Spatial variability of snow water equivalent in a mountainous area of the Japanese Central Alps

2008 ◽  
Vol 113 (F3) ◽  
Author(s):  
Motoki Tanaka ◽  
Keisuke Suzuki
Keyword(s):  
Spatial Variability ◽  
Snow Water Equivalent ◽  
Mountainous Area ◽  
Central Alps ◽  
Snow Water

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

The effect of spatial variability on the sensitivity of passive microwave measurements to snow water equivalent

2013 ◽  
Vol 136 ◽  
pp. 163-179 ◽  
Author(s):  
Benjamin J. Vander Jagt ◽  
Michael T. Durand ◽  
Steven A. Margulis ◽  
Edward J. Kim ◽  
Noah P. Molotch
Keyword(s):  
Spatial Variability ◽  
Snow Water Equivalent ◽  
Passive Microwave ◽  
Microwave Measurements ◽  
Snow Water

Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

2020 ◽  
Vol 21 (11) ◽  
pp. 2713-2733 ◽  
Author(s):  
Graham A. Sexstone ◽  
Colin A. Penn ◽  
Glen E. Liston ◽  
Kelly E. Gleason ◽  
C. David Moeser ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Rio Grande ◽  
Winter Precipitation ◽  
Time Step ◽  
The North ◽  
Spatial Coverage ◽  
Snow Water ◽  
Snowpack Properties

AbstractThis study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

scholarly journals Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

2016 ◽  
Vol 64 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Pavel Krajčí ◽  
Michal Danko ◽  
Jozef Hlavčo ◽  
Zdeněk Kostka ◽  
Ladislav Holko
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Flood Forecasting ◽  
Snow Accumulation ◽  
Small Scale ◽  
Climatic Data ◽  
Tatra Mountains ◽  
Snow Water

AbstractSnow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

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 A model for the spatial distribution of snow water equivalent parameterised from the spatial variability of precipitation

2016 ◽  
Author(s):  
Thomas Skaugen ◽  
Ingunn H. Weltzien
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Snow Distribution ◽  
Rainfall Runoff Model ◽  
Log Normal

Abstract. Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation (SD_G), is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The latter model (SD_LN) has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution. The two models are implemented in the already parameter parsimonious rainfall runoff model Distance Distribution Dynamics (DDD) and their capability for predicting runoff, SWE and snow covered area (SCA) are tested and compared for 71 Norwegian catchments. Results show that SD_G better simulates SCA when compared with MODIS satellite derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash-Sutcliffe and Kling Gupta Criterion of 0.01, but it is shown that high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Snow Layer ◽  
Data Set ◽  
Correlation Lengths ◽  
Snow Water ◽  
Snowpack Properties ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across 9 trenches) collected over two winters at Trail Valley Creek, NWT, Canada, were applied in synthetic radiative transfer experiments. This allowed robust assessment of the impact of first guess information of snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability of total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths were sub-metre for all layers. Depth hoar was consistently ~ 30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and SSA of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks

scholarly journals Modelling the spatial variability of snow water equivalent at the catchment scale

2007 ◽  
Vol 11 (5) ◽  
pp. 1543-1550 ◽  
Author(s):  
T. Skaugen
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Gamma Distribution ◽  
Snow Water Equivalent ◽  
Temporal Correlation ◽  
Catchment Scale ◽  
Snow Season ◽  
Dynamical Parameters ◽  
Snow Water ◽  
Alpine Catchments

Abstract. The spatial distribution of snow water equivalent (SWE) is modelled as a two parameter gamma distribution. The parameters of the distribution are dynamical in that they are functions of the number of accumulation and melting events and the temporal correlation of accumulation and melting events. The estimated spatial variability is compared to snow course observations from the alpine catchments Norefjell and Aursunden in Southern Norway. A fixed snow course at Norefjell was measured 26 times during the snow season and showed that the spatial coefficient of variation change during the snow season with a decreasing trend from the start of the accumulation period and a sharp increase in the melting period. The gamma distribution with dynamical parameters reproduced the observed spatial statistical features of SWE well both at Norefjell and Aursunden. Also the shape of simulated spatial distribution of SWE agreed well with the observed at Norefjell. The temporal correlation tends to be positive for both accumulation and melting events. However, at the start of melting, a better fit between modelled and observed spatial standard deviation of SWE is obtained by using negative correlation between SWE and melt.

scholarly journals Elevation-dependent behavior of hoar-prominent snowpack on forest slopes in the Japanese Central Alps based on a decade of observations

2018 ◽  
Vol 59 (77) ◽  
pp. 77-86
Author(s):  
Yusuke Harada ◽  
Ryuzo Wakabayashi ◽  
Yoshikage Inoue
Keyword(s):  
Linear Correlation ◽  
Snow Water Equivalent ◽  
Quadratic Function ◽  
Central Alps ◽  
Monthly Basis ◽  
Air Temperatures ◽  
Dependent Behavior ◽  
Strong Linear Correlation ◽  
Snow Water ◽  
The Impact

ABSTRACTFull snow-pit observations were performed on a monthly basis over ten winter seasons from 1995 to 2004, at 15 study plots spaced at 100 m elevation intervals (1300–2700 m a.s.l.) in the mountainous forest of the Japanese Central Alps. We observed 514 pits with an average depth of 1.12 m. Density measurements were taken in 2610 snow layers in total. Monthly trends indicate that snow depth has a strong linear correlation with elevation and that the mean density of snow cover has a moderate linear correlation with elevation in midwinter. Snow water equivalent can increase as a quadratic function of elevation in January and February. For this reason, the influence of overburden load and wind packing is elevation-dependent from January to February, a period when a facet-prominent snowpack existed on account of low snow and air temperatures. The density of depth hoar is greater at higher elevations than it is for rounded grains in midwinter due to densification. On forested slopes, with increasing elevation, snowfall frequency and the impact of wind upon snow increases while air temperature decreases, causing elevational variance in grain shapes.

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