Comparative Study of Satellite and Gauge-Based Rainfall Data by Using Advanced Rainfall Correction Methods

Comparative Study of Satellite Rainfall Data

Abstract

Rainfall data is the primary source for flood forecasting. Ungauged basins or the basin with limited ground-based observation need satellite rainfall products to compensate the scarcity. This problem becomes serious in the rugged terrain areas like the river Jhelum catchment, the area of interest for this research. The focus of this research is to evaluate the accuracy of satellite-based GSMaP_NRT rainfall products with gauge-based rainfall data. GSMaP_NRT-global satellite mapping of precipitation, near real time is a component of the GPM mission. The data for GSMaP_NRT is supplied by JAXA (Japan Aerospace Exploration Agency). GSMaP_NRT offers freely available rainfall datasets with 3 h and 24 h accumulated. These datasets are provided in two resolutions: 0.1 and 0.25 degrees. In this analysis, the rainfall dataset is used of 0.1 degree resolution. Applied correction methods in this research included, are regression method and GSMaP_NRT rainfall correction methods. It has been observed that the results provided by GSMaP_NRT (uncorrected) are not satisfactory. For this, bias correction methods GSMaP_IF2 (inter face-2) and IF3 (interface-3) have been applied. The result shows an under estimation of the precipitation at some specific locations and an over estimation where gauge-based rainfall is zero. Additionally, low- elevation areas give better results than high-elevated areas. The highest correlation coefficient is 0.90 using IF3. Spatially, IF2 follows the pattern of ground-based rainfall and IF3 follows the peak but deviates temporally and spatially at some points. Based on the conclusion of this research, the implementation of the correction methods, GSMaP_IF2 and IF3, resulted in improved estimations from GSMaP_NRT, bringing it closer to the ground-based data. This outcome aligns with the primary objective of the research, which aimed to improve the accuracy and alignment between GSMaP_NRT and ground-based rainfall data.