Groundwater最新文献

Computational demands for uncertainty quantification and optimization often exceed available resources for high-fidelity environmental models. While surrogate modeling (or model emulation) offers a pragmatic solution, widespread adoption is hindered by a significant “implementation gap”: practitioners often lack standardized, robust tools to integrate emulation techniques directly into existing modeling workflows, relying instead on bespoke implementations. To bridge this gap, we present the Emulator module within the open-source pyEMU package. This framework provides a “plug-and-play” architecture for deploying Gaussian Process Regression (GPR), Data-Space Inversion (DSI) and other model emulation approaches. The framework automates the complex “plumbing” of emulation-based workflows, including non-Gaussian data transformation and the generation of PEST interface files, allowing trained surrogates to serve as drop-in replacements for physics-based models. We believe this one-to-one correspondence between physics-based model and emulator-based workflows will facilitate direct comparisons between the two so that the community in general can build up the knowledge of when and how to effectively and appropriately deploy emulation. We demonstrate the utility of these tools through a benchmarking optimization problem and a history-matching application on a synthetic groundwater model.

Climate change and human activities increasingly affect interconnected stream–aquifer systems, motivating continued evaluation of how stream–aquifer interactions are represented in integrated groundwater–surface water models. Most groundwater models quantify stream–aquifer exchange using an empirical riverbed leakance parameter that is predefined or calibrated from observed head differences between the stream and aquifer. Here, we describe the numerical implementation of the analytical stream aquifer flow exchange (SAFE) formulation (Morel-Seytoux et al., 2018) within the finite-element-based Integrated Water Flow Model (IWFM). SAFE represents stream–aquifer exchange using physically based parameters and provides an alternative to commonly used first order exchange (FOE) formulations, such as the MODFLOW River Package (RIV). A key extension is the ability to quantify asymmetric stream–aquifer exchange arising from different groundwater heads on opposing sides of a stream, a capability not available in standard stream–aquifer interaction packages. Numerical experiments using hypothetical test cases and a large-scale regional application demonstrate that differences in conductance and connectivity parameterizations produce distinct spatial and temporal patterns of simulated exchange. Compared with FOE, the SAFE formulation produces smoother temporal transitions and reduced short-term variability in simulated exchange. These results demonstrate the importance of conceptual model formulation when interpreting simulated stream–aquifer exchange and provide a physically based alternative for large-scale integrated groundwater–surface water modeling.

Data centers are energy end users with the fastest growing need for electricity in the United States, mainly because of the rapid expansion of cloud computing and artificial intelligence (AI). A substantial portion of this electricity, between 10% and 40%, is used for cooling. As the number of data centers increases and the sector's energy demand continues to rise exponentially, there is an urgent need to explore the use of alternative energy systems that are more efficient and sustainable. This article explores aquifer thermal energy storage (ATES) as a technically feasible and currently underutilized solution for data center cooling in the United States. Previous case studies from Europe and assessments based in the United States are considered, and the potential of ATES for reducing electricity usage for data centers, which would reduce overall greenhouse gas emissions and support sustainable energy operations.

Quantifying and localizing groundwater discharge is inherently difficult. It requires knowledge about hydraulic conductivity and the hydraulic gradient on the scale of interest. Conventional hydraulic testing, such as pumping tests, may fail in the presence of heterogeneity and complex structural boundaries. While advanced 2D and 3D hydraulic tomography may resolve small-scale heterogeneity, it is typically limited to small spatial scales and requires costly field installations. We propose a simplified tomographic approach using a limited number of pumping and observation wells spatially distributed over a well profile in the order of 100 m transverse to the direction of ambient flow. To infer the spatially variable hydraulic-conductivity field from drawdown data with its uncertainty, we apply an iterative ensemble smoother. Subsequently, the posterior ensemble of hydraulic-conductivity fields is used to calculate total and specific discharge based on the observed ambient hydraulic heads in the same wells. We test our approach in a synthetic scenario mimicking a channel-like aquifer such as the quaternary fill in a small river valley. The results demonstrate that multiple spatially distributed pumping tests are suitable to quantify total discharge and its associated uncertainty. The approach is more reliable than a conventional one that estimates effective transmissivity from fitting analytical solutions to pumping-test data. The tomographic analysis additionally allows locating spatial patterns of specific discharge at a resolution similar to the spacing of the wells, which may be important when assessing and remediating contaminant plumes.

Leakage from aging sewer and stormwater pipes into the subsurface poses significant environmental risks and threatens the integrity of urban infrastructure, primarily through the degradation of groundwater quality and the alteration of urban water balances. While the presence of defects within pipe networks is well-documented, accurately quantifying volumetric exchange fluxes remains a challenge due to the complex, nonlinear interactions between the pipe, the surrounding variably saturated soil, and the fluctuating groundwater level. Current modeling approaches often overlook the threshold behaviors of these systems, leading to potential inaccuracies in leakage estimation. In this study, we show for the first time that leaky pipes can become hydraulically disconnected from the underlying groundwater, a phenomenon analogous to well-known river–groundwater interactions that include disconnection. In a sewer–groundwater context, hydraulic disconnection is restricted to point sources and is strongly influenced by colmation/clogging. Through numerical modeling of a hypothetical case study, we show that the leakage flux from the pipe (in absolute terms) initially increases with declining groundwater levels, until a critical depth below the leaky pipe is reached. After this point, the hydraulic communication from the groundwater to the leaky pipe stops and the leakage flux can be considered constant. In a sensitivity analysis, we demonstrate the impact of the individual hydraulic parameters of the leaky-pipe-groundwater system on the hydraulic disconnection. We further modify the properties of the aquifer material, resulting in a hydraulic disconnection depth of 0.89 m, 1.77 m and 4.00 m below pipe for sand, loamy sand and sandy loam aquifers, respectively. This insight has important implications for leakage modeling: once hydraulic disconnection occurs, the leakage flux becomes independent of groundwater dynamics. The present study provides a proof-of-concept for the mechanism by which leaky sewers hydraulically disconnect from groundwater.

A random forest classification (RFC) model was developed to predict hydrochemical facies (HCFs) of groundwater in three dimensions across the conterminous United States (CONUS). Major-ion data from 152,673 sites were used to categorize groundwater into one of six HCFs (CaMg-HCO₃, NaK-HCO₃, CaMg-SO₄, NaK-SO₄, Cl, or Mixed). These six HCFs were used as targets for RFC modeling. Model features that represent relevant geochemical processes and/or physical conditions were derived from previously published data. Additional model features were specifically engineered to support this analysis: elevation of the bottom of a well relative to the base of drinking water (ERDW) and six flags that relate geologic units to HCFs. The most important model feature was ERDW. The model was used to map HCFs at a 1-km² resolution across CONUS and to depths of 400 m below the base of drinking water (which varies from 22 m to 2 km). Model predictions are consistent with expectations. CaMg-HCO₃ is predicted to occur near the water table in more humid settings, and areas underlain by carbonate or crystalline rocks. At depths below the base of drinking-water supplies, the model predicts a rapid transition from HCO₃ HCFs to Cl. Model predictions are accurate based on point data, and data averaged across hydrogeologic regions and with depth. Model predictions of HCFs could be used for multiple purposes, including the mapping of salinity and other groundwater characteristics.