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This paper presents an optimization model to simulate short-term pair-wise spot-market trading of surface water abstraction licenses (water rights). The approach uses a node-arc multicommodity formulation that tracks individual supplier-receiver transactions in a water resource network. This enables accounting for transaction costs between individual buyer-seller pairs and abstractor-specific rules and behaviors using constraints. Trades are driven by economic demand curves that represent each abstractor's time-varying water demand. The purpose of the proposed model is to assess potential hydrologic and economic outcomes of water markets and aid policy makers in designing water market regulations. The model is applied to the Great Ouse River basin in Eastern England. The model assesses the potential weekly water trades and abstractions that could occur in a normal and a dry year. Four sectors (public water supply, energy, agriculture, and industrial) are included in the 94 active licensed water diversions. Each license's unique environmental restrictions are represented and weekly economic water demand curves are estimated. Rules encoded as constraints represent current water management realities and plausible stakeholder-informed water market behaviors. Results show buyers favor sellers who can supply large volumes to minimize transactions. The energy plant cooling and agricultural licenses, often restricted from obtaining water at times when it generates benefits, benefit most from trades. Assumptions and model limitations are discussed.Key PointsTransaction tracking hydro-economic optimization models simulate water marketsProposed model formulation incorporates transaction costs and trading behaviorWater markets benefit users with the most restricted water access

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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