Indicator Assessment

Vegetation response to water deficit in Europe

Indicator Assessment
Prod-ID: IND-510-en
  Also known as: LSI 011
Published 30 Oct 2020 Last modified 30 Oct 2020
25 min read

Monitoring vegetation response to water deficit due to droughts is necessary to be able to introduce effective measures to increase the resilience of ecosystems in line with the EU’s nature restoration plan — a key element of the EU biodiversity strategy for 2030. Between 2000 and 2016, Europe was affected by severe droughts, causing average yearly vegetation productivity losses covering around 121 000 km2. This was particularly notable in 2003, when drought affected most parts of Europe, covering an estimated 330 000 km2 of forests, non-irrigated arable land and pastures. Drought impact was also relatively severe in 2005 and 2012.

Intensity and area affected by water deficit in Europe, 2000-2016

Note: The map shows the long-term impact of water deficit on vegetation productivity, and the area of low vegetation productivity under water deficit impact, aggregated by NUTS3 regions. Negative anomalies are expressed in standard deviation and indicate vegetation productivity conditions that are lower than the long-term average under normal, non-drought conditions.

Data source:

Severe and frequent droughts cause a deficit in water that is available for plant growth. This has long-lasting socio-economic and environmental impacts and is considered the most damaging type of natural disaster (Dai, 2011)Droughts cause habitat loss, the migration of local species and the spread of invasive alien species, leading to biodiversity loss. In Europe, this is a major obstacle to achieving the targets of the EU biodiversity strategy for 2030, which aims to protect and restore nature.

Droughts also affect the implementation of other EU policies. They affect water resources and agriculture, cause soil erosion and reduce carbon sequestration, contributing to land degradation. By hampering nature’s ability to deliver benefits for the environment and society, and support climate change adaptation and mitigation, droughts impinge on the implementation of the EU strategy on green infrastructure. They also impact on key long-term objectives of the Common Agricultural Policy: viable food production, sustainable resource management, climate action and balanced territorial development.

Monitoring the severity of and area affected by the impact of water deficit — by assessing changes in vegetation productivity — is vital for informing policy aimed at mitigating their effects and increasing ecosystem resilience, as set out in the biodiversity strategy’s nature restoration plan. 

Between 2000 and 2016, Europe was affected by severe drought events (see time series break-down here). In the 27 EU Member States and the United Kingdom, the impact was most severe in 2003 in terms of both the intensity and the affected area, amounting to approximately 330 000 km2. Although not as intense, 2005 and 2012 also saw severe water deficit, with vegetation productivity loss affecting approximately 180 000 and 220 000 km2, respectively.

The type of land affected by drought also varied from year to year. In 2003, pastures and forests suffered the most, with the forest area affected amounting to around 101 000 km2. Almost 57 000 km2 of scrub and herbaceous areas and heterogeneous agricultural areas also lost productivity in this year. 

In 2012, annual and permanent crops and agroforestry areas suffered the most severe productivity losses, although forests again accounted for the largest area affected (57 000 km2). Forests also accounted for the largest area of productivity loss in 2005 (52 000 km2), but the intensity of impact was most severe for agroforestry and irrigated lands, indicating that droughts were so severe that irrigation was unable to counterbalance the negative impacts.

Average annual area affected by water deficit, by land cover type and country

Data sources:
Data sources:

Impacts of water deficit due to drought varied from country to country in 2000-2016, with Portugal having the highest percentage of its territory (13.2 %) affected, mostly in the southern regions. This was followed by Hungary (9 %), then Kosovo (under UN Security Council Resolution 1244/99), Serbia and Albania. Other areas badly affected by long-term droughts included the Southern-Iberian Peninsula, the Balkan countries, the interior of France and southern Germany.

The impacts of water deficit on forests were severe in all countries. In terms of agricultural land, the impacts on productivity varied by country:

  • Non-irrigated arable land was most affected in Croatia (40 %) and Serbia (30 %).
  • Hotspots of arable land affected were also seen in Hungary and southern Germany.
  • Permanently irrigated lands were affected most in Portugal, followed by Spain, Greece and Turkey.
  • Rice fields were relatively resistant in all countries, with the exception of North Macedonia, where 12 % of the country was affected.
  • Fruit trees and vineyards were affected most in Portugal, followed by Hungary and Croatia.
  • Pasture productivity loss was most severe in Kosovo (25 % of the country’s area), followed by Hungary.

Supporting information

Indicator definition

The indicator addresses anomalies and long term trends of vegetation productivity derived from remote sensing observed time series of vegetation indices in areas that are pressured by drought.

Drought pressure is computed as the combination of significant Pearson correlation coefficients (r) derived between time series anomalies of yearly vegetation productivity and the anomalies of various drought hazards (dH) during the growing season.

Drought impact is indicated as the most sever negative productivity anomaly under drought pressured areas as well as long term decreasing linear trends of annual vegetation productivity for areas that are pressured by drought hazards. Detailed indicators’ specifications are presented under Methodology.


Area of drought impact (km2);

Normalized vegetation productivity anomalies (standard deviation);

Long term linear trend(%)


Policy context and targets

Context description

Environmental policy is highly dependent on the monitoring and assessment of its targets, be it land take, biodiversity loss, environmental pollution or land degradation. In May 2011, the EU adopted a Biodiversity Strategy to 2020, which identifies 6 priority targets and 20 actions for the European Union to reach the target of "Halting the loss of biodiversity and the degradation of ecosystem services in the EU by 2020, and restoring them in so far as feasible, while stepping up the EU contribution to averting global biodiversity loss". For the EU, the opportunity cost of not reaching the 2020 biodiversity headline target of halting the loss of biodiversity and ecosystem services has been estimated at 50 billion EUR per year[1]. In addition, to undermining these economic benefits, loss of biodiversity means that ecosystems and societies that rely upon them are more fragile and less resilient in the face of challenges such as climate change, pollution and habitat destruction. Droughts have an impact on several land and soil functions, as well as ecosystem services, both in urban and rural areas. For example, droughts have an impact on water resources available for human use in agriculture, cause habitat loss, migration of local species and their replacement by alien ones in open rural systems, and consequently soil erosion and biodiversity degradation. By pressuring natural ecosystems, droughts hamper the achievement of EU Biodiversity 2020 objectives.

Drought pressure on natural ecosystems has also an important role on the implementation of the EU Strategy on Green Infrastructure (GI). In contrast to the most common ‘grey’ (man-made, constructed) infrastructure approaches that serve one single objective, GI promotes multifunctionality, which means that the same area of land is able to perform several functions and offer multiple benefits if its ecosystems are in a healthy state. More specifically, GI aims to enhance nature's ability to deliver multiple valuable ecosystem goods and services, potentially providing a wide range of environmental, social, climate change adaptation and mitigation, and biodiversity benefits. Drought diminishes the normal condition of ecosystems and their capacity to provide services that could be integrated in green infrastructures.

Under EU legislation adopted in May 2018, EU Member States have to ensure that greenhouse gas emissions from land use, land use change and forestry (LULUCF) are offset by at least an equivalent removal of CO₂ from the atmosphere in the period 2021 to 2030. Ultimately, the capacity of forests and soils on a given area of land to remove carbon from the atmosphere will depend on a number of natural (regional/geographical) circumstances such as variations in growing conditions (temperature, precipitation and droughts) and natural disturbances (storms, fires) as well as past and present management practices (e.g. rotation lengths which affect the distribution of age classes in forest stands). By measuring changes in emissions and removals relative to business-as-usual projections, these circumstances (such as drought pressure) will be "factored out" so that only changes related directly human-induced activities are measured. This also provides incentives for improving on the current situation and gives an equal value to mitigation whether through sequestration or conservation or material and energy substitution.

The role of the CAP is to provide a policy framework that supports and encourages producers to address economic, environmental (i.e. relating to resource efficiency, soil and water quality and threats to habitats and biodiversity) and territorial challenges, while remaining coherent with other EU policies. This translates into three long-term CAP objectives: viable food production, sustainable management of natural resources and climate action and balanced territorial development. Given the pressure of drought on natural resources, agriculture has to improve its environmental performance through more sustainable production methods. Farmers have also to adapt to challenges stemming from changes to the climate by pursuing climate change mitigation and adaption actions (e.g. by developing greater resilience to disasters such as flooding, drought and fire). Understanding the spatio-temporal distribution of drought pressures on land, will contribute to a better, faster and more informed implementation of CAP reforms and improve the quality of life of rural populations in Europe.


No specific targets.

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Methodology for indicator calculation

Drought pressure is derived at the pixel level and is based on the combination of Pearson correlation values (r) computed for statistically significant (p-value < 0.05) regression coefficients of three Ordinary Least Squares (OLS) models. The estimated OLS models fit yearly anomalies of vegetation productivity to the severity of three drought hazard indicators (i.e. Soil Moisture, SPI-3 and -12 - see under Data specifications) occurring during the growing seasons from the years 2000 to 2016. Individual OLS models, commonly known as linear regressions, are computed for each individual grid-cell in the raster database, as follows:




where Y denotes the yearly anomalies of vegetation productivity, B01-B03 are the intercept terms, B1-B3 are the regression coefficients, S1-S3 are the drought severity indices during the growing season (GS), and e1-e3 are the error terms of the models. As defined by Spinoni et al. (2014), drought severity can be computed as the sum of negative drought Hazard (dH) values (represented by the bars in red to yellow colours in the plot below) from the start to the end month of the vegetation GS, as follows:

S=∑gs Xm

where Xm is a negative drought Hazard, dH, intensity value occurring in month m within the GS. Severity should not be mistaken for intensity, which is usually referred to the dH value in each month during the GS.

Adapted from the JRC Global Drought Observatory:

Figure 1. Timeseries of monthly dH (i.e. SPI-3) and changes in the start and length of annual GS periods for a single grid-cell in the raster database (adapted from a report of the JRC Global Drought Observatory:

Input data for estimating individual OLS models, i.e. the yearly vegetation productivity and the S values during GS, were first de-trended so as to avoid exaggerated statistically significant relations from spurious regressions (Granger and Newbold 1974). Detrending was performed by substituting the original values of both independent (S) and dependent (Y) variables by the residuals of their respective linear trend estimations. The significance of the OLS models was based on the p-value of the Student’s T-Test statistic; whenever the p-value < 0.05, then the  of the respective model was assumed to be statistically significant.

The correlation coefficients of the drought pressure index are selected as the maximum r value among all significant and positive regression coefficients (B1-B2) of the individual OLS models. Since an increase of drought hazard severity is expected to increase vegetation stress conditions, then only positive correlation coefficients were taken into account in the final indicator. Negative correlation coefficients might be indicative of management activities, such as irrigation practices, which are not addressed by the indicator. Moreover, positive correlation coefficients were kept only when all regression coefficients for the three OLS models were positive.

The proposed approach follows that of Vicente-Serrano et al. (2013), who mapped the response of vegetation to drought across global land biomes according to the highest correlation coefficient found between monthly GIMMS-NDVI and SPEI values measured at time-scales varying from 1- to 24-months. It was shown that different ecosystems react differently to the type and temporal scale of drought hazard values during the GS. Some vegetation types respond immediately to a short lack of precipitation (as measured by SPI-3), while other ecosystems respond more intensively to anomalies in the soil moisture content (anomSM). Moreover, Hofer et al. (2012) have shown that perennial vegetation types in the Iberian Peninsula, such as forests, are less sensitive to short term water deficits, thus requiring an evaluation of the association with large scale drought indices, such SPI-12. Similar conclusions were also drawn for the Iberian Peninsula by Gouveia et al. (2016) and at the global level by Vicente-Serrano et al. (2015). These studies have shown that by consequence of different physiological, anatomical or edaphic factors, some vegetation communities show a response to short periods of water deficit, whereas others may be resistant to soil water deficit, and respond to longer drought time scales representative of water deficits of longer duration.


Drought impact was quantified as:

-the per pixel minimum of the standardized vegetation productivity anomaly during 2000-2016;

-timing of the minimum anomaly, which records the year of the most sever drought impact on vegetation productivity;

-long term significant negative trends in the yearly vegetation productivity indicating lasting impact of intense drought events.

When analyzing drought impact, only those pixels were taken into account which were under drought pressure, i.e. where the regression among the drought indices and the productivity anomalies were significant and positive. Moreover, to account for outliers in the trends, which are occurring due to erroneous satellite image measurements or human land use activities in drought affected areas (such as irrigation or clear cuts), it was decided to remove all trend values below the 10th and above the 90th percentiles. Finally, to avoid the inclusion of trends derived from spurious regressions, only statistically significant (p-value < 0.05) negative trends were kept as part of the final indicator. The significance of the trends was based on the Getis-Ord Gi inferential statistic (Getis and Ord, 1992).

Methodology for gap filling

All input dataset are derived from global sources with a wall-to-wall coverage of the land surface. No gap filling was needed.

Methodology references



Methodology uncertainty

The approach cannot account for land use/land cover changes that have occurred within a pixel for the period of analysis. For example, clear cuts within forest ecosystems or the use of irrigation systems as part of the management in agricultural areas might increase or decrease the vegetation productivity independently of drought occurrences. This can introduce noise in the datasets that might bias the pixel-based relationships between drought pressure and vegetation productivity.

Another source of uncertainty is related with the simplification of the drought impact model for its implementation in the operational setting. In this study, only meteorological drought distribution and intensity is considered. Still, in some cases, the start, end, severity, and spatial extent of a drought, as well as the propagation of its impacts through the whole land systems might be changing due to additional climate and/or surrounding biophysical conditions, such temperature, snowpack, albedo and soil water holding capacity.

Data sets uncertainty

The dataset represents the average trend of productivity of all terrestrial ecosystems within an area covered by a pixel of 500x500m. Therefore, the dataset can only be used at the ecosystem level indicating drought impacts on main terrestrial ecosystems. As opposed to field measurements, remote sensing products measure vegetation light absorption from a satellite at several hundred km height which might introduce bias due to atmospheric disturbances.

Rationale uncertainty

No uncertainty has beenidentified

Data sources

Other info

DPSIR: Impact
Typology: Descriptive indicator (Type A - What is happening to the environment and to humans?)
Indicator codes
  • LSI 011
Frequency of updates
Updates are scheduled once per year
EEA Contact Info


Geographic coverage

Temporal coverage