Estimating regional groundwater level by fusing satellite, model and large-sample observations inputs

The Yellow River basin (YRB) is the second-largest river basin in China , flowing through arid regions. The development and utilization of water resources, including irrigation, urban water supply, and industrial use, face significant challenges (Lin et al., 2019;Qu et al., 2020). Although groundwater resources are abundant, they are constrained by excessive extraction and declining water tables (Lin et al., 2020), posing substantial challenges for water resource management, especially as the global water scarcity issue becomes increasingly prominent. It is challenging to estimate groundwater level at a regional or catchment scale due to its natural heterogeneity.

Here, we use a large sample of groundwater observations, together with datasets from the Global Land Data Assimilation System (GLDAS) and the Gravity Recovery and Climate Experiments (GRACE), to build a machine learning approach — random forest— for predicting regional groundwater levels in the Yellow River Basin of China.

We demonstrated the robustness of this model, with an R² of 0.95 at calibration mode and R² of 0.91±0.009 at a 10-fold cross-validation mode with 100 repetitions. Compared to the spatial predictability, its temporal predictability is less accurate, with R² value of 0.72 for a test period of April-May in 2023. The spatial distribution maps of the groundwater levels in Yellow River Basin showed strong seasonal declines in fall and winter, with severe decreases concentrated in the middle and lower reaches. Overall, this paper shows that it is promising to estimate regional groundwater levels based on machine learning with a large sample of groundwater observations, providing a robust and comprehensive data foundation for groundwater analysis.

References

Lin, M.,  Biswas, A., & Bennett, E. M. (2019), Spatio-temporal dynamics of groundwater storage changes in the yellow river basin. Journal of Environmental Management, 235, 84-95.  https://doi.org/10.1016/j.jenvman.2019.01.016.

Lin, M.,  Biswas, A., & Bennett, E. M. (2020), Socio-ecological determinants on spatio-temporal changes of groundwater in the yellow river basin, china. Science of The Total Environment, 731, 138725. https://doi.org/10.1016/j.scitotenv.2020.138725.

Qu, S.,  Wang, L.,  Lin, A.,  Yu, D.,  Yuan, M., & Li, C. a. (2020), Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the yangtze river basin, china. Ecological Indicators, 108, 105724. https://doi.org/10.1016/j.ecolind.2019.105724.

Keywords:Random forest model; Groundwater level depth; GLDAS; GRACE