{DA8C85C8-1508-405C-AC06-3A7B6CC183A8} eng; USA utf8 dataset EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact 2025-02-13 ISO 19115 Geographic Information - Metadata ISO 19115:2003/Cor.1:2006(E) EnviroAtlas - Austin, TX - Ecosystem Services by Block Group 2015-10-06 publication U.S. EPA Office of Research & Development (ORD) - National Exposure Research Laboratory (NERL) Research Triangle Park NC publisher US Environmental Protection Agency (US EPA) (additional partners are listed in Supplemental) originator This EnviroAtlas dataset presents environmental benefits of the urban forest in 750 block groups in Austin, Texas. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). This EnviroAtlas dataset identifies some of the ecosystem services that are provided by trees in a community. The data could be used to promote awareness of the benefits that trees bring to people and the environment. The overall goal of EnviroAtlas is to employ and develop the best available science to map indicators of ecosystem services production, demand, and drivers for the nation. completed EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact asNeeded Ecosystem Environment theme EPA GIS Keyword Thesaurus Air Census Block Groups Climate Communities Ecosystem Services EnviroAtlas Environment Environmental Atlas Modeling Surface Water Water Trees theme EnviroAtlas Texas Austin, TX place None https://edg.epa.gov/EPA_Data_License.html otherRestrictions Use Constraints: None. Please check sources, scale, accuracy, currency and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of the EPA would be appreciated. otherRestrictions Access Constraints: None. PUBLIC https://project-open-data.github.io/schema/#accessLevel Standard Technical Controls eng; USA environment -98.007554 -97.488224 30.095348 30.772723 2010-01-01 2011-01-01 2015-01-01 This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company. true Entity and Attribute Information Entity and Attribute Overview: For more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets. This final product integrates two datasets provided by the EPA, as well as a U.S. Census derived dataset, with local land cover, elevation, weather, and pollution data. Carbon, temperature, pollution and runoff results are included for selected Census block groups in Austin, TX. Some of the processes described in the processing steps use i-Tree applications (available at www.itreetools.org), particularly i-Tree Eco and Hydro, or make use of the methods and models that are incorporated within the i-Tree applications. Datasets and sources: * ATX_BG: - dataset provided by the U.S. EPA - dataset includes U.S. Census derived block groups of Austin, TX * AUTX_WISTGA_EPA_1K_040915.tif: - dataset provided by the U.S. EPA - dataset includes land cover classes for Austin, TX * USGS NLCD 2011: - dataset derived from the USGS's National Land Cover Database (http://www.mrlc.gov/) - dataset includes land cover data * USGS 10m NED: - dataset derived from the USGS's National Elevation Dataset (http://ned.usgs.gov/) - dataset includes elevation data. DISCUSSION OF EVENT MEAN CONCENTRATION (EMC): When the EMC is multiplied by the runoff volume, an estimate of the loading to the receiving water is provided. The instantaneous concentration during a storm can be higher or lower than the EMC, but the use of the EMC as an event characterization replaces the actual time variation of C versus t in a storm with a pulse of constant concentration having equal mass and duration as the actual event. This process ensures that mass loadings from storms will be correctly represented. EMCs represent the concentration of a specific pollutant contained in stormwater runoff coming from a particular land use type or from the whole watershed. Under most circumstances, the EMC provides the most useful means for quantifying the level of pollution resulting from a runoff event (USEPA, 2002). Since collecting the data necessary for calculating site-specific EMCs can be cost-prohibitive, researchers or regulators will often use values that are already available in the literature. If site-specific numbers are not available, regional or national averages can be used, although the accuracy of using these numbers is questionable. Due to the specific climatological and physiographic characteristics of individual watersheds, agricultural and urban land uses can exhibit a wide range of variability in nutrient export (Beaulac and Reckhow 1982). To understand and control urban runoff pollution, The U.S. Congress included the establishment of the Nationwide Urban Runoff Program (NURP) in the 1977 Amendments of the Clean Water Act (PL 95-217). The U.S. Environmental Protection Agency developed the NURP to expand the state knowledge of urban runoff pollution by applying research projects and instituting data collection in selected urban areas throughout the country. In 1983, the U.S. Environmental Protection Agency (U.S. EPA, 1983) published the results of the NURP, which nationally characterizes urban runoff for 10 standard water quality pollutants, based on data from 2,300 station-storms at 81 urban sites in 28 metropolitan areas. Two important conclusions from NURP investigations are: * The variance of the EMCs when data from sites are grouped by land use type or geographic region is so great that differences in measures of central tendency among groups statistically are not statistically significant; * Statistically, the entire sample of EMCs, and the medians of all EMCs among sites, are lognormally distributed. Thus the numbers do not distinguish between different urban land use types. Subsequently, the USGS created another urban stormwater runoff base (Driver et al. 1985), based on data measured through mid-1980s for over 1,100 stations at 97 urban sites located in 21 metropolitan areas. Additionally, many major cities in the United States collected urban runoff quality data as part of the application requirements for stormwater discharge permits under the National Pollutant Discharge Elimination System (NPDES). The NPDES data are from over 30 cities and more than 800 station-storms for over 150 parameters (Smullen et al, 1999). The data from the three sources (NURP, USGS and NPDES) were used to compute new estimates of EMC population means and medians for the 10 pollutants with many more degrees of freedom than were available to the NURP investigators (Smullen et al, 1999). A "pooled" mean was calculated representing the mean of the total population of sample data. NURP or pooled mean EMCs were selected because they are based on field data collected from thousands of storm events. These estimates are based on nationwide data, however, so they do not account for regional variation in soil types, climate, and other factors. For i-Tree-Hydro, the pooled median and mean EMC value for each pollutant were applied to the runoff regenerated from pervious and impervious surface flow, not the base flow values, to estimate effects on pollutant load across the entire modeling time frame. All rain events are treated equally using the EMC value, which mean some events may be over-estimated and others underestimated. In addition, local management actions (e.g., street sweeping) can affect these values. However, across the entire season, if the EMC value is representative of the watershed, the estimate of cumulative effects on water quality should be relatively accurate. Accuracy of pollution estimates will be increased by using locally derived coefficients. It is not known how well the national EMC values represent local conditions. MODEL CALIBRATION GRAPHS: Model calibration procedures adjust several model parameters (mostly related to soils) to find the best fit between the observed flow and the model flow on an hourly basis. However, there are often mismatches between the precipitation data, which is often collected outside of the watershed, and the actual precipitation that occurs in the watershed. Even if the precipitation measurements are within the watershed, local variations in precipitation intensity can lead to differing amounts of precipitation than observed at the measurement station. These differences in precipitation can lead to poorer fits between the observed and predicted estimates of flow as the precipitation is a main driver of the stream flow. The observed and simulated results will diverge, which is often an artifact of the precipitation data. For example, observed flow will rise sharply, but predicted flow does not, which is an indication of rain in the watershed but not at the precipitation measurement station. Conversely, the simulated flow may rise but the observed flow does not, which is an indication of rain at the precipitation station, but not in the watershed. As the model simulations are comparisons between the base simulation flows and another simulated flow where surface cover is changed (e.g., increase or decrease in tree cover), both model runs are using the same simulation parameters. What this means is that the effects of changes in cover types are comparable, but may not exactly match the flow of the stream. That is, the estimates of the changes in flow are reasonable (e.g., the relative amount of increase or decrease in flow is sound, as both are using the same model parameters and precipitation data), but the absolute estimate of flow may be off. Thus, the model results can be used to assess the relative differences in flow due to changes in cover parameters, but are likely off in predicting the actual effects on stream flow due to precipitation and calibration imperfections. The model can be used to compare the changes in flow (e.g., increased tree cover leads to an x% change in stream flow), but will likely not exactly match the flow observed in the stream. The model is more diagnostic of cover change effects than predictive of actual stream flow due to imperfections of models and input data used in the model. Overall the model tends to underestimate observed peak flows, particularly flows over about 15,000 cubic meters per hour, which did not occur too often during the simulation period. DISCUSSION OF PM2.5 AIR QUALITY IMPROVEMENT: PM2.5 air quality improvement (%) values may be negative, even when PM2.5 removal (kg/year) values are positive. This is because when PM2.5 flux is negative (PM2.5 is resuspended from leaves to the air, increasing ambient PM2.5 concentration) hourly air quality improvement is calculated as negative (air quality is worsened). Even when the annual total removal is positive, the annual average of air quality improvement may become negative, depending on the magnitude of negative air quality value in each hour that is affected by hourly mixing height and measured concentration. This type of interaction occurs when the atmospheric boundary layer height changes. During the day, the height is high and at night it reduces. Depending upon when particles are captured and released, relative to the height of the atmosphere, there is a chance that particles removed during high boundary heights can be resuspended during low boundary layer height times. Even though pollution was removed, the overall concentration change was increased because the trapped particles were rereleased in a smaller volume of air than the volume where they were originally captured. REFERENCES: Beaulac, M. N., and K. H. Reckhow. 1982. An examination of land use-nutrient export relationships. Water Resources Bulletin 18(6):1013-1024. Charbeneau, R. J., and M. Barretti. 1998. Evaluation of methods for estimating stormwater pollutant loads. Water Environment Research 70:1295-1302. Driver, N. E., M. H. Mustard, R. B. Rhinesmith, and R. F. Middelburg. 1985. U.S. Geological Survey urban-stormwater data base for 22 metropolitan areas throughout the United States. Open-File Report 85-337. United States Geological Survey, Lakewood, Colorado, USA. Heisler, G., J. Walton, D. Nowak, R. Pouyat, R. Grant, and K. Belt. 2006. Land-cover influences on below-canopy temperatures in and near Baltimore, MD. 6th International Conference on Urban Climate, Gothenburg, Sweden, 392-395. Heisler, G., J. Walton, I. Yesilonis, D. Nowak, R. Pouyat, R. Grant, S. Grimmond, K. Hyde, G. Bacon. 2007. Empirical modeling and mapping of below-canopy air temperatures in Baltimore, MD and vicinity. Proceedings of the seventh urban environment symposium. San Diego, CA, American Meteorological Society, 2007 September 10-13. Hirabayashi, S., C. N. Kroll, D. J. Nowak., 2011. Component-based development and sensitivity analyses of an air pollutant dry deposition model. Environmental Modeling & Software 26:804-816. Hirabayashi, S., C. N. Kroll, D. J. Nowak., 2013. i-Tree Eco Dry Deposition Model Descriptions. i-Tree Archives, http://www.itreetools.org/eco/resources/iTree_Eco_Dry_Deposition_Model_Descriptions.pdf. Nowak, D., E. Greenfield, R. Hoehn, and E. Lapoint. 2013. Carbon storage and sequestration by trees in urban and community areas of the United States. Environmental Pollution. 178: 229-236. Sansalone, J. J., and S. G. Buchberger. 1997. Partitioning and first flush of metals in urban roadway storm water. Journal of Environmental Engineering ASCE 123:134-143. Smullen, J. T., A. L. Shallcross, and K. A. Cave. 1999. Updating the U.S. nationwide urban runoff quality database. Water Science Technology 39(12):9-16. U.S. Environmental Protection Agency (USEPA). 1983. Results of the Nationwide Urban Runoff Program: Volume I - final report. PB84-185552. U.S. Environmental Protection Agency, Washington, DC, USA. U.S. Environmental Protection Agency (USEPA). 2002. Urban Stormwater BMP Performance Monitoring, A Guidance Manual for Meeting the National Stormwater BMP Database Requirements. Wang, J., T. A. Endreny, and D. J. Nowak. 2008. Mechanistic simulation of urban tree effects in an urban water balance model. Journal of American Water Resource Association 44(1):75-85. Entity and Attribute Detail Citation: https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas distributor https://enviroatlas.epa.gov/download/ATX_metrics_Apr2020_CSV_Shapes.zip download Download The URL providing direct access to the downloadable dataset. download https://enviroatlas.epa.gov/download/ATX_metrics_Apr2020_FGDB.zip ESRI:ArcGIS API The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint). webMapService https://enviroatlas.epa.gov/arcgis/rest/services/Communities/Community_BGmetrics/MapServer information Documentation URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc. information https://www.epa.gov/enviroatlas/enviroatlas-data information Home Page Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API information dataset Horizontal Positional Accuracy Report Data were collected using methods that have unknown accuracy (EPA National Geospatial Data Policy [NGDP] Accuracy Tier 10). For more information, please see EPA's NGDP at https://www.epa.gov/geospatial/geospatial-policies-and-standards Features represented have not been tested for completeness Features represented have not been tested for completeness Tests for integrity have not been performed. This lineage was generated by the FGDC CSDGM to 19139 transformation Generated land cover and carbon attributes using ArcGIS 10.0 software: 2015-05-01T00:00:00 1. ATX_BG (dataset provided by the U.S. EPA that includes U.S. Census derived block groups) was rasterized and then was used to extract values from AUTX_WISTGA_EPA_1K_040915.tif (dataset provided by the US EPA that includes land cover classes for the community) using Tabulate Areas in Spatial Analyst. 2015-05-01T00:00:00 2. The information from AUTX_WISTGA_EPA_1K_040915.tif was used to populate the fields in AustinTXwLCdataB. 2015-05-01T00:00:00 3. Carbon Storage, Carbon Sequestration and their respective dollar values were calculated using Nowak et al 2013 with the following rates: a. Storage = 7.69 kg C per square meter of canopy; b. Sequestration = 0.368 kg C per square meter of canopy per year; c. $78.5 per metric ton of C based on the 2010 social cost of carbon dioxide ($21.4 per metric ton of CO2) from Interagency Working Group on Social Cost of Carbon, United States Government. 2010 Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 2015-05-01T00:00:00 Generated temperature reduction values using ArcGIS 10.0, Access 2010, and Python 6.5: 2015-08-01T00:00:00 1. Generation of Land Cover predictor variables - Impervious, Canopy and Water Cover 2015-08-01T00:00:00 a. Impervious, Canopy and Water Cover extracted from the EPA land cover dataset and resampled to 10m using Spatial Analyst tools (Reclassify, Aggregate, RasterCalculator). 2015-08-01T00:00:00 b. Classified and resampled USGS NLCD 2011 data mosaicked to the processed EPA land cover data to create land cover datasets extending 5km beyond the extent of the selected Census Block Groups. 2015-08-01T00:00:00 c. Land cover predictor variables generated using Focal Statistics to determine upwind cover densities at a range of distances out to 5km. 2015-08-01T00:00:00 2. Generation of Elevation predictor variables - Elevation Difference and Cold Air Drainage. 2015-08-01T00:00:00 a. Elevation Difference and Cold Air Drainage datasets generated by running spatial analysis on the USGS 10m NED dataset (RasterCalculator). 2015-08-01T00:00:00 3. Analysis of Austin Camp Mabry Weather Station data to develop interaction terms to be used with the cover data - Turner Class (atmospheric stability), Rain, Vapor Pressure Deficit, Wind Direction, and Wind Speed. 2015-08-01T00:00:00 a. Wind, clouds and solar elevation angle were combined to develop an indicator of atmospheric stability or Turner Class. 2015-08-01T00:00:00 b. Hourly wind direction was determined by averaging the wind direction from the ASOS 15 minute interval data. Wind direction with an average azimuth greater than 360° was deemed Variable. 2015-08-01T00:00:00 4. Weather station data explored to determine which days, between June 1st and Aug. 31st, 2008, to map temperature: a. High Wind Day - 6/05/2008; b. Low Wind Day - 6/18/2008; c. Average (summer) Temperature Day - 7/9/2008; d. Maximum Temperature Day - 8/3/2008 2015-08-01T00:00:00 5. Hourly temperature datasets (raster) generated using a regression equation (Heisler et al, 2012) combining predictor variables with interaction terms (Raster Calculator). 2015-08-01T00:00:00 a. Temperature maps generated based on the current canopy cover according to the EPA land cover dataset. 2015-08-01T00:00:00 b. Temperature maps generated under a 0% canopy cover scenario. 2015-08-01T00:00:00 6. Generation of raster datasets containing average Temperature Reduction values by block group. 2015-08-01T00:00:00 a. For each hour of the selected days, temperature values from the 0% canopy scenario were subtracted from the current canopy scenario to get the temperature reduction value for each pixel (RasterCalculator). 2015-08-01T00:00:00 b. For each of the selected days, daytime (6am - 5pm) and night time temperature reduction values were averaged (RasterCalculator). 2015-08-01T00:00:00 c. Daytime and night time Temperature Reduction values were averaged for each Census Block Group (RasterCalculator). 2015-08-01T00:00:00 7. Raster dataset containing average temperature reduction values for each census block group were generated for: a. Each of the four days; b. Each of the four nights; c. The warmest hour in each of the four days. Note that the nighttime temperatures are on a single day, from the hours of 1am through 5am and that same day through 12pm. 2015-08-01T00:00:00 8. Raster datasets converted to tables (Zonal Statistics as Table) and Temperature Reduction values added (Join) to the Austin, TX census block group dataset. More information on the methodology for estimating temperature reduction can be attained from work by Heisler et al. 2006 and Heisler et al. 2007. 2015-08-01T00:00:00 Generated annual air quality improvement and pollution removals and value using Visual Studio 2010, ArcGIS 10.0, and Access 2010. 2015-05-01T00:00:00 1. Derivation of local hourly weather data for 2008 - the nearest weather station to each block group was selected (AUSTIN/MUELLER MUNI - USAF: 722540 WBAN: 13904; AUSTIN CAMP MABRY - USAF: 722544 WBAN: 13958; GEORGETOWN (AWOS) - USAF: 722547 WBAN: 53942). 2015-05-01T00:00:00 2. Derivation of local hourly mixing height data - estimated based on upper air data measured at the nearest radiosonde station to each block group (FT WORTH - WMO: 72249; CORPUS CHRISTI - WMO: 72251). 2015-05-01T00:00:00 3. Derivation of local hourly pollution data: 2015-05-01T00:00:00 a. The nearest CO monitor to each block group was selected (State: 48, County: 453, SiteID: 0014) 2015-05-01T00:00:00 b. The nearest NO2 monitor to each block group was selected (State: 48, County: 209, SiteID: 0614; State: 48, County: 453, SiteID: 0020) 2015-05-01T00:00:00 c. The nearest O3 monitor to each block group was selected (State: 48, County: 209, SiteID: 0614; State: 48, County: 453, SiteID: 0014; State: 48, County: 453, SiteID: 0020) 2015-05-01T00:00:00 d. The nearest PM10 monitor to each block group was selected (State: 48, County: 453, SiteID: 0020; State: 48, County: 453, SiteID: 0021) 2015-05-01T00:00:00 e. The nearest SO2 monitor to each block group was selected (State: 48, County: 029, SiteID: 0055; State: 48, County: 029, SiteID: 0622; State: 48, County: 309, SiteID: 1037) 2015-05-01T00:00:00 f. For PM2.5, EPA's Fused Air Quality Surfaces for census tracts were used. A tract to which a block group belongs was used for each block group. If a block group's tract was missing, the nearest tract was used. As PM2.5 data were daily data, the value was assumed to remain the same across a day. 2015-05-01T00:00:00 4. Derivation of location-related values: 2015-05-01T00:00:00 a. Latitude/longitude determined at the centroid of each block group. 2015-05-01T00:00:00 b. Leaf on/off day of year, GMT offset, and other model parameters for Austin, TX derived from USFS's location database were used for each block group. 2015-05-01T00:00:00 5. Derivation of forest area data: 2015-05-01T00:00:00 a. Used total land area (LNDSQM) to represent the area of each block group. 2015-05-01T00:00:00 b. Constant leaf area index of 4.9 was used for each block group. 2015-05-01T00:00:00 c. Percent canopy (PCTCAN) for each block group derived from EPA land cover dataset (TAFSQM divided by LNDSQM). 2015-05-01T00:00:00 d. Percent evergreen was derived from NLCD 2011. The value of percent evergreen (71.2%) was applied to each of the block groups. 2015-05-01T00:00:00 6. Generation of air quality improvement and annual pollution removal (kg/yr) by i-Tree. Note that PM10 removal is for particulate matter greater than 2.5 microns and less than 10 microns. This helps to avoid double counting of PM2.5 removal as it is listed separately. 2015-05-01T00:00:00 7. Generation of annual pollution removal value (dollars/yr) is based on local incidence of adverse health effects and median externality values. The number of adverse health effects and associated economic value is calculated for O3, SO2, NO2, and PM2.5 using the U.S. Environmental Protection Agency's Environmental Benefits Mapping and Analysis Program (BenMAP). The model uses a damage-function approach that is based on the local change in pollution concentration and population. The change in pollution concentration (ppb or micrograms/m3) is generated in i-Tree. Population data is generated by the U.S. Census Bureau. National median externality costs for 2008 were used to calculate the value of CO and PM10 (2.5-10 microns) removal. Note that PM10 value is for particulate matter greater than 2.5 microns and less than 10 microns. This helps to avoid double counting of PM2.5 value as it is listed separately. More information on the methodology for estimating pollution removal can be attained from work by Hirabayashi et al. 2011 and Hirabayashi et al. 2013. 2015-05-01T00:00:00 Generated runoff effects using Excel 2010 and i-Tree Hydro. The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results. Calibration coefficients (0-1 with 1.0 = perfect fit) were calculated for peak flow, base flow, and balance flow (peak and base). Calibrations can often be off, particularly for peak flows, due to mismatching of steam flow and weather data as the weather stations are often outside of the watershed area. For example, it may be raining at the weather station and not in the watershed or vice versa. To estimate the effect of trees at the block group level for Memphis, the Hydro model was run for: Gauging Station Name: Walnut Ck at Webberville Rd, Austin, TX, Gauging Station Location: 30°16'59",-97°39'17", Gauging Station Number: 08158600, Weather Station Name: Austin/Mueller Muni, Weather Station Location: 30°18'00", -97°42'00", Weather Station ID: 722540-13904, Calibration coeff - peak flow (CRF1): 0.778671, Calibration coeff - Base flow (CRF3): 0.6826, Calibration coeff - balanced flow (CRF2): 0.490886, Area km2: 138.1, Simulation period (dates): 01/01/2012-12/30/2012. 2015-05-01T00:00:00 Ran Model Scenarios: After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed. See the overview for details on calibration. 2015-05-01T00:00:00 a. For tree cover simulations, impervious cover was held constant at the original value with tree cover varying between 0 and 100%. Increasing tree cover was assumed to fill bare soil spaces first, then grass and shrub covered areas, and then finally impervious covered land. At 100% tree cover, all impervious land is covered by trees. This assumption is unreasonable as all buildings, road and parking lots would be covered by trees, but the results illustrate the potential impact. Reductions in tree cover were assumed to be filled with grass and shrub cover. 2015-05-01T00:00:00 b. For impervious cover simulations, tree cover was held constant at the original value with impervious cover varying between 0 and 100%. Increasing impervious cover was assumed to fill bare soil spaces first, then grass and shrub covered areas, and then finally under tree canopies. The assumption of 100% impervious cover is unreasonable, but the results illustrate the potential impact. In addition, as impervious increased from the current conditions, so did the percent of the impervious cover connected to the stream, such that at 100% impervious cover, all (100%) impervious cover is connected to the stream. Reductions in impervious cover were assumed to be filled with grass and shrub cover. 2015-05-01T00:00:00 The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. It is defined as the total constituent mass divided by the total runoff volume, although EMC estimates are usually obtained from a flow-weighted composite of concentration samples taken during a storm. Mathematically (Sansalone and Buchberger, 1997; Charbeneau and Barretti, 1998): (1) EMC = C = M/V = (∫C(t) Q(t)dt)/(∫Q(t)dt) ≈ (∑ C(t) Q(t) ∆(t))/(∑ Q(t) ∆(t)) where C(t) and Q(t) are the time-variable concentration and flow measured during the runoff event, and M and V are pollutant mass and runoff volume as defined in Equation 1. It is clear that the EMC results from a flow-weighted average, not simply a time average of the concentration. EMC data is used for estimating pollutant loading into watersheds. EMCs are reported as a mass of pollutant per unit volume of water (usually mg/L). The pollution Load (L) calculation from the EMC method is (2) L = EMC * Q = EMC * dr* A where EMC is event mean concentration (mg/l, mg/m3, ...), Q is runoff of a time period associated with EMC (l/h, m3 /day...), dr is runoff depth of unit area (mm/h, m/h, m/day...), and A is the land area (m2, ...), which is catchment area in i-Tree Hydro. See the Overview for a more complete discussion of the EMC. 2015-05-01T00:00:00 To estimate block group effects, the block group was assumed to act similarly to the watershed in terms of hydrologic effects. To estimate the block group effect, the outputs of the watershed were determined for each possible combination of tree cover (0-100%) and impervious cover (0-100%). Thus, there were a total of 10,201 possible responses (101 x 101). For each block group, the percent tree cover and percent impervious cover combination (e.g., 30% tree / 20% impervious) was matched to the appropriate watershed hydrologic response output for that combination. The hydrologic response outputs were calculated as either percent change or absolute change in units of cubic meters of water per square meter of land area for water flow or kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level. 2015-05-01T00:00:00 unknown 2029-02-13 This metadata was automatically generated from the FGDC Content Standard for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2024-02-09T11:11:00 version of the FGDC CSDGM to ISO 19139 transform. EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact