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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 11 | Copyright
Hydrol. Earth Syst. Sci., 20, 4655-4671, 2016
https://doi.org/10.5194/hess-20-4655-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Nov 2016

Research article | 22 Nov 2016

Towards simplification of hydrologic modeling: identification of dominant processes

Steven L. Markstrom1, Lauren E. Hay1, and Martyn P. Clark2 Steven L. Markstrom et al.
  • 1US Geological Survey, P.O. Box 25046, MS 412, Denver Federal Center, Denver, Colorado, 80225, USA
  • 2National Center for Atmospheric Research, P.O. Box 3000, Boulder, Colorado, 80307, USA

Abstract. parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters.

The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many.

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Results of this study indicate that it is possible to identify the influence of different hydrologic processes when simulating with a distributed-parameter hydrology model on the basis of parameter sensitivity analysis. Identification of these processes allows the modeler to focus on the more important aspects of the model input and output, which can simplify all facets of the hydrologic modeling application.
Results of this study indicate that it is possible to identify the influence of different...
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