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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]

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]

• Hydraulic conductivity is a key control of the way precipitation signals are filtered in the subsurface. • Hydrologic controls of the signal filtering depend on the timescale being investigated. • The filtering that occurs in streamflow, unsaturated storage, and saturated storage signals have varying physical controls. Watersheds act as low-pass filters, damping and attenuating climatic signals as water moves through the surface and subsurface. This is a well observed phenomenon; however, the ways in which watershed properties control the nature of this filtering are less well documented, especially with respect to groundwater surface water interactions. Here, we use a physically based groundwater surface water model to simulate idealized hillslope ensembles with varying watershed properties (hillslope slope, hydraulic conductivity, and precipitation magnitude) to quantitively explore the impact of watershed configuration on temporal filtering in both the surface and subsurface. To limit the complexities of this system an idealized titled-v domain is used. Multi-decadal simulations (95 years) are run, and then power spectral densities and transfer functions are used to quantify the temporal dynamics and damping of each simulation. Overall, we show that the degree of filtering and the degree of signal transformation is controlled by the total time spent in the subsurface and the degree of groundwater surface water exchanges. The ratio of precipitation to hydraulic conductivity controls the partitioning between infiltration and runoff. Greater infiltration results in less filtering in the subsurface and more filtering in streamflow. For a given precipitation conductivity ratio, deeper water table depths lead to greater streamflow filtering for periods less than 5 years. For time periods greater than 5 years the streamflow filtering is most strongly related to hydraulic conductivity which controls the baseflow dynamics. The majority of the input signal is filtered in the subsurface for short periods less than one year. For longer time scales, hydraulic conductivity is found to be the primary control of filtering and power shift taking place in the subsurface with larger conductivities correlated to less filtering and less of a signal transformation. Deeper water table depths lead to more signal transformation in saturated storage but are not correlated to filtering in unsaturated storage. This is likely due to counteracting effects of higher conductivity (which decreases filtering) and deeper water table depths (which increase filtering). [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]

Understanding freshwater fluxes at continental scales will help us better predict hydrologic response and manage our terrestrial water resources. The partitioning of evapotranspiration into bare soil evaporation and plant transpiration remains a key uncertainty in the terrestrial water balance.We used integrated hydrologic simulations that couple vegetation and land-energy processes with surface and subsurface hydrology to study transpiration partitioning at the continental scale. Both latent heat flux and partitioning are connected to water table depth, and including lateral groundwater flow in the model increases transpiration partitioning from 47 ± 13 to 62 ± 12%.This suggests that lateral groundwater flow, which is generally simplified or excluded in Earth system models, may provide a missing link for reconciling observations and global models of terrestrial water fluxes. [ABSTRACT FROM AUTHOR]

Spatial patterns in the physical controls of groundwater depth and flux are assessed quantitatively using results from a first of its kind, integrated groundwater surface water simulation over the majority of the contiguous US. We apply a novel, k-regression algorithm to the simulated system to simultaneously identify spatial subsets of grid cells with similar relationships between explanatory variables and groundwater metrics while quantifying behavior using multiple linear regression. The combination of this statistical approach with the results of a large-scale, high-resolution groundwater simulation allows us to evaluate the ability to represent complex groundwater behavior with simple linear models across an unprecedented range of climates and physical settings. In almost all of the eight major basins considered, we identify at least some areas where the coefficient of determination for the linear regression model is larger than 0.7, and in many cases this is achieved for more than 50% of the total basin area. In general, we show that water table depth is most strongly related to location within a basin and slope, while conductivity and recharge are more important predictors for groundwater flux metrics. Results also illustrate spatial variability in these relationships; further demonstrating the historic difficulty in developing spatially contiguous classifications of groundwater behavior. This work highlights the potential to combine new statistical techniques with integrated hydrologic models to help improve our understanding of complex heterogeneous systems. [ABSTRACT FROM AUTHOR]

Highlights: [•] This study analyzes the impact of aquifer characterization on land energy fluxes. [•] We use an integrated physical hydrology model with a coupled land surface model. [•] We simulate the Upper Klamath basin using four subsurface parameterizations. [•] Subsurface heterogeneity does not fundamentally alter groundwater surface water connections. [•] Regional results are consistent but local differences are significant between scenarios. [ABSTRACT FROM AUTHOR]

Highlights: [•] This study details development of a coupled physical hydrology management model. [•] We couple water allocation algorithms to an integrated physical model, ParFlow. [•] The management module uses linear optimization to maximize satisfaction of demand. [•] Proof of concept is demonstrated for management scenario in a real world basin. [•] Results highlight ability to balance competing demands in a water-limited system. [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]

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]

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]

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]

• 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]

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]