,HYDRUS-1D is a Microsoft Windows-based modeling environment for analysis of water flow and solute transport in variably saturated porous media. The software package includes the one-dimensional finite element model HYDRUS (version 7.0) for simulating the movement of water, heat, and multiple solutes in variably saturated media. The model is supported by an interactive graphics-based interface for data-preprocessing, discretization of the soil profile, and graphic presentation of the results.,The HYDRUS program is a finite element model for simulating theone-dimensional movement of water, heat, and multiple solutes in variably saturated media. The program numerically solves the Richards' equation for saturated-unsaturated water flow and Fickian-based advection dispersion equations for heat and solute transport.,TheFlow equation incorporates a sink term to account for water uptake by plant roots.,TheHeat transport equation considers conduction as well as convection with flowing water.,TheSolute transport equations consider advective-dispersive transport in the liquid phase, and diffusion in the gaseous phase.,The transport equations also include provisions for:,Nonlinear and/orNonequilibrium reactions between the solid and liquid phases,,Linear equilibrium reactions between the liquid and gaseous phases, Zero order production, and TwoFirst order degradation reactions: One which is independent of other solutes, and One which provides the coupling between solutes involved in sequential first-order decay reactions. The program may be used to analyze water and solute movement in unsaturated, partially saturated, or fully saturated porous media.,The flow region itself may be composed of nonuniform soils. Flow and transport can occur in the vertical, horizontal, or a generally inclined direction. The water flow part of the model can deal with (constant or time-varying) prescribed head and flux boundaries, boundaries controlled by atmospheric conditions, as well as free drainage boundary conditions. Soil surface boundary conditions may change during the simulation from prescribed flux to prescribed head type conditions (and vice versa).,For solute transport the code supports both (constant and varying) prescribed concentration (Dirichlet or first-type) and concentration flux (Cauchy or third-type) boundary conditions. The dispersion coefficient includes terms reflecting the effects of molecular diffusion and tortuosity.,The Unsaturated Soil Hydraulic Properties are described using van Genuchten [1980], Brooks and Correy [1964] and modified van Genuchten type analytical functions. Modifications were made to improve the description of hydraulic properties near saturation. The HYDRUS code incorporates hysteresis by using the empirical model introduced by Scott et al. [1983] and Kool and Parker [1987]. This model assumes that drying scanning curves are scaled from the main drying curve, and wetting scanning curves from the main wetting curve.,HYDRUS also implements a scaling procedure to approximate hydraulic variability in a given soil profile by means of a set of linear scaling transformations which relate the individual soil hydraulic characteristics to those of a reference soil.,Root growth is simulated by means of a logistic growth function. Water and salinity stress response functions can be defined according to functions proposed by Feddes et al. [1978] or van Genuchten [1987].,The governing flow and transport equations are solvednumerically using Galerkin type linear finite element schemes. Integration in time is achieved using an implicit (backwards) finite difference scheme for both saturated and unsaturated conditions. Additional measures are taken to improve solution efficiency for transient problems, including automatic time step adjustment and adherence to preset ranges of the Courant and Peclet numbers. The water content term is evaluated using the mass conservative method proposed by Celia et al. [1990]. Possible options

Mesh, properties, initial conditions, injection/withdrawal rates for modelling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, for unscheduled two hour injection at 133 % designed capacity, on fine scale grid, with some results. Second simulation of J.T. Smith, E. Sonnenthal, P. Dobson, P. Nico, and M. Worthington, 2021. Thermal-hydrological-mechanical modeling of Stockton University reservoir cooling system, Proceedings of the 46th Workshop on Geothermal Reservoir Engineering, Stanford University, SGP-TR-218, from which Figures 6-9, pertain.

Mesh, properties, initial conditions, injection/withdrawal rates for modelling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, for unscheduled two hour injection at 133 % designed capacity, on fine scale grid, with some results. Second simulation of J.T. Smith, E. Sonnenthal, P. Dobson, P. Nico, and M. Worthington, 2021. Thermal-hydrological-mechanical modeling of Stockton University reservoir cooling system, Proceedings of the 46th Workshop on Geothermal Reservoir Engineering, Stanford University, SGP-TR-218, from which Figures 6-9, pertain.

Mesh, properties, initial conditions, injection/withdrawal rates for modeling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, on large scale grid, with some results. First simulation of J.T. Smith, E. Sonnenthal, P. Dobson, P. Nico, and M. Worthington, 2021. Thermal-hydrological-mechanical modeling of Stockton University reservoir cooling system, Proceedings of the 46th Workshop on Geothermal Reservoir Engineering, Stanford University, SGP-TR-218, from which Figures 1-5 pertain.

This research effort is a modeling study using the HYDRUS (2D/3D) computer program (www.pc-progress.com) and described in the manuscript/journal article entitled “Comparison of recharge from drywells and infiltration basins: a modeling study.” All the tables and figures in the journal article will be documented within an Excel spreadsheet that will include worksheet tabs with data associated with each table and figure. The tabs, columns, and rows will be clearly labeled to identify table/figures, variables, and units. The information supporting the model runs will be supported in the example library of HYDRUS (2D/3D) maintained by PC-Progress. Non-standard HYDRUS subroutines for the drywell and for the infiltration pond simulations that were funded by this research will be added and made available for viewing and download. After the 1 year embargo period the site will include a link to the PubMed Central manuscript. For example, the HYDRUS library for the transient head drywell associated with the Sasidharan et al. (2018) paper is now active (https://www.pcprogress.com/en/Default.aspx?h3d2-lib-Drywell ). This dataset is associated with the following publication: Sasidharan, S., S. Bradford, J. Simunek, and S. Kraemer. Comparison of recharge from drywells and infiltration basins: A modeling study. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 594: 125720, (2021).

The dataset supported findings in the study: "Evaluation of SWAT reservoir, ponds, and wetlands tools in water and sediment simulation in the Rock River watershed". Results of this study demonstrate the impact of impoundments in SWAT modeling.The dataset includes sources of the SWAT input data. This dataset is associated with the following publication: Jalowska, A., and Y. Yuan. Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 55(1): 209-227, (2019).

The dataset supported findings in the study: "Evaluation of SWAT reservoir, ponds, and wetlands tools in water and sediment simulation in the Rock River watershed". Results of this study demonstrate the impact of impoundments in SWAT modeling.The dataset includes sources of the SWAT input data. This dataset is associated with the following publication: Jalowska, A., and Y. Yuan. Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 55(1): 209-227, (2019).

The Excel file contains the model input-out data sets that where used to evaluate the two-layer soil moisture and flux dynamics model. The model is original and was developed by Dr. Hantush by integrating the well-known Richards equation over the root layer and the lower vadose zone. The input-output data are used for: 1) the numerical scheme verification by comparison against HYDRUS model as a benchmark; 2) model validation by comparison against real site data; and 3) for the estimation of model predictive uncertainty and sources of modeling errors. This dataset is associated with the following publication: He, J., M.M. Hantush, L. Kalin, and S. Isik. Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 613 part A: 128327, (2022).

The BOREAS HYD-08 team made measurements of surface hydrological processes that were collected at the SSA-OBS Tower Flux site in 1996 to support its research into point hydrological processes and the spatial variation of these processes. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution. This particular data set contains the gravimetric moss moisture measurements from July to August 1996. To collect these data, a nested spatial sampling plan was implemented to support research into spatial variations of the measured hydrological processes and ultimately the impact of these variations on modeled carbon and water budgets.

A histrogram-based boosted regression tree (HBRT) method was used to predict the depth to the surficial aquifer water table (in feet) throughout the State of Wisconsin. This method used a combination of discrete groundwater levels from the U.S. Geological Survey National Water Information System, continuous groundwater levels from the National Groundwater Monitoring Network, the State of Wisconsin well-construction database, and NHDPlus version 2.1-derived points. The predicted water table depth utilized the HBRT model available through Scikit-learn in Python version 3.10.10. The HBRT model can predict the surficial water table depth for any latitude and longitude for Wisconsin. A total of 48 predictor variables were used for model development, including basic well characteristics, soil properties, aquifer properties, hydrologic position on the landscape, recharge and evapotranspiration rates, and bedrock characteristics. Model results indicate that the mean surficial water table depth across Wisconsin is 28.3 feet below land surface, with a root mean square error of 7.40 feet for the holdout data to the HBRT model. Aside from the overall HBRT methods contained as part of the Python script, this data release includes a self-contained model directory for recreating the HBRT model published in this data release. The model directory also includes a model object for the HBRT model used to predict the surficial aquifer water table depth (in feet) for the State of Wisconsin. Three separate directories are available within this data release that define the input predictor variables, water levels, and NHD points for the HBRT model. The 'bedrock-overlay' sub-directory contains geospatial data that define the special selection zones used in the depth-to-water well selection (DTW_well_selection_zones.docx). The 'water-levels' sub-directory contains input files for the NHDPlus version 2.1 points, the State of Wisconsin well construction spreadsheets, and water level summary files. The 'python-attributes' sub-directory contains predictor variable rasters and vector data that predict the surficial water table depth for Wisconsin and a Jupyter Notebook used for the attribution and input files for well and NHD points.