Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan
Slim Mtibaa, Shiho Asano
Abstract
The Terauchi catchment, southwestern Japan This paper evaluates two gauge-merged precipitation datasets derived from weather radars (R/A) and satellites (GsMAP_G) based on their capability to improve streamflow simulation using the Soil and Water Assessment Tool (SWAT) model. A third dataset includes measurements from local rain gauges used for producing the R/A database but not the GsMAP_G product was prepared for comparison reasons. The R/A and GsMAP_G data were first compared to gauge observations. The performance and prediction uncertainty of the SWAT model forced by the evaluated datasets were subsequently quantified and compared. The R/A dataset overestimated the precipitation, while the GsMAP_G underestimated it. After calibration, the R/A performed best (NSE = 88–91%), followed by the Gauge (NSE = 84%) and GsMAP_G (NSE = 54%) scenarios. The R/A product improved the overall simulation performance by 6.50% and 62.40% in terms of NSE and absolute percent bias compared to the Gauge model. The performance of the evaluated datasets varied depending on streamflow occurrence exceedance probability (OEP). The R/A dataset improved the simulation of extremely high (OEP < 1%) and low (OEP > 60%) streamflow events as it resulted in the lowest simulation biases and errors. The current investigation suggests the use of the R/A product for improving the simulation of daily streamflow, including hydro-climatic extremes.