Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin

Kha Dang Dinh,Tran Ngoc Anh,Raghavan Srinivasan,Nhu Y Nguyen,Du Duong Bui

doi:10.3390/rs12111858

Abstract

Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.

Highlights

It is well recognized in the literature that precipitation is one of the key factors in hydrological application practices [1,2,3] Existing precipitation products generally include gauge observations, estimates inferred from satellite imagery, and outputs from various numerical models [4]
probability of detection (POD) is typically used to describe the proportions of rainy days that are correctly detected by Gridded precipitation products (GPPs) to the total observations [40]; critical success index (CSI) reflects the overall proportion of rainfall events that are correctly detected by GPPs; and false alarm ratio (FAR) describes the proportions of rainy days that are not recorded by the rain gauges to the total observations
The spatial distributions and precipitation amount of Climate Prediction Center (CPC) and Global Satellite Mapping of Precipitation (GSMaP) are similar, but they are different to the observation pattern

Summary

Introduction

It is well recognized in the literature that precipitation is one of the key factors in hydrological application practices [1,2,3] Existing precipitation products generally include gauge observations, estimates inferred from satellite imagery, and outputs from various numerical models [4]. The strengths and weaknesses of different kinds of precipitation data vary greatly. Gridded precipitation products (GPPs; i.e., re-analysis, satellite, and model-based products) have high spatial and temporal resolutions, but they have a high level of uncertainty, affected by retrieval algorithms, data sources [11], and gauge adjustment procedures [12,13,14]. Precipitation itself strongly varies in relation to different climate properties, altitudes, and surface conditions [15]. These strengths and weaknesses and the variability of precipitation affect hydrological simulation and water balance components

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Results

Conclusion