Snow water equivalents exclusively from snow depths  and their temporal changes: the Δsnow model

Winkler, Michael; Schellander, Harald; Gruber, S.

doi:10.5194/hess-25-1165-2021

Cited by 29 publications

(74 citation statements)

References 47 publications

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“…The data were corrected in many ways. Only a few tasks are shortly outlined here for a more comprehensive illustration the reader is referred to the report on the "Schneelast.Reform" project (Winkler et al, 2021b). A common observational issue is to simply add new snow values over several days and use that as snow depth.…”

Section: Methodsmentioning

confidence: 99%

Towards a reproducible snow load map – an example for Austria

Schellander

Winkler²,

Hell³

2021

Adv. Sci. Res.

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Abstract. The European Committee for Standardization defines zonings and calculation criteria for different European regions to assign snow loads for structural design. In the Alpine region these defaults are quite coarse; countries therefore use their own products, and inconsistencies at national borders are a common problem. A new methodology to derive a snow load map for Austria is presented, which is reproducible and could be used across borders. It is based on (i) modeling snow loads with the specially developed Δsnow model at 897 sophistically quality controlled snow depth series in Austria and neighboring countries and (ii) a generalized additive model where covariates and their combinations are represented by penalized regression splines, fitted to series of yearly snow load maxima derived in the first step. This results in spatially modeled snow load extremes. The new approach outperforms a standard smooth model and is much more accurate than the currently used Austrian snow load map when compared to the RMSE of the 50-year snow load return values through a cross-validation procedure. No zoning is necessary, and the new map's RMSE of station-wise estimated 50-year generalized extreme value (GEV) return levels gradually rises to 2.2 kN m−2 at an elevation of 2000 m. The bias is 0.18 kN m−2 and positive across all elevations. When restricting the range of validity of the new map to 2000 m elevation, negative bias values that significantly underestimate 50-year snow loads at a very small number of stations are the only objective problem that has to be solved before the new map can be proposed as a successor of the current Austrian snow load map.

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Section: Methodsmentioning

confidence: 99%

Towards a reproducible snow load map – an example for Austria

Schellander

Winkler²,

Hell³

2021

Adv. Sci. Res.

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“…In the snow load model, the key information for structural design is snow load on ground [31]. In modern codes and standards, representative values of snow load on ground, characterized by a given annual probability of exceedance, are usually derived by extreme statistics, analyzing measurements of snow cover depth or of snow water equivalent (SWE) [32,33]. In the past, the SWE was measured directly only in some weather stations of European countries such as Germany, Finland, Switzerland and partially UK, in the other cases, the height of snow cover was converted into snow load by means of analytical snow density laws depending on the climatic conditions and the snow lasting period [33].…”

Section: Snow Loadsmentioning

confidence: 99%

Extreme Ground Snow Loads in Europe from 1951 to 2100

Croce

Formichi

Landi

2021

Climate

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Lightweight roofs are extremely sensitive to extreme snow loads, as confirmed by recently occurring failures all over Europe. Obviously, the problem is further emphasized in warmer climatic areas, where low design values are generally foreseen for snow loads. Like other climatic actions, representative values of snow loads provided in structural codes are usually derived by means of suitable elaborations of extreme statistics, assuming climate stationarity over time. As climate change impacts are becoming more and more evident over time, that hypothesis is becoming controversial, so that suitable adaptation strategies aiming to define climate resilient design loads need to be implemented. In the paper, past and future trends of ground snow load in Europe are assessed for the period 1950–2100, starting from high-resolution climate simulations, recently issued by the CORDEX program. Maps of representative values of snow loads adopted for structural design, associated with an annual probability of exceedance p = 2%, are elaborated for Europe. Referring to the historical period, the obtained maps are critically compared with the current European maps based on observations. Factors of change maps, referred to subsequent time windows are presented considering RCP4.5 and RCP8.5 emission trajectories, corresponding to medium and maximum greenhouse gas concentration scenarios. Factors of change are thus evaluated considering suitably selected weather stations in Switzerland and Germany, for which high quality point measurements, sufficiently extended over time are available. Focusing on the investigated weather stations, the study demonstrates that climate models can appropriately reproduce historical trends and that a decrease of characteristic values of the snow loads is expected over time. However, it must be remarked that, if on one hand the mean value of the annual maxima tends to reduce, on the other hand, its standard deviation tends to increase, locally leading to an increase of the extreme values, which should be duly considered in the evaluation of structural reliability over time.

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“…Alternatively, models deriving SWE statistically from HS and considering elevation, region and season could be integrated (e.g., Jonas et al, 2009;Winkler et al, 2021 first need to be inverted so that HS could be derived from SWE and an ad hoc calibration would be necessary for each climatic region.…”

Section: Density and Snow Depth Estimationmentioning

confidence: 99%

“…However, for the conversion of the HS products into SWE additional density information, e.g. using modelling approaches (Jonas et al, 2009;Winkler et al, 2021) or additional measurements, is still needed (Dozier et al, 2016), which is not at every location available or easy to obtain. SWE can also be derived by physically-based modelling (e.g.…”

Section: Introductionmentioning

confidence: 99%

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Capelli

Koch

Henkel³

et al. 2021

Preprint

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Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.

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Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model

Cited by 29 publications

References 47 publications

Towards a reproducible snow load map – an example for Austria

Towards a reproducible snow load map – an example for Austria

Extreme Ground Snow Loads in Europe from 1951 to 2100

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

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