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.
Abstract. Model benchmarking is needed in order to establish how newly developed forecasting approaches perform against current state-of-the-art systems. In many cases, resources for re-forecasting long periods of time are limited and therefore, a period of parallel-operations is evaluated. For this study, the forecasting chain presented in the companion paper by Antonetti et al. (2018) has been set up for the Verzasca basins in the southern Swiss Alps. In this region, an operationally running system is available from previous studies on probabilistic flash flood (FF) forecasts. This current system relies on the calibrated semi-distributed hydrological model PREVAH. The new model RGM-PRO includes the concept of dominant runoff processes and requires a priori estimation of parameters but no direct discharge observations for calibration. This is a significant benefit to FF prediction in ungauged catchments. Both FF forecasting chains are forced by information from numerical weather prediction COSMO-1 and COSMO-E. Real-time rainfall is provided by the CombiPrecip product, which combines rain gauge and weather radar data. As RGM-PRO is an event-based model, initial conditions are not computed internally. Such initial conditions are obtained from operationally available gridded simulations of the PREVAH model. The current PREVAH-HRU setup uses rainfall data as obtained by interpolating real-time data of the station network maintained by MeteoSwiss. Initial conditions are tracked internally day-by-day. The PREVAH-HRU runs forced by COSMO-1 and COSMO-E during real-time operations in the period May to August 2016 have been compiled. Corresponding model runs using RGM-PRO have been computed a posteriori. Both sets of forecasts are evaluated against discharge observations using deterministic and probabilistic verification metrics. Results showed that the novel approach was able to compete with the operational benchmark prediction system and was consistently superior for high-flow situations. The new forecasting chains were able to react faster on precipitation in comparison with the benchmark forecasts. Confirming previous studies for all forecasting chains, a clear preference for using a meteorological ensemble as forcing data was found. In a synthesis of the two companion papers, more skill was found in the Verzasca basin than in the Emme catchment, suggesting a better forecast performance in strongly topography driven basins with shallow soils and weak dependence on initial conditions. The findings of the two studies suggest that the novel forecasting chains can compete with the traditional ones in operational setup without the need of long-term discharge measurements and extensive calibration. With the new runoff generation module, extension of FF prediction to ungauged catchments is possible, provided that spatially distributed information on dominant runoff processes is available.
Abstract. Flash floods (FFs) evolve rapidly during and after heavy precipitation events and represent a 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 FF forecasting chain is presented based on: (i) a nowcasting product which combines radar and 5 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 (RTs), which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is then parametrised a priori 10 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-size FF prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of RTs, which allowed sensitivity of the forecast performance on 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 15 a conventional hydrological model relying on calibration.Results showed a low sensitivity of the predictive power on the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of RTs 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 as a prediction system including a conventional hydrological model. In the small nested subcatchments, the process-based forecasting chains 20 outperformed the conventional system, however, no forecasting chain showed satisfying skill.The outcomes of this study show that operational FF predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.
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