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 110 000 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