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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]

The "MODFLOW-and-More" conference series, which began in 1998, recently held its 10th rendition at Princeton University in 2022. Despite the challenges posed by COVID-19, the conference was successful in bringing together scientists from various sectors to share research and development on groundwater modeling. The conference focused on the theme of a changing world and emphasized advances in computational methods and codes for evaluating groundwater data. Topics discussed included quantum computing, model inputs, groundwater quality concerns, hydrostratigraphy, and software advancements. The conference attendees expressed joy at being able to connect with others in person and engage with the authors. The next conference is scheduled for June 2024 at Princeton University. [Extracted from the article]

Over the past century, groundwater levels in California's San Joaquin Valley have dropped by more than 30 m in some areas mostly due to excessive groundwater extraction used to irrigate agricultural lands and sustain a growing population. Between 2012 and 2015, California experienced the worst drought in its recorded history, depleting surface water supplies and further exacerbating groundwater depletion in the region. Due to a lack of groundwater regulation, exact quantities of extracted groundwater in California are unknown and hard to quantify. Recent adoption of the Sustainable Groundwater Management Act has intensified efforts to identify sustainable groundwater use. However, understanding sustainable use in a highly productive agricultural system with an extremely complex surface water allocation system, variable groundwater use, and spatially extensive and diverse irrigation practices is no easy task. Using an integrated hydrologic model coupled with a land surface model, we evaluated how water management activities, specifically a suite of irrigation and groundwater pumping scenarios, impact surface water–groundwater fluxes and storage components and how those activities and the relationships between them change during drought. Results showed that groundwater pumping volume had the most significant impact on long‐term water storage changes. A comparison with total water storage anomaly (TWSA) estimates from NASA's Gravity Recover and Climate Experiment (GRACE) provided some insight regarding which combinations of pumping and irrigation matched the GRACE TWSA estimates, lending credibility to these scenarios. In addition, the majority of long‐term water storage changes during the recent drought occurred in groundwater storage in the deeper subsurface. Article Impact Statement: Integrated hydrologic modeling to assess the impacts of water management on water availability and terrestrial water fluxes during drought. [ABSTRACT FROM AUTHOR]

In mountain, snow driven catchments, snowmelt is supposed to be the primary contribution to river streamflows during spring. In these catchments the contribution of groundwater is not well documented because of the difficulty to monitor groundwater in such complex environment with deep aquifers. In this study we use an integrated hydrologic model to conduct numerical experiments that help quantify the effect of lateral groundwater flow on total annual and peak streamflow in predevelopment conditions. Our simulations focus on the Upper Colorado River Basin (UCRB; 2.8 × 105 km2) a well‐documented mountain catchment for which both streamflow and water table measurements are available for several important sub‐basins. For the simulated water year, our results suggest an increase in peak flow of up to 57% when lateral groundwater flow processes are included—an unexpected result for flood conditions generally assumed independent of groundwater. Additionally, inclusion of lateral groundwater flow moderately improved the model match to observations. The correlation coefficient for mean annual flows improved from 0.84 for the no lateral groundwater flow simulation to 0.98 for the lateral groundwater flow one. Spatially we see more pronounced differences between lateral and no lateral groundwater flow cases in areas of the domain with steeper topography. We also found distinct differences in the magnitude and spatial distribution of streamflow changes with and without lateral groundwater flow between Upper Colorado River Sub‐basins. A sensitivity test that scaled hydraulic conductivity over two orders of magnitude was conducted for the lateral groundwater flow simulations. These results show that the impact of lateral groundwater flow is as large or larger than an order of magnitude change in hydraulic conductivity. While our results focus on the UCRB, we feel that these simulations have relevance to other headwaters systems worldwide. [ABSTRACT FROM AUTHOR]

Groundwater Modeling and Beyond: MODFLOW-and-More-2019 Special Issue This Foreword introduces the fifth special issue of I Groundwater i comprised of MODFLOW and More conference presentations hosted at the Integrated GroundWater Modeling Center (IGWMC). The theme for the 2019 edition of the "MODFLOW and More" conference series was " I Groundwater Modeling and Beyond i .". [Extracted from the article]

An introduction to the journal is presented on topics including groundwater management; groundwater quality; and characteristics of a watershed that affect transit time distributions of groundwater.