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Changes in glacial lakes and the consequences of these changes, particularly on the development of water resources and management of glacial lake outburst flood (GLOF) risk, has become one of the challenges in the sustainable development of high mountain areas in the context of global warming
. This paper presents the findings of a study on the distribution of, and area changes in, glacial lakes in the Koshi basin in the central Himalayas. Data on the number of glacial lakes and their area was generated for the years 1977, 1990, 2000, and 2010 using Landsat satellite images. According to the glacial lake inventory in 2010, there were a total of 2168 glacial lakes with a total area of 127.61 km2 and average size of 0.06 km2 in the Koshi basin. Of these, 47% were moraine dammed lakes, 34.8% bedrock dammed lakes and 17.7% ice dammed lakes. The number of glacial lakes increased consistently over the study period from 1160 in 1977 to 2168 in 2010, an overall growth rate of 86.9%. The area of glacial lakes also increased from 94.44 km2 in 1977 to 127.61 km2 in 2010, a growth rate of 35.1%. A large number of glacial lakes in the inventory are small in size (≤ 0.1 km2). End moraine dammed lakes with area greater than 0.1 km2 were selected to analyze the change characteristics of glacial lakes in the basin. The results show that, in 2010, there were 129 lakes greater than 0.1 km2 in area; these lakes had a total area of 42.92 km2 in 1997, increasing to 63.28 km2 in 2010. The distribution of lakes on the north side of the Himalayas (in China) was three times higher than on the south side of the Himalayas (in Nepal). Comparing the mean growth rate in area for the 33 year study period (1977-2010), the growth rate on the north side was found to be a little slower than that on the south side. A total of 42 glacial lakes with an area greater than 0.2 km2 are rapidly growing between 1977 and 2010 in the Koshi basin, which need to be paid more attention to monitoring in the future and to identify how critical they are in terms of GLOF
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In recent years attention has been given to assess the impacts of warming on the plant flowering phenology
. There is a growing realization that herbarium-based collections could offer a reliable and relatively time-saving baseline data source to identify these effects. This article examines the magnitude and trends of warming effects on the average flowering timing (AFT) of plants in Tibet Autonomous Region using analysis of herbarium specimens collected for 4 decades. Mixed model with randomized blocks was used to analyze a set of 41 species (total 909 specimens) which were collected during the period of 1961–2000. Results showed that an earlier AFT emerged within 40 years period in comparison to the recorded data of the year of 2000 (0.5 days per year), and that 7.5 days early flowering was contributed by mean summer (i.e., June–August) temperature. It is proposed that temporary shifts in flowering phenology responding to continuing temperature rise could quantify the extent to which climate affects plant species. Analysis of well recorded herbarium specimens could provide a reasonable indication on the impacts of rising temperature on plant phenology. The result of this study could also facilitate a bridge between the scientific knowledge and indigenous knowledge of Tibetan communities
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Christensen, J. H.; Hewitson, B.; Busuioc, A.; Chen, A.; Gao, X.; Held, R.; Jones, R.; Kolli, R. K.; Kwon, W. K.; Laprise, R.
Increasingly reliable regional climate change projections are now available for many regions of the world due to advances in modelling and understanding of the physical processes of the climate system
. Atmosphere-Ocean General Circulation Models (AOGCMs) remain the foundation for projections while downscaling techniques now provide valuable additional detail. Atmosphere-Ocean General Circulation Models cannot provide information at scales finer than their computational grid (typically of the order of 200 km) and processes at the unresolved scales are important. Providing information at finer scales can be achieved through using high resolution in dynamical models or empirical statistical downscaling. Development of downscaling methodologies remains an important focus. Downscaled climate change projections tailored to specific needs are only now starting to become available
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