Advertisement

Improving drought predictability in Arkansas using the ensemble PDSI forecast technique

  • Yan Liu
  • Yeonsang HwangEmail author
Original Paper

Abstract

Drought prediction is important for improved water resources management and agriculture planning. Although Arkansas has suffered severe droughts and economic loss in recent years, no significant study has been done. This study proposes a local nonparametric autoregressive model with designed stochastic residual-resampling approach to produce ensemble drought forecasts with associated confidence. The proposed model utilizes historical climate records, including drought indices, temperature, and precipitation to improve the quality of the short-term forecast of drought indices. Monthly forecasts of Palmer Drought Severity Index (PDSI) in Arkansas climate divisions show remarkable skills with 2–3 month lead-time based on selected performance measure such as, Normalized Root Mean Square Error (NRMSE) and the Kuiper Skill Score (KSS). Rank histograms also show that the model captures the natural variability very well in the produced drought forecasts. The incorporation of categorical long-term precipitation prediction significantly enhances the performance of the monthly drought forecasts.

Keywords

Drought Ensemble forecasts Residual-resampling Arkansas climate division 

References

  1. Alley WM (1984) The Palmer drought severity index: limitations and assumptions. J Clim Appl Meteorol 23:1100–1109CrossRefGoogle Scholar
  2. Arkansas Farm Bureau Federation (1981) Water: Its uses and the implications for Arkansas agriculture, 38Google Scholar
  3. Arkansas Natural Resource Commission (2011) Arkansas Ground-Water Protection and Management Report for 2011. ANRCGoogle Scholar
  4. Axelrod J (2012) Drought dries up 200 miles of Arkansas River, CBS Interactive Inc. http://www.cbsnews.com/video/watch/?id=7421528n. Accessed 07 Nov 2012
  5. Bayazıt M, Şen Z (1977) Dry period statistics of monthly flow models. In: Proceedings of the 3rd International Symposium on Hydrology, Fort Collins, 1977Google Scholar
  6. Beven KJ, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298CrossRefGoogle Scholar
  7. Carbone GJ, Dow K (2005) Water resource management and drought forecasts in South Carolina. J Am Water Resour As 41(1):145–155CrossRefGoogle Scholar
  8. Carpenter TM, Georgakakos KP (2004) Impacts of parametric and radar rainfall uncertainty on the ensemble streamflow simulations of a distributed hydrologic model. J Hydrol 298:202–221CrossRefGoogle Scholar
  9. Cebrián AC, Abaurrea J (2006) Drought analysis based on a marked cluster Poisson model. J Hydrometeorol. 7(4):713–723CrossRefGoogle Scholar
  10. Chang TJ (1990) Effects of drought on streamflow characteristics. J Irrig Drain Eng 116(3):332–341CrossRefGoogle Scholar
  11. Chung C, Salas JD (2000) Drought occurrence probabilities and risk of dependent hydrologic processes. J Hydrol Eng 5(3):259–268CrossRefGoogle Scholar
  12. Cleveland WS, Loader C (1996) Smoothing by local regression: principles and methods. In: Michael S, Haerdle W (eds) Statistical theory and computational aspects of smoothing. Springer, New York, pp 10–49CrossRefGoogle Scholar
  13. Day GN (1985) Extended streamflow forecasting using NWSRFS. J Water Resour Plan Manag 111(2):157–170CrossRefGoogle Scholar
  14. De Roo APJ, Bartholmes J, Bates PD, Beven K et al (2003) Development of a European flood forecasting system. Intl J River Basin Manag 1(1):49–59CrossRefGoogle Scholar
  15. Douc R, Capp´e O, Moulines E (2005) Comparison of resampling schemes for particle filtering. In: Proceedings of the 4th international symposium on image and signal processing and analysis, Zagreb, 15–17 September 2005, pp 64–69Google Scholar
  16. Durdu ÖF (2010) Application of linear stochastic models for drought forecasting in the BÜyÜk Menderes river basin, Western Turkey. Stoch Environ Res Risk Assess 24(8):1145–1162CrossRefGoogle Scholar
  17. Gront D, Kolinski A, Skolnick J (2000) Comparison of three Monte Carlo conformational search strategies for a proteinlike homopolymer model: folding thermodynamics and identification of low-energy structures. J Chem Phys 113(12):5065–5071. doi: 10.1063/1.1289533 CrossRefGoogle Scholar
  18. Guerrero-Salazar P, Yevjevich VM (1975) Analysis of drought characteristics by the theory of runs. Colorado State University Hydrology, Fort Collins, p 80Google Scholar
  19. Guttman NB (1998) Comparing the palmer drought index and the standardized precipitation index. J Am Water Resour As 34:113–121CrossRefGoogle Scholar
  20. Hamill TM (2001) Notes and correspondence: Interpretation of rank histograms for verifying ensemble forecasts. Am Meteorol Soc 129:500–560Google Scholar
  21. Hansen E (2008) The verification of severe weather—Present techniques and plans for development. In: Presentation PPT for WGCEF meeting in Copenhagen. http://www.euroforecaster.org/presentation2008/erik_verify.pdf. Accessed 26 Jun 2012
  22. Hantush MM, Kalin L (2008) Stochastic residual-error analysis for estimating hydrologic model predictive uncertainty. J Hydrol Eng 13(7):585–596CrossRefGoogle Scholar
  23. Hayes MJ (1999) Drought indices. National Drought Mitigation Center. http://www.civil.utah.edu/~cv5450/swsi/indices.htm. Accessed 23 Sep 2012
  24. Heim Jr RR (2002) A review of twentieth-century drought indices used in the United States. Bull Am Meteorol Soc 83:1149–1165CrossRefGoogle Scholar
  25. Heim Jr RR (2006) Station-Based Indices For Drought Monitoring. In: The U.S. Ashville, NC: NOAA/NESDIS/National Climate Data Center. http://www1.ncdc.noaa.gov/pub/data/drought/RHeim-extended-abstract-NADM-workshop-Oct2006.pdf. Accessed 23 Feb 2012
  26. Hersbach H (2000) Decomposition on the continuous ranked probability Score for Ensemble Prediction Systems. Wea Forecasting 15:559–570. doi: 10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2 CrossRefGoogle Scholar
  27. Holland TW (2007) Water use in Arkansas, 2005: U.S. Geological Survey Scientific Investigations Report 2007–5241, p 32 http://www.anrc.arkansas.gov/groundwater/2011_gw_report.pdf. Accessed 26 Mar 2012
  28. Hwang Y, Carbone GJ (2009) Ensemble forecasts of drought indices using a conditional residual resampling technique. J Appl Meteor Climatol 48:1289–1301CrossRefGoogle Scholar
  29. Intergovernmental Panel on Climate Change (2007) Climate change 2007: the physical science basis, Summary for policymakers. IPCC WGI Fourth Assessment Report. http://news.bbc.co.uk/2/shared/bsp/hi/pdfs/02_02_07_climatereport.pdf Accessed by 12 May 2012
  30. Jonathan R (2008) Statistic 2: Bayesian prediction. University of Bristol. http://www.maths.bris.ac.uk/~mazjcr/stats2/HOBayes3.pdf. Accessed 19 Sep 2012
  31. Karl TR, Melillo JM, Peterson TC (2009) Global climate change impacts in the United States. Cambridge University Press, New YorkGoogle Scholar
  32. Kim TW, Juan BV, Chulsang Y (2003) Nonparametric approach For estimating return periods of droughts in arid regions. J Hydrol Eng 8:237–246CrossRefGoogle Scholar
  33. Krzysztofowicz R (1999) Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resour Res 35(9):2739–2750CrossRefGoogle Scholar
  34. Loader C (1999) Local Regression and Likelihood. Springer, New York, pp 59–65 45–48Google Scholar
  35. Loader C (2011) Package locfit, CRAN R project. http://cran.rproject.org/web/packages/locfit/locfit.pdf. Accessed 3 Sept 2012
  36. Loaiciga HA, Leipnik RB (1996) Stochastic renewal model of low-flow Streamflow sequences. Stoch Hydrol Hydraul 10(1):65–85. doi: 10.1007/BF01581794 CrossRefGoogle Scholar
  37. Lohani VK, Loganathan GV (1997) An early warning system for drought management using the palmer drought index. J Am Water Resour As 33:1375–1386CrossRefGoogle Scholar
  38. Mark S et al (2002) The drought monitor. Bull Am Meteorol Soc 83(8):1181–1190CrossRefGoogle Scholar
  39. Mathews JH (2004) Module for hermite polynomial interpolation. In: John H. Mathews. http://math.fullerton.edu/mathews/n2003/HermitePolyMod.html. Assessed 06 Oct 2012
  40. McKee TB, Doesken NJ, Kliest J (1995) Drought monitoring with multiple-time scales. In:Proceedings of the 9th conference on applied climatology, Dallas. Amer Meteor Soc, 233–236Google Scholar
  41. Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Environ Res Risk Assess 19:326–339CrossRefGoogle Scholar
  42. Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198:127–138CrossRefGoogle Scholar
  43. Mishra AK, Singh VP (2011) Drought modeling-A review. J Hydrol 403(1–2):157–175. doi: 10.1016/j.jhydrol.2011.03.049 CrossRefGoogle Scholar
  44. Moon Yl, Oh TS, Kim MS, Kim SS (2010) A drought frequency analysis for palmer drought severity index using boundary kernel function. In: Palmer (ed) World environmental and water resources congress 2010, American society of civil engineers, providence, pp 2708–2716. doi: 10.1061/41114(371)279
  45. Moye LA, Kapadia AS, Cech IM, Hardy RJ (1988) The theory of run with application to drought prediction. J Hydrol 103:127–137CrossRefGoogle Scholar
  46. National Drought Mitigation Center (2012). ENSO and Drought Forecasting, NDMC. http://drought.unl.edu/DroughtBasics/ENSOandForecasting.aspx. Accessed 27 Sept 2012
  47. National Oceanic and Atmospheric Administration (2012) NOAA study: human-caused climate change a major factor in more frequent Mediterranean droughts, NOAA. http://www.noaanews.noaa.gov/stories2011/20111027_drought.html. Accessed 3 Sept 2012
  48. Nurmi P (2003) Recommendations on the verification of local weather forecasts. ECMWF Techn. Mem 430Google Scholar
  49. Palmer WC (1965) Meteorological Drought. U.S. Department of Commerce Weather Bureau. Washington, D.C. Research Paper No. 45 Google Scholar
  50. Prairie JR, Rajagopalan B, Fulp TJ, Zagona EA (2006) Modified K-NN model for stochastic streamflow simulation. J Hydrol Eng 11:371–378. doi: 10.1061/(ASCE)1084-0699(2006)11:4(371) CrossRefGoogle Scholar
  51. Rajagopalan B, Lall U (1998) Locally weighted polynomial estimation of spatial precipitation. J Geogr Inf Decis Anal 2(2):44–51Google Scholar
  52. Rao AR, Padmanabhan G (1984) Analysis and modeling of Palmer’s Drought index series. J Hydrol Eng 68:211–219. doi: 10.1016/0022-1694(84)90212-9 CrossRefGoogle Scholar
  53. Saldariaga J, Yevjevich VM (1970) Application of run-lengths to hydrologic series. Colorado State University Hydrology Paper No. 40. Fort CollinsGoogle Scholar
  54. Sen Z (1990) Critical drought analysis by second-order Markov chain. J Hydrol 120(1–4):183–202. doi: 10.1016/0022-1694(90)90149-R CrossRefGoogle Scholar
  55. Smith SA, Popp MP, Nathan K (2012) Estimate of the Economic Impact of Drought on Commercial Beef Cow/Calf Operations in Arkansas: A Comparison of August 2011 to July 2012 with a Typical Production Year. University of Arkansas, United States Department of Agriculture and County Governments Cooperating. http://www.uaex.edu/depts/ag_economics/publications/Ark_Drought_Report_Comm_Beef_September2012.pdf. Accessed 30 Sept 2012
  56. Talagrand O, Vautard R, Strauss B (1997) Evaluation of probabilistic prediction systems. In: Proceedings, ECMWF Workshop on predictability. ECMWF. pp 1–25Google Scholar
  57. U.S. Census Bureau (2012) Arkansas Quick Fact, United State Census. http://quickfacts.census.gov/qfd/states/05000.html. Accessed 2 Oct 2012
  58. University of Arkansas (2012) Arkansas Agriculture, Division of Agriculture. http://www.aragriculture.org/. Accessed 3 Oct 2012
  59. Wang W, Van Gelder PHAJM, Vrijling JK (2005) Constructing prediction intervals for monthly streamflow forecasts. In: Proceeding of ISSH, Stochastic Hydraulics, Njmegen, 23–24 May 2005, pp 158–161Google Scholar
  60. Wilhite DA, Glantz MH (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10(3):111–120CrossRefGoogle Scholar
  61. Wilks D (1995) Statistical methods in the atmospheric sciences. Academic Press, LondonGoogle Scholar
  62. Williams J (2005) Long-range forecasts have limitations, USATODAY.com. http://usatoday30.usatoday.com/weather/woutwhat.htm. Accessed 8 Aug 2012
  63. Yevjevich VM (1967) An objective approach to definitions and investigations of continental hydrologic droughts. Colorado State University Hydrology Paper no 23. Fort Collins, ColoradoGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.CTL Engineering IncColumbusUSA
  2. 2.Arkansas State UniversityJonesboroUSA

Personalised recommendations