Watershed Planung innerhalb eines Quantitative Szenarioanalyse Rahmen

The overall goal of this methodology is to provide researchers and resource managers with a framework for accessing and managing aquatic systems within actively developing watersheds affected by multiple land use activities. The watershed assessment planning approach described in this video will benefit researchers and aquatic resource managers by enabling characterization and prediction of cumulative impacts associated with multiple land use activities. A main advantage of this technique is that it incorporates a cumulative analysis framework within a GI space scenario analysis framework.

This allows managers to interactively access the outcomes of regulatory decisions, such as permitting and mitigation. For example, the presented approach makes it possible to facilitate both economic and development activity, while also producing that benefits to aquatic ecosystems through targeted remediation of other stressors. In preparation, select landscape-based measures of dominant land use activities within the targeted watershed, such as land cover attributes within the National Land Cover Database.

Then, in the GIS, open the NHD catchment file for the targeted area. Before beginning the summarization, ensure that each catchment has a unique identifier. To begin, allocate vector land use data to each polygon catchment.

Use the tabulate intersection tool to calculate landscape attributes for each catchment. Select the catchments layer as the input zone feature, the unique identifier as the zone field, and the vector land use data as the input class feature. Next, allocate raster land use data to each catchment.

Use the tabulate area tool to calculate the attributes for each catchment. Select the catchment's layer as the feature zone data, the unique identifier as the zone field, and the land cover data set as the input raster. Now, join the tabulated land use attributes to the catchment layer.

Right click on the catchment layer in the table of contents. In the dialogue box, select joins and relates and then join. Select the tabulated vector output as the table to be joined and then select the catchment unique identifier as the field that the join will be based on.

Repeat this step to join the tabulated raster output. Then, accumulate all the landscape attributes and the area field for each catchment using an automated script. This step calculates total upstream basin areas and landscape attributes and can be accomplished for one to 100, 000 scale NHD catchments using the catchment attribute allocation and accumulation tool.

Select NHD catchments as study sites based on their landscape attributes. First, create a scatter plot of all the NHD catchments with respect to their accumulated values of major land use activities. Select approximately 40 catchments as study sites within each eight digit hydrologic unit code watershed.

These sites should represent the full range of influences from the dominant land use activities found within the target watershed. Select sites within independent stressor gradients, which are sites influenced by single land use activity. Also, select sites with stressor combinations which are influenced by multiple land use activities.

Be sure that the sites are well distributed in the watershed and independent of one another with respect to their downstream drainage. Ensure that sites falling within each individual and combined stressor gradient also have similar average basin areas. In the field, delineate the sampling reach as 40 times the active channel width with maximum and minimum length of 300 and 150 meters.

Begin with collecting water samples. Choose moving water characteristic of the entire sampling site. First, obtain instantaneous measures of dissolved oxygen, specific conductivity, temperature, and pH, using hand held sensors.

Next, collect a filtered sample. First, rinse the filtration equipment with deionized water. Then, filter 250 milliliters of water for analysis of dissolved metals and fix the sample to a pH of less than two to ensure that the metals remain dissolved in the solution.

Next, collect 250 milliliters of unfiltered water by completely submerging a sample bottle into the water column. Gentle squeeze the bottle to expunge any remaining air and simultaneously place the cap on the sample bottle. If necessary, fix the sample to a pH of less than two to kill biological activity that might affect the analytes.

Select the analytes based on local land use activities. Collect a negative control once during each sampling event by following all water sampling protocols to obtain samples of deionized water. This is to ensure there is no cross-contamination between sampling sites.

Store all water samples at four degrees Celsius. The next procedure is to measure discharge at each sample site. To do this, first divide the waded stream width into equally sized increments using a depth gauge rod, measure the depth as the distance from the stream bed to the water surface, then, using a current meter, measure the water's velocity at 60%of the water's depth.

Now, calculate the discharge as the sum of all the products of the velocity, depth, and width at each section. To sample the macro invertebrate at each site, take kick samples from four separate riffles distributed throughout the full length of the sampling reach. At each location, place the kick net perpendicular to the stream flow and by foot, disturb a 50 square centimeter area immediately upstream to collect matter in the kick net.

Once the four samples are collected, combine them and immediately preserve them with 95%ethanol. The next procedure is to measure the physical habitat quality and complexity throughout the stream reach by taking measurements at equally spaced points along the thow wake, which is the location within the stream channel with the most rapid flow. Finally, count all pieces of large woody debris within the active channel.

Sub-sample the organisms contained within each macroinvertebrate sample obtained at the test site. Place the entire composite sample into a 100 square inch gridded sorting tray and randomly assign each square inch of the grid a number from one to 100. Remove organisms and debris from a randomly selected grid location and using a stereo microscope, count and identify all organisms.

Continue to count and identify organisms from randomly selected grid locations until the total number of sorted individuals is between 160 and 240. Identify the organism to genus using a macroinvertebrate key. Then, compile the genus level abundance data into community metrics to use as response variables for statistical modeling.

Such variables include total richness and percent EPT. After using the data to construct statistical models that predict the physical, chemical and biological conditions, use the GIS software to visualize the predictions. First, join the predictions to the NHD catchments.

Right click on the catchments layer in the table of contents and select joins and relates and then join. Select the model predictions as the table to be joined and select the catchment unique identifier as the field that the join will be based on. Next, right click on the catchments layer and select properties.

In the layer properties dialogue box, click on the symbology tab and select quantities. Select the predicted value of interest as the value field and click apply. If needed, use the classify option to manually change the range values to match recognized ecological criteria.

Now, conduct scenario analysis. Update the current landscape data set by directly editing the catchment layers attribute table with the field calculator function. For example, change a previously forested catchment to mining land cover.

Users can also edit multiple catchments to quantify likely effects of multiple activities occurring across large spatial scales. Another editing option not shown here is to edit the original vector or raster landscape data sets. Now, using procedures already presented, reallocate and re-accumulate the updated land use attributes for all NHD catchments.

Predict in-stream conditions as function of the updated landscape data set and visualize predicted conditions. 41 to 24, 000 scale NHD catchments were selected as study sites within the Coal River, West Virginia. The study sites were selected to span a range of influences including surface mining, residential development, and underground mining.

After collecting data and constructing statistical models, two sub-watersheds with similar surface mining were analyzed for various land use development and mitigation scenarios. What sets Drawdy Creek apart form Laurel Fork is that Drawdy Creek is influenced by residential structures and underground mining. Scenario analysis suggested that Laurel Fork can assimilate a 21%increase in surface mining land cover or 22 residential structures prior to biological impairment.

Before chemical impairment occurs, Laurel Creek could assimilate a 14%increase in surface mining land or eight underground mines. In contrast, the outflow of Drawdy Creek is predicted to exceed both the chemical and biological criteria, so mitigating scenarios were tested. Neither fully mitigating the effect of residential development, nor fully mitigating underground mining was enough to meet biological or chemical criteria.

Instead, it was predicted that to successfully make the Drawdy Creek outflow meet biological and chemical criteria, residential development and underground mining would need to be mitigated by 94 and 75%respectively, as noted by the dashed lines. This approach addresses previously identified limitations associated with managing aquatic systems and actively developing watersheds. Notably, the targeted watershed assessment produces data capable of quantifying complex cumulative effects at relevant spatial scales and it integrates models with existing GIS capabilities to create an easily interpretable and implementable scenario analysis framework.

It'll be important for us to place this methodology within an adaptive management framework where we make predictions and then access management activities over time, and particularly moving forward, we would like to incorporate the effects of climate change and incorporate these effects into our future scenario models. This framework is applicable to regions and watersheds impacted by any number of land use activities and can be used to conserve aquatic resources in the face of socio-economic and political pressures to continue development activities.