Spatial-Temporal Evolution of Proglacial Lake Volumes and Estimation Models in the Himalaya and Nyainqentanglha Ranges.
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| Title: | Spatial-Temporal Evolution of Proglacial Lake Volumes and Estimation Models in the Himalaya and Nyainqentanglha Ranges. |
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| Authors: | Zhang, Miaohui1,2 (AUTHOR), Wang, Hao2,3 (AUTHOR) hwang@imde.ac.cn, Cui, Peng1,3 (AUTHOR), Tang, Jinbo3,4 (AUTHOR), Yu, Yilong1,2,3 (AUTHOR), Cao, Jingxuan2,3 (AUTHOR), Liu, Xuan3 (AUTHOR), Yang, Jingxi4 (AUTHOR), Liu, Yunpeng4 (AUTHOR), Li, Qingchun4 (AUTHOR) |
| Source: | Remote Sensing. Jul2026, Vol. 18 Issue 13, p2249. 17p. |
| Subjects: | Glacial lakes, Spatiotemporal processes, Empirical research, Depth sounding, Floods |
| Geographic Terms: | Himalaya Mountains |
| Abstract: | Highlights: What are the main findings? We developed optimized regional empirical models by integrating field-based bathymetric surveys of 10 proglacial lakes, showing superior performance in volume and depth estimation compared to 14 established global formulas. Spatiotemporal reconstruction reveals a significant and heterogeneous expansion from 1990 to 2020, with lake volumes in the Nyainqentanglha range increasing by 92.9% over the past three decades. What is the implication of the main finding? These refined scaling relationships offer critical parametric constraints for satellite-based monitoring, substantially improving the accuracy of GLOF hazard assessments and peak discharge estimations across the Himalaya and Nyainqentanglha Range. Volume quantification of proglacial lakes is a fundamental prerequisite for reliable hydrodynamic modeling and peak discharge estimation during glacial lake outburst floods (GLOFs). In this study, we integrated in situ bathymetric surveys of 10 proglacial lakes across the Himalaya and Nyainqentanglha ranges with a comprehensive regional dataset to derive optimized empirical models for lake volume and maximum depth. The predictive robustness of these models was rigorously validated using statistical error metrics and independent datasets. Comparative analysis with 14 established formulas demonstrates that our region-specific models yield superior performance in capturing local geomorphological characteristics. Leveraging these refined scaling relationships, we reconstructed the spatiotemporal volume changes in proglacial lakes across the study region from 1990 to 2020. Our analysis reveals significant lake expansion over the past three decades: lake volumes in the Western and Central Himalayas increased by 46.7% and 46.4%, respectively. Notably, the Eastern Himalayas exhibited a volume increase of 51.5%, while the Nyainqentanglha Mountains experienced a substantial expansion of approximately 92.9%. These findings provide critical parametric constraints for satellite-based hydrological monitoring and significantly enhance the reliability of GLOF hazard assessments in the Himalaya and Nyainqentanglha ranges. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We developed optimized regional empirical models by integrating field-based bathymetric surveys of 10 proglacial lakes, showing superior performance in volume and depth estimation compared to 14 established global formulas. Spatiotemporal reconstruction reveals a significant and heterogeneous expansion from 1990 to 2020, with lake volumes in the Nyainqentanglha range increasing by 92.9% over the past three decades. What is the implication of the main finding? These refined scaling relationships offer critical parametric constraints for satellite-based monitoring, substantially improving the accuracy of GLOF hazard assessments and peak discharge estimations across the Himalaya and Nyainqentanglha Range. Volume quantification of proglacial lakes is a fundamental prerequisite for reliable hydrodynamic modeling and peak discharge estimation during glacial lake outburst floods (GLOFs). In this study, we integrated in situ bathymetric surveys of 10 proglacial lakes across the Himalaya and Nyainqentanglha ranges with a comprehensive regional dataset to derive optimized empirical models for lake volume and maximum depth. The predictive robustness of these models was rigorously validated using statistical error metrics and independent datasets. Comparative analysis with 14 established formulas demonstrates that our region-specific models yield superior performance in capturing local geomorphological characteristics. Leveraging these refined scaling relationships, we reconstructed the spatiotemporal volume changes in proglacial lakes across the study region from 1990 to 2020. Our analysis reveals significant lake expansion over the past three decades: lake volumes in the Western and Central Himalayas increased by 46.7% and 46.4%, respectively. Notably, the Eastern Himalayas exhibited a volume increase of 51.5%, while the Nyainqentanglha Mountains experienced a substantial expansion of approximately 92.9%. These findings provide critical parametric constraints for satellite-based hydrological monitoring and significantly enhance the reliability of GLOF hazard assessments in the Himalaya and Nyainqentanglha ranges. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18132249 |
