Steve Pye LA 221 04/22/16

Final Report: Determining regional locations of reference sites based on slope and soil type.

Client: Sonoma Land Trust Deliverables: Results and working model that determine and identify regional locations with similar soil types and slope steepness to those of the reference site.

Introduction Sears Point is located in Southern Sonoma county adjacent to San Pablo bay (Figure 1). The Sonoma Land Trust currently owns approximately 2,300 acres of land here, which extends from the slopes of Cougar mountain down to San Pablo bay. This gradient of land is managed with consideration given to the Sears Point Final Preliminary Plan, a multicomponent document that guides land managers in their efforts and maintaining and restoring the properties. One defined area of the Management plan is known as the uplands, which encompasses all lands north of Highway 37 at the base of Cougar Mountain. This land is comprised of annual non native and native grasslands, with an underlying soils comprised largely of diablo valley clays and alluvial deposits in the lower lying areas. Virtually all of the small stream courses flowing off Cougar mountain are currently experiencing severe erosion in their steeper reaches. This is believed to be at least in part due to the intensive land uses on the property in past decades. This area of the Sears Point property has been used for cattle grazing operations for over century, with high very high stocking rates in past decades. Currently, there are 200 head of cattle on the property. In the past there have been as many as 800 cows on the property at a given time. One of the most impacted systems in this grazing area is a small drainage on the southern slope of cougar mountain (Cougar mountain creek). This one­mile long creek drains an area of approximately 116 acres, and passes through a culvert beneath Highway 37 before dissipating in an alluvial plane (See figure 1). Chronic erosion in the west branch of this creek has caused mass sloughing of side slopes and channel incision, which clogs the poorly designed culvert during winter storms. Because of the degraded state of this channel network and its implications, it offers a prime opportunity for restoration and alteration to land management practices in order to achieve an ecologically sustainable and stable drainage condition.

To better understand the processes occurring on the property, a spatial analysis tool has been developed to find similar sites to cougar mountain creek through combining slope and soil data across a region of the Cougar Mountain­Sonoma Mountain range. Locating reference sites will allow for further field and remote analysis, ideally providing data to explain the present conditions within the Cougar Mountain creek Drainage.

Data Acquisition Data for use throughout the project was acquired from both the Natural Resource Conservation Service (NRCS) and the Sonoma County Vegetation Mapping and LiDAR Program (SCVMLP). A polygon of the desired study area was used to acquire soil data within the study region. The NRCS Web Soil Survey allows soil data to be collected for an area of interest (AOI) by uploading a polygon feature to their web browser, which then delineates a boundary around the study site in which soil data is shown (Figure 2). This soil data may then be downloaded in tabular or spatial format. Soil data was downloaded for this study in spatial format so that spatial analysis could be conducted on regional soils throughout Southern Sonoma County. A Digital Elevation Model (DEM) was acquired from SCVMLP, which offers watershed based delineations for download. 3 Watershed DEMs were acquired to provide a larger region for study. In Arcmap, these DEM layers were converted from meter to foot values, and subsequently mosaiced to generate one model of the study region (Figure 3). Defining the Study Region A decision was made to define the study region by using polygons from initial data acquisition. The three DEM layers acquired from SCVMLP were mosaiced into one regional layer to allow for a larger area to be analyzed. Combined, the three DEMs allow for the entire southern range of the Sonoma Mountain formation to be included. This range extends north to south from Highway 116 east of Petaluma to San Pablo Bay; and east to west from shellville to the Petaluma River (Figure 4). Conceptual Model Evolution Initial design of the conceptual model was conducted over a period of several weeks, during which time the model was run through multiple iterations to determine the most suitable representation of reference sites. Model processes were first drawn schematically by hand to better understand logical progression, and were thereafter built upon in ArcMap’s modelbuilder feature. The initial model was designed to assess areas within the study region with similar slopes, but was later altered to search for steep slopes where soils were of the same type. This decision was based on NRCS soil data that shows soil stability relative to slope steepness. Model iterations may been in the appendices of this document (Figures 5­8). Final Model Design The final model product (Figure 8) is a refined product of the conceptual model, and sufficiently addresses the research question’s need to find reference sites across the region of study. This model utilizes soil and elevation data to define areas with similar slopes (30­50 degrees) that are of the same soils type (namely Diablo clays). The following is a descriptive representation of the model processes:

1. DEM processing: the digital elevation model was run through the slope tool,

which output a regional slope map. This output was then used as the input feature for the ‘Extract By Attributes’ tool, which utilized an SQL statement to extract slopes between 30 and 50 degrees. This range was chosen because slope data indicates that diablo clay soils within this range are highly susceptible to erosion. This output was then run through the ‘Reclassify’ tool, which converted all selected steep slope values into the number ‘1’. Because the cell values of this slope raster were then in integer values, the raster of steep slopes was able to be converted to a polygon feature using the ‘Raster to Polygon’ tool (this tool requires integer values). Once in vector format, the slopes were given a new field and a weight of 1 was added to that field for all records. At this point, processing of the soils data was merged to produce the final product.

2. Soil Data Processing: soil data was initially processed by using the ‘Select’ tool. This tool was used to select all soils that fell within the Diablo clay complex (soils within the study site). An SQL statement was used here to select the soils associated with the site. A field was then added to this new layer, and was calculated to have a value of 1 (This value would later be added to the slopes value to produce a combined feature of slope and soil similarity).

3. Combined Soil and DEM processing: once processing was complete for individual soil and DEM variables, the vector data was merged and a new field was added to the merged featureclass. This field was calculated to be the sum of soil weight and slope weight, which proved a new definition for all areas with the same slope and soil type. These features then had a value of ‘2’. The ‘Select’ tool was used a final time to extract these new features with a value of ‘2’, which would be the final output result for the model.

Parameterization: after finalizing the model and assessing its function, specific elements were parameterized to allow the user to input different variables than the default specifications (Figure 9). This will allow the user to: define their own study region; define their own soils layer; select specific soil types; select specific slopes; and define an output location. This allows the user to use the tool for other purposes than the specific location and soils specified. Results: the results of this model reveal that chronic erosion is present throughout the region. Vector data given as the model result selected all diablo clay soil areas where slopes were steeper than 30 degrees and less than 50. This range is determined by NRCS to be the range within which diablo soil types are highly unstable and susceptible to erosion. This study reveals that the majority of gullies and ravines within the study area are suffering from erosion when compared with high resolution satellite imagery and ground truthing. The vector results cover virtually all small stream channels and side slopes within the study region, indicating that erosion is either a widespread issue or naturally occurs on slopes of the aforementioned conditions (See Figure 10 for an image of the results).

Model Iteration Experiment: a For iteration was inserted into the model to provide multiple outputs of differing slope steepness. This would allow multiple areas to be assessed on a scale of higher chance of erodibility. This model alteration was attempted, but two fundamental errors in the logic after the addition of the iteration prevented the model from running. The first error occurs when the iterator calls for integer values of slope and increments them by 10. This is not possible because the slope data is continuous, and the iteration can only call upon integers or decimal numbers, but cannot run where the data is continuous. The second error occurs where the iteration of Extract Slopes values is unable to be reclassified because the reclassify tool requires that the old values be manually predefined before the model is run. The output of the iterator is also just one number value of slope (e.g. 10), where all slopes with a value of 10 are selected. Because this value is different than the reclassify tools value, and is not a range of slopes, the reclassify tool cannot perform its function. The only solution to this problem would be to have two separate models, where the first would require the user to select different ranges of slopes to produce several outputs, and the second model would use the Iterate by Files tool, which would select files from a geodatabase or folder and iterate the inputs for reclassify based on the files. However, there still remains an error because the Reclassify tool would need to be changed manually each time to match the values specified for the “Old Values” column. Literature References: Sarr DA (2002). Riparian livestock exclosure research in the western United States: a critique and some recommendations. Environmental Management 30(4):516–526

M.M. Haan, et. al. (2010). Grazing Management and Microclimate Effects on Cattle Distribution Relative to a Cool Season Pasture Stream. Rangeland Ecology and Management 63: 572­580 Al­hamdan Z., Osama, et. al. (2015). Rangeland Hydrology and Erosion Model (RHEM) Enhancements for Applications on Disturbed Rangelands. Hydrological Processes 29: 445­457 Flores­Cervantes, Javier H., et al (2006). Development of gullies on the landscape: A model of headcut retreat resulting fro plunge pool erosion. Journal of Geophysical Research (111) 1­14 Vandekerckhove, L., et. al. (2001). Short­term bank gully retreat rates in Mediterranean environments. Catena 44: 133­161

Appendices: Figure 1: the project or study site, model inputs are based off characteristics at this location

Figure 2: NRCS’s polygon­based AOI download window

Figure 3: SonomaVegMap.org’s interface for watershed data downloads.

Figure 4: the AOI or regional study area

Figure 5: conceptual model, 1st design.

Figure 6: model iteration 1.

Figure 7: model iteration 2.

Figure 8: model iteration 3, final model design and logic. (note the tool and output names).

Figure 9: the wizard for the final model with all inputs.

Figure 10: final product­ results reveal that virtually all gullies within the study areas have steep slopes and diablo clay soil types. Satellite imagery also reveals that the majority of these systems are eroding, which may indicate a widespread erosion problem, but does not explain underlying causes.

Figure 11: Attempted Iterator Insertion to classify multiple slope areas

Figure 12: error message received in attempting the iterator. The logic of the Reclassify tool prevents iteration because the values for reclassification must be assigned outside Model Builder and must be predefined.