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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]

Several studies have demonstrated the ability of long short-term memory (LSTM) machine-learning-based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model. Here, we propose and test a modification of LSTM architecture that is calibrated in a manner that is analogous to a hydrological system. Our architecture, called "HydroLSTM", simulates the sequential updating of the Markovian storage while the gating operation has access to historical information. Specifically, we modify how data are fed to the new representation to facilitate simultaneous access to past lagged inputs and consolidated information, which explicitly acknowledges the importance of trends and patterns in the data. We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weight patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system. [ABSTRACT FROM AUTHOR]

All the major river systems in the contiguous United States (CONUS) (and many in the world) are impacted by dams, yet reservoir operations remain difficult to quantify and model due to a lack of data. Reservoir operation data are often inaccessible or distributed across many local operating agencies, making the acquisition and processing of data records quite time-consuming. As a result, large-scale models often rely on simple parameterizations for assumed reservoir operations and have a very limited ability to evaluate how well these approaches match actual historical operations. Here, we use the first national dataset of historical reservoir operations in the CONUS domain, ResOpsUS, to analyze reservoir storage trends and operations in more than 600 major reservoirs across the US. Our results show clear regional differences in reservoir operations. In the eastern US, which is dominated by flood control storage, we see storage peaks in the winter months with sharper decreases in the operational range (i.e., the difference between monthly maximum and minimum storage) in the summer. In the more arid western US where storage is predominantly for irrigation, we find that storage peaks during the spring and summer with increases in the operational range during the summer months. The Lower Colorado region is an outlier because its seasonal storage dynamics more closely mirrored those of flood control basins, yet the region is classified as arid, and most reservoirs have irrigation uses. Consistent with previous studies, we show that average annual reservoir storage has decreased over the past 40 years, although our analyses show a much smaller decrease than previous work. The reservoir operation characterizations presented here can be used directly for development or evaluation of reservoir operations and their derived parameters in large-scale models. We also evaluate how well historical operations match common assumptions that are often applied in large-scale reservoir parameterizations. For example, we find that 100 dams have maximum storage values greater than the reported reservoir capacity from the Global Reservoirs and Dams database (GRanD). Finally, we show that operational ranges have been increasing over time in more arid regions and decreasing in more humid regions, pointing to the need for operating policies which are not solely based on static values. [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]

Several studies have demonstrated the ability of Long Short-Term Memory (LSTM) machine learning based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model. Here, we propose and test a modification of LSTM architecture that represents internal system processes in a manner that is analogous to a hydrological reservoir. Our architecture, called HydroLSTM, simulates behaviors inherent in a dynamic system, such as sequential updating of the Markovian storage. Specifically, we modify how data is fed to the new representation to facilitate simultaneous access to past lagged inputs, thereby explicitly acknowledging the importance of trends and patterns in the data. We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weights patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system. [ABSTRACT FROM AUTHOR]

This article presents a hydrological reconstruction of the Upper Colorado River Basin with an hourly temporal resolution, and 1-km spatial resolution from October 1982 to September 2019. The validated dataset includes a suite of hydrologic variables including streamflow, water table depth, snow water equivalent (SWE) and evapotranspiration (ET) simulated by an integrated hydrological model, ParFlow-CLM. The dataset was validated over the period with a combination of point observations and remotely sensed products. These datasets provide a long-term, natural-flow, simulation for one of the most over-allocated basins in the world. Measurement(s) streamflow • groundwater • evaporation • evapotranspiration • total water storage Technology Type(s) integrated hydrological model Factor Type(s) temporal interval • geographic location Sample Characteristic - Environment river Sample Characteristic - Location Colorado River Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16632454 [ABSTRACT FROM AUTHOR]

Recent advancements in computational efficiency and Earth system modeling have awarded hydrologists with increasingly high-resolution models of terrestrial hydrology, which are paramount to understanding and predicting complex fluxes of moisture and energy. Continental-scale hydrologic simulations are, in particular, of interest to the hydrologic community for numerous societal, scientific, and operational benefits. The coupled hydrology–land surface model ParFlow–CLM configured over the continental United States (PFCONUS) has been employed in previous literature to study scale-dependent connections between water table depth, topography, recharge, and evapotranspiration, as well as to explore impacts of anthropogenic aquifer depletion to the water and energy balance. These studies have allowed for an unprecedented process-based understanding of the continental water cycle at high resolution. Here, we provide the most comprehensive evaluation of PFCONUS version 1.0 (PFCONUSv1) performance to date by comparing numerous modeled water balance components with thousands of in situ observations and several remote sensing products and using a range of statistical performance metrics for evaluation. PFCONUSv1 comparisons with these datasets are a promising indicator of model fidelity and ability to reproduce the continental-scale water balance at high resolution. Areas for improvement are identified, such as a positive streamflow bias at gauges in the eastern Great Plains, a shallow water table bias over many areas of the model domain, and low bias in seasonal total water storage amplitude, especially for the Ohio, Missouri, and Arkansas River basins. We discuss several potential sources for model bias and suggest that minimizing error in topographic processing and meteorological forcing would considerably improve model performance. Results here provide a benchmark and guidance for further PFCONUS model development, and they highlight the importance of concurrently evaluating all hydrologic components and fluxes to provide a multivariate, holistic validation of the complete modeled water balance. [ABSTRACT FROM AUTHOR]

Topography is a fundamental input to hydrologic models critical for generating realistic streamflow networks as well as infiltration and groundwater flow. Although there exist several national topographic datasets for the United States, they may not be compatible with gridded models that require hydrologically consistent digital elevation models (DEMs). Here, we present a national topographic dataset developed to support gridded hydrologic simulations at 1 km and 250 m spatial resolution over the contiguous United States. The workflow is described step by step in two parts: (a) DEM processing using a Priority Flood algorithm to ensure hydrologically consistent drainage networks and (b) slope calculation and smoothing to improve drainage performance. The accuracy of the derived stream network is evaluated by comparing the derived drainage area to drainage areas reported by the national stream gage network. The slope smoothing steps are evaluated using the runoff simulations with an integrated hydrologic model. Our DEM product started from the National Water Model DEM to ensure our final datasets will be as consistent as possible with this existing national framework. Our analysis shows that the additional processing we provide improves the consistency of simulated drainage areas and the runoff simulations that simulate gridded overland flow (as opposed to a network routing scheme). The workflow uses an open-source R package, and all output datasets and processing scripts are available and fully documented. All of the output datasets and scripts for processing are published through CyVerse at 250 m and 1 km resolution. The DOI link for the dataset is 10.25739/e1ps-qy48 (Zhang and Condon, 2020). [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]

Recent advancements in computational efficiency and earth system modeling have awarded hydrologists with increasingly high-resolution models of terrestrial hydrology, which are paramount to understanding and predicting complex fluxes of moisture and energy. Continental-scale hydrologic simulations are, in particular, of interest to the hydrologic community for numerous societal, scientific and operational benefits. The coupled hydrology-land surface model ParFlow-CLM configured over the continental United States (PFCONUS) has been employed in previous literature to study scale-dependent connections between water table depth, topography, recharge, and evapotranspiration, as well as to explore impacts of anthropogenic aquifer depletion to the water and energy balance. These studies have allowed for an unprecedented, process-based understanding of the continental water cycle at high resolution. Here, we provide the most comprehensive evaluation of PFCONUS version 1.0 (PFCONUSv1) performance to date, comparing numerous modeled water balance components with thousands of in situ observations and several remote sensing products, and using a range of statistical performance metrics for evaluation. PFCONUSv1 comparisons with these datasets are a promising indicator of model fidelity and ability to appropriately reproduce the continental-scale water balance at high resolution. Areas for improvement are identified, such as a positive streamflow bias at gauges in the eastern Great Plains, a shallow water table bias over many areas of the model domain, and low bias in seasonal total water storage amplitude especially for the Ohio, Missouri and Arkansas river basins. We discuss several potential sources for model bias and suggest that minimizing error in topographic processing and meteorological forcing would considerably improve model performance. Results here provide a benchmark and guidance for further PFCONUS model development, and they highlight the importance of concurrently evaluating all hydrologic components and fluxes to provide a multivariate, holistic validation of the complete modeled water balance. [ABSTRACT FROM AUTHOR]

Topography is a fundamental input to hydrologic models critical for generating realistic streamflow networks as well as infiltration and groundwater flow. Although there exist several national topographic datasets for the United States, they may not be compatible with gridded models that require hydrologically consistent Digital Elevation Models (DEMs). Here, we present a national topographic dataset developed support physically based hydrologic simulations at 1km and 250m spatial resolution over contiguous United States. The workflow is described step-by-step in two parts (a) DEM processing using a Priority Flood algorithm to ensure hydrologically consistent drainage networks and (b) slope calculation and smoothing to improve drainage performance. The accuracy of derived stream network is evaluated by comparing the derived drainage area to drainage areas reported by the national stream gage network. The slope smoothing steps are evaluated using the runoff simulations with an integrated hydrologic model. The processed DEM is designed to capture the topographic features and improve the runoff simulations for the models solving partial differential equations. The workflow uses an open-source R package and all output datasets and processing scripts are available and fully documented here. All of the output datasets and scripts for processing are published through Cyverse at 250m and 1km resolution. [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]

Surface flow and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs are dynamically evolving with improvements in technology, and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves the Richards equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite-difference scheme and a conservative finite-volume method for subsurface flow and transport, respectively. ParFlow uses multigrid-preconditioned Krylov and Newton–Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g., the Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective. [ABSTRACT FROM AUTHOR]

Surface and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs is dynamically evolving with improvement in technology and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves Richards' equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite difference scheme and a conservative finite volume method for subsurface flow and transport, respectively. ParFlow uses multigrid preconditioned Krylov and Newton-Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g. Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and the atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective. [ABSTRACT FROM AUTHOR]

Abstract We present a topographic processing tool, PriorityFlow, designed to improve the topographic input datasets which are required for physically based hydrologic models. PriorityFlow is available on GitHub, combines multiple processing approaches, and includes several key innovations over previous work: (1) a novel approach for incorporating a-priori stream network information with priority flood routing DEM processing, (2) improved slope calculations that incorporate flow directions, (3) options for incorporating slopes perpendicular to the primary flow direction, (4) more flexible options for defining subbasins and enforcing slopes along stream reaches. Here we document the approach and demonstrate the impact of topographic processing choices on hydrologic simulations. Results demonstrate that the modified priority flood algorithm can better reproduce observed stream networks without relying on traditional stream burning approaches. Additionally, we demonstrate the impact of slope processing options on runoff timing and magnitude. Specifically, we highlight the sensitivity of the hydrograph to slope processing choices along stream reaches. Highlights • Modified priority flood algorithm to prioritize predefined stream networks. • Topographic processing for integrated hydrologic models. • Hydrograph sensitivity to stream segment smoothing and slopes. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster that may become more common in the future under climate change. It involves changes to temperature, precipitation and/or land cover, but the relative contributions of each of these factors to overall drought severity is not clear. Here we apply a high-resolution integrated hydrologic model of the High Plains to explore the individual importance of each of these factors and the feedbacks between them. The model was constructed using ParFlow-CLM, which represents surface and subsurface processes in detail with physically based equations. Numerical experiments were run to perturb vegetation, precipitation and temperature separately and in combination. Results show that decreased precipitation caused larger anomalies in evapotranspiration, soil moisture, stream flow and water table levels than increased temperature or disturbed land cover did. However, these factors are not linearly additive when applied in combination; some effects of multifactor runs came from interactions between temperature, precipitation and land cover. Spatial scale was important in characterizing impacts, as unpredictable and nonlinear impacts at small scales aggregate to predictable, linear large-scale behavior. [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]

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]

Integrated hydrologic models solve coupled mathematical equations that represent natural processes, including groundwater, unsaturated, and overland flow. However, these models are computationally expensive. It has been recently shown that machine leaning (ML) and deep learning (DL) in particular could be used to emulate complex physical processes in the earth system. In this study, we demonstrate how a DL model can emulate transient, three-dimensional integrated hydrologic model simulations at a fraction of the computational expense. This emulator is based on a DL model previously used for modeling video dynamics, PredRNN. The emulator is trained based on physical parameters used in the original model, inputs such as hydraulic conductivity and topography, and produces spatially distributed outputs (e.g., pressure head) from which quantities such as streamflow and water table depth can be calculated. Simulation results from the emulator and ParFlow agree well with average relative biases of 0.070, 0.092, and 0.032 for streamflow, water table depth, and total water storage, respectively. Moreover, the emulator is up to 42 times faster than ParFlow. Given this promising proof of concept, our results open the door to future applications of full hydrologic model emulation, particularly at larger scales. [ABSTRACT FROM AUTHOR]

Modeling the terrestrial water cycle at the global scale has recently been referred to as the "grand challenge to hydrology", which would enhance our understanding of scale- dependent relationships between subsurface and surface regimes, between land and atmosphere, and between human-induced ecohydrologic impacts, including groundwater withdrawals and climate change. Modern advancements in remote sensing have awarded scientists with the ability to measure mesoscale changes in Earth's terrestrial water storage. The Gravity Research and Climate Recovery (GRACE) Program measures fluctuations in Earth's gravity attributable to mass flux, or changes in total water storage (TWS). However, GRACE TWS estimates are not without error and are limited in scale. GRACE uncertainty is still a topic of much discussion, and its products range in lateral resolution from 3° to 0.5°. Further, while we can infer from GRACE products the change in mass storage in a given region from a baseline value, we do not know the total mass available, nor can we distinguish between processes driving mass change such as recharge anomalies, natural variability or human influence (e.g. pumping, irrigation, or diversions). Determining the availability and fluctuations in water resources for governance at the regional or major aquifer scale either involves downscaling coarse remote observations such as GRACE, or interpolating point observations (wells), both of which introduce uncertainty. Here, we use an integrated hydrologic model, ParFlow, to support and better understand estimates of TWS from GRACE products in the continental United States. The CONUS ParFlow configuration extends over a 6.3 million square kilometer are of the continental United States at 1-km lateral resolution. In previous studies, the CONUS model has been used to analyze continental-scale patterns of water table depth and its mechanistic relationship with topographic indices, recharge and evapotranspiration. This study seeks to use ParFlow to explain sources of groundwater storage loss or gain in major U.S. watersheds that are observed by the GRACE remote sensing products. The CONUS model was run for water years 2009 through 2013 and compared to GRACE GFZ, JPL, and CSR gain-corrected time series. We identify regions in which ParFlow CONUS simulated storage change results fall inside the spread of GRACE leakage and measurement error, adding confidence to both the physically model and remote sensing product. We also use ParFlow to explain partitioning of GRACE TWS into changes in snow water equivalent, soil moisture, groundwater, and surface water. This study illustrates the benefit of extreme-scale hydrologic modeling, in that it may be used to help bridge existing scale gaps between point observations and remote sensing, attribute sources of large scale storage trends, and inform water resource governance at a range of scales. [ABSTRACT FROM AUTHOR]

Topography is one of the most basic inputs to hydrologic models, providing critical gradients that drive flow and determine the configuration of stream networks. There are a multitude of established approaches for developing hydrologically consistent Digital Elevation Models (DEMs) suitable for modeling. However, existing approaches still have limitations which can be particularly problematic for PDE based simulations. We present a topographic processing tool, PriorityFlow, designed to improve the topographic inputs datasets which are required for physically based hydrologic models. PriorityFlow, combines multiple approaches and includes several key innovations over previous work: (1) a novel approach for incorporating a-priori stream network information with priority flood routing DEM processing, (2) improved slope calculations that incorporate flow directions, (3) options for incorporating slopes perpendicular to the primary flow direction, (4) more flexible options for defining subbasins and enforcing slopes along stream reaches. Here we document the approach and explore the impact of topographic processing choices on hydrologic simulations for an integrated hydrologic model spanning the continental US. Results demonstrate that the modified priority flood algorithm can better reproduce observed stream networks across spatial scales without relying on traditional stream burning approaches. Additionally, we demonstrate the impact of slope processing options on runoff timing and magnitude. Specifically, we highlight the sensitivity of the hydrograph to slope processing choices along stream reaches for a variety of hydrologic settings. [ABSTRACT FROM AUTHOR]

Understanding the movement of water in the earth system is important for sustaining ecosystems, municipal, agricultural and industrial consumption. Continental scale simulation of this flow of water through rivers, streams and groundwater is among the tools that can be used to provide this insight. Here we discuss advancements to PF-CONUS 2.0, an integrated hydrologic model of the watersheds contributing to flow over the entire United States. This model is aligned with the US National Water Model and provides enhanced capability beyond that operational framework. Simulations using this model explore connections between components of the terrestrial hydrologic system. We also discuss a model coupling framework built upon the integrated hydrology model ParFlow and WRF-Hydro. Examples highlighting differences in connection created by model parameterizations are presented. Shifts in insight with resolution and scale, moving from highly instrumented headwaters catchments out to the continent are shown. Finally, changes in model behavior under stress and land cover perturbations are used to help inform water management. [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]

• The ParFlow CONUS modeling platform is a large-scale, hyper-resolution, integrated surface-water and groundwater model. • The roughly five years of technical development of the latest ParFlow CONUS 2.0 platform is presented in this study. • Simulated steady-state water table depth and streamflow by CONUS 2.0 were evaluated using more than 635 K USGS observations and other compiled datasets. • Our results demonstrate improvement in both groundwater and surface water simulations over CONUS 1.0 for all USGS Hydrologic Unit Code (HUC) basins. • ParFlow CONUS models demonstrate better groundwater performance than previous large-scale groundwater models. Large-scale, high-resolution hydrologic modeling is an important tool to address questions of water quantity, availability, and potential recharge. Continental-to-Global scale models, particularly those that include groundwater, are growing in number. However, many of these approaches simplify aspects of the system and the connections between surface water and groundwater. The ParFlow CONUS modeling platform is a large-scale, hyper-resolution, hydrologic model that relies on the integrated solution of 3D partial differential equations that describe groundwater, soil, and 2D surface water flow. The prior version, ParFlow CONUS 1.0, was the first large-scale hydrologic model that included an explicit treatment of lateral groundwater flow for the contiguous US (CONUS). Here, we present the ParFlow CONUS 2.0 integrated hydrologic model. This model extends to the coastlines and contributing basins for CONUS and is consistent with the NOAA National Water Model. Here we document the roughly five years of technical development of this platform, present steady-state simulation results, rigorously compare these results to the prior ParFlow CONUS 1.0 simulations, and evaluate the model performance based on observations. Simulated water table depth and streamflow were evaluated using more than 635 K observations from USGS monitoring wells, other compiled groundwater datasets, and NHD and USGS streamflow gauges. Our results demonstrate improvement in both groundwater and surface water simulations over the prior generation model for all USGS Hydrologic Unit Code (HUC) basins. These results suggest that this current generation hydrologic model has good to excellent streamflow performance over the entire CONUS, with almost half of the HUC subbasins exhibiting excellent performance based on normalized root-square error (RSR). These results suggest that the current generation model approaches good performance for water table depth over the CONUS, a metric not usually compared directly at all in large-scale studies, with good-to-excellent performance exhibited over some HUC regions. We also delineate two regions that influence model performance, one where microtopography around streams dominates (D2), and another where a mix of subsurface heterogeneity and topographic gradients dominate (D1). Improvements in topography from CONUS1 to CONUS2 generally result in better streamflow and water table depth performances. Advancements in the subsurface depth and structure also produce better water table estimates. [ABSTRACT FROM AUTHOR]