Hydrological Projections in the Third Pole Using Artificial Intelligence and an Observation‐Constrained Cryosphere‐Hydrology Model

• Published in: Earth's future Vol. 12; no. 4
• Main Authors: Long, Junshui, Wang, Lei, Chen, Deliang, Li, Ning, Zhou, Jing, Li, Xiuping, Guo, Xiaoyu, Liu, Hu, Chai, Chenhao, Fan, Xinfeng
• Format: Journal Article
• Language: English
• Published: Bognor Regis John Wiley & Sons, Inc 01.04.2024; Wiley
• Subjects: Air temperature; Artificial intelligence; Basins; Climate change; Cryosphere; Datasets; Earth and Related Environmental Sciences; Evapotranspiration; Food security; Geovetenskap och miljövetenskap; Glacial runoff; Glacier melting; Glaciers; glacio-hydrological projection; Glaciohydrology; headwater basins; Headwaters; Hydrologic models; Hydrology; Long short-term memory; LSTM-grid; Melting; Meltwater; Monthly precipitation; Monthly runoff; Performance evaluation; Precipitation; River basins; Rivers; Runoff; Runoff increase; Spatial distribution; Temporal distribution; the Third Pole; Time series; Water resources; Westerlies; Brahmaputra River; United States > US; Ganges River; Mekong River; Salween River; Indus River; Yangtze River; Yellow River
• ISSN: 2328-4277; 2328-4277
• DOI: 10.1029/2023EF004222

The water resources of the Third Pole (TP), highly sensitive to climate change and glacier melting, significantly impact the water and food security of millions in Asia. However, projecting future spatial‐temporal runoff changes for TP's mountainous basins remains a formidable challenge. Here, we've leveraged the long short‐term memory model (LSTM) to craft a grid‐scale artificial intelligence (AI) model named LSTM‐grid. This model has enabled the production of hydrological projections for the seven major river basins of TP. The LSTM‐grid model integrates monthly precipitation, air temperature, and total glacier mass changes (total_GMC) data at a 0.25‐degree model grid. Training the LSTM‐grid model employed gridded historical monthly runoff and evapotranspiration data sets generated by an observation‐constrained cryosphere‐hydrology model at the headwaters of seven TP river basins during 2000–2017. Our results demonstrate the LSTM grid's effectiveness and usefulness, exhibiting a Nash‐Sutcliffe Efficiency coefficient exceeding 0.92 during the verification periods (2013–2017). Moreover, river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies region. Intra‐annual projections indicated notable increases in spring runoff, especially in basins where glacier meltwater significantly contributes to runoff. Additionally, the LSTM‐grid model aptly captures the runoff changes before and after the turning points of glacier melting, highlighting the growing influence of precipitation on runoff after reaching the maximum total_GMC. Therefore, the LSTM‐grid model offers a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions by tapping into AI's potential to drive scientific discovery and provide reliable data. Plain Language Summary Water resources of the Third Pole (TP) significantly impact the water and food security in Asia. However, projecting future spatial‐temporal runoff changes for the TP's mountain basins remains a challenge. Here, we've leveraged the long short‐term memory (LSTM) model to craft a gridded artificial intelligence model (named LSTM‐grid). Trained by the outputs of an observation‐constrained distributed cryosphere‐hydrology model, the LSTM‐grid has enabled reliable spatiotemporal runoff and evapotranspiration projections for the headwaters of seven TP rivers (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, Indus) till 2100. Our projections show that the river basins in the monsoon region exhibit a higher rate of runoff increase compared to those in the westerlies region. In particular, the proposed approach in this study can reasonably capture the runoff changes before and after the turning points of glacier melting without prior knowledge, highlighting the growing influence of precipitation on runoff after reaching the maximum total glacier mass changes (of a river basin). Hence, the LSTM‐grid model provides a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions. Key Points We use artificial intelligence and an observation‐constrained cryosphere‐hydrology model to project future runoff for seven high‐mountain Third Pole basins Results show that river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies region The proposed approach can aptly simulate runoff changes before and after the turning points of glacier melting without prior knowledge