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Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan
Study region: The Terauchi catchment, southwestern Japan Study focus: 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. New hydrological insights for the region: 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.
Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan
Study region: The Terauchi catchment, southwestern Japan Study focus: 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. New hydrological insights for the region: 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.
Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan
Slim Mtibaa (author) / Shiho Asano (author)
2022
Article (Journal)
Electronic Resource
Unknown
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