Abstract
Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (>27%) and a high missing ratio (>33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD > 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.
Highlights
Even though precipitation is expected to increase in the future, evaporation is expected to increase significantly as well, and precipitation variation is expected to be extremely variable [1]
This paper aims to inter-compare the performance of three Long-term satellite-based precipitation estimates (LSPE)
The majority of northwestern China is characterized by typical ranging from
Summary
Even though precipitation is expected to increase in the future, evaporation is expected to increase significantly as well, and precipitation variation is expected to be extremely variable [1]. The semi-arid and arid regions are still facing high drought risk [2,3,4]. Droughts have significant impacts on both natural ecosystems and human society, and they are severe in semi-arid and arid regions with fragile ecosystems, such. Drought indices are commonly used to quantify the drought condition and its impacts on global and regional scales [5,6]. Many drought indices have been developed in recent years to characterize drought conditions [7]. SPI, Standardized Precipitation Evapotranspiration Index (SPEI), and Palmer Drought
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