HS4.1 | Anticipation of flash floods and rainfall-induced hydro-geomorphic hazards: short-range observational and forecasting strategies
EDI PICO
Anticipation of flash floods and rainfall-induced hydro-geomorphic hazards: short-range observational and forecasting strategies
Co-organized by NH1
Convener: Clàudia Abancó | Co-conveners: Olivier Payrastre, Jonathan Gourley, Pierre Javelle, Massimiliano Zappa
PICO
| Fri, 28 Apr, 10:45–12:30 (CEST)
 
PICO spot 4
Fri, 10:45
Flash floods triggered by heavy precipitation in small- to medium-sized catchments often cause catastrophic damages, which are largely explained by the very short response times and high unit peak discharge. Often, they are also associated with geomorphic processes such as erosion, sediment transport, debris flows and shallow landslides. The anticipation of such events is crucial for efficient crisis management. However, their predictability is still affected by large uncertainties, due to the fast evolution of triggering rainfall events, the lack of appropriate observations, the changes due to a warming climate, the high variability and non-linearity in the physical processes, the high variability of societal exposure, and the complexity of societal vulnerability.
This session aims to illustrate current advances in monitoring, modeling, and short-range forecasting of flash floods and associated geomorphic processes, including their societal impacts.
Contributions related to recent and significant floods are particularly encouraged.
This session aims to specifically cover the following scientific themes:
- Monitoring and nowcasting of heavy precipitation events based on radar and remote-sensing systems (satellite, lightning, ..), to complement rain gauge networks
- Short-range (0-6h) heavy precipitation forecasting based on NWP models and/or ML-based approaches, with a focus on seamless forecasting strategies, and ensemble or probabilistic strategies for the representation of uncertainties.
- Understanding and modeling of flash floods, rainfall-induced hydro-geomorphic processes and their cascading effects, at appropriate space-time scales.
- Development of integrated hydro-meteorological forecasting chains and new modeling approaches for predicting flash floods and/or rainfall-induced geomorphic hazards in gauged and ungauged basins.
- New direct and indirect (proxy data) observation techniques and strategies for the observation or monitoring of hydrological reactions and geomorphic processes, and the validation of forecasting approaches.
- Development of impact-based modeling and forecasting approaches, including inundation mapping and/or specific impacts modeling approaches for the representation of societal vulnerability.

PICO: Fri, 28 Apr | PICO spot 4

Chairpersons: Clàudia Abancó, Olivier Payrastre
Monitoring and short-range forecasting approaches
10:45–10:47
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PICO4.1
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EGU23-3163
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ECS
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Virtual presentation
Urban flood monitoring and early warning by surveillance camera networks
(withdrawn)
Xing Wang, Yihao Yu, and Yunfeng Nie
10:47–10:49
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PICO4.2
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EGU23-5831
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Virtual presentation
Philippe Baron, Kouhei Kawashima, Dong-Kyun Kim, Hiroshi Hanado, Takeshi Maesaka, Shinsuke Satoh, Seiji Kawamura, and Tomoo Ushio

Temporal extrapolation of radar observations of precipitation is a means of nowcasting sudden localized heavy rains, i.e., restricted convective rains on a spatial scale of less than 10 km and a lifetime of a few tens of minutes. Such nowcasts are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, their restricted area, and nonlinear behavior that are not well captured by current operational systems, even for a lead time of only 10 minutes. Often, conventional approaches use radar observations with 5 min resolution and a Lagrangian advection based extrapolation model with a poor description of the vertical dimension. In this study, we use a new Multi-Parameter Phased-Array Weather Radar (MP-PAWR) with a temporal resolution of 30 sec and a 3D recurrent neural network to improve 10-minute nowcasts of sudden localized rains. The MP-PAWR has been operational in Japan (Saitama prefecture) since 2018. The nowcast model is a supervised neural network trained with adversarial technique. It considers the 3D volume surrounding the instrument up the height of 10 km and the polarimetric information of the measurement.  Improvements with conventional nowcasting techniques will be discussed with some typical examples.

How to cite: Baron, P., Kawashima, K., Kim, D.-K., Hanado, H., Maesaka, T., Satoh, S., Kawamura, S., and Ushio, T.: Nowcasting localized heavy precipitation using a multi-parameter phased array weather radar (MP-PAWR) and a 3D recurrent neural network., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5831, https://doi.org/10.5194/egusphere-egu23-5831, 2023.

10:49–10:51
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PICO4.3
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EGU23-9979
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On-site presentation
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Yuval Reuveni, Saed Asaly, and Lee-Ad Gottlieb

Flash floods occur when heavy rain causes a fast and powerful flow of water in a drainage area. In the Eastern Mediterranean region, which contains arid and semi-arid areas, the location and timing of rainfall is the most significant factor in the formation of flash floods. Predicting when and where extreme weather events such as storms, heavy rainfall, and flooding are likely to happen is a key challenge in the effort to prevent natural disasters. Here, we present an improved version of a previous work by Ziskin and Reuveni, which investigated the use of precipitable water vapor (PWV) data from ground-based global navigation satellite system (GNSS) stations, along with surface pressure measurements to predict flash floods in an arid region of the eastern Mediterranean. The previous study involved training three machine learning models to perform a binary classification task, using multiple unique flash flood events and testing the models using a nested cross-validation technique. The results showed that the support vector machine (SVM) model had the highest mean area under the curve (AUC) and the lowest AUC variability compared to random forest (RF) and multi-layer perceptron (MLP) models.  When tested on an imbalanced dataset simulating a more realistic flash flood occurrence scenario, all models demonstrated a decrease in the false alarm rate (precision) with a high hit rate (recall) performance.

In this study, we extend the previous work by integrating nearby lightning data as a new feature in our studied dataset. The inclusion of this feature is motivated by the observation that heavy rainfall, which can lead to flood events, is often accompanied before by an increase in lightning activity. The experimental results show that the adding a 24-hour vector of nearby lightning activity improves the precision score significantly.

How to cite: Reuveni, Y., Asaly, S., and Gottlieb, L.-A.: Flash flood predictions over the Eastern Mediterranean using artificial intelligence techniques with precipitable water vapor, pressure, and lightning data., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9979, https://doi.org/10.5194/egusphere-egu23-9979, 2023.

10:51–10:53
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PICO4.4
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EGU23-11757
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ECS
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Highlight
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Virtual presentation
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Maryse Charpentier-Noyer, Pierre Nicolle, Olivier Payrastre, Eric Gaume, François Bouttier, and Hugo Marchal

Flash floods (FF) represent an important part of the flood damages and fatalities in the world. Today, operational FF nowcasting and warning systems are often based on the use of precipitation weather radars, and therefore still offer limited anticipation. They also generally rather represent the intensity of the flood events than their severity in terms of impacts, which may limit the capacity of emergency services to take relevant decisions.

This contribution aims at evaluating the value of a new ensemble FF impacts forecasting chain for the decision making of an emergency service.  The case study corresponds to the Aude River flash floods that occurred on October 15 and 16, 2018, and which are among the most important FF observed in southeastern France in the recent years. This event is responsible for the death of 15 people (99 people injured), as well as particularly large material damages.

The tested FF impacts forecasting chain combines three new rainfall ensemble forecast products (provided by CNRM), specifically designed for short-range forecasting (0-6h), and a highly distributed rainfall-runoff model (Charpentier-Noyer et al., 2022). A simple impacts model is built and applied for each river reach based on a catalog of 8 inundation scenarios corresponding to return periods of 2 to 1000 years. The impacts are represented in terms of a number of inundated buildings.

The value of the ensemble impacts forecasts is finally evaluated based on the implementation of a multi-agent model, for the simulation of the field decisions taken by an emergency service. This new evaluation approach, based on simple but realistic hypotheses, allows to illustrate and measure the gains associated with a better anticipation of impacts, and the costs associated with false alarms, which lead to the unnecessary mobilization of rescue teams, to the detriment of really impacted locations. In case of extremely limited means for safety operations (low number of rescue teams), the decisions based on a naive zero future rainfall scenario may sometimes appear better than those using ensemble rainfall forecasts. Nevertheless, in all the simulated cases, the decisions taken from the ensemble rainfall forecasts appear more efficient than those based only on field observations.

 

 

Charpentier-Noyer, M., Peredo, D., Fleury, A., Marchal, H., Bouttier, F., Gaume, E., Nicolle, P., Payrastre, O., and Ramos, M.-H.: A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-182, in review, 2022.

How to cite: Charpentier-Noyer, M., Nicolle, P., Payrastre, O., Gaume, E., Bouttier, F., and Marchal, H.: Relevance of using ensemble forecasts of flash-flood impacts for an emergency service: an evaluation for the October 2018 flood event in the Aude river basin, France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11757, https://doi.org/10.5194/egusphere-egu23-11757, 2023.

10:53–10:55
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PICO4.5
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EGU23-3147
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ECS
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On-site presentation
Ruben Imhoff, Athanasios Tsiokanos, Jerom Aerts, Lesley De Cruz, Claudia Brauer, Klaas-Jan van Heeringen, Albrecht Weerts, and Remko Uijlenhoet

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, rainfall nowcasting can provide an alternative. This observation-based method, however, quickly loses skill after the first few hours of the forecast due to growth and dissipation processes that are not accounted for. In addition, providing an additional forecasting method can let users drown in the amount of available information. A promising way forward is a seamless forecasting system, which combines the aforementioned forecasting methods. By optimally combining (blending) rainfall nowcasts with NWP forecasts, we can extend the skillful lead time of short-term rainfall forecasts and provide users with more consistent, seamless forecasts.

We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library. In this implementation, the blending of the extrapolation (ensemble) nowcast, (ensemble) NWP and noise components is performed level-by-level, which means that the blending weights vary per spatial cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a multi-day rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.

We evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments, focusing on both the rainfall forecasts and the resulting discharge forecasts using the fully distributed wflow_sbm hydrological model. We benchmark the results of the 48-member blended forecasts against the deterministic Belgian NWP forecast, a 48-member nowcast and a simple 48-member linear blending approach. When focusing on the resulting rainfall forecasts, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged CRPS and CSI values) and adds value compared to NWP for the first hours of the forecast. This holds for both the radar domain and catchment scale, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. We find similar results for the resulting discharge forecasts, although the effect of the catchment size and corresponding lag times becomes influential and determines the added value of nowcasting over NWP. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless hydrometeorological forecasting systems.

How to cite: Imhoff, R., Tsiokanos, A., Aerts, J., De Cruz, L., Brauer, C., van Heeringen, K.-J., Weerts, A., and Uijlenhoet, R.: Seamless rainfall and discharge forecasting using a scale-dependent blending of ensemble rainfall nowcasts and NWP, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3147, https://doi.org/10.5194/egusphere-egu23-3147, 2023.

10:55–10:57
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PICO4.6
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EGU23-14123
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On-site presentation
Fiona Johnson and Ze Jiang

Reliable flood forecasts are dependent on accurate quantitative precipitation forecasts. Despite improvements in the resolution and schematisation of Numerical Weather Prediction (NWP) models, there are still substantial biases in their precipitation forecasts. Biases are present at a range of time scales and correctly representing the multi-temporal scale properties of precipitation including its persistence and variability is vital. In this presentation a new method for post-processing NWP model precipitation forecasts is developed. The new method is based on continuous wavelet transforms (CWT) which correct the statistical characteristics of the precipitation forecasts across a range of time scales. The precipitation amounts are corrected using a simple quantile mapping of the amplitude of each time scale of the wavelet decomposition. To account for uncertainty in precipitation timing, we also adjust the phase of the CWT randomly to create an ensemble of post-processed forecasts. Spatial correlations are preserved by maintaining the same phase adjustments at each different precipitation forecast location.  

The new method is demonstrated using hourly forecast data from the ACCESS model over the period March 2018 to September 2021  for a network of 158 gauges around Sydney, in eastern Australia. The new method improves the correlation of the forecasts and reduces the root mean square error. The spatial correlation structure of the post-processed forecasts is also improved. Correctly representing spatial patterns of precipitation is vital to ensure that catchment averaged precipitation and the resulting flood forecasts are correct.

How to cite: Johnson, F. and Jiang, Z.: Wavelet-based post-processing of NWP precipitation forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14123, https://doi.org/10.5194/egusphere-egu23-14123, 2023.

10:57–10:59
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PICO4.7
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EGU23-7151
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ECS
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Highlight
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On-site presentation
Juliette Godet, François Bouttier, Pierre Javelle, and Olivier Payrastre

Flash floods have dramatic economic, natural and social consequences, and efficient adaptation policies are required to reduce these impacts, especially in a context of global warming. This is why it remains essential to develop more efficient flash flood forecasting systems. This study was carried out in order to assess the ability of a new seamless short range ensemble rainfall forecast product, called PIAF-EPS and recently developed by Meteo France, to predict flash floods when it is used as input in an operational hydrological forecasting chain.

For this purpose, eight flash flood events that occurred in the French Mediterranean region between 2019 and 2021 were reproduced, using a similar forecasting chain as the one implemented in the French “Vigicrues-Flash” operational flash flood monitoring system. The hydrological forecasts obtained from PIAF-EPS were compared to the hydrological simulations obtained from the radar observations, and to three deterministic forecasts using varied scenarios (future constant rain, deterministic PIAF, and a numerical nowcasting system called AROME-NWC).

The verification method applied in this work uses rank diagrams and scores calculated on contingency tables, in an original way. The verification process has been conducted on each 1km² pixel of the territory.

The results illustrate the added value of the ensemble approach for flash flood forecasting, and the benefits of the use of a “seamless” product combining radar observations and numerical nowcasting.   

How to cite: Godet, J., Bouttier, F., Javelle, P., and Payrastre, O.: Assessing the ability of a seamless short-range ensemble rainfall product to detect flash floods on the French Mediterranean area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7151, https://doi.org/10.5194/egusphere-egu23-7151, 2023.

10:59–11:01
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PICO4.8
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EGU23-7798
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ECS
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On-site presentation
Ilhan Özgen-Xian, Mario Morales-Hernández, Michael Nones, and Daniel Caviedes-Voullième

Great advancement has been achieved in the last decade in 2D shallow water solvers for flood modelling. However, their application to physically-based flood forecasting continues to be experimental and not widespread. One of the central challenges towards operational flood forecasting with 2D solvers is their computational cost, which needs to be reconciled with the required lead times for forecasts to be of use. Nonetheless, these solvers have great potential to improve flood forecasting predictions, especially when it comes to flash floods, for which the established 1D and conceptual models may be significantly less applicable.

The shallow water solver SERGHEI-SWE leverages on robust and efficient numerical techniques and is implemented for High Performance Computing (HPC), allowing its use in supercomputers and opening new opportunities in 2D flood forecasting. In this contribution, we present proof-of-concept simulations of several flood events in different catchment and river systems. We show that, with SERGHEI-SWE, it is possible to run very high resolution flood simulations for large hydrological systems with runtimes significantly lower than the event duration. This property is essential to enable operational forecasting with useful lead times.

We run simulations on three river reaches, in the Italian river Po (125 km reach between Boretto and Pontelagoscuro) and in one of its tributaries, the river Secchia (20 km reach), and a meandering reach of the Ebro river through the city of Zaragoza. We also perform flash flood simulations on a 5 km2 district of Nice (France), and in a 50 km2 agricultural catchment in Jaén (Spain). The focus of the exercise is on the computational performance aspect and not on the model performance. The results show that high resolution simulations can be done with runtimes in the order of 100 times faster than real time, potentially allowing a very good forecast lead time. We also explore different combinations of computational resources, model resolution and ensemble size to explore the flexibility of the modelling approach under different computational systems, which may be available for flood forecasting.

How to cite: Özgen-Xian, I., Morales-Hernández, M., Nones, M., and Caviedes-Voullième, D.: Towards 2D flood forecasting with the HPC-enabled shallow water solver SERGHEI-SWE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7798, https://doi.org/10.5194/egusphere-egu23-7798, 2023.

Hydrological and hydro-geomorphic processes: characterization and modeling
11:01–11:03
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PICO4.9
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EGU23-13338
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ECS
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Virtual presentation
Gavkhar Mamadjanova, Maria Shahgedanova, and Fatima Pillosu

Accurate predictions of heavy and intense rainfall are vital for impact-based forecasting that can be essential for mitigating the significant damage and loss of life across the globe. However, producing reliable forecasts capable of capturing the rainfall values is challenging in complex mountain terrain due to the forecast uncertainty and computational cost especially in data-scarce regions. Central Asia is one of these regions, where extreme rainfall leads to flash floods, landslides and debris flows in the mountains and foothills. The risk of these events increases with global warming, and the early warning systems based on reliable forecasts are particularly important to manage the risk in the region and adapt to climate change.

In this study, we have evaluated and compared the skills of two probabilistic forecasts developed by the European Centre for Medium-Range Weather Forecasts (ECMWF): standard Ensemble Forecasts (ENS) which consists of an ensemble of 51 members and ecPoint Rainfall produced by statistical post-processing of the ENS and delivers probabilistic forecasts of rainfall totals for points within a model gridbox (18 km resolution) that can be particularly useful in the mountains. Skills of both forecasts were assessed in relation to the forecast of debris flows in Central Asia.

Both forecast products were verified against SYNOP (surface synoptic observations) data for stations over Central Asia, mainly for the debris flow season (March-October) in 2022. In this case, two popular verification methods were used: Brier Score and Receiver Operating Characteristics (ROC) diagram for the exceedance of precipitation thresholds of 1 mm, 10 mm and 25 mm.

Verification trials over the 2022 debris flow season in Central Asia show that the performance of ecPoint Rainfall depending on the forecast lead-time can be a good proxy for the range of point rainfall values to define the warning areas of debris flow risk over the study area. The ecPoint Rainfall is recommended for the operational application of heavy rainfall leading to debris flow formation which can support impact-orientated forecasting and early warning systems in Central Asia.

How to cite: Mamadjanova, G., Shahgedanova, M., and Pillosu, F.: Debris flows risk assessment for Central Asia by application of Global Ensemble Output and Post-processed Precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13338, https://doi.org/10.5194/egusphere-egu23-13338, 2023.

11:03–11:05
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PICO4.10
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EGU23-16248
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On-site presentation
Integrated post-event survey of the record-breaking Central Italy flash flood of September 2022: observation strategy and lessons learned
(withdrawn)
Marco Borga, Lorenzo Marchi, Simone Gabellani, Umberto Morra Di Cella, Giacomo Fagugli, Andrea Libertino, Maurizio Brocchini, Francesco Ballio, Daniela Molinari, Francesco Comiti, Christian Massari, Federica Fiorucci, Eleonora Dallan, Francesco Marra, Marco Cavalli, Stefano Crema, Jacopo Rocca, Velio Coviello, Alessandra Saretta, and Luca Solari and the central Italy 2022 flash flood post event survey team
11:05–11:07
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PICO4.11
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EGU23-16420
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Highlight
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Virtual presentation
Fast-track assessment of flash flood - erosion - landslide risks in fire-stricken river basins of the region of Attica, Greece
(withdrawn)
Charalampos (Haris) Kontoes, Constantinos Loupasakis, Alexia Tsouni, Paraskevas Tsangaratos, Stavroula Sigourou, and Vasiliki Pagana
11:07–11:09
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PICO4.12
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EGU23-766
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ECS
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On-site presentation
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Llanos Valera-Prieto, Virginia Ruiz-Villanueva, and Glòria Furdada

Recent large floods across Europe, including those in Belgium and Germany in 2021 or, more recently, in Italy in October 2022, showed that major obstructions of bridges due to the mobilized large wood (LW) significantly influenced the flood-related damages. However, in principle, none of the dangers posed by wood was inherent to the wood itself but to the obstacles and infrastructures that were not designed to allow the wood to pass. Understanding this legacy effect on wood in rivers due to the increased artificial trapping efficiency of river structures (bridges, dam reservoirs) still needs to be completed.

The Francolí River in Catalonia, NW Iberian Peninsula (853 Km2 area and 59 km length) underwent a major flash flood on October 22, 2019, that caused six fatalities. The rainfall recorded in the NW basin was 293 mm in 24 hours. Consequently, significant bio-geomorphological changes occurred; a large amount of sediment was eroded, transported and deposited, and many trees were damaged or uprooted with subsequent large wood (LW) supply and transport. In addition, infrastructures were severely damaged (e.g., three bridges collapsed).

The legacy effects on instream large wood related to the human infrastructures in river systems is an essential factor to consider when assessing the effects of floods and potential risks. Therefore, this study's main objective was to evaluate the influence of bridges on large wood accumulation during floods. 

We analyzed a reach of 30 km along the Francolí River in which there were 23 bridges. The reach was split into 52 sub-reaches based on their morphological characteristics (i.e., the width of the valley bottom, slope, and sinuosity), the presence of infrastructures, or lithologic and anthropic knickpoints, and the junction with tributaries. The 52 sub-reaches were grouped into four main typologies based on statistical segmentation and clustering.

Individual pieces of LW and accumulations were digitalized using post-flood high-resolution orthophotos (i.e., 0.10 m resolution). They were characterized using four attributes: orientation with respect to the channel (parallel, perpendicular, oblique), transported (yes or not), location (active channel or floodplain), and length. Average Nearest Neighbour, Spatial Autocorrelation (Global Moran's I test) and Density were computed and revealed the depositional pattern of LW along the study reach.

Preliminary results showed that morphological characteristics favoured LW trappings: wide valley bottoms and sinuous bends. In addition, the standing vegetation and other in-channel obstacles were crucial to trap wood. The most significant aspect, however, was the presence of bridges. A significantly more considerable amount of wood (i.e., the highest density observed, ranging between 33 and 101 pieces/ha) was trapped upstream from bridges, where wood was deposited at significantly higher elevations. Further analyses will explore the characteristics of the bridges and upstream sub-reaches.

This study will provide crucial information to understand large wood accumulation at bridges during floods and will inform flood-hazard assessments and river management.

How to cite: Valera-Prieto, L., Ruiz-Villanueva, V., and Furdada, G.: Bridges influence large wood trapping efficiency during large floods: insights from the Francolí River flood in 2019, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-766, https://doi.org/10.5194/egusphere-egu23-766, 2023.

11:09–11:11
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PICO4.13
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EGU23-1241
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ECS
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On-site presentation
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Paul Voit and Maik Heistermann

In the last 20 years a variety of heavy precipitation events (HPEs) have caused severe floods and large damages in Germany. However, the impact of an HPE is not solely determined by the event itself, but also by the geomorphologic characteristics of the location where it occurs.

Previous studies have shown that HPEs can happen anywhere in Germany. To find out where in Germany historical HPEs could have caused a potential hazard, we extracted the 10 most extreme HPEs by using the cross-scale weather extremity index (xWEI) from the last 20 years of radar data (RADKLIM) and shifted these events to every mesoscale subbasin in Germany.

We use the geomorphological instantaneous unit hydrograph as a simple screening tool to investigate the runoff concentration at the mesoscale and the following flood wave propagation in these subbasins as response to historical HPEs. While this method might not be sufficient to model precise discharge, it can be used to spot rapid increase in direct runoff and shed light on the peak development further downstream, depending on the spatiotemporal characteristics of the HPE. 

By using historical HPEs as benchmarks, our method can help to identify areas in Germany which are prone to flood hazard and assist to adjust mitigation measures accordingly.

How to cite: Voit, P. and Heistermann, M.: Downward counterfactual analysis of historical rainfall events in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1241, https://doi.org/10.5194/egusphere-egu23-1241, 2023.

11:11–11:13
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PICO4.14
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EGU23-6096
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ECS
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On-site presentation
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Ashish Manoj J, Franziska Villinger, Mirko Mälicke, Ralf Loritz, and Erwin Zehe

Convective rainfall extremes usually trigger due to their highly localised and intense input of mass and momentum ‘hot moments’ in water and matter cycling. Terrestrial systems then respond with strong Hortonian overland flow and erosion up to the formation of flash floods. While heavy precipitation events are characterised by multi-decadal variability, it is noteworthy that the largest observed floods in many rivers of Europe have occurred in the last three decades. Similarly, flash floods have also intensified. The recent clustering of extremes likely reflects the ongoing acceleration of the hydrological cycle, with expected increasing frequencies of intense convective rainstorms and related flash flood and erosion events due to Clausius-Clapeyron scaling. This urgently calls for an improved understanding and models that allow the design of strategies to mitigate onsite and catchment-wide offsite damages of flash floods and erosion events.

Hortonian overland flow occurs when precipitation intensity exceeds the soil’s infiltration capacity. The latter depends on the soil water content, soil hydraulic properties and the density and connectivity of vertical preferential flow paths and are often biologically mediated, as in the case of worm borrow and root channels. Whether locally generated surface runoff reaches the stream depends on the generated spatial connectivity of overland flow paths to the river network.

Here we propose that land use management and soil surface preparation bear the key to reducing the formation of Hortonian overland flow and the connectivity of its flow path, e.g., through a locally elevated infiltration capacity and roughness, thereby reducing the overland flow velocity and favouring its re-infiltration. Moreover, we demonstrate that physically based hydrological models are key to quantifying how changes in landuse and surface preparation techniques (including buffer areas, vegetation barriers, and fascines) in combination with local flood defense reservoirs reduce the formation of flood runoff during convective extremes. Specifically, we use the model CATFLOW and the representative hillslope approach to investigate flash floods observed in four ungauged headwaters catchments in the Kraichgau, Baden-Württemberg (Germany) in 2016. While each catchment drains into a regulated flood defense reservoir, we inverted the flood hydrograph/ inflow into the flood reservoirs using water level measurements and reservoir geometry equations. LULC maps are derived from LANDSAT images using spectral profiles obtained from field surveys over the region. Since flash floods are often associated with localised short-duration, high-intensity rainfall of convective origin, the model is forced using commercial radar-based precipitation products. The CATFLOW model was set up separately for the four headwaters by transferring a completed hillslope setup (soil catena, soil hydraulic properties, plant roughness parameters) from a gauged Weiherbach experimental catchment in the same landscape while deriving the representative hillslope profiles from the digital elevation data. Our results indicate that physically based models perform well in capturing the dynamics of the reconstructed hydrographs, which speaks a) for the transferability of physically based model structures within the same hydrological landscape and b) the feasibility of representative hillslope approach and c) the usefulness of the radar product.

How to cite: Manoj J, A., Villinger, F., Mälicke, M., Loritz, R., and Zehe, E.: Representative Hillslope Approach for Modeling Flash Flood Generation in Ungauged Catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6096, https://doi.org/10.5194/egusphere-egu23-6096, 2023.

11:13–11:15
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PICO4.15
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EGU23-17171
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Virtual presentation
Strategies for crisis organisations and management of torrential floods and important urban runoffs
(withdrawn)
Sylvain Chave
11:15–12:30