Journal ArticleDOI
High-resolution mapping of global surface water and its long-term changes
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TLDR
Using three million Landsat satellite images, this globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities.Abstract:
A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.read more
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Google Earth Engine: Planetary-scale geospatial analysis for everyone
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Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
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The global methane budget 2000–2017
Marielle Saunois,Ann R. Stavert,Ben Poulter,Philippe Bousquet,Josep G. Canadell,Robert B. Jackson,Peter A. Raymond,Edward J. Dlugokencky,Sander Houweling,Sander Houweling,Prabir K. Patra,Prabir K. Patra,Philippe Ciais,Vivek K. Arora,David Bastviken,Peter Bergamaschi,Donald R. Blake,Gordon Brailsford,Lori Bruhwiler,Kimberly M. Carlson,Mark Carrol,Simona Castaldi,Naveen Chandra,Cyril Crevoisier,Patrick M. Crill,Kristofer R. Covey,Charles L. Curry,Giuseppe Etiope,Giuseppe Etiope,Christian Frankenberg,Nicola Gedney,Michaela I. Hegglin,Lena Höglund-Isaksson,Gustaf Hugelius,Misa Ishizawa,Akihiko Ito,Greet Janssens-Maenhout,Katherine M. Jensen,Fortunat Joos,Thomas Kleinen,Paul B. Krummel,Ray L. Langenfelds,Goulven Gildas Laruelle,Licheng Liu,Toshinobu Machida,Shamil Maksyutov,Kyle C. McDonald,Joe McNorton,Paul A. Miller,Joe R. Melton,Isamu Morino,Jurek Müller,Fabiola Murguia-Flores,Vaishali Naik,Yosuke Niwa,Sergio Noce,Simon O'Doherty,Robert J. Parker,Changhui Peng,Shushi Peng,Glen P. Peters,Catherine Prigent,Ronald G. Prinn,Michel Ramonet,Pierre Regnier,William J. Riley,Judith A. Rosentreter,Arjo Segers,Isobel J. Simpson,Hao Shi,Steven J. Smith,L. Paul Steele,Brett F. Thornton,Hanqin Tian,Yasunori Tohjima,Francesco N. Tubiello,Aki Tsuruta,Nicolas Viovy,Apostolos Voulgarakis,Apostolos Voulgarakis,Thomas Weber,Michiel van Weele,Guido R. van der Werf,Ray F. Weiss,Doug Worthy,Debra Wunch,Yi Yin,Yi Yin,Yukio Yoshida,Weiya Zhang,Zhen Zhang,Yuanhong Zhao,Bo Zheng,Qing Zhu,Qiuan Zhu,Qianlai Zhuang +95 more
TL;DR: The second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations) as discussed by the authors.
Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)
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A high-accuracy map of global terrain elevations
Dai Yamazaki,Daiki Ikeshima,R. Tawatari,Tomohiro Yamaguchi,Fiachra O'Loughlin,J. C. Neal,Christopher C. Sampson,Shinjiro Kanae,Paul D. Bates +8 more
TL;DR: In this article, a high-accuracy global digital elevation model (DEM) was proposed by eliminating major error components from existing DEMs, such as absolute bias, stripe noise, speckle noise, and tree height bias.
References
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Journal ArticleDOI
Development and validation of a global database of lakes, reservoirs and wetlands
TL;DR: The Global Lakes and Wetlands Database (GLWD) as mentioned in this paper was created by combining the best available sources for lakes and wetlands on a global scale and the application of Geographic Information System (GIS) functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands.