Deriving snow-cover depletion curves for different spatial scales from remote sensing and snow telemetry data

Fassnacht, S. R.; Sexstone, G. A.; Kashipazha, Amir; López‐Moreno, Juan I.; Jasinski, Michael F.; Kampf, Stephanie K.; Thaden, Benjamin C. Von

doi:10.1002/hyp.10730

Cited by 23 publications

(15 citation statements)

References 37 publications

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“…The inverse relation of SCA and z 0 ( Figure 3) [50] is affected by the underlying terrain and size of the roughness features. As the snow accumulation increases, the roughness elements become buried, and the topography appears to be smooth [50,51]. This relation indicates that as snow accumulates over topographic features the snow will begin to level out at a z 0 height dependent scale.…”

Section: Discussionmentioning

confidence: 98%

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

et al. 2018

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When applied to a snow-covered surface, aerodynamic roughness length, z0, is typically considered as a static parameter within energy balance equations. However, field observations show that z0 changes spatially and temporally, and thus z0 incorporated as a dynamic parameter may greatly improve models. To evaluate methods for characterizing snow surface roughness, we compared concurrent estimates of z0 based on (1) terrestrial light detection and ranging derived surface geometry of the snowpack surface (geometric, z0G) and (2) vertical wind profile measurements (anemometric, z0A). The value of z0G was computed from Lettau’s equation and underestimated z0A but compared well when scaled by a factor of 2.34. The Counihan method for computing z0G was found to be unsuitable for estimating z0 on a snow surface. During snowpack accumulation in early winter, z0 varied as a function of the snow-covered area (SCA). Our results show that as the SCA increases, z0 decreases, indicating there is a topographic influence on this relation.

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Section: Discussionmentioning

confidence: 98%

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

et al. 2018

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“…Because this technique assumes uniform snowmelt conditions, the derivation of the SDCs did not change when changing the mean pre-melt SWE or snowmelt rate and were only dependent on the CV value. Each SDC represents how the aerial SCA of an HRU decreases with the fractional decrease of SWE below the threshold SWE value (SWE 100 ) for the given HRU (Donald et al, 1995;Fassnacht et al, 2016;Markstrom et al, 2015). As described above, the simulated snowmelt input from the SCA in PRMS is uniformly broadcasted across the entire HRU.…”

Section: Model Experimentsmentioning

confidence: 99%

“…Using these SDCs as a starting point, targeted calibration strategies (based on the sensitivity results presented in this study) to remotely sensed SCA observations could then be completed (Hay, 2019). Calibration could then be evaluated using both SWE and SCA observations from both remote sensing platforms and ground-based observations (e.g., Arsenault & Houser, 2018;Fassnacht et al, 2016) as well as results from fine-scale snow modelling applications (e.g., Broxton, van Leeuwen, & Biederman, 2019;Hedrick et al, 2018;Sexstone et al, 2018). Additionally, known relations between SDCs and topography and climatic variables (Driscoll et al, 2017) could be utilized for evaluation.…”

Section: Implications For Future Modelling Applicationsmentioning

confidence: 99%

“…Luce and Tarboton () highlight that dimensionless SDCs can show good inter‐annual agreement, but also suggest there may be environments or scales where dimensionless SDCs may change from year to year. Furthermore, SDCs are difficult to derive over large regions (Driscoll et al, ; Fassnacht et al, ; Shamir & Georgakakos, ), variable in their representation between models (Essery & Pomeroy, ; Liston, ), and often applied consistently over heterogeneous landscapes. Where more realistically, the representation of the SWE‐SCA relation should be regionally variable (Driscoll et al, ), with differences between landscape type and melt energy accounted for by the HRUs (DeBeer & Pomeroy, , ; Donald, Soulis, Kouwen, & Pietroniro, ; Dornes, Pomeroy, Pietroniro, Carey, & Quinton, ), that change based on the scale of interest (Blöschl, ; Seyfried & Wilcox, ).…”

Section: Introductionmentioning

confidence: 99%

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Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

Sexstone

Driscoll

Hay

et al. 2020

Hydrological Processes

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The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day −1 resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications.

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“…Snow depletion curves (SDCs) are a simple and dynamic method used in hydrologic models to describe snowmelt processes (Martinec and Rango, ; Liston, ; Markstrom et al ., ). SDCs relate snow covered area (SCA) to snow water equivalent (SWE) for a given hydrologic response unit (HRU) over a snowmelt season (Luce and Tarboton, ; Kolberg and Gottschalk, ; Fassnacht et al ., ). Snowmelt is a unidirectional process over time, so SDCs inherently include a temporal factor.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Driscoll

Hay

Bock

2017

J American Water Resour Assoc

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Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.

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Deriving snow-cover depletion curves for different spatial scales from remote sensing and snow telemetry data

Cited by 23 publications

References 37 publications

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

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