We thank the numerous field and laboratory helpers that were involved in various aspects of this work, especially Donna Hartman, Aaron Maxwell, Eric Miller, and Alison Anderson. Funding for this study was provided by the US Geological Survey through support from US Environmental Protection Agency (EPA) Region III. This study was partially developed under the Science To Achieve Results Fellowship Assistance Agreement number FP-91766601-0 awarded by the US EPA. Although the research described in this article has been funded by the US EPA, it has not been subjected to the agency&#39;s required peer and policy review and, therefore, does not necessarily reflect the views of the agency, and no official endorsement should be inferred.

1. Target Sites for Inclusion in Watershed Assessment
<ol>
	<li>Identify the dominant land use activities within the target 8-digit hydrologic unit code (HUC) watershed that are impacting physicochemical and biological condition3, 7. 
	Note: This methodology assumes pre-existing knowledge of important stressors within the watershed of interest. However, consulting regulatory agencies or watershed groups familiar with the system can aid in this effort.</li>
	<li>Select landscape-based measures of dominant land use activities [e.g., 2011 National Land Cover Database (NLCD)] 3, 7.
	<ol>
		<li>Consult published literature to help identify the best landscape-based measures for each land use activity10. Contact natural resource agencies to identify and obtain region-specific landscape datasets that are available for use. However, it may be necessary to create new landscape variables or datasets.</li>
	</ol>
	</li>
	<li>Tabulate land cover and use attributes to the 1:24,000 or 1:100,000 national hydrography dataset (NHD) catchments using Geographic Information (GIS) software.
	<ol>
		<li>Ensure each 1:24,000 or 1:100,000 catchment has a unique identifier. Use any user-defined numeric or categorical identifier as the unique identifier.</li>
		<li>Tabulate vector data (e.g., points or lines) falling within each catchment.
		<ol>
			<li>Summarize all vector features within each catchment using the Tabulate Intersection tool within the Statistics toolset of the Analysis toolbox. Select the NHD catchment layer as the Input Zone Feature, the catchment unique identifier as the Zone Field, and the vector dataset of interest as the Input Class Feature.</li>
			<li>Join the tabulated landscape attributes to the catchment layer. Right click on the catchments layer in the table of contents and select Joins and Relates from the dropdown menu and Join from the subsequent menu. Select the unique identifier as the field that the join will be based on, the output table from 1.3.2.1 as the table to be joined, and the unique identifier as the field in the table that the join will be based on.</li>
		</ol>
		</li>
		<li>Tabulate raster data using the Tabulate Area tool located within the Zonal toolset of the Spatial Analyst toolbox.
		<ol>
			<li>Load the Spatial Analyst extension. Select Extensions from the Customize menu. In the Extensions dialog box, check the box that corresponds to the Spatial Analyst extension.</li>
			<li>In the Tabulate Area dialog box, select the NHD catchment shapefile as the input raster or feature zone data, the unique identifier (e.g., FEATUREID) as the zone field, and the land cover dataset (e.g., NLCD) as the input raster or feature class data.</li>
			<li>Join the tabulated landscape attributes to the catchment layer following protocols in step 1.3.2.2, with the tabulate area results table as the join table.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Accumulate landscape attributes for all NHD catchments.
	<ol>
		<li>Download the NHDPlusV2 Catchment Attribute Allocation and Accumulation Tool (CA3TV2) at http://www2.epa.gov/waterdata/nhdplus-tools. Use the accumulation function of CA3TV2 for accumulation of attributes for 1:100,000 NHD catchments 11. 
		Note: We used custom written code that accumulates landscape attributes for 1:24,000 scale NHD watersheds12. Detailed instructions for using CA3TV2 are integrated into the tool and can be accessed via the Help function.</li>
	</ol>
	</li>
	<li>Select NHD catchments as study sites based on accumulated landscape attributes.
	<ol>
		<li>Create a scatter plot of all NHD catchments with respect to accumulated values of major land use activities (Figure 1A).</li>
		<li>Select study sites (approximately 40 sites per 8-digit HUC watershed) to represent the full range of influence from dominant land use activities found within the target watershed (Figure 1B). Select sites within independent stressor gradients (i.e., influenced by a single land use activity) and stressor combinations (i.e., influenced by multiple land use activities) (Figure 1B).</li>
		<li>Ensure that study sites are spatially distributed throughout the target watershed and independent of one another with respect to downstream drainage. Ensure that sites falling within each individual and combined stressor gradient also have similar average basin areas.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/54095/54095fig1.jpg" /> 
Figure 1. Hypothetical scatter plot of NHD catchments with respect to influence from 2 land use activities. Magnitude of influence of 2 land use activities across all NHD catchments within the hypothetical watershed (n=4,229) (A). Selected study sites (n=40) that represent the full range of observed conditions within the watershed with respect to independent and combined stressor gradients (B). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54095/54095fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Field Protocols for Collection of Physicochemical and Biological Data
Note: All data for each site should be collected during the same site visit at normal base flow conditions. Protocols presented herein represent standard operating procedures for the West Virginia Department of Environmental Protection (WVDEP)13. It may be more appropriate to use state or federally recognized procedures for the specific watershed being assessed.
<ol>
	<li>Delineate the sampling reach for each site as 40&#215; active channel width (ACW), with maximum and minimum lengths of 150 and 300 m3, 7.</li>
	<li>Sample water quality attributes from locations with moving water that are characteristic of the entire sampling site (e.g., not directly influenced by tributary or drainage pipe inputs).
	<ol>
		<li>Obtain instantaneous measures of dissolved oxygen, specific conductivity, temperature, and pH using handheld sensors. Calibrate sensors prior to each sampling event following manufacturer instructions.</li>
		<li>Rinse filtration equipment with deionized water prior to water sample collection.</li>
		<li>Filter 250 ml of water (mixed cellulose ester membrane filter, 0.45 &#181;m pore size) for analysis of dissolved metals. Fix to a pH&#60;2 to ensure metals remain dissolved in solution. 
		Note: The correct volume of acid can be added to the water sample following sample collection. Alternatively, the correct volume can be added to the bottle prior to the sampling event. The volume required to fix to a pH&#60;2 is dependent upon acid strength.
		<ol>
			<li>For the study described here, collect a single filtered sample from each site and fix with nitric acid for determination of dissolved Al, Ca, Fe, Mg, Mn, Na, Zn, K, Ba, Cd, Cr, Ni, and Se3, 7. 
			Note: Selection of analytes should be guided by watershed-specific land use activities.</li>
		</ol>
		</li>
		<li>Collect 250 ml unfiltered sample(s) by completely submerging the sample bottle in the water column. Gently squeeze the bottle to expunge any remaining air and simultaneously place the cap on the sample bottle. Fix the sample(s) to a pH &#60;2 if necessary (e.g., prevent biological activity from affecting nutrients).
		<ol>
			<li>For the study described here, collect two unfiltered samples from each site. Fix the first with sulfuric acid for determination of NO2 and NO3 and total P. Do not fix the second unfiltered sample and use it to determine total and bicarbonate alkalinity, Cl, SO4, and total dissolved solids3, 7. 
			Note: Selection of analytes should be guided by watershed-specific land use activities.</li>
		</ol>
		</li>
		<li>Obtain a field blank for each fixative used during each sampling event. Obtain field blanks by following all protocols for sample collection (i.e., rinsing, filtering, fixing) using deionized water as the final sample. 
		Note: Field blanks are used to identify contamination in sample collection and analysis.</li>
		<li>Store all water samples at 4 &#176;C until all analyses are completed. Ensure all analytes are measured within their specified holding time14.</li>
	</ol>
	</li>
	<li>Measure discharge at each sample site.
	<ol>
		<li>Divide the wetted stream width into equal-sized increments.</li>
		<li>Measure depth and average current velocity at the mid-point of each section.
		<ol>
			<li>Using a depth gauge rod, measure depth as the distance from the stream bed to the water&#39;s surface.</li>
			<li>Using a current meter, measure water velocity at 60% water depth.</li>
		</ol>
		</li>
		<li>Calculate discharge as the sum of the product of velocity, depth, and width across all sections.</li>
	</ol>
	</li>
	<li>Sample the macroinvertebrate community at each site.
	<ol>
		<li>Obtain kick samples (net dimensions 335 &#215; 508 mm2 with 500-&#181;m mesh) from 4 separate representative riffles distributed throughout the entire length of the sampling reach.
		<ol>
			<li>At each kick location, place the kick net perpendicular to stream flow and disturb a 0.50 &#215; 0.50 m2 (i.e., 0.25 m2) area of the stream bed immediately upstream. Ensure all organisms and debris flow downstream into the kick net.</li>
			<li>Combine organisms and debris from the 4 kick samples into a single composite sample (representing 1.00 m2 of the stream bed) and immediately preserve with 95% ethanol.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Measure physical habitat quality and complexity throughout the stream reach.
	<ol>
		<li>Take measurements of water depth, hydraulic channel-unit type, sediment class, and distance to fish cover object at evenly spaced points along the thalweg (portion of the stream through which the main or most rapid flow occurs). Take measurements every 1 ACW for streams &#60;5 m wide and every 0.5 ACW for streams &#62;5 m wide15.
		<ol>
			<li>Classify the channel unit within which each thalweg location is located (e.g., riffle, run, pool, or glide)16.</li>
			<li>Using a depth gauge rod, measure depth as the distance from the stream bed to the water&#39;s surface.</li>
			<li>Randomly identify a piece of sediment and determine its Wentworth size classification (silt, sand, gravel, cobble, boulder)17.</li>
			<li>Estimate the distance from each thalweg point to the nearest cover object. 
			Note: Fish cover is defined as any structure in the active channel capable of concealing a 20.32 cm (8 in) fish18.</li>
		</ol>
		</li>
		<li>Count all pieces of large woody debris within the active channel.</li>
		<li>Estimate habitat quality with US Environmental Protection Agency (EPA) rapid visual habitat assessments (RVHA) protocols19.</li>
	</ol>
	</li>
	<li>Obtain duplicate measurements and samples from a randomly selected 10% of study sites. Duplicate measures are used to estimate sampling and laboratory analysis precision.</li>
</ol>
3. Laboratory Protocols for Physicochemical and Biological Data
Note: Describing laboratory protocols for quantifying water chemistry attributes is outside the scope of this manuscript. However, the current study used standard chemical methods for water and wastes14.
<ol>
	<li>Subsample organisms contained within each macroinvertebrate sample (collected using protocols in section 2.4) to obtain a representative subsample of the macroinvertebrate community at each site.
	<ol>
		<li>Place the entire composite macroinvertebrate sample into a 100 in2 gridded sorting (measuring 5 &#215; 20 in2). Randomly assign each 1 in2 grid a number from 1 to 100.</li>
		<li>Use a stereo microscope to count and identify all organisms within randomly selected 1 in2 grids until the total number of sorted individuals is 200 &#177; 20%. Identify organisms to genus using macroinvertebrate keys, such as those published by Merritt and Cummins20.</li>
		<li>Compile genus-level abundance data into community metrics [e.g., total richness and % Ephemeroptera, Plecoptera, and Trichoptera (EPT)] for use as response variables in statistical models and subsequent scenario analysis3, 7.</li>
	</ol>
	</li>
</ol>
4. Statistical and Scenario Analyses
<ol>
	<li>Construct generalized linear models for predicting in-stream physical, chemical, and biological conditions from landscape-based indicators of dominant land use activities. 
	Note: Protocols and analyses were performed in the R language and environment for statistical computing (version 3.2.1)21.
	<ol>
		<li>Test for normality using Shapiro-Wilk [shaprio.test() function in R package stats21] tests and transform variables to meet assumptions of parametric analyses and linearize relationships.</li>
		<li>Fit initial maximal models specifying 2-way interactions among all land use predictors [glm() function in R package stats21].</li>
		<li>Apply a backward deletion to identify the minimum adequate model3, 7, 22.
		<ol>
			<li>Identify the least significant (i.e., explains the least amount of variation) variable in the maximal model [summary() function in R package stats21] and fit a new model with this variable excluded [glm () function in R package stats21].</li>
			<li>Continue removing variables until all remaining predictors are significantly different from 0 and explanatory power does not differ significantly from the maximal model for each response variable using analysis of deviance tables and likelihood ratio tests [lrtest() function in R package lmtest23].</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Predict current conditions.
	<ol>
		<li>Use final models to predict physicochemical and biological condition given current landscape characteristics within all un-sampled NHD catchments throughout the target watershed [predict() function in R package stats21].</li>
		<li>Visualize predictions in GIS software.
		<ol>
			<li>Join predictions to NHD catchments. Right click on the catchments layer in the table of contents and select Joins and Relates from the dropdown menu and Join from the subsequent menu. Select the unique identifier as the field that the join will be based on, the predictions file as the table to be joined, and the unique identifier as the field in the table that the join will be based on</li>
			<li>Right click on the catchments layer and select Properties. In the Layer Properties dialog box, click on the Symbology tab and select Quantities. Select the predicted value of interest as the Value field and click Apply. 
			Note: Range values can be manually changed to match recognized ecological criteria using the Classify button.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Conduct scenario analyses to compare predicted changes in aquatic conditions under various land use scenarios.
	<ol>
		<li>Update the current landscape dataset to simulate plausible future development or mitigation scenarios. For the study described here, manually update accumulated landscape values for the catchment of interest within the attribute table (e.g., change 10 acres of forested to mining land cover).
		<ol>
			<li>Select the catchment of interest using the Select by Attribute function located within the Selection drop down menu. In the Select by Attribute dialog box, choose the NHD catchments as the Layer. Double click on the unique identifier attribute, select =, and then type the identifier for the catchment of interest in the equation box.</li>
			<li>Open the NHD catchment attribute table by right clicking the catchments layer in the table of contents and selecting Open Attribute Table from the drop down menu. Choose to show only selected catchments.</li>
			<li>With only selected catchments showing, right click on the column of interest and select Field Calculator and input the new simulated value. Note: Multiple catchments can be altered to simulate multiple spatially explicit development or management activities occurring across large spatial scales. 
			Note: Alternatively, original vector and raster datasets can be updated by digitizing new features or altering and removing original features to simulate new land use activity or management of a pre-existing land use impact24. This can be accomplished using the Editor Toolbar.</li>
		</ol>
		</li>
		<li>Re-allocate and re-accumulate landscape attributes for all NHD catchments using protocols presented in steps 1.3-1.4.</li>
		<li>Predict physicochemical and biological condition as a function of the updated landscape dataset [predict() function in R package stats21].</li>
		<li>Visualize predicted conditions under alternative land use scenarios using protocols presented in step 4.2.2.</li>
	</ol>
	</li>
</ol>

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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scenario+analysis+and+the+Watershed+Futures+Planner:+predicting+future+aquatic+condiditons+in+an+intensively+mined+Appalachian+watershed.">Scenario analysis and the Watershed Futures Planner: predicting future aquatic condiditons in an intensively mined Appalachian watershed.</a> Environmental Considerations in Energy Productions. , 5-19 (2013).</li><li>Daraio, J. A., Bales, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+land+use+and+climate+change+on+stream+temperature+I:+daily+flow+and+stream+temperature+projections.">Effects of land use and climate change on stream temperature I: daily flow and stream temperature projections.</a> J. Am. Water Resour. As. 50, 1155-1176 (2014).</li><li>Mantyka-Pringle, C. S., Martin, T. G., Moffatt, D. B., Linke, S., Rhodes, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+and+predicting+the+combined+effects+of+climate+change+and+land-use+change+on+freshwater+macroinvertebrates+and+fish.">Understanding and predicting the combined effects of climate change and land-use change on freshwater macroinvertebrates and fish.</a> J. Appl. Ecol. 51, 572-581 (2014).</li><li>Piggott, J. J., Townsend, C. R., Matthaei, C. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Climate+warming+and+agricultural+stressors+interact+to+determine+stream+macroinvertebrate+community+dynamics.">Climate warming and agricultural stressors interact to determine stream macroinvertebrate community dynamics.</a> Glob. Change Biol. 21, 1897-1906 (2015).</li><li>Elith, J., Leathwick, J. R., Hastie, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+working+guide+to+boosted+regression+trees.">A working guide to boosted regression trees.</a> J. Anim. Ecol. 77, 802-813 (2008).</li><li>Mattson, K. M., Angermeier, P. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+human+impacts+and+ecological+integrity+into+a+risk-based+protocol+for+conservation+planning.">Integrating human impacts and ecological integrity into a risk-based protocol for conservation planning.</a> Environ. Manage. 39, 125-138 (2007).</li><li>(US, U. S. E. P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EPA+841-B-11-002.">EPA 841-B-11-002.</a> USEPA. Identifying and protecting healthy watersheds. Concepts, assessments, and management approaches. , (2012).</li><li>Merriam, E. R., Petty, J. T., Strager, M. P., Maxwell, A. E., Ziemkiewicz, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+contaminant+mixtures+in+multi-stressor+Appalachian+riverscapes.">Complex contaminant mixtures in multi-stressor Appalachian riverscapes.</a> Environ. Toxicol. Chem. , (2015).</li></ol>

The authors have nothing to disclose.

We provide a framework for assessing and managing cumulative effects of multiple land use activities in heavily impacted watersheds. The approach described herein addresses previously identified limitations associated with managing aquatic systems in heavily impacted watersheds5-6. Most notably, the targeted watershed assessment design (i.e., sampling along individual and combined stressor axes) produces data that are well suited for quantifying complex cumulative effects at relevant spatial scales (<...

Anthropogenic alteration of natural landscapes is among the greatest current threats to aquatic ecosystems throughout the world1. In many regions, continued degradation at current rates will result in irreparable damage to aquatic resources, ultimately limiting their capacity to provide invaluable and irreplaceable ecosystem services. Thus, there is a critical need for tools and methodologies capable of managing aquatic systems within developing watersheds2-3. This is particularly important given that managers are often tasked with conserving aquatic resources in the face of socioeconomic and political pressures to continue development activities.
Management of aquatic systems within actively developing regions requires an ability to predict likely effects of proposed development activities within the context of pre-existing natural and anthropogenic landscape attributes3, 4. A major challenge to aquatic resource management within heavily degraded watersheds is the ability to quantify and manage complex (i.e., additive or interactive) cumulative effects of multiple land use stressors at relevant spatial scales2, 5. Despite current challenges, however, cumulative effects assessments are being incorporated into regulatory guidelines throughout the world5-6.
Targeted watershed assessments designed to sample the full range of conditions with respect to multiple land use stressors can produce data capable of modeling complex cumulative effects7. Moreover, incorporating such models within a scenario analysis framework [predicting ecological changes under a range of realistic or proposed development or watershed management (restoration and mitigation) scenarios] has the potential to greatly improve aquatic resource management within heavily impacted watersheds3, 5, 8-9. Most notably, scenario analysis provides a framework for adding objectivity and transparency to management decisions by incorporating scientific information (ecological relationships and statistical models), regulatory goals, and stakeholder needs into a single decision-making framework3, 9.
We present a methodology for assessing and managing cumulative effects of multiple land use activities within a scenario analysis framework. We first describe how to appropriately target sites for inclusion within the watershed assessment based on known land use stressors. We describe field and laboratory techniques for obtaining data on the ecological effects of multiple land use activities. We briefly describe modeling techniques for producing landscape-based cumulative effects models. Lastly, we discuss how to incorporate cumulative effects models within a scenario analysis framework and demonstrate the utility of this methodology in aiding regulatory decisions (e.g., permitting and restoration) within an intensively mined watershed in southern West Virginia.

<table><tbody><tr><td>Slack Invert Sampling Kit</td><td>Wildco</td><td>3-425-N56</td><td></td></tr><tr><td>HDPE Square Jars</td><td>US Plastic Corp</td><td>66188</td><td>32 oz; for storing fixed, composite invertebrate samples</td></tr><tr><td>Ethyl Alcohol 190 Proof</td><td>PHARMCO-AAPER</td><td>111000190</td><td>For fixing and storing invertebrate samples</td></tr><tr><td>5 in. by 20 in. Macroinvertebrate sub-samplilng grid</td><td>N/A</td><td>N/A</td><td>This item cannot be purchased and must be made in house</td></tr><tr><td>Stereomicroscope Stemi 2000 with stand C LED</td><td>ZEISS</td><td>000000-1106-133</td><td>For macroinvertebrate sorting and identification</td></tr><tr><td>Thermo Scientific Nalgene Reusable Filter Holders with Receiver</td><td>Fisher Scientific</td><td>09-740-23A</td><td></td></tr><tr><td>Immobilon-NC Transfer Membrane</td><td>Millipore</td><td>HATF04700</td><td>Triton-free, mixed cellulose exters, 0.45&amp;nbsp;&amp;#956;m, 47&amp;nbsp;mm, disc</td></tr><tr><td>Actron Vacuum Pump Brake Bleeder Kit</td><td>Advanced Auto Parts</td><td>CP7835</td><td></td></tr><tr><td>Nitric Acid Solution</td><td>HACH</td><td>254049</td><td>1:1, 500&amp;nbsp;ml</td></tr><tr><td>Oblong NDPE Wide Mouth Bottles</td><td>Thomas Scientific</td><td>1229Z38</td><td>250 ml; for collection of water samples</td></tr><tr><td>650 Multi-parameter display, standard memory</td><td>Fondriest Environmental</td><td>650-01</td><td></td></tr><tr><td>600XL Sonde with temperature/conductivity sensor</td><td>Fondriest Environmental</td><td>065862</td><td></td></tr><tr><td>pH calibration buffer pack</td><td>Fondriest Environmental</td><td>603824</td><td>2 pints each of pH 4, 7, &amp;amp; 10</td></tr><tr><td>conductivity standard</td><td>Fondriest Environmental</td><td>065270</td><td>1 quart, 1,000 &amp;micro;S</td></tr><tr><td>Flo-Mate 2000</td><td>TTT Environmental</td><td>2000-11</td><td></td></tr><tr><td>Keson English/Metric Open Reel Fiberglass Tape</td><td>Forestry Suppliers</td><td>40025</td><td>300&#039;/100 m</td></tr><tr><td>ArcGIS 10.3.1</td><td>ESRI</td><td></td><td></td></tr></tbody></table>

watershed planning within a quantitative scenario analysis framework

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall short as a result of an inability to quantify and predict complex cumulative effects of current and future land use scenarios at relevant spatial scales. The goal of this manuscript is to provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework. Sites are first selected for inclusion within the watershed assessment by identifying sites that fall along independent gradients and combinations of known stressors. Field and laboratory techniques are then used to obtain data on the physical, chemical, and biological effects of multiple land use activities. Multiple linear regression analysis is then used to produce landscape-based cumulative effects models for predicting aquatic conditions. Lastly, methods for incorporating cumulative effects models within a scenario analysis framework for guiding management and regulatory decisions (e.g., permitting and mitigation) within actively developing watersheds are discussed and demonstrated for 2 sub-watersheds within the mountaintop mining region of central Appalachia. The watershed assessment and management approach provided herein enables resource managers to facilitate economic and development activity while protecting aquatic resources and producing opportunity for net ecological benefits through targeted remediation.

Forty 1:24,000 NHD catchments were selected as study sites within the Coal River, West Virginia (Figure 2). Study sites were selected to span a range influence from surface mining (% land area24), residential development [structure density (no./km2)], and underground mining [national pollution discharge elimination system (NPDES) permit density (no./km2)] such that each major land use activity occurred both in isolation and in combination ...

Watch this Scientific Journal Video about Watershed Planning within a Quantitative Scenario Analysis Framework at JoVE.com

Watershed Planning within a Quantitative Scenario Analysis Framework

There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall ...

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7.9K Views.  West Virginia University. There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.

Video: Watershed Planning within a Quantitative Scenario Analysis Framework

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Urban stormwater and agriculture irrigation runoff contain a complex mixture of contaminants that are often toxic to adjacent receiving waters. Runoff may be treated with simple systems designed to promote sorption of contaminants to vegetation and soils and promote infiltration. Two example systems are described: a bioswale treatment system for urban stormwater treatment, and a vegetated drainage ditch for treating agriculture irrigation runoff. Both have similar attributes that reduce contaminant loading in runoff: vegetation that results in sorption of the contaminants to the soil and plant surfaces, and water infiltration. These systems may also include the integration of granulated activated carbon as a polishing step to remove residual contaminants. Implementation of these systems in agriculture and urban watersheds requires system monitoring to verify treatment efficacy. This includes chemical monitoring for specific contaminants responsible for toxicity. The current paper emphasizes monitoring of current use pesticides since these are responsible for surface water toxicity to aquatic invertebrates.

This article summarizes the design attributes and the effectiveness of treatment systems that treat urban stormwater and agriculture irrigation runoff to remove pesticides and other contaminants associated with aquatic toxicity.

Urban stormwater and agriculture irrigation runoff contain a complex mixture of contaminants that are often toxic to adjacent receiving waters. Runoff may ...

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Pollutant concentrations and loads in watersheds vary considerably with time and space. Accurate and timely information on the magnitude of pollutants in water resources is a prerequisite for understanding the drivers of the pollutant loads and for making informed water resource management decisions. The commonly used &#34;grab sampling&#34; method provides the concentrations of pollutants at the time of sampling (i.e., a snapshot concentration) and may under- or overpredict the pollutant concentrations and loads. Continuous monitoring of nutrients and sediment has recently received more attention due to advances in computing, sensing technology, and storage devices. This protocol demonstrates the use of sensors, sondes, and instrumentation to continuously monitor in situ nitrate, ammonium, turbidity, pH, conductivity, temperature, and dissolved oxygen (DO) and to calculate the loads from two streams (ditches) in two agricultural watersheds. With the proper calibration, maintenance, and operation of sensors and sondes, good water quality data can be obtained by overcoming challenging conditions such as fouling and debris buildup. The method can also be used in watersheds of various sizes and characterized by agricultural, forested, and/or urban land.

With the advancement of technology and the rise in end-user expectations, the need and use of higher temporal resolution data for pollutant load estimation has increased. This protocol describes a method for continuous in situ water quality monitoring to obtain higher temporal resolution data for informed water resource management decisions.

Pollutant concentrations and loads in watersheds vary considerably with time and space. Accurate and timely information on the magnitude of pollutants in ...

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events

The purpose of this study is to describe the methods used to capture flow-weighted water and suspended particulates from farm canals during drainage discharge events. Farm canals can be enriched by nutrients such as phosphorus (P) that are susceptible to transport. Phosphorus in the form of suspended particulates can significantly contribute to the overall P loads in drainage water. A settling tank experiment was conducted to capture suspended particulates during discrete drainage events. Farm canal discharge water was collected in a series of two 200 L settling tanks over the entire duration of the drainage event, so as to represent a composite subsample of the water being discharged. Imhoff settling cones are ultimately used to settle out the suspended particulates. This is achieved by siphoning water from the settling tanks via the cones. The particulates are then collected for physico-chemical analyses.

Nutrients present in particulate form can contribute significantly to the overall loads in agricultural drainage waters. This study describes a novel method to capture flow-weighted water and suspended particulates from farm canal drainage over the entire duration of the drainage event.

The purpose of this study is to describe the methods used to capture flow-weighted water and suspended particulates from farm canals during drainage ...

Wastewater Irrigation Impacts on Soil Hydraulic Conductivity: Coupled Field Sampling and Laboratory Determination of Saturated Hydraulic Conductivity

Since the early 1960s, an alternative wastewater discharge practice at The Pennsylvania State University has been researched and its impacts monitored. Rather than discharging treated wastewater to a stream, and thereby directly impacting the stream quality, the effluent is applied to forested and cropped land managed by the University. Concerns related to reductions in soil hydraulic conductivity occur when considering wastewater reuse. The methodology described in this manuscript, matching soil sample size with the size of the laboratory-based hydraulic conductivity measurement apparatus, provides the benefits of a relatively rapid collection of samples with the benefits of controlled laboratory boundary conditions. The results suggest that there may have been some impact of wastewater reuse on the soil&#39;s ability to transmit water at deeper depths in the depressional areas of the site. Most of the reductions in the soil hydraulic conductivity in the depressions appear to be related to the depth from which the sample was collected, and by inference, associated with the soil structural and textural differences.

Here we present a methodology which matches a soil sample size and a hydraulic conductivity measurement device to prevent the so-called wall flow along the inside of the soil container from being erroneously included in water flow measurements. Its use is demonstrated with samples collected from a wastewater irrigation site.

Since the early 1960s, an alternative wastewater discharge practice at The Pennsylvania State University has been researched and its impacts monitored. ...

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&ndash;Environment Interactions

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant&#8211;environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment&#8217;s effects on the plants&#8217; physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (&#952;) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions

This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant&#8211;environment interactions.

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating ...

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Accurate estimation of drainage discharge (flow rate or water volume per unit time) is essential for calculating nutrient loads delivered to surface water and assessing the performance of edge-of-field conservation practices. Determining drainage flow rates requires monitoring the head in a drainage control structure and applying an appropriate equation for the weir installed in the structure. Previous studies that developed calibrated equations for weirs in control structures have produced different equations (i.e., different weir flow equation coefficients) for the same type of weir and control structure size. This study describes procedures for setting up experimental water flow rate and head measurements within a drainage control structure and developing a weir equation for flows contained within a V-notch weir. Additionally, the procedure for obtaining a weir equation for overtopping flows -- when the water level exceeds the top of the V-notch weir -- is outlined. This process involves using the weir equation developed in the laboratory for a V-notch weir and an online tool, the &#34;Weir Flow Equation Coefficients Calculator&#34;, developed by the authors. The online tool can also generate weir flow equation coefficients for both V-notch-contained and overtopping flows across various sizes of Agri Drain water control structures, even when users lack site-specific flow rate vs. head relationship.

This protocol describes methods for obtaining weir flow equation coefficients for a V-notch weir within a drainage control structure using (1) a laboratory calibration procedure and (2) an online tool, the "Weir Flow Equation Coefficients Calculator", developed by the authors. These methods apply to flows contained within the V-notch and overtopping flows.

Accurate estimation of drainage discharge (flow rate or water volume per unit time) is essential for calculating nutrient loads delivered to surface water ...

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Oil sands process-affected water (OSPW), a by-product of bitumen extraction through surface mining in Alberta, Canada, contains various constituents of concern, including naphthenic acid fraction compounds (NAFCs). These organic compounds are particularly worrisome due to their toxicity and persistence in the environment. Constructed wetland treatment systems (CWTS) use plants and their associated microbes to attenuate contaminants in wastewater. Field-scale CWTS have been presented as a potential large-scale treatment option for OSPW, specifically for degrading NAFCs. To optimize the use of CWTS for large-scale treatment of NAFCs in OSPW, it is essential to deepen our understanding of various design parameters and explore ways to enhance efficacy.
Mesocosm-scale experiments serve as a valuable intermediary, bridging the gap between complex field trials and controlled laboratory settings. Mesocosms provide a controlled, replicable environment to study the effects of various parameters such as substrate, plant species, temperature, and retention time while incorporating ecological complexities in their design. Published and previous work has shown that this method is successful in evaluating the impacts of different parameters on the efficacy of CWTS to attenuate NAFCs in OSPW. This protocol outlines the design and setup of a surface flow wetland mesocosm, along with the experimental approach for treating NAFCs in OSPW. This method can be adapted to treat other wastewaters across diverse geographical locations.

Constructed wetland treatment systems have been used for decades to treat wastewater, but their application to treat oil sands process-affected waters is relatively new. To explore this potential, a surface flow mesocosm design and experimental methods are outlined. This approach aims to enhance our understanding of key design parameters and improve treatment efficacy.

Oil sands process-affected water (OSPW), a by-product of bitumen extraction through surface mining in Alberta, Canada, contains various constituents of ...

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Computational analyses of toxicological processes enables high-throughput screening of chemical substances and prediction of their endpoints in biological systems. In particular, quantitative structure-activity relationship (QSAR) models have been increasingly applied to assess the environmental effects of a plethora of toxic materials. In recent years, some more highlighted types of toxicants are endocrine disruptors (EDs, which are chemicals that can interfere with any hormone-related metabolism). Because EDs may significantly affect animal development and reproduction, rapidly predicting the adverse effects of EDs using in silico techniques is required. This study presents an in silico method to generate prediction data on the effects of representative EDs in aquatic vertebrates, particularly fish species. The protocol describes an example utilizing the automated workflow of the QSAR Toolbox software developed by the Organization for Economic Co-operation and Development (OECD) to enable acute ecotoxicity predictions of EDs. As a result, the following are determined: (1) calculation of the numerical correlations between the concentration for 50% of lethality (LC50) and octanol-water partition coefficient (Kow), (2) output performances in which the LC50 values determined in experiments are compared to those generated by computations, and (3) the dependence of estrogen receptor binding affinity on the relationship between Kow and LC50.

Quantitative structure-activity relationship (QSAR) modeling is a representative bioinformatics-assisted method in toxicological screening. This protocol demonstrates how to computationally assess the risks of endocrine disruptors (EDs) in aquatic environments. Utilizing the OECD QSAR Toolbox, the protocol implements an in silico assay for analyzing toxicity of EDs in fish.

Computational analyses of toxicological processes enables high-throughput screening of chemical substances and prediction of their endpoints in biological ...

Bioengineering

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.
Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7 with a multiobjective evolutionary algorithm SPEA226, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals ...