A Holistic Modelling Approach for the Estimation of Return Levels of Peak Flows in Bavaria

Abstract

This study introduces a holistic approach for the hydrological modelling of peak flows for the major Bavarian river basins, referred to as Hydrological Bavaria. This approach, intended to develop a robust modelling framework to support water resources management under climate change conditions, comprises a regionalized parameterization of the water balance simulation model (WaSiM) for 98 catchments in high temporal (3 h) and spatial (500 m) resolution using spatially coherent information and an automatized calibration (dynamically dimensioned search–simulated annealing, DDS-SA) for storage components. The performance of the model was examined using common metrics (Nash & Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE)). The simulations provided the means for the calculation of a level of trust (LOT) by comparing observed and simulated high flows with a five, ten, and 20-year return period. These estimates were derived by the Generalized Pareto Distribution (GPD) applying the peak over threshold (POT) sampling method. Results show that the model overall performs well with regard to the selected objective measures, but also exhibits regional disparities mainly due to the availability of meteorological inputs or water management data. For most catchments, the LOT shows moderate to high confidence in the estimation of return periods with the hydrological model. Therefore, we consider the holistic modelling approach applicable for climate change impact studies concerned with dynamic alterations in peak flows.

Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

“…They have been used to create an observational large ensemble (McKinnon et al, 2017;McKinnon and Deser, 2018) and test dynamical adjustment techniques (Deser et al, 2016;Lehner et al, 2017). They have also been used to develop new methodologies such as utilising the ensemble dimension for analysis (Herein et al, 2017;Maher et al, 2018Maher et al, , 2019Haszpra et al, 2020a, b) and to develop and test statistical methods for isolating the forced response (Sippel et al, 2020;Wills et al, 2020). Such ensembles have additionally provided important information for policy makers, such as whether emission reductions are likely to be detectable in the coming years, or whether they could be masked by internal variability (Lehner et al, 2016;Tebaldi and Wehner, 2018;Marotzke, 2019;Spring and Ilyina, 2020).…”

Floods and droughts: a multivariate perspective on hazard estimation

“…Examples of physically-based large climate ensembles include single-model-initialized ensembles (SMILES Deser et al, 2012Deser et al, , 2020 and reforecast simulations, i.e., forecasts generated for past periods (Hamill et al, 2006). Climate SMILEs and reforecast simulations have been used in combination with hydrological models to generate large ensembles of streamflow time series (van der Wiel et al, 2019;Willkofer et al, 2020;Brunner et al, 2021c;Brunner and Slater, 2022).…”

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 Large Ensemble

“…The Hydrological Bavaria covers approximately 100,000 km² and features a diverse landscape ranging from the Alps (with the highest point being Piz Bernina at 4049 meters above sea level; m.a.s.l) and the alpine foreland in the south to the southern German escarpment in the north of the study area (with the lowest point being 90 m.a.s.l at Frankfurt-Osthafen) and the eastern mountain ranges to the east (Willkofer et al, 2020;Poschlod et al, 2020).…”

“…The discharge of many rivers within the Hydrological Bavaria is influenced by artificial retention structures (i.e., dams, retention basins), naturally formed lakes, or transfer systems (drinking water supply, low flow elevation) (Willkofer et al, 2020). The major river catchments were divided into a total of 98 smaller sub-catchments based on a common interest in flood protection and a more detailed variation in catchment characteristics, using a selection of gauges (Willkofer et al, 2020).…”