We developed a GIS watershed-based planning tool to identify where landscapes via their connectivity to waterbodies have the: 1) greatest impact on aquatic resources as assessed by ecosystem functions and values; and 2) identify a prioritization using a human impact assessment on aquatic resources. The product is a Decision Support System (DSS) which, identifies and prioritizes potential sites for restoration at multiple spatial scales, from statewide to local watersheds. Furthermore, the DSS identifies cumulative human impacts based upon past, present and future threats to aquatic resources assessed at a watershed scale. The prioritized areas represent a landscape level assessment of the spatial location and configuration where these sites may provide most of the ecosystem services desired by resource managers.
Key ecosystem functions and values were identified by a stakeholder group of natural resource and environmental managers. These functions and values represented in the watershed models include: water quality and water quantity protection; flood control and flow regulation; biodiversity conservation; connectivity; ease of restoration; education; recreation; scenic value; and wildlife habitat. Three separate models were developed which include, the Potential Wetland Function Restoration Site Index (PWFRSI), which represents sites that can provide ecosystem functions and are not currently wetlands, and the Wetland Condition Index (WCI), which assesses the ability of existing wetlands to provide ecosystem functions based upon their position in the landscape and the interaction of the wetland with the watershed. A third model, the human disturbance index (HDI), provides a ranking of all HUC-12 watersheds in Georgia based upon past, present and future human disturbance. Taken together the output of these models are included in a single tool: The Landscape Assessment and Water Services (LAWS) model.
Decision Support Model Description
The existing wetlands are removed from the PWRFSI analysis and ranked separately in the WCI model. The PWFSI is calculated by summing the values of layers 1.1 – 1.9, which are described below. The summed value for each individual grid cell is ranked by low (1), medium (2), or high (3) restoration value to create the final dataset. Based on this analysis, grid cells which are ranked high (3) provide the greatest potential for restoring wetland functions in the watersheds. In contrast, those cells ranked low, will be less likely to produce benefits from restoration.
Layer 1.1 – Restorable land
This layer represents land that will be most successfully be used to restore or create wetland function. The criteria used to assign high restorability include smaller waterbodies (less than 5 acres), or land that identified as wetland in 1974, but is now classified as agriculture or forest in the 2015 GLUT data. Medium potential for restorability success is defined as lands that are currently classified as forest or agriculture (2015) and were classified as open water in 1974. Those lands that are classified as urban, mines, and open water bodies greater than 5 acres are removed from the layer. Low restorability success is defined lands classified as forest and agriculture in 2015 and were forest and agricultural lands in 1974.
Layer 1.2 Hydric Soils
A map identifying hydric soils was extracted from the USDA SSURGO data. Soils identified as having hydric properties were ranked high, nonhydric soils were ranked as low. Layers 1.1 and 1.2 are combined to create an overlay used to identify non-restorable lands.
Layer 1.3 Jurisdictional protection
This layer identifies which wetlands are most likely to be protected through determination by the US Army Corps of Engineers. In Georgia this includes all wetlands found within 100 feet of navigable waters and within the 100-year floodplain. In addition, a 100-foot buffer was placed around these features and those pixels found within the buffer are ranked medium. All cells not found in either the high or medium categories are classified as low for jurisdictional protection.
Layer 1.4 Water quality and Quantity Index
This layer identifies grid cells which will provide the greatest potential improvement for the removal of nonpoint source pollution, by capturing and filtering water before it enters a water body. The index is created from two different sub-models; the first estimates potential runoff from the landscape and the second estimates the distance that any one grid cell is from a receiving body of water. Runoff is calculated using curve numbers which account for effects of land use, topography and soil characteristics. The distance is measured through the path that water will flow between the grid cell of interest and the nearest receiving waterbody.
Layer 1.5 Connectivity to existing conservation lands
Using the Georgia Conservation lands database as the base for this model, we identified grid cells that would increase landscape connectivity to support increasing ecological functions as services. Each grid cell value is calculated by its distance to each of the existing conservation lands and summed to calculate its total value. These values are then ranked from high to low in their support of connectivity.
Layer 1.6 Terrestrial dispersal corridor between wetland sites
This layer assesses the ability of a newly restored wetland site to support colonization of new habitat by terrestrial vertebrate species which are dependent upon wetland habitat for part of their life-cycle. The Green frog was used as a surrogate species for this model. Habitat and dispersal requirements for the green frog is well studied and literature was used to provide input to the model. The model calculates the distance that a frog would need to travel based upon the preference the frog would have to move through the landscape in search of habitat. Each grid cell was ranked based upon the frog’s preferences. For example, frogs would be less likely to move through an urban grid cell than a forested or wetland grid cell, resulting in the frog needing to travel a longer path to find the desired habitat. Finally, each grid cell is weighted by the number of species of terrestrial vertebrates that are supported by the habitat the grid cell represents (data from the Georgia GAP Program). Each patch is ranked from low (resistant to movement) to high (attracting movement) value based on the model results.
Layer 1.7 Hydrologic Connectivity
This layer assesses how well a new wetland grid cell would increase the hydrologic connectivity of the landscape. This is especially important for adding water storage capacity and improving flood control. The model searches the surrounding landscape for existing wetlands and calculates the total size of storage capacity based upon the proximity to the individual grid cell. The model assumes that effects of hydrologic connectivity provide greater benefit when new sites expand the capacity of existing and larger wetlands. The results are ranked from low (isolated to low addition) to high (expand existing capacity).
Layer 1.8 Natural upland habitat surrounding a chosen site
This layer accounts for the potential for increasing biodiversity through the interaction of wetlands with upland habitat. The 2001 natural vegetation map, created by the GAP analysis program was modified by removing agriculture, urban and wetland areas identified from the 2015 landcover, to create a map of natural upland vegetation. For each grid cell the model then searched a radius of 500 meters and calculated the total upland habitat with in the searched area. The results are ranked from high (lots of natural habitat) to low (natural habitat).
Layer 1.9 Maintenance of high biodiversity streams
Streams and rivers supporting high biodiversity have been mapped by Georgia DNR Wildlife Resources Division, as part of the State Wildlife Action Plan. The model identifies areas where wetland restoration would best reduce nonpoint source runoff. The model uses the same approach as that described for layer 1.4. This was a two-component model that accounts for runoff off as well as distance to streams supporting high biodiversity. Results are ranked from low to high.
The second model is an assessment of current and future threats to aquatic resources, by human activities. This assessment was performed and reported for each 12-digit HUC. This information can be used to help prioritize mitigation bank locations within service areas. For each data layer, the raw datasets are reclassified from 1 (lowest) to 9 (highest) human threats using natural breaks (Jenks). This index is an additive model that reports the sum of the eight input layers:
Stream Fragmentation – Layer 2.1
Stream impoundments alter the hydrologic flow of streams and rivers affecting the in-stream, upstream and downstream functions of wetlands. A stream fragmentation index was calculated for each 12-digit HUC which sums the length of free-flow stream and rivers within the HUC and compares change in total length between 1974 and 2015. The index was ranked from low to high, with low having the most free flowing stream length to high having impoundments.
Percent of impaired streams and rivers – Layer 2.2
The length of streams and rivers that are considered impaired were calculated using the 2014 303(d) and 305(b) listing of impaired streams and rivers, which was obtained from the GA EPD (2008). The GA EPD dataset was clipped by 12-digit HUC, and then the length of all stream segments are summed to get a total length of impaired streams and rivers. Percent is calculated using the total length of streams in the HUC – 12
Wetland Activity Index – Layer 2.3
The wetland activity index was developed to determine the change in wetland density within each 12-digit HUC from 1974 -2015. Wetland area was calculated from the 1974 and 2015 Georgia Land Use Trends database and compared. HUC’s with heavy loss of wetlands are ranked high.
Percent impervious surface – Layer 2.4
The percent of impervious surface for each 12-digit HUC was calculated using the 2015 Georgia Impervious Surface Cover database developed by the Natural Resources Spatial Analysis Laboratory (NARSAL 2010). HUC’s were ranked low to high based upon their impervious surface density.
Projected future development in 2050 – Layer 2.5
The projected future threats to wetlands from development was developed to highlight the 12-digit HUCs where potential urban development may have the most impact to existing wetland complexes. The SLUETH model forecasts potential growth scenarios based on a variety of input datasets and exclusion layers. The output of a projected growth model is the probability that a pixel will be considered urban in the year 2025. The model was run for the state of Georgia as part of the regional water planning process for water availability and demand modeling. To simplify the results, all pixels that had 50% or greater probability of being urban in 2050 were retained and given a value of one. All other pixels, 49% probability and less, were given a value of zero. The reclassified projected growth in 2050 was then compared to the area of urban land cover classes in 2015 by 12-digit HUC to determine the potential change of this 20-year period.
Change in average wetland contiguity from 1974 to 2015 – Layer 2.6
The change in contiguity of wetlands by 12-digit HUC is indicative of historic pressures placed upon wetlands and their ability to provide essential ecosystem services. A reduction in the contiguity of wetlands would impacts the flood storage capacity of watersheds thus increasing the risk of flooding and reducing potential recharge. HUC’s are ranked low to high, with high values showing loss of wetland connectivity.
Change in average wetland proximity from 1974 to 2015 – layer 2.7
The change in the average proximity of wetlands is an indication of wetland complexity within 12-digit HUCs which represents the ability to provide essential ecosystem services such as supporting biodiversity or enhance flood control. As wetlands become increasingly isolated the stability of populations of species that rely upon wetlands becomes increasingly unstable and the flood water storage capacity becomes reduced. HUC’s are ranked low to high, where high represents a loss of larger connected wetlands from the watersheds.
Riparian Fragmentation 1974 to 2015 – Layer 2.8
Continuous and adequate riparian buffers are essential for maintaining desired ecosystem services. The change in the mean length of continuous riparian buffers along streams in each 12-digit HUC. HUC’s are ranked low to high, where high values indicate a loss of riparian forest vegetation.
The WCI assesses the impact that the surrounding landscape plays on the health and condition of an existing wetland. Wetlands were extracted from the 2015 GLUT data. The index is calculated by summing the values of layers 3.1 – 3.9, which are described below. The summed value for each individual wetland grid cell is then rescaled to a measure of low (1), medium (2), or high (3) restoration value to create the final dataset. Low (1) value grid cells are wetland areas that are least impacted by humans and restoration of these areas would be of limited value for mitigation banking. In contrast, areas that are ranked high (3), are wetland areas that are highly impacted by human activity in the surrounding landscape and would greatly benefit from restoration.
Potential Runoff Index – Deviation from Reference – Layer 3.1
This layer estimates the effect of watershed landuse on an individual wetland. The index estimates potential runoff from the current landscape and estimates the divergence from a reference forested landscape. For each layer, runoff is calculated using curve numbers which account for effects of land use, topography and soil characteristics. To understand how the watershed supporting the individual wetland might be impacting the condition of the wetland, we calculated the deviation of the from a reference condition. The reference condition was forested. Grid cells are ranked low to high, where low values are wetlands which most resemble the reference condition (good condition), and high values are those wetlands where show greater deviation from the reference condition and would benefit from restoration.
Connectivity to Existing Conservation Areas – Layer 3.2
Using the Georgia Conservation lands database as the base of the model we identified wetland areas that are connected to or protected by the network of existing conservation lands. These values are then ranked from high to low in their support of connectivity. High values if restored will support higher connectivity of healthy wetlands.
Terrestrial Dispersal Corridors Between Wetlands – Layer 3.3
This layer assesses the ability of a wetland to support terrestrial vertebrate species which are dependent upon wetland habitat for part of their life-cycle. The green frog’s habitat requirements were used as a representative vertebrate species. The model calculates the length that a frog would travel based upon the preference the frog would have to move through a site in search of habitat. For example, frogs would be less likely to move through an urban grid cell and select a forested or wetland grid cell, resulting in the frog needing to travel a longer path to find the desired habitat. Finally, each grid cell is weighted by the number of species of terrestrial vertebrates that are supported by the habitat the pixel represents (data from the Georgia GAP Program). Each wetland grid cell is ranked from low to high value based on the model results. With high values supporting better restoration outcomes.
Hydrologic Connectivity of Wetlands – Layer 3.4
This layer assesses how a wetland supports hydrologic connectivity. This is especially important for adding water storage capacity and improving flood control. The model searches for existing wetlands and calculates the total size of storage capacity based upon the proximity to the individual wetland grid cells. The model assumes that effects of hydrologic connectivity provide greater benefit when new sites are closer to existing and larger wetlands. The results are ranked from low to high. With higher valued wetland grid cells providing greater hydrologic connectivity when restored.
Natural Upland Habitat Surrounding Wetlands- Layer 3.5
This layer assesses the extent of intact upland vegetation around an existing wetland. The 2001 natural vegetation map, created by the GAP analysis program was modified by removing agriculture, urban and wetland areas identified from the 2015 landcover, to create a map of natural upland vegetation. For each grid cell the model then searched a radius of 500 meters and calculated the total upland habitat with in the searched area. The results are ranked from high to low.
Maintenance of High Biodiversity Streams – Layer 3.6
This layer uses the same model that was developed for layer 1.9 and is described in the Potential Wetland Function Site Index section. In order to acknowledge the influence of the surrounding wetland grid cells a circular focal mean was calculated with a 500-meter radius.
Percent of Impervious Surface within a Basin – Layer 3.7
Impervious surfaces contribute to the non-point source pollution and flooding potential of our streams and rivers by modifying the natural hydrologic processes of a watershed and impacts wetlands. The percent of impervious surface within the watershed supporting the wetland was calculated using the 2015 Georgia Impervious Surface Cover database developed by the Natural Resources Spatial Analysis Laboratory. Low values will correspond to pixels that have the greatest percentage of impervious surface upstream, and thus more potential to receive non-point source pollution and have negative impacts on flood control.
Percent of Impaired Streams and Rivers – Layer 3.8
This index measures the percentage of listed stream length for all 12-digit HUS and each wetland was then conflated with these HUC’s to provide an estimate of impairment effecting the wetland. The results are scaled from low to high.
Percent Wetland Change – Layer 3.9
The percent wetland change index was developed to determine the percent change in wetland area within a 500-meter radius of each wetlands from 1974 to 2015. The percent gain and percent loss in wetland area was calculated from the 1974 and the 2015 GLUT databases. Net loss was then calculated by subtracting the percent gain from the percent loss.