Datasets
This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6).
This global dataset provides the estimated volatility of corn yields due to heat stress for 20 climate models in NEX-GDDP-CMIP6 data at 0.25-degree resolution.
In the arid and semi-arid Western U.S., access to water is regulated through a legal system of water rights. Individuals, companies, organizations, municipalities, and tribal entities have documents that declare their water rights. State water regulatory agencies collate and maintain these records, which can be used in legal disputes over access to water. While these records are publicly available data in all Western U.S. states, the data have not yet been readily available in digital form from all states. Furthermore, there are many differences in data format, terminology, and definitions between state water regulatory agencies. Here, we have collected water rights data from 11 Western U.S. state agencies, harmonized terminology and use definitions, formatted them for consistency, and tied them to a Western U.S.-wide shapefile of water administrative boundaries.
Citation |
Lisk, M., Grogan, D., Zuidema, S., Caccese, R., Peklak, D., Zheng, J., Fisher-Vanden, K., Lammers, R., Olmstead, S., & Fowler, L. (2023): Harmonized Database of Western U.S. Water Rights (HarDWR), MSD-LIVE Data Repository, DOI:10.57931/2205619. |
Associated Paper |
Lisk, M.D., Grogan, D.S., Zuidema, S., Zheng, J., Caccese, R., Peklak, D., Fisher-Vanden, K., Lammers, R., Olmstead, S., Fowler, L. (2024): Harmonized Database of Western U.S. Water Rights (HarDWR) v.1. . Scientific Data, 11, 598. https://doi.org/10.1038/s41597-024-03434-6. |
The MERIT-Plus river network datasets in 5 and 15 arc minute resolution add value to the original upscaled IHU MERIT data with the main purpose of this work to identify the endorheic and exorheic basin types that are missing in the source datasets. Merging (cleanup) of small endorheic basins introduced few local changes in flow direction and basin identification data but made the datasets more suitable for a broader range of hydrological modeling applications that simulate water balance and accumulation in the endorheic lakes or land depressions.
Citation |
Prusevich, A., Lammers, R., & Glidden, S. (2023): MERIT-Plus Dataset: Delineation of endorheic basins in 5 and 15 min upscaled river networks (v2.2) [Data set], MSD-LIVE Data Repository, DOI:10.57931/2248064. |
Associated Paper |
Prusevich, A.A., Lammers, R.B. & Glidden, S.J. (2024): Delineation of endorheic drainage basins in the MERIT-Plus dataset for 5 and 15 minute upscaled river networks. Scientific Data, 11, 61. https://doi.org/10.1038/s41597-023-02875-9. |
A price-adjusted quantity index is introduced for the total production of crops for irrigated and rainfed farms for the Contiguous U.S. (without crop details) at 5 arc-min resolution. Here, the production of all crops in each grid cell is aggregated into two crop composites: irrigated (QCROP_irr) and rainfed (QCROP_rfd). Each composite may have a slightly different mix of crops with different prices. To consider price differences, a “corn-equivalent” index is generated for production. The dataset also represents the cropland area for irrigated (QLAND_irr) and rainfed (QLAND_rfd) around the year 2010 provided in NetCDF, GeoTIFF, CSV, and HAR file formats.
Citation |
Haqiqi, I., Bowling, L., Jame, S., Baldos, U. L., Liu, J., Hertel, T. (2023): A Gridded Price-Adjusted Quantity Index for Total Production of Crops for Irrigated and Rainfed Farms for the Contiguous U.S., MyGeoHUB, DOI:10.13019/RQ0D-JH17. |
Associated Paper |
Haqiqi, I., Bowling, L. C., Jame, S. A., Baldos, U. L., & Liu, J. Hertel, T. W., (2023): Global Drivers of Local Water Stresses and Global Responses to Local Water Policies in the United States. Environmental Research Letters, 18: 065007. https://doi.org/10.1088/1748-9326/acd269. |
Three scenarios of “low”, “medium”, and “high” levels of restriction on groundwater are developed. This dataset includes likely groundwater sustainability restriction policies (GSPs) considering 2010 levels of groundwater withdrawals in the United States. Groundwater sustainability is defined in a rather simplified way assuming the groundwater extraction should not exceed the average recharge rates. The data is provided in NetCDF, GeoTIFF, CSV, and HAR file formats.
Citation |
Haqiqi, I.(2023): A Gridded Dataset for Groundwater Sustainability Restriction Policy Scenarios for the Contiguous U.S., MyGeoHUB, DOI:10.13019/AHZR-4843. |
Associated Paper |
Haqiqi, I., Bowling, L. C., Jame, S. A., Baldos, U. L., & Liu, J. Hertel, T. W., (2023): Global Drivers of Local Water Stresses and Global Responses to Local Water Policies in the United States. Environmental Research Letters, 18: 065007. https://doi.org/10.1088/1748-9326/acd269. |
Associated Paper |
Baldos, U. L. C., Haqiqi, I., Hertel, T., Horridge, M., and Liu, J. (2020): SIMPLE-G: A Multiscale Framework for Integration of Economic and Biophysical Determinants of Sustainability. Environmental Modelling & Software, 133: 104805. https://www.sciencedirect.com/science/article/pii/S1364815220304205. |
This document describes a global database of inter-basin hydrological transfers that has been used by the University of New Hampshire Water Balance Model for several years. The database has focused primarily on large scale water transfers that are built, under-construction, or proposed.
Download the Singularity image and data files associated with the open source release of the University of New Hampshire Water Balance Model from
the University of New Hampshire Water Balance Model Ancillary Data Download Website.
Citation |
Grogan D.S., Zuidema S., Prusevich A., Wollheim W.M., Glidden S., and Lammers R.B. (2022): University of New Hampshire Water Balance Model Ancillary Data for use with the WBM Open Source Release Version 1.0. https://wbm.unh.edu/, University of New Hampshire Scholars Repository, DOI:10.34051/d/2022.2. |
Associated Paper |
Grogan, D.S., Zuidema S., Alex Prusevich, Wollheim W. M., Glidden S,, and Lammers R. B. (2022): Water Balance Model (WBM) v.1.0.0: A Scalable Gridded Global Hydrologic Model with Water-Tracking Functionality. Geoscientific Model Development, 15 (19): 7287–7323. https://doi.org/10.5194/gmd-15-7287-2022. |
An update to global gridded monthly crop datasets. This new dataset uses the crop categories established by the Global Agro-Ecological Zones (GAEZ) Version 3 model, which is based on the Food and Agricultural Organization of the United Nations (FAO) crop production data. We used publicly available data from the GAEZ+_2015 Annual global gridded dataset (Frolking et al., 2000) along with data from the MIRCA2000 dataset (Portmann et al. 2008) on crop rotations, cropping intensity, and planting and harvest dates to generate c.2015 monthly crop physical area by crop production system (irrigated and rainfed) for 26 crops and crop categories globally at 5-minute resolution. Each crop category can have up to 5 subcrop categories to represent crop rotations, resulting in a total of 93 individual crop/subcrop/production system combinations (93 files total). These data are in standard georeferenced gridded format (netCDF files), and can be used by any global hydrology, land surface, or other earth system model that requires gridded annual or monthly crop data inputs.
Citation |
Grogan, DS, A Prusevich, S Frolking, D Wisser and S Glidden (2021): GAEZ+_2015 Monthly Cropland Data: Global gridded monthly crop physical area for 26 irrigated and rainfed crops, MyGeoHUB, DOI:10.13019/J2BH-VB41. |
Associated Paper |
Grogan, DS, S Frolking, D Wisser, A Prusevich and S Glidden (2022): Global Gridded Crop Harvested Area, Production, Yield, and Monthly Physical Area Data circa 2015. Scientific Data, 9 (1): 1–16. https://doi.org/10.1038/s41597-021-01115-2. |
To accurately represent the routing and storage of water through terrestrial river networks, macro-scale hydrologic models require accurate representations of impounded water-bodies throughout the model domain. The Hydrologically-Consistent Dams (HydroConDams) dataset provides a critical compilation and correction of available datasets for the Conterminous United States (CONUS). HydroConDams provides locations for the single major point of outflow of reservoirs, maximum capacity information, inundated surface area, upstream catchment area, dam construction year, and primary dam purpose. These data are considered appropriate for representing the routing and storage effects of impounded waterbodies along river networks for meso- to macro-scale models of terrestrial hydrology and the land-surface (pixel-sizes from 100’s m to 100’s km).
Here, we combine a high-resolution weather product (PRISM) with fine-scale outputs of a hydrologic model (WBM) to construct functional indicators of compound hydroclimatic extremes for agriculture. Data for US counties for 1981-2015. The main variables are: degree days above 29C; degree days from 10C to 29C; seasonal mean volumetric soil moisture; seasonal mean soil moisture fraction; seasonal mean evapotranspiration; cumulative precipitation; corn irrigated area share; and metrics for the daily interaction of heat and soil moisture.
Citation |
Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2020): Data Sets for: Quantifying the Impacts of Compound Extremes on Agriculture and Irrigation Water Demand, Purdue University Research Repository, DOI:10.4231/0M14-EY38. |
Associated Paper |
Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2021): Quantifying the impacts of compound extremes on agriculture, Hydrology and Earth System Sciences, 25(2), 551-564, DOI: 10.5194/hess-25-551-2021. |
Source Code
Interactive Jupyter dashboard visualizing the relative importance of different uncertainty sources in local climate projections. Learn more about creating and using dashboards for interactive visualization of scientific data and results with Panel in
this blog post by David Lafferty.
This is release v1.0.0 of the WBM open source code.
This utility summarizes the USGS Water Use data available in the public domain in a convenient Jupyter notebook utilizing python.
This Stata code estimates a model to investigate the impacts of compound extremes, water stress, and heat stress on crop yields. It requires "soilMoistureData.dta", which can be found in the data product cited above, Haqiqi et al 2020 (DOI:10.4231/0M14-EY38). The code will estimate 1) the marginal impacts of heat stress on crop yields; 2) the marginal impact of daily soil moisture extremes on crop yields, and 3) the conditional marginal impact of heat and soil moisture on crop yields. This code can be used for studying the impacts of compound extremes on agriculture. The code generates figures and tables too.
Citation |
Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2020): Model Code for: Quantifying the Impacts of Compound Extremes on Agriculture and Irrigation Water Demand, Purdue University Research Repository, DOI:10.4231/Q07D-J369 |
Associated Paper |
Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2021): Quantifying the impacts of compound extremes on agriculture, Hydrology and Earth System Sciences, 25(2), 551-564, DOI: 10.5194/hess-25-551-2021. |
Code for interpolating inundation return periods for parcels of land between the 10 year and 500 year FEMA flood water surface elevations. The code's goal is to improve flood risk communication and understanding by interpolating flood probabilities from existing FEMA flood maps and data. This analysis focuses on two locations: a model testing case in the Sims Bayou, Houston, TX. and an application case at the town of Muncy, PA.
SIMPLE-on-a-Grid (SIMPLE-G) is a multi-region, partial equilibrium model of gridded cropland use, crop production, consumption and trade. It is an extension of the SIMPLE model that has been applied to study long run sustainability issues in the global food-water-environment nexus. Rather than looking at regions or country aggregates, SIMPLE-G divides the world into georeferenced grid-cell units. This allows SIMPLE-G to explicitly incorporate local environmental constraints in its projections, account for sub-national heterogeneity of global drivers such as climate change and water scarcity, and assess local land and water use given future trends the global farm and food system.
In SIMPLE, the world is split into sixteen economic regions. Regional consumption is disaggregated into four commodities (crops, livestock, processed foods and biofuels). Regional demand is driven by population, per capita income, and biofuel mandates (all exogenous in the model) as well as prices (endogenous to the model). SIMPLE-G extends the existing SIMPLE model by disaggregating rainfed and irrigated production and modeling these processes at the individual grid-cell level. Regional crop output is obtained by aggregating across the grid cells (30 arc-min resolution) within each region. Crop production follows a nested constant elasticity of substitution (CES) function. Water is an explicit input used by the irrigated sector only. Water consumption is computed as the product of gridded irrigated cropland area and a grid cell-specific consumptive water use parameter in m3/ha. By aggregating water use across grid cells within a sub-basin (defined below), we obtain total irrigation consumption. Water availability at each grid cell is exogenous in SIMPLE-on-a-Grid, and is obtained from the hydrological model.
MyGeoHub |
SIMPLE-on-a-Grid (SIMPLE-G) model |
Global Trade Analysis Project |
Information hub for the SIMPLE-G model at Purdue Univrsity |
Associated Paper |
Baldos, U. L. C., Haqiqi, I., Hertel, T., Horridge, M., and Liu, J. (2020): SIMPLE-G: A Multiscale Framework for Integration of Economic and Biophysical Determinants of Sustainability. Environmental Modelling & Software, 133: 104805. https://www.sciencedirect.com/science/article/pii/S1364815220304205. |