Predicting climate change impacts on groundwater aquifer levels in the Henan North China Plain - Rabia Dars; Jianhua Ping; Xuemei Mei; Chun Chen; Abdul Raheem Shahzad; Joshua Mahwa; Muhammad Afzal Jamali (Applied Water Science)
Abstract Monitoring GWL over extended periods is crucial for comprehending the fluctuations of groundwater resources in the present context for ongoing global changes. This study analyzed the effects of climate variations on the GWL in Henan Province North China Plain using two deep-learning models Bidirectional Long Short-Term Memory (BidLSTM) and Gated Recurrent Unit (GRU). These models predicted monthly variations in GWL at 85 monitoring wells across the area using a dataset from 1980 to 2015. For validation and evaluation, both models were quantitatively calibrated using training set (1980–2015) to predict GWL from 2016 to 2100. The dataset was partitioned, with 80% allocated for training and 20% for testing. The result interpreted that in AHP3 well, GWL declined to 120 m in 1980 due to reduced precipitation 57 mm and Et 62 mm, while temperature stayed at 10 °C as of 2070, In the Zhengzhou and Keifing regions GWL declined by 98 m in the 1980 s despite rising precipitation 72 mm and Et 60 mm, due to insufficient recharge by 2100, GWL is expected to reach 140 m, driven by climate changes, including a temperature increase to 17 °C. The results indicated significant changes with the effect of precipitation, significant increase in temperature and surface Et. Anthropogenic activity also impacted GWL in the area. The trained models demonstrated good performance, with a prediction error of 0.0350, 0.0346 m, and the root mean square error (RMSE) was recorded at 0.1870, 0.1860 m. By accurately predicting GWLs, the BidLSTM model can help ensure that groundwater resources are used sustainably and efficiently.