Water Resources Management

, Volume 28, Issue 10, pp 3291–3305 | Cite as

Climate Change and Hydrological Response in the Trans-State Oologah Lake Watershed–Evaluating Dynamically Downscaled NARCCAP and Statistically Downscaled CMIP3 Simulations with VIC Model

  • Lei Qiao
  • Yang Hong
  • Renee McPherson
  • Mark Shafer
  • David Gade
  • David Williams
  • Sheng Chen
  • Douglas Lilly


Statistically and dynamically downscaled climate projections are the two important data sources for evaluation of climate change and its impact on water availability, water quality and ecosystems. Though bias correction helps to adjust the climate model output to behave more similarly to observations, the hydrologic response still can be biased. This study uses Variable Infiltration Capacity (VIC) model to evaluate the hydrologic response of the trans-state Oologah Lake watershed to climate change by using both statistically and dynamically downscaled multiple climate projections. Simulated historical and projected climate data from the North American Regional Climate Change Assessment Program (NARCCAP) and Bias-Corrected and Spatially Downscaled–Coupled Model Intercomparison Phase 3 (BCSD-CMIP3) forced the hydrologic model. In addition, different river network upscaling methods are also compared for a higher VIC model performance. Evaluation and comparison shows the following the results. (1) From the hydrologic point of view, the dynamically downscaled NARCCAP projection performed better, most likely in capturing a larger portion of mesoscale-driven convective rainfall than the statistically downscaled CMIP3 projections; hence, the VIC model generated higher seasonal streamflow amplitudes that are closer to observations. Additionally, the statistically downscaled GCMs are less likely to capture the hydrological simulation probably due to missing integration of climate variables of wind, solar radiation and others, even though their precipitation and temperature are bias corrected to be more favorably than the NARCCAP simulations. (2) Future water availability (precipitation, runoff, and baseflow) in the watershed would increase annually by 3–4 %, suggested by both NARCCAP and BCSD-CMIP3. Temperature increases (2.5–3 °C) are much more consistent between the two types of climate projections both seasonally and annually. However, NARCCAP suggested 2–3 times higher seasonal variability of precipitation and other water fluxes than the BCSD-CMIP3 models. (3) The hydrologic performance could be used as a potential metric to comparatively differentiate climate models, since the land surface and atmosphere processes are considered integrally.


Climate change NARCCAP Statistical downscaling VIC Oologah Lake watershed 



This research was funded by the Responses to Climate Change program, U.S. Army Corps of Engineers Institute for Water Resources and the South Central Climate Science Center, U.S. Geological Survey. We wish to thank the North American Regional Climate Change Assessment Program (NARCCAP) for providing the data used in this paper. “Bias Corrected and Downscaled WCRP CMIP3 Climate Projections” archive at http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/.


  1. Abdulla FA, Lettenmaier DP, Wood EF, Smith JA (1996) Application of a macroscale hydrologic model to estimate the water balance of the Arkansas-Red River Basin. J Geophys Res 101(D3):7449–7459CrossRefGoogle Scholar
  2. Andreadis KM, Lettenmaier DP (2006) Trends in 20th century drought over the continental United States. Geophys Res Lett 33(10), L10403Google Scholar
  3. Bowling LC, Storck P, Lettenmaier DP (2000) Hydrologic effects of logging in western Washington, United States. Water Resour Res 36(11):3223–3240CrossRefGoogle Scholar
  4. Castro CL, Pielke RA Sr, Leoncini G (2005) Dynamical downscaling: assessment of value retained and added using the Regional Atmopsheric Modeling System (RAMS). J Geophys Res D Atmos 110(5):1–21Google Scholar
  5. Christensen N, Lettenmaier DP (2006) A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River basin. Hydrol Earth Syst Sci Discuss 3(6):3727–3770CrossRefGoogle Scholar
  6. Christensen J, Carter T, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of regional climate models: the PRUDENCE project. Clim Chang 81:1–6CrossRefGoogle Scholar
  7. Christensen J, Rammukainen M, Lenderink G (2009) Formulation of very-high-resolution regional climate model ensembles for Europe. In: Van der Linden P, Mitchell JFB (eds) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Center, pp. 47–58Google Scholar
  8. Christensen J, Kjellstr E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Clim Res 44(2–3):179–194CrossRefGoogle Scholar
  9. Chu W, Gao X, Sorooshian S (2010) Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: application to the calibration of the Sacramento soil-moisture accounting model. Water Resour Res 46(9):W09530CrossRefGoogle Scholar
  10. Chu W, Gao X, Sorooshian S (2011) A solution to the crucial problem of population degeneration in high-dimensional evolutionary optimization. IEEE Syst J 5(3):362–373CrossRefGoogle Scholar
  11. Daly C, Neilson RP, Phillips DL (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158CrossRefGoogle Scholar
  12. Duan QY, Gupta HV, Sorooshian S (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76(3):501–521CrossRefGoogle Scholar
  13. Duan QY, Sorooshian S, Gupta VK (1994) Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol 158(3–4):265–284CrossRefGoogle Scholar
  14. Fiseha BM, Setegn SG, Melesse AM, Volpi E, Fiori A (2014) Impact of climate change on the hydrology of upper Tiber River Basin using bias corrected regional climate model. Water Resour Manag 28(5):1327–1343CrossRefGoogle Scholar
  15. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578CrossRefGoogle Scholar
  16. Gao H, Wood EF, Drusch M, McCabe MF (2007) Copula-derived observation operators for assimilating TMI and AMSR-E retrieved soil moisture into land surface models. J Hydrometeorol 8(3):413–429CrossRefGoogle Scholar
  17. Ghosh S, Katkar S (2012) Modeling uncertainty resulting from multiple downscaling methods in assessing hydrological impacts of climate change. Water Resour Manag 26(12):3559–3579CrossRefGoogle Scholar
  18. Giorgi F, Mearns LO (2003) Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method. Geophys Res Lett 30(12):1629CrossRefGoogle Scholar
  19. Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113(D6), D06104Google Scholar
  20. Gutowski WJ Jr, Arritt RW, Kawazoe S, Flory DM, Takle ES, Biner S, Caya D, Jones RG, Laprise R, Leung LR, Mearns LO, Moufouma-Okia W, Nunes AMB, Qian Y, Roads JO, Sloan LC, Snyder MA (2010) Regional extreme monthly precipitation simulated by NARCCAP RCMs. J Hydrometeorol 11(6):1373–1379CrossRefGoogle Scholar
  21. Hanel M, Mrkvičková M, Máca P, Vizina A, Pech P (2013) Evaluation of simple statistical downscaling methods for monthly regional climate model simulations with respect to the estimated changes in Runoff in the Czech Republic. Water Resour Manag 27(15):5261–5279Google Scholar
  22. Intergovernmental Panel on Climate Change (IPCC) (2000) Special report on emissions scenarios. Cambridge Univ. Press, CambridgeGoogle Scholar
  23. Knutti R, Abromowitz G, Collins M, Eyering V, Gleckler P, Hewitson B, Mearns L (2010) Good practice guidance paper on assessing and combining multi model climate projections. In: IPCC (ed) Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, p 15Google Scholar
  24. Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99(D7):14415–14428CrossRefGoogle Scholar
  25. Liang X, Wood EF, Lettenmaier DP (1996) Surface soil moisture parameterization of the VIC-2L model: evaluation and modification. Glob Planet Chang 13(1–4):195–206CrossRefGoogle Scholar
  26. Liang X, Wood EF, Lettenmaier DP (1999) Modeling ground heat flux in land surface parameterization schemes. J Geophys Res 104(D8):9581–9600CrossRefGoogle Scholar
  27. Liu L, Hong Y, Hocker J, Shafer M, Carter L, Gourley J, Bednarczyk C, Yong B, Adhikari P (2012a) Analyzing projected changes and trends of temperature and precipitation in the southern USA from 16 downscaled global climate models. Theor Appl Climatol 109(3–4):345–360CrossRefGoogle Scholar
  28. Liu L, Hong Y, Bednarczyk C, Yong B, Shafer M, Riley R, Hocker J (2012b) Hydro-climatological drought analyses and projections using meteorological and hydrological drought indices: a case study in Blue River Basin, Oklahoma. Water Resour Manag 26(10):2761–2779CrossRefGoogle Scholar
  29. Lo JCF, Yang ZL, Pielke Sr RA (2008) Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J Geophys Res D Atmos 113(9)Google Scholar
  30. Lohmann D, Nolte-Holube R, Raschke E (1996) A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus Ser A 48(5):708–721CrossRefGoogle Scholar
  31. Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J Clim 15(22):3237–3251CrossRefGoogle Scholar
  32. Maurer EP, Brekke L, Pruitt T, Duffy PB (2007) Fine-resolution climate projections enhance regional climate change impact studies. EOS Trans Am Geophys Union 88(47):504CrossRefGoogle Scholar
  33. Mearns LO, Gutowski W, Jones R, Leung R, McGinnis S, Nunes A, Qian Y (2009) A regional climate change assessment program for North America. EOS Trans Am Geophys Union 90(36):311CrossRefGoogle Scholar
  34. Mearns LO, Arritt R, Biner S, Bukovsky MS, McGinnis S, Sain S, Caya D, Correia J, Flory D, Gutowski W, Takle ES, Jones R, Leung R, Moufouma-Okia W, McDaniel L, Nunes AMB, Qian Y, Roads J, Sloan L, Snyder M (2012) The North American Regional climate change assessment program: overview of phase i results. Bull Am Meteorol Soc 93(9):1337–1362CrossRefGoogle Scholar
  35. Nijssen B, Schnur R, Lettenmaier DP (2001) Global retrospective estimation of soil moisture using the variable infiltration capacity land surface model, 1980–93. J Clim 14(8):1790–1808CrossRefGoogle Scholar
  36. Phillips TJ, Gleckler PJ (2006) Evaluation of continental precipitation in 20th century climate simulations: the utility of multimodel statistics. Water Resour Res 42(3):W03202CrossRefGoogle Scholar
  37. Pierce DW, Barnett TP, Santer BD, Gleckler PJ (2009) Selecting global climate models for regional climate change studies. Proc Natl Acad Sci 106(21):8441–8446CrossRefGoogle Scholar
  38. Sobolowski S, Pavelsky T (2012) Evaluation of present and future North American Regional Climate Change Assessment Program (NARCCAP) regional climate simulations over the southeast United States. J Geophys Res 117(D1), D01101Google Scholar
  39. Su F, Adam JC, Bowling LC, Lettenmaier DP (2005) Streamflow simulations of the terrestrial Arctic domain. J Geophys Res 110(D8), D08112Google Scholar
  40. Takle ES, Gutowski WJ Jr, Arritt RW, Pan Z, Anderson CJ, da Silva RR, Caya D, Chen S-C, Giorgi F, Christensen JH, Hong S-Y, Juang H-MH, Katzfey J, Lapenta WM, Laprise R, Liston GE, Lopez P, McGregor J, Pielke RA Sr, Roads JO (1999) Project to Intercompare Regional Climate Simulations (PIRCS): description and initial results. J Geophys Res 104(D16):19443–19461CrossRefGoogle Scholar
  41. Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498CrossRefGoogle Scholar
  42. te Linde AH, Aerts JCJH, Hurkmans RTWL, Eberle M (2008) Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets. Hydrol Earth Syst Sci 12(3):943–957CrossRefGoogle Scholar
  43. Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour Res 42(2):W02419CrossRefGoogle Scholar
  44. Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res D Atmos 107(20):6–1–6–15Google Scholar
  45. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62(1):189–216CrossRefGoogle Scholar
  46. Wu H, Kimball JS, Li H, Huang M, Leung LR, Adler RF (2012) A new global river network database for macroscale hydrologic modeling. Water Resour Res 48(9):W09701CrossRefGoogle Scholar
  47. Zhang H, Huang G (2012) Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation. Clim Dyn:1–17Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Lei Qiao
    • 1
    • 2
    • 3
  • Yang Hong
    • 1
    • 2
    • 6
    • 7
  • Renee McPherson
    • 4
  • Mark Shafer
    • 4
  • David Gade
    • 5
  • David Williams
    • 5
  • Sheng Chen
    • 1
    • 2
  • Douglas Lilly
    • 5
  1. 1.School of Civil Engineering and Environmental SciencesUniversity of OklahomaNormanUSA
  2. 2.Advanced Radar Research CenterUniversity of OklahomaNormanUSA
  3. 3.Department of Natural Resource Ecology and ManagementOklahoma State UniversityStillwaterUSA
  4. 4.Oklahoma Climatological Survey and Department of Geography and Environmental SustainabilityUniversity of OklahomaNormanUSA
  5. 5.U.S. Army Corps of EngineersTulsa DistrictUSA
  6. 6.Department of Hydraulic EngineeringTsinghua UniversityBeijingChina
  7. 7.Hydrometeorology and Remote Sensing Lab (hydro.ou.edu)National Weather Center Advanced Radar Research Center Suite 4610NormanUSA

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