Assessing GSMaP Satellite-Based Precipitation for Runoff Modeling in a Data-Scarce Mountainous Basin: A Case Study of the Abra River Basin, Philippines
Ma. Gloann Leizel P. Longboy(1), Nathaniel R. Alibuyog(2), Christopher Zamuco(3), and Rodel T. Utrera(4)

(1)Research Directorate, Mariano Marcos State University, mplongboy[at]mmsu.edu.ph
(2)Coastal Engineering Research and Management Center, Mariano Marcos State University, nralibuyog[at]mmsu.edu.ph
(3)Research Directorate, Mariano Marcos State University, ctzamuco[at]mmsu.edu.ph
(3)Research Directorate, Mariano Marcos State University, rtutrera[at]mmsu.edu.ph

Abstract

Accurate runoff estimation is vital for flood risk modeling and disaster preparedness, particularly in river basins with limited ground-based hydrometeorological observations. This study evaluates the applicability of GSMaP (Global Satellite Mapping of Precipitation) satellite-based rainfall data for hydrologic simulation in the Abra River Basin, a mountainous and data-scarce watershed in Northern Luzon, Philippines. A hydrologic model was developed using the HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) software, incorporating topographic inputs from a 5-meter IFSAR Digital Elevation Model (DEM). Land cover and soil data were processed in a GIS environment to generate a spatially distributed Curve Number (CN) map for estimating runoff potential.
The model was calibrated using observed discharge and GSMaP rainfall data from Typhoon Ompong (Mangkhut, 2018), achieving a Nash-Sutcliffe Efficiency (NSE) of 0.763 and a percent bias of only 0.06%. Model validation using Typhoon Marce (Sinlaku, 2008) resulted in an NSE of 0.614, indicating reasonable model performance. These results demonstrate that GSMaP data can be effectively used to support hydrologic modeling and runoff estimation in ungauged or poorly instrumented basins.
This study highlights the practical use of satellite-derived precipitation in flood modeling and early warning applications, contributing to disaster risk reduction and climate resilience. The integration of remote sensing and GIS in hydrologic modeling offers valuable insights for water resource planning and supports the broader application of remote sensing technologies in disaster-prone regions.

Keywords: Satellite-based precipitation, Remote sensing for flood modeling, GIS-integrated hydrologic modeling, Disaster risk reduction, Abra River Basin

Topic: Topic B: Applications of Remote Sensing