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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]

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]

• 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]

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]