Monitoring groundwater (GW) level variations, or anomalies in multiple wells, over long periods of time is essential to understanding changes in regional groundwater resource availability. However, it is challenging to predict these GW anomalies over the long term in agricultural areas due to complicated boundary conditions, heterogeneous hydrogeological characteristics, and groundwater extraction, as well as nonlinear interactions among these factors. To overcome this challenge, we developed an advanced modeling framework based on a recurrent neural network of long short-term memory (LSTM) as an alternative to complex and computationally expensive physical models. GW anomalies were forecast two months in advance (t + 2) based on the evaluation of drivers that influence GW dynamics in densely irrigated agricultural regions. An application and evaluation of this new approach was conducted in the Wisconsin Central Sands (WCS) region in the U.S., one of the most productive agricultural regions. The modeling framework was developed for the period 1958–2020 by utilizing easily accessible dynamic and static variables to represent hydrometeorological and geological characteristics. GW anomaly observations were acquired from 26 piezometers (wells) installed in the sandy aquifer in WCS over 10–60 years. The subset of GW observations from ∼ 10–60 years, not used in model training, can forecast GW anomalies two months out with a coefficient of determination R ∼ 0.8. Additionally, MAE was less than 0.34 m/month across the study region for both temporal and spatial modeling frameworks. Groundwater anomalies showed high spatiotemporal variability, and their responses are influenced differently by boundary conditions, catchment geology, climate, and topography across locations. Sites with higher autocorrelation with previous two-months GW anomalies reduced bias by increasing R. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. The novelty of this study is identifying the regional drivers of GW fluxes. This case-specific information and location-related simplification, modification, and assumption of LSTM is a unique contribution to the existing literature. Our framework can be used as an alternative method of simulating water availability and groundwater level changes in areas where subsurface properties are unknown or difficult to determine.

中文翻译：

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经历气候和土地利用变化的复杂含水层地下水异常的时空预测


长期监测地下水 (GW) 水位变化或多口井的异常对于了解区域地下水资源可用性的变化至关重要。然而，由于复杂的边界条件、异质的水文地质特征、地下水抽取以及这些因素之间的非线性相互作用，长期预测农业区的这些GW异常具有挑战性。为了克服这一挑战，我们开发了一种基于长短期记忆循环神经网络 (LSTM) 的高级建模框架，作为复杂且计算成本高昂的物理模型的替代方案。根据对影响密集灌溉农业地区水温动态的驱动因素的评估，提前两个月 (t + 2) 预测水温异常。这种新方法的应用和评估是在美国生产力最高的农业地区之一威斯康星州中部沙地 (WCS) 地区进行的。该建模框架是针对 1958 年至 2020 年期间开发的，利用易于获取的动态和静态变量来表示水文气象和地质特征。 GW 异常观测数据是在 10-60 年间从安装在 WCS 沙质含水层中的 26 个测压计（井）获得的。约 10-60 年的引力波观测子集（未用于模型训练）可以预测两个月后的引力波异常，确定系数 R ∼ 0.8。此外，整个研究区域的时间和空间建模框架的 MAE 均小于 0.34 m/月。地下水异常表现出较高的时空变异性，其响应受到不同地点边界条件、流域地质、气候和地形的不同影响。 与前两个月 GW 异常自相关性较高的站点通过增加 R 来减少偏差。土地利用变化和灌溉抽水对 GW 异常预测具有交互作用。这项研究的新颖之处在于确定了GW通量的区域驱动因素。这种针对特定情况的信息和与位置相关的 LSTM 简化、修改和假设是对现有文献的独特贡献。我们的框架可以用作模拟地下属性未知或难以确定的地区的可用水量和地下水位变化的替代方法。