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Groundwater is by far the largest unfrozen freshwater resource on the planet. It plays a critical role as the bottom of the hydrologic cycle, redistributing water in the subsurface and supporting plants and surface water bodies. However, groundwater has historically been excluded or greatly simplified in global models. In recent years, there has been an international push to develop global scale groundwater modeling and analysis. This progress has provided some critical first steps. Still, much additional work will be needed to achieve a consistent global groundwater framework that interacts seamlessly with observational datasets and other earth system and global circulation models. Here we outline a vision for a global groundwater platform for groundwater monitoring and prediction and identify the key technological and data challenges that are currently limiting progress. Any global platform of this type must be interdisciplinary and cannot be achieved by the groundwater modeling community in isolation. Therefore, we also provide a high-level overview of the groundwater system, approaches to groundwater modeling and the current state of global groundwater representations, such that readers of all backgrounds can engage in this challenge.

There is a growing understanding of the role that bedrock weathering can play as a source of nitrogen (N) to soils, groundwater and river systems. The significance is particularly apparent in mountainous environments where weathering fluxes can be large. However, our understanding of the relative contributions of rock-derived, or geogenic, N to the total N supply of mountainous watersheds remains poorly understood. In this study, we develop the High-Altitude Nitrogen Suite of Models (HAN-SoMo), a watershed-scale ensemble of process-based models to quantify the relative sources, transformations, and sinks of geogenic and atmospheric N through a mountain watershed. Our study is based in the East River Watershed (ERW) in the Upper Colorado River Basin. The East River is a near-pristine headwater watershed underlain primarily by an N-rich Mancos Shale bedrock, enabling the timing and magnitude of geogenic and atmospheric contributions to watershed scale dissolved N-exports to be quantified. Several calibration scenarios were developed to explore equifinality using >1600 N concentration measurements from streams, groundwater, and vadose zone samples collected over the course of four years across the watershed. When accounting for recycling of N through plant litter turnover, rock weathering accounts for approximately 12% of the annual dissolved N sources to the watershed in the most probable calibration scenario (0-31% in other scenarios), and 21% (0-44% in other scenarios) when considering only "new" N sources (i.e. geogenic and atmospheric). On an annual scale, instream dissolved N elimination, plant turnover (including cattle grazing) and atmospheric deposition are the most important controls on N cycling.

The continued growth of Southwestern cities depends on reliable water export from Rocky Mountain headwaters, which provide ~85% of Colorado River Basin (CRB) streamflow. Despite being more sensitive to warming temperatures, alpine systems are simplified in the regional-scale models currently in use to plan for future water supply. We used an integrated hydrologic model that couples groundwater and surface water with snow and vegetation processes to examine the effect of topographic simplifications as a result of grid coarsening in a representative CRB headwater basin. High-resolution (100 m) simulations predicted headwater streamflow losses of 16% by 2050 while coarse-resolution (1 km) simulations predict only 12%, suggesting that regional-scale models (coarser than 1 km) likely overestimate future Colorado River Basin water supplies.

An increasing reliance on groundwater resources has been observed worldwide during the past 50-70 years and has led to unsustainable groundwater abstraction in many regions, especially in semi-Arid and arid alluvial groundwater basins. Managed aquifer recharge (MAR) has been promoted to replenish overdrafted groundwater basins and augment surface water supply. However, MAR feasibility in alluvial groundwater basins is complicated by complex geologic architecture that typically includes laterally continuous, fine-Texture confining units that can impede both recharge rates and regional propagation of increases in the hydraulic head. A greater feasibility of MAR hinges on identifying locations where rapid, high-volume recharge that provides regional increases in pressure head are possible, but relatively little research has evaluated the factors that control MAR feasibility in alluvial groundwater basins. Here, we combine a transition probability Markov chain geostatistical model of the subsurface geologic heterogeneity of the eastern side of the northern Central Valley, California, with the three-dimensional, variably saturated water flow code ParFlow to explore the variability of MAR feasibility in this region. We use a combination of computationally efficient local-and global-sensitivity analyses to evaluate the relative importance of factors that contribute to MAR feasibility. A novel proxy parameter approach was used to describe the configuration and proportions of subsurface hydrofacies and the water table depth for sensitivity analyses, and results suggest that recharge potential is relatively more sensitive to the variability of this proxy parameter than to the variability of individual hydrofacies hydraulic properties. Results demonstrate that large variability of MAR feasibility is typical for alluvial aquifer systems and that outsized recharge rates are possible in select locations where interconnected, coarse-Texture hydrofacies occur..

Widespread groundwater overdraft in alluvial aquifer systems like the Central Valley (CV) in California, USA, has increased interest in managed aquifer recharge (MAR). Like most clastic sedimentary basins, recharge to the productive semi-confined CV aquifer system remains a challenge due to the presence of nearly ubiquitous, multiple confining units (silt and clay) that limit recharge pathways. Previous studies suggest the presence of interconnected networks of coarse-texture sand and gravel deposits that bypass regional confining units over a small fraction of the CV near the American and Cosumnes rivers. Here, variably saturated infiltration and recharge processes were simulated across a domain that includes high-resolution representation of the heterogeneous alluvial geologic architecture in this area. Results show that recharge potential is highly dependent on subsurface geologic architecture, with a nearly 2 order-of-magnitude range of recharge across the domain. Where interconnected coarse-texture recharge pathways occur, results show that these features can (1) accommodate rapid, high-volume MAR and (2) propagate widespread and rapid pressure responses over multi-kilometer distances in the semi-confined aquifer system. For all MAR simulations, results show that the majority of MAR is accommodated by filling unsaturated-zone (UZ) pore volume. Results also show that coarse-texture UZ facies (where present) accommodate the majority of MAR volume during early time, but fine-texture facies ultimately accommodate the majority of the total MAR volume, even for coarse-dominated sites. These findings highlight the large variability of MAR potential across the landscape and demonstrate the importance of fine-texture facies for accommodating MAR in alluvial aquifer systems.

Abstract. Increasing reliance on groundwater resources has been observed worldwide during the past 50–70 years and has led to unsustainable groundwater abstraction in many regions, especially in semi-arid and arid alluvial groundwater basins. Managed aquifer recharge (MAR) has been promoted to replenish overdrafted groundwater basins and augment surface water supply. However, MAR feasibility in alluvial groundwater basins is complicated by complex geologic architecture that typically includes laterally-continuous, fine-texture confining units that can impede both recharge rates and regional propagation of increases in hydraulic head. Greater feasibility of MAR hinges on identifying locations where rapid, high-volume recharge that provides regional increases in pressure head are possible, but relatively little research has evaluated the factors that control MAR feasibility in alluvial groundwater basins. Here, we combine a transition probability Markov-chain geostatistical model of the subsurface geologic heterogeneity of the east side of the northern Central Valley, California, with the 3D, variably-saturated water flow code, ParFlow, to explore the variability of MAR feasibility in this region. We use a combination of computationally-efficient local and global sensitivity analyses to evaluate the relative importance of factors that contribute to MAR feasibility. A novel proxy parameter approach was used to describe the configuration and proportions of subsurface hydrofacies and water table depth for sensitivity analyses, and results suggest that recharge potential is relatively more sensitive to the variability of this proxy parameter than to the variablity of individual hydrofacies hydraulic properties. Results demonstrate that large variability of MAR feasibility is typical for alluvial aquifer systems and that outsize recharge rates are possible in select locations where interconnected, coarse-texture hydrofacies occur.

This study examines the question of how much information one can extract from a tracer-based hydrograph separation in a remote and minimally gaged alpine catchment in Chile. We combine PCA-based endmember mixing analysis to identify the sources of flow contribution to the Diguillín River with a hierarchical Bayesian mixing model to integrate spatial and temporal variability in endmember concentration and quantify the source contributions to streamflow over time. The PCA-analysis shows that precipitation isotopes do not vary by elevation (e.g. snow and rainfall had identical signatures) but vary significantly by season, and that a third endmember is necessary to bound streamflow variability at the basin outlet, which was not captured by our field sampling. One of the main advantages of Bayesian methods is the quasi-machine learning capabilities, where we treated the third endmember as a parameter from which the mixing model could both estimate proportional contributions as well as posterior estimates for the tracer concentrations. The two tracer, three endmember hydrograph separation revealed groundwater to be the largest and precipitation (rain and snow) to be the smallest contributor, on average, to streamflow with the third unknown endmember contributing around 40% of streamflow during the Winter wet season. We hypothesize that interflow is occurring as the third endmember in the Alto Diguillín subwatershed, based on inferred tracer values and the presence of alluvium atop impermeable bedrock along certain reaches. More work is necessary to observe and sample these flowpaths, which was not possible during this study. The results of this work have implications for water resource management, since groundwater sustains the majority of streamflow in the Diguillín, and climate change will impact the timing and quantity of baseflow and interflow. Overall, we demonstrate the utility of combining PCA with Bayesian statistical modeling and inference to extract maximum information from a limited field dataset in a remote alpine catchment. The findings of this work can guide future water management in the Diguillín, but also provide clear questions for future research.

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an "emulator" as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case. [ABSTRACT FROM AUTHOR]

The Amazon basin has experienced periodic droughts in the past, and intense and frequent droughts are predicted in the future. Landscape heterogeneity could play an important role in how tropical forests respond to drought by influencing water available to plants. Using the one-dimensional ACME Land Model and the three-dimensional ParFlow variably saturated flow model, numerical experiments were performed for a catchment in central Amazon to elucidate processes that influence water available for plant use and provide insights for improving Earth system models. Results from ParFlow show that topography has a dominant influence on groundwater table and runoff through lateral flow. Without any representations of lateral processes, ALM simulates very different seasonal variations in groundwater table and runoff compared to ParFlow even if it is able to reproduce the long-term spatial average groundwater table of ParFlow through simple parameter calibration. In the ParFlow simulations, even in the plateau with much deeper water table depth during the dry season in the drought year of 2005, plant transpiration is not water stressed as the soil saturation is still sufficient for the stomata to be fully open based on the empirical wilting formulation in the models. This finding is insensitive to uncertainty in atmospheric forcing and soil parameters, but the empirical wilting formulation is an important factor that should be addressed using observations and modeling of coupled plant hydraulics-soil hydrology processes in future studies. The results could be applicable to other catchments in the Amazon basin with similar seasonal variability and hydrologic regimes.

Although important for riverine solute and nutrient fluxes, the connections between biogeochemical processes and subsurface hydrology remain poorly characterized. We investigate these couplings in the East River, CO, a high-elevation shale-dominated catchment in the Rocky Mountains, using concentration-discharge (C-Q) relationships for major cations, anions, and organic carbon. Dissolved organic carbon (DOC) displays a positive C-Q relationship with clockwise hysteresis, indicating mobilization and depletion of DOC in the upper soil horizons and emphasizing the importance of shallow flow paths during snowmelt. Cation and anion concentrations demonstrate that carbonate weathering, which dominates solute fluxes, is promoted by both sulfuric acid derived from pyrite oxidation in the shale bedrock and carbonic acid derived from subsurface respiration. Sulfuric acid weathering dominates during base flow conditions when waters infiltrate below the inferred pyrite oxidation front, whereas carbonic acid weathering plays a dominant role during snowmelt as a result of shallow flow paths. Differential C-Q relationships between solutes suggest that infiltrating waters approach calcite saturation before reaching the pyrite oxidation front, after which sulfuric acid reduces carbonate alkalinity. This reduction in alkalinity results in CO2 outgassing when waters equilibrate to surface conditions, and reduces the riverine export of carbon and alkalinity by roughly 33% annually. Future changes in snowmelt dynamics that control the balance of carbonic and sulfuric acid weathering may substantially alter carbon cycling in the East River. Ultimately, we demonstrate that differential C-Q relationships between major solutes can provide unique insights into the complex subsurface flow and biogeochemical dynamics that operate at catchment scales.

Land cover change due to drought and insect-induced tree mortality or altered vegetation succession is one of the many consequences of anthropogenic climate change. While the hydrologic response to land cover change and increases in temperature have been explored independently, few studies have compared these two impacts in a systematic manner. These changes are particularly important in snow-dominated, headwaters systems that provide streamflow for continental river systems. Here we study the hydrologic impacts of both vegetation change and climate warming along three transects in a mountain headwaters watershed using an integrated hydrologic model. Results show that while impacts due to warming generally outweigh those resulting from vegetation change, the inherent variability within the transects provides varying degrees of response. The combination of both vegetation change and warming results in greater changes to streamflow amount and timing than either impact individually, indicating a nonlinear response from these systems to multiple perturbations. The complexity of response underscores the need to integrate observational data and the challenge of deciphering hydrologic impacts from proxy studies.

A better understanding of how microbial communities interact with their surroundings in physically and chemically heterogeneous subsurface environments will lead to improved quantification of biogeochemical reactions and associated nutrient cycling. This study develops a methodology to predict potential elevated rates of biogeochemical activity (microbial "hotspots") in subsurface environments by correlating microbial DNA and aspects of the community structure with the spatial distribution of geochemical indicators in subsurface sediments. Multiple linear regression models of simulated precipitation leachate, HCl and hydroxylamine extractable iron and manganese, total organic carbon (TOC), and microbial community structure were used to identify sample characteristics indicative of biogeochemical hotspots within fluvially-derived aquifer sediments and overlying soils. The method has been applied to (a) alluvial materials collected at a former uranium mill site near Rifle, Colorado and (b) relatively undisturbed floodplain deposits (soils and sediments) collected along the East River near Crested Butte, Colorado. At Rifle, 16 alluvial samples were taken from 8 sediment cores, and at the East River, 46 soil/sediment samples were collected across and perpendicular to 3 active meanders and an oxbow meander. Regression models using TOC and TOC combined with extractable iron and manganese results were determined to be the best fitting statistical models of microbial DNA (via 16S rRNA gene analysis). Fitting these models to observations in both contaminated and natural floodplain deposits, and their associated alluvial aquifers, demonstrates the broad applicability of the geochemical indicator based approach.

Climate change threatens water resources in snowmelt-dependent regions by altering the fraction of snow and rain and spurring an earlier snowmelt season. The bulk of hydrological research has focused on forecasting response in streamflow volumes and timing to a shrinking snowpack; however, the degree to which subsurface storage offsets the loss of snow storage in various alpine geologic settings, i.e. the hydrogeologic buffering capacity, is still largely unknown. We address this research need by assessing the affects of climate change on storage and runoff generation for two distinct hydrogeologic settings present in alpine systems: a low storage granitic and a greater storage volcanic hillslope. We use a physically based integrated hydrologic model fully coupled to a land surface model to run a base scenario and then three progressive warming scenarios, and account for the shifts in each component of the water budget. For hillslopes with greater water retention, the larger storage volcanic hillslope buffered streamflow volumes and timing, but at the cost of greater reductions in groundwater storage relative to the low storage granite hillslope. We found that the results were highly sensitive to the unsaturated zone retention parameters, which in the case of alpine systems can be a mix of matrix or fracture flow. The presence of fractures and thus less retention in the unsaturated zone significantly decreased the reduction in recharge and runoff for the volcanic hillslope in climate warming scenarios. This approach highlights the importance of incorporating physically based subsurface flow in to alpine hydrology models, and our findings provide ways forward to arrive at a conceptual model that is both consistent with geology and hydrologic principles. Copyright © 2016 John Wiley & Sons, Ltd.

Water table depth (WTD) has a substantial impact on the connection between groundwater dynamics and land surface processes. Due to the scarcity of WTD observations, physically‐based groundwater models are growing in their ability to map WTD at large scales; however, they are still challenged to represent simulated WTD compared to well observations. In this study, we develop a purely data‐driven approach to estimating WTD at continental scale. We apply a random forest (RF) model to estimate WTD over most of the contiguous United States (CONUS) based on available WTD observations. The estimated WTD are in good agreement with well observations, with a Pearson correlation coefficient (r) of 0.96 (0.81 during testing), a Nash‐Sutcliffe efficiency (NSE) of 0.93 (0.65 during testing), and a root mean square error (RMSE) of 6.87 m (15.31 m during testing). The location of each grid cell is rated as the most important feature in estimating WTD over most of the CONUS, which might be a surrogate for spatial information. In addition, the uncertainty of the RF model is quantified using quantile regression forests. High uncertainties are generally associated with locations having a shallow WTD. Our study demonstrates that the RF model can produce reasonable WTD estimates over most of the CONUS, providing an alternative to physics‐based modeling for modeling large‐scale freshwater resources. Since the CONUS covers many different hydrologic regimes, the RF model trained for the CONUS may be transferrable to other regions with a similar hydrologic regime and limited observations. [ABSTRACT FROM AUTHOR]

Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling. [ABSTRACT FROM AUTHOR]

This study synthesizes two different methods for estimating hydraulic conductivity (K) at large scales. We derive analytical approaches that estimate K and apply them to the contiguous United States. We then compare these analytical approaches to three‐dimensional, national gridded K data products and three transmissivity (T) data products developed from publicly available sources. We evaluate these data products using multiple approaches: comparing their statistics qualitatively and quantitatively and with hydrologic model simulations. Some of these datasets were used as inputs for an integrated hydrologic model of the Upper Colorado River Basin and the comparison of the results with observations was used to further evaluate the K data products. Simulated average daily streamflow was compared to daily flow data from 10 USGS stream gages in the domain, and annually averaged simulated groundwater depths are compared to observations from nearly 2000 monitoring wells. We find streamflow predictions from analytically informed simulations to be similar in relative bias and Spearman's rho to the geologically informed simulations. R‐squared values for groundwater depth predictions are close between the best performing analytically and geologically informed simulations at 0.68 and 0.70 respectively, with RMSE values under 10 m. We also show that the analytical approach derived by this study produces estimates of K that are similar in spatial distribution, standard deviation, mean value, and modeling performance to geologically‐informed estimates. The results of this work are used to inform a follow‐on study that tests additional data‐driven approaches in multiple basins within the contiguous United States. [ABSTRACT FROM AUTHOR]