Forest fire. Credit: gilitukha

Winter drought and fire risk in southern Europe

January 2022

Forest fire. Credit: gilitukha
Forest fire. Credit: gilitukha

A significant precipitation deficit over the south-western parts of Europe, together with above normal amount of sunshine, led to serious drought in the region in January 2022. The drought was so severe that even in the middle of winter the risk for wild fires was high for large parts of the Iberian peninsula.

Last Updated

10 June 2022

Published on

28 February 2022

By Christine Traeger-Chatterjee (EUMETSAT), David Fairbairn (H SAF/ECMWF), Isabel Trigo (LSA SAF/IPMA), Steffen Kothe (CM SAF/DWD), Markus Ziese (GPCC/DWD)

Large parts of Europe received below average precipitation during January 2022 (Figure 1).

Precipitation Anomaly January 2022 compared to the long-term average
Figure 1: Precipitation Anomaly January 2022 compared to the long-term average of precipitation in January during the normal period 1991–2020. Regions with above average precipitation in blue and below average in brown. Source: GPCC.

According to measurements of the Global Precipitation Climatology Centre (GPCC), some regions in Portugal had no rain during the entire month of January. Combined with the very sunny conditions southern Europe experienced in January 2022 (Figure 2) and the already dry soil conditions in this region (Figure 4), this led to serious drought conditions (Figure 3).

Monthly anomaly of sunshine duration for January 2022 in reference to 1991 to 2020
Figure 2: Monthly anomaly of sunshine duration for January 2022 in reference to 1991 to 2020. Regions with above average sunshine duration are red and below average sunshine duration are blue.
Anomaly of Soil Wetness Index in the root zone, January 2022
Figure 3: January 2022 anomaly of soil wetness index in the root-zone (0-1m depth) over Europe compared to the January average value over the period 1992–2021. Blue cross indicates the location of Antequera in Spain for the annual cycle plot in Figure 4. Unusually dry conditions are evident over large parts of the Iberian Peninsula.

Since August 2021, the soil moisture available to plants has been significantly below average in Antequera, Spain. Figure 4 shows the monthly average liquid soil wetness index (SWI) in the root-zone (0-1 m depth) for latitude 37N, longitude 4.57W, near Antequera. Lines show: (i) the monthly average from March 2021 to February 2022 (cyan); (ii) the long term (1992-2021) monthly average (green); (iii) the 95th climate percentile of the 1992-2021 time series (blue dashed); (iv) the 5th percentile of the 1992-2021 time series (red dashed). The H26 NRT product is used for 2021/2022 monthly values and the H141/H142 climate data record (CDR) is used for the long-term average. Evidently the soil moisture had mostly been running lower than the 5th percentile for August 2021-February 2022 (i.e., extremely dry conditions).

Monthly average liquid soil wetness index (SWI) in the root-zone (0-1 m depth) for Antequera
Figure 4: monthly average liquid soil wetness index (SWI) in the root-zone (0-1 m depth), Antequera.

As a consequence, the risk for a potential wildfires in large parts of the Iberian Peninsula was rated to be in the high, very high, or even the highest category (Figure 5). Usually, this risk is rated to be in the low or moderate category in January.

Fire risk map (FRM) for 8 February 2022
Figure 5: Fire risk map (FRM) for 8 February 2022. The fire risk map provides risk categories on the likelihood of a potential fire event getting out of control.

The EUMETSAT Satellite Application Facilities (SAFs) in support of Hydrology and Water Management (H SAF), Land Surface Applications (LSA SAF), and Climate Monitoring (CM SAF), as well as the Global Precipitation Climatology Centre (GPCC), help to monitor the situation by making their data freely available.

The maps and plots shown here are derived from products of the SAFs and the GPCC. Precipitation Data from GPCC are quality controlled interpolated station measurements. All SAF products are retrieved from EUMETSAT satellites.

The anomaly map on precipitation (Figure 1) is calculated using GPCCs First Guess Monthly product and the long-term mean value for January of the period 1991-2020. Global real-time weather observations (SYNOP), distributed via the WMO global telecommunication system, are the data base for the regularly updated First Guess Monthly. The long-term mean is calculated from the Precipitation Climatology Version 2020. This climatology focus on the period from 1951–2000, but includes sub-periods like 1991–2020, and is periodically updated. More details on both products are on the GPCC website.

The anomaly map on sunshine (Figure 2) duration is created using the CM SAF Climate Data Record SARAH-2.1 (available for the period 1983–2017), combined with the associated Interim Climate Data Record, which covers from 2018 and to-date. SARAH-2.1 contains solar radiation parameters, such as global radiation, direct normalized radiation, sunshine duration and more, derived from MVIRI and SEVIRI measurements on board the Meteosat first and second generation satellites, respectively. The associated Interim Climate Data Record is based on SEVIRI measurements. Note: the Climate Data Record SARAH will be updated during 2022 and will then cover the time period 19832021. Find out more about this and other satellite-derived climate data records on the CM SAF website.

The anomaly map on soil wetness (Figure 3) and annual cycles of monthly means of soil wetness for Antequera (Figure 4) are created using the H SAF Climate Data Record on soil moisture in the root zone and the associated near-real time product RZSM-ASCAT-NRT-10 (H26). Scatterometer measurements from ERS and ASCAT provide information on surface soil moisture, which is used together with other parameters as input to derive the soil wetness in the root zone. More information on H SAF soil moisture and other products are available on the H SAF website.

The LSA SAF Fire Risk Map (FRM) provides information on the likelihood of a potential wild fire getting out of control. Probabilities at a given place and time are estimated using empirical correlations of fire radiative power and weather conditions. The radiative power emitted by a fire (FRP) is derived from MSG SEVIRI. Find out more about the Fire Risk Map and other land surface products on the LSA SAF website.