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      Degrading permafrost puts Arctic infrastructure at risk by mid-century

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          Abstract

          Degradation of near-surface permafrost can pose a serious threat to the utilization of natural resources, and to the sustainable development of Arctic communities. Here we identify at unprecedentedly high spatial resolution infrastructure hazard areas in the Northern Hemisphere’s permafrost regions under projected climatic changes and quantify fundamental engineering structures at risk by 2050. We show that nearly four million people and 70% of current infrastructure in the permafrost domain are in areas with high potential for thaw of near-surface permafrost. Our results demonstrate that one-third of pan-Arctic infrastructure and 45% of the hydrocarbon extraction fields in the Russian Arctic are in regions where thaw-related ground instability can cause severe damage to the built environment. Alarmingly, these figures are not reduced substantially even if the climate change targets of the Paris Agreement are reached.

          Abstract

          Permafrost thaw poses a serious threat to the sustainable development of Arctic communities. Here the authors show that most fundamental Arctic infrastructure and population will be at high hazard risk, even if the Paris Agreement target is achieved.

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          A scaling method for priorities in hierarchical structures

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            Decision making with the analytic hierarchy process

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              SoilGrids1km — Global Soil Information Based on Automated Mapping

              Background Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.

                Author and article information

                Contributors
                jan.hjort@oulu.fi
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 December 2018
                11 December 2018
                2018
                : 9
                : 5147
                Affiliations
                [1 ]ISNI 0000 0001 0941 4873, GRID grid.10858.34, Geography Research Unit, , University of Oulu, ; P.O. Box 3000, Oulu, 90014 Finland
                [2 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, Department of Geosciences and Geography, , University of Helsinki, ; P.O. Box 64, Helsinki, 00014 Finland
                [3 ]Finnish Meteorological Institute, Weather and Climate Change Impact Research, P.O. Box 503, Helsinki, 00101 Finland
                [4 ]ISNI 0000 0004 1936 8921, GRID grid.5510.1, Department of Geosciences, , University of Oslo, ; P.O. Box 1047, Oslo, 0316 Norway
                [5 ]ISNI 0000 0004 1936 981X, GRID grid.70738.3b, Geophysical Institute, , University of Alaska Fairbanks, ; Fairbanks, AK 99775 AK USA
                [6 ]Earth Cryosphere Institute, Tyumen Science Centre, Siberian Branch of the Russian Academy of Science, Tyumen, 625026 Russian Federation
                [7 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Geography, Environment, and Spatial Sciences, , Michigan State University, ; East Lansing, MI 48824 MI USA
                [8 ]ISNI 0000 0000 8725 6180, GRID grid.261138.f, Department of Earth, Environmental, and Geographical Sciences, , Northern Michigan University, ; Marquette, MI 49855 MI USA
                Author information
                http://orcid.org/0000-0002-4521-2088
                http://orcid.org/0000-0003-2429-4595
                http://orcid.org/0000-0001-6819-4911
                http://orcid.org/0000-0001-6203-5143
                Article
                7557
                10.1038/s41467-018-07557-4
                6289964
                29317637
                d23c553a-eeff-4053-84ab-b3c340f55025
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 February 2018
                : 7 November 2018
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