Heavy precipitation events in small and medium size catchments can trigger flash floods, which are characterized by very short response times and high specific peak discharges, and often occur in ungauged basins. Under appropriate geomorphological conditions, such rainstorms also cause debris flows or shallow landslides mobilizing large amounts of unconsolidated material. Although significant progress has been made in the management of these different hazards and related risks, they remain poorly understood and their predictability is affected by large uncertainties, due to the fast evolution of triggering rainfall events, the lack of appropriate observations, the high variabilities and non-linearities in the physical processes, and the high variability and complexity of societal vulnerability.
This session aims to illustrate current advances in monitoring, understanding, modelling, and forecasting flash floods and associated geomorphic processes, and documenting and anticipating the societal impacts and social responses.
Contributions on the following scientific themes are more specifically expected:
- Development of new measurement techniques adapted to flash floods and/or rainfall-induced geomorphic hazards monitoring (including in-situ sensors and remote sensing data, such as weather radar, and lightning ..), and quantification of the associated uncertainties,
- Identification of processes leading to flash flood events and/or rainfall-induced geomorphic hazards from data analysis and/or modelling, and of their characteristic space-time scales,
- Possible evolutions in hazard characteristics and frequency related to climate change,
- Development of short-range (0-6h) rainfall forecasting techniques adapted to heavy precipitation events, and representation of associated uncertainties,
- Development of hydro-meteorological forecasting chains for predicting flash floods and/or rainfall-induced geomorphic hazards in gauged and ungauged basins,
- Development of inundation mapping approaches specifically designed for an integration in flash floods monitoring or forecasting chains,
- Use of new criteria such as specific “hydrological signatures” (high water marks, impacts and damages, ..) or other proxy data for model and forecast evaluation,
- Observation, understanding and prediction of the societal vulnerability and social responses to flash floods and/or associated hydro-geomorphic hazards.
vPICO presentations: Wed, 28 Apr
Most radar quantitative precipitation estimation (QPE) products systematically deviate from the true rainfall amount. This makes radar QPE adjustments unavoidable for operational use in hydro-meteorological (forecasting) models. Most correction methods require a timely available, high-density network of quality-controlled rain gauge observations. Here, we introduce a set of fixed bias reduction factors for the Netherlands, which vary per grid cell and day of the year. With this approach, we aim to provide an alternative to current practice, because the climatological factors are both operationally available and independent of the real-time rain gauge availability.
The correction factors were based on 10 years of 5-min radar QPE and reference rainfall data. We tested this method on the resulting rainfall estimates and subsequent discharge simulations for twelve Dutch catchment and polder areas. In addition, we compared the results to the operational mean field bias (MFB) corrected rainfall estimates and a reference dataset. This reference consisted of the radar QPE, spatially adjusted with a network of 356 validated rain gauge observations. Of this network, only 31 are automatic gauges. Hence, only these were available in real-time for the operational MFB corrections.
The climatological correction factors show clear spatial and temporal patterns. The factors are higher far from the radars and higher during winter than in summer. The latter pattern is likely a result of sampling above the melting layer during the months December–March, which causes higher underestimations. Estimated yearly rainfall sums are generally comparable to the reference and outperform the MFB corrected rainfall estimates for catchments far from the radars (south and east of the country). This difference is absent for catchments closer to the radars, where both products tend to marginally overestimate the rainfall sums. The differences amplify when both QPE products are used to force the hydrologic models. Discharge simulations based on the proposed QPE product outperform the MFB corrected rainfall estimates for all but one basin. Moreover, the climatological factor derivation shows little sensitivity to the moving window length and to leaving individual years out of the training dataset. The presented method provides a robust and straightforward operational alternative. It can serve as a benchmark for further QPE algorithm development in the Netherlands and elsewhere.
How to cite: Imhoff, R., Brauer, C., van Heeringen, K.-J., Leijnse, H., Overeem, A., Weerts, A., and Uijlenhoet, R.: A climatological approach for operational radar rainfall bias correction , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6127, https://doi.org/10.5194/egusphere-egu21-6127, 2021.
The localized severe heavy rainfalls, which has not been experienced in the past, have frequently occurred in Japan due to the effects of climate change. Especially, the Guerrilla heavy rainfall (abbreviated as GHR) by isolated rapidly growing single cumulonimbus is triggering flash floods in a small river basin and has caused huge damage to human life and property. If we alert the hazardous rainfall in 5 to 10 min earlier for evacuation, we could minimize human injuries such as isolation, death, and disappearance. For hydrometeorological disaster prevention, a system of the early detection and quantitative risk prediction methods is necessary to detect the initial stage of a cumulonimbus cloud before it is generated into heavy rainfall. In previous research, by analyzing the volume scan with some heavy rainfall events, an important sign named as the first echo (Baby-rain cell) was verified. Also, the vertical vortex tubes with positive and negative pairs did exist in the GHR. Most of the severely developed storm had a certain criterion of vertical vorticity. By using those analyses, we developed the early detection and quantitative risk prediction method as follows. We collect the radar variables (i.e. the vorticity, doppler velocity, and reflectivity, etc.) at each event and set the risk level when the maximum rainfall reached the ground. Then, we select an appropriate set of explaining variables considering the risk level. With the Receiver Operating Characteristic (ROC) analysis, we could find the most appropriate method to predict the risk level. However, we would like to improve the early detection and quantitative risk prediction method by estimating vertical vorticity, divergence and convergence with real wind field data. So, we apply the multiple-doppler radar analysis to estimate the variables reflecting real phenomena. As a result, the improved early detection and quantitative risk prediction method could predict the risk of GHR development accurately by using only the observed radar data. It is expected that the quantitative risk prediction could represent realistic flood prediction system and increase the leading time enough to reduce disaster.
How to cite: Kim, H. and Nakakita, E.: Improvement of the early detection and quantitative risk prediction method with the three-dimensional wind field from multiple-doppler radar analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6943, https://doi.org/10.5194/egusphere-egu21-6943, 2021.
Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding. Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network.
In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented.
How to cite: Velasco-Forero, C., Pudashine, J., Curtis, M., and Seed, A.: Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13673, https://doi.org/10.5194/egusphere-egu21-13673, 2021.
On 27-29 October 2018, heavy precipitation over the Eastern Italian Alps (the so-called “Vaia Storm”) led to an extreme flood, causing several casualties and extensive damages to buildings and infrastructures. The event, which occurred at the end of a climatic anomaly of prolonged drought, developed into two phases: a first phase (October 27-28) and a short, but more intense second phase on the 29th. The event was characterized by extreme accumulated precipitation, and several flash floods in the second phase. A previous work focused on the implementation of two NWP models (MOLOCH and WRF) at convection permitting resolution and showed a general good predictability of the precipitation event, associated with a well-defined large-scale forcing. This work aims at providing an outline of the hydrological predictability, focusing on different river systems in the area (the Upper Adige, the Piave and the Bacchiglione-Astico river systems), with different characteristics in terms of drainage areas, elevations and positions within the region hit by the event. For this, the hydro-meteorological forecasting chain includes the two mesoscale models (MOLOCH and WRF), driven by two global analysis systems (GFS-NCEP and IFS-ECMWF), and a grid-based spatially distributed hydrologic model termed GRIS (Grid-based runoff simulation model). We examine different ensemble strategies for the initialization of the hydro-meteorological chain and focus on the assessment of hydrological predictability, paying specific attention to basins with high regulation capacity thanks to the presence of hydropower storage.
How to cite: Pérez Ciria, T., Zaramella, M., Dallan, E., Giovannini, L., Zardi, D., Davolio, S., and Borga, M.: Ensemble strategies for Flash Flood Forecasting: the 29 October 2018 event in the Eastern Italian Alps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14852, https://doi.org/10.5194/egusphere-egu21-14852, 2021.
The Aude river flash floods that occurred on October 15 and 16, 2018 are among the most important in southeastern France in recent years. The triggering rainfall of the event was characterized by a very fast evolution and low predictability. During the night, nearly 243.5mm of rain in 6 hours was recorded near the city of Carcassonne. In addition to significant considerable material damage, 15 people lost their lives during this flood and 99 people were injured. After the event, the CNRM proposed new forecast ensembles, targeting the possibility of short-term nowcasting (0-6h) of this phenomenon. These ensembles are based on the several NWP models of Météo France: the first ensemble corresponds to the operational AROME-PE product (12 members), the second is a combination of the AROME-PE and AROME-PI models (18 members); finally, the last ensemble corresponds to the second one with a spatial perturbation (90 members). In addition to these ensemble forecasts, ANTILOPE J+1 high resolution observed precipitation data are available. The work presented here aims to evaluate, from a hydrological point of view, these three rainfall ensembles specifically designed to improve short-range rainfall now casting. Based on the CINECAR distributed hydrological model, discharge ensembles are calculated for nearly 1200 sub-watersheds with an elementary drainage area of 5km². These forecasts are compared for each sub-basin with the CINECAR simulation obtained with ANTILOPE J+1 rainfall data.This evaluation approach enables to compensate the lack of discharge observations during the event and to enlarge the dataset used for evaluation. The evaluation results presented combine synthetic scores (CRPS and rank diagrams) often used for ensemble forecasts, but also a user-oriented evaluation framework based on threshold exceedance detection and anticipation. Thresholds for each sub-watershed correspond to the 5, 10, 20 and 50 year return period discharges (SHYREG database). ROC curves are at first established independently of the level of anticipation. In a second time, the anticipation delays are analyzed,. This work finally reveals that (1) synthetic ensemble forecast evaluation scores are not always sufficient to evaluate forecasts; (2) the user oriented evaluation shows a clear hierarchy between the three forecast product ensembles in terms of threshold exceedance detection, but not in terms of anticipation levels.
How to cite: Charpentier-Noyer, M., Payrastre, O., Gaume, E., Nicolle, P., Bouttier, F., and Marchal, H.: Evaluation of three short-range (0-6h) rain ensemble forecasts: study of the Aude October 2018 flash floods (southeastern France), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11946, https://doi.org/10.5194/egusphere-egu21-11946, 2021.
This study aims to compare flood forecasting approaches adapted to the context of Morocco, for two catchments (Rheraya and Ourika) located in the High Atlas Mountains. We evaluated the performances of flash-flood forecasts using two approaches; one relying on event-based hydrological modelling, and the second, a generalized least squares regression model linking event rainfall, antecedent soil moisture and runoff. The meteorological forecasts considered were provided by the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) models. For both approaches, three soil moisture data sources (in-situ measurements, ESA-CCI remote sensing data and ERA5 reanalysis) were compared to estimate the initial soil wetness conditions before flood events. Results showed that the AROME and WRF models better simulate precipitation amounts than ALADIN, mostly due to their better ability to reproduce convective events. The comparison between the two flood forecasting approaches showed that the regression model outperforms the hydrological model-based approach, due to fewer calibration parameters and a better robustness. The best results were obtained with the combination of the WRF forecasts with antecedent soil moisture from ERA5. This type of approach needs to be tested in other basins of North Africa where data are available, in order to develop flood forecasting in these regions, which are strongly vulnerable to flash floods.
How to cite: El Mahdi, E. K., Tramblay, Y., Amengual, A., Homar, V., Romero, R., Saidi, M. E. M., and Alaouri, M.: Comparison of two flood forecasting approaches over High Atlas Mountains basins in Morocco, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5502, https://doi.org/10.5194/egusphere-egu21-5502, 2021.
Since March 2017, the French flash flood warning system, Vigicrues Flash, provides warnings for small-to-medium ungauged basins for about 10,000 municipalities to help emergency services better mitigate potential impacts of ongoing and upcoming flash flood events. Set up by the Ministry in charge of Environment, this system complements flood warnings produced by the Vigicrues procedure for French monitored rivers. Based on a discharge-threshold flood warning method called AIGA, Vigicrues Flash currently ingests radar-gauge rainfall grids at a 1-km resolution into a conceptual distributed rainfall-runoff model. Real-time peak discharge estimated on any river cell are then compared to regionalized flood quantiles (estimated with the same hydrological model). Automated warnings are issued for rivers exceeding the high flood and very high flood thresholds (defined as years of return periods) and for the associated municipalities that might be impacted. This service shares a web platform for the dissemination and communication of early warnings and hazard map displays with the APIC heavy rainfall warning service from Météo-France.
To better anticipate flash flood events and extend the coverage of the Vigicrues Flash service, the hydrological modeling is being enhanced within the SMASH (Spatially-distributed Modelling and ASsimilation for Hydrology) platform developed by INRAE (formerly Irstea). For the upcoming operational update of Vigicrues Flash, a simplified distributed hydrologic model is continuously run at a 15-minute time step and a 1-km resolution. It includes only 2 parameters per cell, controlling respectively a production reservoir and a transfer reservoir from the Génie Rural (GR) conceptual models. Cross-validation and regionalization of these two parameters have been improved to better account for basins spatial heterogeneities while optimizing flash flood warning performance. Evaluation results for 921 French basins on the 2007-2019 period show improvements in terms of flash flood event detection and effective warning lead time. Current developments aim to integrate a cell-to-cell routing component and improve parameters estimation at the national scale with the variational calibration schemes recently developed on the SMASH platform by Jay-Allemand et al. (2020). Challenges of including high-resolution precipitation nowcasts and accounting for the hydrometeorological uncertainties via data assimilation and ensemble forecasting are also discussed based on ongoing SMASH research.
Jay-Allemand, M., Javelle, P., Gejadze, I., Arnaud, P., Malaterre, P.-O., Fine, J.-A., and Organde, D.: On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment, Hydrol. Earth Syst. Sci., 24, 5519–5538, https://doi.org/10.5194/hess-24-5519-2020, 2020.
How to cite: Demargne, J., Fouchier, C., Organde, D., Piotte, O., and Belleudy, A.: Advances and challenges of the French operational flash flood warning system, Vigicrues Flash, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16063, https://doi.org/10.5194/egusphere-egu21-16063, 2021.
Faced with the major challenges of floods and droughts forecasting, especially with the ongoing climate change and potential intensification of the hydrological cycle, advanced modeling tools are needed to perform effective predictions. Nevertheless, hydrological models, regardless of their complexity, encounter difficulties in accurately and reliably predicting quantities of interest such as river discharge or soil saturation dynamics and its spatial variability. Because of physical processes complexity and their limited observability, of the absence of an easily exploitable "first-principle", hydrological modeling remains a difficult task involving emprism and the internal fluxes are generally tinged with large uncertainties. Moreover, multiple model and parameter combinations can lead to comparable performances in discharge simulation at locations where models are evaluated (unicity problem, so called equifinality in hydrology).
This contribution investigates flash flood modeling with models of different complexities: lumped GR4H (Perrin et al. 2003, Mathevet 2005) or distributed SMASH (Jay-Allemand et al. 2020) conceptual models, process oriented distributed MARINE model (Roux et al. 2011). Considering two flash flood prone catchments (the Gardon at Anduze and the Ardèche at Vogüé, France) a methodology consisting in model global sensitivity analysis, calibration and hydrological signatures analysis is used. Model robustness and accuracy is analyzed in the light of model response surfaces, parameter sensitivity rankings and functionning points found with the different models and global calibration algorithms. Next, event performances and flow signatures are analyzed for contrasted events, but also simulated soil moisture evolutions (or equivalently available “soil” storage) compared to root zone soil moisture from the operational SIM hydro-meteorological model (Habets et al. 2008). This analysis is aimed at understanding how each model simulates the catchment behaviour: what are the differences between the simulated dynamics and how this understanding can be used to improve the relevance of the models. Finally, this study paves the way for extended model hypothesis testing and intercomparison in the light of multi-sourced signatures, for future improvements of vertical and lateral flow components of the SMASH* platform along with its variational calibration and assimilation algorithm.
• Habets F., A. Boone, J.L Champeaux, et al. (2008)) : The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research 113, D06113 (2008) 18
• Jay-Allemand M., P. Javelle, I. Gejadze, et al., On the potential of variational calibration for a fully distributed hydrological model: application on a mediterranean catchment. HESS, pages 1–24, 2019
• Mathevet, T., 2005. Which lumped rainfall-runoff models for the hourly time-step? Empirical development and comparison of models on a large sample of catchments. PhD Thesis. ENGREF, Cemagref (Irstea), Paris, France, pp. 463.
• Roux H., D. Labat , P.-A. Garambois, M.-M. Maubourguet, J. Chorda, D. Dartus, A physically-based parsimonious hydrological model for flash floods in mediterranean catchments. NHESS, 11(9):2567–2582, 2011.
• Perrin C., C. Michel, V. Andréassian, Improvement of a parsimonious model for streamflow simulation. Journal of hydrology, 279(1-4):275–289, 2003
*SMASH : Spatially-distributed Modelling and ASsimilation for Hydrology, platform developped by INRAE-Hydris corp., operationally applied in the french flashflood forecast system VigicruesFlash - see presentation by J. Demargne et al.).
How to cite: Garambois, P.-A., Haruna, A., Roux, H., Javelle, P., and Jay-Allemand, M.: Signature & sensitivity-based comparison of conceptual and process oriented models GR4H, MARINE and SMASH on French Mediterranean flash floods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11101, https://doi.org/10.5194/egusphere-egu21-11101, 2021.
High-impact flood events in the Mediterranean region are often the result of a combination of local climate and topographic characteristics of the region. Therefore, the way runoff generation processes are represented in hydrological models is a key factor to simulate and forecast floods. In this study, we adapt an existing model in order to increase its versatility to simulate flood events occurring under different conditions: during or after wet periods and after long and dry summer periods. The model adaptation introduces a dependency on rainfall intensity in the production function. The impact of this adaptation is analysed considering model performance over selected flood events and also over a continuous 10-year period of flows. The event-based assessment showed that the adapted model structure performs better than or equal to the original model structure in terms of differences in the timing of peak discharges, regardless of the season of the year when the flood occurs. The most important improvement was observed in the simulation of the magnitude of the flood peaks. A visualisation of model versatility is proposed, which allows detecting the time steps when the new model structure tends to behave more similarly or differently from the original model structure in terms of runoff production. Overall, the results show the potential of the model adaptation proposed to simulate floods originated by different hydrological processes and the value of increasing hydrological model versatility to simulate extreme events.
How to cite: Peredo Ramirez, D., Ramos, M.-H., Andréassian, V., and Oudin, L.: Investigating hydrological model versatility to simulate extreme flood events , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2984, https://doi.org/10.5194/egusphere-egu21-2984, 2021.
As the climate changes are expected to generally increase the hydrogeological hazard, a better knowledge of the catchment-scale response to intense rainfalls is a relevant issue. Hence, in this work, different approaches of infiltration processes estimation are analysed within hydrological modelling of the extreme rainfall event which involved in 1996 the Cardoso area (southern Alpi Apuane, Tuscany, Italy).
On June 19, 1996, a convective supercell storm produced extreme rainfall rates (478 mm/12 h with maximum intensity of 158 mm/h) within a restricted area of the southern Alpi Apuane. The heavy rainstorm principally affected the Cardoso river watershed, where shallow landslides and debris flows were triggered in the steep slopes of the low-order hydrographic network covered by thick unconsolidated materials. Consequently, severe hyperconcentrated flows destroyed the Cardoso village, with 13 deaths and hundreds million Euros damages.
The FLO-2D flood routing model was used for the numerical modelling of the infiltration processes occurred during the event, by implementing both the SCS-CN method and the Green-Ampt (GA) equation. FLO-2D is a combined hydrologic-hydraulic model, in which there is no need to separate rainfall/runoff and flood routing and spatially varying rainfall/infiltration may be simulated. The main advantage of the GA model arises in the temporal variation of the rainfall intensity, which is not considered in the CN model.
In north-western Tuscany and especially in the Cardoso basin, the Authors have available a large set of engineering geological data obtained by field surveys performed under the coordination of the Geomatics lab of the University of Siena. Namely, the field saturated hydraulic conductivity (Ks) and soil depth measurements were used to implement both the GA equation (I) and the CN method (II, III). For each lithological unit, the continuous maps of the soil depth and the Ks were obtained by integrating the field data with the landforms extracted by processing a set of morphometric DTM derivatives. Regarding the CN method, the Hydrologic Soil Groups (HSG) were determined following the procedure proposed by the USDA-NRSC (Hydrology National Engineering Handbook, version 2009) (II), and by applying the subjective interpretation criteria (III) which does not consider the values of Ks and soil depth.
Methods (I) and (II) show similar results and are consistent with the historical literature data and information, considering three relevant transects close to the village, as well as with post-event field evidences. The results obtained by applying the method (III) are strongly conditioned by the subjective assignment of the HSG and they can be different in terms of peak discharge, flood wave arrival time and maximum water level, if compared to (I) and (II). Moreover, the spatial distribution of soil depth and Ks allows a comprehensive representation of the hydrological-morphological framework of the Cardoso catchment.
How to cite: Amaddii, M., D'Addario, E., Disperati, L., and Fantozzi, P. L.: Comparison of different infiltration methods for flood numerical analysis: modelling the 19 June 1996 extreme event of Cardoso (Alpi Apuane, Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3498, https://doi.org/10.5194/egusphere-egu21-3498, 2021.
Landslides and debris flows represent natural phenomenon with high geomorphic impact and of significant cascading hazards to human lives and built environment. Intense rainfall events are key triggers of landslides and, as a result, landslides end up interacting with river channels during floods. Large masses of sediment can overwhelm the sediment transport capacity of a river channel and result in the formation of a dam. Nevertheless, this build-up process is not always evident in the aftermath of the event: when a dam burst occurs, a surge of mixed solid and fluid material is produced resulting in significant erosion in the downstream channel. Eventually, the blockage is removed, leaving the process of dam build-up and bursting undocumented. Due to the abrupt nature of this phenomenon, field observations are difficult to obtain.
In this study, we carried out a preliminary analysis by using a computational model to replicate the formation of a channel blockage downstream of a series of landslides during an event that occurred in the North St Vrain Creek in Colorado, USA, during the Great Colorado Storm in September 2013 (estimated to be a 1 in 1000 years event). In this case, there is limited documented evidence of a blockage, but a dam and its busting were hypothesised by analysing very large erosional patterns in a downstream reach that could not be explained by typical erosive processes (e.g. stream power). We employed the free source code r.avaflow, which is a two-phase model. This code can simulate complex chain phenomena, rapid routing mass flows, and entrainment-deposition processes. Topography of the area was obtained by using high resolution LiDAR DEM before and after the flood event in 2013 and was used as basal topography for simulations, as well as to estimate the amount of sediment released by the landslides. The flood flow employed for the simulation was based on estimated rainfall-runoff and kept constant, since the total simulation time was small compared to the actual flood curve duration. We also tested a limited range of parameters to account for the inherent uncertainties in the variables used.
The model was able to represent the erosion from the landslides and on the river channel, but also displayed the formation of a dam downstream of the landslides across all simulations. Although the topographic change and volume of mobilised sediments were affected by the variation of the model parameters, the formation of the channel blockage was always observed. This modelling will provide the basis for further modelling on landslide-channel interactions and will explain those phenomena that have only been postulated but not directly observed.
How to cite: Panici, D. and Bennett, G.: Modelling landslide-flood interactions: an example from Colorado, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7611, https://doi.org/10.5194/egusphere-egu21-7611, 2021.
Explosive volcanic eruptions are among the most significant natural disturbances to landscapes on Earth. The widespread and rapid influx of pyroclastic sediment, together with subsequent changes to topography and vegetation cover, drives markedly heightened runoff responses to rainfall and increased downstream water and sediment fluxes; principally by way of hazardous lahars. The nature and probability of lahar occurrence under given rainfall conditions evolves as the landscape responds and subsequently recovers following the disturbance. The relationship between varying sediment supply, rainfall patterns, vegetation cover and lahar activity is complex, and impedes forecasting efforts made in the interest of hazard and land use management. Thus, developing an improved understanding of how these systems evolve in response to volcanic eruptions is of high importance.
Here we present SedCas_Volcano[MOU1] , a conceptual sediment cascade model, designed to simulate the first-order trends, such as magnitude-frequency distributions or seasonal patterns, in lahar activity and sediment transport. We use the Belham River Valley, Montserrat, as a case study. This small (~15km2) catchment has been repeatedly disturbed by five phases of volcanic activity at the Soufrière Hills Volcano since 1995. The multi-phase nature of this eruption, together with the varying nature and magnitude of disturbances throughout the eruption, has driven a complex disturbance-recovery cycle, which is further compounded by inter-annual climatic variations (e.g. ENSO). Lahars have occurred frequently in response to rainfall in the Belham River Valley, and their occurrence has evolved through the repeated disturbance-recovery cycle. This activity has resulted in significant net valley floor aggradation and widening, consequent burial and destruction of buildings and infrastructure, as well as coastal aggradation of up to ~250m. Within SedCas_Volcano, we account for evolving sediment supply, vegetation cover and rainfall, to simulate the lahar activity and channel change observed in the Belham River Valley since January 2001. Following this, we test the model under different hypothetical eruptive scenarios. [MOU2] Our goal is to assess the efficacy of such models for reproducing patterns of lahar activity and geomorphic change in river systems that are repeatedly disturbed by volcanic activity.
How to cite: Christie, J., Bennett, G., Hirschberg, J., Barclay, J., and Herd, R.: SedCas_Volcano: Simulating decadal patterns of lahar hazard and sediment transfer following volcanic disturbance in the Belham River Valley, Montserrat , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5694, https://doi.org/10.5194/egusphere-egu21-5694, 2021.
Bedrock landslides are a major hazard, with influences on erosion, weathering, and organic carbon transfer. Understanding the controls of the magnitudes of bedrock landslides is central for predicting and managing landslide hazards. Previous studies hypothesize that the geometric sizes of bedrock landslides are controlled by bedrock fractures that set the strength of subsurface materials. Recent studies show that topographic stress, resulted from the interplay between tectonics stress and topography, sets the extent of subsurface open-fracture zones, but how topographic stress affects bedrock landslides remains less well understood. Here, we investigate whether topographic stress influences the magnitudes of bedrock landslides in a granitic terrain in the eastern Tibetan mountains where landslides prevail. We constructed two new landslide inventories of earthquake- and rainfall-induced landslides in the study area. We examine the relationships between landslide sizes and the proxies for topographic stress, topography, and landslide triggers (i.e. seismic shaking and rainfall). We demonstrate that topographic stress exerts a dominant control on the sizes of large bedrock landslides. Our study provides new insights into how landslides occur in different topographic and tectonic conditions, as well as how topographic stress influences earth surface processes.
How to cite: Li, G. and Moon, S.: Topographic stress influences on bedrock landslides, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8085, https://doi.org/10.5194/egusphere-egu21-8085, 2021.
The 2016 implementation of the EU Flood Directive in Spain defines within the flood-prone zones the Preferential Flow Zone (Zona de Flujo Preferente, ZFP). This zone includes a) broadly, the area where the floods flow is concentrated; b) for the 100 years return period flood, the intensive drainage waterway and the zone dangerous to persons. The ZFP is usually defined for the 100 years flood applying hydraulic modelling. However, the calculation of the 100 years flood poses multiple limitations. For instance, different probability distributions produce different results for the same data series, or for rainfall and discharge data, depending on the time interval considered in the calculation, the results are also different. Regarding rainfall, the meteorological radar data are still too new to extrapolate to 100 years. The destruction of meteorological and gauging stations during storms and floods is not rare; hence, a lack of data on major events in the data series can deeply affect the calculations. Furthermore, similar rainfall can produce different discharges due to differences in the antecedent conditions or to land use changes. All the above and the climate change, question the hypothesis of stationarity at the base of the floods return period concept1 and, thus, its calculation reliability.
Since the middle of the 20th century, significant socio-economic and land use changes occurred in the western Mediterranean region, resulting in changes in the morphology of rivers (e.g., reduced channel section, entrenchment). The record of these morphological changes, including the effects of major floods, can provide insights to define the high-energy flow zone or ZFP. This work contributes to determine the flash flood effects and, therefore, to define the ZPF, through multitemporal geomorphological analysis applied to a case study of the upper basin of the Francolí river in Catalonia, Spain. It was affected by several major floods in 1874, 1930, 1994 and 2019, where the first and the last events were the largest and of quite similar, centenial magnitude. Different reaches of the river are studied and compared to validate the analysis: reaches where 1994 and 2019 flood were similar and reaches where these floods were of very different magnitude; reaches where all the basic dataset is available (1946, 1956, 1995 post flood, pre and post 2019 orthophotos; 2003 detailed DTM; stereo photographs, post 2019 flood field data and GNSS-RTK data of river cross sections) and reaches with lack of some data (especially of the 1995 post flood image). Historical information (water levels attained by the past floods and the calculated discharges) are also used to complement and validate the geomorphological analysis results.
With this work we test whether the main geomorphic effects of the 2019 flood could have been predicted using the multitemporal geomorphological analysis. The ZFP can be reasonably determined for major floods in this Mediterranean river. This multitemporal geomorphological analysis appears as a good complementary tool to inform flood risk.
1 Sofia, G., E. I. Nikolopoulos, L. Slater (2020), It’s time to revise estimates of river flood hazards, Eos, 101, https://doi.org/10.1029/2020EO141499. 16 March 2020.
How to cite: Furdada, G., Valera-Prieto, L., Cortés, S., González, M., Pinyol, J., Balasch, J. C., Tuset, J., Khazaradze, G., and Calvet, J.: Multitemporal geomorphological analysis to predict flash flood impacts: its contribution to inform flood risk management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15286, https://doi.org/10.5194/egusphere-egu21-15286, 2021.
Urban pluvial floods are considered as a ubiquitous hazard. The increase in intensity and frequency of extreme rainfall events, combined with high population density makes urban areas vulnerable to pluvial flooding. Pluvial floods could occur anywhere depending on the existence of minimal areas for surface runoff generation and concentration. Detailed hydrologic and hydrodynamic simulations are computationally expensive and resource-intensive. This study applies two computationally inexpensive approaches to identify risk areas for pluvial flooding. One approach uses common GIS operations to detect flood-prone depressions from a high-resolution 1m x 1m Digital Elevation Model (DEM), to identify contributing catchments, and to represent runoff concentration by a fill-spill-merge approach. The second approach employs GIS to identify pluvial flood-prone hotspots in terms of the topographic wetness index (TWI). Based on the exceedance of a TWI threshold, flood-prone areas are identified using a maximum likelihood method. The threshold is estimated by comparing the TWI to inundation profiles from a two-dimensional (2D) hydrodynamic model (TELEMAC 2D), calculated for various rainfall depths within a given spatial window. The two approaches are applied to two flooding hotspots in Berlin, which have been repeatedly subject to pluvial flooding in the last decades and the outputs are compared against the detailed output from TELEMAC 2D.
How to cite: Seleem, O., Heistermann, M., and Bronstert, A.: Towards pluvial flooding hazard assessment in an urban environment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11141, https://doi.org/10.5194/egusphere-egu21-11141, 2021.
After having developed an operational flood risk management service in France for more than 15 years, PREDICT Services was asked to deploy this service internationally.
PREDICT Services has started the transfer of the methods internationally and particularly in Morocco. The first feedbacks of experiences show that spatial data take an essential part in it but that today the quality of these products needs to be improved and that the durability of the access to the data must be secured.
PREDICT Services has partnered with Météo-France and CEREMA to carry out this project.
We have developed with Météo-France a global precipitation estimation data thanks to an artificial intelligence algorithm specific to rainfall estimation computation which considers the discrimination of stratiform and convective clouds, their precipitation estimates, the evaporation correction of precipitation, seasonality, latitude.... These data are also processed in a training file to calibrate the data according to several parameters and in particular existing radar data.
This data is available every 30 minutes at a resolution of 5km around the world.
In addition, with CEREMA, we can generate flood zones thanks to its Exzeco model, applied on the new Airbus digital terrain models (WorldDEM) based on satellite data, thus improving the overall accuracy.
A pilot project has started and will last 2 years to validate the methods and data developed during the project in the Indian Ocean area (Reunion - Mauritius - Mayotte - Madagascar) so that they can be deployed worldwide. Global rainfall estimation as well as the estimation of floodable areas elaborated thanks to satellite data.
These data will be shared via a web platform which will allow users (insurers, meteorological direction, States) to take control of these tools. A real time assistance service 24/24, 365/365, already set up by PREDICT Services for more than 15 years, will support them in the management of risk events.
This data, essential for crisis management and anticipation of natural risks, will allow the different actors to better anticipate and be better prepared during risk events.
How to cite: Guillaume, L., Alix, R., and Sylvain, C.: Spatial Contribution to Flood Risk Analysis (COSPARIN), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3948, https://doi.org/10.5194/egusphere-egu21-3948, 2021.
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