Summary:
The authors of this article utilized computer modeling and genetic algorithms to attempt to determine ways to locate Best Management Practices (BMPs) within a watershed to most effectively reduce overall peak flows at the basin outlet. The BMPs are considered that cause greater infiltration at certain points within the watershed (and thus reducing the flow available for peak flow). They are meant to be an extension of traditional detention basins in the watershed. The analysis process consisted of first developing a watershed model, calibrating the model (through various calibration parameters) for a specific watershed based upon actual storms that occurred, application of a genetic algorithm to determine which areas in the watershed could implement a BMP that could best reduce the peak runoff (based upon a specified number of BMPs to implement in the system), and then further analysis on the results to determine managment strategies and statistical similarities between inputs and response.
In the model formulation, the authors utilized a system where the watershed was broken into hydrologic response units (HRUs) that are sized as 120m squares aligned as a grid throughout the watershed. Each HRU receives the runoff, allows for initial infiltration into the soil, allows for transport of the available runoff to one downstream HRU (based on the greatest elevation change), has groundwater storage, and allows for the transfer of groundwater via underground transport to adjacent HRUs. Additionally, streams are located throughout the watershed such that the runoff entering an HRU designated as a stream location is collected and moved to the outlet of the basin as a faster rate. After applying the mathematical model, they imported a watershed to Excel via ArcGIS and then calibrated the various model parameters to meet the outflow from specific storms.
The genetic algorithm programming approach was applied for the system based upon some limitations. The watershed had over 4,000 HRUs, and each potentially could have a BMP in place; however, allowing all of these to be availabile as decision variables resulted in a model that had too large of a decision space for the GA too be applied effectively, and thus, they limited the potential BMP locations to 1) the most impervious (highest Curve Number) and 2) the closest HRUs to the streams (as they have the most effect on the initial peak flow at the basin outlet). They ran the simulations by assuming a certain number of BMPs would be in place (and then determining which locations would result in the minimum outflow runoff). They eventually found that after implementing about 25 to 100 BMPs, the rate of flow loss for adding additional BMPs was decreasing.
Further analysis on the system was performed to determine some information about management practices. The central question at this stage was to determine if the watershed should be planned on a master planning level such that initial projects be completed that may not have the greatest impact on the overall outlet flow (if implementing another combination of BMPs in the future would then result in a global minimum being able to be found later on). The results showed that this was not necessary—the best solution would be to choose the most effective BMPs initially and then not worry about which locations would be best in the future. Additionally, the authors were unable to find statisical relationships between relevant parameters (such as location upstream from outlet, etc.) that would provide an optimum solution; thus, the authors demonstrated that a GA application is really necessary for this application.
Discussion:
I thought the authors did a very good job of explaining their methodology. Overall, I think they used a sound methodology to determine a very complex question. I think that the following points are relevant for discussion:
1) In formulating the model, the authors ignored the type of BMP and just assumed that having a BMP would reduce the curve number by 5. I'm not sure if this is really a good assumption as different locations could naturally result in a better potential for different BMPs to have greater or lesser effects. What if a BMP just could not be put in place in one of the "optimum" locations--particularly if only a few BMPs (25 or so) would be put in place.
2) Is the model developed physically accurate for the system? The ability of the calibration to meet the example storm appears to indicate that the model can work, but I still wonder about some of the assumptions made in the formulation process--sometimes this is just hard to follow.
3) How valid is the conclusion that the master plan does not need to be considered? From their writeup and model, it seems valid to me; however, question 1 seems to come into play. Given some of their assumptions, if they were made differently, would the respone prove different and thus nullify their results?
4) Are the parameters used to limit the potential number of sites for BMPs valid? They basically reduced the amount of potential BMP sites in half. Logically, the parameters they used seem to make sense, but you could wonder if this is necessarily appropriate.
