In an urban watershed, the first flush effect (FFE) is one of the biggest issues facing the urban environment. This indicates a greater unloading of the load from the early part in the rainfall event. However, these practices require various monitoring and modeling approaches to identify characteristics and purpose of LID.
INTRODUCTION 1.1 Background and objective
Contents of each chapter
This thesis described research studies that focused on model development to improve the LID simulation and optimization to design the optimal LID scheme. In the model development of LID, the first new model is about improving hydrological simulation in LID, and the second new model is about improving water quality simulation in LID. The first optimization process focused on a small scale consisting of a single urban land use, and the second was conducted to optimize LID at a large scale consisting of multiple land uses, including urban catchments.
Both developed models were evaluated using monitoring data in a single lid and performed scenarios analysis in an urban area. It is important to apply the scenario analysis in a real urban area because the original and new LID model generated different data from the LID effect to the urban area (Elliott and Trowsdale, 2007). In the optimization of LID, there are two different research results depending on the urban scales.
A novel water quality module of the SWMM model for assessing Low Impact Development (LID) in Urban watersheds
Therefore, the different optimization approach depending on whether the watershed is small or large is needed to increase the effectiveness of LID in managing runoff and water quality.
Conclusion remark
Literature review 2.1 Low Impact Development (LID)
2013) applied the SWMM model to calculate the flood reduction effects of low-impact development techniques. The EPA SWMM model is the most popular model to simulate low-impact development techniques. The following equation is the water quality simulation of low impact development in EPA SWMM.
Assessment of a green roof practice using the coupled SWMM and HYDRUS models
- Introduction
- Materials and methods .1 Study sites
- SWMM-H
- Conclusions
The parameters used in the LID modules of the SWMM and SWMM-H models were optimized using the data collected from the pilot-scale green roof system (see figure). The RMSE values of the SWMM were two times higher than those of the SWMM-H Calibrated flow-related parameters of the SWMM and SWMM-H in the urban sub-basin Parameter description (James and Huber, 2003) Value.
Comparison of the total runoff calculated by the SWMM and SWMM-H, taking into account the size and soil type of the green roof (the base scenario indicated the scenario without installation of a green roof). We evaluated the performance of the LID modules of the SWMM-H and SWMM for simulating soil moisture and runoff volume.
- Introduction
- Materials and methods .1. Study sites
These extreme weather conditions cause droughts and severe floods, which can transport pollutants (Hirabayashi et al., 2013). Therefore, it is important to improve how urban management sectors address water-related challenges, especially accelerated pollutant generation and urban runoff in the environmental cycle (Barbosa et al., 2012; Eshtawi et al., 2016; Li and Wang , 2009). In the past decade, low impact development (LID) practices have been proposed to provide better hydrological and environmental functions (Elliott and Trowsdale, 2007; Hunt et al., 2008).
Before installing LID, it is difficult to identify the effectiveness of LID in terms of how it maintains hydrological and environmental functions even after urban development (Ahiablame et al., 2012a; Baek et al., 2017). Although LID practices have many advantages in the urban environment, the management and installation of LID structures are often expensive (Elliott and Trowsdale, 2007; Brunetti et al., 2017). Therefore, governments and research groups established guidelines for the efficient implementation of LID through modeling approaches, which are useful in evaluating the performance of LID with different urban management scenarios (Prez-Pedini et al., 2005; Elliott and Trowsdale, 2007).
LID simulation includes a surface layer, soil layer, and storage layer that have different hydrological behaviors (Qin et al., 2013). The sensitivity analysis of the model was performed using elementary effects (EEs) analysis (Morris et al., 1994). This method has been established as an effective approach in terms of quantifying the effect of the interactions between parameters of non-linear models (Campolongo and Braddock, 1999; Saltelli et al, 2008; Morris et al, 2014).
In this study, we performed sensitivity analysis of EE using the SAFE (Sensitivity Analysis for All) toolbox in MATLAB (Pianosi et al.
Bioretention 2 Infiltration trench
- Results and Discussion 4.3.1 Model sensitivity analysis
- Conclusions
These measures were used to quantitatively describe the accuracy of the model (Moriasi et al., 2007). On the other hand, the results of the infiltration trench were relatively close to the observed values although it shows a slightly exaggerated simulation. Moreover, the 𝐷𝑚𝑖𝑥 of the infiltration trench was much smaller than that of bioretention covers.
Both historical scenarios and RCP 8.5 for the 2-year return period generated similar trends and values of the total volume and peak flow for all cover sizes. Meanwhile, RCP 8.5 generated higher values of the total volume and peak flow than those of the historical scenarios for the 5-year and 10-year return periods. In contrast to the result of the total volume and peak flow, the total load and peak load in the base case showed a negligible difference between historical scenarios and RCP 8.5.
The results of the water quality simulations of LID revealed that the total and peak load of the historical and RCP 8.5 scenarios did not show significant differences. The bioretention 1 and 2 had a greater reduction effect for the total load and peak load compared to the relatively weak reduction effect of the infiltration ditch. These differences were due to the bioretention covers having higher values of the soil mixing zone thickness and the attachment rates of pollutants in the soil (Table 4.3).
The total load and the peak load of bioretention 1 and 2 decreased as the Huff quartile number and the proportion of LID increased.
Optimizing Low Impact Development (LID) for Stormwater Runoff Treatment in Urban Area, Korea: Experimental and Modeling Approach
- Introduction
- Material and Methods
- Results and discussion
- Conclusion
Low Impact Development should be designed more effectively by optimizing the removal efficiency for the first part of the runoff (Kang et al., 2008). Also Li et al. 2010) emphasized first flush (FF) treatment for effective stormwater management, instead of treating only the total water volume. We performed sensitivity analyzes using the Latin Hypercube-One-factor-At-a-Time (LH-OAT) method ( Van Griensven et al., 2006 ).
Next, we ranked the parameters from the sensitivity analysis according to their efficiency to calibrate the model (Cho et al., 2012) (Fig. A sensitivity analysis was applied to identify sensitive parameters for effective model calibration (Park et al., 2014 ).For FFE quantification, Ma et al., 2002) suggested the concept of mass initiation ratio (MFF).
These values are greater than 0.5 and can be considered as acceptable performance (Moriasi et al., 2007). Using a green roof to reduce suspended solids, A~D: SS load (blue circles) and EMC (red producers) for 1st ~ 4th quartile, E~H: FF curves for 1st ~ 4th quartile where red line (ie 45°). .line) indicates that pollutants are uniformly distributed (Verdaguer et al., 2014), and the blue line is the FF curve before LID application. Using pore pressure to reduce suspended solids, A~D: SS load (blue circles) and EMC (red creators) for 1st ~ 4th quartile, E~H: FF curves for 1st ~ 4th quartile where red line (ie 45°). line) means that the pollutants are uniformly distributed (Verdaguer et al., 2014), and the blue line is the FF curve before applying LID.
Porous pavement is characterized by a pavement layer that allows infiltration of rainwater (Nichols et al., 2015).
Developing a Hydrological Simulation Tool to Design Bioretention in a watershed
- Conclusion
There are few studies on long-term LID simulations and optimization of LID size and position in the watershed, which some researchers consider an important part of LID installation (Ahiablame et al., 2012a). The Korea Low Impact Development Model (K-LIDM) is a decision-making tool based on the World Hydrological Model Version 4 (WWHM4) (Beyerlein, 2011) and the Hydrological Simulation Program - Fortran (HSPF) (Bicknel et al., 2001). ). These two processes can be simulated in different layers by the following processes (Figure 6.3): (1) The rate of water movement through the upper soil layer was determined by the Van Genuchten and Darcy equations, (2) The onset of infiltration into the second layer was calculated after taking into account metric heights as the soil approaches field capacity, (3) Water enters the downspout toward the vent and riser (Clear Creek Solutions, 2014).
The index for sensitivity is the sum of squared error (SSE), which took into account the comparison between observed and simulated values (Cho et al., 2012). GSA is one of the mathematical techniques that can identify how the output of the model changes according to the change of the set of inputs (Pianosi et al., 2015). The bioretention parameters shown by Cho et al. 2013) and Minnesota Pollution Control Agency (2016) (Table 4.1) was used for bioretention optimization.
This method is one of the most efficient, multi-objective, evolutionary algorithms using the elitist approach (Deb et al., 2002). This means that the performance of the model can be rated as "very good" and has a good representation of the hydrology of the watershed (Moriasi et al., 2007). There were Pareto optimal solutions, circled in Figure 6.9 in the most convex areas of the curve (Caramia and Dell'Olmo, 2008; Hu et al., 2011).
This result indicates that biological retention for floods needs considerable size (Ahiablame and Shakya, 2016; Lee et al., 2012).
Conclusion remark
Sang-Soo Baek, Mayzonee Ligaray, Jongcheol Pyo, Jong-Pyo Park, Joo-Hyon Kang, Jong Ahn Chun, Kyung Hwa Cho*, A New Water Quality Module of the SWMM Model for Assessing Low Impact Development (LID) in Urban Watersheds , Journal of Hydrology, In revision (first author). Representation and Evaluation of Low Impact Development Practices with L-THIA-LID: An Example of Site Planning. On the Use of Surrogate-Based Modeling for the Numerical Analysis of Low Impact Development Techniques.
Choi, H.S., 2010, Application and effects of low-impact development in the urban renewal of the waterfront area, Korea Environment Institute. Evaluating the effectiveness of low-impact development practices in an urban area: non-point pollutant removal measures using EPA-SWMM, Journal of Korean Society on Water Environment. A hyrbird approach to designing detention and infiltration-based retentions to promote a healthy urban hydrological cycle, Journal of KSEE Evaluating the Effectiveness of Low Impact Development Practices in an Urban Area: Pollutant Removal Measures Without point using EPA-SWMM.
Simulation optimization approach to design low-impact development for managing peak load changes in urbanizing watersheds. Modeling urban district-level stormwater management in response to land-use change and low-impact development. Runoff control potential for low impact development design types in small development areas using XPSWMM.
Analysis of the impact of low-impact construction on runoff from a new district in Korea.
