scholarly journals Dynamic Changes of NDVI in the Growing Season of the Tibetan Plateau During the Past 17 Years and Its Response to Climate Change

2019 ◽  
Vol 16 (18) ◽  
pp. 3452 ◽  
Author(s):  
Xianglin Huang ◽  
Tingbin Zhang ◽  
Guihua Yi ◽  
Dong He ◽  
Xiaobing Zhou ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Tibetan Plateau ◽  
Time Series Analysis ◽  
Environmental Changes ◽  
Climatic Factors ◽  
Growing Season ◽  
Lag Time ◽  
The Tibetan Plateau ◽  
Series Analysis

The fragile alpine vegetation in the Tibetan Plateau (TP) is very sensitive to environmental changes, making TP one of the hotspots for studying the response of vegetation to climate change. Existing studies lack detailed description of the response of vegetation to different climatic factors using the method of multiple nested time series analysis and the method of grey correlation analysis. In this paper, based on the Normalized Difference Vegetation Index (NDVI) of TP in the growing season calculated from the MOD09A1 data product of Moderate-resolution Imaging Spectroradiometer (MODIS), the method of multiple nested time series analysis is adopted to study the variation trends of NDVI in recent 17 years, and the lag time of NDVI to climate change is analyzed using the method of Grey Relational Analysis (GRA). Finally, the characteristics of temporal and spatial differences of NDVI to different climate factors are summarized. The results indicate that: (1) the spatial distribution of NDVI values in the growing season shows a trend of decreasing from east to west, and from north to south, with a change rate of −0.13/10° E and −0.30/10° N, respectively. (2) From 2001 to 2017, the NDVI in the TP shows a slight trend of increase, with a growth rate of 0.01/10a. (3) The lag time of NDVI to air temperature is not obvious, while the NDVI response lags behind cumulative precipitation by zero to one month, relative humidity by two months, and sunshine duration by three months. (4) The effects of different climatic factors on NDVI are significantly different with the increase of the study period.

scholarly journals InSAR time series analysis of seasonal surface displacement dynamics on the Tibetan Plateau

2019 ◽  
Author(s):  
Eike Reinosch ◽  
Johannes Buckel ◽  
Jie Dong ◽  
Markus Gerke ◽  
Jussi Baade ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Time Series Analysis ◽  
Surface Displacement ◽  
The Tibetan Plateau ◽  
Permafrost Degradation ◽  
Nam Co ◽  
Freeze Thaw ◽  
Series Analysis ◽  
Spatial Coverage

Abstract. Climate change and the associated rise in air temperature have affected the Tibetan Plateau to a significantly stronger degree than the global average over the past decades. This has caused deglaciation, permafrost degradation and increased precipitation, heavily changing the water balance of this region. Surface displacement processes are likely to change as the ground continues to warm up and as such it is vital to understand both seasonal and interannual processes dynamics. The Nam Co area is well suited to studying these processes via Interferometric Synthetic Aperture Radar (InSAR) time series analysis, due to its lack of higher vegetation and relatively thin snow cover. The short revisit time of the Sentinel-1 system further reduces the risk of temporal decorrelation, making it possible to produce surface displacement models with good spatial coverage. We created three different surface displacement models to study freeze-thaw processes, seasonal sliding and linear creep. Most slopes of the area are unstable, with velocities of 8 to 17 mm yr−1, and some landforms reach velocities of up to 18 cm yr−1. The monsoonal climate accelerates those movements during the summer months through high temperatures and heavy rainfall. The fastest moving landforms, some of which have been identified as rock glaciers, do not follow this seasonal pattern of accelerated velocity in summer, instead they follow a linear sliding pattern. It is unclear if this linearity is connected to the ice content in those landforms. Flat regions at Nam Co are mostly stable on a multiannual scale but some experience subsidence, which could be caused by permafrost degradation. We observe a very clear seasonal freeze-thaw cycle in the valleys, where thawing and subsequent freezing of the active layer cause a vertical oscillation of the ground of up to a few centimeters, especially near streams and other water bodies.

Analysis of the hydrological and tectonic deformation in the eastern part of the Tibetan plateau, from FLATSIM automated time series analysis of Sentinel-1 InSAR

2021 ◽  
Author(s):  
Laëtitia Lemrabet ◽  
Marie-Pierre Doin ◽  
Cécile Lasserre ◽  
Anne Replumaz ◽  
Marianne Métois ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Time Series Analysis ◽  
Surface Deformation ◽  
Tectonic Deformation ◽  
The Tibetan Plateau ◽  
Continental Scale ◽  
Series Analysis ◽  
Fault Systems ◽  
Seasonal Signal

<p>The global and systematic coverage of Sentinel-1 radar images allows characterizing, by radar interferometry (InSAR), surface deformation on a continental scale.</p><p>Our study focuses on the eastern part of the Tibetan plateau, where a combination of major strike-slip and thrust fault systems accommodates part of the deformation related to the collision between the Indian and Eurasian plates.</p><p>We use an automated Sentinel-1 InSAR processing chain based on the NSBAS approach (Doin et al., 2011, Grandin, 2015) to measure the interseismic deformation across these fault systems. Processing is made on the CNES high-performance computer center in Toulouse in the FLATSIM project framework (ForM@Ter LArge-scale multi-Temporal Sentinel-1 Interferometric Measurement, Durand et al., 2019). We perform a time series analysis of the 2014-2020 Sentinel-1 InSAR data set, for 1200 km-long tracks (acquired along 7 ascending and 7 descending orbits), covering a 1 700 000 km<sup>2</sup> area, with a 160 m spatial resolution. From about 130 acquisitions per track, we perform about 600 interferograms, with short, three months, and one-year temporal baselines. After inversion, we obtain time series of line-of-sight (LOS) delay maps, including residual atmospheric delay and network misclosure measurements. The time series are fitted by a seasonal signal plus a velocity trend. The velocity field on overlap areas agrees within less than 1~mm/yr.</p><p>Finally, we decompose the LOS velocity maps into a vertical and a horizontal contribution.</p><p>InSAR velocity maps highlight surface deformation patterns mostly localized on known major faults, short-wavelength patterns attributed to slope instabilities phenomena, and hydrological signals.</p><p>The seasonal signal combines residual atmospheric phase delays and widespread hydrological phenomena in sedimentary basins, which we interpret in parallel with the regional geological map.  Masking areas affected by dominant gravitational slope or hydrological deformation allows to better focus on tectonic deformation.</p><p>We finally discuss slip partitioning on the various fault systems from the velocity maps and 2D profiles’ analysis.</p>

scholarly journals Contrasting Evolution Patterns of Endorheic and Exorheic Lakes on the Central Tibetan Plateau and Climate Cause Analysis during 1988–2017

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1962
Author(s):  
Zhilong Zhao ◽  
Yue Zhang ◽  
Zengzeng Hu ◽  
Xuanhua Nie
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Climatic Factors ◽  
Global Climate ◽  
Rapid Expansion ◽  
Annual Precipitation ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Climate Change Research ◽  
Mean Temperature

The alpine lakes on the Tibetan Plateau (TP) are indicators of climate change. The assessment of lake dynamics on the TP is an important component of global climate change research. With a focus on lakes in the 33° N zone of the central TP, this study investigates the temporal evolution patterns of the lake areas of different types of lakes, i.e., non-glacier-fed endorheic lakes and non-glacier-fed exorheic lakes, during 1988–2017, and examines their relationship with changes in climatic factors. From 1988 to 2017, two endorheic lakes (Lake Yagenco and Lake Zhamcomaqiong) in the study area expanded significantly, i.e., by more than 50%. Over the same period, two exorheic lakes within the study area also exhibited spatio-temporal variability: Lake Gaeencuonama increased by 5.48%, and the change in Lake Zhamuco was not significant. The 2000s was a period of rapid expansion of both the closed lakes (endorheic lakes) and open lakes (exorheic lakes) in the study area. However, the endorheic lakes maintained the increase in lake area after the period of rapid expansion, while the exorheic lakes decreased after significant expansion. During 1988–2017, the annual mean temperature significantly increased at a rate of 0.04 °C/a, while the annual precipitation slightly increased at a rate of 2.23 mm/a. Furthermore, the annual precipitation significantly increased at a rate of 14.28 mm/a during 1995–2008. The results of this study demonstrate that the change in precipitation was responsible for the observed changes in the lake areas of the two exorheic lakes within the study area, while the changes in the lake areas of the two endorheic lakes were more sensitive to the annual mean temperature between 1988 and 2017. Given the importance of lakes to the TP, these are not trivial issues, and we now need accelerated research based on long-term and continuous remote sensing data.

Climate change impacts on Indian Sunderbans: a time series analysis (1924–2008)

2012 ◽  
Vol 21 (5) ◽  
pp. 1289-1307 ◽  
Author(s):  
Atanu Raha ◽  
Susmita Das ◽  
Kakoli Banerjee ◽  
Abhijit Mitra
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Analysis ◽  
Climate Change Impacts ◽  
Series Analysis

Introduction to the special issue on “hydro-meteorological time series analysis and their relation to climate change”

2018 ◽  
Vol 66 (3) ◽  
pp. 317-318 ◽  
Author(s):  
Javier Estévez ◽  
Amanda García Marín ◽  
Julián Báez Benitez ◽  
M. Carmen Casas Castillo ◽  
Luciano Telesca
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Analysis ◽  
Special Issue ◽  
Series Analysis
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