Iterative Ensemble Calibration of WRF-Hydro for Improved Hydrological Modeling

Hydrological studies often depend on model simulations to analyze flood occurrences and frequency. A major challenge in this domain is quantifying and reducing uncertainty in simulations, particularly when dealing with complex models like WRF-Hydro, which involve extensive parameterization. To address this, we present a novel parameter estimation approach using Iterative Ensemble Smoothers (iES). While iES has been widely applied in calibrating parameters for general circulation models and groundwater models, its potential in improving surface water predictions remains underexplored. This study leverages iES to efficiently estimate and refine parameters, generating ensembles of equally plausible parameter sets. These ensembles yield streamflow predictions that incorporate parameter uncertainty. Unlike traditional sequential simulation methods, iES reduces computational costs by running ensembles of simulations (e.g., 100 members) parallelly refining the parameter space iteratively. Typically, only 3–4 iterations are sufficient to achieve convergence, resulting in reliable parameter sets with low wall clock times. We applied the iES-based calibration framework to the Carson River watershed in the mountainous western United States, focusing on 16 parameters spanning the land surface model, terrain routing, and channel routing components of WRF-Hydro. These parameters capture soil properties, runoff characteristics, groundwater dynamics, vegetation attributes, and snow processes. By refining these parameters, our approach improved the simulation of high-flow events, particularly by better representing snowmelt dynamics critical for flood modeling. Enhanced simulation of snow accumulation and melt processes led to more accurate streamflow predictions, providing valuable insights for flood risk management and water resource planning in snow-dominated regions. Specifically, the iES algorithm demonstrated convergence by the third iteration, with the KGE value improving from 0 in the initial run to 0.41 in the first iteration, 0.65 in the second, and 0.71 in the third Our results highlight significant advancements in computational efficiency, parameter precision, and uncertainty quantification.