Browsing by Author "Aalto, Juha"

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  • Aalto, Juha (Helsingin yliopisto, 2015)
    Climate, Earth surface processes and soil thermal hydrological conditions drive landscape development, ecosystem functioning and human activities in high latitude regions. These systems are at the focal point of concurrent global change studies as the ongoing shifts in climate regimes has already changed the dynamics of fragile and highly specialized environments across pan Arctic. This thesis aimed to 1) analyze and model extreme air temperatures, soil thermal and hydrological conditions, and the main Earth surface processes (ESP) (cryoturbation, solifluction, nivation and palsa mires) controlling the functioning of high latitude systems in current and future climate conditions; 2) identify the key environmental factors driving the spatial variation of the studied phenomena; and 3) develop methodology for producing novel high quality datasets. To accomplish these objectives, spatial analyses were conducted throughout geographical scales by utilizing multiple statistical modelling approaches, such as regression, machine learning techniques and ensemble forecasting. This thesis was based on unique datasets from the northern Fennoscandia; climate station records from Finland, Sweden and Norway, state of the art climate model simulations, fine scale field measurements collected in arctic alpine tundra and remotely sensed geospatial data. In paper I, accurate extreme air temperature maps were produced, which were notably improved after incorporating the influence of local factors such as topography and water bodies into the spatial models. In paper II, the results showed extreme variation in soil temperature and moisture over very short distances, while revealing the factors controlling the heterogeneity of ground thermal and hydrological conditions. Finally, the modelling outputs in papers III and IV provided new insights into the determination of geomorphic activity patterns across arctic alpine landscapes, while stressing the need for accurate climate data for predictive geomorphological distribution mapping. Importantly, Earth surface processes were found to be extremely climatic sensitivity, and drastic changes in geomorphic systems towards the end of 21st century can be expected. The increase of current temperature conditions by 2 ˚C was projected to cause a near complete loss of active ESPs in the high latitude study area. This thesis demonstrated the applicability of spatial modelling techniques as a useful framework in multiple key challenges of contemporary physical geography. Moreover, with the utilized model ensemble approach, the modelling uncertainty can be reduced while presenting the local trends in response variables more robustly. In future Earth system studies, it is essential to further assess the dynamics of arctic alpine landscapes under changing climatic conditions and identify potential tipping points of these sensitive systems.
  • Aalto, Juha (Helsingin yliopisto, 2011)
    Periglacial processes act on cold, non-glacial regions where the landscape deveploment is mainly controlled by frost activity. Circa 25 percent of Earth's surface can be considered as periglacial. Geographical Information System combined with advanced statistical modeling methods, provides an efficient tool and new theoretical perspective for study of cold environments. The aim of this study was to: 1) model and predict the abundance of periglacial phenomena in subarctic environment with statistical modeling, 2) investigate the most import factors affecting the occurence of these phenomena with hierarchical partitioning, 3) compare two widely used statistical modeling methods: Generalized Linear Models and Generalized Additive Models, 4) study modeling resolution's effect on prediction and 5) study how spatially continous prediction can be obtained from point data. The observational data of this study consist of 369 points that were collected during the summers of 2009 and 2010 at the study area in Kilpisjärvi northern Lapland. The periglacial phenomena of interest were cryoturbations, slope processes, weathering, deflation, nivation and fluvial processes. The features were modeled using Generalized Linear Models (GLM) and Generalized Additive Models (GAM) based on Poisson-errors. The abundance of periglacial features were predicted based on these models to a spatial grid with a resolution of one hectare. The most important environmental factors were examined with hierarchical partitioning. The effect of modeling resolution was investigated with in a small independent study area with a spatial resolution of 0,01 hectare. The models explained 45-70 % of the occurence of periglacial phenomena. When spatial variables were added to the models the amount of explained deviance was considerably higher, which signalled a geographical trend structure. The ability of the models to predict periglacial phenomena were assessed with independent evaluation data. Spearman's correlation varied 0,258 - 0,754 between the observed and predicted values. Based on explained deviance, and the results of hierarchical partitioning, the most important environmental variables were mean altitude, vegetation and mean slope angle. The effect of modeling resolution was clear, too coarse resolution caused a loss of information, while finer resolution brought out more localized variation. The models ability to explain and predict periglacial phenomena in the study area were mostly good and moderate respectively. Differences between modeling methods were small, although the explained deviance was higher with GLM-models than GAMs. In turn, GAMs produced more realistic spatial predictions. The single most important environmental variable controlling the occurence of periglacial phenomena was mean altitude, which had strong correlations with many other explanatory variables. The ongoing global warming will have great impact especially in cold environments on high latitudes, and for this reason, an important research topic in the near future will be the response of periglacial environments to a warming climate.