Best Management Practices for Controlling the Urban Surface Runoff in Coastal Zones: A Case Study in Guarujá, State of São Paulo, Brazil

Author(s):  
Vinicius Roveri ◽  
Luciana Lopes Guimarães ◽  
Alberto Teodorico Correia
2017 ◽  
Vol 94 ◽  
pp. 109-119
Author(s):  
Mohammad Hossein Rashidi Mehrabadi ◽  
Bahram Saghafian ◽  
Mohammad Reza Bazargan-Lari

2006 ◽  
Vol 41 (3) ◽  
pp. 283-295 ◽  
Author(s):  
Renaud Quilbé ◽  
Alain N. Rousseau ◽  
Pierre Lafrance ◽  
Jacinthe Leclerc ◽  
Mohamed Amrani

Abstract Numerous models have been developed over the last decades to simulate the fate of pesticides at the watershed scale. Based on a literature review, we inventoried thirty-six models categorized as management, research, screening or multimedia models, each of them having specific strengths and weaknesses. Given this large number of models, it may be difficult for potential users (stakeholders or scientists) to find the most suited one with respect to their needs. To help in this process, this paper proposes a pragmatic approach based on a multi-criteria analysis. Selection criteria are defined following the user's needs and classified in five classes: modelling characteristics, output variables, model applicability, possibilities to simulate best management practices (BMPs) and ease of use. The relative importance of each criterion is quantified by a weight and the total score of a model is calculated by adding the resulting weights of satisfied criteria. This selection framework is illustrated with a case study that consists in selecting a model to develop water quality standards at the watershed scale with respect to the implementation of BMPs. This resulted in the selection of three models: BASINS, SWAT and GIBSI.


PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7093
Author(s):  
Solmaz Rasoulzadeh Gharibdousti ◽  
Gehendra Kharel ◽  
Arthur Stoecker

Best management practices (BMPs) are commonly used to reduce sediment loadings. In this study, we modeled the Fort Cobb Reservoir watershed located in southwestern Oklahoma, USA using the Soil and Water Assessment Tool (SWAT) and evaluated the impacts of five agricultural BMP scenarios on surface runoff, sediment yield, and crop yield. The hydrological model, with 43 sub-basins and 15,217 hydrological response units, was calibrated (1991–2000) and validated (2001–2010) against the monthly observations of streamflow, sediment grab samples, and crop-yields. The coefficient of determination (R2), Nash-Sutcliffe efficiency (NS) and percentage bias (PB) were used to determine model performance with satisfactory values of R2 (0.64 and 0.79) and NS (0.61 and 0.62) in the calibration and validation period respectively for streamflow. We found that contouring practice reduced surface runoff by more than 18% in both conservation tillage and no-till practices for all crops used in this modeling study. In addition, contour farming with either conservation tillage or no-till practice reduced sediment yield by almost half. Compared to the conservation tillage practice, no-till practice decreased sediment yield by 25.3% and 9.0% for cotton and grain sorghum, respectively. Using wheat as cover crop for grain sorghum generated the lowest runoff followed by its rotation with canola and cotton regardless of contouring. Converting all the crops in the watershed into Bermuda grass resulted in significant reduction in sediment yield (72.5–96.3%) and surface runoff (6.8–38.5%). The model can be used to provide useful information for stakeholders to prioritize ecologically sound and feasible BMPs at fields that are capable of reducing sediment yield while increasing crop yield.


Author(s):  
Rohit Dwivedula ◽  
R. Madhuri ◽  
K. Srinivasa Raju ◽  
A. Vasan

Abstract Urban floods cause massive damage to infrastructure and loss of life. This research is being carried out to study how Best Management Practices (BMPs) can mitigate the negative effects of urban floods during extreme rainfall events. Strategically placing BMPs throughout open areas and rooftops in urban areas serves multiple purposes of storage of rainwater, removal of pollutants from surface runoff and sustainable utilisation of land. This situation is framed as a multiobjective optimisation problem to analyse the trade-offs between multiple goals of runoff reduction, construction cost and pollutant load reduction. Output includes a wide range of choices to choose from for decision makers. Proposed methodology is demonstrated with a case study of Greater Hyderabad Municipal Corporation (GHMC), India. Historical extreme rainfall event of 237.5 mm which occurred in year 2016 and extreme rainfall event of 1,740.62 mm corresponding to Representative Concentration Pathway (RCP) 2.6 were considered for analysis. Two multiobjective optimisation algorithms, namely, Non-dominated Sorting Genetic Algorithm – III (NSGA-III) and Constrained Two-Archive Evolutionary Algorithm (C-TAEA) are employed to solve the BMP placement problem, following which the resulting pareto-fronts are ensembled. K-Medoids-based cluster analysis is performed on the resulting ensembled pareto-front. The proposed ensembled approach identified ten possible BMP configurations with costs ranging from Rs. to surface runoff reduction ranging from to and pollutant load removal ranging from tonnes. Use of BMPs in future event has the potential to reduce surface runoff from , while simultaneously removing tonnes of pollutants for cost ranging from The proposed framework forms an effective and novel way to characterise and solve BMP optimisation problems in context of climate change, presenting a view of the urban flooding scenario today, and the likely course of events in the future.


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