Integrating climate and local factors for geomorphological distribution models

Abstract

Earth surface processes (ESPs) drive landscape development and ecosystem processes in high‐latitude regions by creating spatially heterogeneous abiotic and biotic conditions. Ongoing global change may potentially alter the activity of ESPs through feedback on ground conditions, vegetation and the carbon cycle. Consequently, accurate modeling of ESPs is important for improving understanding of the current and future distributions of these processes. The aims of this study were to: (1) integrate climate and multiple local predictors to develop realistic ensemble models for the four key ESPs occurring at high latitudes (slope processes, cryoturbation, nivation and palsa mires) based on the outputs of 10 modern statistical techniques; (2) test whether models of ESPs are improved by incorporating topography, soil and vegetation predictors to climate‐only models; (3) examine the relative importance of these variables in a multivariate setting. Overall, the models showed high transferability with the mean area under curve of a receiver operating characteristics (AUC) ranging from 0.83 to 0.96 and true skill statistics (TSS) from 0.52 to 0.87 for the most complex models. Even though the analyses highlighted the importance of the climate variables as the most influential predictors, three out of four models benefitted from the inclusion of local predictors. We conclude that disregarding local topography and soil conditions in spatial models of ESPs may cause a significant source of error in geomorphological distribution models. Copyright © 2014 John Wiley & Sons, Ltd.

Permafrost Map for Norway, Sweden and Finland

“…The palsas are mainly located at elevations below 1000 m asl, with almost 60 per cent at elevations between 350 and 500 m asl (Figure ). The number of annual FDDs is mainly higher than ‐1500 °C‐days, with more than 60 per cent of the locations higher than ‐2000 °C‐days; this accords with the findings by Aalto and Luoto (). Annual TDDs for most palsa areas are around 1000 °C‐days, while maximum snow depths are around 0.5 m, a pattern reproduced fairly well by the model (Figure ).…”

Revealing topoclimatic heterogeneity using meteorological station data

“…Moreover, various studies have identified climatic gradients being the key factors determining the geographical variation in, e.g. species distributions (Pearson et al, ; Zimmermann et al, ), land surface processes (Scherrer and Körner, ; Knight and Harrison, ; Aalto and Luoto, ) and human activity worldwide (Tallavaara et al, ). Regional and continental average air temperatures (spatial scales 10–1000 km 2 ) are constrained by latitude, longitude and altitude reflecting the influence of moving air masses, cloud cover and baroclinic instability (Barry and Chorley, ; Dobrowski et al, ).…”

Potential for extreme loss in high-latitude Earth surface processes due to climate change

“…The climate of the study area is characterized by subarctic conditions: mean annual temperature (average of 1971–2000) varies from 0.1°C to −4.7°C, and the mean annual precipitation total varies from 546 mm to 385 mm, with notable mesoscale variation caused by topography, water cover, and proximity to the Arctic Ocean [ Aalto et al ., ]. For a more detailed description of the study area, see Aalto and Luoto [].…”

“…Detailed information on the explanatory variables and the data sampling strategy is provided in Aalto and Luoto []; thus, they are only briefly described here. The spatial distribution of ESPs was related to various predictors of climate, topography, and soil.…”

“…Earlier studies have discovered strong relationships between permafrost‐related ESPs (i.e., palsa mires) and climatological gradients (temperature and precipitation) at broad spatial scales [e.g., Luoto et al ., ; Fronzek et al ., , ]. In a recent study, Aalto and Luoto [] highlighted the additional importance of local factors (topography, soil characteristics, and vegetation) for the activity of multiple ESPs and permafrost. Thus, the inclusion of local factors to the climate impact models of ESPs is crucial, as topographically complex areas can produce fine‐scale variability in, e.g., radiation conditions and snow distribution.…”