scholarly journals 57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.)

2018 ◽  
Author(s):  
Yves Lejeune ◽  
Marie Dumont ◽  
Jean-Michel Panel ◽  
Matthieu Lafaysse ◽  
Philippe Lapalus ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Meteorological Data ◽  
Long Period ◽  
Several Variables ◽  
Mountain Site ◽  
Snow Water ◽  
The Mean ◽  
Meteorological Observations ◽  
Measurements Errors

Abstract. In this paper, we introduce and provide access to a daily (1960–2017) and hourly (1993–2017) dataset of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l, Charteuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to April 2015. A detailed study of the uncertainties originating from both measurements errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse to total shortwave broadband irradiance is affected by an uncertainty of ± 0.21. The estimated root mean squared deviation, that can be mainly attributed to spatial variability, is ± 10 cm for snow depth, ± 25 kg m−2 for snow water and ± 1 K for soil temperature (± 0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site with a reduction of the mean snow depth (Dec. 1st to April 30th of 39 cm from 1960–1990 to 1990–2017 and an increase in temperature of + 0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) datacenter: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.

scholarly journals 57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude)

2019 ◽  
Vol 11 (1) ◽  
pp. 71-88 ◽  
Author(s):  
Yves Lejeune ◽  
Marie Dumont ◽  
Jean-Michel Panel ◽  
Matthieu Lafaysse ◽  
Philippe Lapalus ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Measurement Errors ◽  
Meteorological Data ◽  
Long Period ◽  
Data Centre ◽  
Several Variables ◽  
Mountain Site ◽  
The Mean ◽  
Meteorological Observations

Abstract. In this paper, we introduce and provide access to daily (1960–2017) and hourly (1993–2017) datasets of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l., Chartreuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to March 2018. A detailed study of the uncertainties originating from both measurement errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse-to-total shortwave broadband irradiance is affected by an uncertainty of ±0.21 (no unit). The estimated root mean square deviation, which mainly represents spatial variability, is ±10 cm for snow depth, ±25 kg m−2 for the water equivalent of snow cover (SWE), and ±1 K for soil temperature (±0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site, with a decrease of the mean snow depth (1 December to 30 April) of 39 cm from the 1960–1990 period to the 1990–2017 period (40 % of the mean snow depth for 1960–1990) and an increase in temperature of +0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data centre: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

scholarly journals What drives basin scale spatial variability of snow water equivalent during two extreme years?

2013 ◽  
Vol 7 (3) ◽  
pp. 2943-2977
Author(s):  
G. A. Sexstone ◽  
S. R. Fassnacht
Keyword(s):  
Linear Regression ◽  
Spatial Variability ◽  
Multiple Linear Regression ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Density Model ◽  
Snow Density ◽  
Depth Measurement ◽  
Basin Scale ◽  
Snow Water

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow water equivalent (SWE) spatial variability at the basin scale. Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE. Bivariate relations and multiple linear regression models were developed to understand the relation of SWE with terrain and canopy variables (derived using a geographic information system (GIS)). Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance and model validation suggests the model is transferable to independent data within the bounds of the original dataset. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time consuming measurements of SWE are often not feasible. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and UTM Northing) were the most important model variables, suggesting that orographic precipitation and storm track patterns are likely consistent drivers of basin scale SWE variability. Terrain characteristics, such as slope, aspect, and curvature, were also shown to be important variables, but to a lesser extent at the scale of interest.

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

scholarly journals Recent fluctuations of meteorological and snow conditions in Japanese mountains

2011 ◽  
Vol 52 (58) ◽  
pp. 209-215 ◽  
Author(s):  
Satoru Yamaguchi ◽  
Osamu Abe ◽  
Sento Nakai ◽  
Atsushi Sato
Keyword(s):  
Air Temperature ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Earth Science ◽  
Meteorological Data ◽  
Winter Precipitation ◽  
Mountainous Areas ◽  
Snow Water ◽  
Notable Increase ◽  
Snow Conditions

AbstarctMeteorological data from mountainous areas of Japan have been collected by the National Research Institute for Earth Science and Disaster Prevention (NIED) for almost 20 years. The collected long-period data indicate that neither a notable increase in mean winter temperature nor a reduction in snow depth has occurred in these areas. The maximum snow depth, SDmax, and maximum snow water equivalent, SWEmax, show similar fluctuation trends, although with large year-to-year variations in value and a larger fluctuation range for SWEmax than for SDmax. This result suggests that monitoring of only SDmax in mountainous areas is not sufficient for understanding the quantitative fluctuation of water resources originating from snow. The SDmax fluctuation trends in mountainous areas sometimes differ from those in flatland areas because mountain SDmax depends more on winter precipitation than on mean winter air temperature, whereas the opposite is true for flatlands. In addition, the dependence ratio of SDmax on fluctuations in winter precipitation changes with altitude because the distributions of precipitation with air temperature change with altitude.

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2017 ◽  
Author(s):  
Esteban Alonso-González ◽  
J.¬Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database.

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2018 ◽  
Vol 10 (1) ◽  
pp. 303-315 ◽  
Author(s):  
Esteban Alonso-González ◽  
J. Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km  ×  10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km  ×  10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

scholarly journals Estimating the snow water equivalent on a glacierized high elevation site (Forni Glacier, Italy)

2018 ◽  
Vol 12 (4) ◽  
pp. 1293-1306 ◽  
Author(s):  
Antonella Senese ◽  
Maurizio Maugeri ◽  
Eraldo Meraldi ◽  
Gian Pietro Verza ◽  
Roberto Sergio Azzoni ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Average Density ◽  
Snow Density ◽  
Alpine Regions ◽  
Yearly Average ◽  
Snow Water ◽  
The Mean ◽  
Observation Period

Abstract. We present and compare 11 years of snow data (snow depth and snow water equivalent, SWE) measured by an automatic weather station (AWS) and corroborated by data from field campaigns on the Forni Glacier in Italy. The aim of the analysis is to estimate the SWE of new snowfall and the annual SWE peak based on the average density of the new snow at the site (corresponding to the snowfall during the standard observation period of 24 h) and automated snow depth measurements. The results indicate that the daily SR50 sonic ranger measurements and the available snow pit data can be used to estimate the mean new snow density value at the site, with an error of ±6 kg m−3. Once the new snow density is known, the sonic ranger makes it possible to derive SWE values with an RMSE of 45 mm water equivalent (if compared with snow pillow measurements), which turns out to be about 8 % of the total SWE yearly average. Therefore, the methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total SWE using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger.

scholarly journals The evaluation of the potential of global data products for snow hydrological modelling in ungauged high alpine catchments

2020 ◽  
Author(s):  
Michael Weber ◽  
Franziska Koch ◽  
Matthias Bernhardt ◽  
Karsten Schulz
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Meteorological Data ◽  
European Alps ◽  
Depth Measurement ◽  
Hybrid Data ◽  
The Mean ◽  
Alpine Catchments ◽  
Global Data

Abstract. Worldwide, there is a strong discrepancy between the importance of high alpine catchments for the water cycle and the availability of meteorological and snow hydrological in situ measurements. Good knowledge on the timing and quantity of snow meltwater is crucial for numerous hydrological applications, also far way downstream. For several decades, the number of global data sets of different meteorological and land surface parameters has been increasing, but their applicability in modelling high alpine regions has been insufficiently investigated so far. We tested such data for a 10-year period with the physically-based Cold Regions Hydrological Model (CRHM). Our study site is the gauged high alpine Research Catchment Zugspitze (RCZ) of 12 km2 in the European Alps. We used a selection of nine different meteorological driver data setups including data transferred from another alpine station, data from an atmospheric model and hybrid data, whereof we investigated data for all meteorological parameters and substituting precipitation only. For one product, we applied an advanced downscaling approach to test the advantage of such methods. The range between all setups is high at 3.5 °C for the mean decadal temperature and at 1510 mm for the mean decadal precipitation sum. The comparison of all model results with measured snow depth and reference simulations driven with in situ meteorological data demonstrates that the setup with the transferred data performs best, followed by the substitution of precipitation only with hybrid data. All other setups were unrealistic or showed plausible results only for some parts of the RCZ. As a second goal, we investigated potential differences in model performance resulting from topographic parameterization according to three globally available digital elevation models (DEMs); two with 30 m and one with 1 km resolution. As reference, we used a 2.5 m resolution DEM. The simulations with all DEM setups performed well at the snow depth measurement sites and on catchment scale, even if they indicate considerable differences. Differences are mainly caused by product specific topography induced differences in solar radiation. Surprisingly, the setup with the coarsest DEM performed best in describing the catchment mean due to averaged out topographic differences. However, this was not the case for a finer resolution. For the two plausible meteorological setups and all DEM setups, we additionally investigated the maximum quantity and the temporal development of the snowpack as well as the runoff regime. Even those quite plausible setups revealed differences of up to 20 % in snowpack volume and duration, which consequently lead to considerable shifts in runoff. Overall, we could demonstrate that global data are a valuable source to substitute single missing meteorological variables or topographic information, but the exclusive use of such driver data does not provide sufficiently accurate results for the RCZ. For the future, however, we expect an increasing role of global data in modelling ungauged high alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

scholarly journals A 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 5 (1) ◽  
pp. 29-45 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist in measurements of air temperature, relative humidity, wind speed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Evaluation data are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information are provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free "settling" disks. This dataset has been partially used in the past to assist in developing snowpack model and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://doi.pangaea.de/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

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