Drought

Precipitation and evaporation indicators

Background information

The graphs show the values of the indicators from 1980 to 2022, expressed as a spatial average over South Tyrol. The spatial average is only calculated for low and middle altitude areas < 1500 m. When reading the graphs, it should be noted that negative SPI (SPEI) values (in red) correspond to precipitation deficits; conversely, positive SPI (SPEI) values (in blue) indicate precipitation (water availability) that is above the average of the thirty-year reference period 1981-2010 for the same time scale.

SPI and SPEI are calculated monthly based on the total precipitation and water balance of the last three or twelve months. The two time scales provide different information. The three-month scale (SPI/SPEI-3) provides information on agricultural drought, i.e. the deficit of water content in the most superficial parts of the soil, which affects plant growth the most. The longer time scale (SPI/SPEI-12), on the other hand, reflects hydrological drought, which exacerbates water scarcity in soils, but also affects deep aquifers and the flow of watercourses.

The time series of the SPI-12 from 1980 to 2022 shows a statistically significant upward trend, i.e. it indicates an increasing trend in cumulative precipitation over 12 months. In particular, the last decades show a clear increase in the frequency of months in which the SPI is positive, i.e. with higher 12-month cumulative precipitation than the 30-year average 1981 to 2010. In contrast, the most severe droughts were recorded in 2003 to 2007 and in the second half of the 1990s (red bars).

The SPEI-12 series shows no significant long-term trend. The temporal trend is consistent with that of the SPI-12 index, although it emphasises the most important droughts and reduces the most recent episodes of excess water.

At shorter timescales (SPI/SPEI-3) there is greater temporal variability. In both cases there is no long-term trend.

Method

The plots are 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. These are supplemented by the observations of some sites in Switzerland and Austria close to the national border. The collected series were interpolated to 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 provides a regional average that is more representative and stable than one based on individual stations.

The SPI index is calculated as the deviation of precipitation from its mean (over the period 1981-2010) on a given time scale (in this case three and twelve months), divided by its standard deviation. As precipitation is not normally distributed, at least on time scales smaller than one year, the variables are adjusted so that the SPI has a distribution with a mean of zero and a unit variance. The standardized index makes it possible to compare values for areas with different climatic conditions and to consider wet and dry periods equally.

The SPEI index is calculated in a similar way, but takes into account the water balance, i.e. the difference between precipitation and potential evapotranspiration compared to the average reference value on the given time scale (in this case three and twelve months). The potential evapotranspiration is calculated from the maximum and minimum temperature values according to the Hargreaves-Samani formula (Hargreaves and Samani, 1985).

As the contribution of evapotranspiration at higher altitudes, where ground vegetation is less abundant, is not very large, the SPEI time series have been calculated as spatial averages over South Tyrol, taking into account only the areas that sit below 1500 m. In order to make the two indicators comparable, the SPEI time series have been calculated as spatial averages over the same areas.

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

• Water

• Agriculture

• Flora and Fauna

• Water management

• Soil

• Natural hazard

Related indicators

Further research at Eurac Research

The 6-month time scale of the SPEI-6 indicator is used to assess possible reductions in river discharge and storage capacity due to medium-term deficits in the water balance. The 6-month SPI and SPEI indices have therefore also been included in the monitoring portal of the ADO project https://ado.eurac.edu/.

SPEI-6

An analysis of the evolution of the SPEI-6 index in recent years for South Tyrol shows particularly negative values for the spring season of 2022. The lowest values were reached in May, when the monthly average of SPEI-6 was -1.4. This corresponds to “moderate drought“ according to the standard classification (Mckee et al., 1993). This condition is caused by the combination of below-average precipitation and high temperatures from the winter season onwards. Using the daily values of the index at the beginning of the month, the SPEI-6 assumes a value below -1.5, which corresponds to the threshold for defining “severe drought“.

Project ADO

Future scenarios

The climate projections for South Tyrol for the SPI-12 indicator show a slight increase in annual humidity over the course of the century, which is in line with the future trend of annual precipitation in the province. The SPEI-12 indicator, on the other hand, shows a decrease in annual values, which is particularly evident for the RCP 8.5 scenario, implying an increase in aridity in South Tyrol. In this case, the continuous increase in temperature predicted by the most pessimistic scenario leads to an increase in water demand for evapotranspiration and consequently to increasingly negative mean water balances compared to the reference values. Both indices are given as spatial averages for all areas that sit below 1500 m altitude.

Climate projections of the annual SPI-12 and SPEI-12 for South Tyrol (spatial mean) over areas the lie below 1500 m altitude) according to the emission scenarios RCP 4.5 and RCP 8.5. The solid line represents the median of the model simulations considered: 11 (RCP 4.5) and 17 (RCP 8.5) for SPI-12 and 8 for SPEI-12. The colored area shows the range of values between the 25th and 75th percentile of the simulations. The values are given as a 10-year moving averages. Source: EURO-CORDEX; Processing and illustration: Eurac Research

According to the long-term annual projections, the frequency of extremely wet and dry months is expected to change in the coming decades. In particular, the SPI-12 shows a continuous increase in the number of months with extremely high precipitation compared to the average reference conditions (1981 to 2010), with the increase being more pronounced under RCP 8.5. In contrast, the SPEI-12 projections show an increase in extreme droughts, especially in the most pessimistic scenario, which indicates an average of more than 50 months of extreme drought in the last three decades of the century. In contrast, the frequency of extreme wet conditions remains unchanged (RCP 4.5) or gradually decreases (RCP 8.5) according to SPEI-12. In the period 2071 to 2100, the values for SPEI-12 in the RCP 8.5 scenario exceed the limit of 50 months per 30-year period.

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 an intermediate 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 represents the most pessimistic scenario, in which greenhouse gas emissions continue to rise and no action is taken to combat climate change.

The projections of daily precipitation and daily maximum and minimum temperatures from 1971 to 2100 provided by different climate models (ensembles) for the two scenarios have been processed by a downscaling procedure 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 the 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. In addition, the QDM method makes corrections 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 performed using the 1 km gridded observational data set.

From the corrected simulations, the SPI-12 and SPEI-12 indices were calculated on a monthly scale from 1971 to 2100 for all available models, using the period 1981 to 2010 as the reference period. In particular, 11 (RCP 4.5) and 17 (RCP 8.5) model simulations for SPI-12 and 8 simulations for SPEI-12 were considered for both scenarios. For the SPEI-12 calculation, only models for which both precipitation and temperature were available were considered. The potential evapotranspiration required to define the SPEI was derived from the maximum and minimum temperature predictions using the Hargreaves-Samani formula (Hargreaves and Samani, 1985).

As the contribution of evapotranspiration is not very significant at higher altitudes where there is less vegetation, the SPEI-12 was only calculated for areas that lie below 1500 meters. In order to make the two indicators comparable, the projections for SPI-12 were defined for the same areas.

Contact

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

Data provided by: Office for Meteorology and Avalanche Warning of the Autonomous Province of Bolzano