Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016
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 monitor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-10-01
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| Series: | The Cryosphere |
| Online Access: | https://www.the-cryosphere.net/11/2329/2017/tc-11-2329-2017.pdf |
| _version_ | 1811235082787094528 |
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| author | T. Smith B. Bookhagen A. Rheinwalt |
| author_facet | T. Smith B. Bookhagen A. Rheinwalt |
| author_sort | T. Smith |
| collection | DOAJ |
| description | 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 (<i>n</i> = 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<sup>−1</sup> 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. |
| first_indexed | 2024-04-12T11:44:52Z |
| format | Article |
| id | doaj.art-501ab6e5135a4226aeb9065917315e68 |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-04-12T11:44:52Z |
| publishDate | 2017-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-501ab6e5135a4226aeb9065917315e682022-12-22T03:34:22ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242017-10-01112329234310.5194/tc-11-2329-2017Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016T. Smith0B. Bookhagen1A. Rheinwalt2Institute for Earth and Environmental Sciences, Universität Potsdam, Potsdam, GermanyInstitute for Earth and Environmental Sciences, Universität Potsdam, Potsdam, GermanyInstitute for Earth and Environmental Sciences, Universität Potsdam, Potsdam, GermanyHigh 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 (<i>n</i> = 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<sup>−1</sup> 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.https://www.the-cryosphere.net/11/2329/2017/tc-11-2329-2017.pdf |
| spellingShingle | T. Smith B. Bookhagen A. Rheinwalt Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016 The Cryosphere |
| title | Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016 |
| title_full | Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016 |
| title_fullStr | Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016 |
| title_full_unstemmed | Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016 |
| title_short | Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016 |
| title_sort | spatiotemporal patterns of high mountain asia s snowmelt season identified with an automated snowmelt detection algorithm 1987 2016 |
| url | https://www.the-cryosphere.net/11/2329/2017/tc-11-2329-2017.pdf |
| work_keys_str_mv | AT tsmith spatiotemporalpatternsofhighmountainasiassnowmeltseasonidentifiedwithanautomatedsnowmeltdetectionalgorithm19872016 AT bbookhagen spatiotemporalpatternsofhighmountainasiassnowmeltseasonidentifiedwithanautomatedsnowmeltdetectionalgorithm19872016 AT arheinwalt spatiotemporalpatternsofhighmountainasiassnowmeltseasonidentifiedwithanautomatedsnowmeltdetectionalgorithm19872016 |