Evaluating the performance of ground-based and remotely sensed near real-time rainfall fields from a hydrological perspective [Evaluation des performances dans une perspective hydrologique de champs de pluie en temps quasi-réel basés sur des données obser
Hydrological Sciences Journal
Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01002, United States; School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, South Africa
The South African Weather Service (SAWS) issues routine experimental, near real-time rainfall maps from daily raingauge networks, radar networks and satellite images, as well as merged rainfall fields. These products are potentially useful for near real-time forecasting, especially in areas of fast hydrological response, and also to simulate the "now state" of various hydrological state variables such as soil moisture content, streamflow, and reservoir inflows. The purpose of this paper is to evaluate their skill as inputs to hydrological simulations and, in particular, the skill of the merged field in terms of better hydrological results relative to the individual products. Rainfall fields derived from raingauge, radar, satellite, conditioned satellite and the merged (gauge/radar/satellite) were evaluated for two selected days with relatively high amounts of rainfall, as well as for a continuous period of 90 days in the Mgeni catchment, South Africa. Streamflows simulated with the ACRU model indicate that the use of raingauge as well as merged fields of satellite/raingauge and satellite/radars/raingauge provides relatively realistic rainfall results, without much difference in their hydrological outputs, whereas the radar and raw satellite information by themselves cannot be used in operational hydrological application in their current status. © 2010 IAHS Press.
ACRU; AS-soils; Current status; Ground based; Hydrological response; Hydrological simulations; Merged field; Radar network; Rain gauges; Rainfall fields; Real-time forecasting; Reservoir inflow; Satellite images; Satellite information; South Africa; State variables; Weather services; Acoustic surface wave devices; Catchments; Computer simulation; Moisture determination; Radar; Rain; Real variables; Remote sensing; Satellites; Soil moisture; Stream flow; Weather forecasting; Reservoirs (water); computer simulation; flow modeling; ground-based measurement; hydrological modeling; numerical model; performance assessment; precipitation assessment; precipitation intensity; rainfall; raingauge; real time; remote sensing; reservoir; satellite data; soil moisture; streamflow; trend analysis; KwaZulu-Natal; Mgeni River; South Africa