{"id" : "85c2000098e446cb979af577fd95e821", 
  "name" : "Predicted_Background_Conductivity_Data_View", 
  "owner" : "wharton.christopher_EPAEXT", 
  "guid" : "", 
  "title" : "Predicted Background Conductivity Data View", 
  "type" : "Feature Service", 
  "typeKeywords" : [
    "ArcGIS Server", 
    "Data", 
    "Feature Access", 
    "Feature Service", 
    "Service", 
    "Singlelayer", 
    "Hosted Service", 
    "View Service"
  ], 
  "description" : "\u003cp style='margin-top:0px; margin-bottom:0px; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eThe information displayed is based on \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003especific conductivity \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003epredictions \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003efor stream segments in the contiguous United States \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003efrom the Predicted Background Conductivity (PBC) model. The PBC model was developed using a random forest modeling approach and enables comparison with measured in-stream conductivity. Geology, soil, vegetation, climate and other empirically measured data were used as inputs. The PBC model was designed for streams with natural background SC < 2000 µS/cm.  Above this level (typical for freshwater), PBC model estimates may be less reliable.  Data for some parameters that affect background SC were not readily available and were therefore not included in the model. These include freshwater and marine interfaces, natural mineral springs, salt deposits which may affect groundwater and streams, and other natural sources of salts.  In such areas, the model is likely to underestimate background SC, but in some arid areas the model may need to be calibrated by as much as 300 \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eµS/cm (See Model validation below)\u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e.\u003c/span\u003e\u003cfont size='3' style='font-family:inherit;'\u003e \u003c/font\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eLocal knowledge is often necessary to assess differences between predicted and measured background SC. \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eMore information about the model and datasets can be found at \u003c/span\u003e\u003ca href='https://epa.maps.arcgis.com/home/item.html?id=13fe4698f35d4a95ba9dfe6f8033f735' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit; font-size:large;'\u003eFreshwater Explorer Metadata\u003c/a\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e\u003cstrong\u003eGeneral Modeling Approach\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eThe \u003ca href='https://github.com/USEPA/StreamCat' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003eStreamCat dataset\u003c/a\u003e and process was used to develop stream-specific model predictions (Hill et al. 2016) based on  watershed averages for  \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eeach NHD+ segment in the contiguous United States \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e(McKay et al. 2012). These stream segments drain an average area of 3.1 square kilometers which characterizes the spatial grain size of this dataset. Natural background SC was not estimated for streams shown as grey line\u003cspan style='font-family:inherit;'\u003es. \u003c/span\u003e\u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eThe empirical background conductivity model was developed using the following steps:\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e \u003c/p\u003e\u003col\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eCreate training and validation data sets of SC \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eobservations from minimally altered stream segments.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eCharacterize temporally and spatially specific watershed \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eenvironments for each observation, including antecedent\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003econditions.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eRelate observed SC to environmental predictors using a \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003emachine learning technique (random forests).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eAssess model performance and validate using multiple \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eobservations made at randomly chosen stream segments.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cp style='margin-top:0px; margin-bottom:1.5rem; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eFor a detailed description of the step used to develop the PBC model, see \u003cem\u003eOlson and Cormier, 2019.\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\u003cdiv style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e\u003cem\u003e\u003cbr /\u003e\u003c/em\u003e\u003c/span\u003e\u003cp style='margin-top:0px; margin-bottom:1.5rem;'\u003e \u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:medium;'\u003e\u003cstrong\u003eTraining and Validation Data\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eThe t\u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003era\u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eining and validation datasets \u003c/span\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003econsist of minimally disturbed sites. More than 2.4 million SC observations were obtained from the Water Quality Portal (WQP) (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012). Data were downloaded from the \u003c/span\u003e\u003ca href='https://www.waterqualitydata.us/' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eWQP website\u003c/span\u003e\u003c/a\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e using the following query criteria: \u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e \u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eCountry - United States.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eSample Media - Water.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eCharacteristics - Conductivity, Specific Conductivity, Specific Conductance, Calculated/Measured Ratio. \u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eDate range - observations between 1 January 2001 and 31 December 2015. This time period was chosen so that \u003ca href='https://modis.gsfc.nasa.gov/data/' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003eModerate Resolution Imaging Spectroradiometer (MODIS) satellite data\u003c/a\u003e (NASA 2019) could be used as predictors in the model. \u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp style='margin-top:0px; margin-bottom:1.5rem;'\u003e \u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eDuring development, 56 potential explanatory parameters were evaluated. The original source data and final datasets are available for download \u003ca href='https://doi.org/10.23719/1500945' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehere\u003c/a\u003e. Predicted Background Conductivity metadata and data are also available on the Geoplatform.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eEach observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropocentric stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014).  Stream segments were considered as minimally stressed when the associated watershed drainage area had \u003c/span\u003e\u003cem\u003e≤\u003c/em\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e0.5% impervious surface, \u003c/span\u003e\u003cem\u003e≤\u003c/em\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e5% urban, \u003c/span\u003e\u003cem\u003e≤\u003c/em\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e10% agriculture, and population densities of 0.8 to 30 people per square kilometer. Watersheds displaying large residuals during initial model predictions were assessed for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Disturbed watersheds with a tidal influence or unusual geologic conditions, such as hot springs, were not removed from the dataset. Some sites with high levels remain in the dataset (e.g., mining influenced but no easily accessible evidence). Some sites with high SC remain in the dataset (e.g., mining influenced but no national record). About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observational dataset was used for model calibration.\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eThe final training data set used for modeling had 1785 stream segments with 11 796 observations, and the validation data set had 92 segments with 581 observations. The majority of segments had a single observation but ranged up to 165 observations per segment.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cstrong\u003e\u003cspan style='font-family:inherit; font-size:medium;'\u003eModel Validation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eThe "Model Validation" view on the Freshwater Explorer shows the predictive performance at reference sites that were used to develop PBC model. In the wetter and more forested areas, reference sites were more abundant and predictions are more precise. In the central U.S., fewer reference sites were available and predictions are more uncertain. In the grass and shrub lands east of the continental divide, measured SC was over-predicted by the model by more than 100 µS/cm at 5% of sites (yellow dots) and under-predicted by more than 100 µS/cm at 3% of sites (red dots).  Calculated differences between measured and predicted SC are reported as residuals in pop-up boxes on this view. There are many potential causes for these differences, including data reporting errors and reference site reliability.  \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eOverall, the model explained most of the variation in SC and produced reasonably accurate predictions for both calibration data (Mean Absolute Error = 22 µS/cm, Nash-Sutcliffe Efficiency = 0.92, and Coefficient of Determination = 0.92) and external validation data (Mean Absolute Error = 29 µS/cm, Nash-Sutcliffe Efficiency = 0.87, and Coefficient of Determination = 0.87). Values reported as background only apply to streams and have not been validated for lakes or wetlands.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eMore details can be found in \u003cem\u003eOlson and Cormier (2019)\u003c/em\u003e.\u003c/span\u003e\u003cbr /\u003e\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0px;'\u003e\u003cspan style='font-family:inherit;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cstrong\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eReferences\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eHill, R.A., Weber, M.H., Leibowitz, M.H., Olsen, A.R., Thornbrugh, D.J., 2016. The Stream-Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States. JAWRA Journal of the American Water Resources Association, 52(1), 120-128. doi: \u003ca href='https://doi.org/10.1111/1752-1688.12372' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehttps://doi.org/10.1111/1752-1688.12372\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eMcKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., Rea, A., 2012. NHDPlus Version 2: User Guide. National Operational Hydrologic Remote Sensing Center, Washington, DC. Available at: \u003c/span\u003e\u003ca href='https://epa.maps.arcgis.com/home/item.html?id=540abb1d015b4bd2b87d30f4c28a58cb&view=table' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003ehttps://nctc.fws.gov/courses/references/tutorials/geospatial/CSP7306/Readings/NHDPlusV2_User_Guide.pdf\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eNASA, 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Available at: \u003ca href='https://epa.maps.arcgis.com/home/item.html?id=540abb1d015b4bd2b87d30f4c28a58cb&view=table' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehttps://modis.gsfc.nasa.gov/data/\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eOlson, J.R., Hawkins, C.P., 2012. Predicting natural base‐flow stream water chemistry in the western United States. Water Resources Research, 48(2). doi: \u003ca href='https://epa.maps.arcgis.com/home/item.html?id=540abb1d015b4bd2b87d30f4c28a58cb&view=table' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehttps://doi.org/10.1029/2011WR011088\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eOlson, J.R. and Cormier, S.M., 2019.  Modeling spatial and temporal variation in natural background specific conductivity. Environmental Science and Technology. doi: \u003ca href='https://epa.maps.arcgis.com/home/item.html?id=540abb1d015b4bd2b87d30f4c28a58cb&view=table' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehttps://dx.doi.org/10.1021/acs.est.8b0677\u003c/a\u003e7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eOlson, J.R. and Cormier, S.M., 2019. Modeling spatial and temporal variation in natural background specific conductivity: Data sets and R-code. doi: \u003ca href='https://doi.org/10.23719/1500945' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehttps://doi.org/010.23719/1500945\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eOmernik, J. M.; Griffith, G. E., 2014. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental. Management. 54 (6), 1249−1266. doi: \u003ca style='color:rgb(0, 121, 193); font-family:inherit;' target='_blank'\u003ehttps://doi.org/10.1007/s00267-014-0364-1\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eU.S. Environmental Protection Agency (U.S. EPA), 2016. STORET.  Available at: \u003c/span\u003e\u003ca href='https://epa.maps.arcgis.com/home/item.html?id=540abb1d015b4bd2b87d30f4c28a58cb&view=table' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003ehttps://www.epa.gov/storet/\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eU.S. Geological Survey (USGS), 2016. National Water Information System. Available at: \u003ca href='https://epa.maps.arcgis.com/home/item.html?id=540abb1d015b4bd2b87d30f4c28a58cb&view=table' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003ehttps://waterdata.usgs.gov/nwis\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan style='font-family:inherit;'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003eStoddard, J. L., Larsen, D. P., Hawkins, C. P., Johnson, R. K., Norris, R. H., 2006. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol. Appl. 2006, 16 (4), 1267−1276. doi: \u003c/span\u003e\u003ca href='https://doi.org/10.1890/1051-0761(2006)016[1267:SEFTEC]2.0.CO;2' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank'\u003e\u003cspan style='font-family:inherit; font-size:large;'\u003ehttps://doi.org/10.1890/1051-0761(2006)016[1267:SEFTEC]2.0.CO;2\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e", 
  "tags" : [
    "Conductivity", 
    "Cormier", 
    "Freshwater", 
    "HC05", 
    "Hydrology", 
    "Ion", 
    "Aquatic Life", 
    "Model", 
    "National", 
    "Olson", 
    "Tetra Tech", 
    "United States", 
    "USEPA", 
    "Wharton", 
    "Zheng", 
    "Water"
  ], 
  "snippet" : "This \"Predicted Background Conductivity\" view consists of a shapefile derived from National Hydrography Dataset Plus Version 2.0 which displays modeled natural background conductivity for the continental United States.", 
  "thumbnail" : "thumbnail/thumbnail1627344433359.png", 
  "extent" : [-135.062619773205, 20.5531768464803, 
    -56.6007058207892, 52.3578225766815
  ], 
  "categorites" : [
    "/Categories/Water", 
    "/Categories/Environmental Indicators"
  ], 
  "spatialReference" : "102965", 
  "url" : "https://services.arcgis.com/cJ9YHowT8TU7DUyn/arcgis/rest/services/Predicted_Background_Conductivity_Data_View/FeatureServer", 
  "culture" : "en-us", 
  "licenseInfo" : "\u003cp style='margin-top:0px; margin-bottom:0.0001pt; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cb\u003e\u003cspan style='font-size:14pt;'\u003eFreshwater Explorer Disclaimer\u003c/span\u003e\u003c/b\u003e\u003cbr /\u003e\u003c/p\u003e\u003cdiv style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003eThe U.S. EPA Freshwater Explorer and related products are intended for exploratory and discussion purposes. Although statutory provisions and U.S. Environmental Protection Agency (EPA) regulations contain legally binding requirements, this document is not a regulation nor does it change or substitute those provisions or regulations. The document does not substitute for the Clean Water Act, a National Pollutant Discharge Elimination System permit, or EPA or state regulations applicable to permits; nor is this document a permit or regulation itself. Thus, it does not impose legally binding requirements on EPA, states, tribes, or the regulatory community. This document does not confer legal rights or impose legal obligations on any member of the public. Mention of any trade names, products, or services is not and should not be interpreted as conveying official EPA approval, endorsement, or recommendation.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003eWhile EPA has used its best efforts to include complete and accurate information in this system, EPA cannot be held responsible for errors or omissions and is not liable for any direct, indirect or consequential damages resulting from using this secondary information. Some potential sources of error have been assessed by the U.S. EPA resulting in the removal of some samples from the original data sets. However, all sources of potential error cannot be eliminated from the measured data reported in the Freshwater Explorer or the data used to develop predictive models. Therefore, the U.S. EPA cannot fully ensure either the original data or the values calculated from them. Conclusions and assessments drawn from the use of the Freshwater Explorer are the responsibility of the user.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003ePlease check sources, scale, accuracy, dates and other available information. Please confirm that you are using the most recent copy of both data and metadata. Reliance on the information contained in this system by any party cannot be used as a defense in any administrative or judicial proceeding.\u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003e \u003c/span\u003e\u003c/p\u003e\u003cp style='margin-top:0px; margin-bottom:0.0001pt;'\u003e\u003cspan style='font-size:large;'\u003eThis mapping tool might be revised periodically. EPA can revise this story map without public notice to reflect changes in EPA policy, guidance, and advancements in the field of biological assessments. EPA welcomes public input on this document at any time. Send comments to \u003c/span\u003e\u003ca href='mailto:FreshwaterExplorer@epa.gov' rel='nofollow ugc' style='color:rgb(0, 121, 193); text-decoration-line:none;' target='_blank'\u003eFreshwaterExplorer@epa.gov\u003c/a\u003e\u003cspan style='font-size:large;'\u003e, National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, 26 W. Martin Luther King Dr, Cincinnati, OH 45268.\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e", 
  "accessInformation" : "John O. Olson, Christopher L. Wharton, Susan M. Cormier", 
  "properties" : "", 
  "access" : "public", 
  "size" : 0
}