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Prediction and Influencing Factors of Precipitation in the Songliao River Basin, China: Insights from CMIP6

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  • Hongnan Yang

    (School of Hydraulic and Electric-Power, Heilongjiang University, Harbin 150080, China
    Institute of Groundwater in Cold Regions, Heilongjiang University, Harbin 150080, China)

  • Zhijun Li

    (School of Hydraulic and Electric-Power, Heilongjiang University, Harbin 150080, China
    Institute of Groundwater in Cold Regions, Heilongjiang University, Harbin 150080, China)

Abstract

The Songliao River Basin (SLRB) is a key agricultural region in China, and understanding precipitation variations can provide crucial support for water resource management and sustainable development. This study used CN05.1 observational data and the Coupled Model Intercomparison Project Phase 6 (CMIP6) data to simulate and evaluate the precipitation characteristics within the SLRB. The optimal model ensemble was selected for future precipitation predictions. We analyzed the historical precipitation characteristics within the SLRB and projected future precipitation variations under SSP126, SSP245, and SSP585, while exploring the driving factors influencing precipitation. The results indicated that EC-Earth3-Veg (0.507) and BCC-CSM2-MR (0.493) from MME2 effectively capture precipitation variations, with MME2 corrected data more closely matching actual precipitation characteristics. From 1971 to 2014, precipitation showed an insignificant increasing trend, with most precipitation concentrated between May and September. Precipitation in the basin decreased from southeast to northwest. From 2026 to 2100, the increasing trend in precipitation became significant. The trend of precipitation growth over time was as follows: SSP126 < SSP245 < SSP585. Future precipitation distribution resembled the historical period, but the area of semiarid regions gradually decreased while the area of humid regions gradually increased, particularly under SSP585. The long-term increase in precipitation will become more pronounced, with a significant expansion of high-precipitation areas. In low-latitude, high-longitude areas, more precipitation events were expected to occur, while the impact of altitude was relatively weaker. From SSP126 to SSP585, the response of precipitation changes to temperature changes within the SLRB shifts from negative to positive. Under SSP585, this response becomes more pronounced, with average precipitation increasing by 4.87% for every 1 °C rise in temperature.

Suggested Citation

  • Hongnan Yang & Zhijun Li, 2025. "Prediction and Influencing Factors of Precipitation in the Songliao River Basin, China: Insights from CMIP6," Sustainability, MDPI, vol. 17(5), pages 1-29, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:2297-:d:1606658
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    References listed on IDEAS

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    1. Lei Chang & Ying Li & Keyi Zhang & Jialin Zhang & Yuefen Li, 2023. "Temporal and Spatial Variation in Vegetation and Its Influencing Factors in the Songliao River Basin, China," Land, MDPI, vol. 12(9), pages 1-15, August.
    2. Siabi, E. K. & Awafo, E. A. & Kabo-bah, A. T. & Derkyi, N. S. A. & Akpoti, Komlavi & Mortey, E. M. & Yazdanie, M., 2023. "Assessment of Shared Socioeconomic Pathway (SSP) climate scenarios and its impacts on the Greater Accra Region," Papers published in Journals (Open Access), International Water Management Institute, pages 1-49:101432.
    3. Yoo-Geun Ham & Jeong-Hwan Kim & Seung-Ki Min & Daehyun Kim & Tim Li & Axel Timmermann & Malte F. Stuecker, 2023. "Anthropogenic fingerprints in daily precipitation revealed by deep learning," Nature, Nature, vol. 622(7982), pages 301-307, October.
    4. Ludovic Touzé‐Peiffer & Anouk Barberousse & Hervé Le Treut, 2020. "The Coupled Model Intercomparison Project: History, uses, and structural effects on climate research," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    5. Hong Du & Jun Xia & Yi Yan & Yumeng Lu & Jinhua Li, 2022. "Spatiotemporal Variations of Extreme Precipitation in Wuling Mountain Area (China) and Their Connection to Potential Driving Factors," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
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