Transferability of geomorphological distribution models: Evaluation using solifluction features in subarctic and Arctic regions

作者: Jan Hjort , Joonas Ujanen , Miia Parviainen , Jon Tolgensbakk , Bernd Etzelmüller

DOI: 10.1016/J.GEOMORPH.2013.08.002

关键词:

摘要: Extrapolation potential of statistically-based geomorphological distribution models (GDMs) has not been scrutinized. Here, the possibility to transfer solifluction within and between six study areas in subarctic Arctic environments was examined. A generalized linear model, additive maximum entropy boosted regression tree methods were used analyses. The transferability success GDMs assessed by area under curve a receiver operating characteristic plot. Based on results, slope angle, mean annual air temperature remote sensing based index vegetation abundance most important variables contributing occurrence at landscape scale. In model extrapolation, over half calibrated transferable from one another. topographical conditions had greater effect than climate extrapolation potential. More precisely, it more difficult extrapolate high-relief environment an with moderate topography. On contrary, transferred better environments. conclusion, (i) region specific environmental may significantly affect relative importance GDMs, (ii) certain limitations across areas, (iii) range calibration critical factor for (iv) machine learning-based performed marginally parametric extrapolation. Extensive knowledge about space is needed before can be reliably change explorations.

参考文章(60)
A.J. Plater, M.D. Daniels, T. Oguchi, 1.18 Present Research Frontiers in Geomorphology Reference Module in Earth Systems and Environmental Sciences#R##N#Treatise on Geomorphology. pp. 349- 376 ,(2013) , 10.1016/B978-0-12-374739-6.00021-X
B.W. Goodfellow, J. Boelhouwers, 7.31 Hillslope Processes in Cold Environments: An Illustration of High-Latitude Mountain and Hillslope Processes and Forms Reference Module in Earth Systems and Environmental Sciences#R##N#Treatise on Geomorphology. pp. 320- 336 ,(2013) , 10.1016/B978-0-12-374739-6.00181-0
J. Hjort, M. Luoto, 2.6 Statistical Methods for Geomorphic Distribution Modeling Reference Module in Earth Systems and Environmental Sciences#R##N#Treatise on Geomorphology. pp. 59- 73 ,(2013) , 10.1016/B978-0-12-374739-6.00028-2
I. Berthling, A. Schomacker, Í.Ö. Benediktsson, 8.28 The Glacial and Periglacial Research Frontier: Where from Here? Reference Module in Earth Systems and Environmental Sciences#R##N#Treatise on Geomorphology. pp. 479- 499 ,(2013) , 10.1016/B978-0-12-374739-6.00224-4
James Franklin, The elements of statistical learning : data mining, inference,and prediction The Mathematical Intelligencer. ,vol. 27, pp. 83- 85 ,(2005) , 10.1007/BF02985802
Jane Elith, Steven J. Phillips, Trevor Hastie, Miroslav Dudík, Yung En Chee, Colin J. Yates, A statistical explanation of MaxEnt for ecologists Diversity and Distributions. ,vol. 17, pp. 43- 57 ,(2011) , 10.1111/J.1472-4642.2010.00725.X
Karianne S. Lilleøren, Bernd Etzelmüller, A REGIONAL INVENTORY OF ROCK GLACIERS AND ICE‐CORED MORAINES IN NORWAY Geografiska Annaler Series A-physical Geography. ,vol. 93, pp. 175- 191 ,(2011) , 10.1111/J.1468-0459.2011.00430.X
Herman Farbrot, Ketil Isaksen, Bernd Etzelmüller, Kjersti Gisnås, Ground Thermal Regime and Permafrost Distribution under a Changing Climate in Northern Norway Permafrost and Periglacial Processes. ,vol. 24, pp. 20- 38 ,(2013) , 10.1002/PPP.1763