Summarize Climate Data within a Custom Boundary

Rising temperatures have broad ranging impacts, posing threats to ecosystems, energy resources, human health, and more. America’s national parks and forests are susceptible to many of these threats; higher temperatures may require increasing emergency services and water resources to support the millions of visitors each year. At the same time, they may threaten the ecosystems, endangered plants and animal species the protected lands are meant to shelter.  

To understand and plan for these potential impacts, it’s important to see what changes may happen within these protected lands. In this tutorial, you’ll extract projected climate data from multidimensional rasters into the National Parks polygon features using zonal statistics to table and join tools.

While this tutorial leverages specific steps in ArcGIS Pro, it could be replicated with any GIS software. OGC web services are also available for each dataset in the layer's documentation or the  CMRA Portal Open Data Hub .  

1) Open ArcGIS Pro and sign in with an organizational account. 

2) In the Catalog pane, click the Portal tab and choose Living Atlas. Add the   USA Parks   dataset.  Hint: Search for the item by ID: f092c20803a047cba81fbf1e30eff0b5 This layer contains polygons showing parks from local to national levels of administration. To focus the analysis on national park and forest lands, you’ll filter the features to show your areas of interest and add a query to ensure the features are large enough to accurately calculate raster statistics for. For this particular combination of USA Parks polygon features and the multidimensional climate data, you’ll want to choose features larger than 15 square miles.

3) On the Map tab, click Select by Attributes and build the expression Feature Type is equal to National Park or Forest AND Forest and SQ_MI is greater than 15.

4) Some of the parks have multiple polygon features. This step will convert those complex features into a single multi-part feature for each park. In the Geoprocessing pane, open the Pairwise Dissolve tool. Set the following parameters:  i. Dissolve Fields: Name ii. Statistics Fields: Area in SQ MI  

For this analysis, you want to compare possible high heat conditions in US national parks and forests under several climate projection scenarios. You'll add two climate projection layers showing Representative Concentration Pathways (RCPs) 4.5 (low emissions) and 8.5 (high emissions, or “business as usual”) through 2050, and one layer showing baseline (historical) conditions.

1) From Living Atlas, add the   Historic (baseline)  ,   RCP 4.5 (2050) (low emissions)  , and  RCP 8.5 (2050) (high emissions)   layers. Hint: Search for item IDs d05d0d54334d4b2b84aae67ba2cc00c0, d892d5daf77c46d58904c4d71d60388f, and b9832655f2eb477e979757e5e0039194.

2) For each of the following layers:  i. On the Multidimensional tab, click Data Management and choose Subset.  ii. In the Geoprocessing pane, for Variables, select Days above 95F and give the output CRF the following names:  a) Baseline_Days_above_95 b) RCP4.5_Days_above_95 c) RCP8.5_Days_above_95

You could pick any of the other variables, such as "Days above 90F" or precipitation thresholds.

This value shows the annual number of days with a maximum temperature greater than 95 degrees Fahrenheit. Next, you’ll use the Zonal statistics tool to extract values from the climate datasets to your areas of interest. The Zonal statistics tool will calculate the mean (you could also choose maximum) value of the raster cells within each park polygon. 

3) In the Geoprocessing pane, open the Zonal statistics to table tool. Run the Zonal Statistics tool for each output CRF with the following parameters:   i. Input Feature Zone Data: USA_Parks_Forests  ii. Zone Field: Name iii. Input Value Raster: [subsetted .CRF layers] iv. Statistic Type: Mean v. Output Table: [Projection]_ZonalStats  Note: The tool may show a warning that some zones haven’t been rasterized. This warning primarily refers to the park and forest land in Alaska, which the climate data doesn’t cover. These tables will be joined back to the USA_Parks_Forests for analysis. Because each table calculated a field named MEAN, you’ll also rename the fields in the target layer as you create the joins. 

4) In the Geoprocessing pane, open the Join Field tool. Run the tool for the Baseline_ZonalStats table with the following parameters: i. Input Table: USA_Parks_Forests ii. Input Join Field: NAME iii. Join Table: Baseline_ZonalStats  iv. Join Table Field: NAME  v. Transfer Fields: MEAN 

5) Open the attribute table for the USA_Parks_Forests layer and open the Fields view by right clicking on any attribute name. Rename the joined MEAN field to MEAN_Baseline. 

6) Repeat steps 4 and 5 for the RCP 4.5 and RCP 8.5 tables, renaming the MEAN field to show what dataset it represents. Save your edits to the Fields view and close it.

To compare changes possible under the two RCP scenarios, you’ll compare the projected annual number of days over 95 degrees to the baseline climate data. 

1) In the USA_Parks_Forests attribute table, click Calculate. In the Calculate Field window, enter the following parameters: i. Field Name: Difference_4.5  ii. Field Type: Double  iii. Expression = Mean_4.5 - Mean_Baseline

 2) Calculate a second field showing the difference between the Mean_8.5 and Mean_Baseline. 

3) Use the Sort Ascending and Sort Descending tools to see the high and low values for both the Difference 4.5 and Difference 8.5 fields. Close the attribute table. 

What to map a relative change instead? Follow the same steps above but use the expression ((Mean_4.5 - Mean_Baseline) / Mean_Baseline)*100). Now you'll have a percentage change, which may be more meaningful for temperature regions.

Finally, you’ll symbolize the results to show the potential changes that may occur in national park and forest lands by 2050 under the two climate scenarios. To visualize the range of the data, you’ll start by creating a histogram, then symbolize the polygons to emphasize areas that may experience the most severe changes. 

1) In the Symbology pane, for USA_Parks_Forests, choose Graduated Colors. For Field, choose Difference 8.5.  The layer redraws to show parks that will experience many more days over 95 degrees than the baseline in reds and parks that will experience a few more days over 95 degrees than the baseline in yellow.

2) In the Symbology pane, click the Histogram tab.  The resulting histogram shows a slight right skew to the data, with most parks experiencing less than 15 additional days over 95 degrees. There are a good number of parks, primarily in the south of the country, that will experience larger changes, up to 79 additional days over 95 degrees.

3) On the Classes tab, change the Upper value columns as follows:  i. 5 ii. 15 iii. 25 iv. 35 v. 80

4) Set the Label column as follows: i. Under 5 additional days ii. 5 to 15 days iii. 15 to 25 days iv. 25 to 35 days v. Over 35 additional days

Finally for this RCP scenario, you'll create a scatter plot showing the relationship between the baseline and the RCP 8.5 predictions.  

5) In the Contents pane, right click the USA_Parks_Forests layer and choose Create Chart, then choose the Scatter Plot. 

6) In the Chart Properties pane, for X-axis, choose MEAN_Baseline and for Y-axis choose MEAN_8.5.

7) In the Contents pane, copy the USA_Parks_Forests layer and paste it into the Contents pane. Rename one layer RCP 4.5 - change in days over 95 degrees, and the other RCP 8.5 - change in days over 95 degrees.

8) For the RCP 4.5 - change in days over 95 degrees layer, in the Symbology pane, change the Field to Difference_4.5. At the top of the pane, click the Options button and choose Import Symbology, then set the RCP 8.5 - change in days over 95 degrees layer as the Symbology layer.

9) In the Contents pane, right-click the chart for the RCP 4.5 - change in days over 95 degrees layer and click Open. Change the Y-axis to MEAN_4.5.

Possible policy interpretation:

National parks and forests with the greatest increase in extreme heat days should consider impacts to local flora and fauna. They should also consider warning visitors about extreme heat and plan for future infrastructure to help improve heat resilience.