Exercise 6: Using Burn Severity Data to Model Erosion Risk

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1 Exercise 6: Using Burn Severity Data to Model Erosion Risk Document Updated: November 2009 Software Versions: ERDAS Imagine 9.3 and ArcGIS 9.3, Microsoft Office 2007 Introduction A common use of burn severity data immediately after a fire is to model the erosion risk on specific hillslopes. Many BAER (Burned Area Emergency Response) teams assigned to wildfires use ERMiT (Erosion Risk Management Tool) to help with the analysis. ERMiT is a web-based model that estimates erosion, in probabilistic terms, on burned and recovering forest, range, and chaparral lands with and without the application of erosion mitigation treatments (mulch, erosion-barriers, and seeding). This exercise will walk you through how to prepare the GIS inputs to use ERMiT, how to run ERMiT, and how to interpret the results. Data and Materials Needed Soil Burn Severity Map 10m DEM 6th-level Watersheds Soils Layer Major Steps I. Recode and group classes from provided GIS data II. Create Pivot Table to determine # of ERMiT runs III. Run ERMiT and summarize erosion risk IV. Interpret ERMiT results Before using the Spatial Analyst toolbar, you must first make sure the extension is turned on. In ArcMap, go to Tools Extensions to verify there is a checkmark next to Spatial Analyst. It is also wise to check the options menu in the Spatial Analyst drop-down menu; you can edit your analysis mask, working directory, extents, etc. I. Load, Study, and Manipulate Data 1. Open ArcMap (Start Programs ArcGIS ArcMap) 2. Load the following data by browsing to..\classdata\data: BuffaloCreek_SoilBS.tif Soils_LTA_alb.shp Huc6_wsheds_alb.shp Dem10m.img BuffaloCreek_Perim.shp 3. Click the Spatial Analyst drop-down menu and select Options. On the General tab, set the fire perimeter as the Analysis Mask by selecting BuffaloCreek_Perim.shp. This will clip any outputs made using Spatial Analyst to the fire perimeter. Click OK. 1

2 You will eventually be intersecting multiple layers together. It is wise to edit the attribute tables of the individual layers before the intersection for easier interpretation layer. For example, converting a raster layer to a vector polygon feature layer will often provide you a field named GRIDCODE. If you convert more than one raster layer to features and then do an intersect, you will have GRIDCODE, GRIDCODE_1, GRIDCODE_2, etc., in your attribute table. Keeping tabs on what each means is difficult without changing the names of the fields. 4. Convert Soil Burn Severity layer to vector shapefile using the Spatial analyst toolbar. Click the Spatial Analyst drop-down menu and select Convert Raster to Features 5. Input raster: BuffaloCreek_SoilBS.tif 6. Field: VALUE 7. Output geometry type: Polygon 8. UNCHECK Generalize Lines 9. Output features:..\classdata\outputs\soil_bs.shp 10. Click Ok. 11. Create new field for ease of interpretation later (see sidebar note) 12. Open the attribute table of soil_bs.shp 13. Go to Options Add Field 14. Name: BS_Code 15. Type: Short Integer 16. Right-click on the BS_Code column and go to Field Calculator 17. Press Yes when prompted with the warning 18. Double-click on GRIDCODE to add it to the window below BS_Code = 19. Press OK. This just populated BS_Code with the values from GRIDCODE. 20. Delete the GRIDCODE field. Some of your BS_Codes are zero. Select all records in the attribute table with a zero BS_Code and, using the Field Calculator, change them all to 1. These records are all artifacts from the clip, are very small polygons, and have little bearing on the overall severity of the fire. 22. Create a slope layer from the DEM 23. Click the Spatial Analyst drop-down menu and select Surface Analysis Slope Input: dem10m.img 25. Output Measurement: Percent 26. Z Factor: Output cell size: Output raster:..\classdata\outputs\slope 29. Click on Save and then OK. The output will be an ESRI GRID raster layer. Study the layer in your viewer and notice the range of values. Remember that 100% slope only equals 45 degrees. 30. Simplify the layer for future analysis by recoding the slope into 3 manageable classes: 0-35%; 35-65%, and > 65%. 31. Click on the Spatial Analyst drop-down menu and click on Reclassify 32. Input raster: slope 33. Reclass field: Leave blank 2

3 NOTE: This separation doesn t split the slope values into equal classes at all. However, we re trying to break the slope layer into more meaningful classes for management. The soils layer has already been recoded and grouped into manageable classes for you. To do this yourself, you can visit the NRCS Web Soil Survey ( websoilsurvey.nrcs.usda.gov/app/ HomePage.htm). You can zoom in to the area of your fire and investigate the soils attributes there. 34. Click on the Classify button on the right side of the window 35. Change the Jenks Natural Breaks class breaks to 3 from the default of In the Break Values list on the far right, change the middle value to 65, and the top value to 35. The 3 values you now have in this window represent the top end of the class (see graphic on left). 37. Click on OK. 38. Output raster:..\classdata\outputs\slope_rcls 39. Click Save, then OK. 40. Convert the reclassified slope layer to a vector shapefile. 41. Click on the Spatial Analyst drop-down menu and select Convert Raster to Features 42. Input raster: slope_rcls 43. Field: VALUE 44. Output geometry type: Polygon 45. UNCHECK Generalize Lines 46. Output features:..\classdata\outputs\slope_reclass.shp 47. Click Save, then OK. 48. Create a new field for ease of interpretation later (see sidebar note on previous page) 49. Open the attribute table of slope_reclass.shp 50. Go to Options Add Field 51. Name: Slope_Grp 52. Type: Short Integer 53. Right-click on the Slope_Grp column and click on Field Calculator 54. Double-click on GRIDCODE to add it to the window below Slope_Grp = 55. Press OK. This just populated Slope_Grp with the values from GRIDCODE. 56. Delete the GRIDCODE field. 57. Open the attribute tables of slope_reclass.shp, Soils_LTA_alb.shp, huc6_wsheds_alb, and soil_bs.shp 58. The key field in each of these tables is, respectively, Slope_Grp, Soil_Grp, HUC_12, and BS_CODE. Those are the fields we will eventually summarize on. II. Prepare Data for ERMiT 1. Intersect all your layers into a single output. 2. In ArcToolBox, go to Analysis Tools Overlay Intersect 3. Load each of your input (slope_reclass, Soils_LTA_alb, huc6_wsheds_alb, soil_bs) layers individually in the Input Features drop-down menu. 4. Output Feature Class:..\ClassData\Outputs\Intersect_All.shp 5. Click Save then OK 6. The order of the fields in the output shapefile attribute table are based on the order of the input features you loaded. You won t need all the fields included in the Intersect, so turn off unnecessary 3

4 When calculating geometry on polygons in shapefiles, be careful not to choose Ares as the unit type. Ares are much different than Acres. The PivotTable instructions were written based on Microsoft Office Change any integer codes to strings by typing in what the codes mean. Click on and then mouse over the integer codes to see what attribute the numbers came from. As you make changes to the table headings, Excel will apply the change across the entire PivotTable. fields. 7. Open the properties of Intersect_all.shp. 8. Click on the Fields tab 9. Turn off everything except Acres, Slope_Grp, Soil_Grp, HUC_12, and BS_CODE. 10. Calculate acres on the intersected features. 11. Open the attribute table, right-click on the Acres field and click on Calculate Geometry. 12. Click Yes on the warning box that comes up. 13. Make sure the Units: drop-down menu is on Acres (see note in sidebar) and click OK. 14. Export the attribute table by going to Options Export and save it as..\classdata\outputs\intersect_table.dbf. 15. Open Intersect_Table.dbf in Microsoft Excel. 16. With your table loaded, go to Insert PivotTable 17. Make sure the entire data set is highlighted and then click OK. 18. From the PivotTable Field list, click and drag BS_Code to the Row Labels box in the bottom right of the screen. Repeat for Slope_Grp and Soil_Grp. The order you add the fields doesn t really matter. 19. Click and drag Acres to the Values box. 20. You should now see the unique combinations of the variables summarized by acres. Edit the values in the heading of each group in the Pivot Table to accurately reflect what the values actually are (see sidebar graphic). 21. The far-left level of data in your PivotTable will be from whatever field is listed first in the Row Labels box in the bottom right. For the BS_CODE row labels, change the numerical values to textual: 1 = Unburned / Very Low 2 = Low 3 = Moderate 4 = High 22. For Slope_Grp, change them to 1 = < 35% Slope 2 = 35-65% Slope 3 = > 65% Slope 23. Right-click on the Sum of Acres field and format to type Number with 0 decimal places. This will help you quickly spot features that cover a significant amount of acres. 24. Explore display and formatting options within PivotTables. Save the file as an Excel spreadsheet (*.xls/*.xlsx). 25. Look over the data to find groupings that may raise red flags 26. Note two groups with significant acres that represent roughly 50% of the land within the perimeter: 27. Moderate Soil BS, <35% Slope, Sandy Loam 20% Rock 4

5 You don t need to make ERMiT runs on Unburned or Low severity polygons since there won t be significant fire-caused increases in run-off potential. Focus on the Moderate and High severity classes. Adding the HUCs won t help with any ERMiT runs, but it is useful for reporting purposes. You can easily add your HUCs field into the Pivot Table to see acres of severity by HUC. We will not use it for the rest of this exercise. 28. Moderate Soil BS, <35% Slope, Sandy Loam 40% Rock 29. You will want to focus your modeling on these two areas since they represent so much of the burn scar. We will make an ERMiT run based on the first of these two groups. III. Make ERMiT Runs 1. Open an Internet browser and navigate to ermit/ermit.pl You ll notice there are 6 input boxes: Climate, Soil Texture, Vegetation Type, Hillslope Gradient, Hillslope Horizontal Length, and Soil Burn Severity. Some of the values come from the intersection you just did, but others will have to be interpolated. 2. Under Climate, chose Custom Climate Choose Colorado Click on SHOW ME THE CLIMATES. 3. CHEESMAN CO is the most similar climate to Buffalo Creek, so click on it and choose ADD TO PER- SONAL CLIMATES. 4. Click on Return to Input Screen and you ll notice CHEESMAN CO is the first climate shown in your list. 5. Choose sandy loam and 20% rock content for your soil texture variables. 6. Choose Forest for your Vegetation type variable since nearly the entire fire was evergreen forest. Had there been more heterogeneity in the vegetation cover, we would include that in our intersection and Pivot Table and therefore ERMiT considerations. 7. Hillslope gradient is best described by the graphic at the left. Choose 0% for top (indicating the slope starts at the top of the hill); 35% for the middle; 35% for toe. We know all polygons in this groupings have a slope of 35% or less so choosing the max value for the middle gradient here is the safe option. Some toe slopes become more gentle at the bottom of the hill; however, others become more steep. Local knowledge and observations in the field are crucial to determine appropriate toe slopes. For this exercise we will use 35%, the same as the middle slope. 8. Hillslope horizontal length (also known as LS Factor) is often calculated by measuring the lengths of slopes on a topographic map and field observations. Rely on local knowledge for this value. For the purposes of this exercise, use 600 as your hillslope horizontal length. 9. Choose Moderate as the Soil Burn Severity. 10. Click on Run ERMiT and observe the outputs. IV. Interpret ERMiT Results 1. Note the Sediment Delivery Exceedance Probability graph. These lines graph the probability of exceeding a certain level of sediment delivery (runoff) by year after fire. For example, in year 2 after wildfire, ERMiT is predicting about a 20% chance the polygons we selected will produce 5 tons/acre of sediment delivery to the bottom of the hillslope. 5

6 Fifth year expected runoff values roughly equal pre-fire condition. When comparing ERMiT results, compare annual expected runoff rates to 5th-year results (pre-fire). For example: Year 1 Untreated polygons with attributes found in Part II Step 27 when run through ERMiT expect tons/acre of sediment delivery. When compared to pre-fire expected delivery (1.13 tons/acre), you can compute an expected increase in sediment delivery of 823%! When mapping results spatially, however, you may want to focus on thematic values rather than numerical. Report on Significant or Moderate, and etc., changes in sediment delivery. NOTE: This graph assumes the polygons had no erosion mitigation treatments applied (such as seeding, mulching, log erosion barriers, etc.) 2. Note the Mitigation Treatment Comparisons table. This allows you to compare the various treatment options and the expected results given the geography of the polygons and climate. For example, you learn that untreated hillslopes and seeded hillslopes in this case will give you exactly the same sediment delivery response in the first year. However, mulching the same hillslopes will drop your expected sediment delivery from over 10 tons/acre to a little more than 3 tons/acres in the first year. 3. You can change the Probability that sediment yield will be exceeded value to whatever you want and recompute the expected sediment delivery by hillslope treatment. 4. Don t focus so much on the actual tons/acre the model suggests; rather, focus on proportional changes when comparing pre- to post-fire condition. See sidebar note. Conclusion 1. You can repeat the same process for other groups from your PivotTable. You ve only considered one grouping. 2. For more information on ERMiT or WEPP, visit 3. This is the end of the exercise. 6

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