Abstract

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 thre... Show moreThis 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 35 km² 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. Show less