Optimizing the operation of a multireservoir system is challenging due to the high dimension of the decision variables that lead to a large and complex search space. A spectral optimization model (SOM), which transforms the decision variables from time domain to frequency domain, is proposed to reduce the dimensionality. The SOM couples a spectral dimensionality‐reduction method called Karhunen‐Loeve (KL) expansion within the routine of Nondominated Sorting Genetic Algorithm (NSGA‐II). The KL expansion is used to represent the decision variables as a series of terms that are deterministic orthogonal functions with undetermined coefficients. The KL expansion can be truncated into fewer significant terms, and consequently, fewer coefficients by a predetermined number. During optimization, operators of the NSGA‐II (e.g., crossover) are conducted only on the coefficients of the KL expansion rather than the large number of decision variables, significantly reducing the search space. The SOM is applied to the short‐term operation of a 10‐reservoir system in the Columbia River of the United States. Two scenarios are considered herein, the first with 140 decision variables and the second with 3360 decision variables. The hypervolume index is used to evaluate the optimization performance in terms of convergence and diversity. The evaluation of optimization performance is conducted for both conventional optimization model (i.e., NSGA‐II without KL) and the SOM with different number of KL terms. The results show that the number of decision variables can be greatly reduced in the SOM to achieve a similar or better performance compared to the conventional optimization model. For the scenario with 140 decision variables, the optimal performance of the SOM model is found with six KL terms. For the scenario with 3360 decision variables, the optimal performance of the SOM model is obtained with 11 KL terms.

In this study, we assess the costs and benefits of dynamic management of water storage to improve flood control in a system of wetlands. This management involves releasing water from wetlands ahead of (e.g., a few hours or days before) a rainfall event that is forecasted to produce flooding. Each project site may present different challenges and topographical conditions, however as long as there is a relatively small hydraulic gradient between the wetland water surface and the drainage ditch (e.g., >0.9 m), wetlands can be engineered for the purpose of flood control. We present a case study for a system comprised of four wetland areas encompassing 925 acres in the coastal plain south of Houston, Texas. The benefit–cost analysis shows that, in general, the benefits of wetland ecosystems far surpass the costs of construction and maintenance for all considered periods of analysis and assumed degrees of dynamic management of wetland storage. The analysis also shows that the benefit/cost ratios increase over the period of analysis. Considering flood protection only (e.g., not considering the value of other ecosystem services), as long as dynamic management of wetland storage increases flood protection by about 50% compared to that with no management (e.g., a typical wetland with no controlled release of water), the construction of a wetland system would have a benefit/cost ratio of at least 1.9.