Identify groundwater vulnerable areas

Groundwater is an important natural resource to humans and the environment. It's estimated that more than one third of the population in the United States relies on groundwater for drinking, irrigation, and other uses. But due to rising populations, urbanization, increasing human activities on land, and other natural events, groundwater resources have been exposed to severe contaminations and are degrading in supply.

In 2015, the United States Geological Survey (USGS) scientists reported that 22 percent of groundwater samples from aquifers contained contaminants from human and geologic sources in the country. Notable causes include leakages from storage tanks and septic systems, uncontrolled hazardous wastes, and other atmospheric contaminants. Morrow County, Oregon, for example, has been exposed to groundwater contamination for decades. County authorities have been working intensively to curb this ongoing problem. Despite numerous efforts, a recent report revealed a worsening situation, and as a result, the county declared a state of emergency in June 2022.

In this tutorial, you'll focus on an important initiative that will help the Oregon Office of Emergency Management and Morrow County in combating groundwater pollution. As a GIS and environmental planning specialist, you'll identify groundwater vulnerable areas and high-risk zones in Morrow County. You'll prepare and use soil and land-cover data to identify areas where mitigating actions could be taken.

This tutorial was last tested on October 6, 2023, using ArcGIS Pro 3.1. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• User, Publisher, or Administrator role in an ArcGIS organization: ArcGIS Online (see options for software access) or ArcGIS Enterprise (learn more)
• ArcGIS Pro (see options for software access)
• ArcGIS Spatial Analyst extension

Explore the data

Land cover and soil are the main datasets that you'll use in this tutorial. You'll start by reviewing both datasets to understand the types, characteristics, and other information that are available for your analysis.

Download and open the project

First, you'll download the project package and open it in ArcGIS Pro.

1. Download the Groundwater Analysis project package.

   Depending on your web browser, you may be prompted to choose a file location before you begin the download. Most browsers download to your computer's Downloads folder by default.

2. Locate and double-click the Groundwater_Vulnerability_Analysis.ppkx file to open it in ArcGIS Pro.
3. If prompted, sign in using your licensed ArcGIS account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   The application opens to display the Groundwater_Analysis map. In the Contents pane, aside from the basemap layers, there are three layers: MC_Boundary, MC_Soils, and MC_Land_Cover.

   

   Currently, the World Topographic Map basemap is displayed. You'll change it to an Imagery basemap.

4. On the ribbon, on the Map tab, in the Layer group, click Basemap and choose the Imagery basemap.

   

   The map is updated to display the Imagery basemap.

   

5. On the Quick Access Toolbar, click the Save button to save the project.

   

Review the data

Next, you'll explore the datasets that will be used in your analysis. You'll explore the attributes of the soil layer and review the land-cover raster layer to understand the distribution of the land-cover classes in the county.

1. In the Contents pane, check the check box for MC_Soils to turn on the layer.

   

   The map updates to display the soils in Morrow County.

   

   The soils data is from the USDA-NRCS Soil Survey Geographic Database for Oregon. From that database, the Map Unit Polygon (MUPOLYON) layer and the Mapunit table (muaggatt) were used and clipped to the extent of Morrow County using the Clip tool.

   Note:

   You can also access these datasets from ArcGIS Living Atlas of the World, as raster datasets, or as vector, using the SSURGO (Soil Survey Geographic Database) Downloader. The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS).

2. In the Contents pane, right-click the MC_Soils layer and choose Attribute Table.

   

   The attribute table appears below the map.

   Note:

   The original soils dataset contained a lot of Null values. Due to the importance of the attribute fields in identifying groundwater vulnerable areas, statistical methods were employed to populate the Null values for the purpose of this tutorial.

   In the MC_Soils attribute table, three fields are important in groundwater analysis: Drainage Class – Dominant Conditions, Hydrologic Group – Dominant Conditions, and Water Table Depth – Annual – Minimum. Drainage conditions describe the movement of water through soil. It explains how water infiltrates into the ground based on the hydrologic group. There are four hydrologic soil groups (A, B, C, D) that are defined based on runoff potentials, hydraulic conductivity, and depth to any layer. In some instances, soils may be assigned dual hydrologic groups [(A/D, B/D, C/D). In these situations, the first letter represents the drainage condition, and the second letter is the soil's natural condition. Finally, the water table depth measures the distance to the water table, usually in centimeters.

   Hydrologic soil group	Composition	Infiltration
   A	Soil with less than 10 percent clay and more than 90 percent sand or gravel.	High (> 0.3 in./hr.)
   B	Soil with 10-20 percent clay and 50-90 percent sand.	Moderate (0.15 to 0.3 in./ hr.)
   C	Soil with 20-40 percent clay and less than 50 percent sand.	Low (0.05 to 0.15 in./hr.)
   D	Soils with more than 40 percent clay, less than 50 percent sand and with clayey textures.	Low (0.0 to 0.05 in./hr.)

   You'll use the soil characteristics to develop a set of criteria to identify groundwater vulnerable areas in this tutorial. Then you'll review the three attribute fields to understand their distribution in the county.

3. In the MC_Soils attributes table, right-click the header of the Hydrologic Group – Dominant Conditions field and choose Statistics.

   

   The Chart Properties pane and chart appear.

4. In the Chart Properties pane, under Variables, ensure that the Category or Date option is set to Hydrologic Group – Dominant Conditions and Aggregation is set to Count.

   

   The chart view shows the distributions of soil groups.

   

5. In the chart view, hover over the bars to view the total counts for each soil group.

   

   The bar chart shows the count of soil groups in Morrow County. Hydrologic soil group C is the majority. This group is composed of about 20-40 percent clay and less than 50 percent sand and has a loam texture. Infiltration rates are low compared to groups A & B. Areas with soil group C are less vulnerable to groundwater contamination.

   Next, you'll review the other relevant fields in the attribute table.

6. In the Chart Properties pane, under Variables, for Category or Date, choose Drainage Class – Dominant Condition.

   

   The chart updates to display the distribution of the drainage conditions.

   

   The majority of the soils in the county are well drained. Infiltration in these areas tends to be high but not as high as excessively drained soils. Areas that have high to extremely high infiltration rates are potential groundwater contamination zones. But you can't determine these areas by relying solely on the rates of infiltration. You'll also review the depth to water table field.

7. Close the Chart Properties pane and the chart.
8. In the MC_Soils attribute table, right-click the header of the Water Table Depth – Annual – Minimum field and click Sort Descending.

   The field updates with the values sorted in descending order.

   

   The deepest depth is 92 cm. This means that for any substance to reach the water table, it will have to flow 92 cm below the ground. Ideally, the farther the distance, the less probability for a substance to reach the water table.

   Based on this data, you'll be able to identify areas that are prone to groundwater contamination to help the county authorities implement appropriate measures to minimize or mitigate the ongoing crisis.

   Another key factor that influences the contamination of groundwater resources is the type of land-use activity. You'll review the land cover of Morrow County to get an idea of the existing land-use activities.

9. Close the MC_Soils attribute table.
10. In the Contents pane, under the MC_Soils layer, in the Charts section, right-click the chart and choose Delete.

    

11. In the Contents pane, uncheck the check box for MC_Soils to turn off the layer. Turn on the MC_Land_Cover layer.
12. Click the arrow next to MC_Land_Cover to expand the layer. Right-click the layer and choose Zoom To Layer.

    

    The map updates to display the land-cover layer.

    

    Examine the distribution of the various land-cover classes in the county on the map. The area is covered largely by herbaceous and cultivated crops. Land-cover classes such as the Developed areas (low to high intensity and open spaces) and agricultural lands (labeled Cultivated Crops and Hay/Pasture) contribute significantly to the pollution of groundwater resources. There tends to be high concentrations of some chemical constituents in activities that take place in these areas, which make them a threat to the environment and human health.

    Note:

    Land-cover raster data was derived from ArcGIS Living Atlas of the World and clipped to the extent of Morrow County using the Extract by Mask tool.

13. Save your project.

So far in this tutorial, you set up your project and explored the datasets that are required to identify and map groundwater vulnerable areas in Morrow County. You have reviewed the components of the soil data and land-cover raster data. You are now familiar with the characteristics of each soil group, how they can contribute to groundwater contamination, and the kinds of land cover in the county.

Determine groundwater vulnerable areas

Now that you have explored the datasets, you'll use a set of criteria to help identify areas that are vulnerable and are at risk of being contaminated. You'll use the Suitability Modeler to weigh the criteria variables to identify areas of best fit. This tool presents an interactive way of iterating through defined variables and presents feedback that is essential in decision making.

Prepare the data for suitability analysis

Before further analysis can be done, you'll convert the Drainage Condition – Dominant Condition and Water Table Depth – Annual – Minimum fields, stored in the MC_Soils layer, to a raster data format.

First, you'll change the coordinate system to a local coordinate system to ensure accuracies in spatial analysis. You'll use the State Plane Coordinate System, a US centric coordinate system, which divides the country into 120 zones. Morrow County falls within the Oregon North State Plane Zone.

Note:

To learn more about coordinate systems, you can use the Introduction to Coordinate Systems web course or the Map Projections learning path.

1. If necessary, open the Groundwater_Vulnerability_Analysis project.
2. In the Contents pane, double-click the Groundwater_Analysis map to open the Map Properties window.

   

   The Map Properties window appears.

3. In the Map Properties window, click the Coordinate Systems tab. In the search box, type NAD 1983 StatePlane Oregon North and press Enter.

   

4. In the XY Coordinate Systems Available list, expand Projected Coordinate System, State Plane, and NAD 1983 (US Feet). Click NAD 1983 StatePlane Oregon North FIPS 3601 (US Feet) to choose this coordinate system.

   

   The Current XY button updates to indicate that the coordinate system of the map has been changed.

5. Click OK.

   The projected coordinate system is applied to the map. Morrow County appears taller and narrower than before.

   

   Next, you'll review the coordinate systems of the layers in the map.

6. In the Contents pane, double-click the MC_Soils layer to open the Layer Properties window.
7. In the Layer Properties window, click Source. Scroll down and expand the Spatial Reference group.

   

   You can see that the MC_Soils layer has a different coordinate system (NAD 1983 Contiguous USA Albers). Changing a map's coordinate system doesn't affect the layers that have already been generated. You can change the coordinate system of existing layers to match the maps coordinate system by using the Project tool.

   But in this tutorial, you're more concerned about the output layers rather than the input layers. So, you'll maintain the coordinate systems of the existing layers and set the analysis environments. Setting the geoprocessing environment before undertaking geoprocessing tasks allows you to specify the coordinate systems, processing extent, raster analysis functionalities, workspace, and many other parameters for the outputs. It is useful to first set the geoprocessing environment to control the analysis.

8. Close the Layer Properties window.
9. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Environments.

   

   The Environments window appears.

10. In the Environments window, set the following parameters:
    • Under Output Coordinates, for Output Coordinate System, choose Current Map [Groundwater_Analysis].
    • Under Processing Extent, click the Extent menu. Under Same As layer, choose MC_Boundary.
    • Under Raster Analysis, for Cell Size, choose Same as layer MC_Land_Cover.

    

11. Click OK.

    You've set the parameters for all outputs that will be generated during the analysis. The coordinate system will match the map's coordinate system and all analysis will be done within the boundaries of the county and with the same cell size for each raster output that will be generated.

    Next, you'll convert the relevant attributes to raster data format so they can be used as inputs in the Suitability Modeler tool.

12. On the ribbon, on the Analysis tab, in the Geoprocessing group, click Tools.

    

    The Geoprocessing pane appears.

13. In the Geoprocessing pane, in the search box, type Polygon to Raster and press Enter.
14. In the search results, click the Polygon to Raster tool to open it.

    

15. In the Polygon to Raster tool, on the Parameters tab, set the following:
    • For Input Features, choose MC_Soils.
    • For Value field, choose Drainage Class – Dominant Condition.
    • For Output Raster Dataset, type Drainage_Conditions and press Tab.

    

    Note:

    You can switch to the Environments tab to confirm the previous environment settings.

16. Click Run.

    A layer is added to the Contents pane and the Drainage_Condition layer appears on the map.

    

    Note:

    The symbology color of the layer are randomly generated and may differ from the example image, but it does not impact the results of the analysis.

    You'll convert the water table depth field to raster.

17. Review the Drainage_Conditions layer's legend in the Contents pane to understand the map display.

    The map shows how soils in the area are drained. More than 80 percent of soils are identified as Well Drained. Areas along the Columbia River are identified as Excessively drained and Somewhat excessively drained. These areas are where most of the developed land-cover classes are located. This gives a clear indication of potential groundwater risk zones. But to be certain, you'll need to include the water table depth.

18. In the Polygon to Raster tool, update the following parameters:
    • For Input Features, choose MC_Soils.
    • For Value field, choose Water Table Depth – Annual – Minimum.
    • For Output Raster Dataset, type Water_Table_Depth and press Tab.
19. Click Run.

    The Water_Table_Depth layer appears on the map, showing the distribution of depths ranging from 0 to 92 centimeters.

    

    Observe the Water_Table_Depth layer in the Contents pane and on the map. Each color represents a depth value in centimeters.

20. Close the Geoprocessing pane.

In this section, you set the geoprocessing environments for analysis. Setting the environments for analysis is an important step to ensure accuracies and it saves time, especially when you need to run multiple geoprocessing tools. You then converted polygon datasets to raster, which will be used as inputs for the rest of the tutorial.

Create a suitability model to identify vulnerable areas

Next, you'll create a suitability model using the Suitability Modeler tool and add the input rasters that are relevant in identifying groundwater vulnerable areas.

1. On the ribbon, click the Analysis tab. In the Workflows group, click Suitability Modeler.

   

   The Suitability Modeler pane appears.

2. In the Suitability Modeler pane, on the Settings tab, set the following parameters:
   • For Model name, type Vulnerability Analysis.
   • Verify that Model input type is set to Criteria.
   • For Set suitability scale, choose 1 to 5.
   • Verify that Weight by is set to Multiplier.
   • For Output suitability raster, type Vulnerable_Areas and press Tab.

   

   Note:

   The suitability model functions like a toolbox that can be saved and opened at any time when needed. Each model can be identified by its model name in the Spatial Analyst toolbox in the Catalog pane.

   The model uses a set of defined variable criteria (model inputs) and assigned scales and weights to identify areas of best fit. After inputting all the necessary model parameters, you'll run the model to generate an output raster.

3. On the ribbon, on the Suitability Modeler tab, in the Suitability Model group, click Save.

   

   The Vulnerability Analysis model is saved.

4. In the Contents pane, verify the addition of a group layer named Vulnerability Analysis.

   

   The layer is currently empty.

5. In the Suitability Modeler pane, click the Suitability tab.

   

   In this tab, you'll begin to build the suitability model by adding criteria rasters to map groundwater vulnerable areas.

6. If necessary, click the Parameters tab. For Criteria, click the Add raster criteria as layers from the Contents list button.

   

   A menu appears showing all raster layers in the Contents pane. You'll add the two raster layers that will be used to identify groundwater vulnerable areas.

7. In the menu, check the check boxes for Drainage_Conditions and Water_Table_Depth and click the Add button.

   

   The two layers are added under Input Rasters and are both assigned a Weight value of 1.

   

   Weights are assigned to input rasters to signify their level of influence in the suitability analysis. Currently, each raster is assigned a weight of 1. This means they are of equal importance in the analysis.

   To focus on the layers that will be generated during the analysis, you'll turn off all the layers in the Contents pane.

8. In the Contents pane, collapse and turn off all the layers including the layers in the Vulnerability Analysis group layer. Ensure that the MC_Boundary layer, the Vulnerability Analysis group layer, and the World Imagery basemap remain turned on.

   

   You've successfully added the two main criteria that'll help in achieving the goal of the analysis. The next step is to transform each criterion based on its data type to a common suitability scale ranging from 1 to 5.

In this section, you created a suitability model that will be used in mapping groundwater vulnerable areas. You added the raster layers that'll be used in performing the analysis. Next, you'll transform each raster layer based on its data type.

Transform input rasters

Ideally, areas that are vulnerable and at risk of contamination have the following characteristics:

1. Well to excessively drained soils (high infiltration rates)
2. A relatively shallow water table depth
3. Found within developed and agricultural lands

Based on these criteria, you'll use the suitability model (Vulnerability Analysis) to locate areas prone to contamination. First, you'll transform each criterion raster based on its data type.

The process of transformation relies on the type of data involved. You must understand the different raster data structures to be able to transform each criterion successfully. Each raster is distinguished as either categorical or continuous raster datasets. With categorical rasters, cells are assigned unique categories. In this tutorial, the land-cover, drainage condition and water table depth rasters are categorical rasters. On the other hand, continuous rasters usually have a numeric value range.

Note:

You can view the general suitability modeling workflow for more information on how transformation works.

You'll start by transforming the Drainage _Conditions raster.

1. In the Suitability Modeler pane, click the circle next to Drainage_Conditions to open the Transformation pane.

   

   The Transformation pane appears below the map. The pane is divided into three sections: the Distribution of Suitability graph, a middle section for defining transformations, and the transformation graph.

2. If necessary, resize and reposition the Transformation pane below the map, so you can see both the pane and the map.
3. In the Contents pane, in the Vulnerability Analysis group layer, two layers have been added: Vulnerable_Areas and Transformed Drainage_Conditions.

   

   The Vulnerable_Areas layer is a combination of all transformed layers in the suitability model: it displays the final suitability results. The Transformed Drainage_Conditions layer displays the Drainage_Conditions layer transformed on the suitability scale from 1 to 5. There is only one transformed layer right now, so the Vulnerable_Areas map is a repetition of Transformed Drainage_Conditions.

   Next, you'll define the transformation for the Drainage_Conditions criterion. Since this layer is categorical, the Unique Categories tab has been chosen by default. You'll change the suitability scale for each category.

4. In the Transformation pane, on the Unique Categories tab, for Field, choose drclassdcd.

   

   The drclassdcd field is the same as the Drainage Class – Dominant Conditions field. The longer name is the field's alias.

   The suitability table updates to display the names of each category. You'll assign different suitability values to each category based on their condition. But first, you'll turn off the Auto Calculate setting to prevent the map from updating automatically anytime you input a value.

5. On the ribbon, click the Suitability Modeler tab. In the Suitability Analysis group, uncheck the check box for Auto Calculate.

   

6. In the Transformation pane, on the Unique Categories tab, edit the Suitability column to match the following table:

   Category	Suitability
   Well drained	3
   Poorly drained	1
   Somewhat excessively drained	4
   Excessively drained	5
   Somewhat poorly drained	2

   Note:

   Suitability values used in this tutorial are based on research on the factors that influence groundwater contamination. You can adopt your own suitability values and assign them accordingly.

   In suitability analysis, high suitability values are usually assigned to the most suitable criterion value or category. In this analysis, they are assigned to those areas most vulnerable to groundwater contamination.

   You've assigned suitability scales to each drainage condition based on their influence in groundwater contamination. Excessively drained soils have high infiltration rates and therefore have a higher influence in groundwater contamination. This explains why this category was assigned the highest suitability value (5). Poorly drained soils have less influence on groundwater.

   

   The Transformation of Drainage_Conditions graph updates to reflect the assigned suitability values. But there are no changes to the map layer yet because you turned off Auto Calculate.

7. On the ribbon, on the Suitability Modeler tab, in the Suitability Analysis, click the Calculate button.

   

   On the map, the Transformed Drainage_Conditions layer updates to reflect the input suitability values, but you can't see it yet. You'll have to turn off the Vulnerable_Areas layer.

   Note:

   Currently, the Vulnerable_Areas layer is just a duplicate of the Transformed Drainage_Conditions layer.

8. In the Contents pane, in the Vulnerability Analysis group layer, turn off the Vulnerable_Areas layer.

   

   The Groundwater_Analysis map now displays the Transformed Drainage_Condition layer.

   

   Observe the Transformed Drainage_Conditions layer's legend and the map to understand this distribution. You can see that soils in the northern part of the county are excessively drained, which poses a threat to groundwater contamination.

   Next, you'll transform the water table depth layer.

9. In the Suitability Modeler pane, on the Suitability tab, click the circle next to Water_Table_Depth to begin transformation.

   

   The Transformation pane updates and in the Contents pane, the Transformed Water_Table_Depth layer appears. Observe the changes in the Vulnerable_Areas layer's legend.

   The model builder identifies the Water_Table_Depth raster layer as categorical because each cell stores only one numeric value (unique value). However, the data actually reflects a range of values between 0 and 92. You'll transform the water table depth values using the Range of Classes method instead.

10. In the Transformation pane, click the Range of Classes tab. Verify that Field is set to Value.

    

    Values are automatically grouped into classes and assigned suitability values. You'll reclassify the data values.

11. In the Transformation pane, on the Range of Classes tab, click the Classify button.

    

    The Classify ranges window appears.

12. In the Classify ranges window, set the following parameters:
    • For Method, choose Natural Breaks (Jenks).
    • For Classes, choose 5.

    

13. Click OK.

    Initially, the classification was done using the Equal Interval method. But the data values are not evenly distributed. The Natural Breaks (Jenks) methods accounts for the uneven distribution of data values.

14. Review the Transformation of Water_Table_Depth graph.

    

    Currently, the graph shows that the most suitable areas are areas with deeper water table depths. But according to your criteria, you're looking for areas with shallow water table depths. That's the reverse of what is being displayed. You'll have to reverse the suitability values.

15. In the Transformation pane, on the Range of Classes tab, click the Reverse button and observe the distribution.

    

    The Transformed Water_Table_Depth map and the Transformation of Water_Table_Depth graph updates. Now, the most suitable areas are areas with shallow water table depth.

    

    Next, you'll calculate the suitability model.

16. On the ribbon, on the Suitability Modeler tab, in the Suitability Analysis group, click Calculate.

    The Transformation of Water_Table_Depth graph and the Transformed Water_Table_Depth layer update to display the changes.

    

    Now you can see the distribution of water table depths in Morrow County. The shallowest areas, with the highest suitability scores, are mainly in the south of the county.

17. Close the Transformation pane and the Suitability Modeler pane.
18. In the Contents pane, in the Vulnerability Analysis group layer, collapse and turn off all the layers and turn on the Vulnerable_Areas layer.

    

19. Closely observe the layer.

    Tip:

    To view the layer at a closer extent, in the Contents pane, you can right-click the layer and choose Zoom To Layer.

    

    The map shows the combination of the two transformed input raster criterion: Drainage_Conditions and Water_Table_Depth raster layers. The dark green areas are highly vulnerable to groundwater contamination.

20. On the ribbon, on the Suitability Modeler tab, in the Suitability Analysis group, click Save to save the model.
21. Save the project.

In this section, you transformed all raster layers based on a set of criteria. Data transformation is an important step in conducting a suitability analysis. Based on these transformations, you can identify groundwater vulnerable areas.

Complete the groundwater vulnerability analysis

Next, you'll complete the suitability analysis by running the model to save the output.

1. If necessary, open the Groundwater_Vulnerability_Analysis project.
2. In the Catalog pane, on the Project tab, expand Spatial Analyst and the Groundwater_Vulnerability_Analysis_0d0923.sam model folder. Right-click the Vulnerability Analysis model and choose Open.

   Note:

   To open the Catalog pane, on the ribbon, click the View tab. In the Windows group, click Catalog Pane.

   

   The Suitability Modeler pane appears. If necessary, close the Transformation pane.

3. On the Suitability Modeler pane, click the Suitability tab. In the Weight column, input the following values:
   • For Drainage_Conditions, type 5.
   • For Water_Table_Depth, type 4.

   

   Note:

   You can also assign the weights to each input raster before beginning the transformation.

   Weights are assigned based on the level of influence of each input raster. Raster layers that have high influence are assigned higher weights and vice versa. In this case, the Drainage_Conditions raster criterion has the highest influence in groundwater contamination because it represents the infiltration characteristics of soils. Contamination begins at the infiltration stage before you can consider the depth to the water table.

4. On the ribbon, calculate the suitability model.
5. In the Contents pane, observe the Vulnerable_Areas layer's legend.

   

   The Vulnerable_Areas value now ranges from 14 to 37. This is because the weights function as multipliers. The final suitability value for each area is calculated by multiplying each criterion's suitability value by the assigned criterion weight. Since a scale of 5 was used, each criterion weight will be multiplied by 5 to get the highest suitability areas.

   
   5 * 5 + 5 * 4 = 45

   Note:

   The highest suitability value is 45 but the suitability values ranges from 14 to 37. The analysis isn't wrong as you would probably be thinking. This means that there are no areas that are highly suitable (with a value of 45). The highest suitable value in this analysis is 37.

   All the analysis you've done so far was on the fly. Thus, nothing was saved as an actual raster dataset. To complete and save the final output from the suitability analysis, you must run the model.

6. In the Suitability Modeler pane, under Output type, verify that Raster dataset is selected and click Run.

   

   In a few minutes or less, the model runs and the display is updated.

7. Explore the Vulnerable_Areas map display.

   

   The map displays areas that are less to highly vulnerable to groundwater contamination taking into consideration the nature and characteristics of the soil. Light to deep green areas are highly vulnerable. You can see these areas are concentrated in the northern part of the county.

8. Save the model and close the Suitability Modeler pane.
9. Save the project.

You've successfully created a model and used it to identify groundwater vulnerable areas in Morrow County. With this, county authorities will be able to make decisions to regulate activities within these areas to avoid groundwater pollution.

So far in the analysis, you have converted vector data to raster, set up an analysis environment, and created a suitability model. You added criteria variables to the model and transformed each on a defined scale. Next, you'll incorporate the land-cover raster into the analysis. To identify risk zones, you'll create a suitability model that'll combine the results from the Vulnerability Analysis model (Vulnerable_Areas) with the land-cover raster data.

Map risk zones and protected areas

For years now, Morrow County has been facing severe water problems due to land-use activities that contribute to groundwater pollution. Authorities recently declared a state of emergency to help combat the problem. They identified that the Port of Morrow, on the Columbia River, has been a major contributor to the contamination of groundwater resources and have fined them accordingly. But there are other land-use activities that are contributing to the situation. You'll map risk zones to help support their impact assessment. You'll then point out areas that the county can protect or regulate to prevent groundwater contamination.

Map risk zones

Groundwater risk zones are areas that are already contaminated or are at risk of being contaminated based on the land-use activity. Potential sources of contamination include septic tanks, land fill sites, uncontrolled waste disposal, and chemicals from agricultural, domestic, commercial, and industrial facilities. These sources are found in developed and agricultural spaces. You'll develop a new suitability model to identify these risk areas. The model will use the results of the previous model (Vulnerability Analysis) in addition to the land-cover layer.

1. If necessary, open the Groundwater_Vulnerability_Analysis project.
2. In the Contents pane, collapse and turn off the Vulnerability Analysis group layer.

   

3. If necessary, on the ribbon, click the Analysis tab. In the Workflows group, click Suitability Modeler.

   Note:

   If the Suitability Modeler tab of the ribbon is already active, you can create a model by clicking New in the Suitability Model group.

   The previous model you set up was to identify vulnerable areas. Using the results from the Vulnerability Analysis model, you can map risk zones.

4. In the Suitability Modeler pane, on the Settings tab, input the following:
   • For Model name, type Groundwater Risk Zones.
   • Verify that Model input type is set to Criteria.
   • Verify that Set suitability scale is set to 1 to 10.
   • For Output suitability raster, type Risk_Zones and press Tab.

   

   In the previous model, you used a suitability scale of 1 to 5 because of the smaller number of criterion values and classes. But in this model, you'll be dealing with a lot of criterion values and classes, hence a scale of 1 to 10.

   Next, you'll add the criteria variables.

5. In the Suitability Modeler pane, click the Suitability tab. Click the Add raster criteria as layers from the Contents list button and choose the following layers:
   • Vulnerability Analysis\Vulnerable_Areas
   • MC_Land_Cover
6. Click Add.

   Two Input Rasters are added to the criteria list. A new group layer is created for your model and added to the Contents pane.

   

   This time, you'll assign the weights to each raster before you start the transformation.

7. In the Weight column, assign the following:
   • For MC_Land_Cover, type 10.
   • For Vulnerability Analysis\Vulnerable_Areas, type 8.

   

   The land-cover criterion has been assigned the highest weight because the type of land use contributes significantly to groundwater contamination.

   Next, you'll transform the Vulnerability Analysis\Vulnerable_Areas layer.

8. Click the circle next to the Vulnerability Analysis\Vulnerable_Areas criterion.

   The button turns green and the Transformation pane appears.

   

   As before, two layers are added to the Groundwater Risk Zones group layer. This time the weights have already been included in the calculation for the result layer as shown in the legend for Risk_Zones in the Contents pane.

   

   Because the Vulnerability Analysis\Vunerable_Areas criterion is a continuous raster layer, the Continuous Functions method has been applied.

   

   Currently, the Function option is set to the default method, MSSmall. The MSSmall method is applicable when smaller criterion values are highly preferred.

   

   In the graph, smaller criterion values are highly preferable (shown in dark green). This is not what you want for this analysis. You want the data to be transformed so an increase in the criterion value will result in an increase in preference. The most suitable method for this is the Linear function.

9. On the Continuous Functions tab, for Function, choose Linear.

   

   The Transformation graph updates.

   

   Now, higher criterion values are the most preferable.

10. If necessary, on the ribbon, on the Suitability Modeler tab, click the Calculate button to apply the transformations.
11. In the Contents pane, turn off the Risk_Zones layer so you can see the Transformed Vulnerability Analysis\Vulnerable_Areas layer on the map.

    As in the Transformation chart, areas in dark green are the most preferable. In this analysis, preferable is a deceiving word, because these are the areas that are most vulnerable to groundwater contamination.

    

    You've successfully transformed a continuous raster criterion using the Linear function. Next, you'll transform the land-cover layer to complete your suitability analysis.

12. In the Suitability Modeler pane, on the Suitability tab, in the Criteria list, click the circle next to the MC_Land_Cover criterion.

    The Transformation pane updates to display the land-cover values and the TransformedMC_Land_Cover layer is added to the Groundwater Risk Zones group layer.

    The MC_Land_Cover layer is categorical, so the Unique Categories tab is active. You'll assign suitability values to each land-cover class based on their degree of influence.

13. In the Transformation pane, on the Unique Categories tab, change Field to NLCD_Land_Cover_Class.

    

    The Category column updates to display the name of each land-cover class. There are 15 land-cover classes. You'll assign suitability scores to them ranging between 1 and 10.

14. Edit the Suitability column to match the following table:

    Land-cover classes	Suitability values
    Open water	1
    Developed, Open Space	6
    Developed, Low Intensity	7
    Developed, Medium Intensity	8
    Developed, High Intensity	10
    Barren Land	4
    Deciduous Forest	3
    Evergreen Forest	1
    Mixed Forest	3
    Shrub/Scrub	3
    Herbaceous	2
    Hay/Pasture	5
    Cultivated Crops	9
    Woody Wetlands	1
    Emergent Herbaceous Wetlands	1

    Tip:

    If the map refreshes after every edit, go to the Suitability Modeler tab of the ribbon. In the Suitability Analysis group, uncheck the Auto Calculate check box.

15. If necessary, on the ribbon, calculate the model.

    The Transformed MC_Land_Cover map updates based on the assigned suitability values. Observe the layer's legend and the distribution on the map.

    

    Land-cover suitability transformation is based on the impact each class has on groundwater contamination. The Transformed MC_Land_Cover layer highlights areas that can significantly contribute to groundwater contamination. Green areas can highly influence contamination and red areas have less influence over groundwater contamination.

16. Close the Transformation pane. Collapse and turn off all the layers in the Groundwater Risk Zones group layer except for Risk_Zones.

    

    The map updates to display the Risk_Zones layer, which is the combination of the two input raster layers: MC_Land_Cover and Vulnerability Analysis\Vulnerable_Areas.

    

    Based on the criterion rasters and their influence in groundwater contamination, you have mapped risk zones. The suitability map displays vulnerability to groundwater contamination based on land-cover type and soil type. Green areas are at a higher risk of being contaminated or are contaminated.

    You've mapped groundwater risk zones in Morrow County. County authorities can now identify high-risk zones for their impact assessment based on this map.

17. In the Suitability Modeler pane, on the Suitability tab, confirm that Output type is set to Raster dataset and click Run.

    In a few minutes or less, the model runs, and the map display updates. You'll now carefully study the Risk_Zones layer.

18. On the ribbon, click the Map tab. In the Navigate group, click Bookmarks and choose Along the Columbia River.

    

19. In the Contents pane, click the Risk_Zones layer to select it.
20. On the ribbon, click the Raster Layer tab. In the Compare group, click the Swipe tool.

    

    On the map, click and drag to view the basemap. Carefully compare the Risk_Zones layer with the basemap to identify the areas within the green high-risk zones.

    

21. On the ribbon, on the Map tab, click Bookmarks and choose the Port of Morrow bookmark.

    

    The Port of Morrow is located within a high-risk zone. This helps to explain why their activities contribute significantly to groundwater contamination in the county. You can use the Swipe tool to compare the risk zones layer with the basemap to get a better view.

22. On the ribbon, click the Map tab. In the Navigate group, click the Explore tool.

    

    The pointer changes from the Swipe tool to the Explore tool.

23. On the ribbon, on the Suitability Modeler tab, in the Suitability Model group, click Save. In the Close Model group, click Close.
24. Save your project.

In this section, you identified and mapped groundwater risk zones using a set of criteria raster layers. You identified high-risk zones for the county authorities. In the next section, you'll finalize your analysis by recommending areas that must be protected to avoid groundwater resource contamination.

Identify areas for protection

You're wrapping up the analysis. County authorities have a final task for you: to help them identify areas that they can protect to minimize or mitigate groundwater contamination.

1. Right-click the MC_Boundary layer and choose Zoom To Layer.
2. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools to open the Geoprocessing pane.
3. In the Geoprocessing pane, in the search box, type Con and press Enter. Click to open the Con (Spatial Analyst Tools) tool.

   

   The Con tool is used to perform conditional evaluations on cell values. You'll use it to extract high-risk zones from the Risk_Zones layer and undeveloped lands from the MC_Land_Cover layer.

   You'll start by extracting high-risk zones.

4. In the Geoprocessing pane, on the Parameters tab, set the following:
   • For Input conditional raster, choose Groundwater Risk Zones\Risk_Zones.
   • For the Expression, build the query Where VALUE is greater than 100.
   • For Input true raster or constant value, choose Groundwater Risk Zones\Risk_Zones.
   • Leave the Input false raster or constant value parameter blank.
   • For Output raster, type High_Risk_Zones and press Tab.

   

   The Con tool will identify areas from the Risk_Zones layer that have values greater than 100. If the condition is true, the tool will return values from the Risk_Zones layer. If the expression is false, it will return nothing.

5. Click Run.

   The High_Risk_Zones layer is added to the Contents pane and the map.

6. Collapse and turn off the Groundwater Risk Zones group layer.

   

   Observe the High_Risks_Zones layer's legend and the distribution on the map. It only includes areas that recorded a suitability value greater than 100.

   

   Now that you've extracted the high-risk areas from the Risk_Zones layer, you can identify undeveloped lands within the risk zones to prioritize for conservation.

7. In the Con tool pane, on the Parameters tab, for Input conditional raster, choose MC_Land_Cover.

   

   Next, you'll write your conditional expression.

8. Under Expression, use the first three menus to start the query Where NLCD_Land_Cover_Class includes the value(s).
9. Click the fourth menu and choose the following land-cover classes:
   • Barren Land
   • Deciduous Forest
   • Emergent Herbaceous Wetlands
   • Hay/Pasture
   • Herbaceous
   • Mixed Forest
   • Shrub/Scrub
   • Woody Wetlands

   

   This expression characterizes undeveloped areas as all land-cover classes except for developed lands, cultivated croplands, and evergreen forests. The remaining areas are usually left unregulated, although development can occur at any point in time. These are the areas that need to be considered for protection.

10. Finish configuring the Con tool by setting the following parameters:
    • For Input true raster or constant value, choose MC_Land_Cover.
    • Leave the Input false raster or constant value parameter blank.
    • For Output raster, type Undeveloped_Areas and press Tab.

    

    For each area, if the expression is true, the tool will return the values of the MC_Land_Cover layer. If the expression is false, it will return nothing.

    You want to identify undeveloped areas only within the high-risk zones, not the entire county. You'll set the tool's Environments parameters to limit the results to a defined region.

11. At the top of the pane, click the Environments tab. In the Raster Analysis section, for Mask, choose High_Risk_Zones.

    

    The Mask parameter limits the results to a defined region. In this case, you'll identify undeveloped lands within high-risk zones only.

12. Click Run.

    The tool runs and a new layer is added to the Contents pane.

    

    Note:

    The symbology color of the layer are randomly generated and may differ from the example image, but it does not impact the results of the analysis

    The tool returned three different land-cover classes that are identified as undeveloped. You can zoom in to view these areas. This layer shows authorities areas they could still protect from future development. County authorities now must evaluate the impact of land uses within the high-risk zones and make informed decisions based on their results.

    Currently, the land-cover classes are labeled with their gridcodes. You'll change the labels to their actual names to make the map easier to understand.

13. In the Contents pane, right-click Undeveloped_Areas and choose Symbology.

    The Symbology pane appears.

14. In the Symbology pane, in the symbol class table, replace Value with Land Cover Classes. For Label, edit the following:
    • Replace 31 with Barren Land.
    • Replace 52 with Shrub/Scrub.
    • Replace 81 with Hay/Pasture.

    

    The label changes are reflected in the Contents pane.

15. Close the Symbology and Geoprocessing panes.
16. On the ribbon, on the Map tab, in the Navigate group, expand Bookmarks and choose Undeveloped Areas.

    The map zooms in to that area.

    

    This area includes some of the undeveloped areas identified by the Con tool. Even though they cover a small area and are spread across the county, the results are relevant to the authorities because of the importance of groundwater resources. You can optionally explore the map to identify other undeveloped areas.

17. On the Quick Access Toolbar, click Save.

In this module, you made a second suitability model to identify groundwater risk zones based on soil types and land-use activities. Additionally, you used a conditional analysis method to identify undeveloped areas that can be protected from groundwater contamination by the county.

Groundwater is an important resource and needs to be protected. Identifying and mapping groundwater vulnerability and risk areas can contribute significantly to achieving sustainable development. In this tutorial, you learned the following skills:

• How to configure geoprocessing environments for a consistent coordinate system and processing extent
• How to convert vector data to raster data to prepare it for a suitability analysis
• How to create and work with suitability models
• How to use the Con tool to identify areas for groundwater conservation

In this tutorial, you learned a workflow to identify groundwater vulnerable areas and risk zones. With this workflow, GIS professionals can help protect groundwater resources within their jurisdictions.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• DeSimone, L.A., P.B. McMahon, and M.R. Rosen, 2014. "The quality of our Nation’s waters—Water quality in Principal Aquifers of the United States, 1991–2010". U.S. Geological Survey Circular 1360: 151.
• Associated Press. 2022. "Oregon County Declares Emergency Over Water Contamination". , June 11, 2022.
• Soil data was obtained from the USDA Natural Resources Conservation Services (NRCS) Soils Survey Geographic (SSURGO) for Oregon. It is also accessible from the SSURGO Downloader.
• The gallery image is from GRID-Arendal/Studio Atlantis.
• USDA – NRCS. 2007. "Chapter 7 - Hydrologic Soil Groups". In Hydrology National Engineering Handbook. May 2007.
• Allen, Vaikko. 2013. "Stormwater Infiltration: Drain-Down Characteristics of Hydrologic Soil Groups" Contech Engineered Solutions. August 30, 2013.

• The gallery image is from Adobe Stock Images.