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Application of Parity Classified Neurogenetic Models to Analyze the Impact of Climatic Uncertainty on Water Footprint

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

Abstract

Water footprint of an individual, community, or business is defined as the total volume of freshwater that is used to produce the goods and services consumed by the individual or community, or produced by the business. Neurogenetic models were widely used in the prediction of hydrologic variables, and outcome of such applications were found to be satisfactory. The irregular rainfall and temperature pattern, and degradation of watersheds were causing worldwide reduction of water availability (UNFCC). As water footprint is directly related to water availability and also shows the demand from industrial consumers, the present study tried to estimate the impact of climate change on water footprint between two river basins of East India with the help of neurogenetic models. The climate change scenarios were generated with the help of PRECIS climate models, and future runoff was estimated by a neurogenetic model trained with orthopareto dataset. The output from the neurogenetic model, named as PARITYCGD, was compared with a neurogenetic model trained with normal dataset (NGHYD) and conceptual hydrologic models. According to the results, the neurogenetic model trained with orthopareto dataset was selected as the better model among the five models, which shows that neural models trained with orthopareto dataset learn a problem better than a neurogenetic model trained with normal dataset. From the prediction of stream flow, water footprint of the sampling regions were calculated and according to the estimations, water footprint would be reduced in both A2 and B2 climate change scenarios where reductions would be more pronounced in A2 than in B2. Although, due to data dependency of neurogenetic models, the PARITYCGD model may not work for other basins but for the present study, it was found to have better accuracy than the conceptual hydrologic model.

Keywords

Classified neurogenetic models climatic uncertainty orthopareto dataset water footprint 

References

  1. Ahmad M, Ghumman A, Ahmad S (2009) Estimation of Clark’s instantaneous unit hydrograph parameters and development of direct surface runoff hydrograph. Water Resour Manage 23:2417–2435CrossRefGoogle Scholar
  2. Blazkova S, Beven KJ, Kulasova A (2002) On constraining topmodel hydrograph simulations using partial saturated area information. Hydrol Process 16(2):441–458CrossRefGoogle Scholar
  3. Das NG (1991) Statistical methods in commerce, accountance and economics, Part – 1. M. Das & Co., Kolkata, pp 25–50Google Scholar
  4. Hoekstra AY, Chapagain AK (2008) Globalization of water: sharing the planet’s freshwater resources. Blackwell, OxfordGoogle Scholar
  5. IPCC (2007) Climate change 2007: the physical sciences basis, retrieved on http://ipcc-wgl.ucar.edu/wg1/wg1-report.html. on 30th April, 2009
  6. Lopez JJ, Gimena FN, Goni M, Agirre U (2005) Analysis of a unit hydrograph model based on watershed geomorphology represented as a cascade of reservoirs. Agric Water Manage 77(1–3):128–143CrossRefGoogle Scholar
  7. Majumder M, Roy P, Mazumdar A (2010) Watershed modeling of river Damodar with the help of neural network and genetic algorithm, PhD thesis, school of water resources engineering, Jadavpur University, Kolkata (in press)Google Scholar
  8. Subramanya K (1994) Engineering hydrology, 2nd edn. Tata McGraw Hill, New Delhi, pp 60–90Google Scholar
  9. Sui J (2005) Estimation of design flood hydrograph for an ungauged watershed. Water Resour Manage 19(6):813–830CrossRefGoogle Scholar
  10. Taskinen A, Bruen M (2007) Incremental distributed modelling investigation in a small agricultural catchment: 1. Overland flow with comparison with the unit hydrograph model. Hydrol Process 21(1):80–91CrossRefGoogle Scholar
  11. Wanielista M, Kersten R, Eaglin R (1987) Hydrology: water quantity and quality control. Wiley, New York, p 208Google Scholar
  12. Yeh K, Yang J, Tung Y (1997) Regionalization of unit hydrograph parameters: 2. Uncertainty analysis. Stochast Hydrol Hydraulic 11(2):173–192CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Mrinmoy Majumder
    • 1
    • 2
    Email author
  • 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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