Chapter 12: Riparian Climate Corridors
Dataset Overview
Riparian areas are expected to be important for promoting adaptive responses to climate change because they often provide cool microclimates and may serve as corridors to facilitate species range movements (Krosby et al., 2014; see chapter glossary for definitions of terms). This dataset represents an integrated assessment—called a riparian index—of how well specific riparian areas may serve as corridors (figure 12.1). The riparian index integrates five attributes that were considered important for riparian areas to support climate adaptation for species: (1) the degree to which riparian areas span temperature gradients, (2) how much canopy cover they have, (3) their levels of solar exposure, i.e., how much sunlight they receive, (4) how wide they are, and (5) how much they have been altered by human activities. Based on these five considerations, the riparian index scores are presented for stream lines and also aggregated to 6-digit hydrologic unit code (HUC-6) watersheds. Using riparian index scores for individual streams, priority riparian areas were identified within each HUC-6 watershed.
Data Access: https://www.sciencebase.gov/catalog/item/53c93990e4b092c1b2565592
Conservation Applications
Potential conservation applications of this dataset could include the following:
- This dataset could be used to identify riparian areas that warrant further examination for their potential role in supporting adaptation to climate change. Because priority riparian areas were identified within HUC-6 watersheds, assessments of individual riparian corridors at the watershed scale could be guided by this dataset. For example, riparian areas with high riparian index scores could be evaluated in the field, studied from the perspective of known species of conservation concern that use those riparian areas for habitat, and monitored over time. Riparian areas with high index values that also demonstrate a capacity to support climate adaptations (e.g., by serving as microrefugia for species seeking cooler or moister microclimates, or by facilitating species movements across the landscape) might be priorities for conservation.
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:
- Development of the five metrics used to create the riparian index was grounded in conceptual ideas about what might best support climate adaptation (and species movements in particular). This conceptual model has not yet been validated, such as by tracking actual species movements with telemetry.
- In addition, the riparian index represented in this dataset does not incorporate possible future changes in climate, land use, or land cover. As a result, it cannot be used to differentiate riparian areas that will experience more dramatic climate change (e.g., based on high climate velocity) from those that will not.
Past or current conservation applications:
- The dataset has not yet been used in any on-the-ground conservation applications to the knowledge of the authors of this chapter.
Dataset citation:
Krosby, M., R. A. Norheim, D. M. Theobald, and B. McRae. 2014. Final report: riparian climate-corridors: identifying priority areas for conservation in a changing climate. North Pacific Landscape Conservation Cooperative.
Krosby, M., R. Norheim, and D. Theobald. 2015. Riparian climate-corridors: analysis extension, improvements, and validation. North Pacific Landscape Conservation Cooperative.
Krosby, M., D. M. Theobald, R. Norheim, and B. H. McRae. 2018. Identifying riparian climate corridors to inform climate adaptation planning. PloS ONE 13:e0205156.
Dataset documentation links:
https://www.sciencebase.gov/catalog/item/53c938c6e4b092c1b256558f (open access)
https://doi.org/10.1371/journal.pone.0205156 (open access)
Data access:
The dataset can be downloaded from: https://www.sciencebase.gov/catalog/item/53c93990e4b092c1b2565592
The dataset can be viewed interactively at: https://nplcc.databasin.org/datasets/46caec8762194138a8a6421322ea170d
Metadata access:
Formal metadata is not available for this dataset.
Dataset corresponding author:
Meade Krosby
University of Washington, Climate Impacts Group
[email protected]
Data type category (as defined in the Introduction to this guidebook):
Landscape connectivity and permeability, stream and riparian
Species or ecosystems represented:
This dataset represents riparian ecosystems and the species that use them for habitat and movement corridors.
Units of mapped values: unitless
Range of mapped values:
Riparian index values attributed to stream lines: 0.00033546 to 0.829571
Riparian index values attributed to HUC-6 watersheds: 0.000414698 to 0.656893
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: North American Datum of 1983 Albers
Spatial extent: Regional
Dataset truncation: The dataset is truncated along the border between the United States and Canada.
Time period represented: Current or recent (2000 to 2018)
Note that the metrics used to develop the riparian index in this dataset represent some landscape characteristics that are relatively static (e.g., topographic shading) as well as others that represent current or recent conditions (e.g., temperature gradients, canopy cover).
Methods overview:
First, potential riparian areas were identified using the topography of the landscape. Then, five metrics for these riparian areas were developed, as follows. (1) The gradient of temperatures spanned by riparian areas was represented by the temperature difference between the headwaters and the stream outlet. (2) Riparian area size and width were calculated. (3) Canopy cover for riparian areas was derived from the National Land Cover Dataset (NLCD). (4) Human alteration of riparian areas was derived from an existing land-cover condition dataset. (5) Potential radiation was calculated from the topography of the landscape to represent how sunny or shaded a riparian area might be based on the surrounding terrain. These five metrics were synthesized into a riparian index. Values were presented for 90-m pixels along stream lines and were aggregated for HUC-6 watersheds. For more information, please consult the dataset citation listed in section 2 of this chapter.
This dataset employed the following specific models:
FlowAccumulation and FlowLength geoprocessing tools in ArcGIS Spatial Analyst (Esri 2013)
Major input data sources for this dataset included:
Indicators of human presence on the landscape, historical climate observations or models, digital elevation models (DEMs) or topography, current land cover
The mapped values of the dataset may be interpreted as follows:
High values of the riparian index, which were generally found in mountainous areas, indicate riparian areas that are most expected to support climate-change adaptation because they: (1) span large temperature gradients, (2) have high canopy cover, (3) have low levels of solar exposure (sunlight), (4) are relatively wide, and/or (5) have minimal human alteration.
Representations of key concepts in climate-change ecology:
This dataset does not represent climate-change exposure or sensitivity, in that it does not quantify the magnitude of climate change that various riparian areas will experience nor how sensitive they will be to climate change. Instead, the dataset primarily represents adaptive capacity to climate change, in that riparian areas with higher index values may better enable species to move across the landscape in response to climate change, or to seek out cooler riparian microenvironments.
Adaptive capacity represents the degree to which a species is able to cope with climate change by persisting in place (such as through behavioral changes or changes in seasonal timing), seeking out more suitable microhabitats nearby, or migrating to other regions with more suitable climate. Human alterations to landscapes and ecosystems can constrain adaptive capacity in some cases (Beever et al. 2016). For example, species that might otherwise be able to use riparian corridors as migration routes to track favorable climates might be constrained from doing so if some riparian areas are degraded or too close to developed areas. These considerations are incorporated into this dataset, which may be relevant to a wide variety of aquatic and terrestrial species that rely on intact riparian habitats.
This dataset involves the following assumptions, simplifications, and caveats:
Local barriers to species movement along riparian corridors could be present on the landscape that were not accounted for in this dataset, such as cliffs or cities. In addition, the quality of habitat provided by riparian areas could be affected by other considerations that were not included in the development of this dataset, such as water quality, human influences from recreation, presence of invasive species, and other factors. It should be noted that the metrics of riparian condition were not independently verified, such as by using high-resolution aerial imagery.
Quantification of uncertainty:
This dataset does not include any quantification of uncertainty relating to the mapped values.
Field verification:
Validation of riparian areas was conducted by comparing modeled riparian area locations and condition (degree of human modification) to high-resolution aerial photographs from 2011 through 2014. Validation was conducted at 30 random locations within 100 randomly selected 1-km2 squares within the study area, for a total of 3,000 validation locations. Validation results are presented in Krosby et al. (2015).
Prior to publication, peer review of Krosby et al. (2018) was conducted by external review (at least two anonymous reviewers, each from a different institution).
Beever, E., J. O’Leary, C. Mengelt, J. M. West, S. Julius, N. Green, D. Magness, L. Petes, B. Stein, A. B. Nicotra, J. J. Hellmann, A. L. Robertson, M. D. Staudinger, A. Rosenberg, E. Babij, J. Brennan, G. W. Schuurman, and G. E. Hofmann. 2016. Improving conservation outcomes with a new paradigm for understanding species’ fundamental and realized adaptive capacity. Conservation Letters 9: 131–137.
Esri. 2013. ArcGIS Spatial Analyst: Release 10.2. Redlands, CA: Environmental Systems Research Institute.
Krosby, M., R. A. Norheim, D. M. Theobald, and B. McRae. 2014. Final report: riparian climate-corridors: identifying priority areas for conservation in a changing climate. North Pacific Landscape Conservation Cooperative.
Krosby, M., R. Norheim, and D. Theobald. 2015. Riparian climate-corridors: analysis extension, improvements, and validation. North Pacific Landscape Conservation Cooperative.
Krosby, M., D. M. Theobald, R. Norheim, and B. H. McRae. 2018. Identifying riparian climate corridors to inform climate adaptation planning. PLoS ONE 13:e0205156.