Temporal inconsistencies in coarse-scale snow water equivalent patterns: Colorado River Basin snow telemetry-topography regressions

Fassnacht, S. R.; Dressler, K. A.; Hultstrand, Douglas M.; Bales, R. C.; Patterson, Glenn G.

doi:10.3989/pirineos.2012.167008

Cited by 25 publications

(28 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third most important predictor is elevation, shown to be an important predictor in previous studies (Fassnacht et al, 2003;Fassnacht et al, 2012;Schneider and Molotch, 2016). The fourth most important variable is longitude, followed by TB 18V -TB 36V , the difference between microwave brightness temperatures at 18 and 36 GHz, showing that the passive microwave SWE retrievals have little predictive power.…”

Section: Resultsmentioning

confidence: 79%

See 1 more Smart Citation

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

et al. 2018

View full text Add to dashboard Cite

Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques -bagged regression trees and feed-forward neural networks -produced similar mean results, with 0-14 % bias and 46-48 mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

show abstract

Section: Resultsmentioning

confidence: 79%

“…A mix of static physiographic (Fassnacht et al, 2012) and dynamic variables were used as predictors (Table 2). All variables were computed at or resampled to 3.125 km resolution using Gaussian pyramid reduction or expansion (Burt and Adelson, 1983) for the initial steps and bilinear interpolation for the final step.…”

Section: Predictors and Targetmentioning

confidence: 99%

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Future basin scale snow studies should focus on incorporating a more accurate representation of the influence of forest canopy and solar radiation on snowpack variability . Given that studies (e.g., Fassnacht et al, 2012) have shown the spatial variability of snow accumulation to be described by different physiographic variables from year to year, additional years of data collection at the basin scale are needed for a more complete evaluation of the drivers of SWE distribution.…”

Section: Discussionmentioning

confidence: 99%

“…The difference in elevation is obvious since field data are located more at higher elevations than the entire domain (Fig. 5a), and the operational data tend to be located in a small elevation zone (Fassnacht et al, 2012). Northness is highly correlated to solar radiation, and both are related to slope so the significance difference for each of these variables is partly based on their correlation.…”

Section: Basin Scale Swe Variabilitymentioning

confidence: 99%

“…Aspect can be problematic as an independent variable due to its continuous range of 0 to 360 • , thus normalizing this variable is necessary. Degrees of northness and eastness were calculated to normalize the aspect variable (Fassnacht et al, 2001;Fassnacht et al, 2012). Degree of northness is the product of the cosine of aspect and the sine of slope , while degree of eastness is the product of the sine of aspect and the sine of slope.…”

Section: Basin Scale Swe Variabilitymentioning

confidence: 99%

See 1 more Smart Citation

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

Sexstone

Fassnacht

2014

The Cryosphere

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

et al. 2015

View full text Add to dashboard Cite

Abstract:During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow-free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate-resolution imaging spectroradiometer images to produce snow-cover depletion curves. The snow depletion curves were created for an 80 000 km 2 domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter-annual consistency.

show abstract

Temporal inconsistencies in coarse-scale snow water equivalent patterns: Colorado River Basin snow telemetry-topography regressions

Cited by 25 publications

References 19 publications

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

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

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

Contact Info

Product

Resources

About