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Use of Forest Index or PLANOBAY in Estimation of Water Availability Due to Climate Change

  • Mrinmoy MajumderEmail author
  • Suchita Dutta
  • Rabindra Nath Barman
  • Bipal K. Jana
  • Pankaj Roy
  • Asis Mazumdar
Chapter

Abstract

The present study tried to estimate future water availability with the help of Forest Index or Plantation-Prioritized Basin Yield Estimation (PLANOBAY) Hydrologic model, which is a multi-event, discharge prediction model based on variation of discharge with basin area and canopy cover. RCM-PRECIS model was applied to generate future weather scenarios. The observed rainfall along with Vegetated Area Index (VAIn) was used as input to estimate basin runoff. Presence of vegetated area (forest, plantations, cropped land) in any basin would impact the quantity of basin runoff as vegetated areas could hold water with greater capacity than any nonvegetated area. Hence the estimation of runoff from vegetated and nonvegetated catchment must differ and for former, models must include or consider the relationship between vegetated area and the amount of basin runoff. In PLANOBAY, VAIn represents the relationship between vegetated area and basin runoff. VAIn represented the variance of basin area and vegetated area with respect to basin runoff. A neurogenetic model was developed to identify the patterns associated with VAIn, rainfall, and basin runoff. Dataset of 3 decades (1970–2002) was employed to train the model. After the successful completion of training, models were compared with three conceptual models, namely, Hydrologic Engineering Centre – Hydrologic Modeling System (HECHMS), Trend Research Manual of 1955 (TR55), and MODified RATional (MODRAT) hydrologic model. The better model among the four was identified with the help of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (E), and first-order uncertainty analysis (U). Future water availability was estimated with the help of estimated stream flow from the selected model, estimated rainfall from PRECIS climatic model-generated weather scenarios, and Water Budget equation. According to the results, PLANOBAY model was selected as better model among the four, and according to the estimations from the same model, future water availability of the two river basins would reduce for both A2 and B2 scenario of climate change where the water scarcity would be more pronounced in A2 than in B2.

Keywords

Climate change forest global warming neurogenetic models 

References

  1. Brooks K et al (1990) Hydrology and the management of watersheds. Iowa State University Press, AmesGoogle Scholar
  2. Callahan TJ, Cook JD, Coleman MD, Amatya DM, Trettin CC (2004) Modeling storm water runoff and soil interflow in a managed forest, upper coastal plain of the southeast US, Proceedings of ASAE annual meeting, American Society of Agricultural and Biological Engineers, Paper number 042254Google Scholar
  3. Das NG (1991) Statistical methods in commerce, accountance and economics, Part – 1. M. Das & Co., Kolkata, pp 25–50Google Scholar
  4. Franco C, Drew AP, Heisler G (2008) Impacts of urban runoff on native woody vegetation at Clark reservation state park, Jamesville, NY, J Urb Habitats. Retrieved from http://www.urbanhabitats.org/v05n01/runoff_full.html on June 2009
  5. Gomi T, Sidle RC, Miyata S, Kosugi K, Onda Y (2008) Dynamic runoff connectivity of overland flow on steep forested hillslopes: scale effects and runoff transfer. Water Resour Res 44:W08411, doi:10.1029/2007WR005894CrossRefGoogle Scholar
  6. GWSP Digital Water Atlas (2008) Map 52: change in runoff due to deforestation (V1.0). Available online at http://atlas.gwsp.org
  7. Hewlett JD (1982) Principles of forest hydrology. University of Georgia Press, AthensGoogle Scholar
  8. Idson PFF (2009) Methods of studying the dependence of river runoff on the forest coverage of its basin. Retrieved from http://www.cig.ensmp.fr/~iahs/redbooks/a048/048032.pdf on June, 2009
  9. IPCC (2007) Climate change 2007: the physical sciences basis, retrieved on http://ipcc-wg1.ucar.edu/wg1/wg1-report.html. on 30th April, 2009
  10. Lau CC, Lee KT, Tung CP, Chang CH (1999) Assessment of climate-change impact on runoff using normalized difference vegetation index. Retrieved from http://www.gisdevelopment.net/aars/acrs/1999/ts2/ts2045.asp on June 2009
  11. Jr M, Tyler G (1990) Living in the environment, 6th edn. Wadsworth Publishing Company, Belmont, CAGoogle Scholar
  12. Oliviera FP (2006) Hydric erosion in forest areas in the Rio DoceValley, central-east region of the state of Minas Gerais, University Federal de Lavras, Brasil. Retrieved from http://biblioteca.universia.net/html_bura/ficha/params/id/17531366.html on June 2009
  13. Perry DA (1994) Forest ecosystems. The Johns Hopkins University Press, Baltimore, MDGoogle Scholar
  14. Spurr SH, Barnes BV (1980) Forest ecology. Wiley, New YorkGoogle Scholar
  15. Statistics Solution (2009) Retrieved from http://www.statisticssolutions.com/reliability-analysis on July 16, 2009
  16. Tiju C, Xiaojing T (2007) Impact of forest harvesting on river runoff in the Xiaoxing’an Mountains of China. J Frontiers Forest China 2(2):143–147CrossRefGoogle Scholar
  17. Wemple BC, Jones JA (2003) Runoff production on forest roads in a steep, mountain catchment. Water Resour Res 39(8):1220, doi:10.1029/2002WR001744CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Mrinmoy Majumder
    • 1
    • 2
    Email author
  • Suchita Dutta
    • 2
  • Rabindra Nath Barman
    • 1
    • 3
  • Bipal K. Jana
    • 1
    • 4
  • Pankaj Roy
    • 1
  • Asis Mazumdar
    • 1
    • 2
  1. 1.School of Water Resources EngineeringJadavpur UniversityKolkataIndia
  2. 2.Regional Center, National Afforestation and Eco-development BoardJadavpur UniversityKolkataIndia
  3. 3.Department of ProductionNational Institute of TechnologyAgartalaIndia
  4. 4.Consulting Engineering ServicesWest BengalIndia

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