Temporal disaggregation of satellite-derived monthly precipitation estimates and the resulting propagation of error in partitioning of water at the land surface

Global estimates of precipitation can now be made using data from a combina tion of geosynchronous and low earth-orbit satellites. However, revisit pat terns of polar-orbiting satellites and the need to sample mixed-clouds scen es from geosynchronous satellites leads to the coarsening of the temporal r esolution to the monthly scale. There are prohibitive limitations to the ap plicability of monthly-scale aggregated precipitation estimates in many hyd rological applications. The nonlinear and threshold dependencies of surface hydrological processes on precipitation may cause the hydrological respons e of the surface to vary considerably based on the intermittent temporal st ructure of the forcing. Therefore, to make the monthly satellite data usefu l for hydrological applications (i.e. water balance studies, rainfall-runof f modelling, etc.), it is necessary to disaggregate the monthly precipitati on estimates into shorter time intervals so that they may be used in surfac e hydrology models. In this study, two simple statistical disaggregation sc hemes are developed for use with monthly precipitation estimates provided b y satellites. The two techniques are shown to perform relatively well in in troducing a reasonable temporal structure into the disaggregated time serie s. An ensemble of disaggregated realisations was routed through two land su rface models of varying complexity so that the error propagation that takes place over the course of the month could be characterised. Results suggest that one of the proposed disaggregation schemes can be used in hydrologica l applications without introducing significant error.