Editorial Type:
Article
Article Type:
Research Article

A Real-Time Gridded Crop Model for Assessing Spatial Drought Stress on Crops in the Southeastern United States

Richard T. McNider Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

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John R. Christy Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

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Don Moss Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

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Kevin Doty Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

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Cameron Handyside Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

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Ashutosh Limaye NASA Marshall Space Flight Center, Huntsville, Alabama

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Axel Garcia y Garcia Research and Extension Center, University of Wyoming, Powell, Wyoming

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Gerrit Hoogenboom Department of Biological and Agricultural Engineering, The University of Georgia, Griffin, Georgia

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Print Publication:
01 Jul 2011
DOI:
https://doi.org/10.1175/2011JAMC2476.1
Page(s):
1459–1475
Restricted access

Abstract

The severity of drought has many implications for society. Its impacts on rain-fed agriculture are especially direct, however. The southeastern United States, with substantial rain-fed agriculture and large variability in growing-season precipitation, is especially vulnerable to drought. As commodity markets, drought assistance programs, and crop insurance have matured, more advanced information is needed on the evolution and impacts of drought. So far many new drought products and indices have been developed. These products generally do not include spatial details needed in the Southeast or do not include the physiological state of the crop, however. Here, a new type of drought measure is described that incorporates high-resolution physical inputs into a crop model (corn) that evolves based on the physical–biophysical conditions. The inputs include relatively high resolution (as compared with standard surface or NOAA Cooperative Observer Program data) (5 km) radar-derived precipitation, satellite-derived insolation, and temperature analyses. The system (referred to as CropRT for gridded crop real time) is run in real time under script control to provide daily maps of crop evolution and stress. Examples of the results from the system are provided for the 2008–10 growing seasons. Plots of daily crop water stress show small subcounty-scale variations in stress and the rapid change in stress over time. Depictions of final crop yield in comparison with seasonal average stress are provided.

Current affiliation: AgWeatherNet, Washington State University, Prosser, Washington.

Corresponding author address: Richard T. McNider, Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35899. E-mail: mcnider@nsstc.uah.edu

Abstract

The severity of drought has many implications for society. Its impacts on rain-fed agriculture are especially direct, however. The southeastern United States, with substantial rain-fed agriculture and large variability in growing-season precipitation, is especially vulnerable to drought. As commodity markets, drought assistance programs, and crop insurance have matured, more advanced information is needed on the evolution and impacts of drought. So far many new drought products and indices have been developed. These products generally do not include spatial details needed in the Southeast or do not include the physiological state of the crop, however. Here, a new type of drought measure is described that incorporates high-resolution physical inputs into a crop model (corn) that evolves based on the physical–biophysical conditions. The inputs include relatively high resolution (as compared with standard surface or NOAA Cooperative Observer Program data) (5 km) radar-derived precipitation, satellite-derived insolation, and temperature analyses. The system (referred to as CropRT for gridded crop real time) is run in real time under script control to provide daily maps of crop evolution and stress. Examples of the results from the system are provided for the 2008–10 growing seasons. Plots of daily crop water stress show small subcounty-scale variations in stress and the rapid change in stress over time. Depictions of final crop yield in comparison with seasonal average stress are provided.

Current affiliation: AgWeatherNet, Washington State University, Prosser, Washington.

Corresponding author address: Richard T. McNider, Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35899. E-mail: mcnider@nsstc.uah.edu
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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Alley, W. M., 1984: The Palmer drought severity index: Limitations and assumptions. J. Climate Appl. Meteor., 23, 1100–1109.

  • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: II. Surface moisture climatology. J. Geophys. Res., 112, D11112, doi:10.1029/2006JD007507.

    • Search Google Scholar
    • Export Citation

  • Anderson, M. C., C. R. Hain, B. Wardlow, J. R. Mecikalski, and W. P. Kustas, 2011: Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Climate, 24, 2025–2044.

    • Search Google Scholar
    • Export Citation

  • Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams, 1998: Large area hydrologic modeling and assessment. Part I: Model development. J. Amer. Water Resour. Assoc., 34, 73–89.

    • Search Google Scholar
    • Export Citation

  • Bannayan, M., and G. Hoogenboom, 2009: Using pattern recognition for estimating cultivar coefficients of a crop simulation model. Field Crops Res., 111, 290–302.

    • Search Google Scholar
    • Export Citation

  • Bannayan, M., N. M. J. Crout, and G. Hoogenboom, 2003: Applying the CERES-Wheat model for real-time forecasting of winter wheat. Agron. J., 95, 114–125.

    • Search Google Scholar
    • Export Citation

  • Brown, J. F., B. D. Wardlow, T. Tadesse, M. J. Hayes, and B. C. Reed, 2008: The vegetation drought response index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GISci. Remote Sens., 45, 16–46.

    • Search Google Scholar
    • Export Citation

  • Carlson, T. N., R. R. Gillies, and E. M. Perry, 1994: A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens. Rev., 9, 161–173.

    • Search Google Scholar
    • Export Citation

  • Diak, G. R., and C. Gautier, 1983: Improvements to a simple physical model for estimating insolation from GOES data. J. Appl. Meteor., 22, 505–508.

    • Search Google Scholar
    • Export Citation

  • Engel, T., G. Hoogenboom, J. W. Jones, and P. W. Wilkens, 1997: AEGIS/WIN—A computer program for the application of crop simulation models across geographic areas. Agron. J., 89, 919–928.

    • Search Google Scholar
    • Export Citation

  • Fang, H., S. Liang, G. Hoogenboom, J. Teasdale, and M. Cavigelli, 2008: Corn yield estimation of remotely sensed data into the CSM-CERES-Maize model. Int. J. Remote Sens., 29, 3011–3032.

    • Search Google Scholar
    • Export Citation

  • Fraisse, C. W., and Coauthors, 2006: AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA. Comput. Electron. Agric., 53, 13–27.

    • Search Google Scholar
    • Export Citation

  • Garcia y Garcia, A., and G. Hoogenboom, 2005: Evaluation of an improved daily solar radiation generator for the southeastern USA. Climate Res., 29, 91–102.

    • Search Google Scholar
    • Export Citation

  • Garcia y Garcia, A., G. Hoogenboom, L. C. Guerra, J. O. Paz, and C. W. Fraisse, 2006: Analysis of the interannual variation of peanut yield in Georgia using a dynamic crop simulation model. Trans. Amer. Soc. Agric. Biol. Eng., 49, 2005–2015.

    • Search Google Scholar
    • Export Citation

  • Garcia y Garcia, A., L. C. Guerra, and G. Hoogenboom, 2008: Impact of generated solar radiation on simulated crop growth and yield. Ecol. Modell., 210, 312–326.

    • Search Google Scholar
    • Export Citation

  • Garcia y Garcia, A., L. C. Guerra, and G. Hoogenboom, 2009: Water use and water use efficiency of sweet corn under different weather conditions and soil moisture regimes. Agric. Water Manage., 96, 1369–1376.

    • Search Google Scholar
    • Export Citation

  • Gautier, C., G. R. Diak, and S. Mass, 1980: A simple physical model for estimating incident solar radiation at the surface from GOES satellite data. J. Appl. Meteor., 19, 1005–1012.

    • Search Google Scholar
    • Export Citation

  • Heim, R. R., Jr., 2000: Drought indices: A review. Drought: A Global Assessment, D. A. Wilhite, Ed., Routledge, 159–167.

  • Heinemann, A. B., G. Hoogenboom, and R. T. de Faria, 2002: Determination of spatial water requirements at county and regional levels using crop models and GIS: An example for the State of Parana. Agric. Water Manage., 52, 177–196.

    • Search Google Scholar
    • Export Citation

  • Hoogenboom, G., 2000: Contribution of agrometeorology to the simulation of crop production and its applications. Agric. For. Meteor., 103, 137–157.

    • Search Google Scholar
    • Export Citation

  • Hoogenboom, G., and Coauthors, 2004: Decision Support System for Agrotechnology Transfer, version 4.0. University of Hawaii, Honolulu, HI, CD-ROM.

    • Search Google Scholar
    • Export Citation

  • Jones, J. W., and Coauthors, 2003: DSSAT Cropping System Model. Eur. J. Agron., 18, 235–265.

  • Keyantash, J., and J. A. Dracup, 2002: The quantification of drought: An evaluation of drought indices. Bull. Amer. Meteor. Soc., 83, 1167–1180.

    • Search Google Scholar
    • Export Citation

  • Kogan, F. N., 1995: Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Amer. Meteor. Soc., 76, 655–668.

    • Search Google Scholar
    • Export Citation

  • McNider, R. T., A. Song, D. M. Casey, P. J. Wetzel, W. L. Crosson, and R. M. Rabin, 1994: Toward a dynamic–thermodynamic assimilation of satellite surface temperature in numerical atmospheric models. Mon. Wea. Rev., 122, 2784–2803.

    • Search Google Scholar
    • Export Citation

  • McNider, R. T., A. Song, and S. Q. Kidder, 1995: Assimilation of GOES-derived solar insolation into a mesoscale model for studies of cloud shading effects. Int. J. Remote Sens., 16, 2207–2231.

    • Search Google Scholar
    • Export Citation

  • Narasimhan, B., and R. Srinivasan, 2005: Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric. For. Meteor., 133, 69–88.

    • Search Google Scholar
    • Export Citation

  • Nemani, R. R., and S. W. Running, 1989: Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR Data. J. Appl. Meteor., 28, 276–284.

    • Search Google Scholar
    • Export Citation

  • Niemeyer, S., 2008: New drought indices. First Int. Conf. on Drought Management: Scientific and Technological Innovations, Zaragoza, Spain, Joint Research Centre of the European Commission. [Available online at http://www.iamz.ciheam.org/medroplan/zaragoza2008/Sequia2008/Session3/S.Niemeyer.pdf.]

    • Search Google Scholar
    • Export Citation

  • Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperatures. Agric. For. Meteor., 77, 263–293.

    • Search Google Scholar
    • Export Citation

  • Palmer, W. C., 1965: Meteorological drought. U.S. Weather Bureau Res. Paper 45, 58 pp. [Available from NOAA Library and Information Services Division, Washington, DC 20852.]

    • Search Google Scholar
    • Export Citation

  • Palmer, W. C., 1968: Keeping track of crop moisture conditions, nationwide: The new crop moisture index. Weatherwise, 21, 156–161.

  • Paz, J. O., and Coauthors, 2007: Development of an ENSO-based irrigation decision support tool for peanut production in the southeastern US. Comput. Electron. Agric., 55, 28–35.

    • Search Google Scholar
    • Export Citation

  • Pinker, R. T., and Coauthors, 2003: Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) Project. J. Geophys. Res., 108, 8844, doi:10.1029/2002JD003301.

    • Search Google Scholar
    • Export Citation

  • Pour-Biazar, A., and Coauthors, 2007: Correcting photolysis rates on the basis of satellite observed clouds. J. Geophys. Res., 112, D10302, doi:10.1029/2006JD007422.

    • Search Google Scholar
    • Export Citation

  • Priestley, C. H., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large scale parameters. Mon. Wea. Rev., 100, 81–92.

    • Search Google Scholar
    • Export Citation

  • Ritchie, J. T., 1972: Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res., 8, 1204–1213.

  • Ritchie, J. T., 1998: Soil water balance and plant water stress. Understanding Options for Agricultural Production, G. Y. Tsuji, G. Hoogenboom, and P. K. Thornton, Eds., Systems Approaches for Sustainable Agricultural Development, Vol. 7, Kluwer Academic, 42–54.

    • Search Google Scholar
    • Export Citation

  • Ritchie, J. T., U. Singh, D. Godwin, and W. Bowen, 1998: Cereal growth, development and yield. Understanding Options for Agricultural Production, G. Y. Tsuji, G. Hoogenboom, and P. K. Thornton, Eds., Systems Approaches for Sustainable Agricultural Development, Vol. 7, Kluwer Academic, 79–98.

    • Search Google Scholar
    • Export Citation

  • Sellers, P. J., 1985: Canopy reflectance, photosynthesis, and transpiration. Int. J. Remote Sens., 6, 1335–1372.

  • Senay, G. B., 2008: Modeling landscape evapotranspiration by integrating land surface phenology and a water balance algorithm. Algorithms, 1, 52–68, doi:10.3390/a1020052.

    • Search Google Scholar
    • Export Citation

  • Soler, C. M. T., P. C. Sentelhas, and G. Hoogenboom, 2007: Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Eur. J. Agron., 27, 165–177.

    • Search Google Scholar
    • Export Citation

  • Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteor. Soc., 83, 1181–1190.

  • Tarpley, J. D., 1979: Estimating incident solar radiation at the surface from geostationary satellite data. J. Appl. Meteor., 18, 1172–1181.

    • Search Google Scholar
    • Export Citation

  • Thornton, P. K., and G. Hoogenboom, 1994: A computer program to analyze single-season crop model outputs. Agron. J., 86, 860–868.

  • Tsuji, G. Y., G. Hoogenboom, and P. K. Thornton, Eds., 1998: Understanding Options for Agricultural Production, Systems Approaches for Sustainable Agricultural Development, Vol. 7, Kluwer Academic, 400 pp.

    • Search Google Scholar
    • Export Citation

  • Wetzel, P. J., 1984: Determining soil moisture from geosynchronous satellite infrared data: A feasibility study. J. Climate Appl. Meteor., 23, 375–391.

    • Search Google Scholar
    • Export Citation

  • Wilhite, D. A., and M. H. Glantz, 1985: Understanding the drought phenomenon: The role of definitions. Water Int., 10, 111–120.

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