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Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

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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. Copyright Springer Science+Business Media Dordrecht 2014

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  • S. Rehana & P. Mujumdar, 2014. "Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3127-3142, August.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:10:p:3127-3142
    DOI: 10.1007/s11269-014-0659-2
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    2. Jose George & P. Athira, 2024. "Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4499-4516, September.
    3. Pham Thi Thu Ha & Nomessi Kokutse & Sophie Duchesne & Jean-Pierre Villeneuve & Alain Bélanger & Ha Ngoc Hien & Babacar Toumbou & Duong Ngoc Bach, 2017. "Assessing and selecting interventions for river water quality improvement within the context of population growth and urbanization: a case study of the Cau River basin in Vietnam," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(5), pages 1701-1729, October.
    4. M. Rajesh & Sachdeva Anishka & Pansari Satyam Viksit & Srivastav Arohi & S. Rehana, 2023. "Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 75-90, January.

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