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 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/.

scholarly journals Snow water equivalent modeling components in NewAge-JGrass

2014 ◽  
Vol 7 (3) ◽  
pp. 725-736 ◽  
Author(s):  
G. Formetta ◽  
S. K. Kampf ◽  
O. David ◽  
R. Rigon
Keyword(s):  
Goodness Of Fit ◽  
Hydrological Modeling ◽  
Snow Water Equivalent ◽  
Short Wave ◽  
Wave Radiation ◽  
Radiation Balance ◽  
Time Step ◽  
Vector Mode ◽  
Snow Water ◽  
Snow Model

Abstract. This paper presents a package of modified temperature-index-based snow water equivalent models as part of the hydrological modeling system NewAge-JGrass. Three temperature-based snow models are integrated into the NewAge-JGrass modeling system and use many of its components such as those for radiation balance (short wave radiation balance, SWRB), kriging (KRIGING), automatic calibration algorithms (particle swarm optimization) and tests of goodness of fit (NewAge-V), to build suitable modeling solutions (MS). Similarly to all the NewAge-JGrass components, the models can be executed both in raster and in vector mode. The simulation time step can be daily, hourly or sub-hourly, depending on user needs and availability of input data. The MS are applied on the Cache la Poudre River basin (CO, USA) using three test applications. First, daily snow water equivalent is simulated for three different measurement stations for two snow model formulations. Second, hourly snow water equivalent is simulated using all the three different snow model formulae. Finally, a raster mode application is performed to compute snow water equivalent maps for the whole Cache la Poudre Basin.

Ash on Snow a Tool – A Tool to Prevent Flooding?

2001 ◽  
Vol 32 (3) ◽  
pp. 195-214 ◽  
Author(s):  
Angela Lundberg ◽  
H Beyerl
Keyword(s):  
Snow Water Equivalent ◽  
Short Wave ◽  
Wood Ash ◽  
Wave Radiation ◽  
Short Wave Radiation ◽  
Index Method ◽  
Temperature Index ◽  
Index Model ◽  
Snow Water ◽  
Natural Snow

Years with late spring in combination with thick snow-pack constitute risk for flooding. To decrease that risk, the possibility of spreading albedo-lowering material (wood ash) on parts of a basin snow has been examined. By blackening the snow more solar radiation is absorbed and the snowmelt is enhanced. If sun-exposed parts of the basin are ash-treated (before normal runoff starts) the runoff will be distributed over a longer period of time and the risk of flooding will be reduced. Wood ash in different concentrations was spread on small snow plots and melt rates and albedo were measured. For snow covered with 0.03 kg ash m−2, the albedo was decreased from ≈ 0.60 for natural snow to ≈ 0.25, resulting in ≈ 90% more absorbed short-wave radiation. Melt on the ash treated surface, (daily average snow water equivalent), was 70% larger than melt on natural snow (12 and 7 mm d−1 respectively). A five times larger concentration (0.15 kg m−2) only increased the melt rate to 14 mm d−1. The temperature-index method was shown to be inadequate for modelling the melt rate for the ash treated snow. A radiation-index model, based on absorbed incoming short wave radiation, was shown to model the melt rate better than the temperature-index method.

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 Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals CrocO_v1.0 : a Particle Filter to assimilate snowpack observations in a spatialised framework

2020 ◽  
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
Emmanuel Cosme ◽  
Clément Albergel ◽  
Louis-François Meunier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Near Infrared ◽  
Snow Water Equivalent ◽  
Sources Of Information ◽  
Average Improvement ◽  
Modelling Uncertainties ◽  
Snow Water ◽  
Spatio Temporal ◽  
Snowpack Properties

Abstract. Monitoring the evolution of the snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In-situ and remotely sensed observations provide precious information on the snowpack but usually offer a limited spatio-temporal coverage of bulk or surface variables only. In particular, visible-near infrared (VIS-NIR) reflectance observations can inform on the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables to estimate any physical variable, virtually anywhere, but is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information, and to propagate information from observed areas to non observed areas. Here, we present CrocO, (Crocus-Observations) an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and VIS-NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties, and a Particle Filter (PF) to reduce them. The PF is known to collapse when assimilating a too large number of observations. Two variants of the PF were specifically implemented to ensure that observations information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Experiments are carried out in a twin experiment setup, with synthetic observations of HS and VIS-NIR reflectances available in only a 1/6th of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of snow water equivalent Continuous Rank Probability Score (CRPS) of 60 % when assimilating HS with a 40-member ensemble, and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a way for the assimilation of real observations of reflectance, or of any snowpack observations in a spatialised context.

scholarly journals Snowpack variability and trends at long-term stations in northern Colorado, USA

2015 ◽  
Vol 371 ◽  
pp. 131-136 ◽  
Author(s):  
S. R. Fassnacht ◽  
M. Hultstrand
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Entire Period ◽  
Northern Colorado ◽  
Snow Water ◽  
Highly Correlated ◽  
The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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