Journal of Water Resources Planning and Management

Advance predictions of seasonal precipitation may provide information to aid water resource management decisions in various sectors. Yet, a disconnect between the spatial scale upon which skillful predictions are issued and the sectoral decision-making scale renders current predictive information inadequate in many cases. This study explores season-ahead precipitation prediction skill for a local region in the Blue Nile basin, Ethiopia, to better understand how model structure may serve to provide more skillful and valuable predictive information to the end user. Statistical downscaling of global dynamic and regional empirical models and development of a high-resolution, locally tailored statistical model indicate that model structure and prediction skill are inextricably linked. Statistical approaches specific to the local region show higher prediction skill at the sectoral decision-making scale compared with dynamic approaches, offering the potential to aid local communities in many regions that are currently vulnerable to highly variable spatial precipitation patterns. Linking the local-scale precipitation predictions with a simple reservoir model in Ethiopia illustrates application of local-scale predictive information for enhanced value in planning and management decisions.

Water transfer systems are increasingly seen as a response to regional water stresses. The expensive capital cost of such massive projects calls for satisfying the desired requirements with a minimum investment, i.e., achieving a high rate of return on investment. This paper develops a new theoretical cost-benefit analysis framework considering the tradeoffs between investment cost and expected water shortage loss to determine the optimal water transfer capacity for water transfer systems. The optimal capacity is determined through theoretical and hydro-economic analysis considering the compensation effects of the capacity of existing reservoirs and reservoir inflow variability. Theoretical analysis show that the ratio between the slopes of the investment cost and water shortage loss functions, which is obtained analytically with the optimality condition, is equal to the water shortage probability (i.e., 1-reliability) when cost and loss functions are linear. Application to the Biliuhe water transfer system, northeast China, further demonstrates the equivalence between the proposed approach and reliability based simulation approach, as well as the advantage of the proposed approach in simplicity, accuracy and computational efficiency. Furthermore, effects of model parameters, demand uncertainty, inflow variability and probability distribution type on the optimal design of the water transfer system are discussed. This paper demonstrates that the proposed approach provides a theoretical basis for explicitly addressing the uncertainties in the design process of water transfer systems.

This study introduces a decision support system for upgrading aged water distribution mains. The network hydraulics is simulated and evaluated by a hybrid reliability measure, and the design period is divided into multiple upgrade phases. In each phase, a multiobjective optimization problem is solved to derive the trade-off between construction cost and network performance reliability. Subject to budget constraints, upgrade decisions, including those related to pipe replacement and laying parallel pipe, are made, leading to a more robust hydraulic performance of the network over time. To implement multiphase upgrade programs, three approaches are introduced, varying in their assumptions regarding design and construction methods. These approaches are applied to a real case study, and results indicate that the dynamic phase-by-phase design and construction approach is computationally efficient and cost-effective while satisfying physical and performance constraints over the simulation time horizon.

This research explores potable water consumers’ willingness to pay to fund up-front investments in their potable water supply services (PWSSs), which will reduce rates in the longer term. An online survey of 1,970 New Zealand PWSS consumers was carried out in 2011 to identify factors that influence individual discount rates (IDRs) related to PWSS investments—a first of its kind—using dichotomous choice questions. Constrained latent class models (LCMs)—an established technique—were used to test the presence of a class of respondents that is unwilling to invest—a phenomenon that has not yet been tested for PWSS consumers. Confirming PWSS consumers’ IDRs will help water suppliers understand the factors more conducive to consumer support of long-term PWSS investments. The research showed that consumers have an 82% chance of having high discount rates of 45%–55%, and an 18% chance of always being likely to support initial payment increases in the interest of future savings. Decision makers should be aware of high IDRs, but they should recognize that there is a class of consumers that strongly supports long-term investments.

Hydropower operating rules can guide reservoir release and storage for maximizing hydropower generation. The optimal hydropower operating rules are usually derived by numerical hydropower simulation methods. This study focuses on the analytical solution to optimal hydropower rules for storage allocation of a multireservoir system. An energy function, the $E$-function, is proposed to describe the objective of hydropower generation. The $E$-function for a multireservoir system can be represented as a function of two variables: the proportional coefficient (alpha, which represents the proportional relationship between any two reservoirs in a multireservoir system) and the total increment in storage. The alpha discriminant deduced from the $E$-function is used to infer the optimal reservoir storage allocation for maximum hydropower generation. With the Ankang-Danjiangkou reservoirs as a case study, the $E$-function and the alpha discriminant were compared with the numerical hydropower simulation. The results indicated that (1) the $E$-function can be used to estimate the hydropower generation for multireservoir systems with acceptable hydropower generation estimation errors between the analytical $E$-function and the numerical hydropower simulation; and (2) the optimal reservoir storage allocation can be explicitly obtained using the alpha discriminant. These findings are helpful for understanding the allocation of reservoir storages in multireservoir systems.

The assessment of profit, productivity, and price change over time is valuable for regulators and companies when setting tariffs. This paper innovates by comparing profit, price, and productivity changes for English and Welsh water services between water and sewerage companies (WaSCs) and water-only companies (WoCs) over the period of 2001–2009. The Bennet-Bowley indicator (BBI) was used to estimate productivity change over time at industry and company levels. The findings of this study show that the imposition of price cap regulation increased companies’ profits, primarily due to a positive price effect. Moreover, the tightened price review in 1999 had a small, positive impact on the productivity of companies. In contrast, the introduction of new price limits in 2004 did not have a positive impact on companies’ productivity: WaSC and WoC performance improved only in 2004 and 2005. Finally, WaSCs showed higher levels of performance than WoCs. This study provides essential information to policy makers and water company managers so that they can improve decision making aimed at enhancing water industry performance.

Pumps as Turbines (PATs) are standard water pumps utilized as hydraulic turbines by reversing the flow direction across them. The off-the-shelf availability of water pumps and their reduced purchase price with respect to conventional hydro turbines makes them an ideal technology for exploiting large portions of uncapped hydro potential that is technically viable but not financially convenient. Such low-cost technology could help to expand hydropower exploitation in water resources worldwide, helping to reduce climate change greenhouse gas emissions. However, in the literature, the few available cost figures relative to PAT purchase price are discordant and often outdated, and such a lack of information is likely a severe barrier to a more widespread PAT implementation. In order to overcome this limitation, data from 343 pumps and 286 generators were compiled to show graphically and analytically how the purchase price of PATs varies over different nominal powers and hydraulic conditions. Besides, a set of equations was developed to allow designers to predict the PAT and generator cost from the nominal flow rate and available hydraulic head.

Operation rules are commonly used to make decisions for cascaded hydropower reservoirs for profit maximization, given the energy price of current and future periods. For large-scale cascaded hydropower reservoirs whose decisions can affect market price, the operation optimization models are transformed into game models to get Nash equilibriums. For market competition of multiple cascaded hydropower reservoirs, a multiplayer game model is established in which the actions are storage energy based operation rules and the payoff function values are simulated profits of cascades using the rules. The game model is solved using a successive low dimensional search method in which only one parameter of the rules is optimized at each step. The proposed method is tested using data of three cascaded reservoirs in Southwestern China in a hypothetical pure hydropower market. Results show the effect of different kind of models on rule curves and the potential impact of market reformation to the operation of the cascaded hydropower reservoirs. For the studied cascaded reservoirs, the profit increasing percentages can be 2.6%–3.9% with 0.5%–2.0% energy losing, comparing the game model to the energy maximization model.

The optimal placement and operation of pressure control valves in water distribution networks is a challenging engineering problem. When formulated in a mathematical optimization framework, this problem results in a nonconvex mixed integer nonlinear program (MINLP), which has combinatorial computational complexity. As a result, the considered MINLP becomes particularly difficult to solve for large-scale looped operational networks. We extend and combine network model reduction techniques with the proposed optimization framework in order to lower the computational burden and enable the optimal placement and operation of control valves in these complex water distribution networks. An outer approximation algorithm is used to solve the considered MINLPs on reduced hydraulic models. We demonstrate that the restriction of the considered optimization problem on a reduced hydraulic model is not equivalent to solving the original larger MINLP, and its solution is therefore sub-optimal. Consequently, we investigate the trade-off between reducing computational complexity and the potential sub-optimality of the solutions that can be controlled with a parameter of the model reduction routine. The efficacy of the proposed method is evaluated using two large scale water distribution network models.

To assess the influence of sampling methods on surrogate models’ accuracy, using two test problems and a nitrobenzene-contaminated aquifer remediation problem, several sampling methods were adopted to collect sample data sets and a Kriging method was adopted to construct surrogate models. The sampling methods adopted include Latin hypercube sampling (LHS), space-filling-based LHS (SFLHS), orthogonal-array-based LHS (OALHS), and space-filling and orthogonal-array-based LHS (SFOALHS). The space-filling properties and orthogonality of sampling results, as well as the corresponding surrogate models’ accuracies, were compared, and the best surrogate model was invoked for assessing the remediation efficiency in a groundwater remediation optimization problem. The results indicated that (1) compared with LHS, SFLHS, and OALHS results, the SFOALHS result had the best trade-off between orthogonality and space-filling property, and better represented the population; and (2) the SFOALHS-based surrogate model was more accurate and better fit the simulation model in both test problems and the case study; therefore it was invoked as a constraint condition for replacing the behavior of the computational simulation model in the optimization process.

Climate change has significant effects on the management of complex water resource systems. The objective of this study was to assess climate change effects on reservoir system operations. The assessment was performed using three greenhouse gas emission scenarios, four global climate models (GCMs), six downscaling methods, one hydrologic model, and a system dynamics simulation model (SDM). The analyses were conducted for a future time period (2036–2065) and results were compared with the historical time period (1984–2013). The Campbell River basin in British Columbia, Canada, was used as a case study area. The Campbell River basin consists of three reservoirs: Strathcona, Ladore, and John Hart. The results show that the inflow in all three reservoirs decreases during summer and fall, which decreases power production. In addition, it was observed that power production from downstream reservoirs (Ladore and John Hart) will decrease more drastically than the upstream reservoir (Strathcona) in the future. Results show that downscaling models contribute the highest level of uncertainty, which propagates from reservoir inflow to power production. The results of the study show that hydropower reliability will decrease more than 50% for all three reservoirs under future changing climate conditions.

This work shows the application of machine learning (ML) methods to the modeling of water demand for the first time. Classification and regression trees (CART) and random forest (RF), a multivariate, spatially nonstationary and nonlinear ML approach, were used to build a predictive model of water demand in the city of Seville, Spain, at the census tract level. Regression trees (RT) allowed estimation of water demand with an error of $22\mathrm{L}/\mathrm{day}/\mathrm{inhabitant}$ and determination of the main driving variables. RF allowed estimation of water demand with error values ranging from 18.89 to $26.91\mathrm{L}/\mathrm{day}/\mathrm{inhabitant}$. The RF method provided better predictions; however, the RT model facilitated better understanding of water demand. This research shows an alternative to the hitherto applied cluster and linear regression approaches for modeling water demand and paves the way for a new set of further scientific investigations based on ML methods.

Option contracts have been successfully established in water-scare regions to facilitate temporary transfers of water between users. The benefits are clear when the contract water quantity can be guaranteed by the seller. Where water right allocations are subject to interannual variability, guaranteed delivery may not be feasible. Skillful season-ahead forecasts of water right allocations and crop market prices have the potential to inform option contracts in such circumstances. Here, two theoretical growers’ cooperatives, representing high-value and low-value cropping within a basin, are modeled as exclusive water-trading partners. A two-firm demand equilibrium model is developed from a coupled crop yield and economic model, conditioned on each cooperative’s production inputs. Subsequently, several combinations of single-stage and multistage season-ahead forecasts of allocations and crop prices inform both the demand and an option contract model. Evaluating combinations of forecast and persistence-informed models against a perfect foresight-informed model, both the efficacy of the forecast-informed option contract framework and the value of forecast information are determined. Results indicate combinations including multistage season-ahead forecasts of allocations produce results that most closely represent expected trade volume, direction of trading, and market-scale joint profitability across a range of intercooperative water rights ownership scenarios. The multistage allocation forecasts exert more influence over option contract utility than forecasts of product market prices. This work highlights the value of forecast-informed decision making as it pertains to promoting market-scale economic and water use efficiency.

Cooperation in international river basins is often challenged by upstream–downstream conflicts over water allocation. In many cases, water allocation is linked to the energy sector through hydropower. In this study, the water value method was used to simulate reservoir operations in an international basin under different assumptions about national priorities and regional energy cooperation. Benefits in the water sector and the power sector were compared considering both cooperative and noncooperative behavior by national players. The approach is demonstrated for a semiarid international river basin characterized by conflict between upstream hydropower production and downstream irrigated agriculture. A scenario assuming regional cooperation in the power sector came closest to the multisectoral basin cooperation benchmark and produced fewer national costs than scenarios assuming noncooperative behavior. The results emphasize that power and water resource allocation should be viewed jointly in international river basins where upstream hydropower operations can impact downstream irrigation supplies. International cooperation in the power sector may ease upstream–downstream conflicts in these cases.

The management of global water resources is increasingly contested. Australia’s Murray-Darling Basin (MDB) is an example relevant to many semiarid basins around the world. In the MDB, government has recovered substantial volumes of water to support environmental values, but irrigators and the industries and communities they support have opposed the objectives and process of water reform. Initiatives have been implemented to minimize the impact of these reforms on agriculture, but little attention has been given to opportunities that better integrate surface and groundwater resources [i.e., conjunctive use (CU)]. This paper presents a case study in the MDB where stakeholders worked together to identify, then assess, CU opportunities to increase agricultural production without compromising other values. The case study demonstrates the benefits of effective stakeholder engagement; that there are new CU opportunities worth investigating in the MDB; and that the framework used to facilitate the assessment is likely to have wider application.

This study examines the impact of weekly and seasonal weather forecasts on the optimal irrigation scheduling problem. We compare stochastic and deterministic simulation-optimization frameworks for irrigation scheduling using perfect weekly (deterministic) and imperfect seasonal (stochastic) forecasts. The analysis is performed on a case study of irrigated chickpeas in Kibbutz HaZore’a in northern Israel. The results demonstrate that, for the case study area and crop, the optimization with seasonal stochastic forecasts outperforms the deterministic optimization that uses perfect weekly forecast.