Journal of Water Resources Planning and Management

The hydraulic system functioning is determined by the boundary conditions (e.g., network topology, pipe resistances/diameters, tank levels, status of control devices, and status of pumps). Shutdown of isolation valves to detach a portion of the hydraulic network for planned or unplanned works generates abnormal working conditions because of the induced topological modification of the network, which may reduce the hydraulic capacity of the water system with respect to the portions still connected. Thus, a challenge for network design is to optimize diameters versus system management under abnormal working conditions, i.e., accounting for the isolation valve system. To this purpose, a methodology for optimal system design accounting for valve shutdowns is herein presented. Because the optimization asks for the evaluation of network configurations that can be generated by the isolation valve system, a strategy to reduce the computational burden is required. In fact, the analysis of a large number of network configurations could be required in real-world applications. A strategy to evaluate only the critical configurations, i.e., those for which the hydraulic capacity becomes insufficient to satisfy water requests in the still connected network, and dominating configurations, i.e., those that are the most critical, is presented. The optimization procedure is explained and discussed using a small sized network, and the computational efficiency is demonstrated using a large sized network.

This paper develops an integrated approach to model heterogeneous household water and energy use and their linkages. The approach considers variations in behavioral and technological water and energy use factors that affect U.S. indoor residential water and energy use for toilets, showers, faucets, clothes washers, and dishwashers. The study uses a recent, large, national, disaggregated household water use data set collected from 11 cities, as well as national energy data on water heater efficiency and setpoint/intake temperatures. First, probability distributions of water and energy use factors are identified and correlated. Then, Monte Carlo simulations are used to calculate probability distributions for estimated household water and energy use. Finally, linear regressions are used to find the relative effects of water and energy factors on household energy use. Results show that water and energy distributions among households are skewed, with the largest 12% of the users consuming 21% and 24% of water and energy, respectively. Water heater setpoint temperature followed by intake temperature, heater efficiency, shower hot water percentage, household size, shower flowrate, and faucet flowrate have the highest relative effect on household energy use and should be targeted to reduce household energy use. The approach improves prior homogenous and deterministic water-energy models and can help utilities select and size cost-effective, collaborative water and energy conservation actions.

Water quality planning is complicated itself but further challenged by the existence of uncertainties and nonlinearities in terms of model formulation and solution. In this study, a simulation-based inexact two-stage chance-constraint quadratic programming (SITCQP) approach was developed for water quality management. The SITCQP model was a hybrid of two-stage stochastic programming (TSP), chance-constraint programming (CCP), inexact quadratic programming (IQP), and multisegment stream water quality simulation. A water quality simulation model was provided for reflecting the relationship between the pollution-control actions before wastewater discharge and the environmental responses after the discharge. Interval quadratic polynomials were employed to reflect the uncertainties and nonlinearities associated with the costs for wastewater treatment. Uncertainties derived from water quality standards were characterized as random variables with normal distributions. The proposed approach was applied to a hypothetical case in water quality management. The modeling solutions were presented as combinations of deterministic, interval and distributional information, which could facilitate predictions for different forms of uncertainties. The results were valuable for helping decision makers generate alternatives between wastewater treatment and stream water quality management.

The generalized likelihood uncertainty estimation (GLUE) framework has been widely used in hydrologic studies. However, the extensive random sampling causes a high computational burden that prohibits the efficient application of GLUE to costly distributed hydrologic models such as the soil and water assessment tool (SWAT). In this study, a multimodal optimization algorithm called isolated-speciation-based particle swarm optimization (ISPSO) is employed to take samples from the search space. A comparison between the ISPSO-GLUE, proposed here, and traditional GLUE approaches shows that the two approaches generate similar uncertainty bounds, but that the convergence rate to stable uncertainty bounds is much faster for ISPSO-GLUE than for GLUE. That is, ISPSO-GLUE needs a much smaller number of samples than GLUE to arrive at a very similar answer. Although ISPSO-GLUE slightly underestimated the prediction uncertainty and missed a number of observed values, the proposed approach is considered to be a good alternative to the typical GLUE approach that employs random sampling.

Regional wastewater systems are used for the collection and treatment of the wastewater generated in a region and aimed at guaranteeing surface water quality. The volumes of wastewater to process depend on future population and thus are affected by the uncertainty inherent to population dynamics. In this article, we present a robust approach to the planning of regional wastewater systems under population dynamics uncertainty. The approach searches for the optimal configuration of the sewer networks and for the best location, type, and size of the possible pump stations and treatment plants to include in the system. It assumes uncertainty to be described by a given number of discrete scenarios of known probabilities and relies on discrete nonlinear optimization models whose objective is to minimize the expected regret of solutions with respect to total costs. As demonstrated through a case study developed for the North Baixo Mondego area in central Portugal, the results obtained through the proposed approach provide clear insights into the wastewater system planning decisions to make and do not require excessive computational effort.

The Modified Interaction Soil Biosphere Atmosphere (MISBA) model, the Water Resources Management Model (WRMM) of Alberta Environment, and the Irrigation District Model (IDM) of Alberta Agriculture Food and Rural Development were used to assess the future outlook of water resource management of the South Saskatchewan River Basin (SSRB) of Alberta under the potential impacts of climate change. Using the SSRB hydrometeorological base data from 1928–1995, potential effects of climate change on the SSRB for 2010–2039, 2040–2069, and 2070–2099 were simulated by MISBA, IDM, and WRMM with the climate scenarios projected by four general circulation models forced by emissions reported by the Intergovernmental Panel on Climate Change. Under these climate projections, MISBA simulated a significant decrease in the mean annual average and mean annual maximum streamflows over selected nodes within the SSRB; the irrigation water demand projected by IDM is expected to increase progressively over the 21st century; and WRMM simulated changes to the number of deficit years out of 68 years (1928–1995) to specific water sectors of the SSRB. Overall, according to the projections of WRMM, the instream flow requirement of SSRB will either not be affected or will only be marginally affected, but irrigation districts will be progressively affected by climate change. The senior private irrigation users will not be affected by climate change in 2010–2039 and will only be marginally affected in 2040–2069 and 2070–2099. In contrast, junior and future private irrigation users are projected to be progressively affected by climate change. On the other hand, for nonirrigation consumptive uses, all senior, junior, and future licenses may be significantly affected by climate change.

Originating at Lake Tahoe, the Truckee River provides 85% of drinking water for the Reno/Sparks metropolitan area. Major highways and a railroad run adjacent to the river, which increases risk of a contaminant spill into the river that could have detrimental effects on drinking water supplies. A one-dimensional solute transport model (OTIS) was applied to the Truckee River. Data from dye studies on the river were used to determine a relationship to estimate dispersion coefficients for the Truckee River and calibrate the model. Two sizes of hypothetical contaminant spills from 9 locations under 13 flow scenarios were simulated. Travel times to the first water intake for a train spill of 130,000 L ranged from 3 to 46 h and maximum simulated concentrations of a conservative water soluble contaminant at the intake ranged from 340 to $\mathrm{4,800}\mathrm{mg}/\mathrm{L}$. Model output was influenced by uncertainties in the equation for longitudinal dispersion, so model runs were executed with estimated dispersion values that were a factor of 1.5 greater and less than the equation-estimated value of dispersion.

In the Rio Grande transboundary basin, environmental flows have not been considered as an integral part of the water management. This research focuses on the Big Bend, a reach located along the Rio Grande mainstem. Important natural regions of the Chihuahuan Desert are threatened due to the lack of environmental flows. In this paper is estimated the maximum volume of water available for environmental flows without affecting human and international water requirements, and without increasing the flood risk in Presidio-Ojinaga. Environmental flows are proposed based on an analysis of the prior reservoir alteration hydrology of the river. A planning model was built to simulate the water allocation system and evaluate alternative policies. A reservoir reoperation policy for Luis L. Leon reservoir is proposed to supply environmental flows without violating the system constraints. The policy that supplies the maximum water to the environment is two-thirds (66%) of the prior reservoir alteration conditions; it also improves human water supply, treaty obligations, and decreases flood risk.

The writers propose a successive improved dynamic programming (SIDP) algorithm for hydropower reservoir operation based on an analysis of concavity, complementarity, and monotonicity of hydropower problems. For single-period hydropower generation, storage and release have diminishing marginal contributions to hydropower generation (i.e., concavity), and there is also a complementary effect between storage and release (i.e., release becomes more productive in accordance with increasing storage). For multiple-period hydropower generation, the complementarity influences the concavity of the objective function and the monotonicity of operation decisions, and is the major cause of complexity in hydropower operation. With mathematical derivations, the writers propose a concave approximation to the hydropower generation function and a SIDP algorithm for hydropower reservoir operation. The efficiency of SIDP is demonstrated with two hypothetical case studies of long-term hydropower scheduling, which shows that the computation time of SIDP increases linearly in accordance with the number of storage intervals [i.e., $O(n)$], whereas dynamic programming (DP) shows a quadratic increase [i.e., $O(n^2)$].

Following three decades of rather low investment in dams, many regions throughout the world are now seeking to further develop new storage capacity to meet exploding demands for water and to hedge against the risk posed by climate change. Storage capacity is often perceived as a key element of climate change adaptation strategies, while at the same time contributing to socioeconomic development through irrigation, energy generation, fish production, and municipal and industrial water supply. The benefits provided by dams must be balanced with the associated environmental and social costs, which can take various forms, such as the degradation of ecosystems because of altered flow regimes and the relocation of people from the impoundment area. The benefits of storage essentially come from the ability to move water in time, making it available during the low-flow season when it becomes more valuable. As river basins develop and new dams are constructed, it may be important for planning and operational purposes to assess the individual contribution of each reservoir to the benefits of storage. This paper presents a methodology to determine the economic value of storage in multireservoir systems based on the marginal net benefit functions of storage and on hydroeconomic modeling. A cascade of reservoirs in the Euphrates river basin is used to illustrate the methodology.

In this paper, a mathematical model of optimal management of prolonged and consecutive drought events is developed to evaluate circumstances under which the adoption of a water-efficient technology may offer long-term drought resilience. The decision over technology adoption and its timing is affected by the objective of surviving a certain number of consecutive droughts besides being influenced by the costs of technology and the farmer’s endowments. A key finding is that planning for surviving through a longer drought period may discourage early technology adoption among poorer farmers faced with groundwater scarcity. Compared with wealthy farmers, the tendency to conserve groundwater for the poorer farmers when the drought planning horizon is longer gets reversed with an increase in the risks of repeated droughts. At low risks, groundwater conservation increases with the drought-planning horizon, but at higher risks, groundwater is depleted faster as the drought-planning horizon increases. Technology adoption may be discouraged when the possibility of repeated droughts is imminent or when the risk of an irreversible loss of groundwater is present.

Stakeholder involvement in watershed management decisions remains challenging, particularly when one is relying upon modeling efforts as one of the primary means to understand and communicate the complicated processes controlling a natural riverine system. To determine how to meet the instream water quality standards as required by the total maximum daily load (TMDL) program, a QUAL2Kw water quality model was developed and calibrated for the Jordan River, Utah. To improve transparency, increase stakeholder engagement, and reach agreement more effectively, a collaborative approach was taken to calibrate the model that included Utah Division of Water Quality (UDWQ) staff, consultants, key stakeholder representatives, and independent experts. The study reach of the Jordan River flows 83 km from Utah Lake to the Great Salt Lake through the urbanized Salt Lake Valley in Utah. Several segments of the river have been listed by UDWQ as impaired for a number of constituents, but the present effort focuses on the dissolved oxygen (DO) impairment. In cooperation with stakeholder groups, four seasonal synoptic sampling surveys and additional research studies were conducted for use in calibrating and validating the model. The collaborative calibration process involved many meetings and workshops where model performance was iteratively evaluated in order to reach consensus on model inputs and parameterization. The end result of the collaborative calibration of the QUAL2Kw model was the development of a scientifically defensible tool used to establish a consensus that decomposition of organic matter and poor reaeration are the primary causes of the DO impairment in the Jordan River. Without the collaborative approach to calibration, agreement regarding the causes of water quality impairments would not likely have occurred. While clearly improving stakeholder believability in the model, a comparison of the collaborative calibration model performance to an autocalibrated version demonstrated that both approaches resulted in similar predictive accuracy.