Chapter 21: Topoclimate Diversity of the Pacific Northwest

Dataset Overview

This dataset represents the local diversity of topoclimates within an area (figure 21.1; see chapter glossary for definitions of terms). North-facing slopes are topographically shaded and thus provide cooler local temperatures than nearby south-facing slopes, which receive greater sunlight. Therefore, a given area that includes a diversity of sunny and shaded slopes provides greater diversity of local temperatures relative to another area the same size that includes only flat land with neither sunny nor shaded slopes. As another example, ridgetops may be well-drained whereas local depressions and valley bottoms accumulate runoff into streams. Therefore, an area with both ridgetops and valley bottoms might provide greater diversity of moisture conditions than an area that contains only one or the other type of landform.

Data Access:

Figure 21.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • This dataset could be used to identify priority areas for conservation (areas with high topoclimate diversity) based on the idea that these areas may help sustain native biodiversity as climate conditions change. Like other datasets described in chapters 19 and 20 of this guidebook, this dataset represents physical features of the landscape rather than representing any individual species or ecosystem. However, a primary reason for examining topoclimates is their importance to species habitat. Species that are shifting their distributions in response to climate change may be able to access suitable microclimates in areas with high enough topoclimate diversity. Also, high topoclimate diversity may provide more habitat niches to a greater number of species, thereby promoting biodiversity.
  • This information could guide conservation investments including land acquisition for protected areas, ecosystem restoration initiatives, and biodiversity monitoring programs. Stratifying the data by habitats or land types (land facets in Buttrick et al. 2015) is useful so that comparisons are made between areas with similar habitats or facets that may nonetheless differ in topoclimate diversity and thus in their resilience to climate change.

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 (Washington, Oregon, or Idaho), a region (multiple states in the Pacific Northwest).

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:

  • It should be noted that this data layer does not explicitly represent current or future biodiversity and has not been tested for the strength of its association with biodiversity. Different species may respond differently to topoclimate diversity within their ranges, so managers may wish to combine this dataset with existing knowledge about microhabitats for particular species of conservation concern. Some areas of high topoclimate diversity may not represent viable habitat for particular species, so it is advisable to consult a variety of other datasets in conjunction (for example, land use, land ownership, species ranges, climate projections) for conservation planning purposes. Also, very small areas of topoclimate diversity may be ecologically important for conservation but not show up in this dataset due to its scale and resolution (90 m).

Past or current conservation applications:

Chapter 21 Case Study

Dataset citation:

Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving nature’s stage: identifying resilient terrestrial landscapes in the Pacific Northwest. The Nature Conservancy, Portland, Oregon, USA.

Dataset documentation link: (open access)

Data access:

The dataset can be downloaded from:

The zipped folder linked above contains the following files in a geodatabase: CTI_ALL_ECOREG, CTI_FOCAL_NRM, HLI_ALL_ECOREG, HLI_FOCAL_NRM, TOPOCLIMATE_ALL_ECOREG. Topoclimate diversity is represented by the data layer TOPOCLIMATE_ALL_ECOREG. Please consult the dataset citation for explanations for each file.

The dataset available for interactive online map viewing at:

Metadata access:

Formal metadata files are included with the datasets available from:

Dataset corresponding authors:
Michael Schindel
The Nature Conservancy in Oregon
[email protected]

Ken Popper
self-employed; formerly with The Nature Conservancy in Oregon
[email protected]

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

Species or ecosystems represented: This dataset does not represent any individual species or ecosystems.

Units of mapped values: unitless

Range of mapped values: 0.2 to 1.0

Spatial data type: a raster dataset (grid)

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

Spatial resolution: 90 m

Geographic coordinate system: North American Datum of 1983

Projected coordinate system: USA Contiguous Albers Equal Area Conic

Spatial extent: Regional (Pacific Northwest of the United States)

Dataset truncation: The dataset is truncated at non-ecological borders along the northern edge at the United States border with Canada.

Time period represented: Static (relatively unchanging over time)

Methods overview:

The only input dataset was a 30-m digital elevation model (DEM) from the National Elevation Dataset (U.S. Geological Survey, 2009). The Geomorphometric and Gradient Metrics Toolbox (Evans, 2011) was used to calculate heat-load index (HLI) and compound topographic index (CTI). Then, ranges of HLI and CTI were calculated using a 450-m moving window approach. The results were standardized from 0 to 1 and multiplied to create a topoclimate diversity value. For more information, please consult the dataset citation listed in section 2 of this chapter.

This dataset relied on the following general types of models: Terrain or geomorphology models

This dataset employed the following specific models: Geomorphometric and Gradient Metrics Toolbox (Evans, 2011)

Major input data sources for this dataset included: Digital elevation models (DEMs) or topography

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

High values of topoclimate diversity indicate areas with a wide variety of local temperature and moisture conditions as determined by topography. Low values indicate areas where, based on topography, there is little spatial variation in temperature and moisture conditions.

Representations of key concepts in climate-change ecology:

This dataset primarily represents climate-change adaptive capacity, in that it represents the availability of microclimates that could facilitate species migration and refugia in which species could persist in place. The dataset does not represent climate-change exposure, meaning that it does not represent the direction or magnitude of projected climate change. This dataset may represent climate-change sensitivity to the degree that, for example, areas without topographic shading are more sensitive to regional climate warming (showing a greater local increase in temperature) than are areas with more topographic shading.

As described in chapter 19, topoclimate diversity is a metric that may be useful as part of a “coarse-filter” approach to conserving biodiversity because areas with high topoclimate diversity may represent areas best able to support climate adaptation for a variety of species, consistent with the idea of “conserving nature’s stage” (Beier et al. 2015). Information about individual species may also be considered (as a “fine-filter” approach) to enhance assessments of climate vulnerability and species conservation options.

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

Because this dataset represents only topographic controls on climate, it provides a simplified depiction of microclimate diversity. In real ecosystems, microclimate conditions (local temperature and moisture) are influenced by many factors and processes other than topography. These include soil drainage characteristics, vegetation (e.g., use of water by plants and shading provided by tree canopies), groundwater discharge from springs and seeps, temperature inversions from cold-air pooling, and effects from disturbances. The input dataset (a DEM) was at 30-m resolution. Therefore, finer-scale topoclimate diversity (i.e., diversity of topoclimate conditions within a single 30-m pixel) is not represented although it may be ecologically important to some species. The dataset represents topoclimate only for terrestrial areas and should not be used to evaluate aquatic ecosystems (streams or lakes), marine areas, or estuaries.

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

Field verification:

Although topoclimate diversity itself was not field-verified by the dataset authors, the intermediate variables CTI and HLI were compared to field measurements by McCune and Keon (2002). The HLI calculations in McCune and Keon (2002) were calibrated based on field measurements of direct solar radiation on various combinations of slope, aspect, and latitude.

Prior to dataset publication, peer review was conducted by internal review (reviewers were from the same institution).

Beier, P., M. L. Hunter, and M. Anderson. 2015. Special section: conserving nature’s stage. Conservation Biology 29:613–617.

Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving nature’s stage: identifying resilient terrestrial landscapes in the Pacific Northwest. The Nature Conservancy, Portland, Oregon, USA.

Evans, J. 2011. Geomorphometric and Gradients Metrics Toolbox (version 1.0). GIS software.

McCune, B., and D. Keon. 2002. Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13:603–606.

U.S. Geological Survey. 2009. National Elevation Dataset (NED).