Pang Min, Du Erhu, Zheng Chunmiao
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China; College of Hydrology and Water Resources, Hohai University, Nanjing, China.
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China.
Sci Total Environ. 2023 Apr 20;870:161998. doi: 10.1016/j.scitotenv.2023.161998. Epub 2023 Feb 3.
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.
地下水枯竭通常是由多个利益相关者（即用水户）在共享水文连通含水层的情况下进行的分布式抽水活动导致的，这在全球许多流域引发了严重的环境和生态问题。传统上，在地下水管理中，抽水对含水层枯竭的影响是通过井流水力学或基于物理的水文模型来量化的。然而，基于井流水力学的解析解的推导需要大量简化假设，而基于物理的地下水流模型的构建和校准需要有关地下特性的详细信息，而这些信息存在很大的不确定性。在本研究中，我们开发了一种不依赖于井流水力学或地下水流模型的新型建模框架。所提出的框架整合了：（1）一个深度学习模型，该模型捕捉含水层响应多个井场分布式抽水活动的时空变化；（2）一个统计因果推理模型，该模型识别利益相关者之间的因果网络，以量化个体抽水活动对含水层枯竭的因果效应。所提出的框架在一个具有不同空间分布和抽水速率的井场的综合案例研究地点进行了测试。建模结果表明，深度学习方法能够有效捕捉受分布式抽水活动影响的地下水位动态，所有观测数据的R>90%。更重要的是，我们的模型能够评估地下水位下降与多个井场抽水活动之间的因果网络，并量化它们的因果强度。这些结果表明，我们的建模框架可用于在系统层面明确评估每个个体利益相关者的抽水活动对含水层枯竭的贡献程度。本研究中开发的概念和技术可用于解决共同池地下水管理背景下的经典外部性问题。
