Merged Rainfall Fields for Continuous Simulation Modeling

Abstract

Long sequences of rainfall at fine spatial and temporal details are increasingly required, not only for hydrological studies, but also to provide inputs for models of crop growth, land fills, tailing dams, liquid waste disposal on land and other environmentally-sensitive projects. Rainfall information derived from raingauges, radar or satellites may not individually be adequate to meet the detail required by hydrological models or other water resource studies. Therefore, a suitable technique is required to estimate rainfall at finer spatial and temporal resolutions. Different techniques have been developed to merge rainfall information from raingauges, radar and satellites in order to obtain the best estimate of the true rainfall field. However, the length of the radar and satellite estimated rainfall records are currently limited. In this study, the mean areal merged rainfall, derived from raingauges and radar, was estimated for 26 subcatchments in the Leibenbergsvlei catchment in South Africa for the period when radar data were available. By using the relationships derived between the merged rainfall and raingauge data, improved subcatchment rainfall may be estimated using the historical data from raingauges locating in and around the subcatchments. In most of the subcatchments the relationship between the mean areal merged rainfall of the subcatchment and the daily rainfall data from raingauges is strong (R2 >=0.5). The relationship between the daily raingauges and mean areal merged rainfall of the subcatchments is used to adjust the historical rainfall data from the daily raingauges in order to estimate subcatchment rainfall for input to continuous simulation models.