Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

Abstract

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

“…The need for systematically transferring parameters to ungauged basins while respecting their landscape heterogeneity and water balance motivated us to expand our previous single‐basin experiments (Demirel, Koch, Mendiguren, & Stisen, 2018; Demirel, Koch, & Stisen 2018; Demirel, Mai, et al., 2018) to a regional scale study. In this study, we elaborated on the value of multi‐basin, multi‐objective model calibration for distributed hydrologic modelers incorporating readily available global remote sensing data in flexible open‐source models with cutting‐edge parameter regionalization schemes like the multi‐parameter regionalization in mHM.…”

“…For the spatial pattern evaluation of simulated AET, the bias‐insensitive Spatial Efficiency metric (SPAEF) was used (Demirel, Koch, Mendiguren, & Stisen, 2018; Demirel, Koch, & Stisen 2018; Demirel, Mai, et al., 2018; Koch et al., 2018). The SPAEF is a reformulation of KGE and is defined as $$$\text{SPAEF}=1-\sqrt{{(r-1)}^{2}+{({\Phi }-1)}^{2}+{(\gamma -1)}^{2}}$$$ where r is the Pearson correlation coefficient between observed and simulated spatial patterns of AET, Φ is the coefficient of determination fraction of observed and simulated AET, and γ quantifies the fraction of the histogram intersection based on the z ‐scores of observed and simulated AET fields.…”

“…The sensitivity analysis was based on an initial parameter set obtained from a previous calibration against KGE for the same model setup across Europe (Rakovec & Kumar, 2022), although with a different parameterization scheme for Kc and root fraction distribution. For the new parametrization we used parameters from a few test runs combined with experience from (Demirel, Koch, Mendiguren, & Stisen, 2018; Demirel, Koch, & Stisen 2018; Demirel, Mai, et al., 2018). We used KGE as OF for discharge performance and SPAEF as OF for spatial pattern performance of the model, and the initial parameter set gave reasonable performance on both OF's.…”

“…The model calibration software Ostrich is available from USBR (2022). The data for the mHM model simulations are publicly available at Demirel and Stisen (2022).…”

“…Therefore, an particular avenue addressing the challenges of distributed model fidelity, is the use of spatial pattern information from remote sensing data to constrain hydrological model parametrization (Dembélé et al., 2020; Demirel, Koch, Mendiguren, & Stisen, 2018; Demirel, Koch, & Stisen 2018; Demirel, Mai, et al., 2018; Henriksen et al., 2022; Soltani, Bjerre, et al., 2021; Zink et al., 2018).…”