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

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.

Predicting Tropical Monsoon Hydrology Using CFSR and CMADS Data over the Cau River Basin in Vietnam

“…(i) Four basic statistical indicators: the correlation coefficient (CC), mean absolute error (MAE), root-mean-square error (RMSE), and percentage bias (PBIAS) [8,17,39]. The calculation formulas of these indicators are shown as follows:…”

“…Accurate and complete weather information provides important inputs into hydrological models, supporting flood forecasting and climate change impact assessments and serving as scientific guidance for water resource management [1][2][3]. Normally, data collected from meteorological stations are the most reliable and accurate data [4,5]; however, these data are insufficient to represent the actual weather conditions occurring in river basins due to their low spatial coverage [6,7], and as they are affected by signal distortions [8][9][10]. Furthermore, the acquisition of reliable temperature and precipitation data is a difficult task because of dynamic climatic conditions, altitudes, and surface properties [11][12][13][14].…”

“…The gridded meteorological products used in hydrological models need to be carefully assessed because of their variable performances among different areas (e.g., microclimates, storm, surface conditions, elevations, and topographic directions) and because of the uncertainty inherent in forecasting models [12,14,31,32]. In addition, in-depth studies on hydrology and the need to improve water resource management require a full awareness of the advantages and disadvantages of the GMPs that are popularly studied across different regions [8]. In general, CFSR and CMADS can be used as inputs in hydrological studies because of their uniform datasets, and these products can be used immediately in flow simulations.…”

Prediction of tropical monsoon hydrology using gridded meteorological products over the Cau river basin in Vietnam

“…Aim for assessing the quantity of CRPs in collecting temperature and precipitation, the following indicators have been used: (i) Four basic statistical indicators such as Correlation coefficient (CC), Mean absolute error (MAE), Root mean square error (RMSE), and Percentage bias (PBIAS); (ii) Three statistical-categorical indicators to evaluate precipitation events, including Probability of detection (POD), False alarm ratio (FAR) and Critical success index (CSI). Calculation formula, unit, range of values, and their significance synthesized from other studies [2], [7], [8].…”

“…This is possibly related to the reduced efficiency of CMADS due to the CMORPH satellite data having rain detection errors below 4mm [6]. Values of 0.1mm / day were selected as the rainfall detection threshold [7], and POD, FAR, CSI indices were used to evaluate the ability of CRPs to detect precipitation. The POD average value with CFSR data is 0.98, indicates that it tends to capture all daily rain events.…”