Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards

Abstract. In a context of climate change, trends in extreme snow loads need to be determined to minimize the risk of structure collapse. We study trends in 50-year return levels of ground snow load (GSL) using non-stationary extreme value models. These trends are assessed at a mountain massif scale from GSL data, provided for the French Alps from 1959 to 2019 by a meteorological reanalysis and a snowpack model. Our results indicate a temporal decrease in 50-year return levels from 900 to 4200 m, significant in the northwest of the French Alps up to 2100 m. We detect the most important decrease at 900 m with an average of −30 % for return levels between 1960 and 2010. Despite these decreases, in 2019 return levels still exceed return levels designed for French building standards under a stationary assumption. At worst (i.e. at 1800 m), return levels exceed standards by 15 % on average, and half of the massifs exceed standards. We believe that these exceedances are due to questionable assumptions concerning the computation of standards. For example, these were devised with GSL, estimated from snow depth maxima and constant snow density set to 150 kg m−3, which underestimate typical GSL values for the snowpack.

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Accounting for Non-stationarity in Extreme Snow Loads: a Comparison with Building Standards in the French Alps

10.5194/nhess-2020-81 ◽

2020 ◽

Author(s):

Erwan Le Roux ◽

Guillaume Evin ◽

Nicolas Eckert ◽

Juliette Blanchet ◽

Samuel Morin

Keyword(s):

Climate Change ◽

Snow Depth ◽

Return Level ◽

Snow Density ◽

French Alps ◽

Return Levels ◽

Ground Snow Load ◽

Snow Loads ◽

Building Standards ◽

Current Standards

Abstract. In a context of climate change, trends in extreme snow loads need to be determined to minimize the risk of structure collapse.We study trends in annual maxima of ground snow load (GSL) using non-stationary extreme value models. Trends in return levels of GSL are assessed at a mountain massif scale from GSL data, provided for the French Alps from 1959 to 2019 by a meteorological reanalysis and a snowpack model. Our results indicate a temporal decrease in 50-year return levels from 900 m to 4200 m, significant in the Northwest of the French Alps until 2100 m. Despite this decrease, in half of the massifs, the return level in 2019 at 1800 m exceeds the return level designed for French building standards under a stationary assumption. We believe that this high number of exceedances is due to questionable assumptions concerning the computation of current standards. For example, these were devised with GSL, estimated from snow depth and constant snow density set to 150 kg m−3, which underestimate typical GSL values for the full snowpack.

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Extreme value analysis of ground snow loads in the French Alps

10.5194/nhess-2020-81-rc1 ◽

2020 ◽

Keyword(s):

Extreme Value ◽

Extreme Value Analysis ◽

Value Analysis ◽

French Alps ◽

Snow Loads

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Non-stationary extreme value analysis of ground snow loads in the French Alps

10.5194/nhess-2020-81-rc2 ◽

2020 ◽

Keyword(s):

Extreme Value ◽

Extreme Value Analysis ◽

Value Analysis ◽

French Alps ◽

Snow Loads

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Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

10.5194/egusphere-egu21-2081 ◽

2021 ◽

Author(s):

Jeremy Rohmer ◽

Rodrigo Pedreros ◽

Yann Krien

Keyword(s):

Confidence Interval ◽

Stochastic Simulation ◽

Extreme Value ◽

Return Level ◽

Extreme Value Analysis ◽

Percentage Error ◽

Value Analysis ◽

Return Levels ◽

Wave Heights ◽

Local Extreme

To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years &#8211; Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).

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Identifying robust bias adjustment methods for extreme precipitation in a pseudo-reality setting

10.5194/hess-2020-318 ◽

2020 ◽

Author(s):

Torben Schmith ◽

Peter Thejll ◽

Peter Berg ◽

Fredrik Boberg ◽

Ole Bøssing Christensen ◽

...

Keyword(s):

Time Scale ◽

Extreme Value ◽

Extreme Value Analysis ◽

Climate Factor ◽

Value Analysis ◽

Model Simulations ◽

Bias Adjustment ◽

Return Levels ◽

Hourly Data ◽

Daily Time

Abstract. Severe precipitation events occur rarely and are often localized in space and of short duration; but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of these rare events. These are usually projected using Regional Climate Model (RCM) scenario simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterizations in the RCMs, the simulated present-day climate usually has biases relative to observations. Therefore, the RCM results are often bias-adjusted to match observations. This does, however, not guarantee that bias-adjusted projected results will match future reality better, since the bias may change in a changed climate. In the present work we evaluate different bias adjustment techniques in a changing climate. This is done in an inter-model cross-validation setup, in which each model simulation in turn plays the role of pseudo-reality, against which the remaining model simulations are bias adjusted and validated. The study uses hourly data from present-day and RCP8.5 late 21st century from 19 model simulations from the EURO-CORDEX ensemble at 0.11° resolution, from which fields of selected return levels are calculated for hourly and daily time scale. The bias adjustment techniques applied to the return levels are based on extreme value analysis and include analytical quantile-matching together with the simpler climate factor approach. Generally, return levels can be improved by bias adjustment, compared to obtaining them from raw scenarios. The performance of the different methods depends of the time scale considered. On hourly time scale, the climate factor approach performs better than the quantile-matching approaches. On daily time scale, the superior approach is to simply deduce future return levels from observations and the second best choice is using the quantile-mapping approaches. These results are found in all European sub-regions considered.

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Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting

Hydrology and Earth System Sciences ◽

10.5194/hess-25-273-2021 ◽

2021 ◽

Vol 25 (1) ◽

pp. 273-290

Author(s):

Torben Schmith ◽

Peter Thejll ◽

Peter Berg ◽

Fredrik Boberg ◽

Ole Bøssing Christensen ◽

...

Keyword(s):

Cross Validation ◽

Extreme Value ◽

Extreme Value Analysis ◽

Climate Factor ◽

Quantile Mapping ◽

Value Analysis ◽

Model Simulations ◽

Changing Climate ◽

Return Levels ◽

Future Return

Abstract. Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation set-up in which each model simulation, in turn, performs pseudo-observations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11∘ resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981–2005) and end-21st-century (2075–2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantile-mapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.

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Future European changes of extreme precipitation : What can we learn from inter-model cross-validation?

10.5194/egusphere-egu2020-18519 ◽

2020 ◽

Author(s):

Torben Schmith ◽

Peter Thejll ◽

Fredrik Boberg ◽

Peter Berg ◽

Ole Bøssing Christensen ◽

...

Keyword(s):

Error Correction ◽

Cross Validation ◽

Extreme Value ◽

Extreme Value Analysis ◽

Value Analysis ◽

Return Levels ◽

Hourly Data ◽

Calibration Techniques ◽

Precipitation Events

Severe precipitation events occur rarely and are often localized in space and of short duration, but are important for societal managing of infrastructure such as sewage systems, metros etc. Therefore, there is a demand for estimating expected future changes in the statistics of these rare events. These are usually projected using RCM scenario runs combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to RCM imperfections, the modelled climate for the present-day usually has errors relative to observations. Therefore, the RCM results are &#8216;error corrected&#8216; to match observations more closely in order to increase reliability of results.In the present work we evaluate different error correction techniques and compare with non-corrected projections. This is done in an inter-model cross-validation setup, in which each model in turn plays the role of observations, against which the remaining error-corrected models are validated. The study uses hourly data (historical & RCP8.5 late 21st century) from 13 models covering the EURO-CORDEX ensemble at 0.11 degree resolution (about 12.5 km), from which fields of selected return levels are extracted for 1 h and 24 h duration. The error correction techniques applied to the return levels are based on extreme value analysis and include analytical quantile-quantile matching together with a simpler climate factor approach.The study identifies regions where the error correction techniques perform differently, and therefore contributes to guidelines on how and where to apply calibration techniques when projecting extreme return levels.

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Extreme Value Analysis of Wet Snow Loads on Power Lines

IEEE Transactions on Power Systems ◽

10.1109/tpwrs.2014.2321008 ◽

2015 ◽

Vol 30 (1) ◽

pp. 457-462 ◽

Cited By ~ 17

Author(s):

Bogdan Ruszczak ◽

Michal Tomaszewski

Keyword(s):

Extreme Value ◽

Power Lines ◽

Extreme Value Analysis ◽

Value Analysis ◽

Wet Snow ◽

Snow Loads

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Towards a new ground snow-load map for structural design in Germany

10.5194/dach2022-101 ◽

2021 ◽

Author(s):

Fabiana Castino ◽

Bodo Wichura

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Snow Depth ◽

Snow Water Equivalent ◽

High Spatial Resolution ◽

Extreme Value Analysis ◽

Value Analysis ◽

Snow Load ◽

Characteristic Values ◽

Snow Loads

The current European standard for snow loads on structures relies on characteristic values (i.e., snow loads with an annual probability of exceedance of 0.02 and referred to as the 50-year mean return levels) derived for Germany in 2005 using about 350 snow water-equivalent (SWE) time series from ground stations operated by the German National Weather Service (DWD) [DIN EN 1991-1-3/NA:2019-04, 2019]. Here we present a methodology for generating a new ground snow-loads map for Germany, which aims at improving the relative coarse spatial resolution and reducing uncertainties and inconsistencies at national borders of the actual standard. Our methodology is based on (1) high-quality and homogeneous snow-cover time series, including both daily snow-depth (from about 6000 stations in Germany and in neighbouring countries) and three-weekly water-equivalent observation (from about 103 German stations) over the period from 1950 to 2020, (2) an integrated model combining an empirical regression model for snow bulk density and the semi-empirical multi-level &#916;SNOW model for generating accurate daily SWE values from 6000 snow-depth time series [Castino et al., 2022], (3) the spatial interpolation of both daily snow-depth and modelled-SWE time series using a universal-kriging method to generate high spatial-resolution (~1km2) rasterised daily snow loads over the period from 1950 to 2020, and (4) the extreme value analysis of the rasterized daily snow loads for estimating the characteristic values at high spatial resolution for the entire German territory. The uncertainties of the obtained characteristic snow-load values will be estimated using a leave-one-out cross validation based on a selection of observed-SWE time series representative of the diversity of the regional snow climatology in Germany. Finally, the characteristic values of the snow-load map generated with this methodology will be compared with the current German standard. &#160;&#160; &#160; References Castino, F., H. Schellander, B. Wichura, and M. Winkler (2022), SWE modelling: comparison between different approaches applied to Germany, abstract submitted to D-A-CH MeteorologieTagung - 21-25.03.2022, Leipzig. DIN EN 1991-1-3/NA:2019-04 (2019), Nationaler Anhang - National festgelegte Parameter - Eurocode 1: Einwirkungen auf Tragwerke - Teil 1-3: Allgemeine Einwirkungen - Schneelasten, edited, p. 22, Deutsches Institut f&#252;r Normung e.V., Beuth-Verlag, Berlin.

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