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Arizona has a long history of groundwater use, and there is concern about long term groundwater sustainability across the state. We explore groundwater trends across Arizona and how they vary with respect to: (1) whether groundwater pumping is regulated, and (2) relative access to local or imported surface water. Well observations from the Arizona Department of Water Resources are used to quantify water table depth trends and groundwater drilling patterns. There are more than 85,000 groundwater wells in Arizona, and new wells are routinely being drilled. The number of new shallow wells (<200 ft) has decreased over time in all parts of the state. But midrange (200–500 ft) to deep (>500 ft) wells have increased in the past 10 years in regulated and groundwater dominated areas. Most wells are small with low pumping capacities that fall below the regulatory limit; however, there are still large wells being drilled in unregulated areas. Results show statewide decreasing water storage and groundwater levels. Groundwater declines are less severe in the parts of the state that have groundwater regulation. However, looking closer at this trend, groundwater recovery is strongest in areas receiving imported Colorado River water which also implement managed groundwater recharge with the imported water. Our findings indicate that groundwater recovery is very localized and driven more by managed recharge from surface water as opposed to decreased groundwater pumping. [ABSTRACT FROM AUTHOR]

Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over‐year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage. Plain Language Summary: Drought in water systems is a major challenge in managing water resources. Reservoirs are important as they can lessen the impacts of drought on water availability for many users. However, they are impacted by drought as well. We use a novel and generally applicable method to identify when reservoir storage is unusually low, potentially from drought, at 250 reservoirs across the conterminous U.S. We find that the maximum amount of water stored in reservoirs is decreasing across the U.S. We also find that periods of unusually low storage are becoming longer and more severe in western and central U.S. regions as well as for certain types of reservoirs. This suggests that reservoir storage may be less reliable and more vulnerable to extreme conditions and may be further impacted by changing climate and hydrology across the U.S. and by sediment building up behind reservoirs. Key Points: Low‐storage periods are longer, more severe, and more variable in over‐year storage reservoirs and in the western and central CONUSLonger periods of low storage for some regions in recent years suggests decreased reservoir reliability in a changing hydroclimateMaximum annual storage is also declining across CONUS, furthered by storage losses from sedimentation [ABSTRACT FROM AUTHOR]

Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow‐CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event‐scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process‐based models without sacrificing fidelity. Plain Language Summary: Computational models are important for understanding and predicting terrestrial hydrology, but our most physically detailed models can be time‐consuming and expensive to run over large regions. In this study, we trained deep learning models to emulate a complex hydrology model that simulates groundwater flow over the contiguous US. We developed a new model architecture called FSTR that captures spatiotemporal patterns better than standard deep learning models. FSTR is over 1,000 times faster than ParFlow, the original hydrologic model. This enables new possibilities like forecasting groundwater changes and estimating uncertainties. Our results show that specialized deep learning architectures can accurately emulate complex hydrologic models while drastically reducing computation time. Key Points: Deep learning models can emulate a complex integrated hydrology model that simulates groundwater over the United StatesWe developed a new model architecture that is more robust over longer simulation periods than off‐the‐shelf neural networksDeep‐learning based emulators of complex models enable new applications such as real‐time forecasting and estimating uncertainties [ABSTRACT FROM AUTHOR]

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]

Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, motivating the development of large‐scale hydrologic modeling for solutions. Water age/quality is as important as water quantity for understanding the terrestrial water cycle. However, scientific progress in tracking water parcels at large‐scale with high spatiotemporal resolutions is far behind that in simulating water balance/quantity owing to the lack of powerful modeling tools. EcoSLIM is a particle tracking model working with ParFlow‐CLM that couples integrated surface‐subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework on distributed, multi‐Graphics Processing Unit platforms with Compute Unified Device Architecture‐Aware Message Passing Interface for accelerating EcoSLIM to continental‐scale. In tests from catchment‐, to regional‐, and then to continental‐scale using 25‐million to 1.6‐billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where the parallelization is inadequate, and a standard parallel framework is also absent. The parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth System Models. Plain Language Summary: Studies of water ages at multiple spatiotemporal scales are urgent to better understand the connections between different hydrologic compartments. Climate change and anthropogenic activities make this requirement more pressing. Lagrangian particle tracking is a powerful tool to simulate water ages. However, it is computationally demanding, which hampers its wide application. In this study, we provide a Lagrangian particle tracking model, EcoSLIM, with a novel parallel framework that enables it to handle large‐scale water age simulations with high spatiotemporal resolutions. We combined the efforts of engineers and scientists from multiple disciplines on this work which cannot be achieved by the knowledge of an individual discipline. To the best of our knowledge, such a modeling tool is absent in communities of hydrology and Earth Surface Processes. In tests from catchment‐, to regional‐, and then to continental‐scale using 25‐million to 1.6‐billion particles, EcoSLIM shows significant speedup and excellent parallel performance. Although we take EcoSLIM as an example here, the parallel framework is portable to other particle tracking models in Earth System Science, such as those in atmospheric and oceanic disciplines. The parallelized EcoSLIM is a promising tool to hydrologic community and Earth System Model developers for scientific exploration. Key Points: Numerical models for large‐scale water age/quality simulations are absent in communities of hydrology and Earth Surface ProcessesA parallel framework for accelerating Lagrangian particle tracking to continental‐scale on distributed, multi‐Graphics Processing Unit platforms is establishedThe parallelized particle tracking model, EcoSLIM, is a promising tool to accelerate our understanding of the terrestrial water cycle [ABSTRACT FROM AUTHOR]

Key to model development is the selection of an appropriate representational system, including both the representation of what is observed (the data), and the formal mathematical structure used to construct the input-state-output mapping. These choices are critical, because they completely determine the questions we can ask, the nature of the analyses and inferences we can perform, and the answers we can obtain. Accordingly, a representation that is suitable for one kind of investigation might be limited in its ability to support some other kind. Arguably, how different representational approaches affect what we can learn from data is poorly understood. This paper explores three representational strategies as vehicles for understanding how catchment scale hydrological processes vary across hydro-geo-climatologically diverse Chile. Specifically, we test a lumped water-balance model (GR4J), a data-based dynamical systems model (LSTM), and a data-based regression tree model (Random Forest). Insights were obtained regarding system memory encoded in data, spatial transferability by use of surrogate attributes, and informational deficiencies of the data set that limit our ability to learn an adequate input-output relationship. As expected, each approach exhibits specific strengths, with LSTM providing the best characterization of dynamics, GR4J being the most robust under informationally deficient conditions, and Random Forest regression-tree method being most supportive of interpretation. Overall, the contrasting nature of the three approaches suggests the value of adopting a multi-representational framework to more fully extract information from the data and, by doing so, find information that better facilities the goals of robust prediction and improved understanding, ultimately supporting enhanced scientific discovery. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Groundwater is an important part of the water cycle but we are still working on the best ways to include it in global models. This study provides an overview of the state of the science for groundwater modeling and outlines a road map for what is needed to improve global groundwater models. Key Points: A global groundwater framework is needed to address critical gaps in our understanding and predictive capacity of the hydrologic cycleWe envision a framework that will combine observations and models to provide spatially and temporally continuous groundwater informationThe proposed framework could improve predictability in existing models and provide valuable new information for water management [ABSTRACT FROM AUTHOR]

High‐resolution, coupled, process‐based hydrology models, in which subsurface, land‐surface, and energy budget processes are represented, have been applied at the basin‐scale to ask a wide range of water science questions. Recently, these models have been developed at continental scales with applications in operational flood forecasting, hydrologic prediction, and process representation. As use of large‐scale model configurations increases, it is exceedingly important to have a common method for performance evaluation and validation, particularly given challenges associated with accurately representing large domains. Here, we present phase 1 of a comparison project for continental‐scale, high‐resolution, processed‐based hydrologic models entitled the Continental Hydrologic Intercomparison Project (CHIP). The first phase of CHIP is based on past Earth System Model intercomparisons and comprised of a two‐model proof of concept comparing the ParFlow‐CONUS hydrologic model, version 1.0 and a NOAA US National Water Model configuration of WRF‐Hydro, version 1.2. The objectives of CHIP phase 1 are: (a) describe model physics and components, (b) design an experiment to ensure a fair comparison, and (b) assess simulated streamflow with observations to better understand model bias. To our knowledge, this is the first comparison of continental‐scale, high‐resolution, physics‐based models which incorporate lateral subsurface flow. This model intercomparison is an initial step toward a continued effort to unravel process, parameter, and formulation differences in current large‐scale hydrologic models and to engage the hydrology community in improving hydrology model configuration and process representation. Key Points: We conduct a proof of concept intercomparison of two continental‐scale, high‐resolution hydrologic models to evaluate model biases and simulated streamflowBoth models tend to better simulate streamflow in the eastern US and have more variable performance in the western USModel intercomparisons help the hydrologic community identify model biases and inform areas for improvement [ABSTRACT FROM AUTHOR]

Mountain‐front recharge (MFR), or all inflow to a basin‐fill aquifer with its source in the mountain block, is an important component of recharge to basin‐fill aquifer systems. Distinguishing and quantifying the surface from subsurface components of MFR is necessary for water resource planning and management, particularly as climate change may impact these components in distinct ways. This study tests the hypothesis that MFR components can be distinguished in long‐screened, basin‐fill production wells by (1) groundwater age and (2) the median elevation of recharge. We developed an MFR characterization approach by combining age distributions in six wells using tritium, krypton‐85, argon‐39, and radiocarbon, and median recharge elevations from noble gas thermometry combined with numerical experiments to determine recharge temperature lapse rates using flow and energy transport modeling. We found that groundwater age distributions provided valuable information for characterizing the dominant flow system behavior captured by the basin‐fill production wells. Tracers indicated the presence of old (i.e., no detectable tritium) water in a well completed in weathered bedrock located close to the mountain front. Two production wells exhibited age distributions of binary mixing between modern and a small fraction of old water, whereas the remaining wells captured predominantly modern flow paths. Noble gas thermometry provided important complementary information to the age distributions; however, assuming constant recharge temperature lapse rates produced improbable recharge elevations. Numerical experiments suggest that surface MFR, if derived from snowmelt, can locally suppress water table temperatures in the basin‐fill aquifer, with implications for recharge elevations estimated from noble gas thermometry. Key Points: MFR is a large recharge component in semiarid valley aquifers, but MFR components are difficult to quantify and sensitive to climate changeAge distributions, noble gas thermometry, and fluid/energy transport modeling combined as an approach to distinguish/quantify MFR componentsNoble gas thermometry can help constrain age distributions, but assuming a constant Tr lapse rate may produce improbable recharge elevations because surface MFR can locally suppress water table temperatures in basin‐fill aquifers [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 is the world's most important freshwater resource. Despite this importance, groundwater flow and interactions between groundwater and other parts of the hydrological cycle are often neglected or simplified in large‐scale hydrological models. One of the challenges in simulating groundwater flow and continental to global scales is the lack of consistent globally available hydrogeological data. These input data are needed for a more realistic physical representation of the groundwater system, enabling the simulation of groundwater head dynamics and lateral flows. A realistic representation of the subsurface is especially important as large‐scale hydrological models move to finer resolutions and aim to provide accurate and locally relevant hydrologic information everywhere. In this study, we aim at improving and extending on current available large‐scale data sets providing information of the subsurface. We present a detailed aquifer representation for the continental United States and Canada at hyper resolution (250 × 250 m). We integrate local hydrogeological information, including observations of aquifer layer thickness, conductivity, and vertical structure, to obtain representative aquifer parameter values applicable to the continental scale. The methods used are simple and can be expanded to other parts of the world. Hydrological simulations were performed using the integrated hydrological model ParFlow and demonstrated improved model performance when using the new aquifer parameterization. Our results support that more detailed and accurate aquifer parameterization will advance our understanding of the groundwater system at larger scales. Plain Language Summary: Groundwater is the largest available freshwater resource humans can use for drinking and growing food. It is stored beneath our feet in thick layers of sand or rock; we call these layers aquifers. For a realistic estimation of how much groundwater is stored and flows through these aquifers, detailed information on the properties of these layers is essential. However, this information is often missing, or not detailed enough, at the larger scales. In this study, we introduce a new method to obtain this detailed data by including information from observations and local‐scale studies what we upscaled to the continent of North America. We estimate aquifer parameters at very high spatial resolution over a large domain. These data were not available previously. We evaluate our newly obtained aquifer parameterization against other existing data sets and tested model sensitivity to different horizontal and vertical resolutions and conductivity values. We used the integrated hydrological model ParFlow. Our results show that estimates of water table depth improved using our new parameterization. The methods we introduce here can be used by other modelers looking to improve their aquifer parameterization. We hope that our study can be used as a road map towards improved and more detailed aquifer parameterization used in current large‐scale hydrological models. Key Points: We integrate local hydrogeological information into a continental‐scale aquifer parameterization for the continental United States and CanadaAquifer thickness and vertical structure are estimated at a level of detail not available beforeHydrological modeling confirmed accurate large‐scale aquifer parameterization will improve our understanding of the hydrological system [ABSTRACT FROM AUTHOR]

Watersheds have served as one of our most basic units of organization in hydrology for over 300 years (Dooge, 1988, https://doi.org/10.1080/02626668809491223; McDonnell, 2017, https://doi.org/10.1038/ngeo2964; Perrault, 1674, https://www.abebooks.com/first-edition/lorigine-fontaines-Perrault-Pierre-Petit-Imprimeur/21599664536/bd). With growing interest in groundwater-surface water interactions and subsurface flow paths, hydrologists are increasingly looking deeper. But the dialog between surface water hydrologists and groundwater hydrologists is still embryonic, and many basic questions are yet to be posed, let alone answered. One key question is: where is the bottom of a watershed? Knowing where to draw the bottom boundary has not yet been fully addressed in the literature, and how to define the watershed "bottom" is a fraught question. There is large variability across physical and conceptual models regarding how to implement a watershed bottom, and what counts as "deep" varies markedly in different communities. In this commentary, we seek to initiate a dialog on existing approaches to defining the bottom of the watershed. We briefly review the current literature describing how different communities typically frame the answer of just how deep we should look and identify situations where deep flow paths are key to developing realistic conceptual models of watershed systems. We then review the common conceptual approaches used to delineate the watershed lower boundary. Finally, we highlight opportunities to trigger this potential research area at the interface of catchment hydrology and hydrogeology. [ABSTRACT FROM AUTHOR]

Mountain‐block recharge (MBR) is the subsurface inflow of groundwater to lowland aquifers from adjacent mountains. MBR can be a major component of recharge but remains difficult to characterize and quantify due to limited hydrogeologic, climatic, and other data in the mountain block and at the mountain front. The number of MBR‐related studies has increased dramatically in the 15 years since the last review of the topic was conducted by Wilson and Guan (2004), generating important advancements. We review this recent body of literature, summarize current understanding of factors controlling MBR, and provide recommendations for future research priorities. Prior to 2004, most MBR studies were performed in the southwestern United States. Since then, numerous studies have detected and quantified MBR in basins around the world, typically estimating MBR to be 5–50% of basin‐fill aquifer recharge. Theoretical studies using generic numerical modeling domains have revealed fundamental hydrogeologic and topographic controls on the amount of MBR and where it originates within the mountain block. Several mountain‐focused hydrogeologic studies have confirmed the widespread existence of mountain bedrock aquifers hosting considerable groundwater flow and, in some cases, identified the occurrence of interbasin flow leaving headwater catchments in the subsurface—both of which are required for MBR to occur. Future MBR research should focus on the collection of high‐priority data (e.g., subsurface data near the mountain front and within the mountain block) and the development of sophisticated coupled models calibrated to multiple data types to best constrain MBR and predict how it may change in response to climate warming. Key Points: Mountain‐block recharge confirmed as important source of recharge to basin aquifers in a variety of climatic and geologic settings globallyRecent work advanced understanding of fundamental controls on mountain‐block recharge and somewhat improved methods for characterizationFuture research should aim to acquire subsurface data in mountain block and at the mountain front [ABSTRACT FROM AUTHOR]

Small unoccupied aerial systems (sUASs) are increasingly applied to study hydrologic processes and water quality. Here, we evaluate a novel application of sUAS to stream turbidity monitoring, with the goal of extending analyses implemented with satellite remote sensing to enable high resolution, rapid collection of turbidity imagery along smaller waterbodies. To accomplish this, we collected multispectral imagery using two sUAS platforms under a range of environmental conditions along a local creek in Syracuse, NY. In addition, we collected in situ turbidity observations immediately after each flight along several transects along the creek, as well as within a clear plume created by a natural spring entering the main channel of the creek. The in situ turbidity values were compared with the mean and standard deviation of several single‐band and multiband indices extracted along similar transects from the sUAS flights. On the basis of data collected across several flights, we found optical metrics obtained from multispectral imagery correlated well with in situ turbidity measurements. Though many optical metrics yielded strong relationships considering only values within the main channel, values associated with the red band were strongly related to turbidity estimates from the main channel as well as lower turbidity values observed in the spring plume. Although there are still limitations of this approach associated with variable field conditions, results from this proof of concept analysis show that sUASs offer a promising avenue for cost‐effective turbidity monitoring. [ABSTRACT FROM AUTHOR]

Abstract: Recent advancements in analytical solutions to quantify water and solute travel time distributions (TTDs) and the related StorAge Selection (SAS) functions synthesize catchment complexity into a simplified, lumped representation. Although these analytical approaches are efficient in application, they require rarely available long‐term and high‐frequency hydrochemical data for parameter estimation. Alternatively, integrated hydrologic models coupled to Lagrangian particle‐tracking approaches can directly simulate age under different catchment geometries and complexity, but at a greater computational expense. Here, we bridge the two approaches, using a physically based model to explore the uncertainty in the estimation of the SAS function shape. In particular, we study the influence of subsurface heterogeneity, interactions between distinct flow domains (i.e., the vadose zone and saturated groundwater), diversity of flow pathways, and recharge rate on the shape of TTDs and the SAS functions. We use an integrated hydrology model, ParFlow, linked with a particle‐tracking model, SLIM, to compute transient residence times (or ages) at every cell in the domain, facilitating a direct characterization of the SAS function. Steady‐state results reveal that the SAS function shape shows a wide range of variation with respect to the variability in the structure of subsurface heterogeneity. Ensembles of spatially correlated realizations of hydraulic conductivity indicate that the SAS functions in the saturated groundwater have an overall weak tendency toward sampling younger ages, whereas the vadose zone gives a strong preference for older ages. We further show that the influence of recharge rate on the TTD is tightly dependent on the variability of subsurface hydraulic conductivity. [ABSTRACT FROM AUTHOR]

We study the influence of topography on groundwater fluxes and water table depths across the contiguous United States (CONUS). Groundwater tables are often conceptualized as subdued replicas of topography. While it is well known that groundwater configuration is also controlled by geology and climate, nonlinear interactions between these drivers within large real-world systems are not well understood and are difficult to characterize given sparse groundwater observations. We address this limitation using the fully integrated physical hydrology model ParFlow to directly simulate groundwater fluxes and water table depths within a complex heterogeneous domain that incorporates all three primary groundwater drivers. Analysis is based on a first of its kind, continental-scale, high-resolution (1 km), groundwater-surface water simulation spanning more than 6.3 million km2. Results show that groundwater fluxes are most strongly driven by topographic gradients (as opposed to gradients in pressure head) in humid regions with small topographic gradients or low conductivity. These regions are generally consistent with the topographically controlled groundwater regions identified in previous studies. However, we also show that areas where topographic slopes drive groundwater flux do not generally have strong correlations between water table depth and elevation. Nonlinear relationships between topography and water table depth are consistent with groundwater flow systems that are dominated by local convergence and could also be influenced by local variability in geology and climate. One of the strengths of the numerical modeling approach is its ability to evaluate continental-scale groundwater behavior at a high resolution not possible with other techniques. [ABSTRACT FROM AUTHOR]

The article discusses objectives of and the rationale behind developing HyperHydro network, which is an open network of scientists for simulating large-scale terrestrial hydrology and water resources. Topics discussed include global environmental change that require hyper-resolution descriptions of terrestrial water resources, hydrological models to support water resources management and significant challenges in providing hydrological information globally.

Groundwater-fed irrigation has been shown to deplete groundwater storage, decrease surface water runoff, and increase evapotranspiration. Here we simulate soil moisture-dependent groundwater-fed irrigation with an integrated hydrologic model. This allows for direct consideration of feedbacks between irrigation demand and groundwater depth. Special attention is paid to system dynamics in order to characterized spatial variability in irrigation demand and response to increased irrigation stress. A total of 80 years of simulation are completed for the Little Washita Basin in Southwestern Oklahoma, USA spanning a range of agricultural development scenarios and management practices. Results show regionally aggregated irrigation impacts consistent with other studies. However, here a spectral analysis reveals that groundwater-fed irrigation also amplifies the annual streamflow cycle while dampening longer-term cyclical behavior with increased irrigation during climatological dry periods. Feedbacks between the managed and natural system are clearly observed with respect to both irrigation demand and utilization when water table depths are within a critical range. Although the model domain is heterogeneous with respect to both surface and subsurface parameters, relationships between irrigation demand, water table depth, and irrigation utilization are consistent across space and between scenarios. Still, significant local heterogeneities are observed both with respect to transient behavior and response to stress. Spatial analysis of transient behavior shows that farms with groundwater depths within a critical depth range are most sensitive to management changes. Differences in behavior highlight the importance of groundwater's role in system dynamics in addition to water availability. [ABSTRACT FROM AUTHOR]

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility. [ABSTRACT FROM AUTHOR]

There is conflicting evidence about whether Parkinson's disease (PD) is associated with deficiencies in recognition memory (RM) and the processes which underlie it, namely recollection (a form of recall) and familiarity (a feeling of memory in the absence of recall). The aims of the current study were to examine forced-choice verbal RM (assessed with the Warrington Recognition memory Test), yes-no RM, recollection, familiarity and executive functioning in 17 patients with moderate PD and 17 healthy volunteers matched for age and premorbid intelligence. We predicted that patients with moderate PD would display a significant recollection deficit on the yes-no RM test, because their strategic memory processing that depends on executive functioning and is necessary for efficient encoding and/or retrieval, was disrupted. In contrast, familiarity memory, which is not dependent on these processes, and forced-choice RM (which is largely dependent on familiarity) should show higher levels of preservation. We also predicted that recollection should be correlated with severity of executive dysfunction. Our findings revealed that the PD patients were as likely to accurately discriminate between targets and distractors as the healthy volunteers on both RM tests. However, the PD patients were significantly less reliant on recollection-driven recognition decisions on the yes-no RM test when compared with the healthy control group. The patients also displayed executive function deficits, but these were not correlated with recollection. The extent to which the PD patients' reliance on familiarity at the expense of recollection is explained by impairments in strategic memory processes/executive function and/or medial temporal lobe retrieval processes needs further exploration. [ABSTRACT FROM AUTHOR]

Understanding the time water takes as it moves from rain or snowmelt through the terrestrial system to arrive as stream discharge, or evapotranspiration (ET) is an important hydrologic quantity. We develop a Lagrangian particle tracking method to capture transient residence times from source to either ET or outflow in an integrated hydrologic model. This method is parallel and efficiently captures time evolution of parcels of water in the model and tracks the source of water for hydrograph or ET separation. We demonstrate this model using hypothetical hillslope simulations driven by snow or rain dominated forcing and two different land cover types. We show that land cover and forcing both impact the outflow residence time distribution, which spans many years. We also introduce the idea of ET residence time distributions and show that while mean ET residence times are typically less than 1 year, land cover affects this quantity and simulated ET processes draw from much older water (many years old) depending on location on the hillslope or seasonal cycle. Finally, we study source water contribution to outflow and ET and explore assumptions about a residence time‐based definition of older, pre‐event, or groundwater end member. We show that simulated plant processes may switch to more opportunistic and younger sources of water, changing the composition of outflow. [ABSTRACT FROM AUTHOR]