The influence of forest and topography on snow accumulation and melt at the watershed-scale

Jost, G.; Weiler, Markus; Gluns, David R.; Alila, Younes

doi:10.1016/j.jhydrol.2007.09.006

Cited by 195 publications

(199 citation statements)

References 26 publications

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“…Bivariate relations showed that SWE increased with increasing elevation, with the steepness of this trend being greater in WY 2011 than 2012. The strength of the correlation between SWE and elevation for WY 2011 (r = 0.75) and WY 2012 (r = 0.68) suggests that elevation is the most important physiographic variable for driving the distribution of SWE across the study domain, which is consistent with previous findings from studies evaluating SWE at the basin scale (e.g., Fassnacht et al, 2003;Jost et al, 2007;Harshburger et al, 2010). As UTM Northing increases, SWE decreases in WY 2011, suggesting northern regions of the study area receive less snow than southern regions (as suggested by J. Meiman, personal communication, 2010), yet this trend was not apparent in the low snow year of 2012.…”

Section: Basin Scale Swe Variabilitysupporting

confidence: 89%

What drives basin scale spatial variability of snowpack properties in northern Colorado?

Sexstone

Fassnacht

2014

The Cryosphere

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Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km 2 ). 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 near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. 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 data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many timeconsuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

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Section: Basin Scale Swe Variabilitysupporting

confidence: 89%

What drives basin scale spatial variability of snowpack properties in northern Colorado?

Sexstone

Fassnacht

2014

The Cryosphere

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“…Value 1 represents south orientation, value 0 represents north orientation and 0.5 represents both east and west orientations. A similar approach was used by Jost et al (2007).…”

Section: Predictors and Response Variablesmentioning

confidence: 99%

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

Jeníček

Pevná

Matějka

2017

Journal of Hydrology and Hydromechanics

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Abstract:The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.

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“…North-facing slopes also have more snow than south-facing slopes due to lower , . Although no maps of observed snow depth are available for comparison, large-scale distributions of 15 snowpack are known to be controlled by elevation, land cover, and slope/aspect (Fassnacht et al, 2017;Jost et al, 2007), which is more consistent with the RTI model. Figure 3 shows the snow depths from the TI and RTI models at all 8 test locations and compares them to the observations.…”

Section: Snow Depth and Swe (Ti Vs Rti)mentioning

confidence: 64%

A Simple Temperature-Based Method to Estimate Heterogeneous Frozen Ground within a Distributed Watershed Model

Follum¹,

Niemann²,

Parno³

et al. 2017

Preprint

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Frozen ground can be important to flood production and is often heterogeneous within a watershed due to spatial 10 variations in the available energy, insulation by snowpack and ground cover, and the thermal and moisture properties of the soil. The widely-used Continuous Frozen Ground Index (CFGI) model is a degree-day approach and identifies frozen ground using a simple frost index, which varies mainly with elevation through a temperature-elevation relationship.Similarly, snow depth and its insulating effect are also estimated based on elevation. The objective of this work is to more accurately represent the spatial heterogeneity of frozen ground in a distributed hydrologic model, Gridded Surface 15 Subsurface Hydrologic Analysis (GSSHA), by modifying the CFGI method. Among the modifications, the snowpack and frost indices are simulated by replacing air temperature (a surrogate for the available energy) with a radiation-derived temperature that aims to better represent spatial variations in available energy. Ground cover is also included as an additional insulator of the soil. Furthermore, the modified Berggren Equation, which accounts for soil thermal conductivity and soil moisture, is used to convert the frost index into frost depth. The modified CFGI model is tested by application at six 20 test sites within the Sleepers River Experimental Watershed in Vermont. Compared to the CFGI model, the modified CFGI model more accurately captures the variations in frozen ground between the sites, inter-annual variations in frozen ground depths at a given site, and the occurrence of frozen ground.

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The influence of forest and topography on snow accumulation and melt at the watershed-scale

Cited by 195 publications

References 26 publications

What drives basin scale spatial variability of snowpack properties in northern Colorado?

What drives basin scale spatial variability of snowpack properties in northern Colorado?

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

A Simple Temperature-Based Method to Estimate Heterogeneous Frozen Ground within a Distributed Watershed Model

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