Abstract

Climate change adaptation (CCA) is a vital strategy for river basin water management which binds together environmental, agricultural and human water requirements in an uncertain future climate. Policy makers face a difficult task balancing demand and supply for conflicting water requirements, especially to justify present day economic costs for future benefits, like in CCA. No-regret adaptation options, applicable in both, current and future uncertain conditions, provide a way of dealing with these issues. However, determination of such options needs to be based on an integrated assessment of hydrologic, environmental, social, economic and institutional characteristics to be suitable in the future. Here, a three step process for determining no-regret options is presented, having been applied to the Kangsabati River basin in India. Firstly a participatory approach is used to identify potential CCA options, followed by a Multi Criteria Analysis (MCA) to determine the no-regret and suitability characteristics for the region. This approach was replicated at three levels; community, district and state (sub-national), targeting different stakeholders. Finally, hydrological modeling using Water Evaluation And Planning (WEAP) model, of the high ranking adaptation options show the expected efficacy in hydrologic terms. MCA generated no-regret options show importance of currently promoted soil and water conservation measures, like afforestation and check dams and the need for future focus on cropping pattern change. Evaluation criteria important to different stakeholders were also determined in the process, a valuable by-product useful for future water management. Present and future scenario based modelling of CCA options provides comparability in terms of suitability, scale of impacts and costs. Such assessments can be valuable tool-set for policymakers to make evidence based decisions on choice of adaptation measures and their spatio-temporal applications to improve water availability in an uncertain climate.

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