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.

Water resource allocation is the process of assessing and determining a mechanism on how water should be distributed among different regions, sectors and users. Over the recent decades, the optimal solution for water resource allocation has been explored both in centralised and decentralised mechanisms. Conventional approaches are under central planner suggesting a solution which maximises total welfare to the users. Moving towards the decentralised modelling, the techniques consider individuals as if they act selfishly in their own favour. While central planner provides an efficient solution, it may not be acceptable for some selfish agents. The contrary is true as well in decentralised solution, where the solution lacks efficiency leading to an inefficient usage of provided resources. This paper develops a parallel evolutionary search algorithm to introduce a mechanism in re-distributing the central planner revenue value among the competing agents based on their contribution to the central solution. The result maintains the efficiency and is used as an incentive for calculating a fair revenue for each agent. The framework is demonstrated and discussed to allocate water resources along the Nile river basin, where there exist eleven competing users represented as agents in various sectors with upstream-downstream relationships and different water demands and availability.

The world is facing a more water constrained future as a result of urbanisation, population growth, industrialisation and the emergence of climate change. This has direct impacts on the resilience and performance of the energy and food industries, as water plays a key role in electricity generation processes and agriculture production. Water, energy and food dependencies are more evident in transboundary river basins where several countries share the same source of water for irrigation demand and energy production. From the perspective of the upstream users, it would be ideal to store the water for hydropower generation and the agriculture sector while protecting the environment, whereas the downstream users need the supply of water for their agricultural growth and municipal requirements. We aim to develop a system thinking study by focusing on the transboundary Blue Nile River basin where the Ethiopian government investment in the Grand Renaissance dam has led to opposition by downstream users due to potential reduction of water resource availability downstream. We propose a system thinking approach for analysing different water management practices that considers all the available resources and the requirements set by all the users. To simulate this interaction, we use system dynamics to model the linkage between food production, water abstraction and energy generation. We link the simulation model to an optimisation engine to achieve effective management of the reservoir’s operation. The study provides a platform to investigate how the reservoir operating policies can improve an understanding of the value of water in its alternative uses, and shows how different optimal reservoir release rules generate different optimal solutions inherently involved in upstream and downstream users’ requirements. The proposed methodology is an attempt to enable Nile riparian countries to make more informed decisions on water resources policy and management.

Nitrogen, an essential nutrient for plant growth, is commonly added to food crops in the form of manure and synthetic fertilizers. Fertilizer use has significantly increased in the past decades to meet the food demands from a rising population. Although this has boosted food production, it has come at a cost to the environment. Indeed, excess fertilizer ends up in water bodies, a pollution that causes losses in aquatic biodiversity. Better fertilizer management is therefore essential to maintaining water sustainability. Here, we develop and evaluate a nitrogen water quality trading scheme to address this challenge. Nitrogen trading incentivizes farmers to work together to invest in pollution reduction measures in order to keep nitrogen water pollution levels within a standardized limit. We build a mathematical model to represent the nitrogen trading and use it to assess the pollution reduction, the effect on the crop yield, and economical outcomes. The model is applied among local farms in the agricultural county of Suffolk, eastern England. We calculate the nitrogen load to the river from each farm and incorporate the abatement cost into the model. The results show how nitrogen water pollution could be reduced cost-effectively while simultaneously increasing the benefit for the whole catchment. Although the benefit does not increase for all the farms, the increase in benefit for the whole catchment is enough to compensate for this loss. The surplus benefit is equally distributed between all the farms, thus increasing their overall benefit. We discuss how the proposed trading model can create a platform for farmers to participate and reduce their water pollution.