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B1311
Title: A spatio-temporal framework for modeling active layer thickness Authors:  Tatiyana Apanasovich - George Washington University (United States) [presenting]
Abstract: The Arctic is experiencing an unprecedented rate of environmental and climate change. The active layer is sensitive to both climatic and environmental changes, and plays an important role in the functioning, planning, and economic activities of Arctic human and natural ecosystems. A methodology is developed for modeling and estimating spatial-temporal variations in active layer thickness using data from several sites of the Circumpolar Active Layer Monitoring network, and demonstrates its use in spatial-temporal interpolation. The simplest models stochastic component exhibits no spatial or spatio-temporal dependency and is referred to as the nave model, against which we evaluate the performance of the other models, which assume that the stochastic component exhibits either spatial or spatio-temporal dependency. The methods used to fit the models are then discussed, along with point forecasting. We compare the predicted fit of the various models at key study sites located in the North Slope of Alaska and demonstrate the advantages of space-time models through a series of error statistics. We find the difference in performance between the spatio-temporal and remaining models is significant for all three error statistics. The best stochastic spatio-temporal model increases predictive accuracy, compared to the naive model, of 33.3, 36.2 and 32.5\% on average across the three error metrics at the key sites for a one-year hold out period.