Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

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.

Error Characteristic Analysis of Satellite-Based Precipitation Products over Mainland China

“…Finally, the bias adjustment for precipitation estimates has been applied by matching the Global Precipitation Climate Project (GPCP) Monthly Product Version 2.2 (GPCPv2.2) at 2.5 • resolution. The spatial coverage of this precipitation product is 60 • N-60 • S and its highest temporal resolution could reach up to 3 h [33]. Where G stands for the rain gauge, S is for satellite, Re is for reanalysis, Ra means radar, and NRT stands for near-real-time.…”

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

“…The Thornthwaite (TW) ( Thornthwaite, 1948 ) and P-M ( Monteith, 1965 ) formulas were commonly used to calculate the PET in the SPEI estimation. The SPEI calculated based on TW formula was easily to implement, while it may indicate excessive dry conditions due to the influence of temperature, under the significantly increased temperatures in recent years ( Zhang et al., 2017 ; Nouri and Homaee, 2020 ; Guo et al., 2022 ). Compared to the TW formula, which only considers temperature, the P-M formula considered variety of meteorological factors, which are more complex, and is generally more consistent with actual evapotranspiration ( Jensen et al., 1990 ), specially it enable to describe the regions influenced by aerodynamic factors ( Liu and Jiang, 2015 ).…”

Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China

“…Based on the run course theory (Yevjevich, 1967), this study identified drought events and quantitatively analyzed the drought characteristics of drought events, as shown in Figure 3. The paper only focuses on drought events with a long duration and a high drought peak: drought (SPI<0) for 3 consecutive months or more and the peak value less than −1.0 (Guo et al, 2022). The time when SPI starts to be negative and recovers to be positive are defined as the beginning and end time of drought event respectively, and the period is the duration of drought (Formula 10).…”

“…Among them, the North and South regions passed the significance level test of 0.05, and the change trend of the West and East only passed the significance level test of 0.1. Since 2000, the overall wetting trend has been intensified, which may be due to the enhanced water vapor transport from high latitudes in the northern Hemisphere with the global warming since the 21st century, and the "three-stage water vapor relay transport" from the tropical Indian Ocean and Arabian Sea to the Central Asian mountains through the western Indian Ocean, the southern Arabian Peninsula and Central Asia (Guo et al, 2022).…”