere-egu22-11572, 2022.

This study proposes a survey methodology to identify areas for combined Sustainable Drainage Systems (SUDS) and Aquifer Storage and Recovery (ASR), (Dearden et al. 2013, Sharp Jr., 1997); these techniques exploit the hydrogeological and geomorphological characteristics of an area, to increase the natural capacity of water to infiltrate the ground.

The target area of this case study is the city of Rome and the aim of such techniques is to reduce the problems related to rainwater which, in case of extreme events, struggles to infiltrate the ground, overloads the undersized hydraulic systems and floods the urban space.

The proposed method involves GIS geospatial analysis of various data: the permeability of outcropping lithology, the piezometric level of the aquifer, hydrogeological units, geomorphology and land use.

In this aim zones characterised by high permeability and a piezometric level that would confer a volumetric capacity to possibly store even large quantities of water without triggering possible problems associated with fluctuations in the water table, have been identified.

The data have been divided into classes and indexed for comparison and overlap them. Finally, hydrogeological units were also taken into account (by analysing their depth trend) in order to identify areas with similar characteristics of permeability with respect to depth. The latter will also be compared with the previous data to identify the areas suitable for SUDS and ASR.

The final product of the suitable areas from a hydrogeological point of view will be compared with the land use map in order to exclude those areas that, for administrative and other legislative reasons, cannot be used for such activities.

How to cite: Meddi, E., Lentini, A., Galve, J. P., Papiccio, C., and La Vigna, F.: Mitigation of stormwater flooding by identifying areas suitable for Sustainable Drainage Systems and Aquifer Storage and Recovery (case study: Rome, Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12694, https://doi.org/10.5194/egusphere-egu22-12694, 2022.

In recent years, scientists have shown that the increasing trend in precipitation and flash floods during the monsoon season, combined with rapid land-use change, is leading to an increase in river discharge and flood inundation in the Kathmandu valley. The Kathmandu valley is a mid to low elevation mountain region (mean ~ 1250 m), surrounded by hills, particularly to the north, south and west, and has a population of over 3 million.  In this study, statistical analysis of 30 to 50 years of historical rainfall and river discharge data indicate a strong spatial variability in daily rainfall over the catchment during the monsoon season. Hilly regions which surround the Kathmandu basin receive significantly more rainfall than the valley, and rainfall intensity can vary greatly between the northern and southern hills, in particular. Combining our statistical analysis with physical-based numerical modelling of a range of historical flood events we demonstrate that the spatial variability in rainfall can lead to large differences in flood inundation patterns across the valley. Traditional flood early warning systems in the Kathmandu valley do not consider the effect of spatial variability of rainfall on flooding in the basin, which can lead to over or under predictions of flood extent in certain regions for a given event. We demonstrate that flood extent is the centre of Kathmandu and to the west of the city will be significantly higher if heavy rainfall occurs in the northern region of the valley.

How to cite: Creed, M. and Muthusamy, M.: Modelling the impact of spatial variability of precipitation on flood hazards in the Kathmandu Valley, Nepal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12829, https://doi.org/10.5194/egusphere-egu22-12829, 2022.

The Intensity and frequency of extreme storms have been increasing due to possible climate change, making it challenging to manage stormwaters in highly urbanized areas. Without an adequate and appropriate stormwater system, these storms may cause significant damage and losses to live and properties. Low Impact Development (LID) is a recent but widely accepted alternative for managing the increased stormwater. However, limited research is available to understand their effectiveness and optimize the mix of LIDs and conventional stormwater systems. This study evaluates the performance of several LIDs under current and future storm conditions, identify the best performing mixes of LIDs and convention stormwater system and provide a decision-making tool for urban stormwater management. The methodologies will be tested for Renton City, which is part of the Seattle Metropolitan Area.

In order to achieve our objective, first, a statistical rainfall-runoff model will be developed to assess the current stormwater system and estimate runoff for the current and future periods. The final results indicate a significant increase in runoff due to the increased rainfall in the future (2020-2040) compared to the past (1995-2014). The Stormwater Management Model (SWMM) will then be used to simulate the rainfall-runoff under conventional and LIDs (e.g., bio-retention, rain barrels, rain gardens, infiltration trenches, and permeable pavement) stormwater system. The final results show that the performance of LIDs in reducing total runoff volume varies with the types and combinations of LIDs. A 30% to 75% reduction in runoff was achieved for the past and future 50-year and 100-year storms. A Genetic Algorithm (GA) is used to optimize the LID and conventional stormwater system considering the reduction in runoff, installation and maintenance costs. The type, size, location, and number of different LIDs will be considered as decision variables for the GA. Finally, the study aims at developing a comprehensive framework to evaluate the performance of LIDs under present and future storms and identify cost and performance effective LIDs in a given urban area. The framework introduced in this study will help local authorities and practitioners to implement appropriate climate change adaptation strategies by maximizing the benefit from LIDs and ensure sustainable stormwater management for the current and future climates.

How to cite: Abduljaleel, Y. and Demissie, Y.: Evaluation and Optimization of Low Impact Development Designs for Sustainable Stormwater Management in a Changing Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13, https://doi.org/10.5194/egusphere-egu22-13, 2022.

Recent evidences suggest that in Europe, flood frequency and precipitation frequencies are often not aligned. Beside other factors pre-storm conditions exert a significant impact on flood generation thus their knowledge is paramount for a proper flood forecasting. A number of predictors have been used in the past to understand how much precipitation is transformed into runoff (i.e., runoff coefficient, RC). Notable examples are the antecedent precipitation index (API), the prestorm river discharge and soil moisture. On top of these new products potentially available from satellite observations like surface soil moisture and total water storage anomalies (TWSA), root zone soil moisture from reanalysis and hydrological models can be used along with precipitation to predict in advance the severity of the storm runoff.Our goal here is to provide an objective description of the role played by different predictors for hydrologic forecasting in Europe. In particular, we aim at answering the following research questions:

• How variable is runoff coefficient across the European catchments?
• How much are surface and root zone soil moisture, river discharge, antecedent precipitation and total water storage anomalies able to explain the RC variability across European floods?
• Under which conditions (climate period, location and flood magnitude) are the different pre-storm indices able to predict this runoff coefficient variability?

We answered these questions using long term (1980-2016) precipitation and river discharge observations from more than 100 basins covering different European regions. Results demonstrated that root zone soil moisture and TWSA are the best predictors of prestorm conditions under a variety of climatic and geographic features and thus their correct representation in land surface and hydrological models is strategic for an effective flood forecasting.

How to cite: Massari, C., Marra, F., Tramblay, Y., Crow, W., Camici, S., Modanesi, S., Brocca, L., and Gruendemann, G.: Prestorm root zone soil moisture conditions critical for flood forecasting in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1830, https://doi.org/10.5194/egusphere-egu22-1830, 2022.

Tropical cyclones (TCs) displace millions of people every year. Displaced people are subject to heightened risks to their physical and mental well-being. We present the first results of a TC impact forecast system for population displacement, aiding the decision-making process for planning early prevention and mitigation actions. For example, planning precautionary evacuations and the allocation of humanitarian aid. We work closely with the Internal Displacement Monitoring Centre (IDMC) to develop a global TC impact forecast system that predicts the number of people potentially affected or displaced.

We build the impact forecast system using a python-based, open-source, globally consistent platform called CLIMADA (CLIMate ADAptation). The platform integrates probabilistic hazard, exposure, and vulnerability information to compute the potential impacts from TC events. The first prototype of the forecast system extracts information from ECMWF ensemble TC forecast tracks, a global population layer at ~1km resolution, and vulnerability functions that relate the exposed people to the intensity of TC wind speed. We show case studies of recent TC events to reveal the potential of the displacement forecast system, the uncertainties of the forecast results

The displacement forecast system will provide richer information for decision-makers and help improve warnings. The open-source data and codes of this implementation are also transferable to other users, hazards, and impact types. 

How to cite: Kam, P. M., Fairless, C., and Bresch, D. N.: Globally consistent tropical cyclones impact forecast system for population displacement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1981, https://doi.org/10.5194/egusphere-egu22-1981, 2022.

Rainfall extremes are of major and increasing importance in semi-arid countries and their variability has strong implications for water resource and climate impacts on the local societies and environment. Here, we examine extremes intraseasonal descriptors (ISDs) in austral summer rainfall (November–February) over South Africa (SA). Using daily observations from 225 rain gauges, ERA5 reanalysis and satellite estimates (TRMM-3B42), we propose a novel typology of wet extreme events based on their spatial fraction, thus differentiating large- and small-scale extremes. Long-term variability of both types of extreme rainfall events is then extensively discussed. The relationship between these two types of rainfall extremes and different modes of climate variability is further explored at multiple timescales. At low-frequency modes, rainfall extremes are assessed at interannual (IV: 2−8 years) and quasi-decadal (QDV: 8−13 years) timescales which are primarily associated with El Niño Southern Oscillation (ENSO) and Interdecadal Pacific Oscillation (IPO) respectively. At high-frequency modes, rainfall extremes are evaluated with synoptic-scale variability related to seven convective regimes of Tropical Temperate Troughs (TTTs: 3–7 days) and intraseasonal variability associated with eight phases of the Madden-Julien Oscillation (MJO: 30–60 days).

The results demonstrate that using 7% of spatial fraction simultaneously exceeding the local threshold of the 90th percentile produces remarkable results in characterizing rainfall extremes into large- and small-scale extremes. Austral summer total rainfall is found to be primarily shaped by large-scale extremes which constitute more than half of the rainfall amount under observation, and nearly half in ERA5. Observation (ERA5) shows an average of 8 ± 5 (20 ± 7) days per season associated with large-scale extremes, which are comprised in 5 ± 3 (10 ± 3) spells with an average persistence of at least 2 days. Overall, we find a strong dependence of total rainfall on the number of wet days and wet spells that are associated with large-scale extremes. We also find that large- and small-scale extremes are well-organized and spatially coherent yet extreme conditions during small-scale events are found sporadic over the region, contrasting with large-scale events for which extreme conditions are found over a larger and coherent region.

Teleconnections with global SSTs confirm that La Niña conditions favor overall wet conditions and wet extremes in SA. The frequency of large-scale extremes is consistently related to warmer SSTs in the North Atlantic while their link with warmer Indian and tropical South Atlantic Ocean found stronger without ENSO influence. At low-frequency timescale, risk ratio assessment shows that the frequency (total rainfall) of large-scale extremes is significantly modified by IV (QDV) timescale. We note strong variations in the frequency (total rainfall) of large-scale (small-scale) extremes when IV timescale lies in strong positive phase (i.e., +0.5 standard deviation). At high-frequency timescale, the synoptic-scale variability associated with TTT events, are mostly responsible for changes in large-scale extremes as nearly 75% of such events occur during early to mature TTT regimes (3−5) whereas small-scale extremes were found equiprobable during all synoptic regimes. A risk ratio assessment suggests that the probability of large-scale extremes in TTT regime 5 significantly enhance (suppress) during MJO phases 6−8 (1−2).

How to cite: Ullah, A., Pohl, B., Pergaud, J., Dieppois, B., and Rouault, M.: Extremes in South African Rainfall: Mean Characteristics and Seamless Variability Across Multiple Timescales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1985, https://doi.org/10.5194/egusphere-egu22-1985, 2022.

In this work a multi-relaxation time (MRT) Lattice Boltzmann model based on the use of non-conventional collision operator is used to simulate the flood event in Venice Lagoon.

Numerical methods (finite difference, finite volume and finite element methods) that solve the macroscopic equations of fluid mechanics (Navier Stokes equations), are usually employed for these aims. Most of these methods put in evidence that the application of bed slope and friction forces can lead to inaccurate solutions due to numerical errors.

In addition, the extension of these schemes to complex geometries is not straightforward and some of these approaches are very computational expensive if applied to real flows. Since the problems are posed at a large scale, it should be the aim to develop a simple and accurate representation of the source term to simulate realistic shallow water flows.

The LBM approach is a versatile method and it has been extensively applied in different fields.

Non-conventional Lattice Boltzmann models based on central moments and cumulants collision operators allows to simulate large-scale hydraulic problems such as flooding events and the use of a GIS environment allows to set the information related to topography, initial conditions (water depth and velocity values distribution), boundary conditions (position and type of solid and inlet/outlet boundaries), external force (value and distribution of roughness coefficients, obstacles position) and to make this data available for the execution of the numerical model.

In order to validate the correctness of the proposed mathematical model for Venice Lagoon, the real flood event that took place on November 12, 2019 is simulated: several field data are available for this test case; the results, in terms of water level and velocity field are compared with recorded data, verifying the accordance. Moreover, technical solutions for hydraulic risk evaluation and mitigation, taking account of the expected sea level rise, due to climate change, are proposed.

How to cite: Padrone, J., Di Francesco, S., and Venturi, S.: Modelling flood events in Venice Lagoon with a cumulant CO lattice Boltzmann shallow water model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2474, https://doi.org/10.5194/egusphere-egu22-2474, 2022.

Landslides represent a critical natural hazard in many mountain and hilly regions worldwide, provoking causalities and property damages. Landslide triggering thresholds are at the basis of early warning systems to protect livelihoods. Traditionally, landslide triggering thresholds are expressed in terms of not more than two or three precipitation variables, mostly rainfall event depth, and duration. Indeed, the availability of soil moisture information and its proxies (such as antecedent precipitation), can improve the performance of landslide triggering thresholds, thus calling for a multivariate approach.  

Given the above context, this study aims to develop regional landslide triggering thresholds by using multivariate statistical analysis to investigate the performance of multiple combinations of rainfall variables and event soil moisture data, in the identification of regional rainfall thresholds for landslide initiation. Lombardy region (northern Italy) was selected as a study area since it is one of the most susceptible Italian regions to landslide risk. The data on landslides were retrieved from the FraneIalia project that is a comprehensive spatio-temporal database of recent landslides affecting the Italian territory from 2010 onwards. For the Lombardy region, from 2010 to 2019, 592 landslides events triggered by rainfall were detected, all distributed within the mountain and hilly areas of the region.

Precipitation and soil moisture time series, instead, were derived from the ERA5-Land reanalysis dataset and the rainfall events were reconstructed using the CTRL-T code developed by IRPI-CNR, which characterizes each rainfall event by duration, mean intensity, total depth, and peak intensity. The most probable rainfall conditions associated with each landslide are, then, computed based on the distance between the rain gauge and the landslide location. Different combinations of precipitation and soil moisture variables are tested using dimensionality reduction multivariate statistical techniques. An optimization procedure is set up with the aim to maximize the True Skill Statistic (TSS) ROC index associated with parametric thresholds. Several multivariate combinations show better performances than the traditional depth-duration power-law thresholds.  

How to cite: Palazzolo, N., Peres, D. J., Creaco, E., and Cancelliere, A.: Identification of regional landslide triggering thresholds in the Lombardy region using multivariate statistical analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2578, https://doi.org/10.5194/egusphere-egu22-2578, 2022.

Local governments are faced with increasing levels of risk from extreme hydro-meteorological events such as (tropical) storms,  flooding, landslides, drought, heatwaves, wildfires, etc. The frequency and interaction of these events, also in combination with other events that do not have a direct climate driver, makes that it is likely that many areas are faced with higher impacts from compounding events. Global trends such as population growth, urbanization, increased dependency on technology also contributed to larger exposure and vulnerability. In order to plan for future developments, and for reducing the increasing levels of risk, local governments require to plan ahead and evaluate the options available for reducing the risk under future scenarios. For this task Spatial Decision Support Systems are required that allow local governments to make informed decisions, considering the current and future levels of risk. RiskChanges is a Spatial Decision Support System for the analysis of current and future multi-hazard risk at a local level, in order to analyze optimal risk reduction alternatives. The system is developed by the University of Twente in collaboration with the Asian Institute of Technology, GeoInformatics Centre. RiskChanges ( http://www.riskchanges.org/ ) is an Open-Source, web-based tool, based on a series of python scripts, which are integrated into a Graphical User Interface. The tool includes several major features: multi-hazard, multiple assets, a vulnerability curve database, multi-user approach, comparison of risk, and spatial analysis. Users can upload their own datasets (in the form of hazard maps, elements-at-risk maps, administrative unit maps, and vulnerability curves). The tool contains an open-source vulnerability curve database, allowing to sharing of physical vulnerability curves among users. Multiple users can collaborate on the same project, and provide different input data. The multi-hazard feature allows performing the risk assessment for multiple natural and manmade hazard interactions. Exposure and vulnerability are combined in a loss calculation for each combination of element-at-risk and hazard. Loss maps are integrated into a risk map, where the user indicates the interaction between the hazard types. The system allows to analyze the risk of multiple asset types with different spatial characteristics.  Users can compare the risk for the current situation and future scenarios and/or planning alternatives.  

How to cite: van Westen, C., Hazarika, M., Dahal, A., Kshetri, T., Shakya, A., and Nashrrullah, S.: The RiskChanges tool for multi-hazard risk-informed planning at local government level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3026, https://doi.org/10.5194/egusphere-egu22-3026, 2022.

Maps of flooding risk and exposure generally assume people and vehicles density constant over time, although this is not the case in the real world, as crowding is a highly dynamic process in urban areas. Monitoring and forecasting people mobility is a relevant aspect for metropolitan areas subjected to high risk of flooding. Information and communication technologies (ICT) along with big data are massively used, e.g., to support the optimization of traffic flows and the study of urban systems. In particular, mobile phone network data suits with the aim of producing dynamic information on people's movements that can be used to develop dynamic exposure to flood risk maps for areas with hydrogeological criticality, as done by Balistrocchi et al. (2020).

In this work we aim at proposing a time series modelling strategy to obtain “real time” traffic flows prediction. To do so we use mobile phone origin-destination signals on the flow of Telecom Italia Mobile (TIM) users among different census areas (ACE of ISTAT, the Italian National Statistical Institute), and for the MoSoRe Project 2020-2022 and recorded at hourly basis from September 2020 to August 2021.

An Harmonic Dynamic Regression (HDR) model (Hyndman, Athanasopoulos, 2021) as it follows:

Flow= α+Fourier.day (K_d )+Fourier.week (K_w )+ Month+ε_(ARIMA(p,d,q))                        (1)

is proposed, where multiple seasonal periods are modelled with a properly selected number of Fourier basis, month is a dummy variable to account for different levels of flows by months and the error component is structured as an ARIMA.

HDR model suits for our purposes due to the strong daily and weekly patterns in traffic flows, as also confirmed by preliminar results on the accuracy of prediction based on a cross-validation strategy.

In future developments, the model in equation 1 may be improved by adding proper features as explanatory variables to increase the prediction accuracy, such as, e.g., the presence of people in the census area of origin and in the census area of destination of the flow, or precipitation data.

People’s and vehicles’ exposure obtained from mobile phone data and processed with the above stochastic model are then combined to flooding hazard maps estimated for different storm return period in a urbanized area close to Brescia to estimate dynamic flood risk maps.      

References

Balistrocchi, M., Metulini, R, Carpita, M., Ranzi, R.: Dynamic maps of human exposure to floods based on mobile phone data. Natural Hazards and Earth System Sciences, 20: 3485{3500 (2020).

Hyndman, R. J., Athanasopoulos, G.: Forecasting: principles and practice. 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3 (2021)

How to cite: Razdar, B., Metulini, R., Carpita, M., and Ranzi, R.: Dynamic flood hazard maps based on traffic flow forecasts using mobile phone data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3200, https://doi.org/10.5194/egusphere-egu22-3200, 2022.

The entire strait is the base of the port aquatorium and a vital shipping artery from the Baltic Sea to the Curonian Lagoon as well as a complex water system connecting two water basins of different sizes and depths and nature: differing considerably in salinity and density. Although the quays are well protected from the waves of the open sea, dangerous water level fluctuations still occur in the port area, the origin of which is not yet well understood. This study aims to identify the occurrence and main characteristics of the long waves, with the period from minutes to several hours, to identify their origin and impact.

Analysis of the spectral composition of these oscillations is based on continuous pressure recordings at a frequency of 4 Hz in Klaipėda harbour during the stormy season 2016-2017 and repeated during calm and stormy seasons in 2021. Most of the oscillation energy is concentrated in two frequency bands. Significant water level changes occurred due to infragravity motions with periods of 30 s (0.03 Hz) and disturbances with the typical periods of wind waves on the Lithuanian coast with periods of 3-10 s (0.1-0.3 Hz). The highest peak in the wind wave frequency band corresponds to typical storm conditions in the Baltic Sea with periods of 5-9 s. While the typical amplitudes of the oscillations in this range were modest, hazardous changes in water level occurred at lower frequencies with amplitudes of 0.5 m. The record shows the presence of harbour oscillations with periods of 30-200 s (0.005-0.03 Hz) and seiches of the Curonian Lagoon with periods of 1200 s (0.0008 Hz).

The largest oscillations are created by a combination of wind waves and infragravity waves with periods that roughly match the natural seiche periods of Klaipėda Strait and harbour oscillations and seiches can be observed not only during the stormy season.

How to cite: Nesteckytė, L., Klepšaitė-Rimkienė, L., and Myrberg, K. A.: Long waves in the Port of Klaipėda, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4011, https://doi.org/10.5194/egusphere-egu22-4011, 2022.

The subject of this presentation is the assessment of the occurrence, intensity, and impact severity of weather-related events with socio-economic implications during the period 2000-2020 in Greece. The aim is to draw critical conclusions through the distribution of events at the temporal and spatial level and in relation to their societal impact as measured by a qualitative impact-severity index. The data derived from the High Impact Weather Events (HIWE) database that has been developed by the METEO Unit at the National Observatory of Athens (NOA), is systematically updated and publicly available. The analysis includes events related to floods, lightning activity, hail, snow/frost, windstorms, and tornados having caused impacts on life (injury or death) and/or infrastructure. The presentation provides an overview of the data used and methodology applied for assessing weather-related hazards, and the results of their analysis that include the evolution of events, the most damaging phenomena, and the areas most exposed to each phenomenon. This work was conducted in the frame of CLIMPACT – National Νetwork on Climate Change and its Impacts, a flagship initiative on climate change to coordinate a Pan-Hellenic network of institutions.

How to cite: Papagiannaki, K., Kotroni, V., Lagouvardos, K., and Bezes, A.: High-impact weather events in Greece: Analysis of the period 2000-2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4523, https://doi.org/10.5194/egusphere-egu22-4523, 2022.