Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Nodal demand calibration of a water distribution system (WDS) is a process of adjusting the nodal demand in WDS models to make its predictions consisting with measurements, which is an inversion problem compared to the conventional forward computation. Most existing methods rely on performing forward computation repeatedly to calculate the sensitivity matrix or generate offspring for searching for optimal solutions. This paper develops an alternative framework, namely an inversion model, to directly calibrate the nodal demand. The model is constructed by separating the known and unknown variables in continuity and energy equations of WDS using the matrix decomposition method. Specifically, the measured and unmeasured nodal demand, nodal head, and pipe flow are taken as knows and unknowns, respectively. The nodal demands with similar user characteristics are grouped (i.e., aggregated) to make the model overdetermined, and the Gauss-Newton based iteration method is applied to solve the model. To evaluate the calibration results when observation errors are involved, the standard deviations of unknowns are calculated using first-order second-moment method for uncertainty quantification, and the results are verified by Monte Carlo simulation. A simple network is used to illustrate the model construction in detail, and two numerical case studies, including a real highly looped network, are applied to further validate its effectiveness and feasibility. Encouraging results obtained clearly demonstrate the proposed method has potential for practical application in real-time nodal demand calibration, state estimation, and uncertainty quantification of WDSs.

If a contamination event occurs in a water distribution system, sensors in the network may observe water quality changes. The data from these sensors can be used to identify the source of the contamination. The sensors can be binary sensors that record the presence or absence of contamination, fuzzy sensors that measure concentration within a set range, or perfect sensors that measure the exact concentration within the bounds of measurement uncertainty. This work presents an adjoint-based probabilistic approach for identifying the source node, source release time, and source strength for an instantaneous release of contamination based on sensor observations and known system hydraulics. In the adjoint approach, information is propagated upgradient from the sensors to the possible source nodes. EPANET is used to simulate the transient hydraulics of the pipe network and the upgradient propagation of the adjoint state through the network. The resulting adjoint states are related to probability density functions of the source release times at all possible source nodes. For fuzzy or perfect sensors, these probabilities can be used to determine the most likely source node. A hypothetical example is used to show that this method is accurate, even for a small number of sensor observations and complex hydraulics.

Droughts continue to be a major natural hazard, and mounting evidence of global warming confronts society with a pressing question: Will climate change aggravate the risk of drought at the local scale? Are current infrastructures sufficient to mitigate the damage of future drought, or is in-advance infrastructure expansion needed for future drought preparedness? To address these questions, this study presents a decision-support framework based on a coupled simulation and stochastic optimization model through a case study area, the Frenchman Creek basin (FCB), part of the Republican River basin. A complex watershed simulation model is established and converted into a statistical surrogate model for computational feasibility. Decisions for drought preparedness include traditional short-term tactical measures (e.g., facility operation) and long-term or in-advance strategic measures, which require capital investment. A scenario-based, three-stage stochastic optimization model assesses the roles of strategic measures and tactical measures in drought preparedness and mitigation. Modeling scenarios of the future climate are developed from multiple general circulation models (GCMs) and regional climate models (RCMs) under different greenhouse gas emission scenarios to represent the various possible climatic conditions in the midterm (2040s) and long-term (2090s) time horizons. The result of the case study shows that current facilities are not enough to mitigate the damage under future climate conditions, indicating the requirement for infrastructure investment; meanwhile, socioeconomic factors (represented by the discount rate) complicate the decision along the planning horizon.

Water quantity and quality joint operation of dams and floodgates is a cost-effective way to improve a river basin’s water environment at present. It has become a hot topic in integrated basin management. The Huai River basin is a unique region in China with a high density of dams and floodgates and is facing serious water pollution problems because accidental water pollution events have occurred many times. Taking Shaying River and Guo River, which are the two seriously polluted tributaries in the Huai River basin, as the study area, this paper considers objectives of flood control, pollution prevention, and water supply, and a water quantity and quality joint-operation model of dams and floodgates was established. By analyzing the operation rules of various composed scenarios, a three-stage operation method (TSOM) of water quantity and quality was proposed based on the model. The study area was decomposed into two subsystems: Subsystem I is the Shaying River and Subsystem II is the Guo River. Two typical operation scenarios were selected to be simulated. The simulation result of the May 1, 2004, scenario demonstrated that the water quality of the main monitoring sections with the joint operation was better than that with the empirical operation under the same water quality standard. The simulation result of the April 10, 2012, scenario explained that Subsystem I had an obvious compensation effect on Subsystem II in the process of joint operation. Furthermore, the TSOM summarized from 216 scenarios under the condition of different water quantity and quality in time and space can make the operation model more practical and exercisable. The joint operation of dams and floodgates will play a positive role in improving the current water quality of the main stream in the Huai River. This paper will provide scientific decision making for antipollution operation and technical support to achieve the integrated management in the Huai River basin.

Stormwater management in urban areas remains a challenging water-resources and environmental problem worldwide. This work develops and tests two methods for optimizing stormwater control measures. The first method relies on a linear programming formulation to find the optimal sizes of stormwater control measures to be deployed at selected locations. The second method uses binary linear integer programming to determine the optimal type of stormwater control measures of standardized dimensions to be deployed at selected locations. Both methods minimize the total cost of deploying stormwater control measures, subject to constraints on available budget, volumetric water balance, allowable stormwater volumes, and water-quality characteristics. Two examples illustrate the step-by-step formulation of the linear programming and the binary linear integer programming methods for the optimization of stormwater control measures, and provide solutions to the problems of their optimal sizing and selection. Our results demonstrate the feasibility of meeting stormwater retention and water-quality objectives by the optimal deployment of stormwater control measures as proposed in this work.

Future projection of air temperature and precipitation due to climate change plays an important role in shaping the water resources management plans in arid and semi-arid countries such as the United Arab Emirates (UAE). The effect of local topography, land use, and sea-land contrasts are generally dwarfed in the coarse global circulation models [ocean atmosphere global climate models (OAGCMs)] results. Statistical downscaling (SD) is a statistical tool relating OAGCM outputs (predictors) to finer local observed weather data (predictands) to improve future predictions. SD is used in this study to downscale the Hadley Model (HadCM3) predictors using local observations at two stations representing the dominating bioclimatic zones in the UAE. The paper proposes a rigorous and systematic methodology to guide the selection of dominant predictors in the projection scenarios. The proposed methodology is applied for the UAE but can be easily adopted for any other area. The calibrated model, used to project future scenarios in the region, revealed a range of increase of the annual mean maximum temperature of 2.79–3.80°C and a range of reduction of annual precipitation of 16.80–37.00% by 2080 at the considered stations. Impacts of climate change on the UAE’s water resources management are discussed in light of the downscaled estimates for temperature and precipitation. Because a major portion of the water resources is devoted to agriculture, temperature impacts are quite significant. However, the impacts of precipitation are hardly detectable at the national scale. Instead, the impacts of reduced precipitation will be of more localized significance in mountainous regions that are critically dependent on precipitation for water resources.

The Everglades is a complex, multiwetland ecosystem that is heavily managed to meet often-competing flood control, water supply, and environmental demands. Using objective measures to balance these demands through operational protocols has always been a challenge in the multibillion-dollar restoration plans for the ecosystem. Physically based models have been the primary tools for planning efforts but for such a complex system, they are laborious and computationally intensive. Development of optimal operations based on iterative runs of these models is a great challenge. This paper presents an inverse modeling framework for formal optimization suited for wetland system operations that helps overcome such limitations. Labor-intensive and computation-intensive physically representative models are emulated in each individual wetland area using an autoregressive artificial neural network with exogenous variables. Using prescribed inflow, outflow, and meteorological input data, such hydrologic model emulators aided by a dimension-reduction technique provide targeted spatial and temporal predictions for water level (stage) within each area of the Everglades, while excluding computation processes that are intensive but insignificant to the predictions. This computer software uses the augmented Lagrangian genetic algorithm technique (subject to linear and nonlinear constraints) to steer predictions of stage spatial variability within individual wetlands towards corresponding desired goals (including restoration targets). In the augmented Lagrangian genetic algorithm, flow releases are coded as the decision variables to be optimized subject to budget, intrahydraulic conveyance, flow capacity, and upstream storage constraints. Optimization is performed by dividing and solving a sequence of subproblems using the genetic algorithm procedures of initialization, selection, elitism, crossover, and mutation. As part of the process, Lagrangian and penalty parameters are updated, and optimization terminates when certain stopping criteria are met. Applying the technique reported in this paper to a specific Everglades restoration plan (the River of Grass Project) showed a sound hydrologic model emulator prediction when compared to the physical model for all wetland areas. Feeding optimal releases predicted by the computer software into a physical model showed equal or better matching of the restoration target with different release patterns compared to that of the physical model base run scenario. Results show that hydraulic conveyance limitations play a significant role in Everglades restoration. Also, results show that employing an adversity tradeoff matrix presented multiple so-called optimal solutions with different optimization weights and a powerful negotiation matrix.

Collaborative negotiation has been widely used for developing water policy. Nevertheless, a serious lacuna remains in the understanding of the factors that determine whether the negotiators in this bargaining process will be able to reach agreement. This paper argues that the literature has focused on the process that is followed in negotiations, to the virtual exclusion of the substance of the issues that are to be resolved. As a consensus can only be reached concerning a change in policy if each party receives compensation for the concessions that it makes, a precondition for collaborative negotiation is that each party must have control over some asset that it can “trade” with the other parties. When this condition is met, it can be said that the process has “substance.” The authors identify a number of situations in which negotiations over water policy may possess this characteristic. However, they also argue that there is a large set of cases in which positive net gains are available, but in which at least one party lacks control over an asset that can be exchanged. In these cases, a number of government policies are investigated that could provide stakeholders with the necessary tradable goods and, therefore, could impart substance to the process. Many situations still remain, however, in which collaboration will lack substance, and stakeholders can be expected either to seek alternative means for pursuing their goals or to waste their effort in endless bargaining.

As pipe breaks in water distribution networks produce serious consequences, water authorities strive to minimize the frequency of their occurrence. Pressure management is an essential tool to reduce the frequency of breaks and it is closely linked to the proper analysis of a maximum pressure indicator. A methodology that compares the unconditional cumulative distribution function (CDF) and the parametric break-conditioned CDF of the maximum pressure indicator is proposed in this paper. The relationship between the CDFs compared is established by means of the Bayes’ theorem, which allows determining a probability ratio. The objective is to identify the range of operation of maximum pressure that is most likely to reduce pipe breaks. The methodology is applied to four sectors of the water distribution network in Madrid (Spain). In three of those sectors, the maximum pressure indicator is a good predictor of the probability of pipe breaks, confirming that the probability of breaks increases for high maximum pressure ranges. The methodology is validated in one sector, and results provide good agreement between predicted and observed failure rates.

The need for more efficient use of energy in water distribution systems is increasing constantly due to increasing energy prices. A new methodology for optimized real-time operation of a water distribution system is developed and presented here. The methodology is based on the integration of three models: (1) real-time demand forecasting model, (2) hydraulic simulation model of the system, and (3) optimization model. The optimization process is driven by the cost minimization of the energy used for pumping and the maximization of operational reliability. The latter is quantified using alternative measures into the optimization process in order to mimic the conservative attitude to pump scheduling often adopted by control room operators in real-life systems. Optimal pump schedules were generated by using a multialgorithm-genetically-adaptive-method (AMALGAM), hydraulic simulations are performed by using the EPANET2 model, and demand forecasting was performed by using the recently developed DAN2-H model. A number of other methodological developments are used to enable pump scheduling in real time. The new methodology is tested, verified, and demonstrated on the water distribution system of Araraquara, in the state of São Paulo, Brazil. The results obtained demonstrate that it is possible to achieve substantial energy cost savings (up to 13% relative to historical system operation) while simultaneously maintaining the level of supply reliability obtained by manually operating the water system.

A two-level linear fractional water management (TLFWM) model based on interactive fuzzy programming is developed. The model can solve multi-objective problems quantitatively, particularly for the ratio multi-objective problems (e.g., benefit per unit of water in water resources management system). Furthermore, it takes the cooperation relationship between decision makers into consideration. Considering the stochastic features of runoff, chance-constrained programming is integrated into the TLFWM model framework. A stochastic two-level linear fractional chance-constrained water management (STLFCWM) model is thus proposed with different flow levels involved in the STLFCWM model in the form of probabilities as well. The developed two models are applied to a real case study to allocate the limited water resources to different water users. The obtained solutions can demonstrate the feasibility and suitability of the TLFWM model and STLFCWM model and thus help decision makers to identify desired water resources management policies under uncertainties.

Ethiopia has begun seriously developing their significant hydropower potential by launching construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile River to facilitate local and regional growth. The GERD, located just upstream of the border with Sudan, is the first dam ever to be constructed directly on the main stem of the Blue Nile and will become the largest dam in Africa. Although this has required substantial planning on Ethiopia’s part, no policy dictating the reservoir filling rate strategy has been publicly issued. This filling stage will have clear implications on downstream flows in Sudan and Egypt, complicated by evaporative losses, climate variability, and climate change. In this study, various filling policies and future climate states are simultaneously explored through a linked set of models (rainfall-runoff, routing, and hydropower) to infer potential streamflow reductions near Sudan’s Gezira Scheme, one of the largest irrigated areas in the world, and at Lake Nasser behind Egypt’s High Aswan Dam. Simulations across 2011–2060 are evaluated at a monthly time step. Given intermediary tributaries and hydroclimatic variability, a clear nonlinear relationship exists between GERD filling rates and streamflow at key locations in Sudan and Egypt; this study quantifies these differences. For example, impounding 10% (25%) of monthly streamflow behind the GERD produces a 6% (14%) average reduction in streamflow entering Lake Nasser during the first 5 years; changes at the Gezira Scheme are even larger owing to fewer contributions from other tributary flows in between the GERD and the Gezira Scheme. Climate changes may shift this notably. A filling policy contingent on the GERD impounding water only if streamflow exceeds the long-term historical average produces a 7% reduction at Lake Nasser over the same 5 years, with a larger expected variance. The goal of this work, however, is not to prescribe a particular filling policy; rather it is to provide regional decision makers with a set of plausible, justifiable, and comparable outcomes for negotiation and consensus.