scholarly journals Inferring Watershed-scale Mean Snow Magnitude and Distribution Using Multidecadal Snow Reanalysis Patterns and Snow Pillow Observations

2021 ◽  
Author(s):  
Justin Pflug ◽  
Steven Margulis ◽  
Jessica Lundquist
Keyword(s):  
Spatial Heterogeneity ◽  
Sierra Nevada ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Airborne Lidar ◽  
Watershed Scale ◽  
Mountainous Regions ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Deposition

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2017 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Mariken Homleid
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Water Resource Management ◽  
Snow Accumulation ◽  
Surface Model ◽  
Snow Melt ◽  
Grid Spacing ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, thirty percent of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of approximately 1 km over an area in southern Norway for two years (01 September 2014–31 August 2016), using two different forcing data sets: 1) hourly meteorological forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing), and 2) gridded hourly observations of temperature and precipitation (1 km grid spacing) in combination with the meteorological forecasts from AROME MetCoOp. We present an evaluation of the modeled snow depth and snow cover, as compared to point observations of snow depth and to MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snow melt. The results are promising. Both experiments are capable of simulating the snow pack over the two winter seasons, but there is an overestimation of snow depth when using only meteorological forecasts from AROME MetCoOp, although the snow-covered area throughout the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season, showing that assimilation of snow depth observations into SURFEX/Crocus might be necessary when using only meteorological forecasts as forcing. When using gridded observations, the simulation of snow depth is significantly improved, which shows that using a combination of gridded observations and meteorological forecasts to force a snowpack model is very useful and can give better results than only using meteorological forecasts. There is however an underestimation of snow ablation in both experiments. This is mainly due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2018 ◽  
Vol 12 (6) ◽  
pp. 2123-2145 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Åsmund Bakketun ◽  
Mariken Homleid
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Surface Model ◽  
Observation Data ◽  
Grid Spacing ◽  
Data Set ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m grid spacing) and wind, and (2) gridded hourly observations of temperature and precipitation (1 km grid spacing) combined with meteorological forecasts from AROME MetCoOp for the remaining weather variables required by SURFEX/Crocus. We present an evaluation of the modelled snow depth and snow cover in comparison to 30 point observations of snow depth and MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snowmelt. Both experiments are capable of simulating the snowpack over the two winter seasons, but there is an overestimation of snow depth when using meteorological forecasts from AROME MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area in the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season. When using gridded observations, the simulation of snow depth is significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm), but the spatial snow cover distribution is not well captured during the melting season. Underestimation of snow depth at high elevations (due to the low elevation bias in the gridded observation data set) likely causes the snow cover to decrease too soon during the melt season, leading to unrealistically little snow by the end of the season. Our results show that forcing data consisting of post-processed NWP data (observations assimilated into the raw NWP weather predictions) are most promising for snow simulations, when larger regions are evaluated. Post-processed NWP data provide a more representative spatial representation for both high mountains and lowlands, compared to interpolated observations. There is, however, an underestimation of snow ablation in both experiments. This is generally due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snowmelt and biases in the forcing data.

scholarly journals Fractional snow-covered area: scale-independent peak of winter parameterization

2021 ◽  
Vol 15 (2) ◽  
pp. 615-632
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Empirical Relationship ◽  
Ground Surface ◽  
Model Parameters ◽  
Data Sets ◽  
Snow Covered Area ◽  
Covered Area ◽  
Geographical Regions

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

scholarly journals Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

2016 ◽  
Vol 10 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Z. Zheng ◽  
P. B. Kirchner ◽  
R. C. Bales
Keyword(s):  
Sierra Nevada ◽  
Snow Depth ◽  
Point Cloud ◽  
Accumulation Point ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Conifer Forest ◽  
Vegetation Effects ◽  
Cloud Data ◽  
Depth Measurement

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

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 Using crowdsourced web content for informing water systems operations in snow-dominated catchments

2016 ◽  
Vol 20 (12) ◽  
pp. 5049-5062 ◽  
Author(s):  
Matteo Giuliani ◽  
Andrea Castelletti ◽  
Roman Fedorov ◽  
Piero Fraternali
Keyword(s):  
Low Cost ◽  
Management Problem ◽  
Web Content ◽  
Multiple Sources ◽  
Mountainous Regions ◽  
Related Information ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wide Range ◽  
Web Images

Abstract. Snow is a key component of the hydrologic cycle in many regions of the world. Despite recent advances in environmental monitoring that are making a wide range of data available, continuous snow monitoring systems that can collect data at high spatial and temporal resolution are not well established yet, especially in inaccessible high-latitude or mountainous regions. The unprecedented availability of user-generated data on the web is opening new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatiotemporally dense. In this paper, we contribute a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic webcams. A fully automated process fetches mountain images from multiple sources, identifies the peaks present therein, and estimates virtual snow indexes representing a proxy of the snow-covered area. Our procedure has the potential for complementing traditional snow-related information, minimizing costs and efforts for obtaining the virtual snow indexes and, at the same time, maximizing the portability of the procedure to several locations where such public images are available. The operational value of the obtained virtual snow indexes is assessed for a real-world water-management problem, the regulation of Lake Como, where we use these indexes for informing the daily operations of the lake. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance.

Dynamic Snow Distribution Modeling using the Fokker-Planck Equation Approach

2021 ◽  
Author(s):  
Noriaki Ohara
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Planck Equation ◽  
Snow Accumulation ◽  
Airborne Lidar ◽  
Equation Approach ◽  
Diffusion Type ◽  
Fokker Planck Equation ◽  
Time Space ◽  
Fokker Planck

<p>The Fokker-Planck equation (FPE) describes the time evolution of the distribution function of fluctuating macroscopic variables.  Although the FPE was originally derived for the Brownian motion, this framework can be applied to various physical processes.  In this presentation, applications in the snow accumulation and thaw process, which attributes to considerable spatial and temporal variations, are discussed. It is well known that snow process is a major source of heterogeneity in hydrological systems in high altitude or latitude regions; therefore, better treatment of the snow sub-grid variability is desirable. The main advantage of the FPE approach is that it can dynamically compute the probability density function (PDF) governed by an advection-diffusion type FPE without a prescribed PDF.</p><p>First, a bivariate FPE was derived from point scale process-based governing equations (Ohara et al., 2008). This FPE can express the evolution of the PDF of snow depth and temperature within a finite space, possibly a computational cell or small basin, whose shape is irrelevant. This conceptual model was proven to be effective through comparing to the corresponding Monte-Carlo simulation.  Then, the more realistic single variated FPE model for snow depth was implemented with the snow redistribution and snowmelt rate as the main sources of stochasticity. In this study, several realistic approximations were proposed to compute the time-space covariances describing effects induced by uneven snowmelt and snow redistribution.</p><p>Meanwhile, observed high-resolution snow depth data was analyzed using statistical methods to characterize the sub-grid variability of snow depth, which is essential to validate the FPE model for representing such sub-grid variability.  Airborne light detection and ranging (Lidar) provided the snow depth measurements at 0.5 m resolution over two mountainous areas in southwestern Wyoming, Snowy Range and Laramie Range (He et al., 2019). It was found that PDFs of snow depth tend to be Gaussian distributions in the forest areas. However, due to the no-snow areas effect, mainly caused by snow redistribution and uneven snowmelt, the PDFs are eventually skewed as non-Gaussian distribution.</p><p>The simulated results of the FPE model were validated using the measured time series of snow depth at one site and the spatial distributions of snow depth measured by ground penetrating radar (GPR) and airborne Lidar. The modeled and observed time series of the mean snow depth agreed very well while the simulated PDFs of snow depth within the study area were comparable to the observed PDFs of snow depth by GPR and Lidar (He and Ohara, 2019). Accordingly, the FPE model is capable to capture the main characteristics of the snow sub-grid variability in the nature.</p><p><strong>References</strong></p><p>Ohara, N., Kavvas, M. L., & Chen, Z. Q. (2008). Stochastic upscaling for snow accumulation and melt processes with PDF approach. Journal of Hydrologic Engineering, 13(12), 1103-1118.</p><p>He, S., Ohara, N., & Miller, S. N. (2019). Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements. Hydrological Processes, 33(11), 1525-1537.</p><p>He, S., & Ohara, N. (2019). Modeling subgrid variability of snow depth using the Fokker‐Planck equation approach. Water Resources Research, 55(4), 3137-3155.</p>

scholarly journals Spatial Variability of Shortwave Irradiance for Snowmelt in Forests

2008 ◽  
Vol 9 (6) ◽  
pp. 1482-1490 ◽  
Author(s):  
John Pomeroy ◽  
Chad Ellis ◽  
Aled Rowlands ◽  
Richard Essery ◽  
Janet Hardy ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Variation ◽  
Snow Cover ◽  
Boreal Forests ◽  
Stand Structure ◽  
Snow Accumulation ◽  
Snow Covered Area ◽  
Covered Area ◽  
Depletion Curve ◽  
Natural Snow

Abstract The spatial variation of melt energy can influence snow cover depletion rates and in turn be influenced by the spatial variability of shortwave irradiance to snow. The spatial variability of shortwave irradiance during melt under uniform and discontinuous evergreen canopies at a U.S. Rocky Mountains site was measured, analyzed, and then compared to observations from mountain and boreal forests in Canada. All observations used arrays of pyranometers randomly spaced under evergreen canopies of varying structure and latitude. The spatial variability of irradiance for both overcast and clear conditions declined dramatically, as the sample averaging interval increased from minutes to 1 day. At daily averaging intervals, there was little influence of cloudiness on the variability of subcanopy irradiance; instead, it was dominated by stand structure. The spatial variability of irradiance on daily intervals was higher for the discontinuous canopies, but it did not scale reliably with canopy sky view. The spatial variation in irradiance resulted in a coefficient of variation of melt energy of 0.23 for the set of U.S. and Canadian stands. This variability in melt energy smoothed the snow-covered area depletion curve in a distributed melt simulation, thereby lengthening the duration of melt by 20%. This is consistent with observed natural snow cover depletion curves and shows that variations in melt energy and snow accumulation can influence snow-covered area depletion under forest canopies.

scholarly journals Topographical effects of Snow Deposition on Restructured Land

1980 ◽  
Vol 26 (94) ◽  
pp. 518-518
Author(s):  
E. Chaco ◽  
M. Molnau
Keyword(s):  
Snow Depth ◽  
Winter Season ◽  
Surface Mining ◽  
Snow Accumulation ◽  
Grid Data ◽  
Grid Pattern ◽  
Contour Maps ◽  
Snow Properties ◽  
Waste Dumps ◽  
Snow Deposition

AbstractThe measurement of snow accumulation and distribution is one of the primary objectives of a study on the melt of snow-drifts and erosion in the phosphate mining region of south-eastern Idaho. The study area is located in an active phosphate mine and is limited to the sites of waste dumps, a product of the surface mining technique used in this area. Four sites are included in the overall study with one dump selected for intensive snow measurement. Snow deposition data have been collected for one winter season (November 1977—June 1978) on a grid pattern over this dump. The area of the study site has been expanded and similar measurements are planned for the coming snow season.The snow measurements were made monthly on a pre-established 23 m (75 ft) square grid overlaid on the dump. The analysis of the snow data consists of contour mapping of any one or all the snow properties measured—snow depth, density, or water equivalent. In addition, since the measurements are made on the same grid each month, mathematical manipulation of grid data allows contour maps of the residual of the monthly snow properties to be plotted. A similar analysis of terrain properties collected on the same grid results in contour maps displaying ground slope, concavity-convexity of the surface, aspect, or distance from snow- deposition obstacles.The aim of the analysis using these types of data is to arrive at a model which will compute patterns of snow accumulation and distribution on the ground surface given a description of terrain type and probable meteorological properties of the region. A preliminary comparison of the maps shows a similar pattern of snow deposition occurring each month with the exposed areas of the dump swept clean and the greatest snow depth occurring in the sheltered concavities.

scholarly journals A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015)

2016 ◽  
Vol 17 (4) ◽  
pp. 1203-1221 ◽  
Author(s):  
Steven A. Margulis ◽  
Gonzalo Cortés ◽  
Manuela Girotto ◽  
Michael Durand
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Reanalysis Dataset ◽  
In Situ Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Fully Bayesian ◽  
Mean Square Errors

Abstract A newly developed state-of-the-art snow water equivalent (SWE) reanalysis dataset over the Sierra Nevada (United States) based on the assimilation of remotely sensed fractional snow-covered area data over the Landsat 5–8 record (1985–2015) is presented. The method (fully Bayesian), resolution (daily and 90 m), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The reanalysis dataset was used to characterize the peak SWE climatology to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years. The pixel-wise peak SWE volume over the domain was found to be 20.0 km3 on average with a range of 4.0–40.6 km3. The ongoing drought in California contains the two lowest snowpack years (water years 2014 and 2015) and three of the four driest years over the examined record. It was found that the basin-average peak SWE, while underestimating the total water storage in snowpack over the year, accurately captures the interannual variability in stored snowpack water. However, the results showed that the assumption that 1 April SWE is representative of the peak SWE can lead to significant underestimation of basin-average peak SWE both on an average (21% across all basins) and on an interannual basis (up to 98% across all basin years).

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