Abstract
Abstract. Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between sets of dominant parameters or processes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between dominant parameters and processes in the absence of observations, such as under nonstationarity.
Highlights
Distributed rainfall–runoff models allow model parameters and forcing data to vary on a spatial grid, aiming to better represent the spatial variability of watershed processes at the cost of increasing model complexity
We aim to extend the event-scale approach to explore the dynamic controls of a distributed watershed model at a finely resolved sub-daily time step, as well as to advance methods capable of computing and visualizing the results of this analysis
This study proposes high-resolution time-varying sensitivity analysis for a spatially distributed rainfall–runoff model, avoiding the biases introduced by representative event selection by identifying key transitions between dominant parameters and processes a posteriori
Summary
Distributed rainfall–runoff models allow model parameters and forcing data to vary on a spatial grid, aiming to better represent the spatial variability of watershed processes at the cost of increasing model complexity. The studies that do exist have been limited to event-scale analyses, which reported highly complex spatial sensitivities arising from the interplay between forcing heterogeneity, proximity to observations, and the timescale of model performance metrics explored (e.g., Muleta and Nicklow, 2005; van Griensven et al, 2006; Tang et al, 2007; Van Werkhoven et al, 2008b; Yatheendradas et al, 2008). These studies suggest the potential for time-varying spatial-sensitivity analyses, computational demands limited their exploration of this issue
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