Articles | Volume 17, issue 12
Hydrol. Earth Syst. Sci., 17, 5109–5125, 2013
https://doi.org/10.5194/hess-17-5109-2013
Hydrol. Earth Syst. Sci., 17, 5109–5125, 2013
https://doi.org/10.5194/hess-17-5109-2013

Research article 17 Dec 2013

Research article | 17 Dec 2013

From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models

J. D. Herman1, J. B. Kollat2, P. M. Reed1, and T. Wagener3 J. D. Herman et al.
  • 1Department of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA
  • 2Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA
  • 3Department of Civil Engineering, University of Bristol, Queen's Building, Bristol, UK

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.

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