Applicability and Hydrologic Substitutability of TMPA Satellite Precipitation Product in the Feilaixia Catchment, China

Xiaoli Chen,Guoru Huang

doi:10.3390/w12061803

Abstract

The assessment of various precipitation products’ performances in extreme climatic conditions has become a topic of interest. However, little attention has been paid to the hydrological substitutability of these products. The objective of this study is to explore the performance of the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) product in the Feilaixia catchment, China. To assess its applicability in extreme consecutive climates, several statistical indices are adopted to evaluate the TMPA performance both qualitatively and quantitatively. The Cox–Stuart test is used to investigate extreme climate trends. The Soil and Water Assessment Tool (SWAT) model is used to test the TMPA hydrological substitutability via three scenarios of runoff simulation. The results demonstrate that the overall TMPA performance is acceptable, except at high-latitudes and locations where the terrain changes greatly. Moreover, the accuracy of the SWAT model is high both in the semi-substitution and full-substitution scenarios. Based on the results, the TMPA product is a useful substitute for the gauged precipitation in obtaining acceptable hydrologic process information in areas where gauged sites are sparse or non-existent. The TMPA product is satisfactory in predicting the runoff process. Overall, it must be used with caution, especially at high latitudes and altitudes.

Highlights

Located in the northwestern region of the catchment, Dadong Mountain has an altitude of 1900 m, performance is similar to that of the probability of detection (POD)
The Soil and Water Assessment Tool (SWAT) model was used to simulate the substitutability of the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) product
After comparing our results to those in the literature, some interesting findings were obtained: 1. The TMPA product exhibited higher daily adaptability at lower latitudes and in flat terrain areas both in terms of qualitative and quantitative performance, while more invalid reports and false forecasts appeared at high latitudes and in areas with greater terrain changes

Summary

Introduction

High-accuracy and fine-resolution precipitation estimates are crucial for many fields such as hydrology, agriculture, water resource management, and disaster forecasting and response [1].Currently, precipitation data are primarily obtained via discrete ground-based rainfall stations.because of the large variations in the precipitation process, it is difficult to truly reflect the spatial and temporal distributions of precipitation using discrete rainfall sites, in areas where rainfall stations are unevenly and less densely distributed, such as in northwestern China and the Tibetan Plateau.With the rapid development of remote-sensing technology, a series of satellite rainfall products with high spatial and temporal resolutions has been created, including the Tropical Rainfall MeasuringMission (TRMM), Climate Prediction Center Morphing (CMORPH) technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global SatelliteMapping of Precipitation (GSMaP), and others. High-accuracy and fine-resolution precipitation estimates are crucial for many fields such as hydrology, agriculture, water resource management, and disaster forecasting and response [1]. Precipitation data are primarily obtained via discrete ground-based rainfall stations. With the rapid development of remote-sensing technology, a series of satellite rainfall products with high spatial and temporal resolutions has been created, including the Tropical Rainfall Measuring. Mission (TRMM), Climate Prediction Center Morphing (CMORPH) technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite. Water 2020, 12, 1803 increasingly used to overcome the limitations of station-based low-accuracy rainfall data [2,3].

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Conclusion