Elias G. Bekele

Evolutionary computation for simulating reservoir release rates of Lake Shelbyville and Carlyle Lake

This paper presents the use of evolutionary computation for simulating reservoir release rates that are central to developing hydrologic reservoir routing models for Lake Shelbyville and Carlyle Lake. The two lakes, which are located in the Kaskaskia River watershed in Illinois, are multipurpose reservoirs operated by the U.S. Army Corps of Engineers to meet flood control, water supply, navigation, recreation, and conservation needs. To date, the reservoir storages apportioned for water supply and navigation needs have not been fully utilized. The objective of the current study is to develop a model for examining the potential impacts of increased water supply and navigation uses on lake levels during severe drought conditions. The reservoirs have their own operating schedules that include ranges of seasonal release rates based on reservoir pool elevations. In this study, storage routing and multi-objective evolutionary algorithms were coupled to simulate reservoir release rates on weekly basis using long-term historical records of daily inflows, storages, reservoir pool elevations and outflows. The simulated weekly release rates for the two lakes complement their respective reservoir routing models and the modified reservoir routing models will ultimately be coupled with previously developed Kaskaskia River watershed model. The resulting coupled model will be able to provide simulations of storages and reservoir pool elevations for the two lakes under varying climate and water use conditions, in addition to simulating flows at different location in the Kaskaskia River watershed. The multiobjective evolutionary algorithm used in this study was effective in simulating the reservoir release rates and its application was instrumental in developing the modified reservoir routing models for Lake Shelbyville and Carlyle Lake.

Modeling the hydrology and hydraulics of the cache river system

The Cache River is located in the extreme southern part of Illinois, just north of the confluence of the Ohio and Mississippi Rivers. Major floods in both of the two rivers have significant impact on the hydrology and hydraulics of the Cache River. In 1915, a cutoff to the Ohio River was constructed east of Karnak that resulted in sub-dividing the Cache River watershed into the Upper and Lower Cache River watersheds. The Upper Cache River watershed consists of the eastern part of the watershed draining directly to the Ohio River through the Post Creek Cutoff. The Lower Cache River watershed consists of the western part of the watershed draining to the Mississippi River through a diversion channel at the outlet. However, part of the Lower Cache River can at times reverse flow, draining to the Upper Cache River and Post Creek Cutoff. Hydrologic and hydraulic models have been developed to simulate the hydrology and hydraulics of the Cache River system to evaluate flow and water level conditions along the river under different alternative scenarios that can be implemented as part of a restoration effort for the Cache River. The hydrologic model that simulates the rainfall-runoff process for tributary watersheds is based on the HEC-HMS model. The model is used to compute runoff from tributary watersheds for selected storm events. Outputs from the HEC-HMS model are then used as inputs to the hydraulic model, UNET. The UNET model, a one-dimensional unsteady-flow dynamic wave routing model, is capable of modeling the complex hydraulics of the Cache River System that experiences flow reversals during flood events in the Lower Cache River.

A decision support system for watershed-scale management of ecosystem services using evolutionary algorithms

Increased production of ecosystem services (e.g., sediment and nutrient load reduction, flood peak reduction, wildlife habitat, and biodiversity) is likely to be achieved through strategic management of agricultural landscapes. This paper explores the role of landscapes in improving ecosystem service generation on a watershed scale. Four ecosystem service related objectives are considered; they involve reduction of flood peaks and sediment, nitrogen and phosphorus loads. The authors have developed a model to identify land uses and management practices that most cost-effectively generate these ecosystem services. Cost effectiveness is integrated by considering resulting impacts to landowner income, interpreted as a fifth objective. The spatial decision support model used to solve the underlying watershed management problem couples the U.S. Department of Agriculture’s Soil and Water Assessment Tool (SWAT) and a fairly new, multi-objective evolutionary algorithm known as SPEA2. Application of the model to the Big Creek watershed, a complex basin located in southern Illinois, demonstrates that it allows the assessment of tradeoffs between competing objectives, namely agricultural commo dity production and generation of ecosystem services. Moreover, it confirms that this integrative modeling approach could enable stakeholders and policy makers in identifying a suitable set of land use and management practices at a watershed scale.