BOREAS HYD-03 SNOW DEPTH DATA: 1996

1999 ◽  
Author(s):  
R. E. DAVIS
Keyword(s):  
Snow Depth ◽  
Depth Data

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

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.

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

scholarly journals Evaluation of homogenization methods for seasonal snow depth data in the Austrian Alps, 1930–2010

2019 ◽  
Vol 39 (11) ◽  
pp. 4514-4530 ◽  
Author(s):  
Giorgia Marcolini ◽  
Roland Koch ◽  
Barbara Chimani ◽  
Wolfgang Schöner ◽  
Alberto Bellin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Depth Data ◽  
Seasonal Snow ◽  
Austrian Alps ◽  
Homogenization Methods

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

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

<p>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.</p><p>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.</p>

scholarly journals Elevation dependency of mountain snow depth

2014 ◽  
Vol 8 (4) ◽  
pp. 3665-3698 ◽  
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.

scholarly journals Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3599
Author(s):  
Hongju Chen ◽  
Jianping Yang ◽  
Yongjian Ding ◽  
Qingshan He ◽  
Qin Ji
Keyword(s):  
Snow Depth ◽  
Meteorological Data ◽  
Baseline Period ◽  
Reference Station ◽  
Maximum Temperature ◽  
Simulation Data ◽  
Depth Data ◽  
Temporal Correlations ◽  
Highly Correlated ◽  
Using Data

In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data.

scholarly journals Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

2010 ◽  
Vol 11 (6) ◽  
pp. 1380-1394 ◽  
Author(s):  
Matthew Sturm ◽  
Brian Taras ◽  
Glen E. Liston ◽  
Chris Derksen ◽  
Tobias Jonas ◽  
...  
Keyword(s):  
United States ◽  
Bulk Density ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Estimation Procedure ◽  
The United States ◽  
Practical Applications ◽  
Continental Scale ◽  
Depth Data ◽  
Snow Water

Abstract In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

scholarly journals Elevation dependency of mountain snow depth

2014 ◽  
Vol 8 (6) ◽  
pp. 2381-2394 ◽  
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).

Sign in / Sign up

Export Citation Format

Share Document