Sensitivity of Streamflow Simulations to Temporal Variability and Estimation of Z–R Relationships

This study focuses on the sensitivity of streamflow simulations to temporal variations in radar reflectivity–rainfall (i.e., Z–R) relationships. The physically based continuous-mode distributed hydrologic model—gridded surface subsurface hydrologic analysis—is used to predict runoff during three major rainfall-runoff periods observed in a 35km2 experimental watershed in southern Louisiana. Z–R relationships are derived at a series of temporal scales ranging from a climatological scale, where interstorm Z–R variations are ignored, down to a subevent scale, where variations in rainfall type (convective versus stratiform) are taken into account. The analysis is first performed using Z and R data pairs derived directly from disdrometer drop size distribution measurements, and then repeated using WSR-88D radar reflectivity data. The degree of sensitivity in runoff simulations to temporal variations in Z–R relationships depends largely on the method used to derive the parameters of these relationships. Using event-specific Z–R relationships results in accurate hydrographs when the parameters are derived based on bias removal and minimization of random errors of rainfall estimates. Methods based on least-squares fitting require refining the derivation of Z–R parameters down to a subevent scale, which is not practically feasible. A simple and practical method based on selection of a climatologically representative exponent of the Z–R relationships and adjusting the multiplier coefficient through bias removal still results in reasonably accurate runoff simulations, but only when event-specific Z–R relationships are used.