• Ahmed, K. F., , G. Wang, , and J. Silander, 2013: Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. Northeast. Global Planet. Change, 100, 320332, doi:10.1016/j.gloplacha.2012.11.003.

    • Search Google Scholar
    • Export Citation
  • Ahuja, L. R., , and L. Ma, 2002: Parameterization of agricultural system models: Current issues and techniques. Agricultural System Models in Field Research and Technology Transfer, L. R. Ahuja, L. Ma, and T. A. Howell, Eds., CRC Press, 273–316.

  • Akaike, H., 1974: A new look at the statistical model identification. IEEE Trans. Autom. Control, 19, 716723, doi:10.1109/TAC.1974.1100705.

    • Search Google Scholar
    • Export Citation
  • Allen, R. G., , L. S. Pereira, , D. Raes, , and M. Smith, 1998: Crop evapotranspiration—Guidelines for computing crop water requirements. U.N. Food and Agriculture Organization Irrigation and Drainage Paper 56, FAO, 300 pp. [Available online at http://www.fao.org/docrep/x0490e/x0490e00.htm.]

  • Arora, V. K., 2002: The use of the aridity index to assess climate change effect on annual runoff. J. Hydrol., 265, 164177, doi:10.1016/S0022-1694(02)00101-4.

    • Search Google Scholar
    • Export Citation
  • Birsan, M. V., , P. Molnar, , P. Burlamdo, , and M. Pfaundler, 2005: Stream flow trends in Switzerland. J. Hydrol., 314, 312329, doi:10.1016/j.jhydrol.2005.06.008.

    • Search Google Scholar
    • Export Citation
  • Bronaugh, D., , and A. Werner, 2013: Package ‘zyp.’ User guide for Zhang + Yue-Pilon trends package, 9 pp. [Available online at cran.r-project.org/web/packages/zyp/zyp.pdf.]

  • Budyko, M. I., 1974: Climate and Life. Academic Press, 507 pp.

  • Burn, D. H., , and N. M. Hesch, 2007: Trends in evaporation for the Canadian prairies. J. Hydrol., 336, 6173, doi:10.1016/j.jhydrol.2006.12.011.

    • Search Google Scholar
    • Export Citation
  • Cleveland, W., 1979: Robust locally weighted regression and smoothing scatterplots. J. Amer. Stat. Assoc., 74, 829836, doi:10.1080/01621459.1979.10481038.

    • Search Google Scholar
    • Export Citation
  • Delitala, A., , D. Cesari, , P. Chesa, , and M. Ward, 2000: Precipitation over Sardinia (Italy) during the 1946–1993 rainy season and associated large scale climate variation. Int. J. Climatol., 20, 519541, doi:10.1002/(SICI)1097-0088(200004)20:5<519::AID-JOC486>3.0.CO;2-4.

    • Search Google Scholar
    • Export Citation
  • Dyer, J., , and A. Mercer, 2013: Assessment of spatial rainfall variability over the lower Mississippi River alluvial valley. J. Hydrometeor., 14, 18261843, doi:10.1175/JHM-D-12-0163.1.

    • Search Google Scholar
    • Export Citation
  • Fisher, D. K., , and H. C. Pringle III, 2013: Evaluation of alternative methods for estimating reference evapotranspiration. Agric. Sci., 4, 5160, doi:10.4236/as.2013.48A008.

    • Search Google Scholar
    • Export Citation
  • Gao, X., , and F. Giorgi, 2008: Increased aridity in the Mediterranean region under greenhouse gas forcing estimated from high resolution simulations with a regional climate model. Global Planet. Change, 62, 195209, doi:10.1016/j.gloplacha.2008.02.002.

    • Search Google Scholar
    • Export Citation
  • Hargreaves, G. H., , and Z. A. Samani, 1982: Estimating potential evapotranspiration. J. Irrig. Drain. Eng., 108, 223230.

  • Helsel, D. R., , and R. M. Hirsch, 2002: Statistical methods in water resources. Techniques of Water Resources Investigations, chapter A3, Book 4, U.S. Geological Survey, 522 pp. [Available online at http://pubs.usgs.gov/twri/twri4a3/html/toc.html.]

  • Hobbins, M. T., , J. A. Ramírez, , and T. C. Brown, 2004: Trends in pan evaporation and actual evapotranspiration across the conterminous US: Paradoxical or complementary? Geophys. Res. Lett., 31, L13503, doi:10.1029/2004GL019846.

    • Search Google Scholar
    • Export Citation
  • Hollinger, S. E., , S. A. Isard, , and M. R. Welford, 1993: A new soil moisture dryness index for predicting crop yields. Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 187–190.

  • Karl, T. R., , J. M. Melillo, , and T. C. Peterson, 2009: Global Climate Change Impacts in the United States. Cambridge University Press, 188 pp.

  • Kendall, M. G., , and J. D. Gibsons, 1990: Rank Correlation Methods. 5th ed. Oxford University Press, 260 pp.

  • Lázaro, R., , F. S. Rodrigo, , L. Gutiérrez, , F. Domingo, , and J. Puigdefábregas, 2001: Analysis of a 30-year rainfall record (1967–1997) in semi-arid SE Spain for implications on vegetation. J. Arid Environ., 48, 373395, doi:10.1006/jare.2000.0755.

    • Search Google Scholar
    • Export Citation
  • Legates, D. R., , and G. J. McCabe, 1999: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35, 233241, doi:10.1029/1998WR900018.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , F.-L. Zheng, , and W.-Z. Liu, 2012: Spatiotemporal characteristics of reference evapotranspiration during 1961–2009 and its projected changes during 2011–2099 on the Loess Plateau of China. Agric. For. Meteor., 154-155, 147155, doi:10.1016/j.agrformet.2011.10.019.

    • Search Google Scholar
    • Export Citation
  • Liu, L., and et al. , 2012: Analyzing projected changes and trends of temperature and precipitation in the southern USA from 16 downscaled global climate models. Theor. Appl. Climatol., 109, 345360, doi:10.1007/s00704-011-0567-9.

    • Search Google Scholar
    • Export Citation
  • Liu, X., , X. Mei, , Y. Li, , Q. Wang, , J. Jensen, , Y. Zhang, , and J. Porter, 2009: Evaluation of temperature-based global solar radiation models in China. Agric. For. Meteor., 149, 14331446, doi:10.1016/j.agrformet.2009.03.012.

    • Search Google Scholar
    • Export Citation
  • Lu, J., , G. Sun, , S. G. McNulty, , and D. M. Amatya, 2005: A comparison of six potential evapotranspiration methods for regional use in the southeastern United States. J. Amer. Water Resour. Assoc., 41, 621633. [Available online at http://www.srs.fs.usda.gov/pubs/ja/ja_lu004.pdf.]

    • Search Google Scholar
    • Export Citation
  • Ma, L., and et al. , 2011: A protocol for parameterization and calibration of RZWQM2 in field research. Methods of Introducing System Models into Agricultural Research, L. Ahuja and L. Ma, Eds., Soil Science Society of America, 1–64.

  • Ma, L., , T. J. Trout, , L. R. Ahuja, , W. C. Bausch, , S. A. Saseendran, , R. W. Malone, , and D. C. Nielsen, 2012: Calibrating RZWQM2 model for maize responses to deficit irrigation. Agric. Water Manage., 103, 140149, doi:10.1016/j.agwat.2011.11.005.

    • Search Google Scholar
    • Export Citation
  • Makridakis, S., , S. Wheelwright, , and R. Hyndman, 1998: Forecasting Methods and Application. 3rd ed. John Wiley and Sons, 636 pp.

  • McKee, T. B., , N. J. Doesken, , and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 1722.

  • Mishra, A. K., , and V. P. Singh, 2010: A review of drought concepts. J. Hydrol., 391, 202216, doi:10.1016/j.jhydrol.2010.07.012.

  • Moriasi, D. N., , J. G. Arnold, , M. W. Van Liew, , R. L. Gingner, , R. D. Harmel, , and T. L. Veith, 2007: Model evaluation guidelines for systematic quantification of accuracy in watershed simulation. Trans. ASABE, 50, 885900, doi:10.13031/2013.23153.

    • Search Google Scholar
    • Export Citation
  • Nastos, P. T., , N. Politi, , and J. Kapsomenakis, 2013: Spatial and temporal variability of the aridity index in Greece. Atmos. Res., 119, 140152, doi:10.1016/j.atmosres.2011.06.017.

    • Search Google Scholar
    • Export Citation
  • Norrant, C., , and A. Douguedroit, 2006: Monthly and daily precipitation trends in the Mediterranean (1950–2000). Theor. Appl. Climatol., 83, 89106, doi:10.1007/s00704-005-0163-y.

    • Search Google Scholar
    • Export Citation
  • Palmer, W. C., 1965: Meteorological drought. Weather Bureau Research Paper 45, 58 pp. [Available online at https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf.]

  • Palmer, W. C., 1968: Keeping track of crop moisture conditions, nationwide: The new crop moisture index. Weatherwise, 21, 156161, doi:10.1080/00431672.1968.9932814.

    • Search Google Scholar
    • Export Citation
  • Renard, B., and et al. , 2006: Evolution des extremes hydrométriques en France à partir de données observées (Observed changes in hydrological extremes in France). Houille Blanche, 6, 4854, doi:10.1051/lhb:2006100.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., , M. T. Hobbins, , and G. D. Earquhar, 2009a: Pan evaporation trends and the terrestrial water balance. II. Energy balance and interpretation. Geogr. Compass, 3, 761780, doi:10.1111/j.1749-8198.2008.00214.x.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., , M. T. Hobbins, , and G. D. Farquhar, 2009b: Pan evaporation trends and the terrestrial water balance. I. Principles and observations. Geogr. Compass, 3, 746760, doi:10.1111/j.1749-8198.2008.00213.x.

    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389, doi:10.1080/01621459.1968.10480934.

    • Search Google Scholar
    • Export Citation
  • Shumway, R., , and D. Stoffer, 2011: Time Series Analysis and Its Applications: With R Examples. 3rd ed. Springer, 596 pp.

  • Turc, L., 1961: Evaluation des besoins en eau d’irrigation, evapotranspiration potentielle: Formule climatique simplifiée et mise a jour (Water requirements assessment of irrigation, potential evapotranspiration: Simplified and updated climatic formula). Ann. Agron., 12, 1349.

    • Search Google Scholar
    • Export Citation
  • Yoder, R. E., , L. O. Odhiambo, , and W. C. Wright, 2005: Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the humid southeast United States. Appl. Eng. Agric., 21, 197202, doi:10.13031/2013.18153.

    • Search Google Scholar
    • Export Citation
  • Yürekli, K., , H. Simsek, , B. Cemek, , and S. Karaman, 2007: Simulating climatic variables by using stochastic approach. Build. Environ., 42, 34933499, doi:10.1016/j.buildenv.2006.10.046.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 258 258 43
PDF Downloads 207 207 40

Trend Analysis and Forecast of Precipitation, Reference Evapotranspiration, and Rainfall Deficit in the Blackland Prairie of Eastern Mississippi

View More View Less
  • 1 U.S. Department of Agriculture Agricultural Research Service, Mississippi State, Mississippi
  • | 2 University of Georgia, Athens, Georgia
  • | 3 U.S. Department of Agriculture Agricultural Research Service, Athens, Georgia
  • | 4 U.S. Department of Agriculture Agricultural Research Service, Stoneville, Mississippi
  • | 5 U.S. Department of Agriculture Forest Service, Mississippi State, Mississippi
© Get Permissions
Restricted access

Abstract

Trend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ETo, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ETo, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894–2014) were analyzed for annual, seasonal, and monthly periods. The analysis showed historical average annual rainfall, ETo, and dryness index (DI) in the location to be 1307 mm, 1210 mm, and 0.97, respectively. Monthly rainfall and ETo ranged from 72 to 118 mm and from 94 to 146 mm, respectively, between May and October, resulting in a monthly rain deficit from 22 to 62 mm. Annual rainfall showed an increasing trend of 1.17 mm yr−1 while annual ETo exhibited a decreasing trend of −0.51 mm yr−1, resulting in an annual DI reduction of 0.001 per year. Seasonal trends were found for rainfall in autumn (1.06 mm yr−1), ETo in summer (−0.29 mm yr−1) and autumn (−0.18 mm yr−1), and DI in autumn (−0.006). An autoregressive, integrated, and moving-average (ARIMA) approach was used to model monthly and annual rainfall, ETo, and DI and to predict those values in the future. Low values of the root-mean-square error (RMSE) and mean absolute error (with both statistics being normalized to the mean of the observed values), low values of average percent bias, and low values of the ratio of the RMSE to the standard deviation of observed data, along with values of 1.0 for Nash–Sutcliffe modeling efficiency and the index of agreement, all suggest that the performance of the models is acceptable. The ARIMA models forecast 1319 mm of mean annual rainfall, 1203 mm of mean annual ETo, and 0.82 of mean annual DI from 2015 to 2024. The results obtained from this research can guide development of water-management practices and cropping systems in the area that rely on this weather station. The approaches used and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed and a model fitted at other locations.

Denotes Open Access content.

Current affiliation: Colorado State University, Fort Collins, Colorado.

Corresponding author address: Gary Feng, Sustainable Agriculture Research Unit, USDA-ARS, 810 Highway 12 East, Mississippi State, MS 39762. E-mail: gary.feng@ars.usda.gov

Abstract

Trend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ETo, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ETo, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894–2014) were analyzed for annual, seasonal, and monthly periods. The analysis showed historical average annual rainfall, ETo, and dryness index (DI) in the location to be 1307 mm, 1210 mm, and 0.97, respectively. Monthly rainfall and ETo ranged from 72 to 118 mm and from 94 to 146 mm, respectively, between May and October, resulting in a monthly rain deficit from 22 to 62 mm. Annual rainfall showed an increasing trend of 1.17 mm yr−1 while annual ETo exhibited a decreasing trend of −0.51 mm yr−1, resulting in an annual DI reduction of 0.001 per year. Seasonal trends were found for rainfall in autumn (1.06 mm yr−1), ETo in summer (−0.29 mm yr−1) and autumn (−0.18 mm yr−1), and DI in autumn (−0.006). An autoregressive, integrated, and moving-average (ARIMA) approach was used to model monthly and annual rainfall, ETo, and DI and to predict those values in the future. Low values of the root-mean-square error (RMSE) and mean absolute error (with both statistics being normalized to the mean of the observed values), low values of average percent bias, and low values of the ratio of the RMSE to the standard deviation of observed data, along with values of 1.0 for Nash–Sutcliffe modeling efficiency and the index of agreement, all suggest that the performance of the models is acceptable. The ARIMA models forecast 1319 mm of mean annual rainfall, 1203 mm of mean annual ETo, and 0.82 of mean annual DI from 2015 to 2024. The results obtained from this research can guide development of water-management practices and cropping systems in the area that rely on this weather station. The approaches used and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed and a model fitted at other locations.

Denotes Open Access content.

Current affiliation: Colorado State University, Fort Collins, Colorado.

Corresponding author address: Gary Feng, Sustainable Agriculture Research Unit, USDA-ARS, 810 Highway 12 East, Mississippi State, MS 39762. E-mail: gary.feng@ars.usda.gov
Save