The maps show the long-term average soil moisture contents (left) and the trends in soil moisture values (right), aggregated by NUTS3 regions.
Soil moisture is equal to 0 when the soil is severely dry (wilting point) and equal to 1 when the soil moisture is above the field capacity. Low long-term average soil moisture values indicate areas where during the 2000-2019 period the soil moisture deficit was the biggest problem. Trends are expressed in standard deviation from the long term average. Negative trends indicate that soil moisture values show a decreasing tendency during the 2000-2019 period. Areas with lower soil moisture content together with decreasing tendency in the soil moisture are in risk of loosing their land functions of supplying ecosystem services.
See also:
https://www.eea.europa.eu/data-and-maps/data/data-viewers/soil-moisture and
Original dataset: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_soilmoisture.pdf
Indicator Assessment
22 Mar 2021
Monitoring the pressure from soil moisture deficits can warn of potential impacts on plant development and soil health, supporting the assessment of drought-tolerant, resilient and vulnerable ecosystems. In 2000-2019, soil moisture in the growing season was several times below the long-term average in the EEA member countries plus the United Kingdom. The largest soil moisture deficits occurred in 2003, 2017 and 2019, affecting over 1.45 million km 2 in 2019. Soil moisture content was also low in 2012, 2015 and 2018, contributing to increasingly frequent and intense drought pressure.
The dataset consists of a collection of annual soil moisture (SM) anomalies during the vegetation growing season (GS) for the years 2000-2019 across EEA 38 area and the United Kingdom. The vegetation growing season is defined by EEA´s phenology data series "Vegetation growing season length 2000-2016" [https://www.eea.europa.eu/data-and-maps/data/annual-above-ground-vegetation-season]. The anomalies are calculated based on the European Commission's Joint Research Centre European Drought Observatory (EDO) Soil Moisture Index (SMI) with respect to the 1995–2019 base period. The yearly start and end of GS periods are dynamic and calculated according to the EEA Phenology Indicators. A positive anomaly indicates that the observed SM was wetter than the long-term SM average for the base period, while a negative anomaly indicates that the observed SM was drier than the reference value. Because SM anomalies are measured in units of standard deviation from the long-term SMI average, they can be used to compare annual deficits/surplus of SM between geographic regions.
EDO is one of the early warning and monitoring systems of the Copernicus Emergency Management Service. As the dataset builds on EDO's SMI, it therefore contains modified Copernicus Emergency Management Service information (2019).
What do many vineyards scattered across idyllic landscapes, industrial sites and landfills have in common? The presence of chemicals might be the answer. From heavy metals to organic pollutants and microplastics, the soil in which we grow our food and the land on which we build our homes might be contaminated with different pollutants. Contaminants are widespread and are accumulating in Europe’s land and soils. How can we tackle this problem?
Dashboard (Tableau)
13 Aug 2020
Monitoring soil moisture shortages is a precondition for managing drought adaptation and resilience of ecosystems, such as foreseen by the EU Nature restoration plan of the EU Biodiversity strategy 2030. This dashboard analyses 20 years (2000-2019) soil moisture content in Europe (EU27, EEA-38 and the UK). Soil moisture deficits, trends in soil moisture values and the area under pressure are presented by countries and land cover.
Land take as a result of urban sprawl is measured from the Copernicus Corine Land Cover dataset between 2000-2018. The map shows for each grid cell the area in km2 which was converted to urban areas. For visualization land take data is presented in a 10km grid. The original data which statistics are derived from is from the 100m spatial resolution CLC dataset series.
Data Visualization
25 Mar 2020
Indicator Assessment
25 Mar 2020
For the reference year 2015 , 85 861 km 2 of the total area covered by the EEA-39 countries were mapped and categorised as 'sealed surface' in the Copernicus imperviousness product. This corresponds to 1.466 % of the total EEA-39 area.
Between 2006 and 2015, soil sealing (imperviousness) in all EEA-39 countries increased by a total of 3 859 km2 , an annual average increase of 429 km 2 . During this period, the average annual increase in soil sealing relative to country area varied from 0 % to 0.088 %.
In 2015, the percentage of a countries' total area that was sealed also varied greatly, with values ranging from 16.17 % (Malta) to 0.07 % (Iceland). The highest sealing values, as a percentage of country area, occurred in small countries with high population densities, while the lowest sealing values can be found in large countries with low population densities.
The average annual increase in sealing was 460 km 2 between 2006-2009, increasing to 492 km 2 for the 2009-2012 period and slowing to 334 km 2 for the 2012-2015 period. The slow-down in the sealing increase between the two reference periods occurred in 31 out of 39 countries. The same trend is visible for sealing figures normalised by the size of the country (the % of the country newly sealed on average annually for the three periods).
The most problematic situation occurs in countries where there is already a high percentage of sealing and where the annual rate of increase relative to country area is high. Even more problematic are situations where, for 2012-2015, the rate of sealing increase is accelerating, in contrast to the general trend of a slowing rate of increase.
The chart shows the vegetation productivity changes (%) over areas with land use change in the period 2000-2018. The values are broken down by major land use change drivers.
The chart shows the effect of temperature variations on vegetation productivity, expressed in standard deviation units of vegetation productivity.
Many global policy frameworks, including the United Nations Sustainable Development Goals (SDGs),directly and indirectly address land and soil. Many of these SGDs cannot be achieved without healthysoils and a sustainable land use. Below is an overview of the SDGs with strong links to soil.
Facebook Live interview on Land and soil in Europe.
We cannot live without healthy land and
soil. It is on land that we produce most
of our food and we build our homes.
For all species — animals and plants
living on land or water — land is vital.
Soil — one of the essential components
of land — is a very complex and often
undervalued element, teeming with life.
Unfortunately, the way we currently use
land and soil in Europe and in the world
is not sustainable. This has significant
impacts on life on land.
Most of the food we eat is produced on land and in soil. What we eat and how we produce it have changed significantly in the last century along with the European landscape and society. The intensification of agriculture has enabled Europe to produce more food and at more affordable prices but at the expense of the environment and traditional farming. It is now time to rethink our relationship with the food we put on our plates and with the land and communities that produce it.
Soil plays a crucial role in nature’s cycles, including the nutrient cycle, which involves how much soil organic matter — i.e. carbon, nitrogen and phosphorus — is taken up and stored in soil. Organic compounds, such as leaves and root tips, are broken down to simpler compounds by organisms living in soil before they can be used by plants. Some soil bacteria convert atmospheric nitrogen into mineral nitrogen, which is essential for plant growth. Fertilisers introduce nitrogen and phosphates to induce plant growth but not all amounts are taken up by plants. The excess can enter rivers and lakes and affect life in these water ecosystems.
GIS Map Application
15 Aug 2019
The intensity of land take is calculated as land take in the given period as a percentage of the area of artificial surfaces in 2000. For easier comparability, land take is summarised within NUTS3 regions.
The raster file is the basis of the indicator for assessing landscape fragmentation due to urban and transport expansion. The computation is based on the method of Effective Mesh Density (seff). The Effective Mesh Density (seff) is a measure of landscape fragmentation, i.e. the degree to which movement between different parts of the landscape is interrupted by a Fragmentation Geometry (FG). FGs are defined as the presence of impervious surfaces and traffic infrastructure. The Effective Mesh Density gives the effective number of meshes (or landscape patches) per 1000 km2, in other words, the density of the meshes. The more FGs fragment the landscape, the higher the effective mesh density. The seff values are reported within the 1km sq regular LEAC grid.
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