This data set contains fifty-three fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). The Noah model was developed as the land component of the NOAA NCEP mesoscale Eta model [Betts et al. (1997); Chen et al. (1997); Ek et al. (2003)]. As used in NLDAS-2, recent modifications were made to Noah's cold-season [Livneh et al. (2010)] and warm-season [Wei et al. (2012)] parameterizations. Noah serves as the land component in the evolving Weather Research and Forecasting (WRF) regional atmospheric model, the NOAA NCEP coupled Climate Forecast System (CFS), and the Global Forecast System (GFS). The model simulates the soil freeze-thaw process and its impact on soil heating/cooling and transpiration, following Koren et al. (1999). The model has four soil layers with spatially invariant thicknesses of 10, 30, 60, and 100 cm. The first three layers form the root zone in non-forested regions, with the fourth layer included in forested regions. Details about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012).

SpatioTemporal Asset Catalog (STAC) Item - nldas3_LIS_HIST_202112 in nldas3

This data set contains forty-four fields simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format). The VIC model was developed at the University of Washington and Princeton University as a macroscale, semi-distributed, grid-based, hydrologic model [Liang et al., 1994; Wood et al., 1997]. The full water and energy balance modes of VIC were used for NLDAS-2. VIC uses three soil layers, with thicknesses that vary spatially. The root zone depends on the vegetation type and its root distribution, and can span all three soil layers. The VIC model includes a two-layer energy balance snow model [Cherkauer et al., 2003]. Details about the NLDAS-2 configuration of the VIC LSM can be found in Xia et al. (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield et al. (2003).

This data set contains the primary forcing hourly data "File A" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB data files).The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. Additionally, the fields of surface pressure, surface downward longwave radiation, near-surface air temperature, and near-surface specific humidity are adjusted vertically to account for the vertical difference between the NARR and NLDAS fields of terrain height. This vertical adjustment applies the traditional vertical lapse rate of 6.5 K/km for air temperature. The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are presented by Cosgrove et al. (2003).The surface downward shortwave radiation field in "File A" is a bias-corrected field wherein a bias-correction algorithm was applied to the NARR surface downward shortwave radiation. This bias correction utilizes five years (1996-2000) of the hourly 1/8th-degree GOES-based surface downward shortwave radiation fields derived by Pinker et al. (2003). The potential evaporation field in "File A" is that computed in NARR using the modified Penman scheme of Mahrt and Ek (1984).The precipitation field in "File A" is not the NARR precipitation forcing, but is rather a product of a temporal disaggregation of a gauge-only CPC analysis of daily precipitation, performed directly on the NLDAS grid and including an orographic adjustment based on the widely-applied PRISM climatology. The precipitation is temporally disaggregated into hourly fields by deriving hourly disaggregation weights from either WSR-88D Doppler radar-based precipitation estimates, 8-km CMORPH hourly precipitation analyses, or NARR-simulated precipitation (based on availability, in order). The latter fields from radar, CMORPH, and NARR are used only to derive disaggregation weights and do not change the daily total precipitation. The field in "File A" that gives the fraction of total precipitation that is convective is an estimate derived from the following two NARR precipitation fields (which are provided in "File B"): NARR total precipitation and NARR convective precipitation (the latter is less than or equal to the NARR total precipitation and can be zero). The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from NARR.The hourly land surface forcing fields for NLDAS-2 are grouped into two files, "File A" and "File B". "File A" is the primary (default) forcing file and contains eleven meteorological forcing fields. Details about the generation of the NLDAS-2.0 forcing datasets can be found in Xia et al. (2012).

This data set contains thirty-eight fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format).Mosaic was developed by Koster and Suarez (1994, 1996) to account for subgrid vegetation variability with a tile approach. Each vegetation tile carries its own energy and water balance and soil moisture and temperature. Each tile has three soil layers, with the first two in the root zone. In NLDAS, Mosaic is configured to support a maximum of 10 tiles per grid cell with a 5% cutoff that ignores vegetation classes covering less than 5% of the cell. Additionally in NLDAS, all tiles of Mosaic in a grid cell have a predominant soil type and three soil layers with fixed thickness values of 10, 30, and 160 cm (hence constant rooting depth of 40 cm and constant total column depth of 200 cm). Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).

The goal of NLDAS is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. NLDAS is currently running in near real-time on a 1/8th-degree grid over central North America; retrospective NLDAS datasets and simulations also extend back to January 1979. NLDAS constructs a forcing dataset from gauge-based observed precipitation data (temporally disaggregated using Stage II radar data), bias-correcting shortwave radiation, and surface meteorology reanalyses to drive several different LSMs to produce model outputs of surface fluxes, soil moisture, and snow cover. NLDAS is a collaboration project among several groups: NOAA/NCEP's Environmental Modeling Center (EMC), NASA's Goddard Space Flight Center (GSFC), Princeton University, the University of Washington, the NOAA/NWS Office of Hydrological Development (OHD), and the NOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project with support from NOAA's Climate Prediction Program for the Americas (CPPA). Data from the project can be accessed from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) as well as from the NCEP/EMC NLDAS website. This service provides access to NASA's North American Land Data Assimilation System (NLDAS) hourly Mosaic land surface model data.

This data set contains the forcing data for Phase 1 of the North American Land Data Assimilation System (NLDAS-1). The data are in 1/8th degree grid spacing and range from 29 Sep 1996 to 31 Dec 2007. The temporal resolution is hourly. The file format is WMO GRIB-1.The chief source of NLDAS-1 forcing is NCEP's Eta model-based Data Assimilation System (EDAS) [Rogers et al., 1995], a continuously cycled North American 4DDA system. It utilizes 3-hourly analysis-forecast cycles to derive atmospheric states by assimilating many types of observations, including station observations of surface pressure and screen-level atmospheric temperature, humidity, and U and V wind components.

EDAS 3-hourly fields of the latter five variables plus surface downward shortwave and longwave radiation and total and convective precipitation are provided on a 40-km grid, and then interpolated spatially to the NLDAS grid and temporally to one hour. Last, to account for NLDAS versus EDAS surface-elevation differences, a terrain-height adjustment is applied to the air temperature and surface pressure using a standard lapse rate (6.5 K/km), then to specific humidity (keeping original relative humidity) and downward longwave radiation (for new air temperature, specific humidity). The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are presented by Cosgrove et al. (2003).

GOES-based solar insolation (Pinker et al., 2003) provides the primary insolation forcing (shorwave down at the surface) for NLDAS-1. GOES insolation is not retrieved for zenith angles below 75 degrees and so is supplemented with EDAS insolation near the day/night terminator. Last from the GOES-based product suite, Photosynthetically Active Radiation (PAR) and surface brightness temperature fields are included in the NLDAS-1 forcing files.

NLDAS-1 precipitation forcing over CONUS is anchored to NCEP's 1/4th degree gauge-only daily precipitation analyses of Higgins et al. [2000]. In NLDAS-1, this daily analysis is interpolated to 1/8th degree, then temporally disaggregated to hourly values by applying hourly weights derived from hourly, 4-km, radar-based (WSR-88D) precipitation fields. The latter radar-based fields are used only to derive disaggregation weights and do not change the daily total precipitation. Last, convective precipitation is estimated by multiplying NLDAS-1 total precipitation by the ratio of EDAS convective to EDAS total precipitation. The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from EDAS.

The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

This data set contains the secondary forcing hourly data "File B" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format isnetCDF (converted from the GRIB data files).The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. NLDAS-2 is providing a second forcing file, "File B", in which the surface temperature, humidity, and wind fields are represented not at 2-meters and 10-meters above the height of the NLDAS terrain, but rather at the same height above the NLDAS terrain as the height above the NARR terrain of the lowest prognostic level of the NARR assimilation system (namely, the same height above the model terrain as the lowest prognostic level of the mesoscale Eta model, which is the assimilating model in NARR). The surface downward surface radiation field in "File B" is taken directly from NARR, without any bias correction. The precipitation and convective precipitation fields in "File B" are also taken directly from NARR, and are used to calculate the convective fraction provided in "File A". The aerodynamic conductance is "File B" is also taken from NARR.The hourly land surface forcing fields for NLDAS-2 are grouped into two files, "File A" and "File B". "File B" is the secondary (optional) forcing file and contains ten meteorological forcing fields. Details about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012).

Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions. We correlated contemporary walleye recruitment success and largemouth bass relative abundance to modeled water temperature, lake morphometry, and lake productivity, and projected lake specific changes in each species under future climate conditions. Walleye recruitment success was negatively related and largemouth bass abundance was positively related to water temperature degree days. Both species exhibited a threshold response at the same degree day value, albeit in opposite directions. Degree days were predicted to increase in the future, although the magnitude of increase varied among lakes, time periods, and global circulation models (GCMs). Under future conditions, we predicted a loss of walleye recruitment in 30-70% of lakes, and an increase to high largemouth bass relative abundance in 17-55% of additional lakes. The percentage of lakes with abundant largemouth bass and failed walleye recruitment was predicted to increase from 59% in contemporary conditions to 86% of lakes by mid-century and to 91% of lakes by late century, based on median projections across GCMs. Conversely, the number of lakes with successful walleye recruitment and low largemouth bass abundance was predicted to decline from 8.5% of lakes in contemporary conditions to only 38 1% of lakes in both future periods. Importantly, we identify nearly 100 resilient lakes predicted to continue to support walleye recruitment. Management resources could target preserving these resilient walleye populations. This data set contains the following parameters: year, WBDY_WBIC, days_12_28, height_12_28, vol_12_28, days_10.6_11.2, height_10.6_11.2, vol_10.6_11.2, days_18.2_28.2, height_18.2_28.2, vol_18.2_28.2, days_18_22, height_18_22, vol_18_22, days_19.3_23.3, height_19.3_23.3, vol_19.3_23.3, days_19_23, height_19_23, vol_19_23, days_20.6_23.2, height_20.6_23.2, vol_20.6_23.2, days_20_30, height_20_30, vol_20_30, days_21_100, days_22_23, height_22_23, vol_22_23, days_23_31, height_23_31, vol_23_31, days_25_29, height_25_29, vol_25_29, days_26.2_32, height_26.2_32, vol_26.2_32, days_26_28, height_26_28, vol_26_28, days_26_30, height_26_30, vol_26_30, days_28_29, height_28_29, vol_28_29, days_28_32, height_28_32, vol_28_32, days_29_100, height_29_100, vol_29_100, days_30_31, height_30_31, vol_30_31, durStrat, winter_dur_0-4, spring_days_in_10.5_15.5, mean_surf_jul, mean_surf_JAS, peak_temp, post_ice_warm_rate, SthermoD_mean, dateOver21, dateOver18, , dateOver8.9, SmetaTopD_mean, SmetaBotD_mean, coef_var_30_60, coef_var_0_30, mean_epi_hypo_ratio, mean_epi_vol, mean_hyp_vol, simulation_length_days, volume_mean_m_3, volume_sum_m_3_day, GDD_wtr_10c, GDD_wtr_5c, optic_hab_8_64, thermal_hab_11_25, optic_thermal_hab, optic_hab_8_64_surf, thermal_hab_11_25_surf, optic_thermal_hab_surf calculated for 2148 lakes

The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate.An overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document.This NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0.NCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product.

Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions. We correlated contemporary walleye recruitment success and largemouth bass relative abundance to modeled water temperature, lake morphometry, and lake productivity, and projected lake specific changes in each species under future climate conditions. Walleye recruitment success was negatively related and largemouth bass abundance was positively related to water temperature degree days. Both species exhibited a threshold response at the same degree day value, albeit in opposite directions. Degree days were predicted to increase in the future, although the magnitude of increase varied among lakes, time periods, and global circulation models (GCMs). Under future conditions, we predicted a loss of walleye recruitment in 30-70% of lakes, and an increase to high largemouth bass relative abundance in 17-55% of additional lakes. The percentage of lakes with abundant largemouth bass and failed walleye recruitment was predicted to increase from 59% in contemporary conditions to 86% of lakes by mid-century and to 91% of lakes by late century, based on median projections across GCMs. Conversely, the number of lakes with successful walleye recruitment and low largemouth bass abundance was predicted to decline from 8.5% of lakes in contemporary conditions to only 38 1% of lakes in both future periods. Importantly, we identify nearly 100 resilient lakes predicted to continue to support walleye recruitment. Management resources could target preserving these resilient walleye populations. This data set contains the following parameters: year, WBDY_WBIC, days_12_28, height_12_28, vol_12_28, days_10.6_11.2, height_10.6_11.2, vol_10.6_11.2, days_18.2_28.2, height_18.2_28.2, vol_18.2_28.2, days_18_22, height_18_22, vol_18_22, days_19.3_23.3, height_19.3_23.3, vol_19.3_23.3, days_19_23, height_19_23, vol_19_23, days_20.6_23.2, height_20.6_23.2, vol_20.6_23.2, days_20_30, height_20_30, vol_20_30, days_21_100, days_22_23, height_22_23, vol_22_23, days_23_31, height_23_31, vol_23_31, days_25_29, height_25_29, vol_25_29, days_26.2_32, height_26.2_32, vol_26.2_32, days_26_28, height_26_28, vol_26_28, days_26_30, height_26_30, vol_26_30, days_28_29, height_28_29, vol_28_29, days_28_32, height_28_32, vol_28_32, days_29_100, height_29_100, vol_29_100, days_30_31, height_30_31, vol_30_31, durStrat, winter_dur_0-4, spring_days_in_10.5_15.5, mean_surf_jul, mean_surf_JAS, peak_temp, post_ice_warm_rate, SthermoD_mean, dateOver21, dateOver18, , dateOver8.9, SmetaTopD_mean, SmetaBotD_mean, coef_var_30_60, coef_var_0_30, mean_epi_hypo_ratio, mean_epi_vol, mean_hyp_vol, simulation_length_days, volume_mean_m_3, volume_sum_m_3_day, GDD_wtr_10c, GDD_wtr_5c, optic_hab_8_64, thermal_hab_11_25, optic_thermal_hab, optic_hab_8_64_surf, thermal_hab_11_25_surf, optic_thermal_hab_surf calculated for 2148 lakes

These are example application notebooks to simulate SUMMA using CAMELS datasets. There are three steps: (STEP-1) Create SUMMA input, (STEP-2) Execute SUMMA, (STEP-3) Visualize SUMMA output Based on this example, users can change the HRU ID and simulation periods to analyze 671 basins in CAMELS datasets.

(STEP-1) A_1_camels_make_input.ipynb - The first notebook creates SUMMA input using Camels dataset using summa_camels_hydroshare.zip in this resource and OpenDAP(https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/). (STEP-2) B_1_camels_pysumma_default_prob.ipynb, B_2_camels_pysumma_lhs_prob.ipynb, B_3_camels_pysumma_config_prob.ipynb, and B_4_camels_pysumma_lhs_config_prob.ipynb - These four notebooks execute SUMMA considering four different parameters and parameterization combinations (STEP-3) C_1_camels_analyze_output_default_prob.ipynb, C_2_camels_analyze_output_lhs_prob.ipynb, C_3_camels_analyze_output_config_prob.ipynb, C_4_camels_analyze_output_lhs_config_prob.ipynb - The final four notebooks visualize SUMMA output of B-1, B-2, B-3, and B-4 notebooks.

Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE-FO satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.This data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) Global Version 3.0 from a global GRACE and GRACE-FO data assimilation and drought indicator product generation (Li et al., 2019). It varies from the other GRACE-DA-DM products which are from the U.S. GRACE-based drought indicator product generation (Houborg et al., 2012).The GRACE-DA-DM Global V3.0 is similar to the GRACE-DA-DM U.S. V4.0 product. Both products are based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). GRACE-DA-DM Global V3.0 drought indicator maps are derived from the GLDAS_CLSM025_DA1_D product, at 0.25 degree resolution, forced by ECMWF meteorological data, and assimilated RL06 GRACE and GRACE-FO data from the University of Texas at Austin (Save et al., 2016; Save, 2020). The GRACE-DA-DM U.S. V4.0 is at 0.125 degree, which is based on a model simulation (not published at GES DISC) forced by NLDAS-2 meteorological data and assimilated with RL06 GRACE/GRACE-FO data. More information on GRACE-DA-DM U.S. V4.0 and previous versions of the data can be found in the README.The GRACE-DA-DM Global V3.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.25 x 0.25 degree with global coverage (60S, 180W, 90N, 180E), and a temporal range from February 2003 to present (with a 3-6 month latency). The data are archived in NetCDF format.The GRACE-DA-DM is an operational project which produces groundwater and soil moisture drought indicators each week. The operational data is available weekly with a 2-9 day latency from the NASA GRACE project home page found under the Documentation tab. The GRACE-DA-DM data distributed here at GESDISC is the final archive version, which is generated after the latest GRACE-FO data are available.

SUMMA Simulation in East Branch Delaware River at Margaretville New York using Camels Datasets in on CyberGIS Jupyter for water There are four Jupyter notebooks to demonstrate SUMMA Simulations 1. Use installation.ipynb to install required dependencies 2. Create SUMMA input using Camels dataset via this HS resource and OpenDAP(https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/) 3. Execute SUMMA using pySUMMA 4. Plot SUMMA output

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

This repository hosts the setup for executing a basin-scale simulation of the Big Spring watershed that drains into Sycamore Creek in northern Arizona, USA, using the TIN-Based Real-Time Integrated Basin Simulator (tRIBS).

This repository consists of :

(1) A data subdirectory, which contains all the necessary data to run the basin-scale simulation of Big Spring, using tRIBS.

2) A doc subdirectory, which contains a jupyter notebook for analyzing and visualizing this specific benchmark case, alongside additional documentation.

(3) A src subdirectory that is currently empty but intended to contain source code for the tRIBS executable, which can be obtained here.

(4) A bin subdirectory, that is currently empty but intended to store the tRIBS executable, with instructions to build the model here.

(5) A results directory, which contains reference model output for results comparison.

Instructions for running this benchmark are available in the README.html file within the repository, which can be opened with your web browser, and can also be found in the latest tRIBS documentation.

Data from this model were obtained from a number of public sources:

Soil properties were obtained from ISRIC (https://www.isric.org), with tRIBS input parameters derived from the ROSETTA pedotransfer functions (https://github.com/usda-ars-ussl/rosetta-soil). Precipitation and vegetation parameters related to tree height and vegetation fraction were provided by Salt River Project. Meteorological forcing was obtained from NLDAS-2 (https://ldas.gsfc.nasa.gov/nldas/v2/forcing),with rainfall data provided from Salt River Project rain gauges.

NOTE: If you encounter a situation where, after downloading and extracting the big_spring.gz file, the extracted file has no extension, please follow these steps:

Rename the File: Manually rename the extracted file to big_spring.zip.

Extract Again: After renaming, unzip the big_spring.zip file to access the full folder with all the necessary files.

This issue may occur if the file extraction process doesn't automatically add the .zip extension. By renaming the file and unzipping it again, you should be able to access the complete set of files without any issues.

These tabular data sets represent the average daily soil moisture water content (kg/m^2) for four different soil layers processed from North American Land Data Assimilation System (NLDAS-2) data (Xia and others, 2012) for the period of record 1980 through 2020 and compiled for three spatial components: 1) select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Upstream watershed values for each reach catchment were computed using the published python software package Xstrm (Wieferich and others). The following mean daily soil moisture water content layers we ...

[You can run this model with the notebook at https://www.hydroshare.org/resource/8fe974c108ca4c6eaaf9b060779329b0/ in CyberGIS-Jupyter for Water platform]

WRFHydro Test Case -- Croton River, NY

Overview This test case includes prepared geospatial data and input files for a

sample domain (region of interest) and prepared forcing data. This domain is a small region (15km x 16km) encompassing the West Branch of the Croton River, NY, USA (USGS stream gage 0137462010) during hurricane Irene, 2011-08-26 to 2011-09-02. The simulation begins with a restart from a spinup period from 2010-10-01 to 2011-08-26. The forcing data prepared for this test case is North American Land Data Assimilation System (NLDAS) hourly data. There are 3 basic routing configurations included in the test case, National Water Model (NWM), Gridded, and NCAR Reach. See the WRF-Hydro V5 Technical Description located at https://ral.ucar.edu/projects/wrf_hydro for a more detailed description of model physics options, configurations, and input files.

The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region. WLDAS uses NASA’s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables.