Summary
This paper presents a large-scale parameter sensitivity analysis of the PRMS hydrologic model applied across the conterminous United States, using the Fourier amplitude sensitivity test procedure to identify dominant hydrologic processes and sensitive parameters. The analysis demonstrates that model complexity can be reduced by focusing simulation efforts on processes associated with sensitive parameters, and reveals that parameter sensitivity is strongly influenced by both the choice of performance statistic and the particular output variables examined. The findings suggest that different hydrologic processes require different numbers of parameters for adequate simulation, with some sensitive parameters influencing multiple processes whilst others affect only single processes.
UK applicability
Whilst this study focuses on CONUS hydrology, the methodological approach of using sensitivity analysis to identify dominant processes and simplify hydrologic models has potential applicability to UK catchment modelling and water resources assessment. The framework could support development of more parsimonious models for UK river basins, particularly in contexts where parameter data are limited or computational resources constrained.
Key measures
Sensitivity values of 35 PRMS calibration parameters computed across 110,000 hydrologically based spatial units; sensitivity summarised by process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, runoff) and performance statistic (mean, coefficient of variation, autoregressive lag 1)
Outcomes reported
The study identified sensitive input parameters and dominant hydrologic processes across 110,000 spatial units in the conterminous US using Fourier amplitude sensitivity testing on 35 PRMS calibration parameters. Results demonstrated how parameter sensitivity varies by performance statistic and output variable, enabling model simplification by focusing on processes associated with sensitive parameters.
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