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Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Abstract

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.

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Correspondence to P. P. Mujumdar.

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Rehana, S., Mujumdar, P.P. Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties. Water Resour Manage 28, 3127–3142 (2014). https://doi.org/10.1007/s11269-014-0659-2

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Keywords

  • Climate change
  • Statistical downscaling
  • Reservoir rule curves
  • Stochastic dynamic programming
  • Fuzzy optimization