You have been hired by the Lake Raystown Watershed Council to identify potential sludge disposal sites within a watershed in south-central Pennsylvania. You must take into account the vulnerability of groundwater contamination, distance from surface water, and area of each potential site. To accomplish this task, you will use ArcGIS Spatial Analyst tools to recode and overlay maps depicting important factors that affect inherent vulnerability. You will then combine your results with information about surface water and site area to identify potential sites for sludge disposal.
At the successful completion of Lesson 8, you will have:
If you have questions now or at any point during this lesson, please feel free to post them to the Lesson 8 Discussion.
Sewage sludge is the solid waste created during the process of domestic wastewater treatment. This material is often inadvertently contaminated with many toxic organic and inorganic compounds. Lesson 8 focuses on the identification of suitable locations within the Lake Raystown Watershed, where processed sewage sludge can be applied to the soil surface as part of a controlled bio-degradation treatment alternative. There area two primary advantages of this process: 1) the natural renovative capabilities of the soil are used to further break down residuals that remain after standard wastewater treatment processes, and 2) the nutrient-rich sludge material serves as a beneficial soil amendment which can complement, and in many cases replace, standard fertilization practices.
An important consideration in evaluating the suitability of potential sewage disposal sites is the potential impact such sites might have on existing groundwater quality. There are a number of approaches that can be used to evaluate pollution problems associated with groundwater resources, ranging from very complex to relatively simple. An example of a complex approach is the use of sophisticated computer models (such as the MODFLOW model [1] developed by the U.S. Geological Survey) to track the dispersion of contaminants through the soil profile and beyond. While this approach may help one to accurately quantify contaminant movement and loads, it does have a very serious limitation in the form of very extensive data needs. For this reason, simpler, empirical approaches are often used to evaluate pollution potential. One such approach is the DRASTIC methodology developed by the U.S. Environmental Protection Agency.
"DRASTIC" is an acronym in which each letter stands for one of seven hydrogeological parameters that directly influence the movement of pollutants into and through the soil and sub-soil layers. Measurements within each parameter are assigned DRASTIC Ratings between 1 and 10 based on how they affect the movement of contaminants. Some of the parameters (e.g., depth to groundwater) have a much greater influence on the overall groundwater vulnerability than others. This is incorporated into the DRASTIC Index calculation by assigning weights to each of the parameters based on their relative importance. Areas with higher DRASTIC Index Scores are more likely to experience groundwater contamination in the event of a release than areas with low DRASTIC Index Scores.
The equation to calculate the DRASTIC Index is:
$$(\mathbf{D} \times 5)+(\mathbf{R} \times 4)+(\mathbf{A} \times 3)+(\mathbf{S} \times 2)+(\mathbf{T} \times 1)+(\mathbf{I} \times 5)+(\mathbf{C} \times 3)$$The seven parameters are briefly described below:
The Lake Raystown watershed is located in South Central Pennsylvania and covers an area of approximately 1,000 square miles. Contained within this watershed is Raystown Lake. This man-made recreational lake was created as a flood control dam designed to protect the populated areas from Huntingdon, Pennsylvania to the mouth of the Susquehanna River.
All of the required readings for Lesson 8 are Esri help articles. Although we will demonstrate how to use these tools in the Step-by-Step Activity, the help topics will provide you with a good overview of what the tools will do when executed.
Find the help articles listed below on ArcGIS Pro Resource Center [2] website.
This section provides links to download the Lesson 8 data, along with reference information about each dataset (metadata). Briefly review the information below so you have a general idea of the data we will use in this lesson.
For this lesson, you will be provided with all of the data in the Lesson 8 Data zip file. All of these data were created by your own work organization. While receiving data from an in-house source may seem like a blessing, it often comes without some of the typical information you receive from well-known data clearinghouses. Two of the most common shortcomings are a lack of metadata and projection information. Therefore, it may be difficult to determine the source of the data, the attribute definitions, the scale of the data, and the coordinate system and datum. As distressing as this may sound, there will generally be someone in your office who can provide some reliable information pertaining to the data.
Note: You should not complete this activity until you have read through all of the pages in Lesson 8. See the Lesson 8 Checklist for further information.
Create a new folder in your GEOG487 folder called "L8." Download a zip file of the Lesson 8 Data [9] and save it in your "L8" folder. Extract the zip file and view the contents. Information about all datasets used in the lesson is provided below:
The Step-by-Step Activity for Lesson 8 is divided into three parts. In Part I, we will review the relevant datasets and organize your Map. In Part II, we will create a DRASTIC Groundwater Vulnerability grid. In Part III, we will determine suitable land areas for sewage sludge application sites based on the DRASTIC ratings, distance from surface water, and size of each region.
Note: You should not complete this step until you have read through all of the pages under the Lesson 8 Module. See the Lesson 8 Checklist for further information.
In Part I, we will review the starting datasets and organize the map for analysis.
Since all of the datasets used in this lesson have the same projection, we do not need to be concerned with the order that we load the data.
Do the all of the provided raster grids have the same cell size?
Do all of the input datasets have the same extent?
What are the units of the "VALUE" attribute in the elevation grid?
How many different types of soil and rock types are in the study area?
How wide a buffer was used to create the streams data?
Where is the Lake Raystown Watershed located in relation to the state of Pennsylvania?
In Part II, we will create a series of grids representing the DRASTIC Ratings for each parameter (D -Depth to Water Table, R- Net Recharge, A - Aquifer Media, S - Soil Media, T - Topography, I - Impact of Vadose Zone, and C- Hydraulic Conductivity). The dataset we will use to create each grid is shown in the graphic below. In this section, we will introduce two new spatial analyst concepts: creating slope grids from elevation and reclassifying ranges of values as opposed to unique values.
Make sure you have the correct answer before moving on to the next step.
The "soilgrid.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step. Be sure to go to Feature to Raster tool > Environments and double-check and the output coordinates and processing extent to the same as "LakeRaystown.” Also, be sure to expand the table columns to view all COUNT totals.
Texture | DRASTIC Rating |
---|---|
Silty Clay Loam | 3 |
Loam | 5 |
Loamy Sand | 6 |
Make sure you have the correct answer before moving on to the next step.
The "s.tif" attribute table should match the example below. If your data does not match this, go back and redo the previous step.
Three of the seven DRASTIC factors (A - Aquifer media, I - Impact of the vadose zone, and C - Hydraulic Conductivity) can be defined on the basis of geology. We will use the Reclassify Tool again to assign DRASTIC ratings corresponding to these three factors for the appropriate surface geology units contained in the geology layer.
Make sure you have the correct answer before moving on to the next step.
The "geologygrid.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
OID | Value | Count | Rock_type |
---|---|---|---|
0 | 1 | 1480784 | Interbedded Sedimentary |
1 | 2 | 643096 | Sandstone |
2 | 3 | 388372 | Shale |
3 | 4 | 256791 | Carbonate |
Rock Type | DRASTIC Rating |
---|---|
Interbedded Sedimentary | 6 |
Sandstone | 6 |
Shale | 2 |
Carbonate | 10 |
Rock Type | DRASTIC Rating |
---|---|
Interbedded Sedimentary | 6 |
Sandstone | 6 |
Shale | 3 |
Carbonate | 10 |
Rock Type | DRASTIC Rating |
---|---|
Interbedded Sedimentary | 2 |
Sandstone | 1 |
Shale | 1 |
Carbonate | 10 |
Make sure you have the correct answer before moving on to the next step.
The "a," "i," and "c" attribute tables should have all of the attributes shown below. If your data does not match this, go back and redo the previous step. Again, be sure to expand the COUNT field to see all the complete values.
When you have data that represents elevation, you can create several different types of raster layers, one is a slope grid. Slope represents steepness, incline, or grade of a line or area. A higher slope value indicates a steeper incline. With Spatial Analyst, it is easy to create a slope layer from elevation data.
Degree vs. Percentage
Be careful when choosing the slope output measurement. There are two ways to express slope values, either as a percent or as a degree. "45 degrees" slope and "45 %" slope are NOT equivalent values.
Degree slope (θ): angle created by a right triangle with sides of length "rise" and "run"
Percent slope: length of "rise"/length of "run" * 100
Topography Range | DRASTIC Rating |
---|---|
0-2 | 10 |
2-6 | 9 |
6-12 | 5 |
12-18 | 3 |
>18 | 1 |
Make sure you have the correct answer before moving on to the next step.
The "t" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Reclassifying Ranges of Numbers vs. Unique Values
When you need to reclassify data based on ranges of values instead of unique values. For example, notice above that the old value of "2" is specified as the upper bound in the range "0-2" and the lower bound in the range "2-6." What new value, either "10" or "9," will be assigned to old values of "2" in the output grid?
In this case, ArcGIS will assign the old value "2" to a new value of "10," and the old value of "2.0001" to a new value "9" in the output grid. The general rule is that ArcGIS will include the break values themselves in the group that it forms the upper range boundary. Notice that you will encounter this same issue for all break values (e.g., "6", "12", and "18" in the example above).
This is particularly important when the break values themselves are meaningful in your analysis. The most common example of this situation is when you encounter specifications of "less than x" vs. "less than or equal to x" in your requirements. If you want to reclassify values "less than 5" to a new value, you would need to specify a break value of "4.99999999," so the value of "5" is not included in your new category. The particular number of decimals you need to specify will depend on the number of decimals in your input data. For example, if your data layer has five decimal places, then you would set the reclassification thresholds as follows: a.aaaaa - b.bbbbb, b.bbbbb - c.ccccc, and so forth.
See the ArcGIS Help for further information regarding reclassification by range [10].
Compare the "d" grid to the "streams_buffer" shapefile. Do areas near streams have high or low vulnerability?
Which input datasets (d, r, a, s, t, i, c) have the highest DRASTIC rating values?
Do you see any spatial patterns in the individual drastic grids?
Now that you have the required data layers, you can create a DRASTIC groundwater vulnerability grid based upon the DRASTIC index equation. This will involve use of the Raster Calculator to combine several grids in a weighted overlay. The graphic below shows an example of how cell values are updated during the calculation.
Combining raster layers is a simple, yet very important process with Spatial Analyst. You will often find that it is necessary to create a single layer that is comprised of several data sets. The idea is similar to that of performing an overlay with vector layers, in that you are making one out of many, with the major exception that the cell values change based on the expression used.
The addition (+) and multiplication (*) signs are the most common arithmetic operators used to combine raster layers. The plus (+) sign performs an addition with each cell, so the value in a given cell of one grid will be added to the value of the same cell in the next grid, and so on. The multiplication (*) sign, as expected, performs a multiplication based on the values in each cell.
Either of these can be used when the purpose is to simply combine grids, although you should use the same operator for all grids. However, when forming an expression that includes additional operations on individual grids, as in the case above, it is important to understand the precedence that the operators will be performed. In mathematical order of operation rules, multiplication always takes precedence over addition. Hence, in the expression above, the values in the "D" grid will be multiplied by 5 before they are added to the values in the "R" grid. If an expression should occur that is out of precedence, enclose that expression with parentheses, as you would when using a calculator.
Make sure you have the correct answer before moving on to the next step.
The" drastic_index" grid should have the following information. The statistics from the "COUNT" field are also provided. If your data does not match this, go back and redo the previous step.
What do the numbers in the "VALUE" field of the "drastic_index" mean in the real world? For example, do high values represent areas with high or low vulnerability to groundwater pollution?
Which parts of the watershed are most vulnerable to groundwater pollution?
Do any of the parameters have a greater influence on the final results?
Now that the groundwater vulnerability layer has been produced, we can use this data to help find the areas in the watershed most suitable for sludge disposal. Along with this dataset, we also need to incorporate the stream buffer dataset. Remember from previous lessons that it is possible to reclassify grid cells to values of "NoData" to exclude them from your analysis. We will use this technique to remove portions of each dataset that do not meet the relevant criteria. For example, we will reclassify suitable areas within each dataset as "1" and unsuitable areas as "NoData."
You can also do the opposite of this by assigning existing values of "NoData" to more meaningful values. We will use this technique to create a grid of areas that are outside of steam buffers. Then, we will use the Raster Calculator to combine the individual suitability results into one grid. We will then use the "RegionGroup” command to create regions from adjacent cells with the same results. This process is illustrated in the graphic below.
For the purposes of this lesson, we assume that state regulations require the following for a site to be considered for sludge disposal:
The calculation performed in the previous step combines the results of two Boolean operations that are either evaluated as:
TRUE (indicated by a value of 1) OR FALSE (indicated by a value of 0)
We are only interested in cells that meet the criteria (values of 1).
Make sure you have the correct answer before moving on to the next step.
The "OK_DRASTIC.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
The "OK_Streams" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
The "OK2criteria.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
The "OK_Regions" statistics for the "COUNT" field should match the example below. If your data does not match this, go back and redo the previous step.
The last criteria we need to incorporate is - Area (sites greater than 0.5 sq km). We learned in Lesson 5 that you can calculate the area of a raster by multiplying the number of cells by the area of each cell. To calculate the area of regions within a raster, we can use this same method.
Why did we use the number "30" to calculate the area?
Make sure you have the correct answer before moving on to the next step.
The "OK_Area" statistics for the "COUNT" field should match the example below. If your data does not match this, go back and redo the previous step.
Try one or more of the optional activities listed below.
Note: Try This! Activities are voluntary and are not graded, though I encourage you to complete the activity and share comments about your experience on the lesson discussion board.
Advanced Activities are designed to make you apply the skills you learned in this lesson to solve an environmental problem and explore additional resources related to lesson topics.
The regulations for sludge disposal sites were revised to help prevent land use change in the watershed. The new regulations require that sludge disposal can only occur on land that is currently used for agriculture. This will prevent areas such as forests and wetlands from being used as disposal sites.
Find suitable sites in the Lake Raystown Watershed that meet all of the following criteria:
In Lesson 8, we created a DRASTIC Groundwater Vulnerability grid and identified the potentially suitable sites for sludge disposal. We used Spatial Analyst tools to convert vector into raster data, calculate slope, reclassify grids, and combine multiple rasters. Next week, you will apply the skills and techniques you learned in the course to explore an environmental challenge on your own.
Lesson 8 is worth a total of 100 points.
If you have anything you'd like to comment on, or add to the lesson materials, feel free to post your thoughts in the Lesson 8 Discussion. For example, what did you have the most trouble with in this lesson? Was there anything useful here that you'd like to try in your own workplace?
This page includes links to resources such as additional readings and websites related to the lesson concepts. Feel free to explore these on your own. If you'd like to suggest other resources for this list, please send the instructor an email.
Links
[1] http://water.usgs.gov/nrp/gwsoftware/modflow.html
[2] https://www.esri.com/en-us/arcgis/products/arcgis-pro/resources
[3] https://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-slope-works.htm
[4] https://pro.arcgis.com/en/pro-app/help/analysis/spatial-analyst/mapalgebra/what-is-map-algebra.htm
[5] https://pro.arcgis.com/en/pro-app/help/analysis/spatial-analyst/mapalgebra/an-overview-of-the-rules-for-map-algebra.htm
[6] https://pro.arcgis.com/en/pro-app/arcpy/spatial-analyst/an-overview-of-the-map-algebra-operators.htm
[7] https://pro.arcgis.com/en/pro-app/help/analysis/spatial-analyst/mapalgebra/working-with-operators.htm
[8] https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/extract-by-attributes.htm
[9] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/activities/lesson08/L8Data.zip
[10] https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/reclass-by-ranges-of-values.htm
[11] http://raystown.uslakes.info/
[12] https://www.nab.usace.army.mil/Missions/Dams-Recreation/Raystown/
[13] https://nepis.epa.gov/Exe/ZyNET.exe/20007KU4.TXT?ZyActionD=ZyDocument&Client=EPA&Index=1986+Thru+1990&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C86thru90%5CTxt%5C00000001%5C20007KU4.txt&User=ANONYMOUS&Password=anonymous&SortMethod=h%7C-&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Display=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Results%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL
[14] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/activities/lesson08/GroundWaterPollution%20in%20Ohio.pdf
[15] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/activities/lesson08/Evans_1997.pdf
[16] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/activities/lesson08/Brandt_1989.pdf