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Water: Green Infrastructure

Modeling Tools

A range of models are available to assess the costs and environmental outcomes associated with green infrastructure approaches. Here we emphasize models that can predict the water quality and water quantity impacts of green infrastructure approaches.  A few models addressing cost, air quality, and energy consumption are also included.  Modeling approaches are arranged from simpler, less resource-intensive approaches, to more complex approaches that require greater time and expertise.

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Modeling Principles
Models extrapolate our understanding of physical processes to different contexts, allowing us to predict the environmental outcomes of different design and management approaches.  For those who are less familiar with modeling approaches, a few basic principles are discussed.

gi_greenarrow Why Model?

Models can inform design and policy decisions by predicting the outcomes of different design and management approaches.

At the site or neighborhood scale: models allow us to link a site’s land cover and stormwater controls to the volume of stormwater discharged by the site and the pollutant loads exported by those discharges.  By allowing site designers to compare the water quantity and quality outcomes associated with different design scenarios, models can guide site designers in meeting mandatory or voluntary performance standards (see the U.S. Green Building Councils Leadership in Energy and Environmental Design (LEED) Program Exit EPA Disclaimer and the Sustainable Sites Initiative Exit EPA Disclaimer).

At the watershed scale: models allow us to link land cover and stormwater controls implemented throughout a watershed to the hydrological, chemical, and ecological outcomes in receiving waters.  Because receiving waters respond to all the activities occurring in a watershed as well as the pattern of precipitation in a given year, models are particularly important at the watershed scale.  Models allow us to isolate the receiving water impacts associated with stormwater management approaches, and compare the environmental outcomes of alternative management scenarios.  Models can therefore guide planners and environmental managers in meeting mandatory or voluntary objectives for receiving waters.

gi_greenarrow Choosing a Model

A large and ever-expanding array of models is available for assessing the performance of green infrastructure practices in the urban environment.  Before delving into the details of all these models, it is important to think carefully about your needs and about the resources at your disposal.

Understand your objective:  No model can accurately predict all environmental outcomes at all scales, but most models are good at predicting a limited range of environmental outcomes within a limited range of scales.  Before selecting your model, it is important to identify the environmental parameters you are most interested in (water quality, streamflow rates, groundwater recharge rates?) and the scale you are most interested in (a single site, a small headwater stream, a large lake?).

Understand your data requirements:  Simpler models require a limited amount of data that may be retrieved from publicly available databases, while more complex models require larger volumes of data for parameterization and calibration. Determine the volume of data required by each model you are considering, as well as the spatial and temporal resolution required by the model.

Choose the simplest model that can meet your objective:  To efficiently allocate staff and budget resources, it is important to weigh the marginal increases in model accuracy against the marginal increases in modeling cost as you select more sophisticated models.  Think about the level of accuracy that is required to meet your objective.  Is the incremental gain in accuracy associated with a particular model really worth the incremental increase in cost? 

gi_greenarrow Interpreting Model Results

The quantitative nature of modeling results may inspire unwarranted confidence in model predictions – particularly when the modeling package includes an attractive user interface and sophisticated visualization tools – but modeling results should always be interpreted in light of model assumptions and model uncertainty.

Understand model assumptions:  Even in the most complex models, simplifying assumptions are built into model algorithms.  Simplifying assumptions are particularly common in the treatment of soils, groundwater, and green infrastructure practices, which have many functional properties that are difficult to measure.  To better understand the limitations inherent in model results, it is important to understand the most fundamental model assumptions.

Understand uncertainty:
 All model results are sensitive to errors in input data, model parameters, and calibration data.  Some effort should always be made to understand the level of uncertainty associated with model results.

Cost Spreadsheet
Sizing and Costing Spreadsheets
Spreadsheet tools allow users to quickly generate cost and/or performance estimates for multiple sets of site designs.  To provide quick estimates, however, these tools rely on a set of simplifying assumptions.  Users should be aware of these assumptions before applying the estimates generated. Here we provide links to several recently developed sizing and costing spreadsheets.  For each tool we summarize the best management practices (BMPs) included, data requirements, and outputs.
Scale:
Site
BMPs:
Green Roof,
Downspout Disconnection,
Permeable Pavement,
Grass Channel,
Dry Swale,
Bioretention,
Infiltration,
Extended Detention Pond,
Sheetflow to Filter,
Wet Swale,
Constructed Wetland,
Wet Pond
User Inputs:
Annual Precipitation. Land Cover Distribution, Soil Type Distribution, BMPs
Outputs:
Runoff Volume Reduction (ft3 /design storm), Phosphorus Load Reduction (lb/yr), Nitrogen Load Reduction (lb/yr)

Scale:
Site - Watershed
BMPs:
Green Roof,
Planters,
Permeable Pavement,
Rain Gardens,
Retention Ponds,
Swales,
Cisterns,
Bioretention,
Extended Detention Basins
User Inputs:
Drainage Area, BMP Characteristics, Capital Costs, Maintenance Costs
Outputs:
Whole Life Costs, Present Value Graphs

Scale:
City
BMPs:
Green Roof,
Green Roofs,
Bioretention,
Vegetated Swales,
Permeable Pavement,
Rain Barrels and Cisterns
User Inputs:
Event Precipitation, Impervious Area, BMPs, BMP Parameters
Outputs:
Runoff Volume Reduction,  CSO Volume, Costs
Notes:
This tool is intended  to aid municipalities in integrating green infrastructure into Long Term Control Plans for Combined Sewer Overflows.


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Simple Models
For the purposes of this web page, we define simple models as those that require relatively limited input data and technical expertise.  Simple models are often very useful as screening- and planning-level tools.  For each model we list the spatial scale and BMPs that the model was designed to represent.  We also summarize the key data requirements and model outputs.
 
 
 
The National Stormwater Calculator is a SWMM-based desktop application that estimates the impact of land cover change and green infrastructure controls on stormwater runoff from a selected site. Estimates of runoff volume and frequency are provided based on local soil, topographic, and climate data and user-provided land cover and BMP data.
Scale:
Site
BMPs:
Disconnection
Rainwater Harvesting
Rain Gardens
Green Roofs
Street Planters
Infiltration Basins
Permeable Pavement
User Inputs:
Land Cover, BMP Characteristics (Soil, Topographic, and Climate data drawn from national databases)
Outputs:
Runoff Volume and Frequency Statistics
Scale:
Site-Watershed
BMPs:
Extended detention,
Bioretention,
Wetlands,
Swales,
Permeable pavement,
Filters
User Inputs:
Hourly Precipitation Record, Monthly Evaporation Rates, Land Use Distribution, BMPs, BMP Parameters,
Outputs:
Runoff Volume, Pollutant Loads, Costs
Notes:
Cost calculations based on WERF Whole Life Cost Model

Scale:
Site
BMPs:
Green Roof,
Planter Boxes,
Rain Gardens,
Cisterns,
Native Vegetation,
Vegetation Filter Strips,
Amended Soil,
Swales,
Trees,
Reduced Street Width,
Permeable Pavement
User Inputs:
Annual or Event Precipitation, Land Cover Distribution, Soil Type, Runoff Reduction Goal, BMPs, BMP Parameters
Outputs:
Runoff Volume Reduction, Costs, Reduced Air Pollutants, Carbon Dioxide Sequestration, Value of Trees, Groundwater Recharge, Reduced Energy Use, Reduced Treatment Benefits

Scale:
Site
BMPs:
Rain Cisterns
User Inputs:
Hourly or Daily Rainfall Record, BMP Parameters, Anticipated Usage, Water Cost, Sewer Cost, Cistern Cost
Outputs:
Runoff Volume Reduction, Usage Replaced, Payback Period

i-Tree Vue allows users to make use of freely available national land cover data maps to assess their community’s land cover, including tree canopy, and some of the ecosystem services provided by their current urban forest. The effects of planting scenarios on future benefits can also be modeled.
Scale:
City
BMPs:
Urban Forest
User Inputs:
NLCD 2001 Land Cover, Tree Cover, and Impervious Cover Datasets; 
Outputs:
Carbon Sequestration, Air Pollutant Removal

Modeling Tool
Complex Models
For the purposes of this web page, we define complex models as those that require relatively extensive input data and technical expertise.  Complex models generally include more mechanistic representations of physical processes and, therefore, require the user to  define a larger number of physical parameters.  For each model we list the spatial scale and BMPs that the model was designed to represent and summarize the key data requirements and model outputs.
SWMM is general purpose urban hydrology and conveyance system hydraulics software that is used extensively throughout the nation. EPA has extended SWMM to explicitly model the hydrologic performance of specific types of low impact development (LID) controls, such as porous pavement, bio-retention areas, rain barrels, infiltration trenches, and vegetative swales. The updated model allows engineers and planners to accurately represent any combination of LID controls within a study area to determine their effectiveness in managing stormwater and combined sewer overflows.
Scale:
Site-Watershed
BMPs:
Bioretention,
Infiltration Trenches,
Porous Pavement,
Rain Barrels,
Vegetative Swales
User Inputs:
Meteorological Data, Land Surface Characteristics (including impervious area and soil characteristics), Drainage Network Characteristics, BMP Characteristics
Outputs:
Include Runoff Volume, Runoff Rate, Mean Pollutant Concentration, Total Pollutant Load
Articles and Applications:

Shuster, W. D. and E. Pappas. "Laboratory Simulation of Urban Runoff and Estimation of Runoff Hydrographs with Experimental Curve Numbers Implemented in USEPA SWMM." Journal of Irrigation and Drainage Engineering. American Society of Civil Engineers (ASCE), Reston, VA, 137(6):343-402, (2011).

Maya P. Abi Aad, Makram T. Suidan, and William D. Shuster. "Modeling Techniques of Best Management Practices: Rain Barrels and Rain Gardens Using EPA SWMM-5" Exit EPA Disclaimer J Hydrol. Eng. 15, 434; 10 p; doi: 10.1061/ (ASCE) HE.1943-5584.0000136 (2010).

Casey Trees "Green Build-Out" Model (PDF) (111 pp, 4.5MB, About PDF) Exit EPA Disclaimer: Casey Trees and LimnoTech developed the Green Build-out Model to quantify the stormwater benefits of trees and green roofs for different coverage scenarios in Washington, DC. This research was funded by the US Environmental Protection Agency (EPA) through a Water Quality Cooperative Agreement. The model was applied to an “intensive greening” scenario and a “moderate greening” scenario.
  SUSTAIN is a decision support system to facilitate selection and placement of BMPs and low impact development techniques at strategic locations in urban watersheds. It was developed to assist stormwater management professionals in developing implementation plans for flow and pollution control to protect source waters and meet water quality goals. From an understanding of the needs of the user community, SUSTAIN was designed for use by watershed and stormwater practitioners to develop, evaluated, and select optimal BMP combinations at various watershed scales on the basis of cost and effectiveness.

US EPA (2006). "BMP Modeling Concepts and Simulation." Publication No.  EPA/600/R-06/033

US EPA (2006). "Methods for Optimizing Urban Wet-Weather Control System." Publication No. EPA/600/R-06/034.

Lee, J. G., J. P. Heaney, and F. Lai. Optimization of Integrated Urban Wet-Weather Control. Strategies. Abstract. Journal of Water Resource and Planning and Management. American Society of Civil Engineers (ASCE). Reston, VA 131(4): 307-315, (2005).
  FORTRAN (HSPF) is a comprehensive package for simulation of watershed hydrology and water quality for both conventional and toxic organic pollutants. HSPF incorporates EPA's watershed-scale Agricultural Runoff Management Model (ARM) and Nonpoint Source Runoff Model (NPS) into a basin-scale analysis framework that includes fate and transport in one dimensional stream channels. It is the only comprehensive model of watershed hydrology and water quality that allows the integrated simulation of land and soil contaminant runoff processes with In-stream hydraulic and sediment-chemical interactions. The result of this simulation is a time history of the runoff flow rate, sediment load, and nutrient and pesticide concentrations, along with a time history of water quantity and quality at any point in a watershed. HSPF simulates three sediment types (sand, silt, and clay) in addition to a single organic chemical and transformation products of that chemical.
  i-Tree Eco provides a broad picture of the entire urban forest. It is designed to use field data from randomly located plots throughout a community along with local hourly air pollution and meteorological data to quantify urban forest structure, environmental effects, and value to communities.
Scale:
Single tree-County
BMPs:
Urban Forest
User Inputs:
Vegetation Characteristics at Sample Locations
Outputs:
Carbon Sequestration, Air Pollutant Removal, Energy Effects from Trees
Notes:
Data must be submitted through an online system and results are returned the same day.
  i-Tree Streets focuses on the ecosystem services and structure of a municipality’s street tree population. It makes use of a sample or complete inventory to quantify and put a dollar value on the trees’ annual environmental and aesthetic benefits, including energy conservation, air quality improvement, carbon dioxide reduction, stormwater control, and property value increases.
Scale:
City
BMPs:
Street Trees
User Inputs:
Street Tree Characteristics at Sample Locations, City Information, Cost Information, Benefit Prices
Outputs:
Energy Conservation, Runoff Volume Reduction, Air Pollutant Reduction, Carbon Sequestration
  i-Tree Hydro (Beta) is the first vegetation-specific urban hydrology model. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level.
Scale:
Watershed
BMPs:
Urban Forest
User Inputs:
Digital Elevation Model for Study Watershed, Land Cover Distribution, Leaf Area Index, Tree Phenology
Outputs:
Stream Flow, Pollutant Load
 
Scale:
Site
BMPs:
Rain Gardens
User Inputs:
Hourly Precipitation Record or Event Precipitation, Hourly Evapotranspiration Record, Drainage Area, Impervious Area, Pervious Area Curve Number, Soil Properties, Rain Garden Properties
Outputs:
Runoff Volume Reduction
Notes:
Given a target retention volume, the required rain garden area may be calculated.

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