Reliability of Gridded Precipitation Products in the Yellow River Basin, China

Yanfen Yang,Jing Wu,Lei Bai,Bing Wang

doi:10.3390/rs12030374

Abstract

Gridded precipitation products are the potential alternatives in hydrological studies, and the evaluation of their accuracy and potential use is very important for reliable simulations. The objective of this study was to investigate the applicability of gridded precipitation products in the Yellow River Basin of China. Five gridded precipitation products, i.e., Multi-Source Weighted-Ensemble Precipitation (MSWEP), CPC Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), were evaluated against observations made during 2001−2014 at daily, monthly, and annual scales. The results showed that MSWEP had a higher correlation and lower percent bias and root mean square error, while CMORPH and GSMaP made overestimations compared to the observations. All the datasets underestimated the frequency of dry days, and overestimated the frequency and the intensity of wet days (0–5 mm/day). MSWEP and TRMM showed consistent interannual variations and spatial patterns while CMORPH and GSMaP had larger discrepancies with the observations. At the sub-basin scale, all the datasets performed poorly in the Beiluo River and Qingjian River, whereas they were applicable in other sub-basins. Based on its superior performance, MSWEP was identified as more suitable for hydrological applications.

Highlights

Precipitation is the main link in the hydrological cycle and one of the most important meteorological input elements of hydrological models
This study showed that Multi-Source Weighted-Ensemble Precipitation (MSWEP) overestimated the daily precipitation overall, which was consistent with its performance over mainland China and Qinghai-Tibet Plateau [23,58]
POD was 0.97 and false alarm ratio (FAR) was 0.7, which means that MSWEP showed better probability of detection but higher false alarms in the Yellow River Basin than that in Qinghai-Tibet Plateau

Summary

Introduction

Precipitation is the main link in the hydrological cycle and one of the most important meteorological input elements of hydrological models. Choosing precipitation data is more important than choosing hydrological models [2]. The use of ground rainfall observation stations is the most direct way to measure precipitation. Rain gauge density in complex terrain is low and unevenly distributed, thereby resulting in scarce or even a lack of observed precipitation, which cannot meet the needs of hydrological simulations. Algorithm uncertainty [3,4,5,6], the number of stations [7,8], and topographical factors [9,10], gridded precipitation data have errors when comparing to gauge observations, and rigorous quality assessment is required before use

Objectives

Methods

Results

Discussion

Conclusion