Retrieval of land surface temperatures on the Tibetan Plateau using passive microwave data

2004 ◽  
Author(s):  
F. Gao ◽  
F. Wang ◽  
Y. Ma
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Passive Microwave ◽  
The Tibetan Plateau ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
Microwave Data

scholarly journals Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017 ◽  
Vol 11 (5) ◽  
pp. 2329-2343 ◽  
Author(s):  
Taylor Smith ◽  
Bodo Bookhagen ◽  
Aljoscha Rheinwalt
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Climate Model ◽  
Single Point ◽  
Passive Microwave ◽  
Spatiotemporal Patterns ◽  
Signal Separation ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Microwave Data

Abstract. 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.

scholarly journals Spatio-temporal Patterns of High Mountain Asia's Snowmelt Season Identified with an Automated Snowmelt Detection Algorithm, 1987–2016

2017 ◽  
Author(s):  
Taylor Smith ◽  
Bodo Bookhagen ◽  
Aljoscha Rheinwalt
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Tibetan Plateau ◽  
Single Point ◽  
Detection Algorithm ◽  
Passive Microwave ◽  
Small Scale ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Microwave Data

Abstract. High Mountain Asia (HMA), encompassing the Tibetan Plateau and surrounding mountain ranges, is the primary water tower 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 leverage passive microwave data across five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987–2016 to track the onset and end of snowmelt across HMA. Compared against a control dataset (n = 2100, 3 variables at 25 locations over 28 years), our algorithm is generally within 3–5 days of the onset and end dates of melt. 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 inter-annual snowfall variations, makes determining trends in melt onset and end 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 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 snowmelt onset dates (positive trends), but with a 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 three decades indicate earlier snowmelt onset and end, data from the last 14 years (2002–2016) exhibit positive trends in both snowmelt onset and end dates 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 in a long-term trend or simply inter-annual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt onset 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 caused earlier snowmelt onset and shortened melt seasons, changes in HMA's crysophere have been spatially and temporally heterogeneous. The complex response of HMA's cryosphere to climate change highlights the importance of both regional and small-scale studies for effective water planning.

scholarly journals Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

2020 ◽  
Vol 12 (11) ◽  
pp. 1722
Author(s):  
Mingxi Zhang ◽  
Bin Wang ◽  
James Cleverly ◽  
De Li Liu ◽  
Puyu Feng ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Global Studies ◽  
Near Surface ◽  
Automated Mapping ◽  
Land Surface Temperatures

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.

Trends of land surface heat fluxes on the Tibetan Plateau from 2001 to 2012

2017 ◽  
Vol 37 (14) ◽  
pp. 4757-4767 ◽  
Author(s):  
Cunbo Han ◽  
Yaoming Ma ◽  
Xuelong Chen ◽  
Zhongbo Su
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Surface Heat ◽  
Heat Fluxes ◽  
The Tibetan Plateau ◽  
Surface Heat Fluxes ◽  
Land Surface Heat Fluxes

Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh

2020 ◽  
Vol 18 ◽  
pp. 100314 ◽  
Author(s):  
Abdulla - Al Kafy ◽  
Md. Shahinoor Rahman ◽  
Abdullah-Al- Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Muhaiminul Islam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Land Surface Temperatures
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