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Hydrological Flood Forecast for the River Salzach Using Open Source and Open Data

Author(s): Gerald Krebs; Sebastian Gegenleithner; Josef Schneider

Linked Author(s): Gerald Krebs, Josef Schneider

Keywords: Open source; Open data; Hydrological modelling; Snow processes

Abstract: Flood forecasts are increasingly based on real-time simulation results from meteorological, hydrological and hydrodynamic models. The real-time forecast poses particular challenges in terms of computing times, the robustness and accuracy of algorithms used and requires concepts for forecast correction and the integration of a large number of different data sets. The hydrological-hydrodynamic simulation of the catchment area of ​​the Salzach is the base of the hydrological forecasting system Hydris. In the course of a system renovation, the catchment area of ​​the Salzach is simulated with the grid-based hydrological model wflow_sbm. Furthermore, new snow and glacier processes are implemented using an energy balance approach. In addition to wflow_sbm, PCRaster and Python are used for the computations. The Salzach has a length of 226 km and drains, together with its most important tributary, the Saalach, an area of ​​6,728 km2. The catchment area extends over an altitude of 244-3600 m. The wflow_sbm model (e.g. Imhoff et al. 2020) is developed by Deltares and allows the calculation of all essential hydrological-hydraulic processes on a grid basis. The surface and subsurface flow and the channel discharge are calculated kinematically. For this study, only open data was used for model creation (ME), operation (MB) and evaluation (EE): • Digital elevation model and imperviousness (Copernicus Land Monitoring) (ME) • Land use (Corine 2018) (ME) • Soil properties (soil grids, Batjes et al. 2020) (ME) • Reservoirs, lakes and glaciers (SAGIS) (ME) • Precipitation, temperature, radiation in hourly resolution (ERA 5 data sets, Hersbach et al. 2018). The potential evaporation was determined according to DeBruin (2016) and the actual evapotranspiration was determined with a monthly Leaf Area Index (MODIS, NASA) (MB). • Snow cover in hourly resolution (ERA 5 data sets, Muñoz Sabater 2019) (MB, EE) Already the uncalibrated NA model delivered consistently good results for the Golling, Salzburg-Stadt and Oberndorf gauges with deviations in volume between 0-12% and NSE values ​​of around 0.60. Literature: Batjes et al 2020: Standardised soil profile data to support global mapping and modeling (WoSIS snapshot 2019). Earth System Sciences 12 (1) deBruin et al. 2016. A Thermodynamically Based Model for Actual Evapotranspiration of an Extensive Grass Field Close to FAO Reference, Suitable for Remote Sensing Application. Journal of Hydrometeorology. Hersbach et al. 2018. ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Imhoff et al. 2020: Scaling Point-Scale (Pedo) transfer Functions to Seamless Large-Domain Parameter Esti-mates for High-Resolution Distributed Hydrologic Model-ing: An Example for the Rhine River. Water Resources Research 56 (4) Muñoz Sabater 2019. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Cli-mate Data Store (CDS)

DOI: https://doi.org/10.3850/IAHR-39WC2521711920221777

Year: 2022

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