Reliability of long-term snow depth data sets from remote sensing over the western arid zone of China

Abstract. The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

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Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06699-y ◽

2021 ◽

Vol 14 (6) ◽

Author(s):

Jinming Yang ◽

Chengzhi Li

Keyword(s):

Remote Sensing ◽

Kalman Filter ◽

Water Resource ◽

Ensemble Kalman Filter ◽

Snow Depth ◽

Resource Assessment ◽

Measured Data ◽

Hydrological Process ◽

Depth Data ◽

Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

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Towards a scale-independent fractional snow-covered area parameterization for complex terrain

10.5194/egusphere-egu2020-14575 ◽

2020 ◽

Author(s):

Nora Helbig ◽

Yves Bühler ◽

Lucie Eberhard ◽

César Deschamps-Berger ◽

Simon Gascoin ◽

...

Keyword(s):

Snow Depth ◽

Hydrological Modeling ◽

Spatial Scales ◽

Ground Surface ◽

Longwave Radiation ◽

Data Sets ◽

Depth Data ◽

Snow Covered Area ◽

Covered Area ◽

Wide Range

Whenever there is snow on the ground, there will be large spatial variability in snow depth. The spatial distribution of snow is significantly influenced by topography due to wind, precipitation, shortwave and longwave radiation, and even snow avalanches relocate the accumulated snow. Fractional snow-covered area (fSCA) is an important model parameter characterizing the fraction of the ground surface that is covered by snow and is crucial for various model applications such as weather forecasts, climate simulations and hydrological modeling.We recently suggested an empirical fSCA parameterization based on two spatial snow depth data sets acquired at peak of winter in Switzerland and Spain, which yielded best performance for spatial scales larger than 1000 m. However, this parameterization was not validated on independent snow depth data. To evaluate and improve our fSCA parameterization, in particular with regards to other spatial scales and snow climates (or geographic regions), we used spatial snow depth data sets form a wide range of mountain ranges in USA, Switzerland and France acquired by 5 different measuring methods. Pooling all snow depth data sets suggests that a scale-dependent parameter should be introduced to improve the fSCA parameterization, in particular for sub-kilometer spatial scales. Extending our empirical fSCA parameterization to a broader range of scales and snow climates is an important step towards accounting for spatio-temporal variability in snow depth in multiple snow model applications.

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Elevation dependency of mountain snow depth

The Cryosphere Discussions ◽

10.5194/tcd-8-3665-2014 ◽

2014 ◽

Vol 8 (4) ◽

pp. 3665-3698 ◽

Cited By ~ 2

Author(s):

T. Grünewald ◽

Y. Bühler ◽

M. Lehning

Keyword(s):

Snow Depth ◽

Elevation Gradient ◽

Systematic Evaluation ◽

Data Sets ◽

Elevation Gradients ◽

Depth Data ◽

Typical Shape ◽

Depth Relationship ◽

Snow Fall ◽

Depth Curves

Abstract. Elevation strongly affects quantity and distribution of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation – snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales ranging from the complete data sets by km-scale sub-catchments to slope transects. We show that most elevation – snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks.

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Elevation dependency of mountain snow depth

The Cryosphere ◽

10.5194/tc-8-2381-2014 ◽

2014 ◽

Vol 8 (6) ◽

pp. 2381-2394 ◽

Cited By ~ 53

Author(s):

T. Grünewald ◽

Y. Bühler ◽

M. Lehning

Keyword(s):

Snow Depth ◽

Elevation Gradient ◽

Distribution Patterns ◽

Snow Accumulation ◽

Systematic Evaluation ◽

Data Sets ◽

Elevation Gradients ◽

Depth Data ◽

Typical Shape ◽

Depth Relationship

Abstract. Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation–snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation–snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

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Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

Remote Sensing ◽

10.3390/rs13040657 ◽

2021 ◽

Vol 13 (4) ◽

pp. 657

Author(s):

Pengtao Wei ◽

Tingbin Zhang ◽

Xiaobing Zhou ◽

Guihua Yi ◽

Jingji Li ◽

...

Keyword(s):

Remote Sensing ◽

Tibetan Plateau ◽

Environmental Factors ◽

Spatial Resolution ◽

Snow Depth ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Passive Microwave Remote Sensing ◽

Depth Data

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.

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The Swiss snow climatology as seen by reanalyses and further gridded datasets

10.5194/ems2021-282 ◽

2021 ◽

Author(s):

Monika Goeldi ◽

Stefanie Gubler ◽

Christian Steger ◽

Simon C. Scherrer ◽

Sven Kotlarski

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Snow Water Equivalent ◽

Spatiotemporal Variability ◽

Data Sets ◽

Data Set ◽

Climate Services ◽

Snow Cover Extent ◽

Snow Water

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations &#8211; e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

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