Are Best-Management-Practice really environmentally friendly?

Article Review

Roesner, L.A., Bledsoe, B.P., Brashear, R.W. (2001) “Are Best-Management-Practice really environmentally friendly?” Journal of Water Resources Planning and Management, v. 127, no. 3 (May/June).

This article examines the argument that best-management-pratice (BMP) doesn’t not protect urban streams properly. The definition of BMP is strategies to control of urban runoff, usually store structures as detention basins. The authors defend that the BMP is not the problem in itself, but rather the design guidance for BMP outlet flow control that doesn’t take into account the geomorphologic characteristics of the receiver stream.

The authors present a description of the impacts of urbanization in the hydrology of urban watershed. Some numbers illustrate the changes: the peak flow can increase by 2 to more than 10 in terms of magnitude. Also frequency of two-year peak runoff rate in the predevelopment state occurs three to eight times per year after urbanization. The effects of this changes cause severe stream channel erosion and downstream flooding. In terms of water quality, the effects of urban runoff also harm aquatic habitats.

Considering the increase of runoff, many municipalities require big storms to be controlled, in order that the post-development peak flow does not exceed the pre-development discharge. Common recurrence times are 10 and 25 years. Few locals control the 2-year storm. The article states that while detention basins reduce flooding, they are not effective in reducing erosion. One reason is due the sustained flow that detention ponds induce downstream. The outlet flow is prolonged, subjecting the stream channel for a longer period of time. The second reason is the basin only attenuates the flow of the design storm. Attenuation of different storms is coincidental. Considering that urbanization increases the frequency of pre-development peakflow and basins are designed to regulate storms higher than the 10 or 25 year-storms, most storms will pass through the structure unregulated, subjecting the downstream channel to erosive velocities more frequently.

For correcting the problem, the writers believe that “integrating the design of storage and outlet controls for flood control and BMPs it is possible to better control wet weather discharges to receiving waters over the entire range of flows that the facility experiences.” The question that stormwater managers should ask is: “given the potential flow of sediment and water out of the watershed, what are our options for achieving desired social and biological endpoints?”

The article proposes a two step approach to link biological indices to watershed parameters. The first step is to develop a relationship between stream geomorphologic characteristics and biologic indicators. The second step is to develop a relationship between watershed hydrogeometric parameters and stream geomorphologic parameters. It is believed that with this approach is possible to design holistic runoff control facilities into urban development so both flood protection and maintenance of ecosystem can be achieved.

Blog 10 - Generating Alternatives

Article Review

Pan TC, Kao JJ (2009) GA-QP Model to Optimize Sewer System Design, JOURNAL OF ENVIRONMENTAL ENGINEERING, 135(1) 17-24

Pan and Kao implemented a Genetic-Algorithm (GA) and Quadratic Programming (QP) to generate alternative to the problem of designing sanitary sewer systems. This approach is proposed in order to reduce excessive computational effort presented by other optimization techniques. An alternative method is discrete differential dynamic programming (DDDP). DDDP, however, restricts the search space decreasing the chances of locating global optimum. Also, DDDP must be manually implemented, difficult to generalize to different systems. Another approach would be a GA-LP (linear programming) model, but linearization of nonlinear functions is complex.

The nice contribution of this work is the recognition that some important design factors and unquantifiable issues (public preference, for example) are not included within the optimization model. So, “the solution obtained from existing sewer design models may not be the best one or even feasible when unmodeled factors or issues are simultaneously evaluated”. Therefore, a model to generate alternatives (MGA) has been developed to evaluate the differences among the designs generated by the GA-QP and DDDP models.

Discussion

This article presents a nice application of generating alternatives, proposed in the previous article by Brill. Specifically to the design of the sewer system, the decision variables considered were slopes and buried depths. However, in real projects, there are a lot of “more important” aspects that may be subject to change by the decision-makers. For example, the network topology. Also, geology is a very important aspect in economic viability.

Assignment # 10 - Alternatives

Article Review

BRILL ED (1979) USE OF OPTIMIZATION MODELS IN PUBLIC-SECTOR PLANNING, MANAGEMENT SCIENCE, 25.

According to the article, traditional optimization and multiobjective models fail in obtaining the best solution for public-sector planning. The limitations of using optimization models are failure in considering equity and difficulties in estimating benefits and costs. The author states that even a multiobjective approach, which should capture different perspectives of a problem, has practical limitations. In many problems, indifference curves are likely to reflect local optima. In a public planning problem, there might be issues not considered, which once introduced in the mathematical framework, likely bring the optimal to the inferior region of the original problem.

The article question which uses should optimization model be designed? There are set of problems that are inherently political in essence, which increase the complexity. In the second post I have addressed the article of Liebman (1978), which focus on “wicked” problems. Based on that, the authors argue that models should be used in a different way it has been used so far (1979).

The main idea the articles brings is that new types of optimization tools should be designed specifically to generate alternative plans and alternatives. To better understand the problem and generate alternatives is important creativity and inventiveness. Ways of using optimization models in a planning process are discussed in more detail in the article, like the use of optimization and simulation models. For this article, simulation model means examining a specific design in details. The optimization model identifies preliminary alternatives and the simulation model examines the solutions in more details. Use of analytical model and optimization models are also cited. The authors emphasize that there are a lot of gains in understanding the problem and feasible space in using a tool box of models.

The article comes up with the question: “optimization models should be capable of generating alternative solutions that: (1) meet minimal requirements, and (2) are different”? The question related to minimal requirements depends on the formulation of objective function and constraints. The issue of how different the solutions are is more difficult. A random sampling approach is suggested as an alternative method.

Discussion

The article brilliantly addresses how optimization problems should be used in order to effectively help the decision making process in the field of public planning. In real life, there will always be factors that cannot be incorporated in the optimization model, so generating multiple alternatives to be evaluated by decision-makers is a good idea. Some question, however, still remains: Brill says that a good set of alternatives should meet minimal requirements and be different. But how evaluate how different solutions are? He proposes an optimization approach to maximize the differences among solutions, but still this approach doesn’t contain all the aspects related to the decision-making process. I mean that two good solutions may be very similar in terms of modeling, but may contain completely different weights when evaluated by society. Or, two solutions may be different by the modeling, but for the decision-making process the solutions may be similar when intangible and incommensurable criteria are used.

Assignment # 9 - Multiobjective optmization

Shiau JT, Wu FC (2006) Compromise programming methodology for determining instream flow under multiobjective water allocation criteria, JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 42(5), pp. 1179-1191.

Instream flow is one of the most challenging issues in water resources systems. Using a multiobjective optimization criterion, this study implements a framework to allocate water for three different uses: agriculture, urban and instream flow. The methodology considered three objectives: overall degree of hydrological alteration, shortage ratio for agriculture withdrawals, and shortage ratio for municipal uses. The degree of hydrological alteration was assessed using the Range of Variability Approach (RVA), which use 32 indicators of hydrological alteration (IHA) to evaluate hydrological impacts. The decision variable is instream flow release, considered to be constant during time.

The methodology was applied in Kaoping watershed in southern Taiwan. A weir operation diverts a maximum of 35 m³/s for municipal uses but more than that is also diverted to irrigation districts.

Results indicate that actual 9.5 m³/s instream flow is not enough to restore the natural flows variability. Weighting the three objective functions equally, optimal instream flow discharge of 26 m³/s could reduce hydrological alterations from a high to moderate state. A discharge of 93 m³/s would bring the hydrological regime to a low alteration state.

Discussion

This paper presents a very important study for water resources management. It combines shortage ratios and hydrological alteration statistics to perform a multiobjective optimization. The degree of hydrological alteration considered a kind of average value of the 32 IHA to represent the overall alteration. Future improvements could consider each of 32 indicators or the five groups of IHA. Considering variable instream flow releases would also be a interesting application.

Assignment # 8

Article Review

Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 126(2) pp. 57-64

Neelakantan and Pundarikanthan address the problem of optimization for reservoir operation. Theoretically the modeling framework is well known. However, this simulation can be very time consuming. This article focuses on “developing a planning model for reservoir operation that uses a simulation-optimization approach”. The study implemented a backpropagation neural network to approximate the simulation model developed. This methodology was applied for an urban water distribution reservoir in Chennai, India. The particular study case seems to be very relevant since it has a high demand and serious hydrologic conditions which makes shortfalls frequent.

The entire framework comprises four stages. The first stage is the training of a backpropagation neural network, which is used to simulate reservoir operation. The second stage links the neural network model as a submodel with Hooke and Jeeves nonlinear optimization programming model, in order to identify operation policies. In the third stage, good operation policies are selected to be refined using the conventional simulation-optimization model.

Two scenarios were studied. Scenario 1 considered the existing reservoirs and the optimal operation was compared with the standard operation policy. Two proposed reservoirs were considered in Scenario 2. With respect of overall deficit index, the addition of proposed reservoirs don’t improve the system performance, although it reduces losses due spills.

Discussion

The article presented an interesting application of neural network in water resources systems. The study case seems to be relevant and highly representative of others systems in developing countries. The conclusion that addition of two reservoirs doesn’t improve the overall performance of the system seems in a first view very surprising. This conclusion is more relevant for developing countries that face difficult financial conditions.

Assignment # 7

Article Review

Perez-Pedini C, Limbrunner JF, Vogel RM (2005) “Optimal location of infiltration-based best management practices for storm water management,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 131(6) pp. 441-448

The article presents a very interesting application of optimization techniques to study location of BMPs and its impacts of storm water management. A genetic algorithm was used to determine the optimal location of infiltration-based BMP’s. A distributed hydrologic model was used to simulate a 6400 ha watershed. The components of hydrological cycle considered in the simulation were precipitation, runoff, infiltration and groundwater flow. The rainfall-runoff separation method was the SCS-Curve Number. The BMP was introduced in the model by a binary integer decision variable that decreases the CN of a hydrological response unit (HFR) in five units.

Discussion

Under the perspective of storm water management and planning, the article presents a very useful tool. However, some issues related to the methodology seems unclear to me. The first one is related to how the infiltration BMP was modeled. Reduction of CN in five units could also be achieved by other kinds of measurements, like land use change, for example. Besides that, there are different kinds of infiltration BMPS (infiltration basins, trenches, pervious pavements etc) with different characteristics, efficiencies and costs. The article doesn’t address these issues properly in my opinion.

Assignment # 6 - Optimization of storm-water detention ponds system

Article Review

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 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.