NH1.4

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).

Algarra I, et al. Significant increase of global anomalous moisture uptake feeding landfalling Atmospheric Rivers. Nat Commun 11, 5082 (2020). https://doi.org/10.1038/s41467-020-18876-w

Gonçalves A, et al. Wind Energy Assessment during High-Impact Winter Storms in Southwestern Europe. Atmosphere. 2021; 12(4):509. https://doi.org/10.3390/atmos12040509

Hénin R, et al. A ranking of concurrent precipitation and wind events for the Iberian Peninsula. Int J Climatol.; 41(2), 1421-1437 (2021) https://doi.org/10.1002/joc.6829

Hénin R, et al. Assigning precipitation to mid-latitudes fronts on sub-daily scales in the North Atlantic and European sector: Climatology and trends. Int J Climatol.; 39(1), 317–330, (2019) https://doi.org/10.1002/joc.5808

Liberato MLR, et al. Rankings of extreme and widespread dry and wet events in the Iberian Peninsula between 1901 and 2016. Earth Syst. Dynam., 12, 197–210 (2021) https://doi.org/10.5194/esd-12-197-2021

Nieto R, et al. Contribution of the main moisture sources to precipitation during extreme peak precipitation months, Adv. Water Res., 131, (2019), 103385 https://doi.org/10.1016/j.advwatres.2019.103385

Ramos AM, et al. Impacts of Atmospheric Rivers in Extreme Precipitation on the European Macaronesian Islands. Atmosphere 2018, 9, 325, (2018) https://doi.org/10.3390/atmos9080325

Reale M, et al. A Global Climatology of Explosive Cyclones using a Multi-Tracking Approach, Tellus A, 71:1, 1-19, (2019) https://doi.org/10.1080/16000870.2019.1611340

Ribeiro SL, et al. Development of a catalogue of damage in Portuguese forest associated with extreme extratropical cyclones. Science of The Total Environment, 151948, 2021 https://doi.org/10.1016/j.scitotenv.2021.151948

Sorí R, et al. Hydrometeorological droughts in the Miño–Limia–Sil hydrographic demarcation (northwestern Iberian Peninsula): the role of atmospheric drivers, Nat. Hazards Earth Syst. Sci., 20, 1805–1832, (2020) https://doi.org/10.5194/nhess-20-1805-2020

Stojanovic M, et al. Consecutive Extratropical Cyclones Daniel, Elsa and Fabien, and Their Impact on the Hydrological Cycle of Mainland Portugal. Water. (2021); 13(11):1476. https://doi.org/10.3390/w13111476

Vázquez M, et al. Atmospheric Moisture Sources Associated with Extreme Precipitation During the Peak Precipitation Month. Weather and Climate Extremes, 30, 100289 (2020) https://doi.org/10.1016/j.wace.2020.100289

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.