Areas of Concern for Non-Point Phosphorus Pollution of the Yahara Watershed

Introduction and Goals:

This project aimed to find areas that were at risk for contributing non-point phosphorus pollution in the Yahara Watershed, WI, by using GIS skills and methods. This This watershed lies mostly in Dane county, but also has small areas extending into Columbia, Rock, and Green Counties. It also is a part of the greater Rock River sub-basin, and the even greater Mississippi River basin (Connors, n.d.). The watershed covers an area of 359 square miles and contains over 540 miles of rivers and stream. It also contains the four Madison chain lakes: Mendota, Monona, Waubesa, and Kegonsa. All four of these lakes, as well as many of the surrounding streams and rivers are listed as impaired by the EPA due to (among other high levels of contaminants) phosphorus pollution exceeding the EPA's TMDL (Total Maximum Daily Load) (EPA, & WDNR, 2017). These limits are set and waterways are routinely monitored due to the Clean Water Act Section 303d. This project specifically examined streams that contributed to pollution of the Madison Lakes. The land examined as at risk was found only with buffering from streams that were listed as impaired downstream and eventually flowed into one of the lakes. Downstream stretches of stream, even if listed as impaired (and being downstream from the listed lakes which had accumulated much phosphorus, they were impaired), were not considered in this research. This research was considering what was leading to the impairment of the lakes.

Study Area Overlaid on Counties

The Madison lakes have had issues with algae and cyanobacteria growth, affecting stakeholders in the lake: swimmers, boaters, fishermen, businesses situated on the lake, the University of Wisconsin, and the city as a whole. Algae and cyanobacteria flourish due to the eutrophication of the lakes (Park et al., 2012). These then block out sunlight, limiting photosynthetic creation of oxygen, and also use up oxygen in their decomposition, leading to a hypoxic state in the waterbody that can harm aquatic life. Cyanobacteria also creates dangerous cyanotoxins which can cause a diverse selection of negative health effects to humans and other animals. Finally, the filling up of the waters with algae and cyanobacteria creates conditions that are not favorable to those wishing to recreate in the waters.

Impairments of a Single Madison Lake

Phosphorus is known to be the primary cause of eutrophication in the Madison lakes (Converse, 2012). This is established by EPA monitoring and also previous research and the ongoing mitigation plan. Also, it has been established that 71% of phosphorus output has been caused by agricultural activity, while 28% was found to occur in urban areas (Converse, 2012). Phosphorus may be applied via manure or chemical means, and slope, amount of phosphorus applied, soil type, and distance from river are all factors which can inform how much phosphorus may runoff in fields it is applied to, among others. This project used distance, slope, land use data, and impaired waterways data to find areas that may have been some of the riskier areas where phosphorus runoff man have come from. The specific criteria for these areas was pulled from recommendations made by the University of Tennessee, but the soil properties of south-central Wisconsin could be studied to dictate changes that could be made in these criteria for use in Wisconsin (Walker, n.d.). Phosphorus runs off via soil particles it is absorbed into (Klapproth & Johnson, 2009). Very little if any infiltrates and is carried in groundwater as nitrogen fertilizer is. Techniques that can be applied in order to mitigate runoff include vegetative buffers such as riparian forests, terracing of farm fields, reducing use of fertilizer, and strategically placing fertilizer only on fields only a minimum distance away from rivers (Walker, n.d.).

Methods:

Stream data from the WDNR was reduced to the areas previously denoted using Clip and polygons created from reclassified WDNR land use raster data. Manual editing was also used to get rid of areas south and downstream from the Madison lakes. WDNR stream data had included lines intersecting waterbodies which could have impacted further statistical length investigation into the streams. Though networking analysis could have been used to narrow data down by selecting areas upstream from listed impaired segments of waterways only for analysis, this was done simply by manual selection, which was appropriate due to the small size of the study area. WDNR stream data network analysis attempts failed. All data other was reduced to the size and shape of the watershed using Clip and the polygon area created from the watershed raster as well. All data including the watershed raster was projected to the Southern Wisconsin State Plane projection before use, and some of this was done automatically with importing into the Feature Datasets that were configured to use this
projection.

Stream End Nodes and Watershed Boundary

Watershed delineation was performed using watershed delineation procedures written about previously on this blog using a USGS NED (National Elevation Dataset) 10 – meter resolution DEM previously clipped to the four county area. The process included the use of the following geoprocessing tools in the order listed: Fill, Flow Direction, Flow Accumulation, Conditional (50k, 125k, 500k), Stream Link, Watersheds. The 50,000 conditional created watersheds adequately small enough to then convert to polygons, select the polygons that fell into the WDNR Yahara Watershed shape files, then export these for an originally created watershed study area data feature class. This area fell fairly close to the DNR created watershed delineation, and DNR stream data only crossed the new delineation in one place, affirming that the method had worked to create a reliably accurate delineation. More research could be done to compare the accuracies of these watersheds.

Agricultural land use was isolated from the WDNR Wiscland 2 raster dataset, as well as urban landuse, and forgeable grassland. The raster to polygon tool was used to create polygons from a reclassified boolean raster, then the polygon feature class was reduced to polygons of only the land use of interest via a selection by attributes and exporting. The NED DEM was used with the Slope tool to find percent grade, which was then reclassified as a boolean raster for 8% grade. This was then converted to a polygon feature class, and areas with 8% grade and above were selected and exported. A buffer of the streams of concern for 100 feet was then created. Using Clip and field statistics, statistics were found, and using intersect, a final product was created. 

Results:

It was found that of the 416,388 meters of stream that was found to feed into or connect the four-lake system:

• 223,120 m or 53.56% passed through agricultural or urban land.
• 171,951 m or 41.30% passed through agricultural land.
• 51,170 m or 12.29% passed through urban land.
• 26,178 m or 6.29% passed through forgeable grassland. 

These findings reinforce the understanding that Phosphorus non-point pollution comes mostly from agricultural and urban lands, and much more pollution comes from agricultural lands than urban areas.

A map product created from this project is shown below. The areas of concern highlighted by yellow should be investigated to make sure phosphorus use is not allowed on fields in these areas. Alternatively, riparian forests, or other vegetative buffers could be planted in these areas. Future investigation could be done using current high spatial resolution remote sensing data to find out if these areas of river or stream are already protected by vegetative buffers, and if there are currently crops being grown in these areas. This project would have the benefit of not requiring travel or work on the ground in order to monitor a wide area of cropland. High resolution and current land use data could also possibly be used for this function.

Areas of Concern for Non-Point Phosphorus Pollution

Sources:

Converse, D. L. (2012, November 9). Yahara CLEAN Strategic Action Plan for Phosphorus Reduction (Rep.). Retrieved December 17, 2017, from Clean Lakes Alliance website: https://cleanlakesalliance.org/wp-content/uploads/2016/10/Strategic-Action-Plan-11092012.pdf

Connors, K. (n.d.). Reducing Total Phosphorus and Sediment Loads in the Yahara Watershed Through Wisconsin’s Adaptive Management Option. Madison, WI: Dave County Land and Water Resources Department.

Walker, F. (n.d.). Best Management Practices for Phosphorus in the Environment (Publication No. PB 1645). Knoxville, TN: The University of Tennessee Agricultural Extension Service.

Klapproth, J. C., & Johnson, J. E. (2009, May 1). Understanding the Science Behind Riparian Forest Buffers: Effects on Water Quality (Publication No. 420-151). Retrieved December 18, 2017, from Virginia Cooperative Extension website: http://pubs.ext.vt.edu/content/dam/pubs_ext_vt_edu/420/420-151/420-151_pdf.pdf

EPA, & WDNR. (2017, December 6). [EPA 303d Listed Impaired Rivers, Streams, and Lakes Shapefiles]. Published Data.

Park, J., Finn, J., Cooke, R., Lawson, C., & Truslove, L. (2012, March). Farming and Habitats. Retrieved December 18, 2017, from http://www.ecifm.rdg.ac.uk/habitat.htm

WDNR. (2017, December 6). [EPA 303d Listed Impaired Rivers, Streams, and Lakes Shapefiles]. Published Data.

WDNR. (2016, August). [Wiscland 2.0]. Published data.

WDNR. (2017, December 11). [24k Hydro Flowlines and Waterbodies]. Published data.

WDNR. (2017, December 6). [Digital Elevation Model (DEM) - 10 Meter]. Published data.

WDNR. (2017, December 6). [Wisconsin Watersheds]. Published data.

Lab 3: Project Data and Geodatabase Developments

Introduction:

This lab asked that students develop a geodatabase and feature classes for their projects, consider data storage formats and projections and other information, and show the file structure for their data. The methods below describe the structure of the data for a non-point phosphorus pollution sourcing project in development.

Methods:

The file geodatabase created for this project contains two feature datasets: one for all outputs of watershed analysis steps (the watershed analysis procedure outputs many different files which could clutter the geodatabase if not separated), and one for general output of all other geoprocessing. The geodatabase is intended for use by only the project author. Both feature datasets are formatted for the NAD 1983 Wisconsin Southern State Plane in feet, and all data, before being used in this project, is projected to this fitting projection for the study area of the Yahara Watershed. The watershed spans much of Dane County, Wisconsin, stretching into small sections of Green, Rock, and Columbia Counties. This area is completely visible with a scale set to 1:450,000.

Metadata will be produced for the end resulting data of this project. Data lineage is especially important to document in this project due to the long string of geoprocessing events.

Some of the data has not yet made it into the file geodatabase but will as soon as geoprocessing begins. Some data is in shapefile format as well and must be stored outside a geodatabase. Line and polygon shapefile EPA impaired waters data was used for locating rivers with excessive phosphorus content so that locations could be found upstream in this river. Fields included in these shapefiles include date last updated, waterbody name, and pollutants. The raster DEM USGS National Elevation Dataset was included for use in delineating watersheds. This file contains no fields, but contains a grid of elevation values. Wisconsin county boundary shapefiles were included for use in clipping statewide data for smaller file size of output features and shorter geoprocessing times. Wiscland 2 land use data was included for use in finding forested areas near rivers and streams buffering agricultural lands. This raster data contains values corresponding to different land use types.

This project is simply finding areas that are at most risk for creating non-point phosphorus pollution running off overland and does not include any use of newly created and populated feature classes using subtypes, domains, or their policies.

Results:

The data that has been created and sourced so far for the project is shown below in the open file structures (Figure 1). More data will be created as geoprocessing begins.

Figure 1

Sources:

Data sourced from WI DNR, USGS, and US EPA.