Automatic Regionalization of Model Parameters for Hydrological Models. Issue 12 (27th December 2022)

• Record Type: Journal Article
• Authors: Feigl, Moritz; Thober, Stephan; Schweppe, Robert; Herrnegger, Mathew; Samaniego, Luis; Schulz, Karsten
• Is Part Of: Water resources research. Volume 58:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-27
• Subjects: regionalization -- machine learning -- rainfall‐runoff modeling -- distributed models -- transfer functions -- optimization; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2022WR031966
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Parameter estimation is one of the most challenging tasks in large‐scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large‐scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org ), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters "saturated hydraulic conductivity" and "field capacity, " which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models. Key Points: This study shows the performance of automatic transfer function (TF) estimation with the Function Space Optimization (FSO) method We show that FSO is able to estimate TFs that perform similar to the mesoscale Hydrologic Model TFs in an ungauged setting This study represents a step toward automatic TF estimation for physically interpretable model parameters