Snow depth derived from passive microwave remote-sensing data in China

Li, Xin; Jin, Rui; Armstrong, R. L.; Zhang, Tingjun

doi:10.3189/172756408787814690

Cited by 342 publications

(244 citation statements)

References 25 publications

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“…A complex pattern of regionally increasing and decreasing spring snow depth in the Tibetan Plateau has been observed since the 1970s (Zhang et al, 2004;Che et al, 2008;Wang et al, 2013), which could help account for the mixed MXPGR trends observed in the Tibetan Interior. Highelevation zones in the upper Indus catchment, running from the Karakorum in a southeastward direction, have seen increased precipitation over the past decades due to increases in the strength of the WWD Norris et al, 2016;Treydte et al, 2006).…”

Section: Spatial Melt Patterns From Hierarchical Clusteringmentioning

confidence: 99%

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

2017

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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)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(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 ge...

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Section: Spatial Melt Patterns From Hierarchical Clusteringmentioning

confidence: 99%

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

2017

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“…The snow data from the microwave remote sensors can more accurately reflect the spatial characteristics. Hence, in this study, the gridded daily snow depths derived by Che et al (2008) In this paper, the Qinghai-Tibetan Plateau is located from 74°E to 104°E and from 25°N to 40°N. Figure 1 shows the domain, topography, and regional mountain ranges in the QinghaiTibetan Plateau.…”

Section: Methodsmentioning

confidence: 99%

“…The original Chang algorithm (Chang et al,1987) for passive remote sensing of snow depth underestimates the snow depth at the beginning of the snow season and overestimates it at the end. By using SMMR and SSM/I remote-sensing data with the global cylindrical equal-area projection and snow-depth data recorded at the China national meteorological stations, the Chang algorithm is modified and validated using meteorological observation data considering the influences from vegetation, wet snow, precipitation, cold desert, and frozen ground; the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density; the algorithm suitable for snow-depth retrieval in China covering Qinghai-Tibetan Plateau is done and the gridded daily snow depths are derived (Che et al, 2008). Based on the observed snow data from 74 weather stations, Wang et al (2013) have analyzed the accuracy of the snow data derived by Che et al (2008) in Qinghai-Tibetan Plateau.…”

Section: Methodsmentioning

confidence: 99%

“…The snow depths derived from PMSR data (Che et al 2008) are selected only to compare the actual spatial distribution with the modeled results. Figure 3 shows the geographical distributions of the mean AMSD observed by PMSR and simulated by the 19 CMIP5 models from 1986 to 2005 in the QinghaiTibetan Plateau.…”

Section: Spatial Distribution and Variation Characteristics Of The Simentioning

confidence: 99%

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Assessment of Simulations of Snow Depth in the Qinghai-Tibetan Plateau Using CMIP5 Multi-Models

Wei

Dong

2015

Arctic, Antarctic, and Alpine Research

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“…[ Figure 4] 20 VIC simulated snow cover was compared with snow depth derived from passive microwave remote-sensing data by Che et al (2008) and Dai et al (2015). Figure 5 shows the spatial distribution of observed and simulated daily average snow depths during evaluation.…”

Section: Hydrological Model Performance 15mentioning

confidence: 99%

Potential application of hydrological ensemble prediction in forecasting flood and its components over the Yarlung Zangbo River Basin, China

Liu¹,

Pan²,

Bai³

et al. 2018

Preprint

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Abstract. In recent year, flood becomes a serious issue in Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the 10 Yarlung Zangbo River Basin (YZR), China has not been systematically investigated. This study adopts Variable Infiltration Capacity (VIC) model to forecast annual maximum floods (MF) and annual first floods (FF) in YZR based on precipitation, maximum and minimum temperature from European Centre for Medium-Range Weather Forecasts (ECMWF). Nsimulations is proposed to account for more scenarios of parameters in VIC and shows improved flood simulation. Ensemble flood forecasting system can skilfully predict MF with a lead time of more than10 days, and has skill in forecasting the 15 snowmelt-related components in about 7 days ahead. The accuracy of forecasts for FF is inferior with a lead time of only 5 days. The performance in 7-day accumulated flood volumes is better than the peak flows. The components in baseflow for FF are irrelevant to lead time, whilst for MF an obvious deterioration in performance with lead time can be perceived. The snowmelt-induced surface runoff is the most poorly captured component by the system, and the well-predicted rainfallrelated components are the major contributor for good performance. From this study, it is concluded that snowmelt-induced 20 flood volume plays an important role in YZR Basin especially in FF.

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Snow depth derived from passive microwave remote-sensing data in China

Cited by 342 publications

References 25 publications

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

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

Assessment of Simulations of Snow Depth in the Qinghai-Tibetan Plateau Using CMIP5 Multi-Models

Potential application of hydrological ensemble prediction in forecasting flood and its components over the Yarlung Zangbo River Basin, China

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