Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland)

Abstract

Abstract. Flash floods evolve rapidly during and after heavy precipitation events and represent a potential risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a flash-flood forecasting chain is presented based on (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip); (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E); (iii) operationally available soil moisture estimations from the PREVAH hydrological model; and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types, which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is event-based and parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-sized flash-flood-prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of runoff types, which allowed sensitivity of the forecast performance to the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including an original version of the runoff generation module of PREVAH calibrated for one event. Results showed a low sensitivity of the predictive power to the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of runoff types with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill to a prediction system including a conventional hydrological model. In the small nested subcatchments, although the process-based forecasting chains outperformed the original runoff generation module, no forecasting chain showed satisfying skill in the sense that it could be useful for decision makers. Despite the short period available for evaluation, preliminary outcomes of this study show that operational flash-flood predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

Ensemble flood forecasting: Current status and future opportunities

“…While flash flooding predominantly is a pluvial process, the land surface hydrology can modulate the transformation of precipitation to runoff and hence the extend of flooding. To represent the effects of land surface hydrology on flash floods, hydrological modeling needs to be undertaken at fine spatial resolutions to better represent the processes that lead to flash flooding (Antonetti et al, ). There are many outstanding research challenges with hyper‐resolution hydrological modeling, particularly with respect to the characterization of hydrological process, availability of high‐resolution observations and growth in computational requirements as model resolution increases (Bierkens et al, ).…”

Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees

“…But reliable spatial and temporal QPF distributions are necessary to render skillful quantitative discharge forecasts when coping with floods over small-and medium-sized basins. Otherwise, the issuance of precise and dependable early flood warnings is inhibited (Le Lay and Saulnier, 2007;Bartholmes et al, 2009;Cloke et al, 2013).…”

Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

“…The blending technique tries to overcome this limit, connecting these two components. Blending has been previously analysed in some studies with the purpose of improving the rainfall forecast (Golding, 1998;Kober et al,2012;Atencia et al, 2010;Kilambi and Zawadzky, 2005;Nerini et al, 2018). In this application, a blending function is written in order to balance the forecast reliability of the two models.…”