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Data Visualization

Area of yearly soil moisture deficit per country and land cover, in % of the country area

Data Visualization Created 11 Jan 2021 Published 22 Mar 2021 Last modified 22 Mar 2021
1 min read

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Unit

% of the country area

Methodology

Standardized anomalies of of soil moisture during 2000-2019 are analyzed within the vegetation growing season.

Anomalies are expressed in standard deviations compared to the long term average soil moisture conditions for each 500m grid cell in the EEA39 region. Negative soil moisture anomalies indicate vegetation soil moisture conditions that are less than the long term normal condition.

The extent of soil moisture deficit is calculated from the sum of those grid cells where the soil moisture anomaly was below -1 standard deviation in the given year.

In order to exclude low vegetation anomaly signals in the time series, only those grid cells were considered in this analysis where the Corine Land Cover (CLC) 2000-2018 series indicated no land use change between 2000-2018.

Data sources

Soil moisture deficit during the vegetation growing season, annual time-series, 2000-2019 provided by European Environment Agency (EEA)

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Based on data

Data Soil moisture deficit during the vegetation growing season, annual time-series, 2000-2019 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).

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For references, please go to https://www.eea.europa.eu/data-and-maps/daviz/area-of-yearly-soil-moisture or scan the QR code.

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