Water Resources Management

, Volume 29, Issue 5, pp 1467–1487 | Cite as

Projected Hydrologic Changes Under Mid-21st Century Climatic Conditions in a Sub-arctic Watershed

  • Debjani Deb
  • Jonathan Butcher
  • Raghavan Srinivasan


The potential effects of mid-21st century climate change on the hydrology of the Cook Inlet watershed in south-central Alaska was analyzed in this study. Climate datasets representing a set of potential change scenarios for the period 2041–2070 were developed from the North American Regional Climate Change Assessment Program (NARCCAP) archive of dynamically downscaled climate products. The NARCCAP 50-km scale regional climate output was converted to realistic daily weather time series using a “change factor” method in which observed meteorological time series used for model calibration are perturbed. The perturbations are based on statistical summaries of change for the different climate scenarios, by month, as calculated from the differences between the 1971–2000 and 2040–2070 climate model simulation periods. The downscaled climate datasets were then used to run the Soil and Water Assessment Tool (SWAT) for the Cook Inlet watershed. Generally, it was observed that increasing rainfall and warmer temperatures across the Cook Inlet watershed led to a predicted increase in the stream flow in the major rivers, increase in 7-day low flows, and considerable increase in 100-year peak flow. Furthermore, under future climatic conditions precipitation is expected to increase in the Cook Inlet watershed but the amount of snowfall is expected to decrease. Also, the amount of snowmelt is expected to increase due to warmer temperature thereby causing the average annual fraction of snowfall as precipitation to decrease leading to a reduction in the glacial mass balance in the watershed. Moreover, average annual water yield, runoff, baseflow, snowmelt across the basin is expected to increase. More specifically the different hydrologic components varied seasonally and monthly driven by the seasonal and monthly changes in precipitation and temperature. However, the overall hydrology of the watershed is projected to remain snowmelt dominated through the mid-21st century without a major shift in regime. These simulations provide a benchmark of hydrologic sensitivity to potential future climate change in this watershed useful for identifying vulnerabilities and informing the development of adaptation responses.


Hydrology Climate change SWAT Cook inlet 



We wish to thank Seth McGinnis of the National Center for Atmospheric Research (NCAR) for processing the NARCCAP output into change statistics for use in the watershed modeling. NCAR is supported by the National Science Foundation. Peter Cada provided GIS support and Mustafa Faizullabhoy constructed the future climate time series. Funding for this work was provided by the U.S. EPA Office of Research and Development. The views expressed in this paper represent those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.


  1. Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Srinivasan R (2007) Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol 333(2):413–430CrossRefGoogle Scholar
  2. Ahl RS, Woods SW, Zuuring HR (2008) Hydrologic calibration and validation of SWAT in a snow‐dominated rocky mountain Watershed Montana, USA. J Am Water Resour Assoc 44(6):1411–1430CrossRefGoogle Scholar
  3. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc 34(1):73–89CrossRefGoogle Scholar
  4. Bekele EG, Knapp HV (2010) Watershed modeling to assessing impacts of potential climate change on water supply availability. Water Resour Manag 24(13):3299–3320CrossRefGoogle Scholar
  5. Brabets TP, Nelson GL, Dorava JM, Milner AM (1999) Water-quality assessment of the Cook Inlet Basin Alaska-- Environmental setting. US Geological Survey Water-Resources Investigations Report 99–4025; 65 pGoogle Scholar
  6. IPCC (2007) Climate Change 2007: synthesis report - summary for policymakers available online at: http://www.ipccch/pdf/assessment-report/ar4/syr/ar4_syr_spm.pdf
  7. Dai A (2006) Precipitation characteristics in eighteen coupled climate models. J Clim 19(18):4605–4630CrossRefGoogle Scholar
  8. Faramarzi M, Abbaspour KC, Vaghefi SA, Farzaneh MR, Zehnder AJ, Srinivasan R, Yang H (2013) Modelling impacts of climate change on freshwater availability in Africa. J Hydrol 480:85–101CrossRefGoogle Scholar
  9. 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
  10. Fontaine TA, Cruickshank TS, Arnold JG, Hotchkiss RH (2002) Development of a snowfall–snowmelt routine for mountainous terrain for the Soil Water Assessment Tool (SWAT). J Hydrol 262(1):209–223CrossRefGoogle Scholar
  11. Glass RL (1999) Water-quality assessment of the Cook Inlet Basin Alaska-Summary of data through 1997. US Geological Survey Water-Resources Investigations Report 99–4116; 110 pGoogle Scholar
  12. Hamlet AF, Lettenmaier DP (2007) Effects of 20th century warming and climate variability on flood risk in the Western US. Water Resour Res 43(6), W06427CrossRefGoogle Scholar
  13. Hartman CW, Johnson PR (1984) Environmental atlas of Alaska. Institute of Water Resources/Engineering Experiment Station, University of Alaska FairbanksGoogle Scholar
  14. Hurd B, Leary N, Jones R, Smith J (1999) Relative regional vulnerability of water resources to climate change. J Am Water Resour Assoc 35(6):1399–1409CrossRefGoogle Scholar
  15. Jayakrishnan R, Srinivasan R, Santhi C, Arnold JG (2005) Advances in the application of the SWAT model for water resources management. Hydrol Process 19(3):749–762CrossRefGoogle Scholar
  16. Johnson TE, Butcher JB, Parker A, Weaver CP (2011) Investigating the sensitivity of US streamflow and water quality to climate change: US EPA Global Change Research Program’s 20 Watersheds Project. J Water Resour Plan Manag 138(5):453–464CrossRefGoogle Scholar
  17. Luzio M, Srinivasan R, Arnold JG (2002) Integration of watershed tools and SWAT model into BASINS. J Am Water Resour Assoc 38(4):1127–1141CrossRefGoogle Scholar
  18. Mango LM, Melesse AM, McClain ME, Gann D, Setegn SG (2011) Land use and climate change impacts on the hydrology of the upper Mara River Basin Kenya: results of a modeling study to support better resource management. Hydrol Earth Syst Sci 15(7):2245–2258CrossRefGoogle Scholar
  19. Mantua N, Tohver I, Hamlet A (2010) Climate change impacts on streamflow extremes and summertime stream temperature and their possible consequences for freshwater salmon habitat in Washington State. Clim Chang 102(1–2):187–223CrossRefGoogle Scholar
  20. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900CrossRefGoogle Scholar
  21. Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2005) Soil and water assessment tool theoretical documentation. Grassland soil and water research laboratory. USDA- Agricultural Research Service Temple, TXGoogle Scholar
  22. Rahman K, Maringanti C, Beniston M, Widmer F, Abbaspour K, Lehmann A (2013) Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: the Upper Rhone River watershed case in Switzerland. Water Resour Manag 27(2):323–339CrossRefGoogle Scholar
  23. Rosenthal WD, Srinivasan R, Arnold JG (1995) Alternative river management using a linked GIS-hydrology model. Trans ASAE 38(3):783–790CrossRefGoogle Scholar
  24. Santhi C, Arnold JG, Williams JR, Dugas WA, Srinivasan R, Hauck LM (2001) Validation of the SWAT model on a large river basin with point and nonpoint sources. J Am Water Resour Assoc 37(5):1169–1188CrossRefGoogle Scholar
  25. Santhi C, Muttiah RS, Arnold JG, Srinivasan R (2005) A GIS-based regional planning tool for irrigation demand assessment and savings using SWAT. Trans ASAE 48(1):137–147CrossRefGoogle Scholar
  26. Spruill CA, Workman SR, Taraba JL (2000) Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Trans ASAE 43(6):1431–1439CrossRefGoogle Scholar
  27. Srinivasan R, Arnold JG (1994) Integration of a basin-scale water quality model with GIS. J Am Water Resour Assoc 30(3):453–462CrossRefGoogle Scholar
  28. Srinivasan R, Ramanarayanan TS, Arnold JG, Bednarz ST (1998) Large area hydrologic modeling and assessment part II: model application. J Am Water Resour Assoc 34(1):91–101CrossRefGoogle Scholar
  29. Sun Y, Solomon S, Dai A, Portmann RW (2006) How often does it rain? J Clim 19(6):916–934CrossRefGoogle Scholar
  30. USEPA (2013) Watershed modeling to assess the sensitivity of streamflow, nutrient and sediment loads to potential climate change and urban development in 20 US watersheds (Final Report). USEPA, Washington DC EPA/600/R-12/058 F (
  31. USEPA (2008) Using the BASINS Meteorological Database (Version 2006). BASINS Technical Note 10. Office of Water, USEPA, Washington DCGoogle Scholar
  32. USEPA (United States Environmental Protection Agency) (2009) BASINS 40 Climate Assessment Tool (CAT): Supporting Documentation and User’s Manual EPA/600/R-8/088 F. Global Change Research Program, National Center for Environmental Assessment, Office of Research and Development, USEPA,Washington DCGoogle Scholar
  33. Wang X, Melesse AM (2005) Evaluation of the SWAT model’s snowmelt hydrology in a northwestern Minnesota watershed. Trans ASAE 48(4):1359–1376CrossRefGoogle Scholar
  34. Weber A, Fohrer N, Möller D (2001) Long-term land use changes in a mesoscale watershed due to socio-economic factors—effects on landscape structures and functions. Ecol Model 140(1):125–140CrossRefGoogle Scholar
  35. Wu Y, Liu S, Abdul-Aziz OI (2012) Hydrological effects of the increased CO2 and climate change in the Upper Mississippi River Basin using a modified SWAT. Clim Chang 110(3–4):977–1003CrossRefGoogle Scholar
  36. Yen H, Jeong J, Feng Q, Deb D (2014) Assessment of input uncertainty in SWAT using latent variables. Water Resour Manag 1–17Google Scholar
  37. Zheng J, Li GY, Han ZZ, Meng GX (2010) Hydrological cycle simulation of an irrigation district based on a SWAT model. Math Comput Model 51(11):1312–1318CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Debjani Deb
    • 1
  • Jonathan Butcher
    • 2
  • Raghavan Srinivasan
    • 1
  1. 1.Department of Ecosystem Science and ManagementTexas A&M UniversityCollege StationUSA
  2. 2.Tetra Tech, Inc.DurhamUSA

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