Journal of Water Resources Planning and Management

This study addresses questions related to flood-control operating procedures followed by the U.S. Army Corps of Engineers, Rock Island District. Application is presented of a mixed integer linear programming model for a reservoir system analysis of three U.S. Army Corps of Engineers' projects on the Iowa and Des Moines rivers. A strategy for evaluating the value of coordinated reservoir operations is developed. Results of this study suggest that operating Coralville Reservoir, on the Iowa River, for flood control on the Mississippi River does not provide appreciable benefits and, therefore, an operation plan coordinating releases from Coralville Reservoir with the two reservoirs on the Des Moines River may be unnecessary. Damage-minimizing results were obtained by operating the three reservoirs independently for 8 of the 10 largest flood events on record. Also, a review of the operating procedures for the flood of 1993 illustrates how much damage could have been reduced if inflows could be predicted months in advance or if the existing operating rules were more averse to extreme flood events.

There often is difficulty enforcing the given constraints when applying a genetic algorithm (a flexible stochastic search method) to optimal ground-water remediation design problems. This paper compares two methods for constraint handling within the genetic algorithm framework. The first method, the additive penalty method (APM), is a commonly used penalty function approach in which a penalty cost proportional to the total constraints violation is added to the objective function. The second method, the multiplicative penalty method (MPM), multiplies the objective function by a factor proportional to the total constraints violation. The APM and MPM, using constant and generation-varying constraint weights, are applied to two pump-and-treat design examples. Overall, the application of the APM resulted in infeasible solutions with small-to-moderate total constraints violations. With the MPM, a set of feasible and near-optimal policies was readily identified for both examples. Additionally, the MPM converges to the solution faster than the APM. These results demonstrate that the MPM is a robust method, capable of finding feasible and optimal or near-optimal solutions while using a range of weights.

An hydroelectric power system known as the Gabcikovo Barrage System was completed on a section of the Danube River between Hungary and Slovakia in 1996. Originally, the utilization of the river for hydropower production in the common reach was a joint goal of the two countries. Hungary voided the contract in 1989 citing potential adverse environmental effects of the project and Slovakia finished the project alone. To put the Gabcikovo Hydropower Plant into operation, the Danube River was diverted into a power channel in Slovakia in 1992. This paper presents the environmental impacts of the Gabcikovo Barrage System in the 6 years since the diversion, as detected and measured by Landsat satellite remote sensing imagery. The study area was limited to a part of the Szigetköz (Hungarian side) and Csallokoz (Slovakian side) region located between flood dikes. This is a unique wetland system with numerous river branches immediately downstream from the dam. The study concluded that a number of important, but limited, environmental impacts are detectable from the satellite imagery and so the imagery provides a suitable means of monitoring future changes in the region. This case study is the only known application of satellite imagery for assessment of the environmental effects of a diversion of a major river.

We present a new methodology for performing Virginia's statewide, watershed-level non-point-source pollution assessment. The problems and inconsistencies inherent in previous methodologies were addressed and improved upon. The revised methodology utilizes geographic information system technology, a database management system, and computer modeling. Statewide land cover, soils, and topographic digital data were used to develop input parameters for a simple nutrient and sediment loading model, resulting in an assessment based on estimated loadings for each of the predefined 493 hydrologic units in the state. The resulting model output includes runoff volume, sediment loading, and dissolved and sediment-attached nitrogen and phosphorus. The estimated loads were used to rank the hydrologic units for potential non-point-source pollution and compared with rankings from a previous assessment. The revised methodology is based on annual pollutant loadings, and no attempts were made to “weight” or subjectively impose importance to sources or constituents as with the previous methodology. The new methodology, resulting watershed priorities, and recommendations for further refinement are presented in this paper.

Prediction of watershed runoff resulting from precipitation events is of great interest to hydrologists. The nonlinear response of a watershed (in terms of runoff) to rainfall events makes the problem very complicated. In addition, spatial heterogeneity of various physical and geomorphological properties of a watershed cannot be easily represented in physical models. In this study, artificial neural networks (ANNs) were utilized for predicting runoff over three medium-sized watersheds in Kansas. The performances of ANNs possessing different architectures and recurrent neural networks were evaluated by comparisons with other empirical approaches. Monthly precipitation and temperature formed the inputs, and monthly average runoff was chosen as the output. The issues of overtraining and influence of derived inputs were addressed. It appears that a direct use of feedforward neural networks without time-delayed input may not provide a significant improvement over other regression techniques. However, inclusion of feedback with recurrent neural networks generally resulted in better performance.

A dynamic model of floodplain management is shown to address nonstationary conditions, including land-use changes, channel modifications, economic development, and climate change and variability. The dynamic approach permits zoning, levee construction, and other decisions to be made sequentially, rather than only at the present. The dynamic model is formulated as a Markov decision process. A single-floodplain, single-objective, stationary model is extended to include multiple floodplains, nonstationarity, and multiple objectives. Linear programming is used for solution, though the problem may be large. The model is applied to a problem at Chester Creek, Pa. The optimal policy for levee building or replacement is found to depend on if a flood has just occurred and on the costs of buying out property owners and rebuilding homes and levees. Two cases of nonstationarity are examined, future bridge construction and future hydrologic changes. With nonstationarity, buyout of property owners following levee overtopping is an optimal policy since increased future flooding reduces the expected benefits of structural flood measures. When economic development is included with management costs and flood damages in a multiobjective formulation, the optimal policies include building larger levees and increasing floodplain development.