HS4.1

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.

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.

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 20^th 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.