Published April 6, 2019 | Version v1.0
Dataset Open

MIRCA-BC-USMX: Irrigated and planted fractions over the continental United States and Mexico for years 1992, 2002, and 2012

  • 1. ASU School of Earth and Space Exploration

Description

The MIRCA-BC-USMX project contains a spatially explicit mean annual cycle of monthly planted and irrigated fractions at 0.0625 degree (6 km) spatial resolution over the continental United States and Mexico for years 1992, 2002, and 2012.

These fractions were generated by (1) reconciling the MIRCA2000 Global Monthly Irrigated and Rainfed Crop Areas dataset (Portmann et al., 2010) with the cropland and pasture classes of year 2001 of the harmonized NLCD_INEGI land cover dataset (Bohn and Vivoni, 2019b); (2) bias-correcting the irrigated and planted fractions to match state-by-state total irrigated and planted areas from government records in the United States (USDA, 2016) and Mexico (SADER, 2014; SAGARPA, 2016).

These fractions have been added to land surface parameter files for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994) version 5.1 (Hamman et al., 2018), extended to include the irrigation module of Haddeland et al. (2006), available on GitHub. The parameter files were taken from the MOD-LSP project, available on Zenodo (Bohn and Vivoni, 2019a). The VIC 5 image driver requires a "domain" file to accompany the parameter file. This domain file is also necessary for disaggregating the daily gridded meteorological forcings to hourly for input to VIC via the disaggregating tool MetSim (Bennett et al., 2018).  We have provided a domain file compatible with the meteorological forcings of Livneh et al (2015) and the MIRCA-BC-USMX parameters, on Zenodo (Bohn et al., 2019a,b).

Contents:

  • Input Files
    • county_codes.csv - table mapping the numerical codes for counties with the county names used by the US Census Bureau and USDA. This was created by parsing this information from US Census tables from years 1990, 2000, and 2010 and USDA tables from years 1992, 2002, and 2012 and manually reconciling discrepancies across years. Thus the names may not match county names in the original files exactly from year to year, but rather represent my own naming convention. However these discrepancies were rare.
    • mun_us.0.01_deg.asc.tgz and mun_mx.0.0.01_deg.asc.tgz - gzipped tar archives containing mun_us.0.01_deg.asc and mun_mx.0.01_deg.asc, which are ascii-format ESRI grid files created by rasterizing publicly available shapefiles of US and Mexican counties/municipios. These have 0.01 degree (1 km) spatial resolution and pixels have numerical values equal to the codes in county_codes.csv.
  • Output Files
    • fplant_firr_bc.$LCYEAR.nc, where $LCYEAR is one of ("s1992","2001", or "2011") - NetCDF-format files at 0.0625 degree (6 km) resolution containing 12 monthly maps each of bias-corrected "fplant" (planted area fraction) and "firr" (irrigated area fraction) for a specific historical year.The value of $LCYEAR indicates the snapshot of the NLCD_INEGI harmonized land cover classification with which fplant and firr were reconciled (so that these area fractions would not exceed the total agricultural/pastoral area given by NLCD_INEGI). Values of fplant and firr were bias corrected so that state-wide total areas matched government records from USDA (USDA, 2014) and SAGARPA (SADER, 2014; SAGARPA, 2016). For $LCYEAR = ("s1992", "2001", "2011"), the agricultural census year used in the bias correction was (1992, 2002, 2012).
    • fplant_firr_bc.2011.mun_mx.nc - same as fplant_firr_bc.2011.nc, but bias-corrected at the municipio level in Mexico. County-level bias correction was not possible in the US due to lack of sufficient resolution USDA records. Similarly, municipio-level records were not available in Mexico prior to year 2003.
    • params.USMX.NLCD_INEGI.$LCYEAR.$YEAR1_$YEAR2.with_irrig.nc - VIC 5 image driver-compliant input parameter files into which fplant and firr of the given $LCYEAR have been inserted.  $YEAR1 and $YEAR2 indicate the first and last years of MODIS data used to estimate the annual cycle of monthly LAI, fcanopy, and albedo (independent of the values of fplant and firr).
    • params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.mun_mx.nc - same as params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.nc but with fplant and firr bias-corrected at the municipio level in Mexico.

These parameters were created with scripts archived on GitHub (Bohn, 2019).

Files

county_codes.csv

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Additional details

References

  • Bennett, A., J. J. Hamman, B. Nijssen, E. A. Clark, and K. M. Andreadis, 2018: UW-Hydro/MetSim: Version 1.1.0 (version 1.1.0). Zenodo, doi:10.5281/zenodo.1256120. http://doi.org/10.5281/zenodo.1256120 (Accessed June 7, 2018).
  • Bohn, T. J., 2019: tbohn/MIRCA-BC-USMX: Tools to Create MIRCA-BC-USMX bias-corrected irrigated and planted areas over the US and Mexico, v1.1 (Version v1.1). Zenodo, doi:10.5281/zenodo.2632043. http://doi.org/10.5281/zenodo.2632043 (Accessed April 8, 2019).
  • Bohn, T. J., and E. R. Vivoni, 2019a: MOD-LSP: MODIS-Based Parameters for Variable Infiltration Capacity (VIC) Model over the Continental US, Mexico, and Southern Canada (Version 1.0) [Data set]. Zenodo, doi:10.5281/zenodo.2612560. https://zenodo.org/record/2612560.
  • Bohn, T. J., and E. R. Vivioni, 2019b: NLCD_INEGI: Harmonized US-Mexico Land Cover Change Dataset, 1992/2001/2011 (Version 1.1) [Data set]. Zenodo, doi:10.5281/zenodo.2591501. https://zenodo.org/record/2591501.
  • Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019a: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling. J. Hydromet., 20(2), 297-317.
  • Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019b: Parameters for PITRI Precipitation Temporal Disaggregation over continental US, Mexico, and southern Canada, 1981-2013 (Version 1) [Data set]. Zenodo, doi:10.5281/zenodo.1402223. https://zenodo.org/record/1402223.
  • Haddeland I, Lettenmaier DP, Skaugen T (2006) Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins. J Hydrol 324(1):210–223.
  • Hamman, J. J., B. Nijssen, T. J. Bohn, D. R. Gergel, and Y. Mao, 2018: The Variable Infiltration Capacity Model, Version 5 (VIC-5): Infrastructure improvements for new applications and reproducibility. Geosci. Model Dev., 11, 3481–3496.
  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmospheres, 99, 14415–14428.
  • Livneh, B., E. A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. M. Andreadis, E. P. Maurer, and D. P. Lettenmaier, 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Clim., 26, 9384–9392.
  • Portmann, F. T., S. Siebert, and Doll, P., 2010: MIRCA2000--Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Global Biogeochemical Cycles, 24(1), GB1011, doi:10.1029/2008GB003435.
  • SADER, 2014: Estadistica. Secretaria de Agricultura y Desarrollo Rural (SADER), Mexico, DF. https://sader.gob.mx/siap/estadistica.
  • SAGARPA, 2016: Anuario Estadistico de la Produccion Agricola. Secretaria de Agricultura, Ganaderia, Desarrollo Rural, Pesca y Alimentacion (SAGARPA), Mexico, DF. http://infosiap.siap.gob.mx/aagricola_siap_gb/icultivo/index.jsp.
  • USDA, 2014: 2012 Census of Agriculture: United States Summary and State Data. United States Department of Agriculture (USDA), Washington, DC, https://www.agcensus.usda.gov (Accessed November 1, 2016).