Capitalizing on the daily time step of snow telemetry data to model the snowmelt components of the hydrograph for small watersheds

Fassnacht, S. R.; Deitemeyer, David C.; Venable, N. B. H.

doi:10.1002/hyp.10260

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…Similarly, further investigations across medium scales, such as at the watershed scale (dendritic features in Figure 1a), could identify the scale of cold air drainage patterns. Such two future examinations could capitalize (Fassnacht et al ., 2014) on the regional density of SNOTEL stations in mountain region data by installing additional inexpensive, stand‐alone temperature sensors, such as those used here.…”

Section: Discussionmentioning

confidence: 99%

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

Collados‐Lara

Fassnacht

Pulido-Velázquez

et al. 2020

Intl Journal of Climatology

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Where land surface air temperature data are not available, satellite land surface temperature are used. However, the coarse spatial resolution of satellitederived products may yield errors at the local scale. This work shows the differences between the MODIS Land Surface Temperature and Emissivity (MOD11A1) product and ground measurements at two different scales. We used data from 21 SNOTEL stations across the northern Front Range of Colorado to represent the coarse scale and 17 iButton temperature sensors across the Colorado State University Mountain Campus to represent the fine scale. We found significant differences in the temperature and its changes with elevation for the two spatial scales. At the fine scale, cold air drainage can induce an inversion of the temperature gradient with elevation. A higher correlation was found during the nighttime at the fine scale, while, at the coarse scale, higher correlations were observed during the daytime. On windy nights, temperatures do not cool as much as on calmer nights, and the coarse scale nearsurface temperature gradient with elevation persists, though the fine scale inversions do not develop. The near-surface temperature gradients with elevation based on the MODIS pixels are similar to the ground-based data at the coarse scale but not at the fine scale. Thus, one must be cautious in selecting the near-surface temperature gradients with elevation for mountainous terrain when different scales are considered, and a proper validation of satellite products is necessary prior to their use to avoid the propagation of uncertainties.

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Section: Discussionmentioning

confidence: 99%

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

Collados‐Lara

Fassnacht

Pulido-Velázquez

et al. 2020

Intl Journal of Climatology

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“…While the monthly analysis is at a finer temporal resolution, a month is still an arbitrary human-defined time step. To determine snowpack climatology, Fassnacht and Derry [22] showed that using daily data was more appropriate [19][20][21]23].…”

Section: Introductionmentioning

confidence: 99%

How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

et al. 2020

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Historically, snowpack trends have been assessed using one fixed date to represent peak snow accumulation prior to the onset of melt. Subsequent trend analyses have considered the peak snow water equivalent (SWE), but the date of peak SWE can vary by several months due to inter-annual variability in snow accumulation and melt patterns. A 2018 assessment evaluated monthly SWE trends. However, since the month is a societal construct, this current work examines daily trends in SWE, cumulative precipitation, and temperature. The method was applied to 13 snow telemetry stations in Northern Colorado, USA for the period from 1981 to 2018. Temperature trends were consistent among all the stations; warming trends occurred 63% of the time from 1 October through 24 May, with the trends oscillating from warming to cooling over about a 10-day period. From 25 May to 30 September, a similar oscillation was observed, but warming trends occurred 86% of the time. SWE and precipitation trends illustrate temporal patterns that are scaled based on location. Specifically, lower elevations stations are tending to record more snowfall while higher elevation stations are recording less. The largest SWE, cumulative precipitation, and temperature trends were +30 to −70 mm/decade, +30 to −30 mm/decade, and +4 to −2.8 °C/decade, respectively. Trends were statistically significance an average of 25.8, 4.5, and 29.4% of the days for SWE, cumulative precipitation, and temperature, respectively. The trend in precipitation as snow ranged from +/−2%/decade, but was not significant at any station.

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“…Understanding how snow accumulation amounts [32,33] and snowmelt rates [34] influence streamflow timing is important for hydrologists for runoff forecasting [35,36]. Various metrics have been used to simplify the assessment of runoff timing.…”

Section: Introductionmentioning

confidence: 99%

Drivers of Dust-Enhanced Snowpack Melt-Out and Streamflow Timing

et al. 2022

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The presence of dust on the snowpack accelerates snowmelt. This has been observed through snowpack and hydrometeorological measurements at a small study watershed in southwestern Colorado. For a 13-year period, we quantified the annual dust-enhanced energy absorption (DEAE) and used this information to model the snowpack melt-out under observed (with dust present) and clean conditions (no dust). We determine the difference in snow cover duration between actual (dust present) and simulated ideal (clean) snowpack (ΔSAG) to characterize the shifts in melt timing for each year. We compute the center of mass of runoff (tQ50) as a characteristic of snowmelt. DEAE, ΔSAG and tQ50 vary from year to year, and are dictated by the quantity of snow accumulation, and to a lesser extent the number of dust events, the annual dust loading, and springtime snowfall.

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Capitalizing on the daily time step of snow telemetry data to model the snowmelt components of the hydrograph for small watersheds

Cited by 10 publications

References 23 publications

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

Drivers of Dust-Enhanced Snowpack Melt-Out and Streamflow Timing

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