Drought impact on ecosystems in Europe

Europe is facing more and stronger climate hazards, including heatwaves and prolonged droughts. Drought conditions were drier-than-average in eastern/south-eastern Europe during 2024. Around 600,000 km 2 of the European Union was exposed to below 2000-2020 average soil moisture levels. In response, vegetation productivity did not recover to the 2000-2020 baseline condition in almost 160,000km 2 of south-eastern Europe and Mediterranean regions. Without effective implementation of global climate mitigation as well as EU and national climate adaptation strategies and plans, the impacts of prolonged drought are likely to further increase.

Metadata

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Workflow

Layout

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Vegetation in Cyprus, Greece\u00a0and Italy\u00a0failed to recover\u00a0in\u00a0more than 10% of each\u00a0country.\u00a0In\u00a0absolute\u00a0terms, Romania\u00a0suffered largest vegetation productivity impact from\u00a0the drought (41,370km 2 ), followed by\u00a0Italy\u00a0(31,856km 2 ) and\u00a0Spain\u00a0(29,304km 2 ).", "value": [ { "children": [ { "text": "Strong regional differences can be seen in the " }, { "children": [ { "text": "2024\u00a0drought impact" } ], "type": "strong" }, { "text": "\u00a0on Figure 2.\u00a0The\u00a0impacted\u00a0area was much higher than the 2000-2020 average area for six\u00a0EU Member States (eight\u00a0in EEA member countries).\u00a0In\u00a0Malta,\u00a0over 57% of the country's vegetation productivity\u00a0could not recover to the 2000-2020\u00a0level.\u00a0The same\u00a0drought\u00a0caused\u00a0lower than\u00a0baseline\u00a0vegetation\u00a0productivity\u00a0in 20%\u00a0of\u00a0Bulgaria\u2019s\u00a0and\u00a017% of Romania\u2019s\u00a0territory. 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Drought impact area during 2024 in comparison to the 2000-2020 average for the EEA-38 regions", "value": [ { "children": [ { "text": "Figure 2. Drought impact area during 2024 in comparison to the 2000-2020 average for the EEA-38 regions" } ], "type": "h3-light" } ] } }, "blocks_layout": { "items": [ "918f89d8-e178-47ce-9cb2-8d0c1594d2ec" ] } }, "id": "figure-metadata-02ba4a04-fcfe-4968-806f-1dac3119cfef", "styles": {} }, "43df8fab-b278-4b0e-a62c-ce6b8e0a881e": { "@type": "dividerBlock", "disableNewBlocks": true, "fitted": false, "fixed": true, "hidden": true, "readOnly": true, "readOnlySettings": true, "required": true, "section": false, "short": true, "spacing": "m", "styles": {} }, "cb9b3ad1-65d9-4392-8601-1410d4cca06c": { "@type": "slate", "plaintext": "For the non-EU region,\u00a0drought impact in Macedonia was most concerning, where\u00a0vegetation failed to recover across\u00a013% of the\u00a0country, 9.4 percentage points above the 2000-2020 baseline.\u00a0The largest area affected by drought\u00a0was seen in T\u00fcrkiye (20,549km 2 ).", "value": [ { "children": [ { "text": "For the non-EU region,\u00a0drought impact in Macedonia was most concerning, where\u00a0vegetation failed to recover across\u00a013% of the\u00a0country, 9.4 percentage points above the 2000-2020 baseline.\u00a0The " }, { "children": [ { "text": "largest area affected" } ], "type": "strong" }, { "text": "\u00a0by drought\u00a0was seen in T\u00fcrkiye (20,549km" }, { "children": [ { "text": "2" } ], "type": "sup" }, { "text": ").\u00a0" } ], "type": "p" } ] }, "d3d49723-14e5-4663-b346-37ee3572f28d": { "@type": "slate", "fixed": true, "instructions": { "content-type": "text/html", "data": " ", "encoding": "utf8" }, "plaintext": "", "readOnlySettings": true, "required": true, "value": [ { "children": [ { "text": "" } ], "type": "p" } ] } }, "blocks_layout": { "items": [ "2b071ffb-64a9-44af-8355-3891f0b91224", "02ba4a04-fcfe-4968-806f-1dac3119cfef", "43df8fab-b278-4b0e-a62c-ce6b8e0a881e", "027a79ba-b656-497d-b4d1-1be2a1a80b66", "cb9b3ad1-65d9-4392-8601-1410d4cca06c" ] } }, "disableInnerButtons": true, "disableNewBlocks": false, 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global\u00a0climate\u00a0mitigation, to meet Paris Agreement targets ( IPCC AR6, 2023 ).", "value": [ { "children": [ { "text": "As set out in the " }, { "children": [ { "text": "European Water Resilience Strategy," } ], "data": { "url": "https://commission.europa.eu/topics/environment/water-resilience-strategy_en" }, "type": "link" }, { "text": " droughts and water scarcity are increasingly challenging the EU, for both environment and society.\u00a0Strengthening ecosystem resilience and reducing drought\u00a0impacts require " }, { "children": [ { "text": "urgent\u00a0climate\u00a0adaptation\u00a0action" } ], "type": "strong" }, { "text": "\u00a0by\u00a0the EU and Member States, alongside stronger global\u00a0climate\u00a0mitigation, to meet Paris Agreement targets (" }, { "children": [ { "text": "IPCC AR6, 2023" } ], "data": { "url": "https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_LongerReport.pdf" }, "type": "link" }, { "text": ").\u00a0" } ], "type": "p" } ] }, "30b97580-d14a-4051-8622-c3ba6b51cf37": { "@type": "group", "className": "figure-metadata", "data": { "blocks": { "41797b97-00e7-4170-99b9-999a6868c134": { "@type": "slate", "plaintext": "Figure 1. Area of drought impact on vegetation productivity in the EU-27", "value": [ { "children": [ { "text": "Figure 1. Area of drought impact on vegetation productivity in the EU-27" } ], "type": "h3-light" } ] } }, "blocks_layout": { "items": [ "41797b97-00e7-4170-99b9-999a6868c134" ] } }, "id": "figure-metadata-b0279dde-1ceb-4137-a7f1-5ab7b46a782c" }, "43df8fab-b278-4b0e-a62c-ce6b8e0a881d": { "@type": "dividerBlock", "disableNewBlocks": true, "fitted": false, "fixed": true, "hidden": true, "readOnly": true, "readOnlySettings": true, "required": true, "section": false, "short": true, "spacing": "m", "styles": {} }, "592e6cd9-3236-45f6-b004-d3d2749dad91": { "@type": "slate", "plaintext": "During\u00a02024, 87,567\u202fkm\u00b2 of EU-27 cropland\u00a0productivity was below the 2000\u20132020 baseline. Heathlands and shrubs were affected across 9,866\u202fkm\u00b2, while forest productivity was\u00a0impacted\u00a0over 33,518\u202fkm\u00b2. This is below\u00a0the 2000-2020 baseline,\u00a0yet still a concern for carbon sequestration and the EU\u2019s 2050 climate goals .\u00a0Grasslands and heathlands, among the EU\u2019s most biodiverse and carbon-rich ecosystems, were\u00a0impacted\u00a0on 24,056\u202fkm\u00b2 in 2024\u2014larger than\u00a0the size of\u00a0Slovenia. Only\u00a0222\u202fkm\u00b2 of wetlands were affected in 2024, reflecting drought concentration in Southeast Europe where wetlands are rare.", "value": [ { "children": [ { "text": "During\u00a02024, 87,567\u202fkm\u00b2 of EU-27 " }, { "children": [ { "text": "cropland\u00a0productivity" } ], "type": "strong" }, { "text": " was below the 2000\u20132020 baseline. Heathlands and shrubs were affected across 9,866\u202fkm\u00b2, while\u00a0" }, { "children": [ { "text": "forest productivity" } ], "type": "strong" }, { "text": "\u00a0was\u00a0impacted\u00a0over 33,518\u202fkm\u00b2. This is below\u00a0the 2000-2020 baseline,\u00a0yet still a concern for carbon sequestration and the " }, { "children": [ { "text": "EU\u2019s 2050 climate goals" } ], "data": { "url": "https://climate.ec.europa.eu/eu-action/climate-strategies-targets/2050-long-term-strategy_en" }, "type": "link" }, { "text": ".\u00a0Grasslands and heathlands, among the EU\u2019s most biodiverse and carbon-rich ecosystems, were\u00a0impacted\u00a0on 24,056\u202fkm\u00b2 in 2024\u2014larger than\u00a0the size of\u00a0Slovenia. Only\u00a0222\u202fkm\u00b2 of wetlands were affected in 2024, reflecting drought concentration in Southeast Europe where wetlands are rare.\u00a0\u00a0" } ], "type": "p" } ] }, "7a53e263-ee5b-4f1e-852e-02e3021dfcce": { "@type": "slate", "plaintext": "Droughts hinder carbon sequestration by natural ecosystems, which leads to\u00a0undermining the EU\u2019s climate mitigation goals ( Climate Impact and Preparedness Portal ).\u00a0It compromises\u00a0ecosystems'\u00a0ability to reduce\u00a0the impact of\u00a0heat waves\u00a0and increases\u00a0the risk of wildfires.\u00a0They also\u00a0impact\u00a0food production, resource management, and territorial development\u2014core aims of the EU Common Agriculture Policy .", "value": [ { "children": [ { "text": "Droughts\u00a0" }, { "children": [ { "text": "hinder carbon sequestration" } ], "type": "strong" }, { "text": " by natural ecosystems, which leads to\u00a0undermining the EU\u2019s climate mitigation goals (" }, { "children": [ { "text": "Climate Impact and Preparedness Portal" } ], "data": { "url": "https://discomap.eea.europa.eu/ClimatePreparedness2025/" }, "type": "link" }, { "text": ").\u00a0It compromises\u00a0ecosystems'\u00a0ability to reduce\u00a0the impact of\u00a0heat waves\u00a0and increases\u00a0the risk of wildfires.\u00a0They also\u00a0impact\u00a0food production, resource management, and territorial development\u2014core aims of the " }, { "children": [ { "text": "EU Common Agriculture Policy" } ], "data": { "url": "https://agriculture.ec.europa.eu/common-agricultural-policy_en" }, "type": "link" }, { "text": ".\u00a0" } ], "type": "p" } ] }, "9160c8b7-6daf-4d2b-96b9-25473c955291": { "@type": "slate", "plaintext": "By mid-century, heatwaves and droughts are expected to increase in frequency and\u00a0in\u00a0intensity\u00a0across most of Europe, outpacing the global average ( IPCC AR6 ).\u00a0Between 2000 and 2024, 12 years saw drought-affected areas above the median, with eight occurring after 2010.\u00a0Given the rising frequency and severity of droughts,\u00a0impacted\u00a0areas are unlikely to decline by 2030.\u00a0Land\u00a0and water\u00a0management\u00a0must become more sustainable which\u00a0includes\u00a0drought-tolerant crops, cover crops, and crop residues\u00a0and\u00a0the\u00a0effective implementation of EU and national\u00a0climate adaptation\u00a0strategies are essential ( Climate-ADAPT ).", "value": [ { "children": [ { "text": "By mid-century, heatwaves and droughts are expected to increase in frequency and\u00a0in\u00a0intensity\u00a0across most of Europe, outpacing the global average (" }, { "children": [ { "text": "IPCC AR6" } ], "data": { "url": "https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_LongerReport.pdf" }, "type": "link" }, { "text": ").\u00a0Between 2000 and 2024, 12 years saw " }, { "children": [ { "text": "drought-affected areas " } ], "type": "strong" }, { "text": "above the median, with eight occurring after 2010.\u00a0Given the rising frequency and severity of droughts,\u00a0impacted\u00a0areas are unlikely to decline by 2030.\u00a0Land\u00a0and water\u00a0management\u00a0must become more sustainable which\u00a0includes\u00a0drought-tolerant crops, cover crops, and crop residues\u00a0and\u00a0the\u00a0effective implementation of EU and national\u00a0climate adaptation\u00a0strategies are essential (" }, { "children": [ { "text": "Climate-ADAPT" } ], "data": { "url": "https://climate-adapt.eea.europa.eu/en/knowledge/adaptation-information/adaptation-options" }, "type": "link" }, { "text": ")." } ], "type": "p" } ] }, "ab112d92-6981-44d4-9cb1-3bc0987e1f05": { "@type": "slate", "plaintext": "In 2024, severe droughts\u00a0exposed\u00a0601,193\u202fkm\u00b2 across Europe, with\u00a0vegetation productivity\u00a0failing to recover\u00a0to the baseline level\u00a0of 156,703\u202fkm\u00b2 \u2014 indicating adaptation stress (Figure 1).\u00a0Since 2017, vegetation impact has consistently exceeded the 2000-2020 baseline level (approx. 152,000km 2 ), except in 2021.", "value": [ { "children": [ { "text": "In 2024, severe droughts\u00a0exposed\u00a0601,193\u202fkm\u00b2 across Europe, with\u00a0vegetation productivity\u00a0failing to recover\u00a0to the baseline level\u00a0of 156,703\u202fkm\u00b2 \u2014 indicating\u00a0" }, { "children": [ { "text": "adaptation stress" } ], "type": "strong" }, { "text": " (Figure 1).\u00a0Since 2017, vegetation impact has consistently exceeded the 2000-2020 baseline level (approx. 152,000km" }, { "children": [ { "text": "2" } ], "type": "sup" }, { "text": "), except in 2021.\u00a0\u00a0" } ], "type": "p" } ] }, "b0279dde-1ceb-4137-a7f1-5ab7b46a782c": { "@type": "embed_content", "disableNewBlocks": true, "fixed": true, "instructions": { "content-type": "text/html", "data": "figure instructions goes here", "encoding": "utf8" }, "readOnlySettings": true, "required": true, "url": "../../../../resolveuid/a0287ee64fd844eba82289c0a7d2af62" }, 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relevant aggregate level (i.e. global, EU, sectoral) and addresses the following: <ol keys=\"dkvn8,e367c,f4lpb,9j981,7ai6k,3g3pd\" depth=\"0\"><li>Explains in one or two sentences on the environmental rationale of the indicator, i.e. why it matters to the environment that we see an increase/decrease in the value measured.</li><li>Explains in one or two sentences the associated policy objective, which can be either quantitative or directional. More information on the policy objective and related references will be included in the supporting information section. Where there is no policy objective associated with the indicator, i.e. where the indicator addresses an issue that is important for future policy formulation, this text should explain instead why this issue is important.</li><li>IF NECESSARY - Explains any mismatch between what the indicator tracks and what the policy objective/issue is.</li><li>Qualifies the historical trend (e.g. steady increase) and explains the key reasons (e.g. policies) behind it. If there is a quantitative target it explains if we are on track to meet it.</li><li>IF NECESSARY - Explains any recent changes to the trend and why.</li><li>IF NECESSARY - Describes what needs to happen to see adequate progress in future, for instance in order to remain on track to meet targets.</li></ol>Please cite your work if necessary using the EEA citation style (i.e. EEA, 2020). A full reference list appears in the supporting information section.", "encoding": "utf8" }, "maxChars": "2000", "placeholder": "Aggregate level assessment e.g. progress at global, EU level..", "readOnlySettings": true, "required": true, "title": "Aggregate level assessment" }, "6400efa6-98d8-4b27-adf2-14843552db3c": { "@layout": "794c9b24-5cd4-4b9f-a0cd-b796aadc86e8", "@type": "group", "allowedBlocks": [], "as": "section", "block": "6400efa6-98d8-4b27-adf2-14843552db3c", "data": { "blocks": { "12d8c532-f7ad-43fe-ada7-330b2d7a7a39": { "@type": "slate", "disableNewBlocks": true, "fixed": true, "instructions": { "content-type": "text/html", "data": " ", "encoding": "utf8" }, "plaintext": "Published: date \u2012 25min read", "readOnly": true, "readOnlySettings": true, "required": true, "value": [ { "children": [ { "text": "" }, { "children": [ { "text": "Published: " }, { "children": [ { "text": "date" } ], "data": { "id": "effective", "widget": "datetime" }, "type": "mention" }, { "text": " \u2012 25min read" } ], "type": "sup" }, { "text": "" } ], "type": "p" } ] }, "1c31c956-5086-476a-8694-9936cfa6c240": { "@type": "description", "disableNewBlocks": true, "fixed": true, "instructions": { "content-type": "text/html", "data": "The summary tells the reader about the indicator trend over the examined period and whether or not it helps to achieve the associated policy objective, which can be either quantitative or directional.In the absence of a policy objective, it explains whether the trend is in the right or wrong direction in relation to the issue examined.If there has been an important change over the most recent period of the time series, e.g. over the last year, this is indicated too.Furthermore, if there is a quantitative target, it also indicates whether we are on track to meet it and if not what are the reasons preventing that, e.g. socio-economic drivers, implementation gap etc.", "encoding": "utf8" }, "placeholder": "Summary", "plaintext": "Europe is\u00a0facing more and stronger climate hazards, including heatwaves and prolonged\u00a0droughts. Drought\u00a0conditions\u00a0were drier-than-average in\u00a0eastern/south-eastern\u00a0Europe during 2024.\u00a0Around 600,000 km 2 of the European Union\u00a0was exposed to\u00a0below\u00a02000-2020 average\u00a0soil moisture levels.\u00a0In response, vegetation productivity did not recover to the 2000-2020 baseline condition in almost 160,000km 2 of\u00a0south-eastern Europe\u00a0and Mediterranean regions.\u00a0Without effective implementation\u00a0of global\u00a0climate\u00a0mitigation\u00a0as well\u00a0as EU\u00a0and national\u00a0climate\u00a0adaptation\u00a0strategies\u00a0and plans,\u00a0the impacts of prolonged\u00a0drought\u00a0are likely to\u00a0further\u00a0increase.", "readOnlySettings": true, "required": true, "value": [ { "children": [ { "text": "Europe is\u00a0facing more and stronger climate hazards, including heatwaves and prolonged\u00a0droughts. Drought\u00a0conditions\u00a0were drier-than-average in\u00a0eastern/south-eastern\u00a0Europe during 2024.\u00a0Around 600,000 km" }, { "children": [ { "text": "2" } ], "type": "sup" }, { "text": "\u00a0of the European Union\u00a0was exposed to\u00a0below\u00a02000-2020 average\u00a0soil moisture levels.\u00a0In response, vegetation productivity did not recover to the 2000-2020 baseline condition in almost 160,000km" }, { "children": [ { "text": "2" } ], "type": "sup" }, { "text": "\u00a0of\u00a0south-eastern Europe\u00a0and Mediterranean regions.\u00a0Without effective implementation\u00a0of global\u00a0climate\u00a0mitigation\u00a0as well\u00a0as EU\u00a0and national\u00a0climate\u00a0adaptation\u00a0strategies\u00a0and plans,\u00a0the impacts of prolonged\u00a0drought\u00a0are likely to\u00a0further\u00a0increase.\u00a0" } ], "type": "p" } ] }, "3cccc2bb-471a-44c7-b006-5595c4713ff2": { "@type": "layoutSettings", "disableNewBlocks": true, "fixed": true, "layout_size": "narrow_view", 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Supporting information

[ { "type": "p", "children": [ { "text": "" }, { "children": [ { "text": "Soil moisture deficit" } ], "type": "strong" }, { "text": " is calculated at the pixel level by deriving z-score anomalies from the Soil Moisture Index, such as: " } ] }, { "children": [ { "text": "SMA = SMI-SMI" }, { "children": [ { "sub": true, "text": "MN" } ], "type": "strong" }, { "sub": true, "text": " " }, { "children": [ { "sub": true, "text": "(2001-2020)" } ], "type": "strong" }, { "text": "/SMI" }, { "children": [ { "sub": true, "text": "SD (2001-2020)" } ], "type": "strong" }, { "sub": true, "text": ", " }, { "text": "(Equation 1) " } ], "type": "p" }, { "children": [ { "text": "Where SMA is Soil Moisture Anomaly, MN is the 2001-2020 average SMA and SD is the 2001-2020 standard deviation of the SMI. The calculated SMA values are then averaged within the growing season to derive the SMA(gs) time series.\u00a0" } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "The aggregation is performed by averaging the monthly SMA values extracted from the EDO within the vegetation growing season. The vegetation growing season was defined by using the start and the end date of the growing period (SOS or Start of Season and EOS or End Of Season, respectively) extracted from the Medium Resolution Vegetation Phenology and Productivity product of the Copernicus Land Monitoring Service. The SOS and EOS datasets can be explored and downloaded from EEA's data repository under sdi.eea.europa.eu. Direct links to the datasets: " } ], "type": "p" }, { "children": [ { "text": "SOS: " } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a7b2369b-dd62-4d02-99e2-e5d74a8ec83a" } ], "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a7b2369b-dd62-4d02-99e2-e5d74a8ec83a" }, "type": "link" }, { "text": " " } ], "type": "p" }, { "children": [ { "text": " " }, { "children": [ { "text": "" } ], "type": "b" }, { "text": "" } ], "type": "p" }, { "children": [ { "text": "EOS: " } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a3cfb2c4-156a-413c-a73b-15ebbb016557" } ], "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a3cfb2c4-156a-413c-a73b-15ebbb016557" }, "type": "link" }, { "text": " " } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "Annual drought pressure" } ], "type": "strong" }, { "text": " is derived at the pixel level and is simply defined as:\u00a0 " } ], "type": "p" }, { "children": [ { "text": "SMA(gs) < -1, (Equation 2) ." } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Negative soil moisture anomalies indicate that the annual average availability of soil moisture for plants is lower than the long-term normal condition and drops to such a level that it might impact vegetation productivity.\u00a0 " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "To indicate " }, { "children": [ { "text": "drought pressure area" } ], "type": "strong" }, { "text": ", strong negative soil moisture anomalies are selected by setting a maximum value at -1 standard deviation (std). The drought pressure area is the sum of those grid cells within each analytical unit (see later), where the growing season aggregated SMA values are < -1. This threshold was selected to allow the monitoring of vegetation responses to only considerable soil moisture deficits. Choosing the threshold of -1 std follows the recommendations of the European Drought Observatory (EDO" }, { "sup": true, "text": "11" }, { "text": ") of the European Commission\u2019s Joint Research Centre. This approach is also followed in the EEA indicator addressing " }, { "type": "link", "data": { "url": "../../../../resolveuid/02362879b05c454c96681b1a00426384" }, "children": [ { "text": "soil moisture deficit" } ] }, { "text": ". By applying this threshold, drought impacts can better be distinguished from response in vegetation anomalies due to other environmental pressures such as e.g. wildfires, storms or insects infestations. As vegetation productivity decline may be also caused by anthropogenic impacts, pixels with land use change were excluded from the statistical population based on the " }, { "children": [ { "text": "Copernicus Corine Land Cover 2000-2018 accounting layers datasets" } ], "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/5a5f43ca-1447-4ed0-b0a6-4bd2e17e4f4d" }, "type": "link" }, { "text": ".\u00a0 " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "The " }, { "children": [ { "text": "drought pressure intensity" } ], "type": "strong" }, { "text": " is defined as the annual, growing season aggregated SMA values where SMA < -1, where aggregation is performed by temporal and spatial averaging within analytical units (see later). " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "Annual drought impact" } ], "type": "strong" }, { "text": " is quantified as: " } ], "type": "p" }, { "children": [ { "text": "SMA(gs)<0 and LINTa<-0.5, (Equation 2)," } ], "type": "p" }, { "children": [ { "text": "where LINTa (lLarge Integral anomaly) refers to the 2000-2022 annual anomalies in growing season productivity derived from remote-sensing data and approximated using vegetation indices (see more explanation below). " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "The LINT anomalies were calculated as standard deviations from the long-term mean: " } ], "type": "p" }, { "children": [ { "text": "LINTa(year x" }, { "sub": true, "text": "i-n" }, { "text": ")=(LINT(x" }, { "sub": true, "text": "i" }, { "text": ")-LINT(" }, { "sub": true, "text": "LTA" }, { "text": "))/LINT(" }, { "sub": true, "text": "std" }, { "text": ")), (Equation 3)." } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Where x" }, { "sub": true, "text": "i-n" }, { "text": " indexes the time series (from i=2000 till n=2021), LINT(" }, { "sub": true, "text": "LTA" }, { "text": ") is the long term (using the background of 2000-2020) average of the LINT values and LINT(" }, { "sub": true, "text": "std" }, { "text": ") is the long term (using the background) standard deviation of the LINT values for the same period. " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "The threshold of a -0.5 standard deviation for the vegetation anomalies was selected to indicate small deviations from the long-term mean and to allow for moderate productivity levels under drought impact to be accounted for. In a Europe-wide study, this is a pragmatic solution that provides a wide overview of drought impact situations in Europe. However, local studies might consider setting a lower or higher threshold to reflect local conditions. " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "The drought impact area is the sum of those grid cells within each analytical unit (see below) where the growing season aggregated SMA values are < -1 and the LINT anomalies are < -0.5. The drought impact intensity is defined as the annual aggregated LINT anomalies where SMA < -1 and LINTa < -0.5. Aggregation is performed by temporal and spatial averaging within analytical units. " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "For the analytical units of this indicator the following datasets were combined: " } ], "type": "p" }, { "type": "p", "children": [ { "text": "" } ] }, { "type": "ul", "children": [ { "children": [ { "text": "Administrative boundaries, aligned with the Corine Land Cover: " }, { "type": "link", "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/08c0e074-4a98-4545-bd85-f58fe3f74d82" }, "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/08c0e074-4a98-4545-bd85-f58fe3f74d82" } ] }, { "text": ";" } ], "type": "li" }, { "children": [ { "text": "Environmental Zones: " }, { "type": "link", "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/6ef007ab-1fcd-4c4f-bc96-14e8afbcb688" }, "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/6ef007ab-1fcd-4c4f-bc96-14e8afbcb688" } ] }, { "text": ";" } ], "type": "li" }, { "children": [ { "text": "Corine Land Cover accounting layers 2000 and 2018:\u00a0" }, { "type": "link", "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/fa9bd2f5-8006-42e7-8090-7b9f9b09bf29" }, "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/fa9bd2f5-8006-42e7-8090-7b9f9b09bf29" } ] }, { "text": " and\u00a0 " }, { "type": "link", "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/5a5f43ca-1447-4ed0-b0a6-4bd2e17e4f4d" }, "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/5a5f43ca-1447-4ed0-b0a6-4bd2e17e4f4d" } ] }, { "text": ". " } ], "type": "li" }, { "type": "li", "children": [ { "text": "MAES ecosystem types derived from the Corine Land Cover as Look Up Tables (can be distributed upon request). " } ] }, { "type": "li", "children": [ { "text": "Land cover flows: " }, { "type": "link", "data": { "url": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/835d25e0-b9dc-4fb9-a8b6-f9e5336fa357" }, "children": [ { "text": "https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/835d25e0-b9dc-4fb9-a8b6-f9e5336fa357" } ] }, { "text": ". " } ] } ] }, { "children": [ { "text": "The combination of the above datasets resulted in analytical units with 2,700,000 records in the database, which is easy to handle with desktop computers.\u00a0 " } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "Vegetation productivity: LINT, or Large Integral" } ], "type": "strong" }, { "text": " " } ], "type": "p" }, { "children": [ { "text": "In summary, vegetation productivity is derived from remote-sensing observed time series data of vegetation indices. The vegetation index used for the LINT index is the Plant Phenology Index (PPI) (" }, { "type": "link", "data": { "url": "https://www.eea.europa.eu/data-and-maps/data/external/plant-phenology-index" }, "children": [ { "text": "Jin and Eklundh, 2014" } ] }, { "text": "). The PPI is based on the MODIS Nadir BRDF-adjusted reflectance product (MODIS MCD43 NBAR). The product provides reflectance data for the MODIS \u2018land\u2019 bands (1-7), adjusted using a bidirectional reflectance distribution function. This function models values as if they were collected from a Nadir view to remove so-called cross-track illumination effects. The PPI is a new vegetation index optimised for the efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in the visible-red (RED) and near-infrared (NIR) spectral domains. The PPI is defined as having a linear relationship with the canopy green Leaf Area Index (LAI) and its temporal pattern is very similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. The PPI is less affected by the presence of snow than other commonly used vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI). " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "The product is distributed with a 500m pixel size (MODIS Sinusoidal Grid) with an 8-day compositing period. The large integral, or LINT, used in this indicator is the mathematical integral calculation of the smoothed and gap-filled PPI time series data between the start and end of the growing season points, being the SOS and EOS datasets described above. " } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "All input data sets are derived with wall-to-wall coverage of the land surface of the EEA-38 region. " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "No gap filling was needed. " } ], "type": "p" } ]

{ "readOnly": true, "data": [ { "@id": "e322ae04-e504-4a93-a1d0-6dee243f277b", "link": "https://www.eea.europa.eu/en/datahub/datahubitem-view/b7b1fa67-6591-48cf-b88b-a8357fe0383a", "organisation": "European Environment Agency (EEA) ", "title": "Vegetation productivity 2000-2024" }, { "@id": "ccf5216b-d636-4d31-be9d-98cf7e5ca73d", "link": "https://data.jrc.ec.europa.eu/dataset/a47c68f3-818f-4c41-92cc-1b94bdefcb6f", "organisation": "European Drought Observatory, Joint Research Centre", "title": "EDO Soil Moisture Index (SMI) (version 3.0.1)" } ] }

[ { "children": [ { "text": "The indicator only addresses droughts, hence the annual deficit in soil moisture due precipitation shortages. The indicator does not address hydrological droughts which occur when low water levels become evident in hydrological systems, especially in streams, reservoirs, and groundwater, usually after many months of meteorological drought.\u00a0The indicator monitors anomalies and long-term trends in vegetation productivity based on remote sensing observed time series data of vegetation indices in areas that are under pressure from soil moisture deficit.\u00a0" } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Drought pressure is computed as soil moisture deficit within the growing season, using the Soil Moisture Index (SMI)" }, { "sup": true, "text": "10" }, { "text": " time series of the Copernicus EMS " }, { "children": [ { "text": "European Drought Observatory of the European Commission Joint Research Centre" } ], "data": { "url": "https://edo.jrc.ec.europa.eu/edov2/php/index.php?id=1000" }, "type": "link" }, { "text": " (" }, { "children": [ { "text": "EDO, 2019" } ], "data": { "url": "https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_soilmoisture.pdf" }, "type": "link" }, { "text": "). " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Drought impact during the growing season is indicated as a severe negative annual productivity anomaly in drought-pressured areas, i.e. areas with negative annual soil moisture anomalies. Detailed indicator specifications are described under \u2018Methodology\u2019. " } ], "type": "p" } ]

[ { "children": [ { "text": "The indicator is a headline indicator for monitoring progress towards the " }, { "type": "link", "data": { "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52022DC0357" }, "children": [ { "text": "8th Environment Action Programme" } ] }, { "text": ". It contributes mainly to monitoring aspects of the 8th EAP priority objective Article 2.2.b that shall be met by 2030: \u2018continuous progress in enhancing and mainstreaming adaptive capacity, including on the basis of ecosystem approaches, strengthening resilience and adaptation and reducing the vulnerability of the environment, society and all sectors of the economy to climate change, while improving prevention of, and preparedness for, weather- and climate-related disasters\u2019" }, { "children": [ { "text": " " } ], "data": { "footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">EU, 2022, Decision (EU) 2022/591 of the European Parliament and of the Council of 6 April 2022 on a general Union environment action programme to 2030, OJ L 114, 12.4.2022, p. 22-36.</div>\n</div>\n", "footnoteTitle": "EU, Decision (EU) 2022/591 of the European Parliament and of the Council of 6\u00a0April 2022 on a general Union environment action programme to 2030", "uid": "qmbuS", "zoteroId": "YZ7GN8RW" }, "type": "zotero" }, { "text": ". More specifically, and in accordance with the European Commission Communication on the 8th EAP monitoring framework, the indicator assesses whether the EU will \u2018decrease the area impacted by drought and loss of vegetation productivity\u2019 by 2030" }, { "children": [ { "text": " " } ], "data": { "footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">EC, 2022, COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS on the monitoring framework for the 8th Environment Action Programme: Measuring progress towards the attainment of the Programme’s 2030 and 2050 priority objectives - COM/2022/357 final</div>\n</div>\n", "footnoteTitle": "EC, 2022, COMMUNICATION FROM THE COMMISSION TO THE", "uid": "zoIPV", "zoteroId": "8B97VMRI" }, "type": "zotero" }, { "text": ".\u00a0 " } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "Justification for indicator selection" } ], "type": "strong" }, { "text": " " } ], "type": "p" }, { "children": [ { "text": "Droughts are extreme climate events that are induced by temporary water deficits and may be related to a lack of precipitation, soil moisture, streamflow or any combination of the three taking place at the same time. Droughts can occur in most parts of the world, even in wet and humid regions, and can have profound impacts on agriculture, industry, tourism and ecosystems and the services they provide. In arid and semi-arid ecosystems (including the Mediterranean regions), limited water availability is a recurrent phenomenon and governs plant growth and phenology. On the other hand, in temperate and boreal regions, sporadic prolonged dry periods can lead to water-limited conditions and have far-reaching impacts on ecosystems\u2019 carbon balance and structure. The immediate impacts of droughts within the growing season (i.e. a few weeks in duration) are, for example, lead to decline in crop production, pasture growth and fodder supplies from crop residues. Prolonged water shortages (e.g. of several months) may, among other things, potentially increase wildfire occurrences. " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "The monitoring and assessment of drought impacts are complex because they vary in their severity and often depend on the different phases of the given drought event. Differences in the physiological response of vegetation to water deficits cause differences in the sensitivity and resilience of terrestrial ecosystems to drought, and ultimately influence the types of impacts that droughts have, i.e. slow growth or reduced greenness, that lead to loss of biomass or may even result in plant mortality. Consequently, significant changes in vegetation productivity provide an indication/early warning of imminent impacts on ecosystems\u2019 equilibrium states. " } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "" }, { "children": [ { "text": "Context description" } ], "type": "strong" }, { "text": " " } ], "type": "p" }, { "children": [ { "text": "In June 2024, the EU adopted the " }, { "type": "link", "data": { "url": "https://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-regulation_en" }, "children": [ { "text": "Nature Restoration Law" } ] }, { "text": " requiring member states to restore at least 30% of habitats covered by legislation from a poor to a good condition by 2030, increasing to 60% by 2040, and 90% by 2050.\u00a0" } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Droughts have an impact on the condition of ecosystems covered by the Nature Restoration law such as forests and grasslands and wetlands and might hamper reaching the restoration targets.\u00a0" } ], "type": "p" }, { "children": [ { "text": "The EU Strategy on Adaptation to Climate Change " }, { "type": "link", "data": { "url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021DC0082" }, "children": [ { "text": "(COM(2021) 82 final)" } ] }, { "text": " sets out important objectives around mainstreaming adaptation across different policy areas. It shows the importance of healthy soils in minimising impacts of floods and droughts. Droughts negatively affect the adaptative capacity of agricultural ecosystems, the resilience of forest ecosystems and in urban ecosystems droughts indirectly affect the ability of green urban spaces to protect people against heatwaves.\u00a0\u00a0" } ], "type": "p" }, { "children": [ { "text": "\u00a0" } ], "type": "p" }, { "children": [ { "text": "The EU legislation for " }, { "type": "link", "data": { "url": "https://unfccc.int/topics/land-use/workstreams/land-use--land-use-change-and-forestry-lulucf" }, "children": [ { "text": "LULUCF" } ] }, { "text": " as part of the 2030 climate target sets clear targets for the LULUCF sector for each Member States.\u202fThe capacity of forests and\u202fother land uses\u202fto\u202fstore and\u202fremove carbon from the atmosphere will depend\u202fon management as well as\u202fa number of natural circumstances. The latter include variations in growing conditions and droughts, which can have an important effect on reaching the national carbon removal targets of Member States by impacting the amount of carbon storge in ecosystems.\u00a0" } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "Targets: " } ], "type": "p" }, { "children": [ { "text": "No specific targets. " } ], "type": "p" } ]

[ { "children": [ { "text": "Methodology uncertainty: " } ], "type": "p" }, { "children": [ { "text": "The approach cannot account for all land use or land cover changes that have occurred within a pixel in the whole period of analysis. For example, clear cuts within forest ecosystems or the use of irrigation systems as part of management processes in agricultural areas might increase or decrease vegetation productivity independently of drought occurrences. This can introduce noise to the data sets that might further bias the assumed pixel-based relationships between drought pressure and vegetation productivity.\u00a0" } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Another source of uncertainty is related to the simplification of the drought impact model for its implementation in the operational setting. On one hand, the same thresholds for deviations in soil moisture and vegetation production imply similar impacts/impact severity in different sectors (agriculture, forestry, etc), which gives an acceptable approximation on the continental scale but might need to be adjusted to local conditions. 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 change as a result of additional climate and/or surrounding biophysical conditions, such as temperature, snowpack, albedo and soil\u2019s water-holding capacity.\u00a0" } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Lastly, insect infestations, wildfires and land use change, in most extreme case soil sealing will also reduce vegetation productivity. For the latter the analytics has excluded those grid cells with known land use change processes. Data on insect infestation are not available on the EU scale and wildfires will be included in the next version of the indicator. However, the analytics of anomalies for every 500m grid cells only retained those events where soil moisture deficit and negative vegetation anomalies occurred the same time and place. As vegetation productivity and soil moisture deficit have been shown to show a strong correlation, we think that the method is appropriate to indicate drought impact to an acceptable certainty.\u00a0" } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "Data set uncertainty:\u00a0" } ], "type": "p" }, { "children": [ { "text": "The datasets represent the average impact on the productivity of all terrestrial ecosystems within an area covered by a pixel of 500m\u00d7500m. Therefore, the indicator can be used at coarse resolution only, indicating drought impacts on main terrestrial ecosystems. As opposed to field measurements, remote-sensing products measure vegetation\u2019s light absorption from a satellite at a height of several hundred kilometres, which might introduce bias due to atmospheric disturbances.\u00a0" } ], "type": "p" }, { "children": [ { "text": "" } ], "type": "p" }, { "children": [ { "text": "In some land use types like e.g. in forestry, land management practices like e.g. clearing and thinning are practices that affect the measurements of vegetation productivity derived from satellite data. These measures are not captured by CORINE land cover and could therefore affect the results. Furthermore, the short vegetation period in the Nordic countries and high degree of cloudiness may potentially affect the results regionally.\u00a0" } ], "type": "p" }, { "children": [ { "text": " " } ], "type": "p" }, { "children": [ { "text": "Rationale uncertainty: " } ], "type": "p" }, { "children": [ { "text": "No uncertainty has been identified. " } ], "type": "p" } ]

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Annual area exposed to drought (top) and impacted by drought (bottom) in the EU, in 2000-2024

The chart shows the annual area in km 2 affected by drought during the period 2000-2024, where vegetation productivity did not recover to the 2000-2020 baseline condition. The values are summed for the EU-27 region, by ecosystems types. Note: the reported figures refer to the thresholds and input data used. The reported absolute values may be different if different method and different data are used.

Share of the country territory with drought impact in 2024 in comparison to the 2000-2020 baseline drought impact (EEA-38 region)

The chart shows the 2024 drought impacted area and the 2000-2020 average annual area impacted by drought in the EEA-38 region, by country (in percentage of the country area). Note: the reported figures refer to the thresholds and input data used. The reported absolute values may be different if different method and different data are used.

Biodiversity Productivity Land cover 8th EAP LSI011 Drought impact Ecosystems