TY - GEN A1 - Smith, Taylor A1 - Zotta, Ruxandra-Maria A1 - Boulton, Chris A. A1 - Lenton, Timothy M. A1 - Dorigo, Wouter A1 - Boers, Niklas T1 - Reliability of resilience estimation based on multi-instrument time series T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1322 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-589120 SN - 1866-8372 IS - 1322 SP - 173 EP - 183 ER - TY - JOUR A1 - Smith, Taylor A1 - Zotta, Ruxandra-Maria A1 - Boulton, Chris A. A1 - Lenton, Timothy M. A1 - Dorigo, Wouter A1 - Boers, Niklas T1 - Reliability of resilience estimation based on multi-instrument time series JF - Earth System Dynamics N2 - Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience. Y1 - 2023 U6 - https://doi.org/10.5194/esd-14-173-2023 SN - 2190-4987 VL - 14 SP - 173 EP - 183 PB - Copernicus Publications CY - Göttingen ER - TY - JOUR A1 - Smith, Taylor A1 - Boers, Niklas T1 - Global vegetation resilience linked to water availability and variability JF - Nature Communications N2 - Quantifying the resilience of vegetated ecosystems is key to constraining both present-day and future global impacts of anthropogenic climate change. Here we apply both empirical and theoretical resilience metrics to remotely-sensed vegetation data in order to examine the role of water availability and variability in controlling vegetation resilience at the global scale. We find a concise global relationship where vegetation resilience is greater in regions with higher water availability. We also reveal that resilience is lower in regions with more pronounced inter-annual precipitation variability, but find less concise relationships between vegetation resilience and intra-annual precipitation variability. Our results thus imply that the resilience of vegetation responds differently to water deficits at varying time scales. In view of projected increases in precipitation variability, our findings highlight the risk of ecosystem degradation under ongoing climate change. Vegetation dynamics depend on both the amount of precipitation and its variability over time. Here, the authors show that vegetation resilience is greater where water availability is higher and where precipitation is more stable from year to year. Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-36207-7 SN - 2041-1723 VL - 14 IS - 1 PB - Springer Nature CY - London ER - TY - GEN A1 - Atmani, Farid A1 - Bookhagen, Bodo A1 - Smith, Taylor T1 - Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1275 KW - ICESat-2 KW - GEDI KW - canopy height KW - lidar KW - savanna Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-569915 SN - 1866-8372 IS - 1275 ER - TY - JOUR A1 - Atmani, Farid A1 - Bookhagen, Bodo A1 - Smith, Taylor T1 - Measuring vegetation heights and their seasonal changes in the Western Namibian Savanna using spaceborne lidars JF - Remote sensing / Molecular Diversity Preservation International (MDPI) N2 - The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes. KW - ICESat-2 KW - GEDI KW - canopy height KW - lidar KW - savanna Y1 - 2022 U6 - https://doi.org/10.3390/rs14122928 SN - 2072-4292 VL - 14 IS - 12 SP - 1 EP - 20 PB - MDPI CY - Basel, Schweiz ET - 12 ER - TY - GEN A1 - Hering, Robert A1 - Hauptfleisch, Morgan A1 - Jago, Mark A1 - Smith, Taylor A1 - Kramer-Schadt, Stephanie A1 - Stiegler, Jonas A1 - Blaum, Niels T1 - Don't stop me now: Managed fence gaps could allow migratory ungulates to track dynamic resources and reduce fence related energy loss T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - In semi-arid environments characterized by erratic rainfall and scattered primary production, migratory movements are a key survival strategy of large herbivores to track resources over vast areas. Veterinary Cordon Fences (VCFs), intended to reduce wildlife-livestock disease transmission, fragment large parts of southern Africa and have limited the movements of large wild mammals for over 60 years. Consequently, wildlife-fence interactions are frequent and often result in perforations of the fence, mainly caused by elephants. Yet, we lack knowledge about at which times fences act as barriers, how fences directly alter the energy expenditure of native herbivores, and what the consequences of impermeability are. We studied 2-year ungulate movements in three common antelopes (springbok, kudu, eland) across a perforated part of Namibia's VCF separating a wildlife reserve and Etosha National Park using GPS telemetry, accelerometer measurements, and satellite imagery. We identified 2905 fence interaction events which we used to evaluate critical times of encounters and direct fence effects on energy expenditure. Using vegetation type-specific greenness dynamics, we quantified what animals gained in terms of high quality food resources from crossing the VCF. Our results show that the perforation of the VCF sustains herbivore-vegetation interactions in the savanna with its scattered resources. Fence permeability led to peaks in crossing numbers during the first flush of woody plants before the rain started. Kudu and eland often showed increased energy expenditure when crossing the fence. Energy expenditure was lowered during the frequent interactions of ungulates standing at the fence. We found no alteration of energy expenditure when springbok immediately found and crossed fence breaches. Our results indicate that constantly open gaps did not affect energy expenditure, while gaps with obstacles increased motion. Closing gaps may have confused ungulates and modified their intended movements. While browsing, sedentary kudu's use of space was less affected by the VCF; migratory, mixed-feeding springbok, and eland benefited from gaps by gaining forage quality and quantity after crossing. This highlights the importance of access to vast areas to allow ungulates to track vital vegetation patches. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1278 KW - veterinary cordon fence KW - ungulate KW - fence ecology KW - resource-tracking KW - energy expenditure KW - accelerometer KW - GPS KW - wildlife and habitat management Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-570087 SN - 1866-8372 IS - 1278 ER - TY - JOUR A1 - Hering, Robert A1 - Hauptfleisch, Morgan A1 - Jago, Mark A1 - Smith, Taylor A1 - Kramer-Schadt, Stephanie A1 - Stiegler, Jonas A1 - Blaum, Niels T1 - Don't stop me now: Managed fence gaps could allow migratory ungulates to track dynamic resources and reduce fence related energy loss JF - Frontiers in Ecology and Evolution N2 - In semi-arid environments characterized by erratic rainfall and scattered primary production, migratory movements are a key survival strategy of large herbivores to track resources over vast areas. Veterinary Cordon Fences (VCFs), intended to reduce wildlife-livestock disease transmission, fragment large parts of southern Africa and have limited the movements of large wild mammals for over 60 years. Consequently, wildlife-fence interactions are frequent and often result in perforations of the fence, mainly caused by elephants. Yet, we lack knowledge about at which times fences act as barriers, how fences directly alter the energy expenditure of native herbivores, and what the consequences of impermeability are. We studied 2-year ungulate movements in three common antelopes (springbok, kudu, eland) across a perforated part of Namibia's VCF separating a wildlife reserve and Etosha National Park using GPS telemetry, accelerometer measurements, and satellite imagery. We identified 2905 fence interaction events which we used to evaluate critical times of encounters and direct fence effects on energy expenditure. Using vegetation type-specific greenness dynamics, we quantified what animals gained in terms of high quality food resources from crossing the VCF. Our results show that the perforation of the VCF sustains herbivore-vegetation interactions in the savanna with its scattered resources. Fence permeability led to peaks in crossing numbers during the first flush of woody plants before the rain started. Kudu and eland often showed increased energy expenditure when crossing the fence. Energy expenditure was lowered during the frequent interactions of ungulates standing at the fence. We found no alteration of energy expenditure when springbok immediately found and crossed fence breaches. Our results indicate that constantly open gaps did not affect energy expenditure, while gaps with obstacles increased motion. Closing gaps may have confused ungulates and modified their intended movements. While browsing, sedentary kudu's use of space was less affected by the VCF; migratory, mixed-feeding springbok, and eland benefited from gaps by gaining forage quality and quantity after crossing. This highlights the importance of access to vast areas to allow ungulates to track vital vegetation patches. KW - veterinary cordon fence KW - ungulate KW - fence ecology KW - resource-tracking KW - energy expenditure KW - accelerometer KW - GPS KW - wildlife and habitat management Y1 - 2022 U6 - https://doi.org/10.3389/fevo.2022.907079 SN - 2296-701X SP - 1 EP - 18 PB - Frontiers CY - Lausanne, Schweiz ER - TY - JOUR A1 - Smith, Taylor A1 - Traxl, Dominik A1 - Boers, Niklas T1 - Empirical evidence for recent global shifts in vegetation resilience JF - Nature climate change N2 - The authors demonstrate that a vegetation system's ability to recover from disturbances-its resilience-can be estimated from its natural variability. Global patterns of resilience loss and gains since the early 1990s reveal shifts towards widespread resilience loss since the early 2000s. The character and health of ecosystems worldwide is tightly coupled to changes in Earth's climate. Theory suggests that ecosystem resilience-the ability of ecosystems to resist and recover from external shocks such as droughts and fires-can be inferred from their natural variability. Here, we quantify vegetation resilience globally with complementary metrics based on two independent long-term satellite records. We first empirically confirm that the recovery rates from large perturbations can be closely approximated from internal vegetation variability across vegetation types and climate zones. On the basis of this empirical relationship, we quantify vegetation resilience continuously and globally from 1992 to 2017. Long-term vegetation resilience trends are spatially heterogeneous, with overall increasing resilience in the tropics and decreasing resilience at higher latitudes. Shorter-term trends, however, reveal a marked shift towards a global decline in vegetation resilience since the early 2000s, particularly in the equatorial rainforest belt. Y1 - 2022 U6 - https://doi.org/10.1038/s41558-022-01352-2 SN - 1758-678X SN - 1758-6798 VL - 12 IS - 5 SP - 477 EP - 484 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Topography and climate in the upper Indus Basin BT - Mapping elevation-snow cover relationships JF - The science of the total environment : an international journal for scientific research into the environment and its relationship with man N2 - The Upper Indus Basin (UIB), which covers a wide range of climatic and topographic settings, provides an ideal venue to explore the relationship between climate and topography. While the distribution of snow and glaciers is spatially and temporally heterogeneous, there exist regions with similar elevation-snow relationships. In this work, we construct elevation-binned snow-cover statistics to analyze 3415 watersheds and 7357 glaciers in the UIB region. We group both glaciers and watersheds using a hierarchical clustering approach and find that (1) watershed clusters mirror large-scale moisture transport patterns and (2) are highly dependent on median watershed elevation. (3) Glacier clusters are spatially heterogeneous and are less strongly controlled by elevation, but rather by local topographic parameters that modify solar insolation. Our clustering approach allows us to clearly define self-similar snow-topographic regions. Eastern watersheds in the UIB show a steep snow cover-elevation relationship whereas watersheds in the central and western UIB have moderately sloped relationships, but cluster in distinct groups. We highlight this snow-cover-topographic transition zone and argue that these watersheds have different hydrologic responses than other regions. Our hierarchical clustering approach provides a potential new framework to use in defining climatic zones in the cyrosphere based on empirical data. KW - Snow-cover KW - Hierarchical clustering KW - Glaciers KW - Upper Indus Basin Y1 - 2021 U6 - https://doi.org/10.1016/j.scitotenv.2021.147363 SN - 0048-9697 SN - 1879-1026 VL - 786 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo T1 - Climatic and biotic controls on topographic asymmetry at the global scale JF - Journal of geophysical research : JGR, Earth surface N2 - Insolation differences play a primary role in controlling microclimate and vegetation cover, which together influence the development of topography. Topographic asymmetry (TA), or slope differences between terrain aspects, has been well documented in small-scale, field-based, and modeling studies. Here we combine a suite of environmental (e.g., vegetation, temperature, solar insolation) and topographic (e.g., elevation, drainage network) data to explore the driving mechanisms and markers of TA on a global scale. Using a novel empirical TA analysis method, we find that (1) steeper terrain has higher TA magnitudes, (2) globally, pole-facing terrain is on average steeper than equator-facing terrain, especially in mid-latitude, tectonically quiescent, and vegetated landscapes, and (3) high-elevation and low-temperature regions tend to have terrain steepened toward the equator. We further show that there are distinct differences in climate and vegetation cover across terrain aspects, and that TA is reflected in the size and form of fluvial drainage networks. Our work supports the argument that insolation asymmetries engender differences in local microclimates and vegetation on opposing terrain aspects, which broadly encourage the development of asymmetric topography across a range of lithologic, tectonic, geomorphic, and climatic settings. KW - erosion KW - freeze-thaw cycling KW - solar radiation KW - topographic asymmetry KW - topography KW - vegetation cover Y1 - 2021 U6 - https://doi.org/10.1029/2020JF005692 SN - 2169-9003 SN - 2169-9011 VL - 126 IS - 1 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Smith, Taylor A1 - Bookhagen, Bodo T1 - Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1020 KW - snow KW - glacier KW - climate change KW - passive microwave KW - special sensor microwave imager KW - special sensor microwave imager/sounder Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-484176 SN - 1866-8372 IS - 1020 ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo T1 - Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data JF - Frontiers in Earth Science N2 - High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances. KW - snow KW - glacier KW - climate change KW - passive microwave KW - special sensor microwave imager KW - special sensor microwave imager/sounder Y1 - 2020 U6 - https://doi.org/10.3389/feart.2020.559175 SN - 2296-6463 VL - 8 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Middeldorp, Christel M. A1 - Mahajan, Anubha A1 - Horikoshi, Momoko A1 - Robertson, Neil R. A1 - Beaumont, Robin N. A1 - Bradfield, Jonathan P. A1 - Bustamante, Mariona A1 - Cousminer, Diana L. A1 - Day, Felix R. A1 - De Silva, N. Maneka A1 - Guxens, Monica A1 - Mook-Kanamori, Dennis O. A1 - St Pourcain, Beate A1 - Warrington, Nicole M. A1 - Adair, Linda S. A1 - Ahlqvist, Emma A1 - Ahluwalia, Tarunveer Singh A1 - Almgren, Peter A1 - Ang, Wei A1 - Atalay, Mustafa A1 - Auvinen, Juha A1 - Bartels, Meike A1 - Beckmann, Jacques S. A1 - Bilbao, Jose Ramon A1 - Bond, Tom A1 - Borja, Judith B. A1 - Cavadino, Alana A1 - Charoen, Pimphen A1 - Chen, Zhanghua A1 - Coin, Lachlan A1 - Cooper, Cyrus A1 - Curtin, John A. A1 - Custovic, Adnan A1 - Das, Shikta A1 - Davies, Gareth E. A1 - Dedoussis, George V. A1 - Duijts, Liesbeth A1 - Eastwood, Peter R. A1 - Eliasen, Anders U. A1 - Elliott, Paul A1 - Eriksson, Johan G. A1 - Estivill, Xavier A1 - Fadista, Joao A1 - Fedko, Iryna O. A1 - Frayling, Timothy M. A1 - Gaillard, Romy A1 - Gauderman, W. James A1 - Geller, Frank A1 - Gilliland, Frank A1 - Gilsanz, Vincente A1 - Granell, Raquel A1 - Grarup, Niels A1 - Groop, Leif A1 - Hadley, Dexter A1 - Hakonarson, Hakon A1 - Hansen, Torben A1 - Hartman, Catharina A. A1 - Hattersley, Andrew T. A1 - Hayes, M. Geoffrey A1 - Hebebrand, Johannes A1 - Heinrich, Joachim A1 - Helgeland, Oyvind A1 - Henders, Anjali K. A1 - Henderson, John A1 - Henriksen, Tine B. A1 - Hirschhorn, Joel N. A1 - Hivert, Marie-France A1 - Hocher, Berthold A1 - Holloway, John W. A1 - Holt, Patrick A1 - Hottenga, Jouke-Jan A1 - Hypponen, Elina A1 - Iniguez, Carmen A1 - Johansson, Stefan A1 - Jugessur, Astanand A1 - Kahonen, Mika A1 - Kalkwarf, Heidi J. A1 - Kaprio, Jaakko A1 - Karhunen, Ville A1 - Kemp, John P. A1 - Kerkhof, Marjan A1 - Koppelman, Gerard H. A1 - Korner, Antje A1 - Kotecha, Sailesh A1 - Kreiner-Moller, Eskil A1 - Kulohoma, Benard A1 - Kumar, Ashish A1 - Kutalik, Zoltan A1 - Lahti, Jari A1 - Lappe, Joan M. A1 - Larsson, Henrik A1 - Lehtimaki, Terho A1 - Lewin, Alexandra M. A1 - Li, Jin A1 - Lichtenstein, Paul A1 - Lindgren, Cecilia M. A1 - Lindi, Virpi A1 - Linneberg, Allan A1 - Liu, Xueping A1 - Liu, Jun A1 - Lowe, William L. A1 - Lundstrom, Sebastian A1 - Lyytikainen, Leo-Pekka A1 - Ma, Ronald C. W. A1 - Mace, Aurelien A1 - Magi, Reedik A1 - Magnus, Per A1 - Mamun, Abdullah A. A1 - Mannikko, Minna A1 - Martin, Nicholas G. A1 - Mbarek, Hamdi A1 - McCarthy, Nina S. A1 - Medland, Sarah E. A1 - Melbye, Mads A1 - Melen, Erik A1 - Mohlke, Karen L. A1 - Monnereau, Claire A1 - Morgen, Camilla S. A1 - Morris, Andrew P. A1 - Murray, Jeffrey C. A1 - Myhre, Ronny A1 - Najman, Jackob M. A1 - Nivard, Michel G. A1 - Nohr, Ellen A. A1 - Nolte, Ilja M. A1 - Ntalla, Ioanna A1 - Oberfield, Sharon E. A1 - Oken, Emily A1 - Oldehinkel, Albertine J. A1 - Pahkala, Katja A1 - Palviainen, Teemu A1 - Panoutsopoulou, Kalliope A1 - Pedersen, Oluf A1 - Pennell, Craig E. A1 - Pershagen, Goran A1 - Pitkanen, Niina A1 - Plomin, Robert A1 - Power, Christine A1 - Prasad, Rashmi B. A1 - Prokopenko, Inga A1 - Pulkkinen, Lea A1 - Raikkonen, Katri A1 - Raitakari, Olli T. A1 - Reynolds, Rebecca M. A1 - Richmond, Rebecca C. A1 - Rivadeneira, Fernando A1 - Rodriguez, Alina A1 - Rose, Richard J. A1 - Salem, Rany A1 - Santa-Marina, Loreto A1 - Saw, Seang-Mei A1 - Schnurr, Theresia M. A1 - Scott, James G. A1 - Selzam, Saskia A1 - Shepherd, John A. A1 - Simpson, Angela A1 - Skotte, Line A1 - Sleiman, Patrick M. A. A1 - Snieder, Harold A1 - Sorensen, Thorkild I. A. A1 - Standl, Marie A1 - Steegers, Eric A. P. A1 - Strachan, David P. A1 - Straker, Leon A1 - Strandberg, Timo A1 - Taylor, Michelle A1 - Teo, Yik-Ying A1 - Thiering, Elisabeth A1 - Torrent, Maties A1 - Tyrrell, Jessica A1 - Uitterlinden, Andre G. A1 - van Beijsterveldt, Toos A1 - van der Most, Peter J. A1 - van Duijn, Cornelia M. A1 - Viikari, Jorma A1 - Vilor-Tejedor, Natalia A1 - Vogelezang, Suzanne A1 - Vonk, Judith M. A1 - Vrijkotte, Tanja G. M. A1 - Vuoksimaa, Eero A1 - Wang, Carol A. A1 - Watkins, William J. A1 - Wichmann, H-Erich A1 - Willemsen, Gonneke A1 - Williams, Gail M. A1 - Wilson, James F. A1 - Wray, Naomi R. A1 - Xu, Shujing A1 - Xu, Cheng-Jian A1 - Yaghootkar, Hanieh A1 - Yi, Lu A1 - Zafarmand, Mohammad Hadi A1 - Zeggini, Eleftheria A1 - Zemel, Babette S. A1 - Hinney, Anke A1 - Lakka, Timo A. A1 - Whitehouse, Andrew J. O. A1 - Sunyer, Jordi A1 - Widen, Elisabeth E. A1 - Feenstra, Bjarke A1 - Sebert, Sylvain A1 - Jacobsson, Bo A1 - Njolstad, Pal R. A1 - Stoltenberg, Camilla A1 - Smith, George Davey A1 - Lawlor, Debbie A. A1 - Paternoster, Lavinia A1 - Timpson, Nicholas J. A1 - Ong, Ken K. A1 - Bisgaard, Hans A1 - Bonnelykke, Klaus A1 - Jaddoe, Vincent W. V. A1 - Tiemeier, Henning A1 - Jarvelin, Marjo-Riitta A1 - Evans, David M. A1 - Perry, John R. B. A1 - Grant, Struan F. A. A1 - Boomsma, Dorret I. A1 - Freathy, Rachel M. A1 - McCarthy, Mark I. A1 - Felix, Janine F. T1 - The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia BT - design, results and future prospects JF - European journal of epidemiology N2 - The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites. KW - Genetics KW - Consortium KW - Childhood traits and disorders KW - Longitudinal Y1 - 2019 U6 - https://doi.org/10.1007/s10654-019-00502-9 SN - 0393-2990 SN - 1573-7284 VL - 34 IS - 3 SP - 279 EP - 300 PB - Springer CY - Dordrecht ER - TY - GEN A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 725 KW - Digital Elevation Model KW - River Incision Model KW - Accuracy Asseessment KW - Landscape Response KW - Error KW - Slope KW - Uncertainties KW - Extraction KW - Expression KW - Patterns Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-430165 SN - 1866-8372 IS - 725 SP - 475 EP - 489 ER - TY - JOUR A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset JF - Earth Surface Dynamics N2 - Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions. KW - Digital Elevation Model KW - River Incision Model KW - Accuracy Asseessment KW - Landscape Response KW - Error KW - Slope KW - Uncertainties KW - Extraction KW - Expression KW - Patterns Y1 - 2019 U6 - https://doi.org/10.5194/esurf-7-475-2019 SN - 2196-6311 SN - 2196-632X VL - 7 SP - 475 EP - 489 PB - Copernicus Publ. CY - Göttingen ER - TY - JOUR A1 - Wuttke, Matthias A1 - Li, Yong A1 - Li, Man A1 - Sieber, Karsten B. A1 - Feitosa, Mary F. A1 - Gorski, Mathias A1 - Tin, Adrienne A1 - Wang, Lihua A1 - Chu, Audrey Y. A1 - Hoppmann, Anselm A1 - Kirsten, Holger A1 - Giri, Ayush A1 - Chai, Jin-Fang A1 - Sveinbjornsson, Gardar A1 - Tayo, Bamidele O. A1 - Nutile, Teresa A1 - Fuchsberger, Christian A1 - Marten, Jonathan A1 - Cocca, Massimiliano A1 - Ghasemi, Sahar A1 - Xu, Yizhe A1 - Horn, Katrin A1 - Noce, Damia A1 - Van der Most, Peter J. A1 - Sedaghat, Sanaz A1 - Yu, Zhi A1 - Akiyama, Masato A1 - Afaq, Saima A1 - Ahluwalia, Tarunveer Singh A1 - Almgren, Peter A1 - Amin, Najaf A1 - Arnlov, Johan A1 - Bakker, Stephan J. L. A1 - Bansal, Nisha A1 - Baptista, Daniela A1 - Bergmann, Sven A1 - Biggs, Mary L. A1 - Biino, Ginevra A1 - Boehnke, Michael A1 - Boerwinkle, Eric A1 - Boissel, Mathilde A1 - Böttinger, Erwin A1 - Boutin, Thibaud S. A1 - Brenner, Hermann A1 - Brumat, Marco A1 - Burkhardt, Ralph A1 - Butterworth, Adam S. A1 - Campana, Eric A1 - Campbell, Archie A1 - Campbell, Harry A1 - Canouil, Mickael A1 - Carroll, Robert J. A1 - Catamo, Eulalia A1 - Chambers, John C. A1 - Chee, Miao-Ling A1 - Chee, Miao-Li A1 - Chen, Xu A1 - Cheng, Ching-Yu A1 - Cheng, Yurong A1 - Christensen, Kaare A1 - Cifkova, Renata A1 - Ciullo, Marina A1 - Concas, Maria Pina A1 - Cook, James P. A1 - Coresh, Josef A1 - Corre, Tanguy A1 - Sala, Cinzia Felicita A1 - Cusi, Daniele A1 - Danesh, John A1 - Daw, E. Warwick A1 - De Borst, Martin H. A1 - De Grandi, Alessandro A1 - De Mutsert, Renee A1 - De Vries, Aiko P. J. A1 - Degenhardt, Frauke A1 - Delgado, Graciela A1 - Demirkan, Ayse A1 - Di Angelantonio, Emanuele A1 - Dittrich, Katalin A1 - Divers, Jasmin A1 - Dorajoo, Rajkumar A1 - Eckardt, Kai-Uwe A1 - Ehret, Georg A1 - Elliott, Paul A1 - Endlich, Karlhans A1 - Evans, Michele K. A1 - Felix, Janine F. A1 - Foo, Valencia Hui Xian A1 - Franco, Oscar H. A1 - Franke, Andre A1 - Freedman, Barry I. A1 - Freitag-Wolf, Sandra A1 - Friedlander, Yechiel A1 - Froguel, Philippe A1 - Gansevoort, Ron T. A1 - Gao, He A1 - Gasparini, Paolo A1 - Gaziano, J. Michael A1 - Giedraitis, Vilmantas A1 - Gieger, Christian A1 - Girotto, Giorgia A1 - Giulianini, Franco A1 - Gogele, Martin A1 - Gordon, Scott D. A1 - Gudbjartsson, Daniel F. A1 - Gudnason, Vilmundur A1 - Haller, Toomas A1 - Hamet, Pavel A1 - Harris, Tamara B. A1 - Hartman, Catharina A. A1 - Hayward, Caroline A1 - Hellwege, Jacklyn N. A1 - Heng, Chew-Kiat A1 - Hicks, Andrew A. A1 - Hofer, Edith A1 - Huang, Wei A1 - Hutri-Kahonen, Nina A1 - Hwang, Shih-Jen A1 - Ikram, M. Arfan A1 - Indridason, Olafur S. A1 - Ingelsson, Erik A1 - Ising, Marcus A1 - Jaddoe, Vincent W. V. A1 - Jakobsdottir, Johanna A1 - Jonas, Jost B. A1 - Joshi, Peter K. A1 - Josyula, Navya Shilpa A1 - Jung, Bettina A1 - Kahonen, Mika A1 - Kamatani, Yoichiro A1 - Kammerer, Candace M. A1 - Kanai, Masahiro A1 - Kastarinen, Mika A1 - Kerr, Shona M. A1 - Khor, Chiea-Chuen A1 - Kiess, Wieland A1 - Kleber, Marcus E. A1 - Koenig, Wolfgang A1 - Kooner, Jaspal S. A1 - Korner, Antje A1 - Kovacs, Peter A1 - Kraja, Aldi T. A1 - Krajcoviechova, Alena A1 - Kramer, Holly A1 - Kramer, Bernhard K. A1 - Kronenberg, Florian A1 - Kubo, Michiaki A1 - Kuhnel, Brigitte A1 - Kuokkanen, Mikko A1 - Kuusisto, Johanna A1 - La Bianca, Martina A1 - Laakso, Markku A1 - Lange, Leslie A. A1 - Langefeld, Carl D. A1 - Lee, Jeannette Jen-Mai A1 - Lehne, Benjamin A1 - Lehtimaki, Terho A1 - Lieb, Wolfgang A1 - Lim, Su-Chi A1 - Lind, Lars A1 - Lindgren, Cecilia M. A1 - Liu, Jun A1 - Liu, Jianjun A1 - Loeffler, Markus A1 - Loos, Ruth J. F. A1 - Lucae, Susanne A1 - Lukas, Mary Ann A1 - Lyytikainen, Leo-Pekka A1 - Magi, Reedik A1 - Magnusson, Patrik K. E. A1 - Mahajan, Anubha A1 - Martin, Nicholas G. A1 - Martins, Jade A1 - Marz, Winfried A1 - Mascalzoni, Deborah A1 - Matsuda, Koichi A1 - Meisinger, Christa A1 - Meitinger, Thomas A1 - Melander, Olle A1 - Metspalu, Andres A1 - Mikaelsdottir, Evgenia K. A1 - Milaneschi, Yuri A1 - Miliku, Kozeta A1 - Mishra, Pashupati P. A1 - Program, V. A. Million Veteran A1 - Mohlke, Karen L. A1 - Mononen, Nina A1 - Montgomery, Grant W. A1 - Mook-Kanamori, Dennis O. A1 - Mychaleckyj, Josyf C. A1 - Nadkarni, Girish N. A1 - Nalls, Mike A. A1 - Nauck, Matthias A1 - Nikus, Kjell A1 - Ning, Boting A1 - Nolte, Ilja M. A1 - Noordam, Raymond A1 - Olafsson, Isleifur A1 - Oldehinkel, Albertine J. A1 - Orho-Melander, Marju A1 - Ouwehand, Willem H. A1 - Padmanabhan, Sandosh A1 - Palmer, Nicholette D. A1 - Palsson, Runolfur A1 - Penninx, Brenda W. J. H. A1 - Perls, Thomas A1 - Perola, Markus A1 - Pirastu, Mario A1 - Pirastu, Nicola A1 - Pistis, Giorgio A1 - Podgornaia, Anna I. A1 - Polasek, Ozren A1 - Ponte, Belen A1 - Porteous, David J. A1 - Poulain, Tanja A1 - Pramstaller, Peter P. A1 - Preuss, Michael H. A1 - Prins, Bram P. A1 - Province, Michael A. A1 - Rabelink, Ton J. A1 - Raffield, Laura M. A1 - Raitakari, Olli T. A1 - Reilly, Dermot F. A1 - Rettig, Rainer A1 - Rheinberger, Myriam A1 - Rice, Kenneth M. A1 - Ridker, Paul M. A1 - Rivadeneira, Fernando A1 - Rizzi, Federica A1 - Roberts, David J. A1 - Robino, Antonietta A1 - Rossing, Peter A1 - Rudan, Igor A1 - Rueedi, Rico A1 - Ruggiero, Daniela A1 - Ryan, Kathleen A. A1 - Saba, Yasaman A1 - Sabanayagam, Charumathi A1 - Salomaa, Veikko A1 - Salvi, Erika A1 - Saum, Kai-Uwe A1 - Schmidt, Helena A1 - Schmidt, Reinhold A1 - Ben Schottker, A1 - Schulz, Christina-Alexandra A1 - Schupf, Nicole A1 - Shaffer, Christian M. A1 - Shi, Yuan A1 - Smith, Albert V. A1 - Smith, Blair H. A1 - Soranzo, Nicole A1 - Spracklen, Cassandra N. A1 - Strauch, Konstantin A1 - Stringham, Heather M. A1 - Stumvoll, Michael A1 - Svensson, Per O. A1 - Szymczak, Silke A1 - Tai, E-Shyong A1 - Tajuddin, Salman M. A1 - Tan, Nicholas Y. Q. A1 - Taylor, Kent D. A1 - Teren, Andrej A1 - Tham, Yih-Chung A1 - Thiery, Joachim A1 - Thio, Chris H. L. A1 - Thomsen, Hauke A1 - Thorleifsson, Gudmar A1 - Toniolo, Daniela A1 - Tonjes, Anke A1 - Tremblay, Johanne A1 - Tzoulaki, Ioanna A1 - Uitterlinden, Andre G. A1 - Vaccargiu, Simona A1 - Van Dam, Rob M. A1 - Van der Harst, Pim A1 - Van Duijn, Cornelia M. A1 - Edward, Digna R. Velez A1 - Verweij, Niek A1 - Vogelezang, Suzanne A1 - Volker, Uwe A1 - Vollenweider, Peter A1 - Waeber, Gerard A1 - Waldenberger, Melanie A1 - Wallentin, Lars A1 - Wang, Ya Xing A1 - Wang, Chaolong A1 - Waterworth, Dawn M. A1 - Bin Wei, Wen A1 - White, Harvey A1 - Whitfield, John B. A1 - Wild, Sarah H. A1 - Wilson, James F. A1 - Wojczynski, Mary K. A1 - Wong, Charlene A1 - Wong, Tien-Yin A1 - Xu, Liang A1 - Yang, Qiong A1 - Yasuda, Masayuki A1 - Yerges-Armstrong, Laura M. A1 - Zhang, Weihua A1 - Zonderman, Alan B. A1 - Rotter, Jerome I. A1 - Bochud, Murielle A1 - Psaty, Bruce M. A1 - Vitart, Veronique A1 - Wilson, James G. A1 - Dehghan, Abbas A1 - Parsa, Afshin A1 - Chasman, Daniel I. A1 - Ho, Kevin A1 - Morris, Andrew P. A1 - Devuyst, Olivier A1 - Akilesh, Shreeram A1 - Pendergrass, Sarah A. A1 - Sim, Xueling A1 - Boger, Carsten A. A1 - Okada, Yukinori A1 - Edwards, Todd L. A1 - Snieder, Harold A1 - Stefansson, Kari A1 - Hung, Adriana M. A1 - Heid, Iris M. A1 - Scholz, Markus A1 - Teumer, Alexander A1 - Kottgen, Anna A1 - Pattaro, Cristian T1 - A catalog of genetic loci associated with kidney function from analyses of a million individuals JF - Nature genetics N2 - Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research. Y1 - 2019 U6 - https://doi.org/10.1038/s41588-019-0407-x SN - 1061-4036 SN - 1546-1718 VL - 51 IS - 6 SP - 957 EP - + PB - Nature Publ. Group CY - New York ER - TY - GEN A1 - Smith, Taylor A1 - Bookhagen, Bodo T1 - Using passive microwave data to understand spatio-temporal trends and dynamics in snow-water storage in High Mountain Asia T2 - active and passive microwave remote sensing for environmental monitoring II N2 - High Mountain Asia provides water for more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow - the vast majority of which is not monitored by sparse weather networks. We leverage passive microwave data from the SSMI series of satellites (SSMI, SSMI/S, 1987-2016), reprocessed to 3.125 km resolution, to examine trends in the volume and spatial distribution of snow-water equivalent (SWE) in the Indus Basin. We find that the majority of the Indus has seen an increase in snow-water storage. There exists a strong elevation-trend relationship, where high-elevation zones have more positive SWE trends. Negative trends are confined to the Himalayan foreland and deeply-incised valleys which run into the Upper Indus. This implies a temperature-dependent cutoff below which precipitation increases are not translated into increased SWE. Earlier snowmelt or a higher percentage of liquid precipitation could both explain this cutoff.(1) Earlier work 2 found a negative snow-water storage trend for the entire Indus catchment over the time period 1987-2009 (-4 x 10(-3) mm/yr). In this study based on an additional seven years of data, the average trend reverses to 1.4 x 10(-3). This implies that the decade since the mid-2000s was likely wetter, and positively impacted long-term SWE trends. This conclusion is supported by an analysis of snowmelt onset and end dates which found that while long-term trends are negative, more recent (since 2005) trends are positive (moving later in the year).(3) KW - Passive Microwave KW - Snow KW - Climate Change KW - High Mountain Asia Y1 - 2018 SN - 978-1-5106-2160-2 U6 - https://doi.org/10.1117/12.2323827 SN - 0277-786X SN - 1996-756X VL - 10788 PB - SPIE-INT Soc Optical Engineering CY - Bellingham ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo T1 - Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009) JF - Science Advances N2 - Snow meltwaters account for most of the yearly water budgets of many catchments in High Mountain Asia (HMA). We examine trends in snow water equivalent (SWE) using passive microwave data (1987 to 2009). We find an overall decrease in SWE in HMA, despite regions of increased SWE in the Pamir, Kunlun Shan, Eastern Himalaya, and Eastern Tien Shan. Although the average decline in annual SWE across HMA (contributing area, 2641 x 10(3) km(2)) is low (average, -0.3%), annual SWE losses conceal distinct seasonal and spatial heterogeneities across the study region. For example, the Tien Shan has seen both strong increases in winter SWE and sharp declines in spring and summer SWE. In the majority of catchments, the most negative SWE trends are found in mid-elevation zones, which often correspond to the regions of highest snow-water storage and are somewhat distinct from glaciated areas. Negative changes in SWE storage in these mid-elevation zones have strong implications for downstream water availability. Y1 - 2018 U6 - https://doi.org/10.1126/sciadv.1701550 SN - 2375-2548 VL - 4 IS - 1 PB - American Assoc. for the Advancement of Science CY - Washington ER - TY - THES A1 - Smith, Taylor T1 - Decadal changes in the snow regime of High Mountain Asia, 1987-2016 T1 - Veränderungen in der Schnee-Regen-Bilanz auf dekadischen Zeitskalen im zentralasiatischen Hochgebirge (1987-2016) N2 - More than a billion people rely on water from rivers sourced in High Mountain Asia (HMA), a significant portion of which is derived from snow and glacier melt. Rural communities are heavily dependent on the consistency of runoff, and are highly vulnerable to shifts in their local environment brought on by climate change. Despite this dependence, the impacts of climate change in HMA remain poorly constrained due to poor process understanding, complex terrain, and insufficiently dense in-situ measurements. HMA's glaciers contain more frozen water than any region outside of the poles. Their extensive retreat is a highly visible and much studied marker of regional and global climate change. However, in many catchments, snow and snowmelt represent a much larger fraction of the yearly water budget than glacial meltwaters. Despite their importance, climate-related changes in HMA's snow resources have not been well studied. Changes in the volume and distribution of snowpack have complex and extensive impacts on both local and global climates. Eurasian snow cover has been shown to impact the strength and direction of the Indian Summer Monsoon -- which is responsible for much of the precipitation over the Indian Subcontinent -- by modulating earth-surface heating. Shifts in the timing of snowmelt have been shown to limit the productivity of major rangelands, reduce streamflow, modify sediment transport, and impact the spread of vector-borne diseases. However, a large-scale regional study of climate impacts on snow resources had yet to be undertaken. Passive Microwave (PM) remote sensing is a well-established empirical method of studying snow resources over large areas. Since 1987, there have been consistent daily global PM measurements which can be used to derive an estimate of snow depth, and hence snow-water equivalent (SWE) -- the amount of water stored in snowpack. The SWE estimation algorithms were originally developed for flat and even terrain -- such as the Russian and Canadian Arctic -- and have rarely been used in complex terrain such as HMA. This dissertation first examines factors present in HMA that could impact the reliability of SWE estimates. Forest cover, absolute snow depth, long-term average wind speeds, and hillslope angle were found to be the strongest controls on SWE measurement reliability. While forest density and snow depth are factors accounted for in modern SWE retrieval algorithms, wind speed and hillslope angle are not. Despite uncertainty in absolute SWE measurements and differences in the magnitude of SWE retrievals between sensors, single-instrument SWE time series were found to be internally consistent and suitable for trend analysis. Building on this finding, this dissertation tracks changes in SWE across HMA using a statistical decomposition technique. An aggregate decrease in SWE was found (10.6 mm/yr), despite large spatial and seasonal heterogeneities. Winter SWE increased in almost half of HMA, despite general negative trends throughout the rest of the year. The elevation distribution of these negative trends indicates that while changes in SWE have likely impacted glaciers in the region, climate change impacts on these two pieces of the cryosphere are somewhat distinct. Following the discussion of relative changes in SWE, this dissertation explores changes in the timing of the snowmelt season in HMA using a newly developed algorithm. The algorithm is shown to accurately track the onset and end of the snowmelt season (70% within 5 days of a control dataset, 89% within 10). Using a 29-year time series, changes in the onset, end, and duration of snowmelt are examined. While nearly the entirety of HMA has experienced an earlier end to the snowmelt season, large regions of HMA have seen a later start to the snowmelt season. Snowmelt periods have also decreased in almost all of HMA, indicating that the snowmelt season is generally shortening and ending earlier across HMA. By examining shifts in both the spatio-temporal distribution of SWE and the timing of the snowmelt season across HMA, we provide a detailed accounting of changes in HMA's snow resources. The overall trend in HMA is towards less SWE storage and a shorter snowmelt season. However, long-term and regional trends conceal distinct seasonal, temporal, and spatial heterogeneity, indicating that changes in snow resources are strongly controlled by local climate and topography, and that inter-annual variability plays a significant role in HMA's snow regime. N2 - Mehr als eine Milliarde Menschen ist von Wasser aus Flüssen, welche im Hochgebirge Asiens (HA) entspringen, abhängig. Diese werden, im Wesentlichen durch Schmelzwasser von Schnee und Gletschern gespeist. Gemeinden auf dem Land sind im hohem Maße auf die Beständigkeit des Wasserabflusses angewiesen, und folglich stark anfällig für durch Klimawandel hervorgerufene Veränderungen der Umwelt auf regionaler Ebene. Der extensive Gletscherrückzug ist ein deutlich sichtbarer und weitgehend erforschter Marker für den Klimawandel auf regionaler und globaler Ebene. In vielen Einzugsgebieten machen jedoch Schnee und Schneeschmelzen einen sehr viel größeren Anteil des jährlichen Wasserbudgets aus also Gletscherschmelzwasser. Dennoch sind die klimaabhängigen Veränderungen auf Schneeressourcen im HA nicht ausreichend untersucht. Passive Mikrowellenradiometer (PM) basierte Fernerkundung ist eine etablierte empirische Methode zur Untersuchung von Schneeressourcen in weit ausgedehnten Gebieten. Seit 1987 wurden täglich konsistente PM Messungen auf globaler Ebene durchgeführt, die zur Abschätzung der Schneehöhe verwendet werden können, und folglich den Anteil des Wassers in der Schneemasse wiederspiegeln – das Schneewasser Äquivalent (SWE). In dieser Studie die lokalen Veränderungen des SWE über dem gesamten HA untersucht. Trotz großer räumlicher und saisonaler Heterogenität, wurde eine Gesamtverringerung des SWE (10,6 mm/yr) festgestellt. Im Winter jedoch hat das SWE in etwa 50% des HAs trotz der negativen Trends im restlichen Verlauf des Jahres zugenommen. Wie aus der Diskussion über die relativen Veränderungen im SWE hervorgeht, wird in dieser Studie mithilfe eines neuentwickelten Algorithmus die Untersuchung der Veränderungen des Zeitlichen einsetzen der Schneeschmelzperiode im HA. Während im nahezu gesamten Gebiet des HA das Ende Schneeschmelzsaison verfrüht einsetzt, so ist in der Hälfte des Gebietes der Begin dieser nach hinten verschoben. Die Schneeschmelzperioden haben im so gut wie gesamten Gebiet des HA abgenommen, was darauf hindeutet dass sich diese über dem gesamten HA generell verkürzt haben und frühzeitig beendet werden. Durch die Untersuchung der räumlich-zeitlichen Verteilung der Schneevolumens und des Schneeschmelzperioden im gesamten HA konnten wir eine lückenlose Bilanz der Veränderungen der Schneeressourcen im HA erstellen. Der allgemeine Trend zeigt eine geringere Speicherung des SWE und kürzere Schneeschmelzperioden im gesamten HA. Langfristige und regionale Trends überdecken jedoch verschiedene saisonale, temporäre und räumliche Heterogenität, was wiederum zeigt dass Veränderungen der Schneebedeckung stark von lokalem Klima und der Topographie abhängen, und dass jährliche Schwankungen zu einem erheblichen Anteil zum Schneeregime des HA beitragen. KW - climate change KW - snow KW - remote sensing KW - Schnee KW - Klimawandel KW - Fernerkundung Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407120 ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo A1 - Rheinwalt, Aljoscha T1 - identified with an automated snowmelt detection algorithm, 1987-2016 JF - The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union N2 - High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous. Y1 - 2017 U6 - https://doi.org/10.5194/tc-11-2329-2017 SN - 1994-0416 SN - 1994-0424 VL - 11 SP - 2329 EP - 2343 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo A1 - Rheinwalt, Aljoscha T1 - Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016 JF - The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union N2 - High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous. Y1 - 2017 U6 - https://doi.org/10.5194/tc-11-2329-2017 SN - 1994-0416 SN - 1994-0424 VL - 11 SP - 2329 EP - 2343 ER - TY - GEN A1 - Smith, Taylor A1 - Bookhagen, Bodo A1 - Rheinwalt, Aljoscha T1 - Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016 N2 - High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 397 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-403911 ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo T1 - Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia JF - Remote sensing of environment : an interdisciplinary journal N2 - Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite - sensors the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987-2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E, 2002-2011), AMSR2 (2012-2015), and the Global Precipitation Measurement (GPM, 2014-2015) - in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multiyear snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (<5%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8%), followed by maximal multi-year snow depth (7.82%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64%), 25th percentile DJF 10-m wind speed (5.44%), and hillslope angle (5.24%). Elevation, relief, and terrain aspect show very low influence on SWE variability (<1%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. (C) 2016 Elsevier Inc. All rights reserved. KW - Snow-Water Equivalent KW - Passive Microwave KW - SSMI/S KW - AMSR-E KW - AMSR2 KW - GPM Y1 - 2016 U6 - https://doi.org/10.1016/j.rse.2016.03.037 SN - 0034-4257 SN - 1879-0704 VL - 181 SP - 174 EP - 185 PB - Elsevier CY - New York ER - TY - GEN A1 - Smith, Taylor A1 - Bookhagen, Bodo A1 - Cannon, Forest T1 - Improving semi-automated glacier mapping with a multi-method approach BT - applications in central Asia T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity. In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10% of glacier areas, as compared to a similar to 750 glacier control data set, and can reliably classify a given Landsat scene in 3-5 min. The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 510 KW - debris-covered glaciers KW - land ice measurements KW - remote-sensing data KW - thematic mapper KW - glims project KW - aster data KW - inventory KW - area KW - deformation KW - parameters Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-408471 SN - 1866-8372 IS - 510 ER - TY - JOUR A1 - Smith, Taylor A1 - Bookhagen, Bodo A1 - Cannon, Forest T1 - Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia JF - The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union N2 - Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity. In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10% of glacier areas, as compared to a similar to 750 glacier control data set, and can reliably classify a given Landsat scene in 3-5 min. The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data. Y1 - 2015 U6 - https://doi.org/10.5194/tc-9-1747-2015 SN - 1994-0416 SN - 1994-0424 VL - 9 IS - 5 SP - 1747 EP - 1759 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Aliu, E. A1 - Archambault, S. A1 - Aune, T. A1 - Behera, B. A1 - Beilicke, M. A1 - Benbow, W. A1 - Berger, K. A1 - Bird, R. A1 - Bouvier, A. A1 - Buckley, J. H. A1 - Bugaev, V. A1 - Byrum, K. A1 - Cerruti, M. A1 - Chen, X. A1 - Ciupik, L. A1 - Connolly, M. P. A1 - Cui, W. A1 - Duke, C. A1 - Dumm, J. A1 - Errando, M. A1 - Falcone, A. A1 - Federici, S. A1 - Feng, Q. A1 - Finley, J. P. A1 - Fortin, P. A1 - Fortson, L. A1 - Furniss, A. A1 - Galante, N. A1 - Gillanders, G. H. A1 - Griffin, S. A1 - Griffiths, S. T. A1 - Grube, J. A1 - Gyuk, G. A1 - Hanna, D. A1 - Holder, J. A1 - Hughes, G. A1 - Humensky, T. B. A1 - Kaaret, P. A1 - Kertzman, M. A1 - Khassen, Y. A1 - Kieda, D. A1 - Krawczynski, H. A1 - Krennrich, F. A1 - Lang, M. J. A1 - Madhavan, A. S. A1 - Maier, G. A1 - Majumdar, P. A1 - McCann, A. A1 - Moriarty, P. A1 - Mukherjee, R. A1 - Nieto, D. A1 - Ong, R. A. A1 - Otte, A. N. A1 - Park, N. A1 - Perkins, J. S. A1 - Pohl, M. A1 - Popkow, A. A1 - Prokoph, H. A1 - Quinn, J. A1 - Ragan, K. A1 - Rajotte, J. A1 - Reyes, L. C. A1 - Reynolds, P. T. A1 - Richards, G. T. A1 - Roache, E. A1 - Rousselle, J. A1 - Sembroski, G. H. A1 - Sheidaei, F. A1 - Skole, C. A1 - Smith, A. W. A1 - Staszak, D. A1 - Stroh, M. A1 - Telezhinsky, Igor O. A1 - Theiling, M. A1 - Tucci, J. V. A1 - Tyler, J. A1 - Varlotta, A. A1 - Vincent, S. A1 - Wakely, S. P. A1 - Weinstein, A. A1 - Welsing, R. A1 - Williams, D. A. A1 - Zajczyk, A. A1 - Zitzer, B. A1 - Abramowski, Attila A1 - Aharonian, Felix A. A1 - Benkhali, Faical Ait A1 - Akhperjanian, A. G. A1 - Angüner, Ekrem Oǧuzhan A1 - Anton, Gisela A1 - Balenderan, Shangkari A1 - Balzer, Arnim A1 - Barnacka, Anna A1 - Becherini, Yvonne A1 - Tjus, J. Becker A1 - Bernlöhr, K. A1 - Birsin, E. A1 - Bissaldi, E. A1 - Biteau, Jonathan A1 - Boettcher, Markus A1 - Boisson, Catherine A1 - Bolmont, J. A1 - Bordas, Pol A1 - Brucker, J. A1 - Brun, Francois A1 - Brun, Pierre A1 - Bulik, Tomasz A1 - Carrigan, Svenja A1 - Casanova, Sabrina A1 - Cerruti, M. A1 - Chadwick, Paula M. A1 - Chalme-Calvet, R. A1 - Chaves, Ryan C. G. A1 - Cheesebrough, A. A1 - Chretien, M. A1 - Colafrancesco, Sergio A1 - Cologna, Gabriele A1 - Conrad, Jan A1 - Couturier, C. A1 - Dalton, M. A1 - Daniel, M. K. A1 - Davids, I. D. A1 - Degrange, B. A1 - Deil, C. A1 - deWilt, P. A1 - Dickinson, H. J. A1 - Djannati-Ataï, A. A1 - Domainko, W. A1 - Dubus, G. A1 - Dutson, K. A1 - Dyks, J. A1 - Dyrda, M. A1 - Edwards, T. A1 - Egberts, Kathrin A1 - Eger, P. A1 - Espigat, P. A1 - Farnier, C. A1 - Fegan, S. A1 - Feinstein, F. A1 - Fernandes, M. V. A1 - Fernandez, D. A1 - Fiasson, A. A1 - Fontaine, G. A1 - Foerster, A. A1 - Fuessling, M. A1 - Gajdus, M. A1 - Gallant, Y. A. A1 - Garrigoux, T. A1 - Giavitto, G. A1 - Giebels, B. A1 - Glicenstein, J. F. A1 - Grondin, M. -H. A1 - Grudzinska, M. A1 - Haeffner, S. A1 - Hahn, J. A1 - Harris, J. A1 - Heinzelmann, G. A1 - Henri, G. A1 - Hermann, G. A1 - Hervet, O. A1 - Hillert, A. A1 - Hinton, James Anthony A1 - Hofmann, W. A1 - Hofverberg, P. A1 - Holler, M. A1 - Horns, D. A1 - Jacholkowska, A. A1 - Jahn, C. A1 - Jamrozy, M. A1 - Janiak, M. A1 - Jankowsky, F. A1 - Jung, I. A1 - Kastendieck, M. A. A1 - Katarzynski, K. A1 - Katz, U. A1 - Kaufmann, S. A1 - Khelifi, B. A1 - Kieffer, M. A1 - Klepser, S. A1 - Klochkov, D. A1 - Kluzniak, W. A1 - Kneiske, T. A1 - Kolitzus, D. A1 - Komin, Nu. A1 - Kosack, K. A1 - Krakau, S. A1 - Krayzel, F. A1 - Krueger, P. P. A1 - Laffon, H. A1 - Lamanna, G. A1 - Lefaucheur, J. A1 - Lemiere, A. A1 - Lemoine-Goumard, M. A1 - Lenain, J. -P. A1 - Lennarz, D. A1 - Lohse, T. A1 - Lopatin, A. A1 - Lu, C. -C. A1 - Marandon, V. A1 - Marcowith, Alexandre A1 - Marx, R. A1 - Maurin, G. A1 - Maxted, N. A1 - Mayer, M. A1 - McComb, T. J. L. A1 - Mehault, J. A1 - Menzler, U. A1 - Meyer, M. A1 - Moderski, R. A1 - Mohamed, M. A1 - Moulin, Emmanuel A1 - Murach, T. A1 - Naumann, C. L. A1 - de Naurois, M. A1 - Niemiec, J. A1 - Nolan, S. J. A1 - Oakes, L. A1 - Ohm, S. A1 - Wilhelmi, E. de Ona A1 - Opitz, B. A1 - Ostrowski, M. A1 - Oya, I. A1 - Panter, M. A1 - Parsons, R. D. A1 - Arribas, M. Paz A1 - Pekeur, N. W. A1 - Pelletier, G. A1 - Perez, J. A1 - Petrucci, P. -O. A1 - Peyaud, B. A1 - Pita, S. A1 - Poon, H. A1 - Puehlhofer, G. A1 - Punch, M. A1 - Quirrenbach, A. A1 - Raab, S. A1 - Raue, M. A1 - Reimer, A. A1 - Reimer, O. A1 - Renaud, M. A1 - de los Reyes, R. A1 - Rieger, F. A1 - Rob, L. A1 - Romoli, C. A1 - Rosier-Lees, S. A1 - Rowell, G. A1 - Rudak, B. A1 - Rulten, C. B. A1 - Sahakian, V. A1 - Sanchez, David M. A1 - Santangelo, Andrea A1 - Schlickeiser, R. A1 - Schuessler, F. A1 - Schulz, A. A1 - Schwanke, U. A1 - Schwarzburg, S. A1 - Schwemmer, S. A1 - Sol, H. A1 - Spengler, G. A1 - Spies, F. A1 - Stawarz, L. A1 - Steenkamp, R. A1 - Stegmann, Christian A1 - Stinzing, F. A1 - Stycz, K. A1 - Sushch, Iurii A1 - Szostek, A. A1 - Tavernet, J. -P. A1 - Tavernier, T. A1 - Taylor, A. M. A1 - Terrier, R. A1 - Tluczykont, M. A1 - Trichard, C. A1 - Valerius, K. A1 - van Eldik, C. A1 - Vasileiadis, G. A1 - Venter, C. A1 - Viana, A. A1 - Vincent, P. A1 - Voelk, H. J. A1 - Volpe, F. A1 - Vorster, M. A1 - Wagner, S. J. A1 - Wagner, P. A1 - Ward, M. A1 - Weidinger, M. A1 - Weitzel, Q. A1 - White, R. A1 - Wierzcholska, A. A1 - Willmann, P. A1 - Woernlein, A. A1 - Wouters, D. A1 - Zacharias, M. A1 - Zajczyk, A. A1 - Zdziarski, A. A. A1 - Zech, Alraune A1 - Zechlin, H. -S. T1 - Long-term TeV and X-RAY observations of the GAMMA- RAY binary hess J0632+057 JF - The astrophysical journal : an international review of spectroscopy and astronomical physics KW - acceleration of particles KW - binaries: general KW - gamma rays: general(HESS J0632+057, VER J0633+057) Y1 - 2014 U6 - https://doi.org/10.1088/0004-637X/780/2/168 SN - 0004-637X SN - 1538-4357 VL - 780 IS - 2 PB - IOP Publ. Ltd. CY - Bristol ER -