Journal of Water Resources Planning and Management

A combination of models including wavelet transform and kernel partial least squares-autoregressive moving average (KPLS-ARMA) is proposed to explore the nonstationarity of the urban annual water demand series, the nonlinear relationships between water demand series and its determinants, and the high correlations among those determinants, based on which a novel forecast model is proposed for urban annual water demand. First, by Mallat algorithm, a nonstationary urban annual water demand series is decomposed and reconstructed into one low-frequency component and one or several high-frequency components. Following that, the kernel partial least squares (KPLS) model is applied to simulating the low-frequency component. An autoregressive moving average (ARMA) model is constructed for each of the high-frequency components. The combined models are applied to understanding the nonstationarity and forecasting the annual water demand of Dalian City. The results are then compared with those from other several methods. It is shown that the proposed method, which combines advanced statistical tools (such as wavelet transform and artificial intelligence) and traditional statistical models, provides the most accurate forecast of urban annual water demand in the city.

Floodways may offer efficient flood damage reduction along flat land rivers, especially if the real-time operation of the floodways is feasible. Given an observed or forecasted upstream flood hydrograph, an optimal control policy of floodways is sought that results in the least flood damage over the whole system. The system consists of a river protected against flooding by existing levee reaches and floodways. The levee reaches are characterized by variable and uncertain levee resistance due to soil heterogeneities. This uncertainty is modeled probabilistically, leading to levee resistance performance functions. A model is developed to calculate failure probabilities for a river section consisting of several levee reaches. The objective function aims at minimizing expected flood losses along all river reaches to determine daily floodway operation. Results for the Tisza River system in Hungary indicate the benefit of using the optimal control policy versus other possible schemes.

Municipal water supply systems dependent on surface water sources that are susceptible to both low flows and contamination can use off-stream blending reservoirs (OSBR) both to increase their yield and avoid investments in expensive treatment technology. Three water quality operating rules (WQORs) are simulated from a retrospective planning perspective using the OSBR system that controls nitrate contamination for Pontiac, Illinois as an example case. These rules regulate pumped reservoir inflow based on (1) a maximum river concentration (Riv-CR), (2) a maximum reservoir concentration (Res-CR), and (3) a hybrid rule incorporating both maximum reservoir and river concentrations (Hyb-CR). The Hyb-CR rule typically registers the highest yield when nitrate is modeled as a conservative contaminant, while Res-CR tends to produce the highest yields when nitrate losses are considered. In general, reservoir concentration-based WQORs are effective for increasing the yield of OSBR systems that control the concentration of nonconservative contaminants, and often produce higher yields than a traditional Riv-CR, even with conservative contaminants. However, numerous circumstances are identified under which Riv-CR may outperform both Res-CR and Hyb-CR.

Even after a century of development, the total hydropower potential from undeveloped rivers is still considered to be abundant in the United States. However, unlike evaluating hydropower potential at existing hydropower plants or nonpowered dams, locating a feasible new hydropower plant involves many unknowns; hence, the total undeveloped potential is harder to quantify. In light of the rapid development of multiple national geospatial data sets for topography, hydrology, and environmental characteristics, a merit matrix–based geospatial algorithm is proposed to identify possible hydropower stream reaches for future development. These hydropower stream reaches—sections of natural streams with suitable head, flow, and slope for possible future development—are identified and compared by using three different scenarios. A case study was conducted in the Alabama-Coosa-Tallapoosa and Apalachicola-Chattahoochee-Flint hydrologic subregions. It was found that a merit matrix–based algorithm, which is based on the product of hydraulic head, annual mean flow, and average channel slope, can effectively identify stream reaches with high power density and small surface inundation. These identified stream reaches can then be evaluated for their potential environmental impact, land development cost, and other competing water usage in detailed feasibility studies. Given that the selected data sets are available nationally (at least within the conterminous U.S.), the proposed methodology will have wide applicability across the country.

As utilities move toward more detailed hydraulic and water quality network models, the potential for the stochastic nature of consumptive demands having an effect on transport characteristics increases. A nonhomogeneous Poisson rectangular pulse model (PRPsym) was used to generate stochastic water demands aggregated at 1-min, 10-min, and 1-h time steps for a small skeletonized network and a large all-pipe network; results were then linked with EPANET to simulate the impacts of water-demand variability on the underlying transport and water quality. In general, the impacts of temporal aggregation—as represented by flow rate and concentration variability and arrival times—increased as the evaluations moved from the main trunk lines toward dead-end nodes or pipes. More specifically, analysis of a Monte Carlo ensemble of results demonstrated that (1) a 10-min temporal aggregation captured most of the arrival time variability relative to the 1-min aggregation step; (2) hourly demand variability (e.g., from one day to another) had a greater effect on transport characteristics than the intra-hour variability from temporal aggregation; and (3) in addition to the expected dead-end nodes and loops, transport characteristics within source blending zones were consistently impacted by stochastic demands at smaller temporal aggregations. A sensitivity analysis illustrated the demand variability associated with temporal aggregation was not further affected by the distribution of demand intensity, but the distribution of demand duration, particularly the variance of demand duration, further affected demand variability through the autocorrelation of demands across time steps. Using the sensitivity results to select parameters associated with greater demand variability, another Monte Carlo ensemble was developed for the large network that only slightly increased the spatial extent of nodes within the distribution system that resulted in significant changes in transport characteristics attributable to the effects of spatial aggregation. These results provide additional insight into the magnitude of impact from demand variability (from both modeling aggregation and daily variations) and the spatial extent of such effects on transport characteristics relative to the typical modeling assumptions of utilizing deterministic, 1-h demands.

Water levels in the Great Lakes–St. Lawrence system located in northeastern North America are critically important to the Canadian and U.S. economies. Water managers are concerned that this system, which is currently managed by control structures at the outlets of Lakes Superior and Ontario, is not able to cope with the highly uncertain impacts of climate change. In particular, the frequency of extreme water levels throughout the system might be substantially increased. This study provides an exploratory conceptual analysis to determine the extent that new control structures at the outlet of Lake Huron or Erie (or both) and corresponding excavation along the St. Clair or Niagara River (or both) might mitigate the risks posed by future extreme water supply scenarios. Multilake parametric rule curves were developed to regulate systems enabled with these new control structures as a whole. Multiple stochastic water supply sequences were adopted that represented different future extreme climate scenarios. A multiscenario, multireservoir (multilake), biobjective simulation-optimization methodology was developed to optimize the rule curve parameters in such a way that the risk of experiencing extreme water levels is robustly minimized and fairly distributed across the system. The biobjective setting was designed to embed the secondary objective function so that the cost of the new control structures and excavation generates trade-offs between the costs and the associated achievable risk reductions. The recently developed Pareto Archived Dynamically Dimensioned Search (PA-DDS) multiobjective optimization algorithm was enabled with the efficiency-increasing “deterministic model preemption” strategy and utilized to solve the optimization problem. Results demonstrate that although systemwide regulation with the new control structures could substantially reduce the risk (i.e., by 86% compared to the current or base level of regulation), it could not eliminate such events entirely and would cause adverse effects on the lower St. Lawrence River. Numerical results also suggest that implementing a single new control point at the outlet of Lake Huron would not be effective despite its high cost; however, a single new control point at the outlet of Lake Erie would be very effective, with considerably less associated cost.

Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a Bayesian approach for probabilistic contamination source identification using a beta-binomial conjugate pair framework to identify contaminant source locations and times and compares the performance of this algorithm to previous work based on a Bayes’ rule approach. The proposed algorithm is capable of directly assigning a probability to a potential source location and updating the probability through the use of a backtracking algorithm and Bayesian statistics. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison, as well as a simulation study in terms of a conservative chemical intrusion event through both a small skeletonized network and a large all-pipe distribution system network. Results from the simple comparison showed that the beta-binomial approach was more responsive to changes in sensor signals. In terms of intrusion events, the beta-binomial approach was more selective than the Bayes’ rule approach in identifying potential source node–time pairs and provided the flexibility to account for multiple possible injection locations.

Floods are the most devastating natural disasters and are occurring with increasing frequency and causing losses worldwide. Structural measures have been used as primary tools to control floods and flood losses. Despite devoting considerable resources to structural measures, increasing trends in flood losses and casualties are observed. It is also realized that probability-based design standards are unable to reduce losses efficiently because population dynamics are not taken into consideration. A risk-based approach was introduced in the 1990s in the field of flood management. So far, the implementation of risk-based flood management is lacking due to its inability to provide a uniform standard approach that could be practiced nationwide. The present research provides a risk-based systematic framework for the design of structural measures. The concept of optimum state (OS) is introduced. This framework is based on costs and benefits using expected annual damages (EAD) for the evaluation of flood risk mitigation. EAD addresses the probabilistic nature of flood events and provides risk distributions in the form of EAD curves and EAD distribution maps. The proposed framework supports considering all probable floods instead of a single design flood.

Restoring functional, self-sustaining floodplain habitats requires explicit consideration of the flow dynamics that once created healthy floodplain ecosystems. This paper describes a new method for quantifying the benefits of river projects by linking the spatial and temporal characteristics of floodplains to define the functional habitat they create. Combining standard hydrologic and hydraulic analysis, habitat can be quantified using area-duration-frequency (ADF) curves for several durations and across multiple frequencies of flood occurrence. From this, a value can be created for expected annual habitat (EAH) analogous to expected annual damages (EAD) used in flood risk analysis. To illustrate this method, a modeling case study was conducted on the Lower San Joaquin River in California. The case study shows that a levee setback to restore floodplain connectivity could benefit splittail fish populations but not rearing salmonids. The development of ADF curves and EAH values is a transparent and replicable means to examine the effects, impacts, and benefits of policy and projects on riverine species and ecosystems.