Precipitation

Annual anomalies of winter precipitation (October to March)

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Annual anomalies of summer precipitation (April to September)

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Description of the results

As can be seen from the maps, the highest precipitation totals on average falls in the summer half year, with the wettest conditions recorded in the eastern part of the province. The graph shows the winter and summer precipitation anomalies for each year from 1980 (1981 for the winter series) to 2024, expressed as a spatial average over South Tyrol. The 2000/2001 winter season was exceptionally rainy, with the highest precipitation anomaly in the series, mainly due to the particularly heavy rainfall in October and November. Regarding the summer season, at the beginning of the 2000s the longest consecutive period with less precipitation than normal (average values over 1981 to 2010) since 1980 was recorded.

Although the calculated trends in both seasons are not statistically significant, the temporal evolution of the anomalies indicates an increasing tendency in precipitation in South Tyrol, especially in the winter half-year (+38 % since 1981 according to the Thei-Sen calculation). However, due to the higher temperatures, precipitation is more frequent in the form of rain, while snowfall is less frequent and occurs at higher altitudes.

Method

The graph is based on the daily meteorological observations of more than 80 of the Office for Meteorology and Avalanche Warning of the Autonomous Province of Bolzano‘s measuring points and are supplemented by the observation data of some sites in Switzerland and Austria close to the national border. The collected series were interpolated on a regular grid with a resolution of 1 km for the whole national territory using a geostatistical method.

Before interpolation, all observation series were checked for measurement error and temporal homogeneity. In addition, missing daily values were reconstructed using a statistical procedure to maximize the temporal continuity of the series.

Interpolation allows a regional mean to be obtained which is more representative and stable than that based on individual stations. Trends are calculated using the Theil-Sen method and significance is determined using the Mann-Kendall test. The trend is considered significant if the resulting p-value is less than 0.05.

 

Sectors affected

• Snow and glacier

• Water

• Flora and Fauna

• Water management

• Soil

• Natural hazard

• Ecosystem service

• Agriculture

• Foresty

• Tourism

• Traffic infrastructure

Related indicators

Future scenarios

In general, precipitation in South Tyrol does not show any significant changes in the coming decades and there are no significant differences between the two emission scenarios considered. The projections for annual precipitation up to the year 2100 show an increasing tendency, and for the average annual totals in the period 2071 to 2100, the median of the simulations shows an increase of about 7% according to the RCP 8.5 scenario compared to the average amounts in the reference period 1981 to 2010. While summer precipitation (April to September) is projected to remain unchanged in the coming decades, winter precipitation (October to March) is projected to increase, with values in the period 2071 to 2100 being on average 12% (RCP 4.5) and 14% (RCP 8.5) higher than in the period 1981 to 2010.

 

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The changes in annual precipitation in South Tyrol predicted by the climate projections show differences in their spatial distribution between the two emission scenarios. Although an increase in annual precipitation is projected everywhere compared to the 30-year period 1981 to 2010, the increase remains moderate in most parts of the province and is less than 10% in all cases. Strong percentage increases in both the medium and long term, especially for the RCP 8.5 scenario, are found in the westernmost part of South Tyrol, in the upper Vinschgau/Val Venosta valley.

Method

The climate scenarios for annual and seasonal precipitation for South Tyrol were derived from EURO-CORDEX climate simulations over Europe for the two emission scenarios RCP 4.5 and RCP 8.5. RCP stands for “Representative Concentration Pathways“, i.e. projections of how greenhouse gas emissions in the atmosphere will develop in the future.

RCP 4.5 represents a mid-range scenario in which greenhouse gas emissions are curbed, but atmospheric concentrations continue to rise over the next 50 years and the +2°C target is not met. RCP 8.5 is the most pessimistic scenario, in which greenhouse gas emissions continue to rise and no action is taken to combat climate change.

The daily precipitation projections from 1971 to 2100 provided by different climate models (ensembles) for the two scenarios (11 for RCP 4.5 and 17 for RCP 8.5) have been processed using a downscaling process that allows the simulated values to be transferred from the original spatial resolution (in this case about 12 km) to a finer resolution (in this case 1 km). This step makes it possible to reduce systematic errors that occur in the model simulations due to the limited spatial resolution of the available models, which do not provide an adequate representation of local features, especially in mountainous regions with complex orography. The downscaling method used is based on the delta-quantile mapping method (QDM, Cannon et al., 2015), in which simulated values are compared with observations over a common reference period and corrected so that the probability distributions match. Furthermore, in the QDM method, the corrections are made in such a way that the long-term climate signal originally present in the simulations is not altered.

In this case, the reference period is 1981 to 2010. The correction was made using the 1 km gridded data set.

The corrected simulations were used to calculate the annual winter (October to March) and summer (April to September) totals from 1971 to 2100 for each ensemble model and for both scenarios. The ensemble indicator values were then aggregated by calculating the median of the 11 (RCP 4.5) and 17 (RCP 8.5) model simulations for each year and using the interquartile range, i.e. the range of values between the 25th and 75th percentiles, to provide an estimate of the variability of the model simulations.

Contact

Eurac Research: Alice Crespi, Center for Climate Change and Transformation

Data provided by: Amt für Meteorologie und Lawinenwarnung of the Autonomous Province of Bolzano