Time-varying sensitivity analysis reveals impacts of watershed model choice on the inference of dominant processes

Open Access
Author:
Herman, Jonathan D
Graduate Program:
Civil Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 27, 2012
Committee Members:
  • Patrick M Reed, Thesis Advisor
  • Thorsten Wagener, Thesis Advisor
  • Michael Gooseff, Thesis Advisor
Keywords:
  • rainfall-runoff modeling
  • sensitivity analysis
  • hydrology
Abstract:
Lumped rainfall-runoff models are widely used for flow prediction, but a long-recognized need exists for diagnostic tools to identify appropriate model structures for a given application based on the dominant processes they are meant to represent. To this end, we develop a comprehensive exploration of dominant processes in the Hymod, HBV, and Sacramento Soil Moisture Accounting (SAC-SMA) model structures. Model controls are isolated using time-varying Sobol′ sensitivity analysis for twelve MOPEX watersheds in the eastern United States over a ten-year period. Sensitivity indices are visualized along gradients of observed precipitation and flow characteristics to determine behavioral consistency between the three models. Results indicate that the models’ dominant processes strongly depend on time varying hydroclimatic conditions. Parameters associated with surface processes such as evapotranspiration and runoff generally dominate under dry conditions, since high evaporative fluxes and small contributions from fast runoff are required for the models to properly simulate low flow conditions. Parameters associated with routing processes typically dominate under high flow conditions, when performance depends on the timing of flow events, even though these parameters might be associated with subsurface processes. Additionally, the models exhibit very different dominant processes relative to one another due to their contrasting mathematical formulations. These results emphasize the importance of scrutinizing how the formulation of a model shapes the scientific inferences drawn its behavior, particularly in applications such as predictions under change where the ability to infer dominant processes from a model is crucial.