The purpose of this exercise is to continue our analysis of the impacts of Frac Sand mining in Western Wisconsin. For the purpose of this exercise we will be looking at the impacts on Trempealeau County. We will be using various raster geoprocessing tools to build models for sand mining suitability, as well as sand mining impacts to the local communities and the environment.
The source of this data will be from the Trempealeau County Geodatabase, which we downloaded for an earlier exercise.
First we will be accessing sand mine locations based on known criteria of what is logistically and physically the best conditions for establishing a new mine in Trempealeau County.
Next we will be using features of Trempealeau County to measure the impacts a new sand mine could have on the community and Trempealeau County in general.
Methods
In order to access sand mining suitability and determine where in Trempealeau County new sand mines could be located we examined the following factors:
• Geology:
The specific type of sand which is used for hydraulic fracing comes from the Jordan and Wonewoc Geological formations
• Land Use Land Cover:
agricultural (herbaceous planted/cultivated) land use, hay, cultivated crops, forest, and human development will all play a roll in suitable mining locations
• Distance to railroad terminals:
Just like in exercise 7, the distance to railroad terminals will have logistical impacts on the County, as well as financial costs on both the County and the mining corporation.
• Slope:
The general slope of the land makes mining easier or harder depending on the incline.
• Water table criteria:
Sand mining requires the use of water, and as such prefers locations that have a water table close to the surface.
Each of these features had to be converted to a raster, and then reclassified based on a criteria ranking of our choosing, based on information of the mining process and were classified in the following manner; 3 = High or desirable criteria, 2 = Medium, 1 = Low or least desirable criteria.
| Feature | Attributes | Rank | Justification For Ranking |
| Geology suitability | Jordan/Wonewoc Geological Formations | (Most) 3 | Geology that is desired |
| All Other Geological Formations in Trempealeau County | (Least) 1 | Non desired geology |
| Land Cover Suitability | Open Space, Barren Land, Shrub, Scrub, Herbaceous, Hay, Pasture | (Most) 3 | Non developed space that can easily be replaced |
Low Intensity Development/Cultivated Crops | (Mid) 2 | Can be replaced with some costs or effort to set back to normal (other than time and natural Processes) | |
| Open Water, Medium intensity to High intensity Developed, Deciduous Forest, Evergreen Forest, Mixed Forest, Woody Wetlands, Emergent Herbaceous Wetlands. | (Least) 1 | Would require substantial effort to return to pre mine condition, or would otherwise be impossible to mine |
| Land Cover Exclusion | Open Space, Barren Land, Shrub, Scrub, Herbaceous, Hay, Pasture, Low Intensity Development/Cultivated Crops, Forest, Medium intensity to High intensity Developed, Woody Wetlands | 1 | |
| Open Water, Emergent Herbaceous Wetlands, Woody Wetlands | 0 | Physically Impossible Locations |
|
| Slope | 0%-5.1% Slope | (Most) 3 | Least amount of slope, easiest to mine |
| 5.2%-12.4% Slope | (Mid) 2 | ||
| 12.5%-37.3% Slope | (Least) 1 | Most amount of slope, hardest to mine |
Figure 1 (left) and Figure 2 (right). Figure 1 shows the distance of the closest mining terminals to Trempealeau County. Looking back at the exercise 7, we have already begun to understand the impacts logistics and transportation can have on an area. The further the distance traveled increases the cost for mining companies as well as damage to local infrastructure. In this map the rank of 3 denotes the area in which the least amount of travel would occur, maximizing efficiency for a mining company, and minimizing damage to infrastructure, and thus would be the best area to locate a new mine. Figure 2 shows the land cover of Trempealeau County that would be excluded int he final model. This land was excluded due to being physically impossible to mine. Areas such as open water, wetlands, or highly developed areas were all excluded in the final model.
Figures 3 (left) and Figure 4 (right). Figure 3 shows the geological formations of interest in Trempealeau County, the Jordan/Wonewoc formations (rank of 3, most desired), while all other formations ranked a 1 for least desired. Figure 4 shows the land cover again in ranks of most to least desired. Figure 4 shows the land cover classes as well as the rankings of suitability. For the reasons those features were chosen and the rankings assigned to each feature, please see the above table.
Figures 5 (left) and Figure 6 (right). Figure 5 shows the slopes of Trempealeau County which would be easiest to mine (rank 3) to most difficult to mine. Figure 6 shows classes of water table depth that would be easiest to access for the mining process, again with a rank of 3 being the areas with the easiest access to the water table.
Figures 7. This map shows all the criteria from figure 1-6 compiled using raster calculator to give an index of suitability for the county. Yellow indicates areas that are least desirable to mine, while blue shows areas that would be best to mine based on all of the above criteria. This map shows which areas that would likely to be chosen if a new mine were to be created.
Sand Mining Impact Criteria
Similar to the above models, knowing a bit about the make up of the county we can predict how sand mining will impact cretin areas. We wanted to look at not only impact on the environment but also on people, so we constructed a risk model that includes a noise/dust shed as well.
| Feature | Attributes | Rank | Justification |
| Streams | Streams -1 through third Order Streams | 3 | Smaller streams are more susceptible to impacts of sand mining as they discharge less water and not be able to handle increased sediment load |
| 4th - 6th order streams | 2 | ||
| Greater than 6th | 1 | Mississippi is a 9 and the Amazon is a 12 on this scale. These Rivers would have less impact from sediment, if it were to be added. |
| Prime Farmland | Highly Erodible | 3 | Erodible land should not be mined as it will lead to greater erosion of the landscape, which may have impacts on vegetation, agriculture, road networks, and other impacts |
| Potentially Highly Erodible | 2 | Okay place to mine, may still cause environmental damage if erosion does occur | |
| Not Highly Erodible | 1 | Best place to mine, least impact |
| Zoning | Residential any type (30-2000 meters) | 3 | Where people would notice the impact of mining most is in their daily lives |
| Agricultural | 2 | While the impact may have more of an effect on animals, potentially finding sand in your corn is not good either | |
| Industrial/Commercial/Utility/
Major road |
1 | Noise and dust wont add to much to factories or major roadways, |
| Schools | 0 - 4,000 | 3 | Children exposed to dust and nose that disrupts learning. To put this in perspective 4000 meters is 2.5 miles |
| 4,000 - 8,000 | 2 | 8000 meters is 5 miles. | |
| 8,000 - 11,582 | 1 | This is about 7 miles, while this is the best distance of the three it is important to note that any mine built in the County will be at a maximum of 7 miles to a school no matter where it is placed. |
| Wild Life | 30 - 5,000 meters | 3 | Similar break down to schools with close proximity having the most impact. Wild life areas have a larger distance than schools |
| 5,000 -1 0,000 | 2 | because there are not as many | |
| 10,000 - 15,700 | 1 | 15,000 meters is about 10 miles. |
Figure 8 (left) and Figure 9 (right). Figure 8 shows the areas of Trempealeau County that are designated as residential zones, and which would have the greatest impact if mines were to open in those locations. In this map the rank of 3 designates area areas of highest impact risk. Residential zones were chosen over land cover, due to land cover being slightly ambiguous, in that land cover only designated development and did not state if this development was residential. Figure 9 shows the distance from schools to the edges of the county. The Euclidean distance tool did not run to the full extent of the county, the implications of which are noted in the discussion below.
Figure 10 (left) and Figure 11 (right). Figure 10 shows the distance from wild life areas that would have the least and greatest potential impact to those wild life areas. As with the other distance maps, the closer to the wild life area the mine would be created would result in a higher impact to those areas. Figure 11 shows the streams of Trempealeau County. In the model the distance of streams with the most impact was supposed to be used, but due to the issues with the feature class to raster tool (noted in the discussion). The streams themselves had to be used.
Figure 12 (left) and Figure 7 (right, from above). This map shows the areas that would be the most affected in Trempealeau County. Yellow and low numbers designate areas of least impact, while blue designates areas of highest impact. Comparing the two maps it would seem that the areas that would be best for mining in figure 7 designated in blue are generally the areas in Figure 12 that would have the highest impact. The location of a new mine would most likely be located in areas of blue in figure 7 and areas of yellow in figure 12 which overlap.
For the weighted risk model, a Python Code was used, or rather would have been but unfortunately it would not run (most likely my error). So the model was ran directly in ArcMap. Using the same equation, the factor that was chosen to weigh more heavily in the model was Wild Life Areas. Below is the Model Builder for both the weighted model and for the View Shed tool.
Figure 13 (left) and Figure 12 (right). Figure 13 is the weighted impact model which emphasizes one of the criteria used in calculating the impact model over the others. The level of weight can be chosen by the user and in this case is a factor of 1.5. The factor that was chosen to be weighted was wild life areas. Wild life areas were chosen due to potential legal issues on the federal level if any of these areas contains migratory birds, it would be a nightmare for a mining company to attempt to establish a mine near that location due to the federal migratory bird act of 1918. It is important to note that at this point in time it is unknown if migratory birds use these areas, these wildlife areas were chosen because I felt that this point may be over looked and that residential or farm areas would have advocates for both of those locations inherently from the who lived there, while people might overlook wildlife areas.
Figure 14. The view shed tool. This map shows what the view from a location would be at a chosen scenic point. For the scenic point I chose Tamarak Creek Wild Life Area in Trempealeau County. It is important to note that limitations exist with the view shed tool and will be discussed below.
Discussion:
In full disclosure some issues which occurred in building this model did effect the outcome. Not that the outcome is biased, the outcome should be understood in the light of these issues. First, the Raster Calculator in arcmap did not run the model to the full extent of the county. The issue seems to stem from the School Distance layer, which truncated itself to a lesser extent than the county for an unknown reason. When using Raster Calculator, not having values for the school layer in these places resulted in the gaps in the model which are seen in the maps. The fix for this issue is unknown at this time, but it is possible that mask for the raster was some set to different limits than for all of the other layers, which resulted in the truncation. Attempting to rerun the tool many times with different masks did not fix the issue.
Secondly, the Feature to Raster tool resulted in much anguish while constructing the model. The tool would run for 15-20 minutes and would often not complete and would crash the program. During these crashes, the first map document was corrupted and would no longer open, taking a model builder for the first section with it. The results of this tool not working well, changed the model in two ways, first the distance from residential areas could not be calculated so just residential areas were used. Secondly and similarly, the Streams layer was supposed to consist of streams that would be the most heavily impacted by having a mine located near them. This did not occur and again the model has different results than what the analysis of the distance from the streams would have produced.
What this means for map interpretation is that the Zoning areas and the streams should have been different. The Euclidean Distance tool should have been ran on residential areas, and on only streams of cretin sizes to measure impact on the distance of both of these layers.
Lastly, their is an issue with the view shed tool, however this issue is actually with how the tool is designed. The view shed tool is limited in a few ways, one with out Z data to know actual elevation the models predictions are going to be off. While the model can predict elevation, it will not take into account vegetation or structure, only the elevation of the point that the view shed is ran from, the elevation, and the curvature of the Earth.
Again, the results of the impact model are limited, but it is important to understand the limitations of the model, how the issues occurred in the model, and in the future, how those issues can be fixed.
Under standing the limitations has an ethical component, which is that as a map maker, we in the GIS industry do not mislead people who view our content or misrepresent our results.
Letting the GIS community and people in general, know of the limitations of the model accomplishes two things, first as stated above it honestly states that the results are not misrepresented, and second, it allows for others in the GIS community to build on the model or help in correcting any errors that have occurred.
Conclusion:
While not perfect, GIS maps help to show off the modeling potential of GIS. We can take data, and just with known information we can attempt to understand or explain geospatial phenomenon of all kinds. This can extend to other disciplines, as well as everyday problems, and goes to show why GIS is becoming such an important tool in our everyday lives.
While showing a partial picture, these maps help shine a light on some of the impacts that may occur if new frac sand mines would be built in Trempealeau County. Knowing the impacts, people can make more informed decision on both the local and County level as to where a mine should be located and the potential impacts on the general population.
Sources:Wisconsin DNR
Trempealeau County Geodatbase (from the Trempealeau County Website)
