Assessment of TOPKAPI-X Applicability for Flood Events Simulation in Two Small Catchments in Saxony

Abstract

Numerical simulations of rainfall-runoff processes are useful tools for understanding hydrological processes and performing impact assessment studies. The advancements in computer technology and data availability have assisted their rapid development and wide use. This project aims to evaluate the applicability of a physically based, fully distributed rainfall-runoff model TOPKAPI-X for the simulation of flood events in two small watersheds of Saxony, Germany. The results indicate that the model was calibrated well for 4.88 km2 Wernersbach catchment (NSE 0.89), whereas 276 km2 Wesenitz catchment calibration was only satisfactory (NSE 0.7). The addition of the second soil layer improved the model’s performance in comparison to the simulations with only one soil layer for Wernersbach (NSE increase from 0.83 to 0.89). During the validation process, the model showed a variable performance. The best performance was achieved for Wernersbach for the year with the highest runoff (NSE 0.95) in the last decade. The lowest performance for the Wernersbach and Wesenitz catchments was 0.64 for both. The reasons for the model’s low performance in some years are discussed, and include: (i) input data quality and data insufficiency, (ii) methods used within the simulations (interpolation, ETP estimation, etc.), and (iii) assumptions made during the calibration (manual calibration, parameter selection, etc.).

Achieving Robust and Transferable Performance for Conservation‐Based Models of Dynamical Physical Systems

“…Because these models are constrained to obey mass, energy, and/or momentum balance within a fixed control volume (Pascolini‐Campbell et al., 2020), we will hereafter use the term “ conservation‐based models (CBMs)” to distinguish them from machine learning (ML) models that focus primarily on information flows and that are not regularized to obey physical conservation principles. CBMs are used across the full range of Earth Science disciplines; hydrological examples used for streamflow prediction (among other things) include relatively simple spatially lumped physical–conceptual rainfall‐runoff models such as SIMHYD (Vaze et al., 2010) and more complex spatially distributed process‐based rainfall‐runoff models such as TOPKAPI (Janabi et al., 2021).…”