Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to 2007

Takala, Matias; Pulliainen, Jouni; Metsämäki, Sari; Koskinen, J.

doi:10.1109/tgrs.2009.2018442

Cited by 86 publications

(71 citation statements)

References 27 publications

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“…PM data also have the advantage of functioning despite cloud cover, which is ubiquitous in much of HMA during both winter and the Indian Summer Monsoon (ISM) season. Using satellite-derived PM measurements, several authors have tracked the onset, duration, and spatial extent of snowmelt events using a range of approaches including the cross-polarized gradient ratio (XPGR) (Abdalati and Steffen, 1995;Hall et al, 2004), the advanced horizontal range algorithm (Drobot and Anderson, 2001), Gaussian edge detection (Joshi et al, 2001), channel differences (Takala et al, 2003), artificial neural networks (Takala et al, 2008(Takala et al, , 2009, diurnal temperature brightness (Tb) variations (Apgar et al, 2007;Monahan and Ramage, 2010;Tedesco, 2007), wavelet-based approaches (Liu et al, 2005), and median filtering of raw PM data (Xiong et al, 2017).…”

Section: T Smith Et Al: Spatiotemporal Patterns Of High Mountain Asmentioning

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: T Smith Et Al: Spatiotemporal Patterns Of High Mountain Asmentioning

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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“…This data set contains monthly 0.25 • resolution estimates of SWE (in mm) for low-relief regions with seasonal snow cover north of 55 • N during 2003-2011. The SWE estimates are derived through a combination of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing Satellite) passive microwave remote sensing and weather station data (Pulliainen, 2006;Takala et al, 2009). The GlobSnow data were aggregated to 1 • resolution.…”

Section: Evaluation Against Observationsmentioning

confidence: 99%

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

Dijk

Renzullo

Wada

et al. 2014

Hydrol. Earth Syst. Sci.

132

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Abstract. We present a global water cycle reanalysis that merges water balance estimates derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission, satellite water level altimetry and off-line estimates from several hydrological models. Error estimates for the sequential data assimilation scheme were derived from available uncertainty information and the triple collocation technique. Errors in four GRACE storage products were estimated to be 11-12 mm over land areas, while errors in monthly storage changes derived from five global hydrological models were estimated to be 17-28 mm. Prior and posterior water storage estimates were evaluated against independent observations of river water level and discharge, snow water storage and glacier mass loss. Data assimilation improved or maintained agreement overall, although results varied regionally. Uncertainties were greatest in regions where glacier mass loss and subsurface storage decline are both plausible but poorly constrained. We calculated a global water budget for [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012]. The main changes were a net loss of polar ice caps (−342 Gt yr −1 ) and mountain glaciers (−230 Gt yr −1 ), with an additional decrease in seasonal snowpack (−18 Gt yr −1 ). Storage increased due to new impoundments (+16 Gt yr −1 ), but this was compensated by decreases in other surface water bodies (−10 Gt yr −1 ). If the effect of groundwater depletion (−92 Gt yr −1 ) is considered separately, subsurface water storage increased by +202 Gt yr −1 due particularly to increased wetness in northern temperate regions and in the seasonally wet tropics of South America and southern Africa. The reanalysis results are publicly available via www.wenfo.org/wald/.

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“…The grain size is allowed to deviate from the initial estimate during this minimisation and the EO uncertainty is adjusted accordingly. Additional dry snow start (Hall et al 2002) and snowmelt start (Takala et al 2009) algorithms are employed. Before the first dry snow and after the onset of melt the EO weight is set to zero.…”

Section: Globsnowmentioning

confidence: 99%

“…All products showed much better agreement for end date, both in bias and RMSE. The start and end dates in Globsnow will be determined by the Chang based snow start method of Hall et al (2002) and the empirical snowmelt algorithm of Takala et al (2009). Therefore we would expect the snow start date of Globsnow to be more conservative than SSM/I and AMSR-E, which it is.…”

Section: Snow Datesmentioning

confidence: 99%

Evaluating global snow water equivalent products for testing land surface models

Hancock

Baxter

Evans³

et al. 2013

Remote Sensing of Environment

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(2013) 'Evaluating global snow water equivalent products for testing land surface models.', Remote sensing of environment., 128 . pp. 107-117. Further information on publisher's website:http://dx.doi.org/10.1016/j.rse. 2012.10.004 Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Remote sensing of environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Remote sensing of environment, 128, 2013, 10.1016/j.rse.2012.10.004 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is payed to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE > 80 -150 mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20+ days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1-6 days) although the spread was still on the order of three weeks.During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to correct or avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.

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Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to 2007

Cited by 86 publications

References 27 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

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

Evaluating global snow water equivalent products for testing land surface models

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