Climatic Change

, Volume 110, Issue 3–4, pp 977–1003 | Cite as

Hydrological effects of the increased CO2 and climate change in the Upper Mississippi River Basin using a modified SWAT

  • Yiping Wu
  • Shuguang Liu
  • Omar I. Abdul-Aziz


Increased atmospheric CO2 concentration and climate change may significantly impact the hydrological and meteorological processes of a watershed system. Quantifying and understanding hydrological responses to elevated ambient CO2 and climate change is, therefore, critical for formulating adaptive strategies for an appropriate management of water resources. In this study, the Soil and Water Assessment Tool (SWAT) model was applied to assess the effects of increased CO2 concentration and climate change in the Upper Mississippi River Basin (UMRB). The standard SWAT model was modified to represent more mechanistic vegetation type specific responses of stomatal conductance reduction and leaf area increase to elevated CO2 based on physiological studies. For estimating the historical impacts of increased CO2 in the recent past decades, the incremental (i.e., dynamic) rises of CO2 concentration at a monthly time-scale were also introduced into the model. Our study results indicated that about 1–4% of the streamflow in the UMRB during 1986 through 2008 could be attributed to the elevated CO2 concentration. In addition to evaluating a range of future climate sensitivity scenarios, the climate projections by four General Circulation Models (GCMs) under different greenhouse gas emission scenarios were used to predict the hydrological effects in the late twenty-first century (2071–2100). Our simulations demonstrated that the water yield would increase in spring and substantially decrease in summer, while soil moisture would rise in spring and decline in summer. Such an uneven distribution of water with higher variability compared to the baseline level (1961–1990) may cause an increased risk of both flooding and drought events in the basin.


Streamflow Water Yield Mississippi River Basin Swat Model Leaf Area Increase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Allen RG (1986) A penman for all seasons. J Irrig Drain Eng-ASCE 112(4):348–368CrossRefGoogle Scholar
  2. Allen RG, Jensen ME, Wright JL, Burman RD (1989) Operational estimates of reference evapotranspiration. Agron J 81(4):650–662CrossRefGoogle Scholar
  3. Arabi M, Frankenberger JR, Enge BA, Arnold JG (2008) Representation of agricultural conservation practices with swat. Hydrol Process 22 (16):3042–3055. doi: 10.1002/hyp.6890 CrossRefGoogle Scholar
  4. Arnell NW, Liv C (2001) Hydrology and water resources. Climate change 2001: impacts, adaptation and vulnerability. Cambridge University Press, CambridgeGoogle Scholar
  5. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment—part 1: model development. J Am Water Resour Assoc 34 (1):73–89CrossRefGoogle Scholar
  6. Arnold JG, Muttiah RS, Srinivasan R, Allen PM (2000) Regional estimation of base flow and groundwater recharge in the upper mississippi river basin. J Hydrol 227(1–4):21–40CrossRefGoogle Scholar
  7. Bates B, Kundzewicz ZW, Wu S, Palutikof J (2008) Climate change and water. Technical Paper VI of the Intergovernmental Panel on Climate Change. IPCC Secretariat, GenevaGoogle Scholar
  8. Betts RA, Cox PM, Lee SE, Woodward FI (1997) Contrasting physiological and structural vegetation feedbacks in climate change simulations. Nature 387(6635):796–799CrossRefGoogle Scholar
  9. Betts RA, Boucher O, Collins M, Cox PM, Falloon PD, Gedney N, Hemming DL, Huntingford C, Jones CD, Sexton DMH, Webb MJ (2007) Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature 448(7157):1037–U1035. doi: 10.1038/nature06045 CrossRefGoogle Scholar
  10. Beven KJ (2001) Rainfall–runoff modelling. Wiley, ChichesterGoogle Scholar
  11. Bouraoui F, Benabdallah S, Jrad A, Bidoglio G Application of the swat model on the medjerda river basin (tunisia). In: Workshop on Sustainable Catchment Management, Nice, FRANCE, Apr 25–30 2004. pp 497–507. doi: 10.1016/j.pce.2005.07.004
  12. Chaplot V (2007) Water and soil resources response to rising levels of atmospheric CO2 concentration and to changes in precipitation and air temperature. J Hydrol 337 (1–2):159–171. doi: 10.1016/j.jhydrol.2007.01.026 CrossRefGoogle Scholar
  13. Duan QY, Sorooshian S, Gupta V (1992) Effective and efficient global optimization for coneptual rainfall–runoff models. Water Resour Res 28(4):1015–1031CrossRefGoogle Scholar
  14. Easterling WE, Rosenberg NJ, McKenney MS, Jones CA, Dyke PT, Williams JR (1992) Preparing the erosion productivity impact calculator (epic) model to simulate crop response to climate change and the direct effects of co2. Agric For Meteorol 59(1–2):17–34CrossRefGoogle Scholar
  15. Eckhardt K, Ulbrich U (2003) Potential impacts of climate change on groundwater recharge and streamflow in a central European low mountain range. J Hydrol 284(1–4):244–252. doi: 10.1016/j.jhydrol.2003.08.005 CrossRefGoogle Scholar
  16. Farquhar GD, Fasham MJR, Goulden ML, Heimann M, Jaramillo VJ, Kheshgi HS, Quéré CL, Scholes RJ, Wallace DWR (2001) The carbon cycle and atmospheric carbon dioxide (chapter 3). IPCC 2001 Working Group I The Scientific Basis. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  17. Ficklin DL, Luo YZ, Luedeling E, Zhang MH (2009) Climate change sensitivity assessment of a highly agricultural watershed using swat. J Hydrol 374(1–2):16–29. doi: 10.1016/j.jhydrol.2009.05.016 CrossRefGoogle Scholar
  18. Field CB, Jackson RB, Mooney HA (1995) Stomatal responses to increased CO2—implications from the plant to the global-scale. Plant Cell Environ 18(10):1214–1225CrossRefGoogle Scholar
  19. Fontaine TA, Klassen JF, Cruickshank TS, Hotchkiss RH (2001) Hydrological response to climate change in the black hills of south dakota, USA. Hydrol Sci J—Journal Des Sciences Hydrologiques 46(1):27–40CrossRefGoogle Scholar
  20. Gassman PW, Secchi S, Jha M, Kurkalova L (2006) Upper Mississippi river basin modeling system part 1: swat input data requirements and issues. Coastal hydrology and processes. Water Resources Publications, LLC, HighlandsGoogle Scholar
  21. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250Google Scholar
  22. Gedney N, Cox PM, Betts RA, Boucher O, Huntingford C, Stott PA (2006) Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439(7078):835–838. doi: 10.1038/nature04504 CrossRefGoogle Scholar
  23. Green CH, van Griensven A (2008) Autocalibration in hydrologic modeling: using swat2005 in small-scale watersheds. Environ Model Softw 23(4):422–434. doi: 10.1016/j.envsoft.2007.06.002 CrossRefGoogle Scholar
  24. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99Google Scholar
  25. Houghton JT, Ding Y, Griggs DJ, Noguer M, Linden PJvd, Dai X, Maskell K, Johnson CA (2001) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  26. IPCC (2006) Gcm experiment data (ar4). Accessed July 30 2010
  27. IPCC (2007a) Climate change 2007: Summary for policymakers. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  28. IPCC (2007b) Climate change 2007: The physical science basis. Contribution of working group i to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  29. Jha M, Pan ZT, Takle ES, Gu R (2004) Impacts of climate change on streamflow in the upper mississippi river basin: A regional climate model perspective. J Geophys Res-Atmos 109(D9). doi: 10.1029/2003jd003686
  30. Jha M, Arnold JG, Gassman PW, Giorgi F, Gu RR (2006) Climate change sensitivity assessment on upper mississippi river basin streamflows using swat. J Am Water Resour Assoc 42(4):997–1015CrossRefGoogle Scholar
  31. Kergoat L, Lafont S, Douville H, Berthelot B, Dedieu G, Planton S, Royer JF (2002) Impact of doubled CO2 on global-scale leaf area index and evapotranspiration: conflicting stomatal conductance and lai responses. J Geophys Res-Atmos 107(D24). doi: 10.1029/2001jd001245
  32. Labat D, Godderis Y, Probst JL, Guyot JL (2004) Evidence for global runoff increase related to climate warming. Adv Water Resour 27(6):631–642. doi: 10.1016/j.advwatres.2004.02.020 CrossRefGoogle Scholar
  33. Legates DR, Lins HF, McCabe GJ (2005) Comments on “evidence for global runoff increase related to climate warming” by labat et al. Adv Water Resour 28(12):1310–1315. doi: 10.1016/j.advwatres.2005.04.006 CrossRefGoogle Scholar
  34. Leipprand A, Gerten D (2006) Global effects of doubled atmospheric CO2 content on evapotranspiration, soil moisture and runoff under potential natural vegetation. Hydrol Sci J—Journal Des Sciences Hydrologiques 51(1):171–185CrossRefGoogle Scholar
  35. Medlyn BE, Barton CVM, Broadmeadow MSJ, Ceulemans R, De Angelis P, Forstreuter M, Freeman M, Jackson SB, Kellomaki S, Laitat E, Rey A, Roberntz P, Sigurdsson BD, Strassemeyer J, Wang K, Curtis PS, Jarvis PG (2001) Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytol 149(2):247–264CrossRefGoogle Scholar
  36. Monteith JL (1965) Evaporation and the environment. Paper presented at the In The state and movement of water in living organisms, XIXth Symposium,Google Scholar
  37. 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. Transactions of the ASABE 50(3):885–900Google Scholar
  38. Morison JIL (1987) Intercellular CO2 concentration and stomatal response to CO2. Stomatal function. Stanford University Press, StanfordGoogle Scholar
  39. Morison JIL, Gifford RM (1983) Stomatal sensitivity to carbon dioxide and humidity. Plant Physiol 71:789–796CrossRefGoogle Scholar
  40. Muleta MK, Nicklow JW (2005) Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. J Hydrol 306(1–4):127–145. doi: 10.1016/j.jhydrol.2004.09.005 CrossRefGoogle Scholar
  41. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  42. Neitsch SL, Arnold JG, Kiniry JR, Srinivasan R, Williams JR (2005a) Soil and water assessment tool input/output file documentation. Version 2005 edn., Grassland, soil and research service, Temple, TXGoogle Scholar
  43. Neitsch SL, Arnold JG, Kiniry JR, Williams JR, King KW (2005b) Soil and water assessment tool theoretical documentation. Version 2005 edn., Grassland, soil and research service, Temple, TXGoogle Scholar
  44. NOAA/ESRL (2010) Noaa esrl data. Accessed 30 July 2010
  45. Priestly CHB, Taylor RJ (1972) Assessment of surface heat-flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92CrossRefGoogle Scholar
  46. Pritchard SG, Rogers HH, Prior SA, Peterson CM (1999) Elevated CO2 and plant structure: a review. Glob Chang Biol 5(7):807–837CrossRefGoogle Scholar
  47. Rind D, Goldberg R, Hansen J, Rosenzweig C, Ruedy R (1990) Potential evapotranspiration and the likelihood of future drought. J Geophys Res-Atmos 95(D7):9983–10004CrossRefGoogle Scholar
  48. Rossi A, Massei N, Laignel B, Sebag D, Copard Y (2009) The response of the mississippi river to climate fluctuations and reservoir construction as indicated by wavelet analysis of streamflow and suspended-sediment load, 1950–1975. J Hydrol 377(3–4):237–244. doi: 10.1016/j.jhydrol.2009.08.032 CrossRefGoogle Scholar
  49. 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
  50. Saxe H, Ellsworth DS, Heath J (1998) Tree and forest functioning in an enriched CO2 atmosphere. New Phytol 139(3):395–436CrossRefGoogle Scholar
  51. Schaake JC (1990) Water resources. In: Waggoner PE (ed) Water resources. vol (Chapter 8). Wiley, New York, pp 177–206Google Scholar
  52. Sellers PJ, Bounoua L, Collatz GJ, Randall DA, Dazlich DA, Los SO, Berry JA, Fung I, Tucker CJ, Field CB, Jensen TG (1996) Comparison of radiative and physiological effects of doubled atmospheric CO2 on climate. Science 271(5254):1402–1406CrossRefGoogle Scholar
  53. Sharpley AN, Williams JR (1990) Epic-erosion productivity impact calculator, 1. Model documentation. U.S. Department of Agriculture, Agricultural Research Service, Tech. Bull. 1768Google Scholar
  54. Singh J, Knapp HV, Arnold JG, Demissie M Hydrological (2003) modeling of the iroquois river watershed using hspf and swat. In: AWRA Spring Specialty Conference on Agricultural Hydrology and Water Quality, Kansas City, MO, May 2003. pp 343–360Google Scholar
  55. SRES (2000) IPCC special report emissions scenarios. United Nations Environment Programme and the World Meteorological Organizatio, SwitzerlandGoogle Scholar
  56. Stockle CO, Dyke PT, Williams JR, Jones CA, Rosenberg NJ (1992a) A method for estimating the direct and climatic effects of rising atmospheric carbon-dioxide on growth and yield of crops. 2. Sensitivity analysis at 3 sites in the midwestern USA. Agric Syst 38(3):239–256CrossRefGoogle Scholar
  57. Stockle CO, Williams JR, Rosenberg NJ, Jones CA (1992b) A method for estimating the direct and climatic effects of rising atmospheric carbon-dioxide on growth and yield of crops. 1. Modification of the epic model for climate change analysis. Agric Syst 38(3):225–238CrossRefGoogle Scholar
  58. van Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio A, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324(1–4):10–23. doi: 10.1016/j.jhydrol.2005.09.008 CrossRefGoogle Scholar
  59. Vicuna S, Maurer EP, Joyce B, Dracup JA, Purkey D (2007) The sensitivity of California water resources to climate change scenarios. J Am Water Resour Assoc 43(2):482–498. doi: 10.1111/j.1752-1688.2007.00038.x CrossRefGoogle Scholar
  60. Wand SJE, Midgley GF, Jones MH, Curtis PS (1999) Responses of wild c4 and c3 grass (poaceae) species to elevated atmospheric CO2 concentration: a meta-analytic test of current theories and perceptions. Glob Chang Biol 5(6):723–741CrossRefGoogle Scholar
  61. Williams JR (1995) Chapter 25. The epic model. In: Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, CO, pp 909–1000Google Scholar
  62. Winchell M, Srinivasan R, Di Luzio M, Arnold JG (2009) Arcswat 2.3.4 interface for swat2005. Version 2005 edn., Grassland, soil and research service, Temple, TXGoogle Scholar
  63. Wolock DM, McCabe GJ (1999) Estimates of runoff using water-balance and atmospheric general circulation models. J Am Water Resour Assoc 35(6):1341–1350CrossRefGoogle Scholar
  64. Xu ZX, Zhao FF, Li JY (2009) Response of streamflow to climate change in the headwater catchment of the yellow river basin. Quat Int 208:62–75. doi: 10.1016/j.quaint.2008.09.001 CrossRefGoogle Scholar
  65. Young CA, Escobar-Arias MI, Fernandes M, Joyce B, Kiparsky M, Mount JF, Mehta VK, Purkey D, Viers JH, Yates D (2009) Modeling the hydrology of climate change in California’s Sierra Nevada for subwatershed scale adaptation1. J Am Water Resour Assoc 45(6):1409–1423. doi: 10.1111/j.1752-1688.2009.00375.x CrossRefGoogle Scholar
  66. Zhang XS, Srinivasan R, Zhao KG, Van Liew M (2009) Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol Process 23(3):430–441. doi: 10.1002/hyp.7152 CrossRefGoogle Scholar

Copyright information

© U.S. Government 2011

Authors and Affiliations

  1. 1.ASRC Research and Technology Solutions at U.S. Geological Survey (USGS)Earth Resources Observation and Science (EROS) CenterSioux FallsUSA
  2. 2.U.S. Geological Survey (USGS)Earth Resources Observation and Science (EROS) CenterSioux FallsUSA
  3. 3.Geographic Information Science Center of ExcellenceSouth Dakota State UniversityBrookingsUSA

Personalised recommendations