Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

Shimelis Gebere,Assefa Melesse,Broder Merkel,Tena Alamirew

doi:10.3390/rs70911639

Abstract

Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42), the Global Satellite Mapping of Precipitation (GSMaP_MVK+), and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN) at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain) and Kiremt (long rain) seasons, the TRMM did better than the others by far. However, during Bega (dry) season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias.

Highlights

Accurate estimations of rainfall on fine spatial and temporal resolutions are vital for several water resource-related applications, such as agricultural water use, water resource management for human consumption, and industrial use, and understanding the ecosystems [1,2,3]
Of the three satellite rainfall, TRMM overestimates rainfall from rain gauge, which is consistent with its positive bias of 1.32 mm, while GSMaP and PERSIANN underestimate rainfall from rain gauge, which is related with their negative bias values of −1.44 and −1.20 mm, respectively
This paper compared the performance of three high-resolution satellite rainfall products (TRMM, PERSIANN, and GSMaP) with rain gauge stations over the data-scarce complex terrain of Eastern

Summary

Introduction

Accurate estimations of rainfall on fine spatial and temporal resolutions are vital for several water resource-related applications, such as agricultural water use, water resource management for human consumption, and industrial use, and understanding the ecosystems [1,2,3]. For a better understanding of the impact of rainfall on the environment, it is crucial that one use good spatial and high temporal resolution rainfall measurements. This is the case in complex mountainous regions where there are insufficient rain gauge stations available and rainfall is characterized by complex patterns [5,6]. Rainfall observation with high spatial and temporal resolution is extremely important to understand the hydrologic processes in these areas [14,15,16,17]

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Conclusion