Behera, P, Papa, F., Adams, B (1999) “Optimization of Regional Storm-Water Management Systems” Journal of Water Resources Planning and Management, 125(2) pp. 107-114.
The present article proposes an optimization framework for detention ponds design parameters (storage, release rate and depth). First is developed a framework for a single detention pond. The methodology is expanded to consider a multiple parallel catchment system with one detention pond per catchment. The detention pond is simulated for quantity and quality constrains. The model considers total cost of implementing storm-water ponds for all of the catchments and provides a solution considering system-wide constraints instead of uniform control in all catchments.
The present article proposes an optimization framework for detention ponds design parameters (storage, release rate and depth). First is developed a framework for a single detention pond. The methodology is expanded to consider a multiple parallel catchment system with one detention pond per catchment. The detention pond is simulated for quantity and quality constrains. The model considers total cost of implementing storm-water ponds for all of the catchments and provides a solution considering system-wide constraints instead of uniform control in all catchments.
The objective function minimizes the total costs of implementation and operation of the ponds. These costs are: land-use values, construction, operation, maintenance and repair. Costs of conveying storm-water from the pond to the collector are not included. The decision variables are: active storage volume (mm), controlled release rate from the pond (mm/hour) and pond depth (m). “The goal is to obtain the least total cost associated with the detention ponds in all catchments, while satisfying the desired levels of overall system performances in terms of runoff quantity and quality control.” (Behera et al. 1999).
The quantity constraint is modeled by a simplified formulation that gives the average annual fraction of runoff quantity control. This model has the following parameters: runoff coefficient, rainfall event duration, rainfall event volume, and inter-event time. For different combination of storage volume and release rates, isoquant expressions for average annual fraction of runoff are developed. The quality model is defined as the average annual fraction of suspended solids removed.
The chosen optimization type is dynamic programming (DP), which “transforms the multiple catchment problem into a series of individual catchment problems (stages), then combines the solutions of the smaller problems to obtain the solution or policy of the overall (multiple catchment) problem” (Behera et al. 1999).
The formulation was implemented in a simple example case of three catchments. The overall constraints are achieve 50 % pollution control and 90 % runoff control at the discharge point. The objective function for these control values is: $ 754,072, while if this control values are considered for each catchment the cost would be $ 850,793 (13 % higher).
Discussion
This is one application of optimization techniques that interest me a lot. My course project in CVEN 665 is a detention ponds optimization framework, but we are considering just wet detention ponds as improve urban water quality. Behera et al. have analyzed both quality and quantity issues. The proposed framework is very relevant since it can help developers and municipalities to better plan their storm-water control systems.
Some issues are unclear to me. The runoff modeling is not explained in details. For example, we know that annual average values may not capture in details the runoff and quality control processes. Other meteorological variables, as evaporation are not considered. Usually is not a substantial for small detention ponds, but can be relevant for large storages. Besides that, the framework considers just suspended solids removal.
Future applications could analyze nutrients removal (nitrogen and phosphorus for example).
This specific formulation was implemented in computer spreadsheet. Future larger applications may be difficult to implement in this kind of computer environment.
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