Journal of Water Resources Planning and Management

Flooding in urban areas has become increasingly common in recent decades, as a result of increased urbanization, decreased infiltration rates, and climate change. Hydraulic rehabilitation plans can be developed and implemented to maintain suitable urban drainage system performance. Determining the effective plans, however, requires the involvement of rainfall uncertainties in the modeling and using special tools. In this study, statistical copula functions are established to determine joint probability distribution of rainfall variables considering their dependence structure. The most credible distribution is then used through the Monte Carlo simulation (MCS) to investigate rainfall uncertainties. A multiobjective optimization model is also developed and, after validation, is linked to the EPA-SWMM model for evaluating urban drainage rehabilitation scenarios. The copula-based multiobjective optimization model represents a range of cost-effective rehabilitation plans in terms of overflow improvements and their confidence intervals. This provides decision makers with valuable information about the robustness of the solutions and the possibility of selecting a trade-off region given constrained rehabilitation resources.

There are many uncertainties in real-time flood control operation of a reservoir, which create risks in flood control decision making. In this paper, three uncertainty factors—reservoir inflow-forecasting errors, outflow errors, and observation errors of reservoir storage capacity curve—are taken into account and quantified methods are proposed. With consideration of the three uncertainties and correlation between inflow-forecasting errors, reservoir water-level errors are derived using the stochastic differential equation of reservoir flood routing. Then the definition and calculation methods for flood risk at each moment and the integrated risk of the entire flood process are proposed. The Dahuofang reservoir in China is selected as the case study. The results shows that the risk resulting from the uncertainties is decreased by the reservoir flood regulation function and that the proposed method can provide a useful way to estimate the risks in real-time reservoir flood control.

Flood management systems involve a variety of complexities, such as multiple uncertainties and their interdependences, as well as multiregion and dynamic features. This paper thus presents an inexact two-stage mixed-integer programming with random coefficients (ITMP-RC) model for flood management in a dynamic and uncertain environment. ITMP-RC is capable of addressing dual uncertainties expressed as random boundary intervals that exist in the coefficients of the objective function. A case study of flood diversion planning is used to demonstrate the applicability of the proposed methodology. Results indicate that total system costs would be rising gradually with increased probabilities of occurrence, implying a trade-off between economic objective and system safety. A variety of decision alternatives can be obtained under different policy scenarios, which are useful for decision makers to formulate appropriate flood management policies according to practical situations. The performance of ITMP-RC is analyzed and compared with an inexact two-stage stochastic programming model.

In applications where demand on a water supply system changes frequently and widely, the operating conditions of pumps always deviate from the design conditions, and this error leads to poor efficiency and reliability. For energy saving and longer service life, a parallel pump system was supplied, with the valves and rotational speed controls approximating the system’s operating conditions closer to the designed conditions for different consumer loads. The developed optimization model employs a genetic algorithm (GA) aiming at the pumps’ maximum efficiency. A theoretical solution based on the Lagrange multiples method was proved. Experiments on two identical pumps were carried out. The presented model gives optimal input data for the pumps’ rotational speed and valve positions. The results show that control valves are especially helpful for improvement of a single pump’s efficiency and reliability. However, in the system of parallel pumps, throttling losses in the valves caused a significant decline in system efficiency. Therefore, the developed optimization model provides balance between efficiency and reliability by offering the suitable operating mode.

This paper presents three new indicators for assessing the energy efficiency of a pressurized water system and the potential energy savings relative to the available technology and economic framework. The first two indicators are the ideal and real efficiencies of the system and reflect the values of the minimum energy required by users—the minimum amount of energy to be supplied to the system (because of its ideal behavior) and the actual energy consumed. The third indicator is the energy performance target, and it is estimated by setting an ambitious but achievable level of energy loss attributable to inefficiencies in the system (e.g., pumping stations, leakage, friction loss). The information provided by these three key performance indicators can make a significant contribution towards increasing system efficiency. The real efficiency indicator shows the actual performance of the system; the energy performance target provides a realistic goal on how the system should be performing; and finally, the ideal efficiency provides the maximum and unachievable level of efficiency (limited by the topographic energy linked to the network topography). The applicability and usefulness of these metrics will be demonstrated with an application in a real case study.

In-stream water-quality models provide guidance in watershed management decisions by linking pollutant loads to changes in water quality. These models are particularly useful for determining wasteload allocations, developing numeric nutrient criteria, and aiding in total maximum daily load (TMDL) analyses. Unfortunately, the routine data collected as part of the governmental monitoring efforts do not typically meet the data requirements for modeling. Consequently, this study presents a data collection methodology suited to meet basic in-stream water-quality modeling requirements for a commonly used model (QUAL2Kw). To set some model parameters directly, methods are provided for estimating maximum sediment oxygen demand and determining appropriate reaeration formulas based on observed oxygen time series. The quantity of data gathered within an example case study was minimized to reduce cost, which resulted in expected challenges (e.g., designation of appropriate loading values from highly variable point source information). Similar to other modeling studies, parameter estimates were also not readily identifiable. However, even simple methods to set key parameters and reduce the number requiring calibration proved beneficial. Although most situations will require additional calibration and data for model corroboration, this approach provides an initial framework to aid in the judicious use of resources to meet watershed management decision making needs within the context of wasteload allocation and/or numeric nutrient criteria development.

Optimal utilization of water resources by means of water transfers and reservoirs in semiarid and arid regions is used to mitigate natural water scarcity. In this context, metaheuristic algorithms for optimum reservoir system operation have become an attractive alternative to traditional operations research algorithms such as linear programming (LP), nonlinear programming (NLP), and dynamic programming (DP). This paper presents the metaheuristic bat algorithm (BA) and its application to the optimal operation of the Karoun-4 reservoir system in Iran and to a hypothetical four-reservoir system. The merits of the performance of the BA in the optimization of reservoir operation are demonstrated by comparison to those of LP, NLP, and genetic algorithm (GA) in terms of the convergence to global optima and of the variance of results about global optima for reservoir optimization problems.

Concurrent incidence of heavy rainfall events and coastal storm surges can cause damages of exponential proportion to the coastal habitants and infrastructures. Super Storm Sandy and hurricane Irene were wake up calls that the floodplains and evacuation zones in New York City need major revision. Federal and State emergency agencies have been working on revising the appropriate maps and spatial tools for planning and management purposes. This paper presents an integrated scheme for floodplain delineation in coastal areas, considering the combined effect of inland and coastal flooding. The proposed scheme is applied to the Bronx River watershed in the Bronx section of New York City. Consideration of inland and coastal flooding is provided by developing a conceptual rainfall-runoff model and a sea level simulation model. Climate change has intensified the intensity and frequency of extreme rainfall events and storm surges in the region. In order to evaluate climate change impacts on floodplain, projections from two GCMs (General Circulation Models) for A1B (balanced) and A2 (medium-high emissions) climate change scenarios are used. Change factor methodology is employed to downscale the GCMs’ outputs. Flooding scenarios are defined based on the frequency analysis of the projected runoff and water level for different recurrence intervals. This is a key issue in planning and management of coastal region considering different levels of acceptable risk. Inundation maps of the coastal floodplain are created for each flooding scenario using a geographic information system (GIS) platform. Result shows that long-term average of runoff and water level in the region in a 30-year period will be increased by 42% and 0.46 m respectively. The study finds that the effect of sea level rises on floodplain extent is greater than changes in runoff. In order to plan for effective flood risk management strategies in urban areas, the spatial analysis and mapping of flood inundation should be implemented. This study emphasizes the importance of updating the delineation of coastal floodplains as a part of planning and management of land use zoning and future developments in coastal regions.

Long-term changes in reservoir inflow due to climate change and human interferences have caused doubts on the assumption of hydrologic stationarity in reservoir design and operation. Incorporating uncertain predictions that consider nonstationarity into an adaptive reservoir operation is a promising strategy for handling the challenges that result from nonstationarity. This study proposes rules for multistage optimal hedging operations that incorporate uncertain inflow predictions for large reservoirs with multiyear flow regulation capacities. Three specific rules for determining the optimal numerical solution are derived. A solution algorithm is then developed based on the optimality conditions and the three rules. The optimal hedging rules and the solution algorithm are applied to the Miyun Reservoir in China, which exhibited a statistically significant decline in reservoir inflow trend from 1957 to 2009, to determine an annual operating schedule from 1996 to 2009. Nonstationary inflows are predicted by using an autoregressive integrated moving average (ARIMA) model on a period-by-period basis. The actual operation (AO) of the reservoir is compared with different operational policy scenarios, including a standard operating policy (SOP; matching the current demand as much as possible), a hedging rule (i.e., leaving a certain amount of water for the future to avoid the risk of a large water deficit) with a prediction from ARIMA (HR-1), and a hedging rule with a perfect prediction (HR-0). With a predefined benefit function, the utility of the reservoir operation under HR-1 is 3.7% lower than that under HR-2, but the utility under HR-1 is 3.1% higher than that of AO and 13.7% higher than that of SOP.

Hydroeconomic models have been used to determine policies for efficient allocation of scarce water resources. Hydropower benefits are typically represented through exogenous electricity prices, but these do not consider the effect that the power market can have on the hydropower release policy and vice versa. To improve the representation of hydropower benefits in hydroeconomic models, an application of stochastic dynamic programming, known as the water value method, was used to maximize irrigation benefits while minimizing the costs of power generation within a power market. The method yields optimal operation rules that maximize current and expected future benefits as a function of reservoir level, week of the year, and inflow state. The method was tested on the Iberian Peninsula and performed better than traditional approaches that use exogenous prices: resulting operation rules were more realistic and sensitive to hydrological variability. Internally calculated hydropower prices provided better results than exogenous hydropower prices and can therefore improve the representation of hydropower benefits in hydroeconomic models.

Most utilities are actively engaged in pipe replacement planning for a variety of reasons: to reduce the volume of lost water in their network by repairing or avoiding main breaks, to alleviate water quality concerns, to improve overall area delivery, and many others. Rarely are whole-of-life energy considerations taken into account when utilities create their pipe replacement schedules. The aim of the paper is to perform a sensitivity analysis on several important parameters related to pipe replacement, i.e., leak volume per break, leak duration, break rate model, and pump efficiency, on the energy use of a large-scale distribution network. The sensitivity analysis is carried out for a no-replacement baseline scenario and three replacement plans. For the specific water network examined, results suggest that the leakage volume per break, leak duration, and break forecasting model coefficients have a small effect on energy use, regardless of the pipe replacement plan. Pump efficiency was found to have the most significant effect on energy use.