Extreme Ground Snow Loads in Europe from 1951 to 2100
Abstract
:1. Introduction
2. Material and Methods
2.1. Snow Loads
- First, annual extremes are collected for the considered period in a proper set of weather stations adequately spread over the investigated area;
- Then, available data for each weather station are fitted to an appropriate extreme value probability distribution, in such a way that representative values can be derived. In Europe, a Gumbel (EVI) distribution is generally adopted for annual maxima [31,33,36], including also non-snowy winters, following a “mixed distribution approach” [31,37];
- After that, homogenous climatic zones, which are characterized by a common load–altitude relationship, are identified;
- Finally, isopleths are drawn to show the variation of the load within the considered geographic area.
2.2. Datasets
2.3. The Factor of Change Approach
- Extraction of annual maxima of the investigated climate variable for the investigated time windows (e.g., 1951–1990, 1961–2000, 1971–2010, …, 2050–2090);
- Elaboration of extreme values for each time period, via the block maxima method [60]. Of course, several extreme value distributions can be assumed for the considered variable. Since ground snow load extremes in Europe are usually described by the extreme value type I (EVI) distribution [31,33,36], an EVI distribution was assumed in the present study too. The cumulative distribution function (CDF) of the EVI distribution, also known as Gumbel distribution, is
- Characteristic values of ground snow load, , are thus evaluated for the -th time window, from
- FC are derived in terms of ratios between the characteristic value in the -th time window and that obtained in the first one
3. Results and Discussion
3.1. Climate Simulations for the Historical Period
3.2. Future Climate Projections
3.3. Evaluation of Current and Future Trends at Selected Weather Stations
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Model Acronym | Institution Acronym | Driving GCM | RCM |
---|---|---|---|
EC-EARTH-CCLM | CLMcom | ICHEC-EC-EARTH | CCLM4-8-17 |
HadGEM2-CCLM | CLMcom | MOHC-HadGEM2-ES | CCLM4-8-17 |
MPI- CCLM | CLMcom | MPI-M-MPI-ESM-LR | CCLM4-8-17 |
EC-EARTH-HIRHAM | DMI | ICHEC-EC-EARTH | HIRHAM5 v2 |
HadGEM2-HIRHAM | DMI | MOHC-HadGEM2-ES | HIRHAM5 v2 |
EC-EARTH-RACMO | KNMI | ICHEC-EC-EARTH | RACMO 22E |
MPI-Remo2009 | MPI-CSC | MPI-M-MPI-ESM-LR | Remo 2009 |
NCC-Remo2015 | MPI-CSC | NCC-NorESM1-M | Remo 2015 |
IPSL-WRF381P | IPSL-INERIS | IPSL-IPSL-CM5A-MR | WRF381P |
Climatic Region | RMSE (kN/m2) | MAE (kN/m2) |
---|---|---|
Alpine | 2.43 | 1.44 |
Mediterranean | 1.10 | 0.73 |
Central East | 0.39 | 0.30 |
Central West | 0.32 | 0.16 |
Iberian Peninsula | 0.83 | 0.34 |
UK–Eire | 0.36 | 0.17 |
Station Id. | Station | Lon [°] | Lat [°] | Alt. [m] | FC Obs. | FC Ensemble Mean |
---|---|---|---|---|---|---|
S01 | Angermünde | 13.99 | 53.03 | 54 | 0.72 | 0.81 |
S02 | Arkona | 13.43 | 54.68 | 42 | 1.01 | 0.94 |
S03 | Augsburg | 10.94 | 48.43 | 462 | 0.61 | 0.81 |
S04 | Basel | 7.58 | 47.54 | 316 | 1.09 | 0.79 |
S05 | Berlin-Dahlem (FU) | 13.30 | 52.45 | 51 | 0.79 | 0.78 |
S06 | Bern | 7.46 | 46.99 | 553 | 0.86 | 0.86 |
S07 | Bernburg/Saale | 11.71 | 51.82 | 85 | 0.84 | 0.94 |
S08 | Bremen | 8.80 | 53.05 | 4 | 0.68 | 0.90 |
S09 | Chemnitz | 12.87 | 50.79 | 418 | 1.10 | 0.90 |
S10 | Cottbus | 14.32 | 51.78 | 69 | 0.72 | 0.82 |
S11 | Elm | 9.18 | 46.92 | 958 | 0.80 | 0.92 |
S12 | Essen-Bredeney | 6.97 | 51.40 | 150 | 0.87 | 0.69 |
S13 | Freiburg | 7.83 | 48.02 | 237 | 0.68 | 0.75 |
S14 | Geisenheim | 7.95 | 49.99 | 110 | 0.88 | 0.94 |
S15 | Görlitz | 14.95 | 51.16 | 238 | 0.94 | 0.86 |
S16 | Göttingen | 9.95 | 51.50 | 167 | 0.82 | 0.79 |
S17 | Hamburg-Fuhlsbüttel | 9.99 | 53.63 | 14 | 0.77 | 0.85 |
S18 | Hannover | 9.68 | 52.46 | 59 | 0.78 | 0.90 |
S19 | Heinersreuth-Vollhof | 11.52 | 49.97 | 350 | 1.01 | 0.80 |
S20 | Hohenpeißenberg | 11.01 | 47.80 | 977 | 0.87 | 0.86 |
S21 | Jena (Sternwarte) | 11.58 | 50.93 | 155 | 0.87 | 0.93 |
S22 | Kahler-Asten | 8.49 | 51.18 | 839 | 0.79 | 0.80 |
S23 | Kaiserslautern | 7.76 | 49.43 | 271 | 0.64 | 0.78 |
S24 | Kirchdorf/Poel | 11.43 | 54.00 | 12 | 0.93 | 0.86 |
S25 | Kleve | 6.10 | 51.76 | 46 | 0.74 | 0.82 |
S26 | Leipzig-Holzhausen | 12.45 | 51.32 | 138 | 0.73 | 0.90 |
S27 | Lindenberg | 14.12 | 52.21 | 98 | 0.79 | 0.83 |
S28 | Lingen | 7.31 | 52.52 | 22 | 0.67 | 0.76 |
S29 | Locarno | 8.79 | 46.17 | 367 | 0.82 | 0.77 |
S30 | Lugano | 8.96 | 46.00 | 273 | 1.05 | 0.74 |
S31 | Magdeburg | 11.58 | 52.10 | 79 | 0.65 | 0.88 |
S32 | Neuchotel | 6.95 | 47.00 | 485 | 0.76 | 0.91 |
S33 | Neukirchen-Hauptschwenda | 9.41 | 50.89 | 500 | 0.79 | 0.77 |
S34 | Oberstdorf | 10.28 | 47.40 | 806 | 0.84 | 1.00 |
S35 | Regensburg | 12.10 | 49.04 | 365 | 0.96 | 0.81 |
S36 | Rostock-Warnemünde | 12.08 | 54.18 | 4 | 0.88 | 0.83 |
S37 | Schwerin | 11.39 | 53.64 | 59 | 0.97 | 0.82 |
S38 | Sigmaringen-Laiz | 9.19 | 48.07 | 581 | 0.80 | 0.76 |
S39 | Stuttgart-Echterdingen | 9.22 | 48.69 | 371 | 0.72 | 0.83 |
S40 | Weißenburg-Emetzheim | 10.93 | 49.01 | 439 | 0.80 | 0.79 |
S41 | Würzburg | 9.96 | 49.77 | 268 | 1.01 | 0.90 |
S42 | Zurich | 8.57 | 47.38 | 556 | 0.91 | 0.76 |
Scenario | Parameter | RMSE Regional Average | RMSE Single Stations Average |
---|---|---|---|
RCP4.5 | 0.021 | 0.113 | |
0.073 | 0.150 | ||
0.108 | 0.239 | ||
RCP8.5 | 0.018 | 0.108 | |
0.055 | 0.139 | ||
0.068 | 0.228 |
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Croce, P.; Formichi, P.; Landi, F. Extreme Ground Snow Loads in Europe from 1951 to 2100. Climate 2021, 9, 133. https://doi.org/10.3390/cli9090133
Croce P, Formichi P, Landi F. Extreme Ground Snow Loads in Europe from 1951 to 2100. Climate. 2021; 9(9):133. https://doi.org/10.3390/cli9090133
Chicago/Turabian StyleCroce, Pietro, Paolo Formichi, and Filippo Landi. 2021. "Extreme Ground Snow Loads in Europe from 1951 to 2100" Climate 9, no. 9: 133. https://doi.org/10.3390/cli9090133