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. 2007 ) are taken a step further into a multidimensional spatiotemporal domain. To achieve this, we apply algebraic similarity mapping, a method for quantitative comparison of any number of input datasets, maps, or models, which was first developed for resource exploration ( Herzfeld and Merriam 1990 ) and is adapted here for climate data analysis and the WALE experiment. Similarity mapping, or algebraic map comparison, utilizes a multidimensional algebraic algorithm to compare any number of input
. 2007 ) are taken a step further into a multidimensional spatiotemporal domain. To achieve this, we apply algebraic similarity mapping, a method for quantitative comparison of any number of input datasets, maps, or models, which was first developed for resource exploration ( Herzfeld and Merriam 1990 ) and is adapted here for climate data analysis and the WALE experiment. Similarity mapping, or algebraic map comparison, utilizes a multidimensional algebraic algorithm to compare any number of input
) datasets for nine years (1992–2000) simultaneously. The map comparison method is based on an algebraic approach that proceeds by 1) standardizing input values in each map or spatial model, 2) forming a functional of pairwise differences of standardized values, and 3) applying a seminorm to the functional in step 2, for each point in the 25-km 2 EASE Grid of the WALE region. The result is a spatial grid model of similarity values, which may be mapped to show areas of similarity versus areas of
) datasets for nine years (1992–2000) simultaneously. The map comparison method is based on an algebraic approach that proceeds by 1) standardizing input values in each map or spatial model, 2) forming a functional of pairwise differences of standardized values, and 3) applying a seminorm to the functional in step 2, for each point in the 25-km 2 EASE Grid of the WALE region. The result is a spatial grid model of similarity values, which may be mapped to show areas of similarity versus areas of
and the perimeters of all large fires that occurred during the period 1950–2000. The map is projected in the EASE projection format. Figure 2. Histogram showing observed total area burned (ha) within the study domain for the period 1950–2000. Figure 3. Comparison of observed vegetation pattern in 1992 (based on an AVHRR classification) vs simulated vegetation at 1992 for the CRU climate dataset and prescribed fire. Histogram presents the number of pixels (1 km × 1 km) in each vegetation type. Maps
and the perimeters of all large fires that occurred during the period 1950–2000. The map is projected in the EASE projection format. Figure 2. Histogram showing observed total area burned (ha) within the study domain for the period 1950–2000. Figure 3. Comparison of observed vegetation pattern in 1992 (based on an AVHRR classification) vs simulated vegetation at 1992 for the CRU climate dataset and prescribed fire. Histogram presents the number of pixels (1 km × 1 km) in each vegetation type. Maps
to climate-change scenarios were determined by comparisons to PRMS simulations of baseline (historical) conditions ( Hay et al. 2011 ). Figure 1. Location of study basins in the United States with shaded topography indicating relief across the basins. Site name colors indicate the degree of change in the 1.5-yr flood resulting from climate change: black indicates relatively little change (less than 10% difference between current conditions and the 2090 projection), red indicates decreasing 1.5-yr
to climate-change scenarios were determined by comparisons to PRMS simulations of baseline (historical) conditions ( Hay et al. 2011 ). Figure 1. Location of study basins in the United States with shaded topography indicating relief across the basins. Site name colors indicate the degree of change in the 1.5-yr flood resulting from climate change: black indicates relatively little change (less than 10% difference between current conditions and the 2090 projection), red indicates decreasing 1.5-yr
parameters while maintaining broadly significant vegetation descriptions. 2.2 Parameterization For each parameter we conducted a literature search for each biome and calculated mean and standard deviation. There were two choices when assigning values: use the mean for each biome or conduct multiple comparison tests to group biome values together into statistically similar groups. Natural variability within biomes and, in some cases, limited sample sizes led the statistical approach to produce a
parameters while maintaining broadly significant vegetation descriptions. 2.2 Parameterization For each parameter we conducted a literature search for each biome and calculated mean and standard deviation. There were two choices when assigning values: use the mean for each biome or conduct multiple comparison tests to group biome values together into statistically similar groups. Natural variability within biomes and, in some cases, limited sample sizes led the statistical approach to produce a
