Performance Evaluation of Multi-Typed Precipitation Products for Agricultural Research in the Amur River Basin over the Sino–Russian Border Region

Abstract

Precipitation data are crucial for research on agricultural production, vegetation growth, and other topics related to environmental resources and ecology. With an increasing number of multi-typed gridded precipitation products (PPs), it is important to validate the applicability of PPs and improve their subsequent monitoring capabilities to ensure accurate precipitation-based research. This study evaluates the performance of four mainstream PPs—European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ERA5), ERA5-Land, Multi-Source Weighted-Ensemble Precipitation (MSWEP), and integrated multi-satellite retrievals for the Global Precipitation Mission (GPM)—in capturing the characteristics of precipitation intensity and derived agricultural drought in the crop-enrichment area over the Sino–Russian border region. The results show that, overall, GPM has the most balanced capability among the different experimental scenarios, with well-identified seasonal precipitation intensities. ERA5-Land had strong abilities in depicting annual distribution from spatial/stationary outcomes and obtained advantages in daily multi-parameter consistency verification. When evaluating monthly data in different agroclimatic areas, MSWEP and GPM had outstanding performances in the regions of Russia and China, respectively. For evaluating precipitation intensities and agricultural drought based on daily and monthly precipitation, MSWEP and GPM demonstrated finer performances based on combined agricultural thematic areas (ATAs). However, seasonal effects and affiliated material features were found to be the main factors in exhibiting identification capabilities under different scenarios. Despite good handling of intensity recognition in the eastern Chinese area, ERA5′s capabilities need to be improved by extending sources for calibrating gauged data and information on dry–wet conditions. Overall, this study provides insight into the characterization of PP performances and supports optimal product selection for different applications.

Keywords

• precipitation estimation
• scenario analysis
• intensity recognition
• agricultural drought
• Amur River Basin