How GPM IMERG and GSMaP advance hydrological applications: A global perspective

Huang Z., Wu H., Gu G., YILMAZ K., Nanding N., Li C.

Journal of Hydrology, cilt.661, 2025 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 661
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.jhydrol.2025.133514
  • Dergi Adı: Journal of Hydrology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: GPM, GSMaP, Hydrological evaluations, IMERG, Satellite precipitation, Streamflow simulations
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

As representative satellite precipitation products in the Global Precipitation Measurement (GPM) era, the latest versions of the Integrated Multi-satellitE Retrievals for GPM (IMERG V07) and Global Satellite Mapping of Precipitation (GSMaP V08) have attracted global attention for their advancements. However, their potential and limitations in continued advancing global hydrological modelling and monitoring remain underexplored, which is crucial for both end-users and algorithm developers. This study aims to (1) assess the overall hydrological performance of IMERG and GSMaP against global gauge observations and nine widely used precipitation products, and (2) examine the impact of different bias-adjustment algorithms on their accuracy. The results show that: (1) both post-real-time (PRT) and near-real-time (NRT) IMERG and GSMaP products outperform their predecessor, the TRMM Multi-Satellite Precipitation Analysis (TMPA), with the most significant improvements in NRT products, particularly in arid and mid-to-high latitude regions; (2) performance gains from PRT gauge corrections and NRT bias adjustments varies considerably across products and regions. For instance, GSMaP relies heavily on the gauge-based correction dataset, and its performance declines in areas with sparse observation networks where the quality of correction data may degrade. Overall, the enhanced IMERG and GSMaP products are poised to improve both scientific research and operational systems, such as the Global Flood Monitoring System (GFMS), advancing global hydrological applications with greater timeliness and efficacy. This study provides valuable insights into the global performance of these products and suggests directions for improving future retrieval and bias-adjustment algorithms.