![]() a –1 from 2012 to 2018 after a period of enhanced mass loss of −0.31 ± 0.16 m w.e. This wider analysis reveals a period of reduced mass loss of −0.13 ± 0.21 m w.e. To place these results into a broader geographical context, the mass balance of a further 15 glaciers from around the Monte San Lorenzo massif was determined from 2000 onwards. Over the periods of 2000–20–2018, the mass budget of these three glaciers remained virtually unchanged at −1.37 ± 0.06 and −1.36 ± 0.17 m w.e. a –1 and a maximum loss of −2.23 ± 0.07 m w.e. The period 1981–2000 had the most negative mass budget, with an area-averaged mass loss of 1.67 ± 0.11 m w.e. Our results indicate that net mass balance was negative throughout the six decades, with a mean mass loss of −1.35 ± 0.08 m w.e. Here, we present geodetic mass and area changes of three valley glaciers from Monte San Lorenzo derived from stereo aerial photos, the Shuttle Radar Topography Mission (SRTM) and satellite imagery (SPOT5 and Pleiades) spanning four periods from 1958 to 2018. 5Departamento de Geografía, Universidad de Chile, Santiago, ChileĪ full understanding of glacier changes in the Patagonian Andes over decadal to century time-scales is presently limited by a lack of detailed and appropriate long-term observations.4Centro de Estudios Científicos, Valdivia, Chile.3School of Geography and Sustainable Development, University of St Andrews, St Andrews, United Kingdom.2Departamento de Geografía, Facultad de Filosofía y Letras, Universidad Nacional de Cuyo, Mendoza, Argentina.1Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales, CCT-Mendoza CONICET, Mendoza, Argentina.As of August, 2013, the GLIMS Glacier Database contains approximately 70% of the contents of the RGI, by both glacier count and area.Daniel Falaschi 1,2*, María Gabriela Lenzano 1, Ricardo Villalba 1, Tobias Bolch 3, Andrés Rivera 4,5 and Andrés Lo Vecchio 1,2 This contribution discusses the status of the GLIMS Glacier Database and the merge of RGI data into GLIMS, showing how the merge is carried out. More data from the RGI, such as from Arctic Canada and the periphery of Greenland, are expected to be merged into GLIMS as resources at NSIDC allow and as sufficient metadata can be obtained. The New Zealand outlines came from the Randolph Glacier Inventory (RGI), a data set created with the express purpose of filling the geographic gaps in GLIMS to produce a globally complete map of glaciers. These outlines had significant overlap with existing outlines in the GLIMS database, necessitating new approaches to the merging process. ![]() New sets of glacier outlines, including 12000 outlines from the Western Himalaya and 3500 outlines from New Zealand, have recently been merged into GLIMS. Otherwise, outlines that are supposed to pertain to the same glacier will appear to be different glaciers, causing errors in summary statistics of the database, such as glacier count or area. ![]() As new glacier outlines are produced for glaciers which have already been mapped within GLIMS, we must ensure that each new outline is assigned the same GLIMS glacier ID as its previous outline. GLIMS is one of the most popular data sets at NSIDC, and a web-based map interface and web map services allow users to obtain the data at no cost. The Global Land Ice Measurements from Space (GLIMS) glacier database was built at the National Snow and Ice Data Center (NSIDC) in 2005, and now contains outlines and metadata for 120,000 glaciers. ![]()
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