After identifying collaborative conservation priority areas (see this report for details), the Arid Lands Initiative needed to assess the relative climate change vulnerability of these areas to better understand what management strategies might be most appropriate in each. This Climate Change Vulnerability Assessment (CCVA) for the ALI’s Priority Core Areas (PCAs) is fully described in this report. An addendum report, here, presents a complementary CCVA that was developed for the ALI’s Priority Linkage Areas (PLAs). PCAs were assessed relative to other PCAs, and PLAs relative to other PLAs, so they both have their own models attached below. Note that a Netweaver2 Developer License (http://www.rules-of-thumb.com/NetWeaver/) is required to view these models.
The following paragraphs, which described the NetWeaver model available for download here, are excerpted from the Phase 2 final report, “Assessing the Condition and Resilience of Collaborative Conservation Priority Areas in the Columbia Plateau Ecoregion.”
The abundance of existing data on climate change and other threats in the Columbia Plateau presents both an opportunity and an analytical challenge to the Arid Lands Initiative. The data sets all represent various aspects of vulnerability, but were developed with differing methods, purposes, and resolutions. Simple additive combinations of these GIS layers would be inappropriate, leading to confusing and potentially misleading outputs. To synthesize these disparate datasets, a fuzzy logic approach was developed.
Fuzzy logic deals with a continuum of values, rather than binary true/false values. To use fuzzy logic, variables must first be remapped into “truth” values by using the data to evaluate a true/false proposition. For example, temperature data could be remapped into “fuzzy space” by assessing the trueness of a proposition such as “It is hot.” If it were 105°F, it would definitely be hot (100% true), whereas 75°F might be described as hot by some, and warm by others. To remap data, a “fuzzy curve” is used. Above a certain threshold, a proposition is completely true, below a certain threshold, it is completely false, and in between the thresholds, it is partially true.
Once all relevant variables are converted into truth values using fuzzy curves, they can then be combined into a tree-based, structured fuzzy logic model that can support decision-making. The key advantages of this approach are that 1) it is akin to human decision making processes, which deal in shades of gray, and 2) it allows for meaningful integration of data.
To create the model, the ALI used NetWeaver developer 2. This software streamlines the preparation of fuzzy logic models and has been used in many applications. NetWeaver allows the modeler to calculate the truth of multiple propositions against a complex database, and roll them up with fuzzy logic operators. Many climate-relevant data values were available for each PCA, and could be used to assess statements such as “Relative to other PCAs, this PCA is highly vulnerable to climate change.” If the proposition was fully supported it was assigned a value of 1 (true). If the proposition had no support, it was assigned a value of -1 (false). PCAs with partial support for the proposition had a fuzzy truth value falling between -1 and 1 (Figure 4).
