A Comparison of Snow Telemetry and Snow Course Measurements in the Colorado River Basin

Dressler, K. A.; Fassnacht, S. R.; Bales, R. C.

doi:10.1175/jhm506.1

Cited by 47 publications

(33 citation statements)

References 12 publications

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“…Some snow courses are collocated with automated snow telemetry (SNOTEL) precipitation stations; however, the majority of SNOTEL data begins less than 20 years ago, too short a time to detect a slowly evolving anthropogenic signal. There also tend to be disagreements between reported snowfall and SWE even for collocated SNOTEL and snow courses, although snow course SWE measurements correlate over longer distances than SNOTEL snowfall measurements (Dressler et al 2006). This is helpful for our purposes, as it suggests the snow course measurements have less noise.…”

Section: B Precipitationmentioning

confidence: 94%

Attribution of Declining Western U.S. Snowpack to Human Effects

et al. 2008

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Observations show snowpack has declined across much of the western United States over the period 1950-99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D-A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean-atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D-A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.

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Section: B Precipitationmentioning

confidence: 94%

Attribution of Declining Western U.S. Snowpack to Human Effects

et al. 2008

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“…At each SNOTEL site, the weight of snow on a liquid-lled pillow is measured hourly by a pressure sensor and converted to SWE [16,36,37]. e high temporal resolution and long time series of the SNOTEL record (since the late 1970s) make it uniquely suited to the primary goals of this study.…”

Section: Snow Water Equivalent (Swe)mentioning

confidence: 99%

Predictive Contributions of Snowmelt and Rainfall to Streamflow Variations in the Western United States

Zheng

Wang

Li-hua

et al. 2018

Advances in Meteorology

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is study used long-term in situ rainfall, snow, and streamflow data to explore the predictive contributions of snowmelt and rainfall to streamflow in six watersheds over the Western United States. Analysis showed that peak snow accumulation, snow-free day, and snowmelt slope all had strong correlation with peak streamflow, particularly in inland basins. Further analysis revealed that the variation of snow accumulation anomaly had strong lead correlation with the variation of streamflow anomaly. Over the entire Western United States, inner mountain areas had lead times of 4-10 pentads. However, in coastal areas, nearly all sites had lead times of less than one pentad. e relative contributions of rainfall and snowmelt to streamflow in different watersheds were calculated based on the snow lead time. e geographic distribution of annual relative contributions revealed that interior areas were dominated by snowmelt contribution, whereas the rainfall contribution dominated coastal areas. In the wet season, the snowmelt contribution increased in the western Pacific Northwest, whereas the rainfall contribution increased in the southeastern Pacific Northwest, southern Upper Colorado, and northern Rio Grande regions. e derived results demonstrated the predictive contributions of snowmelt and rainfall to streamflow. ese findings could be considered a reference both for seasonal predictions of streamflow and for prevention of hydrological disasters. Furthermore, they will be helpful in the evaluation and improvement of hydrological and climate models.

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“…The objectives of this research were (1) to evaluate basin scale snow density variability from historic snow course measurements and develop a snow density model specific to our study area that can be used to estimate SWE from snow depth measurements; this is a different domain and scale than used in previous studies, and (2) to combine operational SNOTEL and snow course measurements, as suggested by Dressler et al (2006), with supporting field-based snowpack measurements to evaluate what is driving variability of the snowpack at the basin scale.…”

Section: G a Sexstone And S R Fassnacht: What Drives Basin Scale mentioning

confidence: 99%

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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A Comparison of Snow Telemetry and Snow Course Measurements in the Colorado River Basin

Cited by 47 publications

References 12 publications

Attribution of Declining Western U.S. Snowpack to Human Effects

Attribution of Declining Western U.S. Snowpack to Human Effects

Predictive Contributions of Snowmelt and Rainfall to Streamflow Variations in the Western United States

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

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