An official website of the United States government
We provide a dataset of irrigation water withdrawals by crop, county, year, and water source within the United States. We employ a framework we previously developed to establish a companion dataset to our original estimates. The main difference is that we now use the U.S. Geological Survey (USGS) variable ‘irrigation — total’ to partition PCR-GLOBWB 2 hydrology model estimates, instead of ‘irrigation — crop’ as used in previous estimates. Our findings for Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) are similar to those of prior estimates but now have better spatial coverage, since several states are missing from the USGS ‘irrigation — crop’ variable that was originally used. Irrigation water use increases in this study, since more states are included and ‘irrigation — total’ includes more categories of irrigation than ‘irrigation — crop’. Notably, irrigation in the Mississippi Embayment Aquifer is now captured for rice and soy. We provide nearly 2.5 million data points with this paper (3,142 counties; 13 years; 3 water sources; and 20 crops).
more »
« less
Data for Total irrigation by crop in the Continental United States from 2008 to 2020
This dataset provides estimates of total Irrigation Water Use (IWU) by crop, county, water source, and year for the Continental United States. Total irrigation from Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2024) "Total irrigation by crop in the Continental United States from 2008 to 2020", Scientific Data, doi: 10.1038/s41597-024-03244-w When using, please cite as: Ruess, P.J., Konar, M., Wanders, N., and Bierkens, M.F.P. (2024) Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, doi: 10.1038/s41597-024-03244-w
more »
« less
- Award ID(s):
- 2115405
- PAR ID:
- 10544947
- Publisher / Repository:
- University of Illinois at Urbana-Champaign
- Date Published:
- Subject(s) / Keyword(s):
- water use irrigation surface water groundwater groundwater depletion counties crops time series
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Zero v1.0 Universal
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract: Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) – the Harmonized Land Nutrient Irrigation Dataset (HLNID) – that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938-2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting. TechnicalInfo: # Harmonized land nutrient irrigation dataset (HLNID) for conterminous United States, 1938-2020 Dataset DOI: [10.5061/dryad.r4xgxd2rh](10.5061/dryad.r4xgxd2rh) ## Description of the data and file structure Here we provide a new dataset – the Harmonized Land Nutrient Irrigation Dataset (HLNID). The raster datasets (GeoTIFFs) consist of single categorical values for each pixel for land use-land cover (and crop type), continuous values for nutrients, and Boolean values for irrigation extent. While we provide data for two spatial resolutions (48 and 150 arc-sec), the method is scale independent. We use the temporally consistent and county-scale USDA Census of Agriculture (CoA) dataset as a foundational data source for the method to allow for harmonization through time and across other historical datasets. The method preserves county-scale estimates from the CoA and other sources (FORE-SCE, LCMAP, NLCD) and uses recent model-based and satellite-based datasets (LCMAP, NLCD, CDL, MIrAD) to spatially allocate LULC, crop type, and irrigation extent across a given county. It also preserves county-scale nutrient mass estimates and spatially allocates within a county according to crop-specific demand. ### Files and variables #### File: HLNID_150sec_20260416.zip **Description:** Harmonized Land Nutrient Irrigation Dataset (HLNID) at 150 arc-second resolution 'irrig' folder: annual irrigation extent raster datasets (GeoTIFFs) from 1938-2020 'irrig_YYYY.tif': boolean data representing irrigation extent 0: no irrigation present 1: irrigation present 'lulc' folder: annual land use-land cover (LULC) raster datasets (GeoTIFFs) from 1938-2020 'vegtype_YYYY.tif': integer data representing LULC category for each individual year | LULC code | LULC description | | :-------- | :--------------------------- | | 4 | Evergreen forest | | 5 | Deciduous forest | | 8 | Mixed forest | | 10 | Grassland | | 11 | Shrubland | | 15 | Perennial ice/snow | | 16 | Corn | | 17 | Soybean | | 18 | Small grains | | 19 | Alfalfa | | 20 | Non-alfalfa hay | | 21 | Pasture / range | | 22 | Developed / high intensity | | 23 | Developed / medium intensity | | 24 | Developed / low intensity | | 25 | Developed / open space | | 26 | Herbaceous wetland | | 27 | Woody wetland | | 30 | Barren | | 98 | Open water | | 99 | Other crop | 'nutrients' folder: annual nutrient (nitrogen and phosphorus) inputs from fertilizer and manure from 1938-2020 NOTE: manure is estimated on an "as-excreted" basis and does not account for losses prior to typical land application 'fertnkg_YYYY.tif': fertilizer nitrogen [kg/ha] 'fertpkg_YYYY.tif': fertilizer phosphorus [kg/ha] 'manurenkg_YYYY.tif': manure nitrogen [kg/ha] 'manurepkg_YYYY.tif': manure phosphorus [kg/ha] #### File: HLNID_48sec_20260406.zip **Description:** Harmonized Land Nutrient Irrigation Dataset (HLNID) at 48 arc-second resolution 'irrig' folder: annual irrigation extent raster datasets (GeoTIFFs) from 1938-2020 'irrig_YYYY.tif': boolean data representing irrigation extent 0: no irrigation present 1: irrigation present 'lulc' folder: annual land use-land cover (LULC) raster datasets (GeoTIFFs) from 1938-2020 'vegtype_YYYY.tif': integer data representing LULC category for each individual year | LULC code | LULC description | | :-------- | :--------------------------- | | 4 | Evergreen forest | | 5 | Deciduous forest | | 8 | Mixed forest | | 10 | Grassland | | 11 | Shrubland | | 15 | Perennial ice/snow | | 16 | Corn | | 17 | Soybean | | 18 | Small grains | | 19 | Alfalfa | | 20 | Non-alfalfa hay | | 21 | Pasture / range | | 22 | Developed / high intensity | | 23 | Developed / medium intensity | | 24 | Developed / low intensity | | 25 | Developed / open space | | 26 | Herbaceous wetland | | 27 | Woody wetland | | 30 | Barren | | 98 | Open water | | 99 | Other crop | 'nutrients' folder: annual nutrient (nitrogen and phosphorus) inputs from fertilizer and manure from 1938-2020 NOTE: manure is estimated on an "as-excreted" basis and does not account for losses prior to typical land application 'fertnkg_YYYY.tif': fertilizer nitrogen [kg/ha] 'fertpkg_YYYY.tif': fertilizer phosphorus [kg/ha] 'manurenkg_YYYY.tif': manure nitrogen [kg/ha] 'manurepkg_YYYY.tif': manure phosphorus [kg/ha] ## Code/software Many different software applications can be used to view and extract data from GeoTIFF files including ArcGIS, QGIS, R, Python, and Matlab.more » « less
-
Irrigated agriculture depends on surface water and groundwater, but we do not have a clear picture of how much water is consumed from these sources by different crops across the US over time. Current estimates of crop water consumption are insufficient in providing the spatial granularity and temporal depth required for comprehensive long‐term analysis. To fill this data gap, we utilized crop growth models to quantify the monthly crop water consumption ‐ distinguishing between rainwater, surface water, and groundwater ‐ of the 30 most widely irrigated crops in the US from 1981 to 2019 at 2.5 arc min. These 30 crops represent approximately 95% of US irrigated cropland. We found that the average annual total crop water consumption for these 30 irrigated crops in the US was 154.2 km3, 70% of which was from irrigation. Corn and alfalfa accounted for approximately 16.7 and 24.8 km3of average annual blue crop water consumption, respectively, which is nearly two‐fifths of the blue crop water consumed in the US. Surface water consumption decreased by 41.2%, while groundwater consumption increased by 6.8%, resulting in a 17.3% decline in blue water consumption between 1981 and 2019. We find good agreement between our results and existing modeled evapotranspiration (ET) products, remotely sensed ET estimates (OpenET), and water use data from the US Geological Survey and US Department of Agriculture. Our data set and model can help assess the impact of irrigation practices and water scarcity on crop production and sustainability.more » « less
-
Efficient irrigation technologies, which seem to promise reduced production costs and water consumption in heavily irrigated areas, may instead be driving increased irrigation use in areas that were not traditionally irrigated. As a result, the total dependence on supplemental irrigation for crop production and revenue is steadily increasing across the contiguous United States. Quantifying this dependence has been hampered by a lack of comprehensive irrigated and dryland yield and harvested area data outside of major irrigated regions, despite the importance and long history of irrigation applications in agriculture. This study used a linear regression model to disaggregate lumped agricultural statistics and estimate average irrigated and dryland yields at the state level for five major row crops: corn, cotton, hay, soybeans, and wheat. For 1945–2015, we quantified crop production, irrigation enhancement revenue, and irrigated and dryland areas in both intensively irrigated and marginally-dependent states, where both irrigated and dryland farming practices are implemented. In 2015, we found that irrigating just the five commodity crops enhanced revenue by ~$7 billion across all states with irrigation. In states with both irrigated and dryland practices, 23% of total produced area relied on irrigation, resulting in 7% more production than from dryland practices. There was a clear response to increasing biofuel demand, with the addition of more than 3.6 million ha of irrigated corn and soybeans in the last decade in marginally-dependent states. Since 1945, we estimate that yield enhancement due to irrigation has resulted in over $465 billion in increased revenue across the contiguous United States (CONUS). Example applications of this dataset include estimating historical water use, evaluating the effects of environmental policies, developing new resource management strategies, economic risk analyses, and developing tools for farmer decision making.more » « less
-
Abstract The average farm size has more than doubled within the United States over the last three decades, transforming the agricultural industry and rural farming communities. It is unclear, however, how this ubiquitous trend has affected and is affected by the environment, particularly groundwater resources critical for food production. Here, we leverage a unique multi-decadal dataset of well-level groundwater withdrawals for crop irrigation over the Kansas High Plains Aquifer to determine the interactions between groundwater depletion and growing farms. Holding key technological, management, and environmental variables fixed, we show that doubling a farm’s irrigated cropland decreases groundwater extractions by 2%–5% depending on the initial farm size. However, a corresponding shift by larger farms to different irrigation technologies offsets this reduction in groundwater use, leading to a slight increase in overall groundwater use. We find groundwater depletion increases the likelihood farmland is sold to a larger farm, amplifying the cycle of groundwater depletion and the consolidation of farmland.more » « less
