Integrating ground-based and satellite rainfall data for hydrological modeling: A SWAT+ application with sensitivity analysis in the Saguling Watershed, Citarum River Basin

In many developing regions and archipelagic nations, the spatial coverage of ground-based rainfall stations (GSR), considered the most reliable source of precipitation data, is often inadequate. To address this challenge, this study integrates GSR data with satellite rainfall data from GSMaP to assess how the choice of precipitation dataset affects the performance of the SWAT+ hydrological model in the Saguling Watershed. Two scenarios were made by utilizing two rainfall datasets: GSR data and calibrated GSMaP satellite data. The research assessed the satellite rainfall accuracy, the performance of the uncalibrated model, and the parameter sensitivity of each scenario. The rainfall accuracy assessment indicated that the GSMaP data demonstrates moderate reliability, tending to overestimate low-intensity rainfall while underestimating high-intensity events, while the data remains to represent the diurnal patterns of rainfall. Additionally, the performance of the GSMaP model revealed suboptimal results, as it significantly overestimated streamflow during extreme high-flow events and inaccurately predicted low-flow streamflow. The sensitivity analysis indicated that both scenarios produced a combination of surface runoff and interflow driven by varying sensitivity parameters. With this outcome, this study underscores the significance impact of rainfall data in hydrological modeling, particularly in relation to GSMaP satellite rainfall.