Towards a reproducible snow load map – an example for Austria

Schellander, Harald; Winkler, Michael; Hell, Tobias

doi:10.5194/asr-18-135-2021

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…A higher number of knots K (see Section 3.2.2) for the cubic spline would allow for more flexibility, but might also lead to overfitting the spline to the data (Schellander et al., 2021). Further, the higher model complexity with larger K leads to longer runtimes.…”

Section: Theory and Methodsmentioning

confidence: 99%

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Poschlod,

Koh

2024

Water Resources Research

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Information about the frequency and intensity of extreme precipitation is generally derived from fitting extreme value models using point‐observations, but the regionalization of these models is challenging. Here we propose using high‐resolution convection‐permitting climate model output as covariates for the estimation of observation‐based spatial rainfall return levels. We apply the Weather and Forecasting Research (WRF) model at a 1.5 km resolution driven by ERA5 reanalysis data over southern Germany, where 1,132 rain gauges provide observations of daily rainfall. For this complex topography, we build three different smooth spatial Generalized Extreme Value (GEV) models: (a) a reference model using latitude, longitude and elevation as covariates; (b) a model adding mean annual precipitation from the WRF; (c) a model adding extreme value statistical model estimates using WRF output. We show that the additional information provided by the WRF model can improve the representation of 10‐year and 100‐year return levels of daily rainfall by lowering the percentage bias, mean absolute error, and root‐mean‐square error. Furthermore, we conduct an extensive cross‐validation, where only 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all rain gauges are considered when building spatial GEV models. Again, the additional information provided by the WRF model can improve results here. This cross‐validation study also highlights the robustness of our approach, showing great potential for use in data‐scarce regions.

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Section: Theory and Methodsmentioning

confidence: 99%

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Poschlod,

Koh

2024

Water Resources Research

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“…This approach was chosen because the international standards for maximum snow load on buildings are based on R50HSmax (see e.g. Schellander et al, 2021). The calculations were performed with R package extRemes (Gilleland and Katz, 2016) in default settings (generalized extreme value distribution (GEV), maximum likelihood estimation method (MLE) and 95 % confidence intervals).…”

Section: Detection Of Trends and Changes In Snow Depth Seriesmentioning

confidence: 99%

“…Snow days are relevant for ecology (Stone et al, 2002;Jonas et al, 2008), climatology (Scherrer et al, 2004; or the ski tourism industry (Abegg et al, 2020), whereas the average and maximum snow depths are particularly applicable to climatology and engineering applications. Trend and extreme value analyses of snow indices (Scherrer et al, 2013;Matiu et al, 2021) are common methods in climate monitoring (Bocchiola et al, 2008;Marty and Blanchet, 2012;Buchmann et al, 2021a) and model verification (Brown et al, 2003;Essery et al, 2013), while extreme value analyses are important for defining snow loads and limits for building codes (Croce et al, 2021;Schellander et al, 2021;Al-Rubaye et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Buchmann

Resch²,

Begert³

et al. 2023

The Cryosphere

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Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

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“…Brown et al, 2003;Essery et al, 2013), whereas extreme value analysis is important for the definition of snow loads and limits for building-codes (e.g. Croce et al, 2021;Schellander et al, 2021;Al-Rubaye et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Homogenizing Swiss snow depth series – Impact on decadal trends and extremes

Buchmann

Resch

Begert

et al. 2022

Preprint

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Abstract. Our current knowledge on snow depth trends is based almost exclusively on these non-homogenized data.Long-term observations of deposited snow are well suited as indicator of climate change. However, like all other long-term observations, they are prone to inhomogeneities that can influence and change trends if not taken into account. We investigated the effects of removing inhomogeneities in the large network of Swiss snow depth observations on trends and extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depth in the period 1961–2021. For this task, three homogenization methods were applied: Climatol and HOMER, which use a median based adjustment method, and interpQM, which applies quantile based adjustments. All three were run using the same break points and input data. This allowed us to investigate and quantify the effects of these methods on the homogenization results. We found that all three methods agree well on trends in seasonal average snow depth, while differences are visible for seasonal maximum snow depth and the corresponding extreme values. Here, the quantile-based method performed slightly better than the two median-based methods, as it had the smallest number of stations outside the 95 % confidence interval for 50-year return periods of maximum snow depth. These differences are mainly caused by the way the reference series are selected. The combination of a high minimum correlation (>0.7) and restrictions in vertical (<300 m) and horizontal (<100 km) distances proved to be better suited than only using correlations or distances respectively as criteria. The adjustments removed all positive trends for snow days in the original data and strengthened the negative mean trend, especially for stations >1500 m. In addition, the number of significant negative stations was increased between 7–21 %, with the strongest changes at higher snow depths.

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Towards a reproducible snow load map – an example for Austria

Cited by 3 publications

References 30 publications

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Homogenizing Swiss snow depth series – Impact on decadal trends and extremes

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