Coupling Support Vector Machine and physically-based Hydrological Modeling for Reducing the Computational Time in Climate Change Studies
Hofmeister F, Spadina A, Chiogna G (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: International Association for Hydro-Environment Engineering and Research
Pages Range: 4827-4836
Conference Proceedings Title: Proceedings of the IAHR World Congress
Event location: Granada, ESP
DOI: 10.3850/IAHR-39WC2521716X2022902
Abstract
Hydrological modeling of Alpine catchments is particularly challenging due to the high variability of hydrological processes in space and time. Although physically-based and fully-distributed hydrological models, such as WaSiM, are able to simulate these small-scale variabilities, the computational time for running a model on hourly time step and 25 m grid resolution in mesoscale catchments (10-100 km2) is significantly high. This becomes particularly relevant when large time periods (>30 years) are to be simulated for climate change studies. Therefore, we applied Support Vector Regression (SVR) to reproduce the results of a high-resolution WaSiM model (25 m grid, hourly time step) using as an input a coarse spatial (100 m grid) and temporal (daily) resolution of the model and hourly meteorological time series. As a result, the computational time was reduced by 93% for the model setup with hourly time step and 25 m grid resolution. The quality of the SVR results was quantified through different indicators: Root Mean Squared Error (RMSE), Standard deviation Ratio of RMSE (RSR), Nash-Sutcliffe Efficiency (NSE) and logarithmic NSE (logNSE). Additionally, the SVR results were compared with the flow duration curve. All indicators show an excellent performance (e.g., NSE=0.89) of the SVR in reproducing WaSiM results. We tested the robustness of the SVR also considering different data, such as meteorological inputs from different stations and simulated discharges of sub-catchments. Except for the cases of small sub-catchments with little glacier contribution, very good performance levels were achieved.
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Germany (DE)How to cite
APA:
Hofmeister, F., Spadina, A., & Chiogna, G. (2022). Coupling Support Vector Machine and physically-based Hydrological Modeling for Reducing the Computational Time in Climate Change Studies. In Proceedings of the IAHR World Congress (pp. 4827-4836). Granada, ESP: International Association for Hydro-Environment Engineering and Research.
MLA:
Hofmeister, Florentin, Alice Spadina, and Gabriele Chiogna. "Coupling Support Vector Machine and physically-based Hydrological Modeling for Reducing the Computational Time in Climate Change Studies." Proceedings of the 39th IAHR World Congress, 2022, Granada, ESP International Association for Hydro-Environment Engineering and Research, 2022. 4827-4836.
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