Regional Environmental Change

, Volume 13, Supplement 1, pp 81–90 | Cite as

Validating climate models for computing evapotranspiration in hydrologic studies: how relevant are climate model simulations over Florida?

  • Jayantha Obeysekera
Original Article


A non-stationary climate requires a new paradigm in the current practice of using past observations for future planning. Next to precipitation, evapotranspiration is the most important variable in water budgets of regions such as Florida, the location of this study. Dynamical simulations using Regional Climate Models (RCMs) provide all the variables necessary to compute potential or reference crop evapotranspiration (RET) for hydrologic modeling under climate change scenarios. Data sets for the variables necessary to compute RET using the Penman–Monteith model were obtained from RCMs provided by the North American Regional Climate Change Assessment Program to evaluate their skills in estimating RET during the late twentieth century simulations. Comparison of RET values simulated from RCMs with those computed from a high-resolution weather-monitoring network and satellite data show significant biases in spatial patterns and seasonality. In particular, seasonal patterns of RET computed from RCMs show a phase shift with peak values occurring during mid-summer months, whereas observed data show earlier peaks in May and June. The phase shift reflects similar behavior in model predictions of incoming solar radiation. In addition, relative humidity values estimated from RCMs are significantly higher than observed values and the wind speed estimates from many RCMs have a significant bias during dry season months. Further improvements to RCMs may be needed before they can be used effectively for hydrologic modeling under future climate change scenarios.


Evapotranspiration Climate change Regional Climate Models Hydrologic modeling 



We wish to thank the NARCCAP for providing the data used in this paper. NARCCAP is funded by the National Science Foundation, U.S. Department of Energy, the National Oceanic and Atmospheric Administration, and the U.S. Environmental Protection Agency Office of Research and Development. The author wishes to acknowledge the valuable assistance from Michelle Irizarry and Joel VanArman at South Florida Water Management District. Comments from the reviewers and the editors are also appreciated.

Supplementary material

10113_2013_411_MOESM1_ESM.docx (470 kb)
Supplementary material 1 (DOCX 470 kb)


  1. Abtew W (1995) Lysimeter study of evapotranspiration of cattails and comparison of three estimation methods. Trans Am Soc Ag Eng 38(1):121–129Google Scholar
  2. Abtew W (1996) Evapotranspiration measurements and modeling for three wetland systems in South Florida. J Am Water Res Assoc 32(3):465–473CrossRefGoogle Scholar
  3. Abtew W, Obeysekera J, Irizarry-Ortiz M, Lyons D, Reardon A (2003) Evapotranspiration estimation for South Florida. In: Bizier P, DeBarry P (eds) Proceedings of the world water and environmental congress 2003, ASCE, Reston, VAGoogle Scholar
  4. Allen R (2005) Penman–Monteith Equation. Elsevier, Encyclopedia of SoilsGoogle Scholar
  5. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements. FAO irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
  6. IPCC (2007) Climate change 2007—the physical science basis. In: Solomon S et al (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  7. German ER (2000) Regional evaluation of evapotranspiration in the Everglades, U.S. Geological Survey Water Resources Investigations Report 00-4217, p 48Google Scholar
  8. Irizarry-Ortiz M, Said W, Trimble P, Trimble B, Brown M, Ali A, Obeysekera J, Tarboton K, Newton T (2007) Estimation of long-term reference evapotranspiration from regional climate model datasets.In: Proceedings of the world environmental and water resources congress 2007: restoring our natural habitat, May 15–19. Tampa, FL, USAGoogle Scholar
  9. Jacobs J, Mecikalski J, Paech S (2008) Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. Report prepared for the South Florida Water Management District, p 138Google Scholar
  10. Kay AL, Davies HN (2008) Calculating potential evaporation from climate model data: a source of uncertainty for hydrological climate change impacts. J Hydrol 358(3–4):221–239. doi: 10.1016/j.jhydrol.2008.06.005 CrossRefGoogle Scholar
  11. Mearns LO, Gutowski WJ, Jones R, Leung LY, McGinnis S, Nunes AMB, Qian Y (2009) A regional climate change assessment program for North America. EOS 90(36):311–312CrossRefGoogle Scholar
  12. Milly PCD, Bettencourt J, Falkenmark M, Hirsch RM, Kundezewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead-Whither water management. Science 319:573–574. doi: 10.1126/science.1151915 CrossRefGoogle Scholar
  13. Obeysekera J, Irizarry M, Park J, Barnes J, Dessalegne T (2011a) Climate change and its implication for water resources management in South Florida. J Stoch Environ Res Risk Assess 25(4):495CrossRefGoogle Scholar
  14. Obeysekera J, Park J, Irizarry-Ortiz M, Trimble P, Barnes J, VanArman J, Said W, Gadzinski E (2011b) Past and projected trends in climate and sea level for South Florida. Interdepartmental Climate Change Group, South Florida Water Management District, West Palm Beach, Florida. Hydrologic and Environmental Systems Modeling Technical Report, July 5, 2011Google Scholar
  15. Penman HL (1948) Natural evaporation from open water, bare soil, and grass. Proc R Soc Lond A 193:120–146CrossRefGoogle Scholar
  16. R Foundation for Statistical Computing (2008) R: a language and environment for statistical computing, R Development Core Team, Vienna, Austria. ISBN 3-900051-07-0, URL, R version 2.7.2 accessed October 22, 2009
  17. Shoemaker B, Lopez CD, and Duever MJ (2011) Evapotranspiration over spatially extensive plant communities in the Big Cypress National Preserve, Southern Florida, 2007–2010. U.S. Geological Survey Scientific Investigations Report 2011-5212Google Scholar
  18. Stefanova L, Misra V, Chan SC, Griffin M, O’Brien JJ, Smith TJ III (2011) A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses. Clim Dyn 38(11–12):2449–2466. doi: 10.1007/s00382-011-1230-y Google Scholar
  19. Sumner DM (1996) Evapotranspiration form successional vegetation in a deforested area of the Lake Wales Ridge, Florida. U.S. Geological Survey Water Resources Investigations Report 96-4244Google Scholar
  20. Sumner DM, Jacobs JM (2005) Utility of Penman–Monteith Priestley–Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration. J Hydrol 308:81–104CrossRefGoogle Scholar
  21. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Hydrologic and Environmental Systems ModelingSouth Florida Water Management DistrictWest Palm BeachUSA

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