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.

Reservoir refill operation modeling attempts to maximize a set of benefits while minimizing risks. The benefits and risks can be in opposition to each other, such as having enough water for hydropower generation while leaving enough room for flood protection. In addition to multiple objects, the uncertainty of streamflow can make decision making difficult. This paper develops a stochastic optimization model for reservoir refill operation with the objective of maximizing the expected synthesized energy production for a cascade system of hydropower stations while considering flood risk. Streamflow uncertainty is addressed by discretized streamflow scenarios and flood risk is controlled by a joint chance constraint restricting the occurrence probability. With the variability of flood risk level, two advancing refill scenarios for exploring operation benefit are presented. Scenario I loosens the current stagewise storage bounds conditions and allows advancing reservoir refills but keeps the flood risk level the same as the refill policies obtained under the current storage bounds. Scenario II keeps the current storage bounds unchanged but allows increases in flood risk level. The proposed methodology is applied to the Xiluodu cascade system of reservoirs in China and investigates the optimal refill policies obtained by both scenarios. Compared with the benchmark obtained under the current storage bounds and lowest flood risk level, the results show (1) the synthesized energy production can be improved by 2.13% without changing the flood risk level under Scenario I, and (2) the synthesized energy production can also be increased by 0.21% at the expense of increasing the flood risk level by 4.4% when Scenario II is employed. As Scenario I produces higher benefit and lower risk than Scenario II, it is recommended to loosen the current stagewise storage bounds but to keep the flood risk level unchanged during refill operations.

Water distribution system (WDS) pipe networks can vary widely in topological layout. The variations in branch and loop combinatorics across large pipe network data sets are ideal for exploring graph-based structural patterns and linkages with engineered performance. To facilitate this exploration, a library of 10,001 lattice-like pipe networks is developed. Each network is equipped with hydraulic, water quality, and diurnal demand information for extended period simulations. Results show strong correlations exist among graph theory metrics (e.g., geodesic, spectral, and combinatorics-based) and performance measures (e.g., maximum hourly unit headloss, average water age, and average chemical concentration). These trends are extended with performance predictions using parametric and semiparametric statistical models. Predictive accuracies are evident for processes that follow diffusion-like behaviors, indicating coupled applications of graph theory and statistical methods may have strong abilities to characterize WDSs at lower computational and analytical costs. Evidence also reveals features from percolation theory, with sharp phase transitions in WDS performance at certain probabilistic thresholds of pipe network connectivity. This may support future probabilistic studies on WDS performance with percolation-like transitions.

This paper presents a collaborative framework to couple historical records with expert knowledge and criteria to define a decision support system (DSS) that supports the seasonal operation of the reservoirs of the Jucar River system. The framework relies on the codevelopment of a DSS tool that is able to explicitly reproduce the decision-making processes and criteria considered by the system operators. Fuzzy logic is used to derive the implicit operating rules followed by the managers, and is based on historical decisions and expert knowledge obtained in the codevelopment process, combining both sources of information. Fuzzy regression is used to forecast future inflows based on the meteorological and hydrological variables considered by the system operators in their decisions on reservoir operation. The DSS was validated against historical records. The developed framework and tools offer the system operators a way to predefine a set of feasible ex ante management decisions, and to explore the consequences associated with any single choice. In contrast with other approaches, the fuzzy-based method used in this study is able to embed inflow uncertainty and its effects in the definition of the decisions on the system operation. Furthermore, the method is flexible enough to be applied to other water resource systems.

This study applies a recently developed evolutionary algorithm (EA) called state of matter search (SMS) to minimize the total energy deficit in the Karun4 reservoir, Iran. The operation of the Karun4 reservoir is influenced by several factors, which requires a multiple criteria framework for selecting the most suitable solution EA. Five EAs, in addition to the SMS, were evaluated for the reservoir operation problem on the basis of four performance criteria and the fitness function (FF) value. The priority assessment on the basis of FF value revealed that the SMS outperformed the other EAs in converging to the optimal solution. However, judged by the other four performance criteria, based on weighted aggregates sum product assessment (WASPAS) technique, particle swarm optimization (PSO) proved superior to the other EAs. This paper’s results show that the selection of a solution EA for solving complex reservoir optimization problem requires a multicriteria decision-making process. Multiobjective evolutionary algorithms (MOEAs) are well-suited for the task.

Flood resilience is a desirable state of natural and built environments in dealing with hazards such as extreme rainfalls, storm surges, and hurricanes. Here, resilience means the capacity to reach an acceptable level of functionality during and after a flood. Observations of the recent history of flood disasters in New Orleans and New York City (NYC) have suggested the necessity of quantifying resilience in these regions utilizing a floodplain’s vulnerability assessment platform. Once a meaningful and well-recognized method is developed, then resources can be allocated to increase resiliency. In this study, redundancy, resourcefulness, robustness, and rapidity metrics based on socioeconomic, manmade, and natural conditions are developed to quantify coastal resilience. Exposure and susceptibility are also defined and evaluated for quantifying vulnerability. The proposed evaluation method, which is based on the linear combination of the developed metrics, uses a multicriteria decision making technique for ranking the factors of the system and their weights. Sensitivity and uncertainty analyses are performed to determine the sensitivity of the developed platform to 54 different factors affecting the system’s vulnerability and the uncertainty associated with estimating the representative values and weights of those factors. The proposed platform allows identifying those factors that have the most significant impact on resiliency. As a result, resource allocation could be directed to strengthen those factors rather than ad hoc allocations without consideration of their overall impact on building a more resilient region. Application of the proposed scheme is investigated using a coastal area in the Bronx Borough of NYC. The proposed algorithm can be applied to other coastal areas in identifying effective factors on their system’s flood resiliency.

Stormwater low-impact development (LID) has become an important method for the reduction of stormwater runoff emanating from impervious surfaces to local water bodies. The extra runoff from human-made impervious surfaces overloads water bodies with polluted runoff. Since many LID best management practices (BMPs) exist, and many areas have multiple subcatchments, it is important to carefully select which BMPs to place in each subcatchment and its placement in order to minimize cost, but also to give the highest chance of owner BMP maintenance. LID BMPs are often constructed on private land and maintenance can be at the owner’s expense and, therefore, not guaranteed. A new algorithm, entitled multi-objective, socio-economic, boundary-emanating, nearest distance (MOSEBEND), that is easily used by practitioners is introduced and demonstrated here for a road in a community showing that the algorithm allows the selection of the set of BMPs on the various subcatchments with the lowest cost, highest runoff restoration to natural values, and highest likelihood of private-owner maintenance. The BMP selection and location starts at the downstream watershed boundary and propagates upstream, and then works in the upstream-to-downstream direction from the upstream watershed boundary. The multiple objectives addressed are cost-benefit ratio (CBR) and maintenance probability factor (MPF). The optimal solution is considered to be the one closest to the origin on a plot of CBR and MPF. It was found that the new algorithm resulted in a solution that is (1) low cost, (2) high benefit, and (3) more likely to be built and maintained than an analysis of any subset of these three objectives. The option with the highest cost-benefit-ratio is that in which LID BMPs are implemented in the upper and steeper portion of the catchment, for the watershed that the algorithm is demonstrated herein. However, that option has a less likely chance to be maintained and, therefore, is not ranked first by the MOSEBEND algorithm proposed here.