Planning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system's ability to adapt to future needs, flexibility in activating, delaying, and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Options Analysis, which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information that quantify the value of adopting adaptive and flexible plans, respectively. An application to London's water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed.

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Staged water infrastructure capacity expansion optimization models help create flexible plans under uncertainty. In these models exogenous uncertainty can be incorporated into the optimization using an a priori hydrological and demand scenario ensemble. However some water supply intervention uncertainties cannot be considered in this way, such as demand management or technological options. In these cases the uncertainty is endogenous or 'decision-dependent', i.e., the optimized timing and selection of interventions determines when and which uncertainties must be considered. We formulate a multistage real-options water supply capacity expansion optimization model incorporating such uncertainty and describe its effect on cost and option selection.

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<p>Water security can be susceptible to demand increases and climate change impacts. In this case interventions (new infrastructure and/or policies) must be made to meet future demands despite the timing and extent of supply-demand changes are unknown in advance. Given the potential large economic costs of water infrastructure, and the uncertainties in both future supplies and demands, formal planning under uncertainty techniques aiming for robustness and/or adaptability are warranted.</p><p>Staged water infrastructure capacity expansion optimization models help create flexible plans under uncertainty. In these models two types of uncertainties are realized. The first category is the exogenous uncertainty that can be incorporated into the optimization using an a priori scenario ensemble. The second category is the endogenous uncertainty for which the optimized timing and selection of interventions determines when and which uncertainties must be considered. Endogenous uncertainty is therefore ‘decision-dependent’ and cannot be considered as a priori set of scenarios.</p><p>This work describes an extension to an adaptive multistage real options water infrastructure planning optimization problem formulation to incorporate endogenous uncertainty and describe its effect on cost and option selection. We show how endogenous uncertainty propagates when making planning decisions over time on a synthetic case study. The results are contrasted with the deterministic formulation in terms of option activations and the expected present value of the cost.</p>