Chapter 22: Soil Sensitivity Index for Western Washington, Oregon & California

Dataset Overview

Land degradation, through processes such as vegetation loss and soil erosion, is a concern globally and regionally in the context of climate change. This dataset consists of a map of soil sensitivity (figure 22.1), conceptualized as a soil's ability to recover from disturbance (see chapter glossary for definitions of terms). Specifically, the study that produced this dataset examined the implications of a drying climate and spatial patterns in soil characteristics that could make soils more (or less) sensitive to drying, which in turn could make them more (or less) sensitive to disturbances such as fire, insects and disease.

Data Access:

Figure 22.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • Soils that have been identified as especially sensitive could be targeted for conservation practices aimed at mitigating soil erosion. These include practices to reduce disturbance (e.g., reducing vehicle traffic, using appropriate grazing practices) and to increase soil productivity through revegetation, including tree planting where appropriate and practicable. In some cases, erosion control practices may be considered to protect the soil surface.

Applicable scales for detailed spatial assessments:

  • For conservation applications requiring detailed assessment of spatial variation of the dataset within a geographic boundary (such as a protected area), the following geographic scales may be most appropriate (see appendix 3 for more information).
  • At the scale of: a local watershed (12-digit hydrologic unit code [HUC-12]), a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest, a level-3 ecoregion (e.g., the North Cascades), a single state (California only), a region (California plus western Oregon and western Washington).

Applicable scales for assessing general patterns:

  • Due to spatial resolution, the dataset may not show detailed spatial variation at the following geographic scales, however, the dataset may be useful to assess general patterns or for comparison to other locations (see appendix 3 for more information).
  • At the scale of: a small (< 1 km2) nature preserve, a state park or state wildlife area.

Use of the dataset in conservation applications may be limited by the following considerations:

  • This dataset represents soil conditions only based on existing soil attribute information from the SSURGO and STATSGO databases. Therefore, soil or vegetation information not represented in those databases is not reflected in this dataset. This includes information such as recent disturbance history, type and structure of vegetation, and history of management practices in a given area.

Past or current conservation applications:

Chapter 22 Case Study

Dataset citation:

Peterman, W. L., and K. Ferschweiler. 2015. A case study for evaluating potential soil sensitivity in aridland systems. Integrated Environmental Assessment and Management 12:388–396.

Note that this publication describes the methods used to produce this dataset but does not present the dataset itself. This publication presents a similar dataset produced using the same methods for a different geographic area (portions of Arizona, Colorado, New Mexico, Utah, Idaho, Wyoming, and Nevada).

Dataset documentation link: (subscription or fee required)

Data access:

The dataset can be viewed online and downloaded from:

Metadata access:

Formal metadata is available from:

Dataset corresponding author:
Wendy Peterman
USDA Forest Service

Data type category (as defined in the Introduction to this guidebook): Topoedaphic

Species or ecosystems represented: This dataset represents the soil component of a variety of ecosystems within the study domain.

Units of mapped values: unitless

Range of mapped values: 0 to 18

Spatial data type: a raster dataset (grid)

Data file format(s): GeoTiff (.tif)

Spatial resolution: Approximately 105 m

Geographic coordinate system: North American Datum of 1983

Projected coordinate system: USA Contiguous Albers Equal Area Conic

Spatial extent: western Washington, western Oregon, entire State of California

Dataset truncation: The dataset is truncated along the northern border of the State of Washington and the borders of the State of California.

Time period represented: Static (relatively unchanging over time)

Methods overview:

Soil attributes were obtained from the Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO) databases (Natural Resource Conservation Service, 2014a,b), and included indicators of soil depth, soil water storage, concentrations of calcium and gypsum, pH, particle size, temperature and moisture regime, and erodibility by wind and water. Soil horizons (vertical layers) were averaged together. In certain areas of the landscape, soil attributes from SSURGO were not available and so were modeled using a machine-learning approach based on available topographic, geologic, and climate datasets.

A set of rules was developed for all the soil attributes to identify soil sensitivity factors (Peterman and Ferschweiler, 2015). For each soil attribute, a threshold was identified beyond which the soil would be considered sensitive. For example, soils shallower than 10 cm were deemed sensitive based on soil depth and soils deeper than this threshold were deemed not sensitive. Finally, an overall soil sensitivity score was calculated by weighting the various soil sensitivity factors, which included (with weights in parentheses): droughtiness (5), shallowness (5), water erodibility (3), wind erodibility (3), gypsic (1), calcic (1), sodic (1), hydric (1), acidic (1), and alkaline (1). The weighted soil sensitivity factors were added together to produce an overall soil sensitivity score (Peterman, 2015). For more information, please consult the dataset citation listed in section 2 of this chapter.

Major input data sources for this dataset included:

Historical climate observations or models, digital elevation models (DEMs) or topography, soil characteristics

The mapped values of the dataset may be interpreted as follows:

Areas with high soil sensitivity index values exhibited a range of characteristics (e.g., droughtiness, shallowness, erodibility) that could be associated with soil sensitivity to climate change, including aridification and soil disturbance. Areas with lower values were deemed less sensitive based on these soil characteristics.

Representations of key concepts in climate-change ecology:

This dataset primarily represents a component of climate-change sensitivity, specifically the sensitivity of certain soils to climate change and related disturbances such as droughts and fires. Soil sensitivity to changing climate conditions and changing disturbance patterns has potential influences for plant communities and, in turn, for whole ecosystems that depend on those plant communities. For example, soils that are most sensitive to drying and least capable of storing and delivering water to plants during droughts may be especially prone to vegetation loss and erosion, with consequences for terrestrial plant and animal communities and for streams that receive eroded sediment. Thus, this dataset helps illustrate the importance of soil characteristics in mediating ecosystem-level and species-level responses to climate change, including climate-driven shifts in drought patterns.

This dataset involves the following assumptions, simplifications, and caveats:

A variety of factors could affect soil sensitivity to disturbance and climate change beyond those that were considered in creating this dataset. These include fine-scale effects of vegetation (e.g., soil stabilization, soil shading), history of disturbances such as recent fires and droughts, and various land-use practices (forest management practices, agricultural practices).

Quantification of uncertainty: This dataset does not include any quantification of uncertainty relating to the mapped values.

Field verification: Creation of this dataset did not involve any field verification of the mapped values.

The methods used to produce the dataset are described in the publication cited in section 2, for which peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).

Peterman, W. 2015. Sensitive soil index for the North Pacific and California Landscape Conservation Cooperative.

Peterman, W. L., and K. Ferschweiler. 2015. A case study for evaluating potential soil sensitivity in aridland systems. Integrated Environmental Assessment and Management 12:388–396.

Natural Resource Conservation Service. 2014a. Soil Survey Geographic (SSURGO) Database.

Natural Resource Conservation Service. 2014b. U.S. General Soil Map (STATSGO2) Database.