GIS Trash Map

Clean Jordan Lake gratefully acknowledges REI Co-op for providing a stewardship grant to improve the efficiency and planning of our shoreline trash removal efforts. With this support, we have developed a GIS map of trash, tires and CJL volunteer effort.  Sue King, a graduate student in the Center for Geospatial Analytics at North Carolina State University, carried out the data analysis.  She presented her findings at the 2018 Annual Conference of the NC Water Resources Research Institute.  This link  is to a .ppt file that includes a voice description by Sue of each slide.

Sue started with CJL data of all cleanups dating back to 2009.  The data set consisted of the number of  trash bags, tires and volunteers in 1O5 cleanups conducted by CJL from January 1, 2009 to July 15, 2017.  The shoreline length covered the Haw River Arm from the entrance of Robeson Creek to the dam and both sides of New Hope River Channel from the dam northward to New Hope Overlook at Jordan Lake State Park. Of this 24 mi. stretch, about 17 mi. were cleaned at least once.  This section was selected because the main source of trash and tires found here is from stormwater flows from the Haw River watershed rather than recreational use of the lake.  The spatial variability in intensity of trash and tires and in volunteer effort was defined by dividing the shoreline length into 43 subsections.

The ArcMap shown in snapshot below is available online at ArcGIS.    Layers of metrics can be viewed for individual cleanups and for all in each subsection.  The example below is for total lb of trash removed per 100 ft of shoreline for all cleanups performed in each of 43 subsections.  The largest circle represents > 183 lb/100 ft and the smallest < 6 lb/100 ft.  The number of cleanups in each section is not the same because the rotation around the shoreline was not systematic.  Nevertheless, the larger intensities of trash are consistent with sections of shoreline most vulnerable by their position to accumulation from the watershed.  Expand the view (click on + sign) to see the individual subsections more clearly.  Click here for a beginners guide to displaying layers of information on the ArcMap.

Cleanups varied in the length, not always covering an entire subsection and sometimes covering fractions of more than one subsection.  The weight of trash and number of tires removed in each cleanup were divided by the shoreline length cleaned to obtain intensity factors.  Bags of trash were converted to pounds by assuming each holds 20 lb.  Volunteer efficiency was expressed as lb of trash and number of tires removed per volunteer-hour.

Map 1. Volunteers and trash and tire intensities (per 100 ft of shoreline) in each subsection

A few examples of GIS layers that can be viewed online are given here. Map 1 shows the total number of volunteers and the total lb of trash and number of tires removed per 100 ft  within each of the 43 subsections.  The largest intensity of trash and tires, and correspondingly, the greatest number of volunteers were at subsections along the Haw River Arm and those directly opposite its confluence with the New Hope River Channel on the east side of the lake.

Map 2. Volunteer efficiency in trash removal in each subsection

The volunteer-hours (number of volunteers x hours worked) and  lb of trash removed per volunteer-hour for each shoreline subsection are shown in Map 2.  In general, efficiency was lower moving northeast along the New Hope River Channel. Lower trash loads in this direction could explain this trend because bags are filled less quickly when more scattered.

Map 3. Impact of rainfall on trash loadings

Lake level rise (LLR) is an indirect measure of the average rainfall across the 1,700 sq. mi. watershed. LLR produced the metric shown in Map 3 to quantify the amount of trash (and also tires) arriving at the shoreline with each significant rainfall. Although not rigorous, visual inspection of the USGS data for lake level at the Moncure, N.C. gaging station suggests a 1-inch rainfall results in an LLR of about approximately 2 ft.  This was taken as the minimum LLR to quantify the effect of rainfall on trash loadings; 47 LLRs of 2 ft or greater occurred from January 1, 2009 to July 15, 2017. 

The very first cleanup of a subsection established the initial clean state.  Trash loading intensity per significant rainfall was then calculated by dividing the lb of trash per 100 ft of shoreline removed in the next cleanup by the number of LLRs that occurred between the two cleanups. The same calculation was repeated for subsequent cleanups from which the average trash loading is reported; 11 of the 43 subsections were excluded because of having only one cleanup.  

Optimized Hot Spot Analysis (OHSA) is a tool within ArcMap that was used to determine significant clustering of high values (hot spot) or low values (cold spot).

Map 4. Example of OHSA examining sections of shoreline having high (red outlined circles) and low (blue outlined circles) trash loadings

The OSHA for average trash intensity (lb/100 ft)  for all cleanups in each subsection is shown on Map 5. The four hot spots (red outlined circles) are on the east side of the Haw River Arm. The remaining hot spot is on the opposite the entrance of the Haw River Arm to the main body on the east side of the lake. These results reinforce the observation that trash has a strong tendency to deposit in these subsections while being transported by high discharge in the Haw River along from the Haw River Arm toward the dam.

Low trash intensity clusters along New Hope River Channel are indicated by 14 cold spots (blue outlined circles). In periods of high discharge during rainfall events, the dam acts to deflect flow to the northeast, opposite to the dry weather direction. While most trash has already been deposited along the Haw River Arm, the remainder moves northeast up the New Hope River Channel. A southwest wind can further the transport.

Map 5. Example of OHSA examining sections of shoreline having high (red outlined circles) and low (blue outlined circles) tire loadings

Similar results were obtained for tire loadings as depicted in Map 5.