Modeling Dynamics of Water Balance for Lakes in the Northwest Tibetan Plateau with Satellite-Based Observations

The hydrological cycle in the Tibetan Plateau is experiencing notable changes in recent decades under a changing climate. The hydrological changes, however, are not well investigated due to the limitations in the availability of ground-based observations. In this study, by incorporating satellite-based observations into a hydrological modeling framework, seasonal and inter-annual dynamics of water balance for lakes in the northwest Tibetan Plateau are examined systematically for the period of 1990 to 2022. Satellite-based observations, including lake water area and water level, have been used to calibrate the hydrological model and to estimate lake water storage. The hydrological model performs satisfactorily, with the Nash–Sutcliffe efficiency coefficient (NSE) exceeding 0.5 for all 15 studied lakes. It is found that inflow contributes over 70% of annual water gain for most lakes, while percolation accounts for a larger portion (>60%) of total water loss than evaporation. The studied lakes have expanded substantially, with regional average increasing rates in lake level and water storage of 0.38 m/a and 3.12 × 108 m3/a, respectively. Some lakes transitioned from shrinking to expanding around 1999, and expansion in most lakes has further accelerated since around 2012, primarily because of increased precipitation over the lake catchments, leading to greater inflow to the lakes. These findings provide important insights into understanding and predicting responses of lake water balance to climate change as well as for developing adaptative strategies.

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Publication Details

Journal
Remote Sensing
Published
2025-05-02
DOI
https://doi.org/10.3390/rs17091618
Citations
4
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
4.42

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article

Modeling Dynamics of Water Balance for Lakes in the Northwest Tibetan Plateau with Satellite-Based Observations

Siqi Zheng, Hao Chen, Jiaheng Yan, X. Teng et al.
4 citations
Remote Sensing
Flood Risk Assessment and Management
4.42
article

Modeling Dynamics of Water Balance for Lakes in the Northwest Tibetan Plateau with Satellite-Based Observations

Siqi Zheng, Hao Chen, Jiaheng Yan, X. Teng, Yongkang Ren, Yanhong Wu
article en
4 citations

Abstract

The hydrological cycle in the Tibetan Plateau is experiencing notable changes in recent decades under a changing climate. The hydrological changes, however, are not well investigated due to the limitations in the availability of ground-based observations. In this study, by incorporating satellite-based observations into a hydrological modeling framework, seasonal and inter-annual dynamics of water balance for lakes in the northwest Tibetan Plateau are examined systematically for the period of 1990 to 2022. Satellite-based observations, including lake water area and water level, have been used to calibrate the hydrological model and to estimate lake water storage. The hydrological model performs satisfactorily, with the Nash–Sutcliffe efficiency coefficient (NSE) exceeding 0.5 for all 15 studied lakes. It is found that inflow contributes over 70% of annual water gain for most lakes, while percolation accounts for a larger portion (>60%) of total water loss than evaporation. The studied lakes have expanded substantially, with regional average increasing rates in lake level and water storage of 0.38 m/a and 3.12 × 108 m3/a, respectively. Some lakes transitioned from shrinking to expanding around 1999, and expansion in most lakes has further accelerated since around 2012, primarily because of increased precipitation over the lake catchments, leading to greater inflow to the lakes. These findings provide important insights into understanding and predicting responses of lake water balance to climate change as well as for developing adaptative strategies.

Remote SensingVol. 17(9)
Nanjing Forestry University (CN), Chinese Academy of Sciences (CN), Beijing Institute of Big Data Research (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 6%
Flood Risk Assessment and Management
4.42
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