Fractional snow-covered area parameterization over complex topography

Helbig, Nora; Herwijnen, A. van; Magnusson, Jan; Jonas, Tobias

doi:10.5194/hess-19-1339-2015

Cited by 52 publications

(119 citation statements)

References 34 publications

(78 reference statements)

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“…(10) and the corresponding description in Magnusson et al, 2014). Second, fractional snow-covered area (SCF) is parameterized using modeled snow depth and terrain parameters that were derived from a 25 m digital elevation model according to Helbig et al (2015). Third, all three model versions allow for the snow cover to hold a fraction of liquid water.…”

Section: Snow Modelmentioning

confidence: 99%

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Griessinger

Seibert

Magnusson

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a timeinvariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.

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Section: Snow Modelmentioning

confidence: 99%

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Griessinger

Seibert

Magnusson

et al. 2016

Hydrol. Earth Syst. Sci.

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show abstract

“…One note of caution is that SNOTEL and especially COOP measurements are probably mostly located in ideal flat settings. Grünewald and Lehning [2015] found that ideal, flat measurements tend to overestimate SWE and snow depth for close surroundings (up to 400 m horizontal distance), though Helbig et al [2015] found that there is much better agreement between flat measurements and spatially averaged snow depth for larger grid cells (>1500 m), such as those used in this study.…”

Section: The Spatial Consistency Of Swe Max and D Max When Normalizedmentioning

confidence: 52%

Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

Broxton

Dawson

Zeng

2016

Earth and Space Science

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It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.

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“…However, the estimation of the coefficient of variation uses many snow depth measurements from terrestrial lidar (e.g. Helbig et al, 2015;López-Moreno et al, 2015) or from manual measurements (Molotch et al, 2014). Such fine resolution snow depth data are not currently available around the operational SNOTEL stations but could be incorporated into the development of snow-cover depletion curves when available.…”

Section: Uses and Limitations Of The Snow-cover Depletion Curvesmentioning

confidence: 99%

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

et al. 2015

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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.

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Fractional snow-covered area parameterization over complex topography

Cited by 52 publications

References 34 publications

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

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

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