Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China

Abstract

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.

Monthly Water Balance of Ungauged Watersheds Using Empirical and Conceptual Models: A Case Study of the Semiarid Mountainous Watersheds, Southwest of Saudi Arabia

“…Generally, the GPM-IMERGE product with monthly time step performed well compared to the rain gauge record and could detect monthly rainfall temporally and spatially [35][36][37][38][39][40]. In addition, several other researchers have reported that the IMERG Final-Run product is a reliable source of rainfall data for various hydrological and hydroclimatic studies [41][42][43].…”

The Global Pattern and Development Trends and Directions on the Drought Monitoring Research from 1983 to 2020 by Using Bibliometric Analysis

“…Thus, improving the accuracy on precipitation estimation with the high temporal resolution was vital for flash drought monitoring, as well as sub-month, near-real-time, early warning of drought monitoring. However, neither satellite precipitation products nor reanalysis datasets had weak performances in daily precipitation estimation (Pedreira et al 2021;Wei et al 2020b), which caused they could not capture the dynamic of flash drought.…”

Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward

“…The IMERG combined TMPA, CMORPH, and PERSIANN algorithms, thus allowing it to take advantage of infrared and microwave information [44]. This feature has allowed IMERG to strongly depict the intra-annual precipitation changes and the onset and extension of drought events [45]. However, the length of the IMERG data (2014-present) is too short for drought monitoring, which is the main challenge of SPPs [46].…”