Optimal Location of Infiltration based Best Management Practices for Storm Water management

Pedini-Perez .Christina, Limbrunner.F.James, Vogel.M.Richard(2005). " Optimal Location of Infiltration based Best Management Practices for Storm Water management", Journal of Water Resorces Planning and Management, Vol 131(2005), Pg 441-448.

Summary

 Storm water management in urban areas has become critical in recent times. Development in a watershed results in greater impervious areas which in turn results in greater runoff thus causing flood. Best Management Practices (BMP) are introduced to minimize the effect of watershed development by reducing the peak flow. This paper aims at determining optimal locations of infiltration based BMPs for storm water management by using Genetic Algorithm (GA) in a distributed hydrologic model of an urban watershed. Although detention based water basins are highly effective in controlling peak flows they are expensive to construct and there is no systematic basin wide approach to their implement. This paper deals with infiltration based BMPs which are collectively called as low impact development (LID). To apply BMPs to a watershed it is first modeled as a network of 4553 Hydrologic Response unit (HRU) which can have a BMP. HRUs were modeled as square cells of 120 m. The SCS Curve Number method for calculating runoff is selected to estimate the flow from each HRU unit and it is than directed to the most negative slope. The spatial characteristics of the HRUs like its slope, position and curve number is developed using ARCGIS and the flow connectivity from cell to cell is built using the D8 algorithm. Each HRU drains to a single adjacent HRU and may receive flow from maximum seven upstream HRU’s. The rainfall that falls on a HRU is partly lost as infiltration that enters the ground storage and the rest contributes to runoff that is routed to the adjacent downstream HRU. The flow continues downstream till it enters a stream network. Stream network cell collects water from its adjacent HRU’s and routes it to the watershed outlet with a lag time that is equal to the stream travel time. The BMP is introduced as a binary integer decision variable which says that if a BMP is introduced in a HRU than its curve number will decrease by 5. The model does not consider the type of BMP that is introduced in a particular HRU as a part of the problem. A single type of BMP is introduced in the HRU and it is assumed that implementation costs are same irrespective of land use and surface conditions. This model is calibrated to real time data to estimate values of initial conditions like α1 (scaling term for curve number method) and So ( Storage capacity) and basin parameters like Coefficient of Initial abstraction (α2), fraction of inactive ground water (h) and groundwater outflow rate constant (r). Thus the goal of the optimization model is to locate those HRUs which if BMPs are applied will lead to maximum reduction in peak flow at the watershed outlet. The number of BMPs applied will be equal to the to the total project cost. A GA called Evolver is used to generate different groups of BMP location with the above objective. To minimize the search space the BMPs were restricted to HRUs that had higher curve number and were located further from the stream network. 2629 HRUs were excluded which has marginal effect on peak flow reduction. This model was applied to the Aberjona River watershed a 6400 ha urban catchment located northwest of Boston,Massachusetts.   

Discussions

Project budget reduction and peak flow reduction are considered as separate objectives and a trade off are obtained between the two objectives. A trade off curve was developed which showed the marginal benefits of reduced flood damage as a function of the number of BMPs that are applied. Thus less return is observed in terms of watershed peak flow reduction with increasing number of BMPS applied.  The GA identified four critical regions for our model and as the budget was increased it spread out to other locations which shows that optimal solution are not obtained by merely placing BMPs in HRUs with the greatest impervious areas. The regions in which BMPs have maximum effect coincide with industrial and commercial developments near major highway intersections. The solution generated for a larger budget is not necessarily inclusive of the smaller budget solution but quite near to it. This indicates that the GA may not find the global optimum solution but it helps to identify a wide range of near optimal solutions.  It is important that the solutions be inclusive so that a phase wise implementation of the BMP can be applied as the budget is increased over a period time.An attempt to find the optimal solution of the BMPS without resorting to a distributedhydrologic model and GA algorithm was made. However the relationship between thepeak outflow reduction due to BMP and HRU s characteristics are highly complicatedand dependent upon multiple factors which makes it necessary that we use the above described approach. The approach in this paper is simple and it also makes uses of one of the most commonlyused methods to calculate runoff. This kind of effort can easily be translated into practical uses. The objective is to reduce the peak flow but it also takes care of the budget limits. The solutions for smaller sets are inclusive of the larger set makes it convenient to plan step wise. The BMPs can be applied to critical HRUs first and than when more funds are available BMPs can be applied to less critical areas.