A data-driven approach to exploring the causal relationships between distributed pumping activities and aquifer drawdown

• Published in: The Science of the total environment Vol. 870; p. 161998
• Main Authors: Pang, Min, Du, Erhu, Zheng, Chunmiao
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.04.2023
• Subjects: Aquifer depletion; aquifers; case studies; Causal inference; Convolutional long short-term memory; Deep learning; drawdown; environment; fluid mechanics; groundwater flow; Groundwater management; hydrologic models; rivers; stakeholders; water management; water shortages; water table; Deep learning; Causal inference; Groundwater management; Aquifer depletion; Convolutional long short-term memory
• DOI: 10.1016/j.scitotenv.2023.161998
• ISSN: 0048-9697; 1879-1026

Groundwater depletion, typically caused by the distributed pumping activities of multiple stakeholders (i.e., water users) that share a hydrologically connected aquifer, has led to severe environmental and ecological problems in many river basins worldwide. Conventionally, the effects of pumping on aquifer depletion are quantified using well hydraulics or physically based hydrological models in groundwater management. However, the derivation of well hydraulics-based analytical solutions requires numerous simplifying assumptions, while the construction and calibration of a physically based groundwater flow model require detailed information about the subsurface properties, which are subject to large uncertainties. In this study, we develop a novel modeling framework that does not rely on well hydraulics or groundwater flow models. The proposed framework integrates (1) a deep learning model that captures the spatiotemporal variations in the aquifer in response to distributed pumping activities in multiple well fields and (2) a statistical causal inference model that identifies the causal networks among stakeholders to quantify the causal effects of individual pumping activities on aquifer depletion. The proposed framework is tested on a synthetic case study site with well fields that have various spatial distributions and pumping rates. The modeling results show that the deep learning method can effectively capture the water table dynamics influenced by distributed pumping activities with R2 >90 % for all observation data. More importantly, our model is capable of assessing the causal networks between the drawdown of water table and the pumping activities of multiple well fields and quantifying their causal strengths. These results suggest that our modeling framework can be used to explicitly assess the extent to which each individual stakeholder's pumping activities contribute to aquifer depletion at the system level. The concepts and techniques developed in this study can be used to resolve classic externality problems in the context of common-pool groundwater management. [Display omitted] •A data-driven approach is developed for common-pool groundwater management.•The new approach combines a deep learning model and a causal inference model.•The proposed method identifies causal relationships between pumping and groundwater drawdowns.•Convolutional long short-term memory works well for subsurface hydrological problems.