Evaluating the Performance of Multiple Precipitation Datasets over the Transboundary Ili River Basin Between China and Kazakhstan

Baktybek Duisebek (), Gabriel B. Senay, Dennis S. Ojima, Tibin Zhang, Janay Sagin and Xuejia Wang
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Baktybek Duisebek: School of Information Technology and Engineering, Kazakh-British Technical University, Almaty 050000, Kazakhstan
Gabriel B. Senay: Natural Resource Ecology Laboratory (NREL), Colorado State University, Fort Collins, CO 80523, USA
Dennis S. Ojima: Natural Resource Ecology Laboratory (NREL), Colorado State University, Fort Collins, CO 80523, USA
Tibin Zhang: State Key Laboratory of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China
Janay Sagin: School of Information Technology and Engineering, Kazakh-British Technical University, Almaty 050000, Kazakhstan
Xuejia Wang: Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

Sustainability, 2025, vol. 17, issue 16, 1-26

Abstract: The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin.

Keywords: remote sensing; hydroclimatic assessment; precipitation variability; spatiotemporal analysis; transmountain water; elevation gradient impact; mountainous basin (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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