A feature class depicting geographic locations where permanent water quality monitoring locations have been established in Great Smoky Mountains National Park. This includes monitoring location sites establised by the National Park Service and other state and federal agencies responsible for water quality monitoring and reporting. Agencies responsible for a monitoring location are listed in the attributes ORGANIZATIONIDENTIFIER and ORGANIZATIONFORMALNAME. For the display, query, and analysis of legacy and current hydrology spatial and tabular data; Consolidate and centralize a very diverse range and quantity of monitoring location site data from numerous programs and protocols; Mitigate the duplication of monitoring location data across shared systems; Allow for single-source identification and management of monitoring location sites that are "co-located"; Provide a single point of data entry, management, query, analysis, and display of water quality data from numerous sources, including STORET which are sourced from an accurate monitoring location database; Enable spatial relationship of water quality monitoring data to High-Resolution USGS NHD Reaches through the use of modern GIS, database, and statistics software; Support USGS and EPA standards for spatial and non-spatial hydrology and water quality data exchange and sharing. Very important details are included in the attached metadata document and should be read thouroughly before these data are used.

The Great Smoky Mountains National Park Hydrology dataset is a value-added attribution of data produced by Great Smoky Mountains National Park and published by the USGS NHD. Not to be confused with the USGS NHD Plus Dataset, the park has published these data as an interim while the NHD Plus "catches up" with recently-updated NHD Stream Data within the park footprint. These data have been attributed in the following way: Strahler Stream Order: In the Strahler method, all links without any tributaries are assigned an order of 1 and are referred to as first order. The stream order increases when streams of the same order intersect. Therefore, the intersection of two first-order links will create a second-order link, the intersection of two second-order links will create a third-order link, and so on. The intersection of two links of different orders, however, will not result in an increase in order. For example, the intersection of a first-order and second-order link will not create a third-order link but will retain the order of the highest ordered link. If the node is a leaf (has no children), its Strahler number is one. If the node has one child with Strahler number i, and all other children have Strahler numbers less than i, then the Strahler number of the node is i again. If the node has two or more children with Strahler number i, and no children with greater number, then the Strahler number of the node is i + 1. The Strahler number of a tree is the number of its root node. Algorithmically, these numbers may be assigned by performing a depth-first search and assigning each node's number in postorder. The same numbers may also be generated via a pruning process in which the tree is simplified in a sequence of stages, where in each stage one removes all leaf nodes and all of the paths of degree-one nodes leading to leaves: the Strahler number of a node is the stage at which it would be removed by this process, and the Strahler number of a tree is the number of stages required to remove all of its nodes. Another equivalent definition of the Strahler number of a tree is that it is the height of the largest complete binary tree that can be homeomorphically embedded into the given tree; the Strahler number of a node in a tree is similarly the height of the largest complete binary tree that can be embedded below that node. Any node with Strahler number i must have at least two descendants with Strahler number i − 1, at least four descendants with Strahler number i − 2, etc., and at least 2i − 1 leaf descendants. Therefore, in a tree with n nodes, the largest possible Strahler number is log2 n. However, unless the tree forms a complete binary tree its Strahler number will be less than this bound. In an n-node binary tree, chosen uniformly at random among all possible binary trees, the expected index of the root is with high probability very close to log4. Sinuosity: A river’s sinuosity is its tendency to move back and forth across its floodplain, in an S-shaped pattern, over time. As the stream meanders across the flood plain, it may leave behind scars of where the river channel once was. A stream that doesn't meander at all has a sinuosity of 1. The more meanders in a stream, the closer the sinuosity value will get to 0. For single-thread stream channels, the sinuosity index is calculated for each reach using its two endpoints (Upstream point A, Downstream point B). The ratio of the sinuous length tho the straight-line distance is Channel Sinuosity value for the reach. The sinuous length is measured down the centerline of the channel. Divide the sinuous length by the straight-line distance between the same two points. Sinuosity values range from 1 to 4 (or so). A completely straight channel will have a sinuosity of 1. Channels with ratios ~1.5 are called sinuous channels. Channels with higher ratios are called meandering channels. Values are commonly reported to two decimal places, but there’s no firm rule. This is a very large

The Great Smoky Mountains National Park Hydrology dataset is a value-added attribution of data produced by Great Smoky Mountains National Park and published by the USGS NHD. Not to be confused with the USGS NHD Plus Dataset, the park has published these data as an interim while the NHD Plus "catches up" with recently-updated NHD Stream Data within the park footprint. These data have been attributed in the following way: Strahler Stream Order: In the Strahler method, all links without any tributaries are assigned an order of 1 and are referred to as first order. The stream order increases when streams of the same order intersect. Therefore, the intersection of two first-order links will create a second-order link, the intersection of two second-order links will create a third-order link, and so on. The intersection of two links of different orders, however, will not result in an increase in order. For example, the intersection of a first-order and second-order link will not create a third-order link but will retain the order of the highest ordered link. If the node is a leaf (has no children), its Strahler number is one. If the node has one child with Strahler number i, and all other children have Strahler numbers less than i, then the Strahler number of the node is i again. If the node has two or more children with Strahler number i, and no children with greater number, then the Strahler number of the node is i + 1. The Strahler number of a tree is the number of its root node. Algorithmically, these numbers may be assigned by performing a depth-first search and assigning each node's number in postorder. The same numbers may also be generated via a pruning process in which the tree is simplified in a sequence of stages, where in each stage one removes all leaf nodes and all of the paths of degree-one nodes leading to leaves: the Strahler number of a node is the stage at which it would be removed by this process, and the Strahler number of a tree is the number of stages required to remove all of its nodes. Another equivalent definition of the Strahler number of a tree is that it is the height of the largest complete binary tree that can be homeomorphically embedded into the given tree; the Strahler number of a node in a tree is similarly the height of the largest complete binary tree that can be embedded below that node. Any node with Strahler number i must have at least two descendants with Strahler number i − 1, at least four descendants with Strahler number i − 2, etc., and at least 2i − 1 leaf descendants. Therefore, in a tree with n nodes, the largest possible Strahler number is log2 n. However, unless the tree forms a complete binary tree its Strahler number will be less than this bound. In an n-node binary tree, chosen uniformly at random among all possible binary trees, the expected index of the root is with high probability very close to log4. Sinuosity: A river’s sinuosity is its tendency to move back and forth across its floodplain, in an S-shaped pattern, over time. As the stream meanders across the flood plain, it may leave behind scars of where the river channel once was. A stream that doesn't meander at all has a sinuosity of 1. The more meanders in a stream, the closer the sinuosity value will get to 0. For single-thread stream channels, the sinuosity index is calculated for each reach using its two endpoints (Upstream point A, Downstream point B). The ratio of the sinuous length tho the straight-line distance is Channel Sinuosity value for the reach. The sinuous length is measured down the centerline of the channel. Divide the sinuous length by the straight-line distance between the same two points. Sinuosity values range from 1 to 4 (or so). A completely straight channel will have a sinuosity of 1. Channels with ratios ~1.5 are called sinuous channels. Channels with higher ratios are called meandering channels. Values are commonly reported to two decimal places, but there’s no firm rule. This is a very large

This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the Great Smoky Mountains National Park. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). Certain wetland habitats may be excluded from this mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. This dataset should be used in conjunction with the Wetlands Project Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.

This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the Great Smoky Mountains National Park. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). Certain wetland habitats may be excluded from this mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. This dataset should be used in conjunction with the Wetlands Project Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.

Procedures are from the NRSA Field Operations Manual 1. In situ Measure in situ DO, pH, water temperature, and conductivity using a calibrated multi-parameter water quality meter (or sonde). Take the measurements mid-channel at the X-site. Take the readings at 0.5 m depth. Measure the site depth accurately before taking the measurements. If the depth at the x-site is less than 1 meter, take the measurements at mid-depth. 2. Water Chemistry The water chemistry samples will be analyzed for total phosphorus (TP), total nitrogen (TN), total ammonium(NH4), nitrate (NO3), basic anions, cations, total suspended solids (TSS),turbidity, acid neutralizing capacity (ANC), alkalinity, dissolved organic carbon (DOC), and total organic carbon (TOC). Using a 3 L Nalgene beaker, collect a grab sample into one 4L cube container (for water chemistry)and one 2L amber Nalgene bottle (for chlorophyll a from the X site at the midpoint of the stream. After collection, store all samples on ice in a closed cooler. Filter the chlorophyll-a sample, the filters must be kept frozen until ready to ship. 3. Benthic Macroinvertebrates Collect benthic macroinvertebrate composite sample using a D-frame net with 500 micron mesh openings. Individual samples will be collected from 11 transects equally distributed along the reach. Composite sample and preserve in 95% ethanol. 4. Periphyton Collect periphyton from the 11 cross transects established withing the sample reach. 5. Physical Habitat Field measurements for physical habitat are made at two scales of resolution along the mid-channel length of the reach, and the results are later aggregated and expressed for the entire reach. The protocol defines the length of each sampling reach proportional to stream channel wetted width and then systematically places measurements to statistically represent the entire reach. Measurements will consist of: Thalweg profile and large woody debris tally, Channel cross section and riparian cross section, channel constraint and torrent evidence, bank slope, canopy cover, instream fish cover, algea, aquatic macrophytes, human influence and stream discharge. 6. Fecal Indicator A fecal indicator sample at the last transect (Transect K) after all other sampling is completed. Filters will be frozen within six hours of collection. A pre-sterilized, 250 ml bottle will be used to collect the sample approximately 1 m off the bank at about 0.3 meter (12 inches) below the water. 7. Fish Assemblage The fish sampling method is designed to provide a representative sample of the fish community, collecting all but the rarest fish taxa inhabiting the site. It is intended to accurately represent species richness, species guilds, relative abundance, size and presence of anomalies. Fish will be collected using a backpack electrofisher and placed into an aerated container then sorted by species, recorded and returned to the stream. Any voucher specimens will be collected by photograph only.

This feature service will help to create fish consumption reports for Abrams Creek and Little River. This will be used to show where pH sample sites will be located as well as where advisory signs will be posted.

These data depict the locations (only) of all Aquatic Macroinvertebrates Monitoring Locations study sites in the park. Great Smoky Mountains National Park (GRSM) contains over 3400 km (2000 mi) of pristine waterways. As streams drain the forested ecosystems of the Park, they integrate and reflect conditions in those ecosystems. Aquatic macroinvertebrates are subjected directly to changes in the physical and chemical conditions of the water, and because of this dependent relationship with the water they live in, aquatic macroinvertebrates are good indicators of ecosystem health. They are found in all aquatic environments, are less mobile than many other groups of organisms, and are of a size that makes them easily collectable. Moreover, benthic macroinvertebrates have been shown to be a cost-effective monitoring tool (Lenat 1988). Aquatic biota exhibit responses to a wide array of stressors, including those having synergistic or antagonistic effects. In the Smokies, these stressors include primarily acid deposition and forest changes due to exotic pest infestations. The overall goal of this program is to maintain a Park-wide system of benthic macroinvertebrate monitoring sites to track the environmental health of Park streams, and to detect and quantify changes in conditions. The specific objectives are: to develop long-term aquatic biota data for large streams; to determine correlations among macroinvertebrates, fish, habitat, and water quality monitoring data; and to develop baseline data on aquatic macroinvertebrates. When conducting a monitoring program, the main goal is to analyze long-term data to evaluate changes in condition, and progress toward meeting a management objective. The aquatic macroinvertebrate monitoring program in GRSM is designed to provide this data through repeated sampling over time to answer the question of whether or not a particular benthic macroinvertebrate population displays trends indicative of ecosystem stress. This data relates directly to many other Inventory and Monitoring components in the Park, particularly the fisheries and water quality programs. Additionally, this data provides a baseline dataset for many areas outside of the Park which may be experiencing greater impacts to their aquatic resources. Benthic macroinvertebrates represent an integral part of lotic systems by processing organic matter and providing energy to higher trophic levels; therefore, an understanding of the effects of anthropogenic, as well as natural stressors, on their distribution and abundance is critical for comprehensive impact assessment of streams and rivers (Carter et al. 2006). Changes in macroinvertebrate population relative abundances, life-history traits, and growth rates are sensitive indicators of perturbations and are routinely used when evaluating the impacts of pollution (Carter et al. 2006). The deleterious effects of acidic stream water, for example, are well established, primarily in terms of reduced numbers of species and individuals (Allan 1995). Direct physiological effects and mortality due to acidification and to the subsequent mobilization of toxic metals, have been observed among various groups of aquatic invertebrates (Burton et al. 1985). Indirect effects of acidification also occur, through behavioral responses and alterations of food availability (Allan 1995).

These data depict the locations (only) of all Aquatic Macroinvertebrates Monitoring Locations study sites in the park. Great Smoky Mountains National Park (GRSM) contains over 3400 km (2000 mi) of pristine waterways. As streams drain the forested ecosystems of the Park, they integrate and reflect conditions in those ecosystems. Aquatic macroinvertebrates are subjected directly to changes in the physical and chemical conditions of the water, and because of this dependent relationship with the water they live in, aquatic macroinvertebrates are good indicators of ecosystem health. They are found in all aquatic environments, are less mobile than many other groups of organisms, and are of a size that makes them easily collectable. Moreover, benthic macroinvertebrates have been shown to be a cost-effective monitoring tool (Lenat 1988). Aquatic biota exhibit responses to a wide array of stressors, including those having synergistic or antagonistic effects. In the Smokies, these stressors include primarily acid deposition and forest changes due to exotic pest infestations. The overall goal of this program is to maintain a Park-wide system of benthic macroinvertebrate monitoring sites to track the environmental health of Park streams, and to detect and quantify changes in conditions. The specific objectives are: to develop long-term aquatic biota data for large streams; to determine correlations among macroinvertebrates, fish, habitat, and water quality monitoring data; and to develop baseline data on aquatic macroinvertebrates.When conducting a monitoring program, the main goal is to analyze long-term data to evaluate changes in condition, and progress toward meeting a management objective. The aquatic macroinvertebrate monitoring program in GRSM is designed to provide this data through repeated sampling over time to answer the question of whether or not a particular benthic macroinvertebrate population displays trends indicative of ecosystem stress. This data relates directly to many other Inventory and Monitoring components in the Park, particularly the fisheries and water quality programs. Additionally, this data provides a baseline dataset for many areas outside of the Park which may be experiencing greater impacts to their aquatic resources.Benthic macroinvertebrates represent an integral part of lotic systems by processing organic matter and providing energy to higher trophic levels; therefore, an understanding of the effects of anthropogenic, as well as natural stressors, on their distribution and abundance is critical for comprehensive impact assessment of streams and rivers (Carter et al. 2006). Changes in macroinvertebrate population relative abundances, life-history traits, and growth rates are sensitive indicators of perturbations and are routinely used when evaluating the impacts of pollution (Carter et al. 2006). The deleterious effects of acidic stream water, for example, are well established, primarily in terms of reduced numbers of species and individuals (Allan 1995). Direct physiological effects and mortality due to acidification and to the subsequent mobilization of toxic metals, have been observed among various groups of aquatic invertebrates (Burton et al. 1985). Indirect effects of acidification also occur, through behavioral responses and alterations of food availability (Allan 1995).

Great Smoky Mountains National Park (GRSM) is committed to monitoring ecological and evolutionary functions and processes of park ecosystems. Brook trout (Salvelinus fontinalis) is the only salmonid native to the Southern Appalachians and functions as a keystone species in some headwater streams. The historic use of hatchery-reared brook trout for supplemental and restorative stocking in GRSM underscores the need to recognize the evolutionary relationship among stream populations. A recent survey of microsatellite DNA variation in GRSM brook trout indicated the presence of highly significant differentiation at all hierarical levels which suggests that the individual stream should be considered the unit of management. Given that management resources are limited and that stream-specific management is often not practical, fisheries managers need to know whether the genetic divergence observed among GRSM brook trout reflect adaptive differences or is the variation due to stochastic processes like random genetic drift. DNA microarrays are a powerful method for the global analysis of steady-state intracellular mRNA levels, and thus identifying genes that are transcriptionally modulated as a consequence of metabolic or bioenergetic demands. The information gathered from these arrays of gene sequences can be used to characterize complex biological processes and interactions providing insight into the adaptive significance of observed genetic differentiation. This research objective, if funded, would represent the first attempt at determining whether GRSM fisheries managers should focus their resources on genetic relatedness or demographics.Brook trout (Salvelinus fontinalis) is the only trout native to the Southern Appalachians. Since the turn of the century, this native trout has lost approximately 75 percent of its range in Great Smoky Mountains National Park (GRSM) (Kelly et al. 1980). Initial range loss (about 50 percent) has been attributed to logging and resultant water quality degradation (King 1937). This activity virtually eliminated brook trout in streams below about 914 m (3,000 ft) in elevation. In turn, residents and loggers became concerned because they had nothing for which to fish. To meet the demand for recreational angling at the time (around 1910), logging companies began stocking both non-native rainbow trout and northern brook trout and continued this activity until the Park was established in 1934. The Park continued to allow the stocking of both species until 1974.Park staff in the 1930s and 1940s saw no harm in stocking rainbows and believed that as reforestation occurred, brook trout would reclaim lost range (King, personal communication). However, distribution surveys in the 1970s showed this not to be true and that 45 percent of the range exclusively occupied by brook trout had been lost since the mid-1930s (Kelly et al. 1980). The decline in allopatric brook trout range was the direct result of rainbow trout encroachment into previously unstocked brook trout streams (Larson and Moore 1985). Native brook trout had become restricted to marginal headwater streams above 1,067 m (3,500 ft), characterized by steep gradients and pH that is naturally slightly acidic. Based on the report by Kelly et al. (1980) it was determined that the only places brook trout could not be displaced are in streams above waterfalls where rainbows could not ascend.Historically, local residents were very vocal about introduced northern strains of brook trout being different from the native brook trout or "speckled trout." Studies in the 1950s showed that physical differences do exist between Southern Appalachian brook trout and hatchery fish. In 1993, a study conducted by the University of Tennessee provided conclusive evidence that Southern Appalachian brook trout are genetically distinct at the subspecies level from northern populations (McCracken et al. 1993). This effort collected brook trout from 47 streams across the Park and dem