Evaluation of the performance of the 1-arc min hydrological model LISFLOOD in German catchments

Floods represent one of the most frequent natural hazards in the world and have a high impact on society. In central Europe and countries like Germany, flooding severity has increased in the last 10 years with devastating effects in well-being and high economic loses. Currently, the European Flood Awareness System (EFAS) is preparing a major release of the system at a spatial scale of 1 arc min. EFAS uses a specific number of hydrological stations for calibration, nevertheless, the availability of information at a higher resolution allows for a further update of the current system, over specific areas and catchments. The objective of this study is to evaluate the performance of the fully distributed rainfall-runoff model by calibrating and testing LISFLOOD for flood forecasting in Germany at 1 arc min with daily resolution. For this study we selected all major rivers and catchments that flow through Germany, choosing a period of analysis from 1990 to 2020. The meteorological forcing entails precipitation, temperature, solar radiation, and vapor pressure, from the high-resolution multi-variable gridded meteorological data set for Europe, EMO-1arcmin (Thiemig et al., 2022; http://data.europa.eu/89h/0bd84be4-cec8-4180-97a6-8b3adaac4d26). The discharge data were collected from the transnational flood portal for all states in Germany and the neighboring countries. The maps, the static maps that characterize the land use, land cover and human activity on the surface, soil and groundwater characteristics of the study area are kindly provided by EFAS. The calibration was conducted using the non-dominated sorting genetic algorithm-II (NSGA-II) in a top-down approach where the Kling–Gupta efficiency criteria (KGE) acted as the objective function for evaluating the model performance. The results show that the calibrated LISFLOOD model could be use in flood scenarios with reduced performance along the coastal areas.

Reference

Thiemig, Vera; Ramos Gomes, Gonçalo Nuno; Skøien, Jon Olav; Ziese, Markus; Rauthe-Schöch, Armin; Rustemeier, Elke; Rehfeldt, Kira; Walawender, Jakub; Kolbe, Christine; Pichon, Damien; Schweim, Christoph; Salamon, Peter (2022). EMO-1arcmin: A high-resolution multi-variable gridded meteorological data set for Europe (1990-2021). [Data set]. European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26