Generating precipitation ensembles for flood alert and risk management

Abstract

Floods are major natural disasters that, in several occasions, can be responsible for life losses and severe economic damages. Flood forecasting and alert systems are needed to anticipate the arrival of these events and mitigate their impacts. They are particularly important for risk management and response in the nowcasting of flash floods. In this case, precipitation fields are crucial and is important to consider uncertainties coming from the observed precipitation fields used as input data to the system. One approach to take into account these uncertainties is to generate an ensemble of possible scenarios of observed precipitation. The aim of this study is to investigate the potential of a framework that applies a geostatistical conditional simulation method to generate an ensemble of precipitation fields that can be used as input to a distributed rainfall-runoff model to produce probabilistic flood alert maps. The Var region (southeastern France) and 17 events are used to validate the approach. Results show that the proposed method can be useful to generate realistic precipitation scenarios and, ultimately, to provide information on the probability of discharges exceeding critical flood thresholds. It can be a solution to combine information from radar fields and rain gauges to generate precipitation ensembles and quantify uncertainties in input data for hydrological modelling.

Investigating the role of geology in the hydrological response of Mediterranean catchments prone to flash-floods: Regional modelling study and process understanding

“…Consequently, the hydrological response of the Cévennes-Vivarais catchments to flood events have long been observed, within the Cévennes-Vivarais Hydro-Meteorological Observatory 1 (Boudevillain et al, 2011), and subject of many hydrological modelling studies (Le Lay and Saulnier, 2007;Bonnifait et al, 2009;Garambois et al, 2013 e.g.). Several works aiming at developing and assessing forecast tools and flash-flood warning systems also focus on the Cévennes-Vivarais region Vincendon et al, 2010;Alfieri et al, 2011), or on neighbouring French Mediterranean regions like the Var department, hit by massive floods in June 2010 (Javelle et al, 2014;Caseri et al, 2015). However, the purpose of the current work significantly diverges from forecast-oriented studies, in the sense that it is governed by the understanding of unknown processes, instead of the will to obtain the best results as possible.…”

A space-time geostatistical approach for ensemble rainfall nowcasting

“…Conditioning is based on the residual substitution kriging approach and MCMC sampling (further details can be found in [12]). In summary, the parameters used by the generator are: the wind velocity and direction of the event, the space-time variogram model, the average percentage of zero rain, the direction and velocity of rain cells, the mean and standard deviation of the precipitation data, and the rain data at gauged locations for the conditioning.…”

“…Ensemble rainfall nowcasts are generated based on space-time properties of precipitation fields given by radar measurements and precipitation data from rain gauges. The approach has been previously tested for the simulation of uncertainty in radar rainfall fields ( [12]) and is here proposed for nowcasting. The method was applied in the Var region, south of France, using several flood events.…”

Dealing with non-stationarity in sub-daily stochastic rainfall models

“…-Hypothesis H3: the structure of rainfall is constant within a single rain storm but changes between storms (Caseri et al, 2016;Benoit et al, In Press). Here 17 rain events are identified following the definition adopted in Sect.…”

“…In the case of stochastic rainfall modelling, the identification of stationary datasets or sub-datasets relies on some phenomenological guesses about rainfall, which serve as fuzzy guidelines to delineate stationary domains. Depending on the application and modeling choices, the statistical structure used for sub-daily stochastic rainfall is considered as changing at scales ranging from seasons (Paschalis et al, 2013;Bárdossy and Pegram, 2016;Peleg et al, 2017) to single rain storms (Caseri et al, 2016;Benoit et al, In Press).…”