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

Pang，Min1,2,3; Du，Erhu1,2; Zheng，Chunmiao2,4,5

2023-04-20

10.1016/j.scitotenv.2023.161998

SCIENCE OF THE TOTAL ENVIRONMENT   Impact Factor & Quartile

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 R >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.

Aquifer depletion Causal inference Convolutional long short-term memory Deep learning Groundwater management

SCI

English

Others

National Natural Science Founda- tion of China["52000100","51909118"] ; Shenzhen Municipal Science and Technology Innovation Committee[KQTD2016022619584022]

Environmental Sciences & Ecology

Environmental Sciences

WOS:000932593600001

ELSEVIER

ENVIRONMENT/ECOLOGY

2-s2.0-85147549976

Scopus

1.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing,China
2.Yangtze Institute for Conservation and Development,Hohai University,Nanjing,China
3.College of Hydrology and Water Resources,Hohai University,Nanjing,China
4.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
5.EIT Institute for Advanced Study,Ningbo,Zhejiang,China

Pang，Min,Du，Erhu,Zheng，Chunmiao. A data-driven approach to exploring the causal relationships between distributed pumping activities and aquifer drawdown[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2023,870.

Pang，Min,Du，Erhu,&Zheng，Chunmiao.(2023).A data-driven approach to exploring the causal relationships between distributed pumping activities and aquifer drawdown.SCIENCE OF THE TOTAL ENVIRONMENT,870.

Pang，Min,et al."A data-driven approach to exploring the causal relationships between distributed pumping activities and aquifer drawdown".SCIENCE OF THE TOTAL ENVIRONMENT 870(2023).