scholarly journals Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2022 ◽  
Author(s):  
Shojaeizadeh ◽  
Geza ◽  
McCray ◽  
Hogue

A site-scale integrated decision support tool (i-DSTss) is developed for selection and sizing of stormwater Best Management Practices (BMPs). The tool has several component modules—hydrology, BMP selection, BMP sizing, and life-cycle cost analysis (LCCA)—integrated into a single platform. The hydrology module predicts runoff from small catchment on event and continuous basis using the Green-Ampt and Curve Number methods. The module predicted runoff from a small residential area and a parking lot with R2 value of 0.77 and 0.74, respectively. The BMP selection module recommends a BMP type appropriate for a site based on economic, technical, social and environmental criteria using a multi-criteria optimization approach. The BMP sizing module includes sizing options for green roofs, infiltration-based BMPs, and storage-based BMPs. A mass balance approach is implemented for all types of BMPs. The tool predicted outflow rates from a permeable pavement with R2 value of 0.89. A cost module is included where capital, operation and maintenance, and rehabilitation costs are estimated based on BMP size obtained from the sizing module. The i-DSTss is built on an accessible platform (Microsoft Excel VBA) and can be operated with a basic skillset. The i-DSTss is intended for designers, regulators, and municipalities for quick analysis of scenarios involving interaction among several factors.

2018 ◽  
Vol 22 (7) ◽  
pp. 3789-3806 ◽  
Author(s):  
Junyu Qi ◽  
Sheng Li ◽  
Charles P.-A. Bourque ◽  
Zisheng Xing ◽  
Fan-Rui Meng

Abstract. Decision making on water resources management at ungauged, especially large-scale watersheds relies on hydrological modeling. Physically based distributed hydrological models require complicated setup, calibration, and validation processes, which may delay their acceptance among decision makers. This study presents an approach to develop a simple decision support tool (DST) for decision makers and economists to evaluate multiyear impacts of land use change and best management practices (BMPs) on water quantity and quality for ungauged watersheds. The example DST developed in the present study was based on statistical equations derived from Soil and Water Assessment Tool (SWAT) simulations and applied to a small experimental watershed in northwest New Brunswick. The DST was subsequently tested against field measurements and SWAT simulations for a larger watershed. Results from DST could reproduce both field data and model simulations of annual stream discharge and sediment and nutrient loadings. The relative error of mean annual discharge and sediment, nitrate–nitrogen, and soluble-phosphorus loadings were −6, −52, 27, and −16 %, respectively, for long-term simulation. Compared with SWAT, DST has fewer input requirements and can be applied to multiple watersheds without additional calibration. Also, scenario analyses with DST can be directly conducted for different combinations of land use and BMPs without complex model setup procedures. The approach in developing DST can be applied to other regions of the world because of its flexible structure.


2011 ◽  
Vol 25 (1) ◽  
pp. 159-164 ◽  
Author(s):  
Hugh J. Beckie ◽  
K. Neil Harker ◽  
Linda M. Hall ◽  
Frederick A. Holm ◽  
Robert H. Gulden

With increasing incidence of glyphosate-resistant weeds worldwide, greater farmer awareness of the importance of glyphosate stewardship and proactive glyphosate-resistance management is needed. A Web-based decision-support tool (http://www.weedtool.com) comprising 10 questions has been developed primarily for farmers in western Canada to assess the relative risk of selection for glyphosate-resistant weeds on a field-by-field basis. We describe the rationale for the questions and how a response to a particular question influences the risk rating. Practices with the greatest risk weighting in western Canadian cropping systems are lack of crop-rotation diversity (growing mainly oilseeds) and a high frequency of glyphosate-resistant crops in the rotation. Three case scenarios are outlined—low, moderate, and high risk of glyphosate-resistance evolution. Based on the overall risk rating, three best-management practices are recommended to reduce the risk of glyphosate resistance in weeds.


Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 370 ◽  
Author(s):  
Angela Rizzo ◽  
Primoz Banovec ◽  
Ajda Cilenšek ◽  
Guido Rianna ◽  
Monia Santini

GOWARE (transnational Guide toward an Optimal WAter REgime) represents a Decision Support Tool (DST) developed to support the implementation of innovative Best Management Practices (BMPs) for drinking water protection and flood/drought risk mitigation. The tool is one of the main outputs of the PROLINE-CE Project, an EU project funded within the Interreg Central Europe (CE) Programme (2014–2020). The aim of this paper is illustrating the design and the methodological approaches proposed for the operative development of the tool. Furthermore, the paper provides the results of a number of tests carried out to evaluate the understandability of the analysis’s processes and assessing the stakeholders’ acceptance. Specifically, GOWARE-DST has been developed for supporting single users or groups of users in the decision-making process. The tool has been provided with a catalogue of 92 BMPs to handle water issues in different land use contexts. The selection of practices suitable for addressing the specific user’s requirements is supported by the Analytic Hierarchy Process, a method that allows filtering a subset of BMPs by accounting for the relative importance that the user assigns to each characterizing criterion. GOWARE-DST represents an innovative tool for supporting users at different levels of planning (operational and strategic) by promoting sustainable land and water management and defining long-term governance activities.


2007 ◽  
Vol 4 (2) ◽  
pp. 747-775 ◽  
Author(s):  
P. Cau ◽  
C. Paniconi

Abstract. Quantifying the impact of land use on water supply and quality is a primary focus of environmental management. In this work we apply a semidistributed hydrological model (SWAT) to predict the impact of different land management practices on water and agricultural chemical yield for a study site situated in the Arborea region of central Sardinia, Italy. The physical processes associated with water movement, crop growth, and nutrient cycling are directly modeled by SWAT. The model simulations are used to identify indicators that reflect critical processes related to the integrity and sustainability of the ecosystem. Specifically we focus on stream quality and quantity indicators associated with anthropogenic and natural sources of pollution. A multicriteria decision support system is then used to develop the analysis matrix where water quality and quantity indicators for the rivers, lagoons, and soil are combined with socio-economic variables. The DSS is used to assess four options involving alternative watersheds designated for intensive agriculture and dairy farming and the use or not of treated wastewater for irrigation.


Author(s):  
Fernanda Santos Araujo ◽  
Vicente Nepomuceno Oliveira ◽  
Denise Alvarez ◽  
Helder Costa

Company recovery is a practice developed by workers who, in the imminence of becoming unemployed, negotiate or fight for access to the means of production of bankrupting companies, and start to manage them collectively, guided by the principles of self-management.  Nevertheless, how to assess self-management in worker-recovered companies (WRCs)? The criteria selected by a bibliographic review on the concept of self-management were used in dealing with the data collected by the Brazilian WRCs national mapping. A multi-criteria decision support tool was used to build a model for analyzing and classifying the companies in three categories related to their form of management. The multi-criteria approach allowed to create an assessment of self-management practices in the WRCs studied.


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