Water Resources Management

, Volume 26, Issue 8, pp 2147–2157

Estimations of Evapotranspiration and Water Balance with Uncertainty over the Yukon River Basin

  • Wenping Yuan
  • Shuguang Liu
  • Shunlin Liang
  • Zhengxi Tan
  • Heping Liu
  • Claudia Young
Article

Abstract

In this study, the revised Remote Sensing-Penman Monteith model (RS-PM) was used to scale up evapotranspiration (ET) over the entire Yukon River Basin (YRB) from three eddy covariance (EC) towers covering major vegetation types. We determined model parameters and uncertainty using a Bayesian-based method in the three EC sites. The 95 % confidence interval for the aggregate ecosystem ET ranged from 233 to 396 mm yr−1 with an average of 319 mm yr−1. The mean difference between precipitation and evapotranspiration (W) was 171 mm yr−1 with a 95 % confidence interval of 94–257 mm yr−1. The YRB region showed a slight increasing trend in annual precipitation for the 1982–2009 time period, while ET showed a significant increasing trend of 6.6 mm decade−1. As a whole, annual W showed a drying trend over YRB region.

Keywords

Evapotranspiration Bayesian approach Markov chain Monte Carlo Model uncertainty Yukon river basin 

References

  1. Baldocchi DD, Vogel CA, Hall B (1997) Seasonal variation of energy and water vapor exchange rates above and below a boreal jack pine forest. J Geophys Res 102:28939–28952CrossRefGoogle Scholar
  2. Barber VA, Juday GP, Finney BP, Wilmking M (2004) Reconstruction of summer temperatures in interior Alaska from tree-ring proxies: evidence for changing synoptic climate regimes. Clim Chang 63:91–120CrossRefGoogle Scholar
  3. Campolongo F, Kleijnen J, Andres T (2000) Screening Methods. In: Saltelli A, Chan K, Scott EM (eds) Sensitivity analysis. Wiley, Chichester, UKGoogle Scholar
  4. Chapin FS III, Mcguire AD, Randerson J et al (2000) Arctic and boreal ecosystems of western North America as components of the climate system. Global Change Biol 6:211–223CrossRefGoogle Scholar
  5. Cleugh HA, Leuning R, Mu Q, Running SW (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens Environ 106:285–304CrossRefGoogle Scholar
  6. Dirmeyer PA, Gao X, Zhao M, Guo Z, Oki T, Hanasaki N (2006) GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull Am Meteorol Soc 87:1381–1397CrossRefGoogle Scholar
  7. Ewers BE, Gower ST, Bond-Lamberty B, Wang CK (2005) Effects of stand age and tree species composition on transpiration and canopy conductance of boreal forest. Plant Cell Environ 28:660–678CrossRefGoogle Scholar
  8. Falge E, Baldocchi D, Olson R et al (2001) Gap filling strategies for defensible annual sums of net ecosystem exchange. Agr Forest Meteorol 107:43–69CrossRefGoogle Scholar
  9. Flannigan MD, Logan KA, Amiro BD, Skinner W, Stocks B (2005) Future area burned in Canada. Clim Chang 72:1–16CrossRefGoogle Scholar
  10. Franchini M, Ventaglio E, Bonoli A (2011) A procedure for evaluating the compatibility of surface water resources with environmental and human requirements. Water Resour Manage 25:3613–3634CrossRefGoogle Scholar
  11. Friedl MA, McIver DK, Hodges JCF et al (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302CrossRefGoogle Scholar
  12. Godden L, Ison RL, Wallis PJ (2011) Water governance in a climate change world: appraising systemic and adaptive effectiveness. Water Resour Manage 25:3971–3976CrossRefGoogle Scholar
  13. Goetz SJ, Bunn AG, Fiske GJ, Houghton RA (2005) Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. PNAS 102:13521–13525CrossRefGoogle Scholar
  14. Hassaballah K, Jonoski A, Popescu I, Solomatine DP (2012) Model-based optimization of downstream impact during filling of a new reservoir: case study of Mandaya/Roseires Reservoirs on the Blue Nile River. Water Resour Manage 26:273–293CrossRefGoogle Scholar
  15. Helton JC, Davis FJ (2000) Sampling-based methods for uncertainty and sensitivity analysis. Sandia National Laboratories, AlbuquerqueCrossRefGoogle Scholar
  16. Hogg EH, Price DT, Black TA (2000) Postulated feedbacks of deciduous forest phenology on seasonal climate patterns in the western Canadian interior. J Clim 13:4229–4243CrossRefGoogle Scholar
  17. Hogg EH, Wein RW (2005) Impacts of drought on forest growth and regeneration following fire in southwestern Yukon, Canada. Can J For Res 35:2141–2150Google Scholar
  18. IPCC (2007) Climate Change 2007: The physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  19. Kelliher FM, Leuning R, Raupach MR, Schulze ED (1995) Maximum conductances for evaporation from global vegetation types. Agr Forest Meteorol 73:1–16CrossRefGoogle Scholar
  20. Kljun NT, Black A, Griffis TJ et al (2006) Response of net ecosystem productivity of three boreal forest stands to drought. Ecosystems 10:1039–1055CrossRefGoogle Scholar
  21. Klos RJ, Wang GG, Bauerle WL, Rieck JR (2009) Drought impact on forest growth and mortality in the southeast USA: an analysis using forest health and monitoring data. Ecol Appl 19:699–708CrossRefGoogle Scholar
  22. Knorr W, Heimann M (2001) Uncertainties in global terrestrial biosphere modeling I: a comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme. Global Biogeochem Cy 15:207–225CrossRefGoogle Scholar
  23. Liu HP, Randerson JT (2008) Interannual variability of surface energy exchange depends on stand age in a boreal forest fire chronosequence. J Geophys Res 13:G01006. doi:10.1029/2007JG000483 CrossRefGoogle Scholar
  24. Mu QZ, Heinsch FA, Zhao MS, Running SW (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ 111:519–536CrossRefGoogle Scholar
  25. Myneni RB, Keeling CD, Tucker CJ et al (1997) Increased plant growth in the northern high latitudes from 1981–1991. Nature 386:698–702Google Scholar
  26. Verbeeck H, Samson R, Verdonck F et al (2006) Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a monte carlo analysis. Tree Physiol 26:807–817CrossRefGoogle Scholar
  27. Wilhite DA, Svoboda MD, Hayes MJ (2007) Understanding the complex impacts of drought: a key to enhancing drought mitigation and preparedness. Water Resour Manage 21:763–774CrossRefGoogle Scholar
  28. Wilson K, Goldstein A, Falge E et al (2002) Energy balance closure at FLUXNET sites. Agr Forest Meteorol 113:223–243CrossRefGoogle Scholar
  29. Yuan WP, Liu SG, Yu GR et al (2010) Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens Environ 114:1416–1431CrossRefGoogle Scholar
  30. Zhang K, Kimball JS, Mu QZ et al (2009) Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005. J Hydrol 379:92–110CrossRefGoogle Scholar
  31. Zhang K, Kimball JS, Hogg EH et al (2008) Satellite-based model detection of recent climate-driven changes in northern high-latitude vegetation productivity. J Geophys Res 113:G03033. doi:10.1029/2007JG000621 CrossRefGoogle Scholar
  32. Zhao M, Heinsch FA, Nemani R, Running SW (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ 95:164–176CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Wenping Yuan
    • 1
    • 2
  • Shuguang Liu
    • 3
    • 4
  • Shunlin Liang
    • 1
    • 2
    • 8
  • Zhengxi Tan
    • 5
  • Heping Liu
    • 6
  • Claudia Young
    • 7
  1. 1.State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing ApplicationsChinese Academic of ScienceBeijingChina
  2. 2.College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina
  3. 3.United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) CenterSioux FallsUSA
  4. 4.Geographic Information Science Center of ExcellenceSouth Dakota State UniversityBrookingsUSA
  5. 5.ASRC Research and Technology Solutions, Contractor to the USGS Earth Resources Observation and Science (EROS) CenterSioux FallsUSA
  6. 6.Department of Civil & Environmental EngineeringWashington State UniversityPullmanUSA
  7. 7.ADNET Systems, Inc., Contractor to the USGS Earth Resources Observation and Science (EROS) CenterSioux FallsUSA
  8. 8.Department of GeographyUniversity of MarylandCollege ParkUSA

Personalised recommendations