Estimating Regional Groundwater Level by Combining Satellite, Model, and Large-Sample Observations Inputs.
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| Title: | Estimating Regional Groundwater Level by Combining Satellite, Model, and Large-Sample Observations Inputs. |
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| Authors: | Cao, Yijing1 (AUTHOR), Zhang, Yongqiang1,2 (AUTHOR) zhangyq@igsnrr.ac.cn, Chen, Yuyin1,3 (AUTHOR), Zhang, Xuanze1 (AUTHOR), Tian, Jing1,2 (AUTHOR), Yang, Xuening1,3 (AUTHOR), Huang, Qi1,3 (AUTHOR), Su, Jianzhong2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1622. 20p. |
| Subjects: | Random forest algorithms, Soil permeability, Water management, Machine learning, Remote sensing, Water table |
| Geographic Terms: | Yellow River (China), China |
| Abstract: | Highlights: It accurately predicts regional groundwater levels with an R2 of 0.91 ± 0.009 in cross-validation mode. Farming activities and high soil permeability can be important factors contributing to a large error in groundwater prediction. Groundwater level in YRB is strongly declined in fall and winter, particularly in the middle and lower reaches. What are the main findings? The model accurately predicts regional groundwater levels, achieving an R2 of 0.91 ± 0.009 in cross-validation. Groundwater levels in the YRB show a strong decline in fall and winter, particularly in the middle and lower reaches. What are the implications of the main findings? Farming activities and high soil permeability are key factors contributing to large errors in groundwater prediction. The identified seasonal and spatial patterns provide critical insights for sustainable groundwater management in the YRB. Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China's Yellow River Basin. The model showed robustness, achieving an R2 of 0.95 in calibration and an R2 of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R2 of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: It accurately predicts regional groundwater levels with an R2 of 0.91 ± 0.009 in cross-validation mode. Farming activities and high soil permeability can be important factors contributing to a large error in groundwater prediction. Groundwater level in YRB is strongly declined in fall and winter, particularly in the middle and lower reaches. What are the main findings? The model accurately predicts regional groundwater levels, achieving an R2 of 0.91 ± 0.009 in cross-validation. Groundwater levels in the YRB show a strong decline in fall and winter, particularly in the middle and lower reaches. What are the implications of the main findings? Farming activities and high soil permeability are key factors contributing to large errors in groundwater prediction. The identified seasonal and spatial patterns provide critical insights for sustainable groundwater management in the YRB. Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China's Yellow River Basin. The model showed robustness, achieving an R2 of 0.95 in calibration and an R2 of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R2 of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101622 |
