MODIS/Terra observed seasonal variations of snow cover over the Tibetan Plateau

Pu, Zhaoxia; Xu, Li; Salomonson, V. V.

doi:10.1029/2007gl029262

Cited by 259 publications

(235 citation statements)

References 8 publications

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“…This results in up to 86 % of precipitation provided as snow (modelled). Better-performing models show higher fractions of precipitation as snowfall, which matches the assumption that most moisture is supplied by the Westerlies in winter and early spring (Immerzeel et al, 2009;Pu et al, 2007;Xiao et al, 2002).…”

Section: Data Set Characteristicsmentioning

confidence: 56%

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Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains

et al. 2015

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Abstract.A clear understanding of the hydrology is required to capture surface processes and potential inherent hazards in orogens. Complex climatic interactions control hydrological processes in high mountains that in their turn regulate the erosive forces shaping the relief. To unravel the hydrological cycle of a glaciated watershed (Gunt River) considered representative of the Pamir Mountains' hydrologic regime, we developed a remotesensing-based approach. At the boundary between two distinct climatic zones dominated by the Westerlies and Indian summer monsoon, the Pamir Mountains are poorly instrumented and only a few in situ meteorological and hydrological data are available. We adapted a suitable conceptual distributed hydrological model (J2000g). Interpolations of the few available in situ data are inadequate due to strong, relief-induced, spatial heterogeneities. Instead of these we use raster data, preferably from remote sensing sources depending on availability and validation. We evaluate remote-sensing-based precipitation and temperature products. MODIS MOD11 surface temperatures show good agreement with in situ data, perform better than other products, and represent a good proxy for air temperatures. For precipitation we tested remote sensing products as well as the HAR10 climate model data and the interpolation-based APHRODITE data set. All products show substantial differences both in intensity and seasonal distribution with in situ data. Despite low resolutions, the data sets are able to sustain high model efficiencies (NSE ≥ 0.85). In contrast to neighbouring regions in the Himalayas or the Hindu Kush, discharge is dominantly the product of snow and glacier melt, and thus temperature is the essential controlling factor. Eighty percent of annual precipitation is provided as snow in winter and spring contrasting peak discharges during summer. Hence, precipitation and discharge are negatively correlated and display complex hysteresis effects that allow for the effect of interannual climatic variability on river flow to be inferred. We infer the existence of two subsurface reservoirs. The groundwater reservoir (providing 40 % of annual discharge) recharges in spring and summer and releases slowly during autumn and winter, when it provides the only source for river discharge. A not fully constrained shallow reservoir with very rapid retention times buffers meltwaters during spring and summer. The negative glacier mass balance (−0.6 m w.e. yr −1 ) indicates glacier retreat, which will ultimately affect the currently 30 % contribution of glacier melt to annual stream flow. The spatiotemporal dependence of water release from snow and ice during the annual cycle likewise implies spatiotemporally restricted surface processes, which are essentially confined to glaciated catchments in late summer, when glacier runoff is the only source of surface runoff. Only this precise constraint of the hydrologic cycle in this complex region allows for unravelling of the surface processes and natural hazards such as floo...

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Section: Data Set Characteristicsmentioning

confidence: 56%

“…3) with an average snow cover of > 50 % from November until March (Immerzeel et al, 2009;Pu et al, 2007;Dietz et al, 2014). A negative temperature gradient is apparent along the westeast gradient in precipitation and relief.…”

Section: Meteorological Station Discharge Measurement Snow and Ice (Modmentioning

confidence: 98%

Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains

et al. 2015

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“…Linear regression is one of the most widely adopted (Pu et al, 2007;Wang et al, 2008;Immerzeel et al, 2009;Gurung et al, 2011a) approaches, and SCA trends were analysed using liner regressions at the 95% confidence level. As a measure of the statistical significance of the observed trends, we used the P-value test, which is a statistical method of testing one or more hypotheses.…”

Section: Snow Cover Area Analysismentioning

confidence: 99%

Climate and topographic controls on snow cover dynamics in the Hindu Kush Himalaya

Gurung

Maharjan

Shrestha³

et al. 2017

Intl Journal of Climatology

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Snow governs interaction between atmospheric and land surface processes in high mountains, and is also source of fresh water. It is thus important to both climate scientists and local communities. However, our understanding of snow cover dynamics in terms of space and time is limited across the Hindu Kush Himalaya (HKH) region, which is known to be a climatically sensitive region. We used MODIS snow cover area (SCA) data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012), APHRODITE temperature data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007), and monthly long term in-situ river discharge data of the Gandaki (1968Gandaki ( -2010, Koshi (1977Koshi ( -2010 and Manas (1987Manas ( -2004 basins to analyse variations among four basins. We gained insights into short term SCA and temperature, long term discharge trends, and regional variability thereby. Strong correlations were observed among SCA, temperature and discharge thereby highlighting the strong nexus between them. Temporal and spatial snow cover variability across the basins is strongly coupled with the variability of two weather systems: Western Disturbances (WD) and Indian Monsoon System (IMS), and strongly influenced by topography. Manifestation of these variability in terms if downstream discharge can have repercussion to water based sectors: hydropower and agriculture, as low flow seasons is seen affected. This study adds to our knowledge of snow fall and melt dynamics in the HKH region, and intra-annual snow melt contributions to downstream discharges. The study is limited by short span of data and it is desirable to perform a similar study using data representing a much longer time span.

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“…The MOD10C1 snow cover product uses the ancillary MODIS cloud mask (i.e. MOD35 product) based on cloud-detection tests indicating a level of confidence that MODIS observes clear skies (Ackerman et al, 1998;Platnick et al, 2003).…”

Section: Methods For Satellite-derived Snow Daysmentioning

confidence: 99%

“…Pu et al, 2007) was constructed to better assess the overall accuracy of the gap-filling technique. Table 4 approach are combined for snow covered (e.g.…”

Section: Analysis Of Daily Scdmentioning

confidence: 99%

Inter-annual variations of snow days over Switzerland from 2000–2010 derived from MODIS satellite data

Foppa

Seiz

2012

The Cryosphere

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Abstract. Snow cover plays a vital role in the SwissAlps and therefore it is of major interest to determine and understand its variability on different spatiotemporal scales. Within the activities of the National Climate Observing System (GCOS Switzerland) inter-annual variations of snow days over Switzerland were derived from 2000 to 2010 based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. To minimize the impact of cloud cover on the MODIS snow product MOD10C1, we implemented a post-processing technique based on a forward and backward gap-filling approach. Using the proposed methodology it was possible to determine the total number of annual snow days over Switzerland from 2000 to 2010 (SCD MODIS ). The accuracy of the calculated snow days per year were quantitatively evaluated against three in situ snow observation sites representing different climatological regimes (SCD in situ ). Various statistical indices were computed and analysed over the entire period. The overall accuracy between SCD MODIS and SCD in situ on a daily basis over 10 yr is 88 % to 94 %, depending on the regional characteristics of each validation site. Differences between SCD MODIS and SCD in situ vary during the snow accumulation period in autumn and smaller differences after spring, in particularly for the Central Alps.

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MODIS/Terra observed seasonal variations of snow cover over the Tibetan Plateau

Cited by 259 publications

References 8 publications

Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains

Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains

Climate and topographic controls on snow cover dynamics in the Hindu Kush Himalaya

Inter-annual variations of snow days over Switzerland from 2000–2010 derived from MODIS satellite data

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