Konar Research Group
Civil and Environmental Engineering Department University of Illinois at Urbana-Champaign
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Total irrigation by crop and water source in the United States from 2008 to 2020
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop and water source. In this study, we estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion in km3. To do this, we scale irrigation estimates from the PCR-GLOBWB 2.0 global hydrology model with the U.S. Geological Survey total irrigation variable. These data present a national-scale assessment of irrigation by crop, county, water source, and year for the United States from 2008 to 2020. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).
Reference: Ruess, P.J., M. Konar, N. Wanders, and M. Bierkens (2024) Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, Vol 11, No 395, doi: 10.1038/s41597-024-03244-w.
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop and water source. In this study, we estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion in km3. To do this, we scale irrigation estimates from the PCR-GLOBWB 2.0 global hydrology model with the U.S. Geological Survey total irrigation variable. These data present a national-scale assessment of irrigation by crop, county, water source, and year for the United States from 2008 to 2020. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).
Reference: Ruess, P.J., M. Konar, N. Wanders, and M. Bierkens (2024) Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, Vol 11, No 395, doi: 10.1038/s41597-024-03244-w.
Irrigation by crop and water source in the United States from 2008 to 2020
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop and water source. In this study, we estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion in km3. To do this, we scale irrigation estimates from the PCR-GLOBWB 2.0 global hydrology model with the U.S. Geological Survey crop irrigation variable. These data present a national-scale assessment of irrigation by crop, county, water source, and year for the United States from 2008 to 2020. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).
Reference: Ruess, P.J., M. Konar, N. Wanders, and M. Bierkens (2023) Irrigation by crop in the Continental United States from 2008 to 2020, Water Resources Research, 59, e2022WR032804, doi: 10.1029/2022WR032804.
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop and water source. In this study, we estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion in km3. To do this, we scale irrigation estimates from the PCR-GLOBWB 2.0 global hydrology model with the U.S. Geological Survey crop irrigation variable. These data present a national-scale assessment of irrigation by crop, county, water source, and year for the United States from 2008 to 2020. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).
Reference: Ruess, P.J., M. Konar, N. Wanders, and M. Bierkens (2023) Irrigation by crop in the Continental United States from 2008 to 2020, Water Resources Research, 59, e2022WR032804, doi: 10.1029/2022WR032804.
Economic and virtual water multilayer networks in China
This dataset includes the blue water intensity by sector (41 industries and service sectors) for provinces in China. The economic and virtual water flow networks for China in 2017 are provided, as well as the corresponding network properties for these two networks.
Reference: Wang, J., M. Konar, C. Dalin, Y. Liu, A. S. Stillwell, M. Xu, and T. Zhu (2022) Economic and virtual water multilayer networks in China, Journal of Cleaner Production, Vol 381, pp. 125041, doi: 10.1016/j.jclepro.2022.135041.
The carbon footprint of cold chain food flows in the United States
This database presents the estimates of our novel model of cold chain food flows between counties in the United States. Specifically, we estimate refrigerated truck transport via roadways for the year 2017. The included Standard Classification of Transported Goods (SCTG) categories are: (SCTG 05) meat, poultry, fish, seafood, and their preparations, and (SCTG 07) other prepared foodstuffs, fats and oils. Our estimates are provided in units of both mass [kg] and dollar value [$].
Reference: Wang, J., D.B. Karakoc, and M. Konar (2022), The carbon footprint of cold chain food flows in the United States, Environmental Research: Infrastructure and Sustainability, Vol 2, pp. 0021002, doi: 10.1088/2634-4505/ac676d.
This database presents the estimates of our novel model of cold chain food flows between counties in the United States. Specifically, we estimate refrigerated truck transport via roadways for the year 2017. The included Standard Classification of Transported Goods (SCTG) categories are: (SCTG 05) meat, poultry, fish, seafood, and their preparations, and (SCTG 07) other prepared foodstuffs, fats and oils. Our estimates are provided in units of both mass [kg] and dollar value [$].
Reference: Wang, J., D.B. Karakoc, and M. Konar (2022), The carbon footprint of cold chain food flows in the United States, Environmental Research: Infrastructure and Sustainability, Vol 2, pp. 0021002, doi: 10.1088/2634-4505/ac676d.
Food flows between counties in the United States over time
An update to our earlier estimates of agri-food flows between counties in the United States that was provided by Lin et al (2019) for the year 2012. In this dataset, we use an improved version of the Food Flow Model to estimate food flows (kg) between all county pairs across all Standard Classification of Transported Goods (SCTG) agri-food commodity groups for the years 2007, 2012, and 2017 (which requires estimating 206.3 million links). The included SCTG categories are: (SCTG 01) animals and fish (live), (SCTG 02) cereal grains (includes seeds), (SCTG 03) agricultural products (excludes animal feed, cereal grains, and forage products), (SCTG 04) animal feed, eggs, honey, and other products of animal origin, (SCTG 05) meat, poultry, fish, seafood, and their preparations, (SCTG 06) milled grain products and preparations, and bakery products, and (SCTG 07) other prepared foodstuffs, fats and oils. Weighted [kg] and directed matrices of food flows from origin to destination county (by fips code) are provided for the years 2007, 2012, and 2017.
Reference: Karakoc, D.B., J. Wang, and M. Konar (2022), Food flows between counties in the United States from 2007 to 2017, Environmental Research Letters, Vol 17, pp. 034035, doi: 10.1088/1748-9326/ac5270.
An update to our earlier estimates of agri-food flows between counties in the United States that was provided by Lin et al (2019) for the year 2012. In this dataset, we use an improved version of the Food Flow Model to estimate food flows (kg) between all county pairs across all Standard Classification of Transported Goods (SCTG) agri-food commodity groups for the years 2007, 2012, and 2017 (which requires estimating 206.3 million links). The included SCTG categories are: (SCTG 01) animals and fish (live), (SCTG 02) cereal grains (includes seeds), (SCTG 03) agricultural products (excludes animal feed, cereal grains, and forage products), (SCTG 04) animal feed, eggs, honey, and other products of animal origin, (SCTG 05) meat, poultry, fish, seafood, and their preparations, (SCTG 06) milled grain products and preparations, and bakery products, and (SCTG 07) other prepared foodstuffs, fats and oils. Weighted [kg] and directed matrices of food flows from origin to destination county (by fips code) are provided for the years 2007, 2012, and 2017.
Reference: Karakoc, D.B., J. Wang, and M. Konar (2022), Food flows between counties in the United States from 2007 to 2017, Environmental Research Letters, Vol 17, pp. 034035, doi: 10.1088/1748-9326/ac5270.
Crop-specific exposure to extreme temperature and moisture for the globe for the last half century
Estimates of crop-specific temperature and moisture shocks during the growing season for the globe from 1961 to 2014. Estimates are provided at the annual time step for a 0.25 degree spatial grid and also aggregated by nations. Data is provided for 17 crops (barley, cassava, groundnuts, maize, millet, oats, potato, rapeseed, rice, rye, sorghum, soybeans, sugarbeet, sunflowers, sweet potato, wheat, and yams) and 4 extreme weather categories (too wet, too dry, too hot, too cold).
Reference: Jackson, N.D., M. Konar, P. Debaere, and J. Sheffield (2021), Crop-specific exposure to extreme temperature and moisture for the globe for the last half century, Environmental Research Letters, Vol 16, Issue 6, pp. 064006, doi: 10.1088/1748-9326/abf8e0.
Estimates of crop-specific temperature and moisture shocks during the growing season for the globe from 1961 to 2014. Estimates are provided at the annual time step for a 0.25 degree spatial grid and also aggregated by nations. Data is provided for 17 crops (barley, cassava, groundnuts, maize, millet, oats, potato, rapeseed, rice, rye, sorghum, soybeans, sugarbeet, sunflowers, sweet potato, wheat, and yams) and 4 extreme weather categories (too wet, too dry, too hot, too cold).
Reference: Jackson, N.D., M. Konar, P. Debaere, and J. Sheffield (2021), Crop-specific exposure to extreme temperature and moisture for the globe for the last half century, Environmental Research Letters, Vol 16, Issue 6, pp. 064006, doi: 10.1088/1748-9326/abf8e0.
Groundwater depletion in agricultural supply chains of the United States
A dataset of the groundwater depletion embedded in the domestic transfers and international exports of the United States. This database provides the volume [km3] of nonrenewable groundwater incorporated in agricultural commodities for the years 2002 and 2012. The groundwater depletion of three Standard Classification of Transported Goods (SCTG) commodity classes is provided: (SCTG 02) cereal grains, (SCTG 03) all other agricultural products excluding animal feed and forage products, and (SCTG 04) animal feed and other products of animal origin.
Reference: Gumidyala, S., P.J. Ruess, M. Konar, L. Marston, C. Dalin, and Y. Wada (2020), Groundwater depletion embedded in transfers and exports of the United States, Water Resources Research, Vol 56, Issue 2, pp. e2019WR024986, doi: 10.1029/2019WR024986.
A dataset of the groundwater depletion embedded in the domestic transfers and international exports of the United States. This database provides the volume [km3] of nonrenewable groundwater incorporated in agricultural commodities for the years 2002 and 2012. The groundwater depletion of three Standard Classification of Transported Goods (SCTG) commodity classes is provided: (SCTG 02) cereal grains, (SCTG 03) all other agricultural products excluding animal feed and forage products, and (SCTG 04) animal feed and other products of animal origin.
Reference: Gumidyala, S., P.J. Ruess, M. Konar, L. Marston, C. Dalin, and Y. Wada (2020), Groundwater depletion embedded in transfers and exports of the United States, Water Resources Research, Vol 56, Issue 2, pp. e2019WR024986, doi: 10.1029/2019WR024986.
Global gridded maps of specific crops for a half century
This database provides the likely locations of specific crops for the last half century. Gridded estimates are provided at the annual temporal resolution from 1961 to 2014. Crop-specific area harvested is provided for a global 0.5-degree grid for 17 major crops: barley, cassava, groundnut, maize, millet, oats, potato, rapeseed, rice, rye, sorghum, soybean, sugarbeet, sunflower, sweet potato, wheat, and yams. The mean and standard deviation of grid cell fractions are available for each of the 17 crops.
Reference: Jackson, N., M. Konar, P. Debaere, and L. Estes (2019) Probabilistic global maps of crop-specific areas from 1961 to 2014, Environmental Research Letters, doi: 10.1088/1748- 9326/ab3b93.
This database provides the likely locations of specific crops for the last half century. Gridded estimates are provided at the annual temporal resolution from 1961 to 2014. Crop-specific area harvested is provided for a global 0.5-degree grid for 17 major crops: barley, cassava, groundnut, maize, millet, oats, potato, rapeseed, rice, rye, sorghum, soybean, sugarbeet, sunflower, sweet potato, wheat, and yams. The mean and standard deviation of grid cell fractions are available for each of the 17 crops.
Reference: Jackson, N., M. Konar, P. Debaere, and L. Estes (2019) Probabilistic global maps of crop-specific areas from 1961 to 2014, Environmental Research Letters, doi: 10.1088/1748- 9326/ab3b93.
Food flows between counties in the United States
A comprehensive database of food flows between all county pairs within the United States. Estimates are provided for all 9,869,022 potential links within the nation by Standard Classification of Transported Goods (SCTG) commodity classes: (SCTG 01) animals and fish (live), (SCTG 02) cereal grains (includes seeds), (SCTG 03) agricultural products (excludes animal feed, cereal grains, and forage products), (SCTG 04) animal feed, eggs, honey, and other products of animal origin, (SCTG 05) meat, poultry, fish, seafood, and their preparations, (SCTG 06) milled grain products and preparations, and bakery products, and (SCTG 07) other prepared foodstuffs, fats and oils. A weighted [kg] and directed matrix of food flows from origin to destination county (by fips code) is provided for the year 2012.
Reference: Lin, X., P.J. Ruess, L. Marston, and M. Konar (2019) Food flows between counties in the United States, Environmental Research Letters, Vol 14, pp. 084011, doi: 10.1088/1748-9326/ab29ae.
A comprehensive database of food flows between all county pairs within the United States. Estimates are provided for all 9,869,022 potential links within the nation by Standard Classification of Transported Goods (SCTG) commodity classes: (SCTG 01) animals and fish (live), (SCTG 02) cereal grains (includes seeds), (SCTG 03) agricultural products (excludes animal feed, cereal grains, and forage products), (SCTG 04) animal feed, eggs, honey, and other products of animal origin, (SCTG 05) meat, poultry, fish, seafood, and their preparations, (SCTG 06) milled grain products and preparations, and bakery products, and (SCTG 07) other prepared foodstuffs, fats and oils. A weighted [kg] and directed matrix of food flows from origin to destination county (by fips code) is provided for the year 2012.
Reference: Lin, X., P.J. Ruess, L. Marston, and M. Konar (2019) Food flows between counties in the United States, Environmental Research Letters, Vol 14, pp. 084011, doi: 10.1088/1748-9326/ab29ae.
Water footprints of production in the United States
A rich dataset for the United States showing the water footprint of production per economic sector at state level and at county level (for some sectors), and distinguishing between surface water footprint and groundwater footprint. The different sectors included are crop production, livestock, aquaculture, mining, thermoelectric power production, and commercial, industrial, and institutional production.
Reference: Marston, L., Y. Ao, M. Konar, M. Mekonnen, and A.Y. Hoekstra (2018), High-resolution water footprints of production of the United States, Water Resources Research, Vol 54, Issue 3, pp. 2288-2316, doi: 10.1002/2017WR021923.