the european drought observatory (edo) scope and data jürgen vogt, stefan niemeyer

Expert meeting on European hazard and disaster data - 19/20 May 2010 - Copenhagen 1

The European Drought Observatory (EDO)Scope and Data

Jürgen Vogt, Stefan NiemeyerEuropean Commission Joint Research Centre

Institute for Environment and SustainabilityIspra (VA), Italy

http://edo.jrc.ec.europa.eu


• Creeping phenomenon

• Not a localized event

• Long-term and far reaching impacts

• To be distinguished from aridity

• To be distinguished from water scarcity

Drought


Communication on Water Scarcity and Drought

yearly aggregated drought indicators

yearly water scarcity indicators

EDOJRC

European Reporting and Information System (WISE)

WS&D-ISEEA

Global Drought Monitoring System

NetworkNetwork

COM(2007)414Final

EDO Context


• Web-based Platform for detection, monitoring, forecasting and information exchange

Provide drought-related information at the European scale Mutual exchange of knowledge & methodologies Real-time monitoring and forecasting (early warning,

preparedness)

EDO - Vision

• Multi-scale approach, integrating Continental level MS level Regional level Commonly agreed products (e.g. drought indices) Joint comparison and analysis of information

• Subsidiarity principle European level information + platform (JRC) National datasets managed at MS level Regional information processed by river basin / regional

environmental authorities De-central data holding


MeteorologicalData

Stations

Fields

Fore- casts

Relevant Authorities: European, Member States, River Basins, …

Communication on Drought and Communication on Drought and Water ScarcityWater Scarcity

Drought Management PlansDrought Management Plans

EDO Map ServerEDO Map Server

EDO System Setup

European Data Layers

LC/LU

Soil

DEM

…

RBs

VEGETATION STATE

Hydrological Processes

MONITORING MONITORING & MODELLING& MODELLING

Land Surface ProcessesRemote SensingData

FAPAR

NDWI

TimeSeries

ProductsProducts

Rainfall Anomalies

Soil MoistureAnomalies

VegetationVigour

…


• Precipitation

Monthly SPI (for different aggregation periods)1 week forecast

• Soil MoistureDaily soil moistureDaily soil moisture anomalyForecasted soil moisture anomalyForecasted soil moisture trend

• Vegetation statusNDWI 10-day daily compositesfAPAR 10-day composites.fAPAR anomalies 10-day composites.

Current EDO Database – Europe

Coverage of the entire European continent


Drought Indicator Data source Spatial resolution Temporal resolution

SPI Ground observations 5 Km monthly

Soil Moisture LISFLOOD hydrological model 5 Km Daily

Soil Moisture Anomaly LISFLOOD hydrological model 5 Km Daily

Soil Moisture 7-day Forecast

LISFLOOD hydrological model 5 Km Daily

Soil Moisture 7-day Forecast anomaly

LISFLOOD hydrological model 5 Km Daily

NDWI 10-day composites MODIS satellite sensor 1 Km Daily

fAPAR 10-day composites MERIS satellite sensor 1.2 Km Decadal (10 days)

fAPAR 10-day composites anomaly MERIS satellite sensor 1.2 Km Decadal (10 days)

Content of the EDO Database


Standardized Precipitation Index (SPI)

http://edo.jrc.ec.europa.eu/

SPI

3-month Standardized Precipitation Index (SPI)for 10-12/2008 aggregated to regional level

Como, IT

0.000 - 0.023Extremely drySPI < -2.00

0.023 - 0.067Severe dry-2.00 ≤ SPI < -1.50

0.067 - 0.159Moderately dry-1.50 ≤ SPI < -1.00

0.159 - 0.841Near normal-1.00 ≤ SPI < 1.00

0.841 - 0.933Moderately wet1.00 ≤ SPI < 1.50

0.933 - 0.977Very wet1.50 ≤ SPI < 2.00

0.977 - 1.00Extremely wetSPI ≥ 2.00

Cumulative probabilityClass nameSPI value

0.000 - 0.023Extremely drySPI < -2.00

0.023 - 0.067Severe dry-2.00 ≤ SPI < -1.50

0.067 - 0.159Moderately dry-1.50 ≤ SPI < -1.00

0.159 - 0.841Near normal-1.00 ≤ SPI < 1.00

0.841 - 0.933Moderately wet1.00 ≤ SPI < 1.50

0.933 - 0.977Very wet1.50 ≤ SPI < 2.00

0.977 - 1.00Extremely wetSPI ≥ 2.00

Cumulative probabilityClass nameSPI value


Aim Detection of short term changes in precipitation as compared to the long-term average & forecast

ProductionProducer: JRCAlgorithm: EDO SPI processing chain.

Input Data

Meteorological precipitation measures at a daily time step derived from point measurements of around 1800 meteorological stations throughout Europe, and communicated via the Global Telecommunication System (GTS) of WMO.

Input Data provider JRC-MARS database

Temporal resolution Monthly

Spatial resolution 5 Km

NotesCalculated at different time scales (1, 3, 6, 9, 12 months). SPI3 is displayed on the EDO Mapserver.

Data availability JRC Desert Action

References McKee et al, 1993 and 1995 (index description)

Standardized Precipitation Index (SPI)


Soil Moisture Information

Based on LISFLOOD5km spatial resolution

Soil Moisture

1.8 2.2 2.6 3.0 3.4 3.8 4.2 4.6 5.0 very wet very dry

13 April 2010

Soil Moisture Anomaly

-4 -3 -2 -1 0 1 2 3 4 wetter normal drier

13 April 2010

-4 -3 -2 -1 0 1 2 3 4 wetter normal drier

Soil Moisture Anomaly Forecast (7 days)

Until 22 April 2010 Until 22 April 2010

Soil Moisture Trend (7 days)

wetter no change drier


Aim Modelling of soil moisture (current situation and one week forecast)

Production Produced by JRC LISFLOOD hydrological model

Input Data Meteorological data, soil data, DEM, …

Input Data provider JRC-MARS database (MARS-STAT activity-IPSC/JRC) and DWD forecast data.

Temporal resolution Daily


Notes Soil moisture is presented in form of soil suction (pF) values of the top soil layer.

Data availability JRC DESERT Action

ReferencesDe Roo et al. (2000) (model)Laguardia and Niemeyer (2008) (quality evaluation against radar soil moisture estimates)

Soil Moisture Information


NDWI, decade 2-11 May 2010.

Spectral index correlated with the vegetation water content.

Calculated every day from the previous 10 MODIS images available.

Time-composite algorithm.

Normalized Difference Water Index (NDWI)


Aim Vegetation water content detection

ProductionProducer: JRC Algorithm: EDO NDWI processing chain.

Input Data MODIS images pre-processed by DLR

Input Data provider JRC FOREST Action

Temporal resolution Daily


Notes 10-day Maximum Value time composites.


References Gao (1996) (index description)

NDWI 10-day Composites


Photosynthetic Activity (fAPAR)

fAPAR, 21-31 May 20091km resolution

fAPAR Anomaly, 21-31 May 2009

• Calculated from MERIS and SeaWIFS data (1997 )• http://fapar.jrc.ec.europa.eu

Fraction of Absorbed Photosynthetically Active Radiation (fAPAR)


Aim Vegetation status indicator

Production

Producer: ESAAlgorithm: JRC MGVI algorithm (adopted by ESA for the operational production of MERIS Level-2 Land Products), time compositing and spatial aggregation level-3 algorithms.

Input Data MERIS Level-2 Land Products (ESA)

Input Data provider ESA

Temporal resolution Decadal (products available 6 days after the end of the considered decade)

Spatial resolution 1.2 Km

Notes 10-day composites produced following the approach by Pinty et al. (2002)

Data availability ESA. Data freely distributed through the ESA Service Support Environment (SSE)

References

Gobron et al. (2004) (MGVI description),Aussedat et al. (2007) and Pinty et al. (2002) (compositing algorithms), Gobron et al. (2007) (evaluation).

fAPAR 10-day Composites


Aim Indicator of anomalies in vegetation development.

ProductionProducer: JRCAlgorithm: EDO fAPAR anomalies production chain.

Input Data MERIS Level-3 Aggregated Products (ESA) and SeaWiFS fAPAR products (JRC).

Input Data provider ESA and JRC

Temporal resolution Decadal (products available 6 days after the end of the considered decade)

Spatial resolution 1.2 Km

Notes

10-day composites produced following the approach by Pinty et al. (2002).MERIS time series (2002-now) are extended with SeaWiFS fAPAR estimations obtained with a fully compatible algorithm developed at the JRC.


References Rossi et al. (2008) (drought detection); Gobron et al. (2002) (SeaWiFS fAPAR algorithm)

fAPAR Anomaly 10-day Composites


EDO Map Server: Output Options

Maps (Grid and administrative units)

Time Series (Specific queries possible)

PDF Reports, JPGs

KML file


Next Steps

1. Further development and validation of indicators2. Building of composite indicator

1. Integration with MS and Regional Systems setup of a data and information infrastructure (EUROGEOSS)

Inter-regional level:- Drought Management Centre for South East Europe (DMCSEE)

National level:- Observatorio Nacional de Sequias (ONS), ES- Portail national d'Accès aux Données sur les Eaux Souterraines, BRGM, F- Italy, UK, Finland (WS&D Expert Network)

Regional level:- Confederacion Hidrografica del Ebro (CHE)


Next Steps

•Study on the Carpathian Climate- data rescue, harmonisation, analysis- drought indicators and link to EDO

•DB on past drought events?- first study end 1990s- media monitor

EDO Prototype to be ready by end 2012


More Information

[email protected]://desert.jrc.ec.europa.eu

http://edo.jrc.ec.europa.eu

the european drought observatory (edo) scope and data jürgen vogt, stefan niemeyer

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