Stressor Indicators

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This web page describes example indicators for the RPS Stressor Indicators category. The brief summary of each indicator provides a name, example metrics used, relevance to watershed condition and basic information about data sources and measurement. Many of these indicator summaries are further hyperlinked to indicator-specific reference sheets with more detailed information including literature excerpts. Many of these example indicators have also been compiled for the conterminous US on HUC12 watershed units and are either already embedded in the data tables of state-specific RPS Tools or publicly available through the Watershed Index Online (WSIO) Indicator Data Library. Some Stressor Indicators that weren't compiled nationally are available at state or local scales, and these can and should be added to the RPS Tool by the user as appropriate to support their screening objectives.

On this page:

% Agriculture 

  • Description: Indicators of the extent and distribution of agricultural cover within the watershed unit. Agricultural land cover types most often include cropland, pasture, and hay. Indicators can characterize the present-day extent of agricultural cover throughout the entire watershed area, within the riparian zone or hydrologically connected zone, or in patches that are contiguous to surface waters. Indicators can also measure changes in the extent of agricultural cover over time, or describe additional terrain conditions such as cropland on steep slopes. See Watershed % Agriculture Reference Sheet; Corridor % Agricultural Reference Sheet; Watershed % Legacy Agriculture Reference Sheet; Corridor % Legacy Agriculture Reference Sheet; Land Use Change Trajectory Reference Sheet; Legacy Land Uses Reference Sheet.
  • Example metrics: % Agriculture in Watershed; % Agriculture in Riparian Zone; % Agriculture Change in Watershed; % Cultivated Crops in Hydrologically Connected Zone; % Pasture/Hay in Watershed; % Agriculture on > 10% Slope in Watershed; % Slope of Cropland, Mean in Watershed; % Cropland on > 3% Slope in Watershed; % Slope of Cropland, Mean % Slope in Riparian Zone; % Pasture on > 10% Slope in Watershed
  • Why relevant: Croplands and pastures have been linked to a wide variety of water quality and biotic impacts on waters, and agriculture within stream corridors is sometimes more highly linked to impairments than agriculture generally distributed in the watershed. Common effects seen at moderate to high agricultural proportions of total watershed land cover include less diverse and more intolerant macrobenthic communities, increased nutrient loading resulting in turbid water, overall homogenization of the fish fauna, accelerated erosion and bank destabilization, suspended sediment particles carrying pesticides, pathogens and heavy metals, habitat degradation and reduced biodiversity and increases in specific conductivity. A past history of agriculture in the watershed and/or riparian corridor can continue to account for adverse effects even after land use change (vegetation succession, transition to residential or other uses) has replaced the agriculture. Some studies suggest that a past history of agricultural use is more strongly correlated with impairment than current non-agricultural land use patterns. In addition, cropland on steep slopes is associated with higher erosion and overland transport of nutrients and other pollutants. Abundant steep-slope agriculture in a watershed may represent a significant difficulty to overcome in restoration efforts.
  • Data sources and measurement: Measured as the percentage of total watershed area that is classified as an agricultural cover type within the watershed, riparian zone, hydrologically connected zone, or contiguous to surface water. Indicators of agricultural cover change over time are calculated as the present-day percentage minus the historical percentage. The National Land Cover Database (NLCD) for 2011, 2006, 2001 and 1992 are available for determining the area of agricultural cover within study watersheds and changes over time; alternative land cover datasets are also often available from state-specific sources. Slope-based metrics are calculated by overlaying geospatial land cover data and elevation data to determine agricultural areas on steep slopes. Steep slopes are typically defined as those greater than 3%, though other thresholds can be explored. For elevation data, the National Elevation Dataset (NED) is available. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Number of CAFOs 

  • Description: The count of Concentrated Animal Feeding Operations (CAFOS) in the watershed.
  • Example metrics: Count of CAFOs in Watershed
  • Why relevant: CAFOs are a spatially concentrated source of nutrients and pathogens that, although frequently managed or regulated, can episodically release pollutants that set back impaired waters recovery. Due to the high magnitude of pollutant loads associated with CAFOs, they can be considered a potential stressor that may reduce recovery potential in some areas.
  • Data sources and measurement: State records may identify or map CAFO locations and likely also the livestock species and numbers.

Number of Septic Systems 

  • Description: The count of septic systems in the watershed.
  • Example metrics: Count of Septic Systems in Watershed
  • Why relevant: Although subject to regulations, private septic systems frequently fail to adequately treat domestic wastewater before it enters surface and groundwaters. Septic effluent that reaches waterways can increase nutrient and pathogen loadings, as well as deliver household toxins such as chlorine. Because of difficulty of detection, failed septic systems are also an obstacle to effective recovery and restoration targeting.
  • Data sources and measurement: Land cover maps overlaid with sanitary sewer service area maps can help identify locations with potential septic usage as developed areas in non-sewered areas. Some municipalities have individual septic records that can be summed by township or watershed. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

% Impervious Cover 

  • Description: Indicators of the extent of impervious cover in the watershed. Indicators can characterize the present-day extent of impervious cover throughout the entire watershed area, within the riparian zone or hydrologically connected zone, or in patches that are near surface waters. Indicators can also measure changes in the extent of impervious cover over time. See Watershed % Impervious Cover Reference Sheet; Corridor % Impervious Cover Reference Sheet.
  • Example metrics: % Imperviousness, Mean in Watershed; Impervious Cover Projected Change; % Waters Near ≥ 5% Impervious Cover; Proximity Waters to ≥ 15% Impervious Cover; % Streamlength Near ≥ 5% Impervious Cover
  • Why relevant: Impervious cover is an indicator of the impacts of urbanization and development on water resources. Impervious cover intensifies multiple stressors to a watershed, such as increased pollutant loads from stormwater runoff, altered stream flow, decreased bank stability, and increased water temperatures. The significance of this metric in reducing recovery potential is based on the multiple impacts to the watershed as well as the nearly irreversible nature of imperviousness at high levels.
  • Data sources and measurement: Measured as the percentage of total watershed area that is classified as impervious within the watershed, riparian zone, or hydrologically connected zone. Alternative measures can focus on the percentage of stream and/or lakeshore length that is near areas with high impervious cover. Indicators of impervious cover change over time are calculated as the present-day percentage minus the historical percentage. Impervious land cover data is available from the National Land Cover Database (NLCD) for 2001, 2006, and 2011. Additionally, the Integrated Climate and Land-Use Scenarios (ICLUS) dataset includes future projections of impervious cover. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

% Tile-Drained Cropland 

  • Description: The percent of the watershed with subsurface agricultural tile drainage. See Watershed % Tile-Drained Cropland Reference Sheet.
  • Example metrics: % Tile Drained Area in Watershed
  • Why relevant: A tile drainage system efficiently drains water from the soil saturated zone of a field to adjacent streams, thereby reducing residence time in areas conducive to denitrification and increasing nitrogen export. Tile draining also has created concern for the delivery of sediment, bacteria, and other contaminants. Tile drains can selectively transport fine-grained sediment from soils to receiving freshwater, increase the size of the contributing area by hydraulically connecting remote areas of the catchment to the stream system, and circumvent management strategies such as buffer strips. Subsurface drain tiling that accompanies wetland drainage can lead to flashy hydrology that can decimate stream biota.
  • Data sources and measurement: The United States Geological Survey (USGS) produced a dataset of tile drained areas summarized by NHDPlus catchment that can be aggregated to larger watershed scales. Custom datasets could also be developed based on verifying the association of tile drain usage with specific hydric soil types that are being cropped. Soil survey data for mapping hydric soils are available for most areas from the Natural Resources Conservation Service (NRCS) Soil Data Access website. The availability and coverage of digital soil survey data varies by state. States with fully digitized county soil survey-level information can use this metric most effectively. Land cover data for mapping cropland include the National Land Cover Dataset (NLCD), the USDA Cropland Data Layer (CDL), and various state sources. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

% U-Index (Non-Natural Land Cover) 

  • Description: Indicators of the extent and distribution of anthropogenic land cover within the watershed. Anthropogenic cover types typically include urban, cropland, and pasture. Barren land may be optionally counted if it does not occur naturally within the study area, such as in areas with vegetation removed as part of surface mining. Indicators can characterize the present-day extent of anthropogenic cover throughout the entire watershed area, within the riparian zone or hydrologically connected zone, or in patches that are contiguous to surface waters. Indicators can also measure changes in the extent of anthropogenic cover over time. See Watershed % U-Index Reference Sheet; Corridor % U-Index Reference Sheet.
  • Example metrics: % Human Use, U-Index in Watershed; % Human Use, U-Index in Riparian Zone; % Human Use Change, U-Index Change in Watershed; % Human Use Change, U-Index Change in Hydrologically Connected Zone; % U-Index Change To/From Water in Watershed
  • Why relevant: Both watershed-wide and riparian/hydrologically connected zone U-Index (anthropogenic) land cover patterns are associated with benthic macroinvertebrate communities that are tolerant of stream degradation, indicating a lower level of aquatic ecological integrity and water quality. As the intensity of human activities increase there is a tendency for the biological integrity of the rivers decreases. Additionally, the change in anthropogenic land cover over time can be explored.  The age of an area with many anthropogenic influences or specifics of its history may have implications for its legacy pollutants in groundwater that can affect recovery. Built-up pollutants from anthropogenic sources in groundwater can continue to be discharged through influent groundwater connections for decades. Increasing substrate embeddedness and bank erosion have also been observed to increase in streams in developing areas.
  • Data sources and measurement: Measured as the percentage of total watershed area that is classified as anthropogenic cover within the watershed, riparian zone, hydrologically connected zone, or contiguous to surface water. Indicators of anthropogenic cover change over time are calculated as the present-day percentage minus the historical percentage. The National Land Cover Database (NLCD) for 2011, 2006, 2001 and 1992 are available for determining the area of anthropogenic cover within study watersheds and changes over time; alternative land cover datasets are also often available from state-specific sources. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

% Urban 

  • Description: Indicators of the extent and distribution of urban (developed) land cover within the watershed. Indicators can characterize the present-day extent of urban cover throughout the entire watershed area, within the riparian zone or hydrologically connected zone, or in patches that are contiguous to surface waters. Indicators can also measure changes in the extent of urban cover over time. See Watershed % Urban Reference Sheet; Corridor % Urban Reference Sheet; Watershed % Legacy Urban Reference Sheet.
  • Example metrics: % Urban in Watershed; % Urban in Hydrologically Connected Zone; % Urban Change in Watershed; % Developed, High Intensity in Watershed; % Developed, Medium Intensity in Riparian Zone
  • Why relevant: Urbanization of a watershed, riparian zone or hydrologically connected zone of a intensifies multiple stressors, such as pollutant loads from stormwater runoff, altered stream flow, decreased bank stability, increased substrate embeddedness, and increased water temperatures. The significance of this metric in reducing recovery potential is based on the multiple impacts to the watershed as well as the nearly irreversible nature of imperviousness (see also watershed impervious cover). Additionally, the change in urban land cover over time can be explored. The age of an urbanized area or specifics of its history may have implications for its legacy pollutants in groundwater that can affect recovery. Built-up urban and industrial pollutants in groundwater can continue to be discharged through influent groundwater connections for decades. Exploring the degree of urbanization in stream/lake corridors may also be important as developed land cover in riparian zones is associated with aquatic biota more tolerant of pollutants. Human shoreline development may lead to loss of littoral habitats. Ecological responses to threshold percentages of development found in corridors have not been observed at the watershed scale, indicating potentially greater significance of corridor versus watershed effects from urbanization.
  • Data sources and measurement: Measured as the percentage of total watershed area that is classified as urban cover within watershed, riparian zone, hydrologically connected zone, or contiguous to surface water. Indicators of urban cover change over time are calculated as the present-day percentage minus the historical percentage. The National Land Cover Database (NLCD) for 2011, 2006, 2001 and 1992 are available for determining the area of urban cover within study watersheds and changes over time; alternative land cover datasets are also often available from state-specific sources. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Road Density 

  • Description: Indicators of the length or density of roads in the watershed or in the riparian zone of the watershed. See Watershed Road Density Reference Sheet; Corridor Road Density Reference Sheet.
  • Example metrics: Density All Roads in Watershed; Density All Roads in Riparian Zone; Length Primary Roads in Watershed; Length Primary Roads in Riparian Zone
  • Why relevant: Roads and associated storm drains appear to be important elements influencing the degradation of water quality with respect to aquatic biota. Fish density, number of intolerant fish species, and invertebrate density are seen to change in association with more roads in watersheds. Studies of Middle Atlantic streams have linked greater road densities to increased conductivity and subsequent impacts on aquatic life. Roads also add to impervious cover and thereby contribute many secondary effects on flashy flows and related destabilized channels, increased urban pollutant transport, and other effects. Riparian corridor roads can affect sedimentation and deposition processes, increase siltation to the detriment of aquatic biota, compact floodplain substrates, reduce recharge that would help maintain base flow and increase pollutants such as road salts that raise conductivity and harm stream invertebrates and fish. Because most stream corridor roads are likely permanent, their relevance to recovery potential is also linked to whether the degraded conditions can be managed or mitigated.
  • Data sources and measurement: Measured as road length in the watershed or road density as length per watershed area. National road geospatial data is obtainable through the US Census Bureau. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Wildfire Risk 

  • Description: The level of wildfire risk or extent of areas with high wildfire risk in the watershed.
  • Example metrics: Wildfire Hazard Potential, Mean in Watershed; % High or Very High Wildfire Hazard Potential
  • Why relevant: Vegetative cover loss from severe wildfires can increase runoff potential and alter historic flow regimes, increase sediment and nutrient loading, and increase water temperatures. Wildfires can also deposit airborne fire debris which can encapsulate aquatic habitat as it settles, or cause fish kills via oxygen depletion through the decomposition process. Additionally, wildfire management often involves the use of flame retardant chemicals which can be harmful to aquatic organisms.
  • Data sources and measurement: The United States Department of Agriculture (USDA) Forest Service maintains a Wildfire Hazard Potential geospatial dataset that can be used to summarize wildfire risk at the watershed scale for the entire conterminous United States. Some states also maintain wildfire risk information that could be summarized by watershed. Additionally, local knowledge of recent wildfires or wildfire prone areas may be used if the data exists. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Mining Activity 

  • Description: Indicators of current or historic mining activity in the watershed. Mining may include surface or subsurface mineral or coal mines, support sites (test cores, water wells, etc.) or processing plants. Indicators can quantify all mining activity in the watershed or focus on specific types of mines or mining activities. 
  • Example metrics: Count Mineral Mining in Watershed; Density Mineral Mining in Watershed; Count Minor Coal Mining in Watershed; Density All Mining in Watershed; Count All Coal Mining in Watershed
  • Why relevant: Mined areas often undergo drastic vegetative cover loss, resulting in increased runoff and nonpoint source pollutant loading potential. Additionally, mining activity often requires large amounts of water for the extraction process, potentially altering historic flow regimes. The introduction of toxic chemicals that are used both for the recovery of mining materials and their processing can also degrade water quality.
  • Data sources and measurement: The USGS Mineral Resources Program maintains a database of non-energy mineral mining locations. Data on coal mine locations are stored in the USGS National Coal Resources Data System. States or local entities may also maintain databases of mine locations or mined areas that could be summarized by watershed. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Linear % of Channel Through Agriculture 

  • Description: The percent of stream channel or lake shore length that passes through agriculture in the watershed.
  • Example metrics: % Streamlength Contiguous to Agriculture in Watershed
  • Why relevant: Croplands and pastures have been linked to a wide variety of water quality and biotic impacts on waters (see watershed percent agriculture). The actual land-water interface along streams that pass through agricultural areas can vary substantially in its relevance to agriculture-related impairment. Corridors or watersheds with high proportions of agriculture may still have well-vegetated buffers or may be farmed or grazed all the way to the channel. Unbuffered channels and lake shores are more erosion-prone, deliver more sediment and pollutants such as pesticides and fertilizers in runoff, can elevate water temperatures, and can have other impacts that hinder and complicate recovery.
  • Data sources and measurement: Measured by overlaying stream hydrography and land cover to determine the percentage of total streamlength that flows through agricultural land cover. Calculation can be performed as a linear measurement if the watershed contains only linear streams, but can also be approximated by calculating the percentage of agricultural area within a very narrow (e.g., 1 meter) buffer. An equivalent approach allows for lake shores to be characterized. Resolution of the land cover source should be considered, as thin vegetated buffers may not be detected or mapped. A slightly different way to quantify the agriculture-flowing water interface involves looking also at flow accumulation paths generated from digital elevation data. These 'low spots' may not have well-defined channels but have some likelihood of transporting agricultural runoff to surface waters nearby.

Corridor Road Crossings 

  • Description: The count or density of road-stream crossings in the watershed. Indicators can measure all road-stream crossings or crossings over a specific stream order. See Reference Sheet.
  • Example metrics: Count Road-Stream Crossings; Density Road-Stream Crossing in Watershed; Count Road-Stream Crossings, 1st Order; Count Road-Stream Crossings, 2nd Order
  • Why relevant: Road crossings are linked with degraded conditions for several reasons but because most crossings are likely permanent, their relevance to recovery potential is also linked to whether the degraded conditions can be managed or mitigated. Road crossings are linked with channel destabilization, tree collapse, hanging tributary junctions as a result of variable incision rates, and erosion around artificial structures including bridges. Local scouring alters sedimentation and deposition processes, and more sediment and chemicals enter streams where a road crosses. Wetland road crossings often block drainage passages and groundwater flows, effectively raising the upslope water table and killing vegetation by root inundation, while lowering the downslope water table. Small road crossings often have culverts that do not allow upstream fish passage and constrict the available useful habitat for salmonids already vulnerable to other impacts.
  • Data sources and measurement: Measured as the number of road crossing in the watershed or number of crossings per stream mile. National road geospatial data is obtainable through the US Census Bureau. Stream hydrography and Strahler stream order (if used) are available from the NHDPlus dataset. Data on unimproved road crossings in remote parts of federal lands may need to be obtained through communication with the appropriate land management agency. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Aquatic Barriers 

  • Description: Indicators of the number or density of dams and other aquatic barriers in the watershed. See Reference Sheet.
  • Example metrics: Dam Density in Watershed; Count of Dams in Watershed
  • Why relevant: This metric is often relevant to evaluating restoration prospects for biological impairments. Barriers that fragment aquatic populations of marginal size may reduce the viability of each fragmented population. Barriers often also can prevent or delay recolonization of areas with diminished or absent populations. Barriers may be natural (waterfalls, major habitat changes) as well as artificial (perched culverts, buried streams, dams) and may also be physio-chemical (temperature, toxicity) as well as structural. Unless species reintroduction is feasible to circumvent a barrier that cannot be removed or modified, barriers are sometimes insurmountable obstacles to aquatic community recovery.
  • Data sources and measurement: Barrier influence is most easily measured as a count per watershed or density per stream mile in the watershed. A more meaningful approach is the number/density of stream segments isolated from waters of similar size (e.g., within 1 Strahler order). Depending on the mobility of species of interest, barrier height or barrier upstream/downstream location may be considered. Aquatic barriers for fish passage are documented through the US Fish and Wildlife Fish Passage Decision Support System. Major dams have been mapped through the US Army Corps of Engineers' National Inventory of Dams but the large numbers of smaller dams on small to medium-scale streams and rivers are not uniformly documented. Some types of barrier information may be available from water quality monitoring. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Channelization 

  • Description: Indicators of the degree of channelization in the watershed. Channelization can include channel straightening, widening, ditching, or other changes to natural channel form. See Reference Sheet.
  • Example metrics: Canal Density in Watershed; % Ditch Drained Area in Watershed
  • Why relevant: Channelization is a major modification of natural form that results in habitat simplification and reduction in the frequency of specific, life-supporting habitat types (e.g. pools, spawning gravels). The process also destabilizes erosion/deposition dynamics, shortens residence time for nutrient processing, and increases risks of downstream erosion and channel destabilization with accompanying loss of use or property. Negative impacts on biological communities are well documented not only within channelized reaches but at substantial distances downstream. The significance of this metric in reducing recovery potential is based on multiple effects: degraded habitat, altered primary physical processes, destabilized instream conditions, persistence of negative effects for decades, and high expense of reengineering channel sinuosity.
  • Data sources and measurement: Measured as the percentage of total streamlength in the watershed that is channelized or as the percentage of watershed area that is drained by channelized reaches. Canals and ditches are mapped at medium resolution (1:100,000 scale) and high resolution (1:24,000 scale) as part of the National Hydrography Dataset (NHD). The United States Geological Survey (USGS) produced a dataset of ditch drained areas summarized by NHDPlus catchment that can be aggregated to larger watershed scales. Manual interpretation from geospatial hydrographic data, although somewhat laborious, is effective for identifying and measuring the length/percent of channelized reaches in each impaired waterbody segment. Channelization presence/absence is sometimes reported as a cause for 303(d) listing and available as attribute data from EPA's ATTAINS data system.

Hydrologic Alteration 

  • Description: Indicators of the degree of hydrologic alteration in the watershed, defined as human-induced changes to the natural flow regime. Indicators can include direct measures of streamflow change relative to reference conditions or indirect measures of sources of hydrologic alteration. See Reference Sheet.
  • Example metrics: Change in Mean Annual Flow; Increase in Low Flow Duration; Dam Storage Volume in Watershed; Dam Storage Ratio in Watershed
  • Why relevant: Most U.S. river systems are hydrologically altered by dams, but water diversions, withdrawals, channelization, land use change, and climate change also cause hydrologic alteration. These source of alteration have resulted in dramatic shifts in river flow regimes, sediment transport and deposition patterns, water temperature, nutrient loadings, fish assemblages, floodplain isolation, altered high and low flow, and floodplain land use. Significant departure of an impaired waterbody from its range of natural flow variability is a common mechanism that negatively influences recovery potential. However, dam removal, adjusting flow regulation at dams or the changing the seasonality or timing of withdrawals is often possible and can bring about recovery in many flow-altered waters.
  • Data sources and measurement: Can be measured as the percent change in a present-day flow metric (mean annual flow, flood flow magnitude, etc.) from reference conditions. Since flow data are typically limited, a scoring process for waterbody segments downstream of dams or withdrawals can implemented that considers dam size, role on flow alteration, and feasibility of flow management. Where detailed dam information is not available, the metric can be measured in terms of dam presence/absence, dam storage volume, or dam storage ratio (ratio of storage volume to annual flow at the watershed outlet). Dam locations are documented in the  the US Army Corps of Engineers' National Inventory of Dams, though the large numbers of smaller dams on small to medium-scale streams and rivers are not uniformly documented in the NID. An example of state withdrawal information can be found through the Michigan Department of Natural Resources and Environment. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Water Use Intensity 

  • Description: Indicators of the magnitude of water use in the watershed. Indicators can measure water withdrawal volumes from streams in the watershed or reported water demand by domestic, agricultural, industrial or other users in the watershed.
  • Example metrics: Water Withdrawal Volume; Water Withdrawal Ratio; Domestic Water Demand in Watershed; Agricultural Water Demand in Watershed; Industrial Water Demand in Watershed
  • Why relevant: Stressors affecting the natural flow regime can have numerous secondary impacts. Ecological responses to flow alterations include loss of sensitive species, reduced diversity, altered assemblages and dominant taxa, reduced abundance, and increases in non-native species. Human water use can affect the magnitude, frequency and duration of streamflows.
  • Data sources and measurement: Measured as the sum of daily or annual water withdrawal volumes in the watershed. A withdrawal ratio can be calculated as the annual withdrawal volume divided by annual streamflow volume at the watershed outlet. Water withdrawal and streamflow information is usually limited to state-specific databases and may not be available for all projects. Alternatively, EPA EnviroAtlas has used USGS estimates of water demand by county to derive HUC12 scale estimates of water demand for domestic, agricultural, industrial, and hydroelectric use categories. Note that these estimates should not be interpreted as water withdrawals since they do not consider the location of water acquisition. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Elevation 

  • Description: The elevation of the watershed or the elevation of one or more river/lake segments of interest in the watershed. See Reference Sheet.
  • Example metrics: Elevation, Mean Value in Watershed; Elevation, Minimum Value in Watershed; Elevation, Maximum Value in Watershed
  • Why relevant: Specific to waters with biological impairments involving coldwater fish populations. For a given state or sub-state region, the range of elevations among different bio-impaired waters may provide part of the basis for comparing the likelihood of reestablishing coldwater temperature regimes, all other factors aside. Lower elevations may correlate with greater vulnerability of coldwater aquatic communities and difficulty in their restoration, especially in consideration of expected climate change. Secondarily, warmer water temperature regimes in lower elevation streams can increase chemical pollutant availability or toxicity and oxygen depletion.
  • Data sources and measurement: Measured as mean, minimum, or maximum elevation of the watershed or the specific stream/river segment of interest. Field data or models may be suitable for determining elevation thresholds below which recovery of a coldwater system or species is unlikely. The National Elevation Dataset (NED) can be used to generate elevation summary statistics. The NHDPlus contains information on maximum and minimum elevation for each flowline in the dataset. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Invasive Species Risk 

  • Description: Indicators of the risk of introduction or further establishment of invasive species in the watershed. See Reference Sheet.
  • Example metrics: Invasive Species Risk Score in Watershed
  • Why relevant: Non-indigenous species (NIS) invasions are widely known to disrupt aquatic system function and inhibit recovery of altered systems. The rapid colonization typical of NIS may act to subvert expected succession pathways and thereby disrupt restoration planning. Altered structure due to aquatic or riparian NIS can reduce shade, inhibit native riparian vegetation cover, and increase sedimentation. Aquatic invaders may compete directly or prey upon key native species, reduce numbers or species diversity, and markedly alter food webs and ecological structure. Presence of NIS may actually be the impairment cause for listing, and recovery in such cases depends on eradication or control. Particularly relevant to recovery potential screening is the fact that some NIS, once established, cannot currently be controlled or eradicated by any known methods.
  • Data sources and measurement: In recovery potential screening, this metric may consider existing invasions or the risk of future invasions, or both. Many options for scoring can be developed. An example scoring process could be: 0 = no established NIS of concern, no immediate risk; 1 = no established NIS of concern, risk due to proximity or other vulnerability; 2 = established NIS of concern exists, control or eradication feasible; 3 = established NIS of concern exists, control or eradication infeasible. The scoring approach can be species-specific or consider multiple NIS per waterbody and may factor in the prospects of re-attaining the unmet water quality standard. Data sources may include waterbody-specific monitoring information or NIS range maps by species; both are available from the USGS Non-Indigenous Aquatic Species Information Resource. The USDA National Invasive Species Information website also contains links to several databases.

Number of 303d Listed Causes 

  • Description: The number of unique causes of impairment for waters listed as impaired under Section 303(d) of the Clean Water Act. See Reference Sheet.
  • Example metrics: Watershed Unique 303d-Listed Causes Count; Unique Impairment Causes Count
  • Why relevant: Section 303(d) listings typically report the pollutant causing the impairment for each listed water body. The number of pollutants affecting an impaired water body is generally a direct indication of the relative complexity, expense and difficulty of its restoration, according to many practitioners. More pollutants causing impairments frequently implies more numbers and diverse types of responsible sources. The number of listed causes also may be associated with greater magnitude of impairment due to cumulative effects.
  • Data sources and measurement: Measured as the number of unique pollutant causes reported for 303(d) listed water body segments in the watershed. If the watershed contains more than one listed water body segment, then causes reported to affect multiple segments should only be counted once. The EPA Assessment TMDL Tracking and Implementation System (ATTAINS) contains information on 303(d) listed waters by state and by semi-annual reporting cycle. States may also have more detailed information.

CSS or MS4 Areas 

  • Description: Indicators of the extent of Combined Sewer Systems (CSS) or Municipal Separate Storm Sewer Systems (MS4s) in the watershed.
  • Example metrics: % MS4 in Watershed; % CSS in Watershed
  • Why relevant: Public sewer systems are designed to handle stormwater runoff and its pollutants, up to a point. Exceeding the capacity of a system can result in an episodic increase in pollutant loadings and complicate efforts at restoration. Existence of CSS or MS4 areas in a watershed as well as the capacity of the systems can be a consideration when evaluating stressors that affect recovery.
  • Data sources and measurement: Generally these areas are available in mapped form at state or municipal level. MS4 areas can be approximated using maps of municipal boundaries and urbanized areas. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Age of Sewer Infrastructure 

  • Description: The age of sanitary and/or stormwater sewer system infrastructure in the watershed.
  • Example metrics: Average Age of Sewer Infrastructure in Watershed
  • Why relevant: Sewer pipe failures and leaks are more common in older systems. The presence of older sewerage infrastructure can therefore be an important stressor and thus affect recovery prospects in some watersheds.
  • Data sources and measurement: The age of sewer infrastructure are generally available from municipalities, sometimes in mapped form.

Severity of Loading 

  • Description: The severity of pollutant loading in the watershed. Severity of loading is determined by comparing the load reduction necessary to achieve water quality standards to the current loading. A higher ratio of reductions to current loading indicates more severe loading. See Reference Sheet.
  • Example metrics: Nitrogen Loading Severity in Watershed; Phosphorus Loading Severity in Watershed
  • Why relevant: For impaired waters where load reductions have been calculated, the magnitude of necessary reductions compared with current loadings has been shown to relate to the likelihood of successful restoration, though greater severity is not necessarily a sign of irreversible degradation. Case studies of restoration show multiple cases where restoration successes were achieved if load reductions were less than 50% of current levels. The 50% figure is likely not a consistent threshold value and data of this sort are limited, thus the metric is best used to array a set of waters into severity categories based on expert judgment.
  • Data sources and measurement: Measured for an individual pollutant as the load reduction needed to achieve water quality standards (typically calculated from a TMDL) divided by the estimated current pollutant load. The EPA Assessment TMDL Tracking and Implementation System (ATTAINS) contains information on 303(d) listed waters and approved TMDLs.

Stressor Persistence 

  • Description: Indicators of the level of persistence of water quality stressors in the watershed. See Reference Sheet.
  • Example metrics: Stressor Persistence Rating in Watershed
  • Why relevant: Stressors causing impairment can vary considerably in their likelihood to persist over long periods, naturally dissipate or respond rapidly to controls. This can be due to the nature of the stressor itself (e.g., radionuclides), its source (e.g., unremediated acid mine drainage), or its setting (e.g., excess fine sediment persistence in lower gradient streams). Comparison of recovery potential across many watersheds can consider differences in persistence across different stressor types and settings.
  • Data sources and measurement: Methods for measurement would be project-specific, and differ with the stressors included. One option for developing persistence metrics involving different stressors and settings is to use high/medium/low categories specific to each stressor.

SPARROW Nitrogen Loading Estimate 

  • Description: The nitrogen load generated in the watershed based on the United States Geological Survey (USGS) Spatially-Referenced Regression on Watershed Attributes (SPARROW) water quality model.
  • Example metrics: SPARROW Total Nitrogen Load in Watershed
  • Why relevant: SPARROW is one of the most widely used geospatial models for estimating nutrient pollution on a watershed basis and has been used to estimate nitrogen and phosphorus loads and yields over large areas at the HUC8 and HUC12 watershed scales. These models identify urban and agricultural sources as major contributors of nutrients to streams and reveal local and regional differences in nutrient contributions from contrasting types of agricultural sources (farm fertilizers versus animal manure) and urban sources (wastewater versus diffuse runoff from developed land).
  • Data sources and measurement: SPARROW model data is available online along with documentation of methods and results.

SPARROW Phosphorus Loading Estimate 

  • Description: The phosphorus load generated in the watershed based on the United States Geological Survey (USGS) Spatially-Referenced Regression on Watershed Attributes (SPARROW) water quality model.
  • Example metrics: SPARROW Total Phosphorus Load in Watershed
  • Why relevant: SPARROW is one of the most widely used geospatial models for estimating nutrient pollution on a watershed basis, and has been used to estimate nitrogen and phosphorus loads and yields over large areas at the HUC8 and HUC12 watershed scales. These models identify urban and agricultural sources as major contributors of nutrients to streams and reveal local and regional differences in nutrient contributions from contrasting types of agricultural sources (farm fertilizers versus animal manure) and urban sources (wastewater versus diffuse runoff from developed land).
  • Data sources and measurement: SPARROW model data is available online along with documentation of methods and results.

Stream Miles Impaired 

  • Description: The length of streams and rivers in the watershed with water quality impairments. Impaired waters can include those listed under Section 303(d) of the Clean Water Act and/or waters with a Total Maximum Daily Load (TMDL). Indicators can measure the length of all impaired waters or focus on impairments for one or more pollutants of interest.
  • Example metrics: % Streamlength Impaired 303d-Listed + TMDLs; % Streamlength Impaired 303d-Listed + TMDLs; % Streamlength 303d-Listed; % Streamlength 303d-Listed Nutrients; % Streamlength 303d-Listed Sediment
  • Why relevant: Although state water quality monitoring programs are generally unable to assess all of their waters in each integrated reporting cycle, the relative quantity of reported impairments provides an important insight into what is currently known. Larger quantities or proportions of impaired stream miles imply a likely more complex and extensive watershed restoration task, and also may be associated with additional unmonitored impairments within adjacent or connected streams and other water bodies.
  • Data sources and measurement: Can be measured as the number of impaired miles per watershed or as a percentage of total stream miles in the watershed that are impaired. National geospatial datasets of 303(d) listed waters and waters with TMDLs are available through EPA's Assessment TMDL Tracking and Implementation System (ATTAINS).

Waterbody Acres Impaired 

  • Description: The area of lakes, reservoirs, and other open waters in the watershed with water quality impairments. Impaired waters can include those listed under Section 303(d) of the Clean Water Act and/or waters with a Total Maximum Daily Load (TMDL). Indicators can measure the area of all impaired waters or focus on impairments for one or more pollutant of interest.
  • Example metrics: % Waterbody Area 303d-Listed + TMDLs; % Waterbody Area 303d-Listed; % Waterbody Area 303d-Listed Nutrients; % Waterbody Area 303d-Listed Sediment
  • Why relevant: Although state water quality monitoring programs are generally unable to assess all of their waters in each integrated reporting cycle, the relative quantity of reported impairments provides an important insight into what is currently known in each watershed. Larger quantities or proportions of impaired water body acres imply a likely more complex and extensive watershed restoration task, and also may be associated with additional unmonitored impairments within adjacent or connected streams and other water bodies.
  • Data sources and measurement: Can be measured as the number of impaired waterbody acres per watershed or as a percentage of total waterbody acres in the watershed that are impaired. National geospatial datasets of 303(d) listed waters and waters with TMDLs are available through EPA's Assessment TMDL Tracking and Implementation System (ATTAINS).

Modeled Aerial Deposition of N 

  • Description: The rate of aerial nitrogen (N) deposition in the watershed. Indicators can measure total nitrogen deposition or deposition of individual forms of nitrogen.
  • Example metrics: Total Nitrogen Deposition in Watershed; Oxidized Nitrogen Wet Deposition in Watershed; Reduced Nitrogen Wet Deposition in Watershed; Reduced Nitrogen Dry Deposition in Watershed; Reduced Nitrogen Total Deposition in Watershed
  • Why relevant: Excessive N loadings to waters cause a number of adverse effects, whether from land-based sources or aerial deposition. Aerial deposition is worth separate consideration as a factor inhibiting recovery potential as the likelihood of successful control of the aerial sources is independent of the likelihood of controlling the within-watershed point or nonpoint sources of N. Where aerial sources continue to be the primary sources of N, watershed-based restoration efforts alone might be expected to have low recovery potential.
  • Data sources and measurement: Air quality computer models are capable of simulating nitrogen transport and can estimate variability in deposition of nitrogen loads geospatially. The Community Multiscale Air Quality modeling system (CMAQW) produced a grid of predicted aerial nitrogen deposition for the conterminous United States that was summarized by HUC12 by EPA EnviroAtlas. Other modeled nitrogen deposition data may be available for selected areas at a resolution that enables aggregation by watershed. It is likely that original model outputs are not on a watershed basis, but areal weighting methods allow for translating values to a watershed basis. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Modeled Aerial Deposition of Hg 

  • Description: The rate of aerial mercury (Hg) deposition in the watershed.
  • Example metrics: Mercury Total Deposition in Watershed
  • Why relevant: Over 8,700 water bodies in 43 states plus the District of Columbia and Puerto Rico are listed as impaired under Section 303(d) of the Clean Water Act due to excessive amounts of mercury in fish tissue or in the water column. Atmospheric deposition is believed to be the dominant avenue by which mercury loads are delivered to most watersheds, although some waters have significant inputs from sources such as historic mine tailings and/or enriched minerals. A discussion of the adverse effects of mercury on human health, especially for unborn children, as well as ecological impacts can be found at https://www.epa.gov/mercury.
  • Data sources and measurement: In order to support development and implementation of TMDLs for mercury in areas impacted by atmospheric deposition, EPA's Office of Water in cooperation with State and Regional partners has completed mercury deposition modeling. The Regional Modeling System for Aerosols and Deposition (REMSAD) was the primary model relied upon in this analysis. The Community Multi-scale Air Quality Model (CMAQ) was also used to provide a "second opinion" of key REMSAD findings. In addition, three different global models were used in order to provide a range of likely impacts from foreign sources. The domain of this modeling was the lower continental US and the spatial resolution was a network of 12 kilometer by 12 kilometer grid cells throughout the domain.

Modeled Aerial Deposition of S 

  • Description: The rate of aerial sulfur deposition in the watershed. Indicators can measure total sulfur deposition or individual forms of sulfur deposition.
  • Example metrics: Sulfur Wet Deposition in Watershed; Sulfur Dry Deposition in Watershed; Sulfur Total Deposition in Watershed
  • Why relevant: Excessive sulfur concentrations in water bodies can increase water acidity, form unpleasant tastes and odors, and be toxic to aquatic organisms. Similar to nitrogen, aerial deposition is worth separate consideration as a factor inhibiting recovery potential as the likelihood of successful control of the aerial sources is independent of the likelihood of controlling the within-watershed point or nonpoint sources of sulfur. Where aerial sources continue to be the primary sources of sulfur, watershed-based restoration efforts alone might be expected to have low recovery potential.
  • Data sources and measurement: Air quality computer models are capable of simulating sulfur transport and can estimate variability in deposition of sulfur loads geospatially. The Community Multiscale Air Quality modeling system (CMAQW) produced a grid of predicted aerial sulfur deposition for the conterminous United States that was summarized by HUC12 by EPA EnviroAtlas. Other modeled sulfur deposition data may be available for selected areas at a resolution that enables aggregation by watershed. It is likely that original model outputs are not on a watershed basis, but areal weighting methods allow for translating values to a watershed basis. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Number of Impaired Segments 

  • Description: The number of water body segments in the watershed with water quality impairments. Impaired waters can include those listed under Section 303(d) of the Clean Water Act and/or waters with a Total Maximum Daily Load (TMDL). Indicators can measure the number of all impaired waters or focus on impairments for one or more pollutant of interest.
  • Example metrics: Impairment 303d and TMDL Segments Count; 303d-Listed Segments Count; Nutrients 303d-Listed Segments Count; Sediment 303d-Listed Segments Count
  • Why relevant: Although state monitoring programs are generally unable to assess all of their waters in each integrated reporting cycle, the relative quantity of reported impairments provides an important insight into what is currently known in each watershed. Larger quantities or proportions of impaired water body acres imply a likely more complex and extensive watershed restoration task, and also may be associated with additional unmonitored impairments within adjacent or connected streams and other water bodies.
  • Data sources and measurement: National geospatial datasets of 303(d) listed waters and waters with TMDLs are available through EPA's Assessment TMDL Tracking and Implementation System (ATTAINS).

Specialized Agricultural Practices 

  • Description: Indicators of the intensity of agricultural practices occurring in the watershed. Agricultural practices may include fertilizer application, manure application, tillage, or other activities with demonstrated effects on water quality.
  • Example metrics: Manure Application in Watershed; Synthetic Nitrogen Fertilizer Application in Watershed
  • Why relevant: Understanding agricultural practices provides insight into where potential sources of nonpoint source pollution occur and opportunities for improved management. These indicators could be particularly useful in identifying watersheds where agricultural best management practices (BMPs) could be implemented or for outreach to communicate with agricultural producers the importance of nutrient and sediment management to water quality.
  • Data sources and measurement: The International Plant Nutrition Institute (IPNI) Nutrient Geographic Information System (NuGIS) contains manure and fertilizer application that have been summarized by HUC12 by EPA EnviroAtlas for the conterminous United States. Alternative state and local datasets may be available for summarizing agricultural practices by watershed. Soil and water conservationist districts are often a useful resource as they can have a thorough knowledge of agricultural practices within their region. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Integrated Watershed Vulnerability Index and Sub-Indices 

  • Description: Watershed Vulnerability Index and Sub-Index scores from the EPA Preliminary Healthy Watersheds Assessment (PHWA). The PHWA calculated a Watershed Vulnerability Index score and three Sub-Index scores (Land Use Vulnerability; Water Use Vulnerability; Wildfire Vulnerability) for each HUC12 watershed in the contiguous US.
  • Example metrics: PHWA Watershed Vulnerability Index; PHWA Land Use Vulnerability Sub-Index; PHWA Water Use Vulnerability Sub-Index; PHWA Wildfire Vulnerability Sub-Index
  • Why relevant: Under the PHWA assessment framework, watershed vulnerability is characterized by the potential for future degradation of watershed health. The PHWA aimed to develop screening-level information to evaluate relative watershed vulnerability (i.e., comparisons of multiple watersheds across states and ecoregions) to help resource managers plan and target future watershed protection efforts.
  • Data sources and measurement: PHWA Watershed Vulnerability Index and Sub-Index scores for each HUC12 in the contiguous US are available for download from the PHWA website. Scores were calculated by first identifying measurable indicators closely associated with each of three sub-index categories (Land Use Vulnerability; Water Use Vulnerability; Wildfire Vulnerability), compiling Sub-Index scores from indicator data, and then developing the integrated Watershed Vulnerability Index from the three sub-indices. Separate “statewide” and “ecoregional” index and sub-index scores are provided. Statewide scores reflect a HUC12’s vulnerability relative to all other HUC12s in the state, while ecoregional scores reflect a HUC12’s vulnerability relative to all other HUC12s in the ecoregion. All scores can range from 0 to 100, with higher scores representing more vulnerable conditions relative to other watersheds in the state/ecoregion. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Empower Density 

  • Description: Empower density in the watershed, riparian zone, or hydrologically connected zone. Empower density is an indicator of the intensity of human activity and measures non-renewable energy and material flow within a given area over time, including electricity, fuels, fertilizers, pesticides, and water.
  • Example metrics: Empower Density, Mean in Watershed; Empower Density, Mean in Hydrologically Connected Zone; Empower Density, Mean in Riparian Zone
  • Why relevant: Empower density provides a standardized measure for quantifying the intensity of human activity in an area. Empower density can be a more informative measure of human disturbance to ecosystems relative to human population size and land use metrics. Studies have documented relationships between higher empower density and degraded water quality and wetland conditions.
  • Data sources and measurement: EPA Region 4 developed a gridded empower density dataset for the conterminous United States that applied empower density conversions by land cover type to the National Land Cover Database Land Cover dataset. It may be possible to calculate empower density datasets using higher resolution or regionally specific land cover data and summarizing by watershed. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Other Stressor-Specific Severity Factors 

  • Description: Indicators that quantify the severity of stressors that are not otherwise described by existing indicators.
  • Example metrics: Illicit Discharge Severity in Watershed; Legacy Pollutant Severity in Watershed; Coastal Pollution Severity in Watershed
  • Why relevant: The broad variety of impairment causes that can affect degraded waters may vary substantially from one another in relative difficulty of restoration. A given area may, for example, find their urban runoff impairments far more difficult to remediate than their rural pathogen impairments. This metric relates to differentiating between specific stressors and their generalized differences in restorability as a recovery potential indicator concept. This concept can also be applied to develop single stressor-based indicators that recognize recovery potential differences related to loading magnitude, frequency, duration, or association with other factors that influence restorability.
  • Data sources and measurement: Project-specific and stressor-specific measurement methods need to be developed to use this indicator.

Neutral Variable (Stressor) 

  • Description: The neutral variable indicator is equal to 0.5 for all watersheds in the RPS Tool. This is a “dummy” indicator used when you wish to omit the Stressor category from a screening.
  • Example metrics: Neutral Variable, Stressor Category
  • Why relevant: Users may want to run a RPS screening that does not include the Stressor indicator category. Selecting only the Neutral Variable for the Stressor category will allow the RPS Tool to run without errors and provide results that focus on just the Ecological and Social indicator categories.
  • Data sources and measurement: The neutral variable indicator is set to 0.5 for all watersheds. One or more forms of this indicator has been measured at the HUC12 scale for all lower 48 states and data are available from Watershed Index Online (WSIO).

Recovery Potential Screening

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Last updated on March 25, 2024