land management and natural hazards unit - unipr.it · land management and natural hazards unit 20...

Land Management and Natural Hazards Unit 1

Land Management and Natural Hazards UnitGuido Schmuck (Head of Unit)

http://ies.jrc.ec.europa.eu/the-institute/units/land-management-and-natural-hazards-unit.html


To support EU policies and programs linked to sustainable land management practices in the fields of forestry, soils and weather-driven natural hazards

with a particular focus on

the development of environmental information systems

The unit directly supports European policies in the fields of Environment and Sustainability, Forestry and Soils, Regional Development, and Civil Protection

Mission


ActivitiesForestry

Establishment of the European Forest Data Centre - a single focal point for forest data and information for the Commission

Customers: DG ENV*, DG AGRI**, DG REGIO***, DG ENTR****, EEA*****, Forest services in Member States

SoilEstablishment of the European Soil Data Centre - a single focal point for soil data and

information for the Commission Customers: DG ENV, DG AGRI, EEA

Desertification, Land Degradation and DroughtsDevelopment of tools for the implementation of policies and conventions related to drought

and desertification in Europe and worldwide.Customers: DG ENV, DG DEV******, DG REGIO, DG AGRI, DG EuropAid

Natural HazardsDevelopment of a better capacity for early warning, monitoring and damage assessment

systems for weather-driven natural hazards, as well as for tools for assessing climate change effects, land use change effects, risk mapping and adaptation to extreme events

Customers: DG ENV, DG REGIO, DG ENTR, EEA, National hydrological and meteorological services

*Environment, **Agriculture, ***Regional Policy, ****Enterprise and Industry, ***** European Environment

Agency, ****** Development


Forestry

International organisations:Ministerial Conference for the Protection of Forests in Europe (MCPFE)Food and Agriculture Organisation of the United Nations (FAO) United Nations Economic Commission for Europe (UNECE) International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) United States Department of Agriculture - Forest Service (USDA-Forest Service)United States Environment Protection Agency (US-EPA)

Research organisations:Over 50 research organisations (on e.g. forestry, forest fire modelling, remote sensing, information systems, socio-economic analysis, climate change, emissions)


±

ClassesMasked

Non-Forest

Forest

Reference data

European forest map


Emissions from Forest firesForest and Climate Change

Applications

Forest spatial pattern (changes in core forest

1990 - 2000)

Impact of fires on forest connectivity (functional biodiversity)


Management and coordination

Data Centers

JRC

European Forest Data Centre(EFDAC)

European Forest Inventory Network

Forest data from other sources (research

projects, LIFE+ project, etc.)

Data from related JRC actions (e.g. GHG-

AFOLU)

European Soil Data Center

Ancillary data from other data centers (EEA,

EUROSTAT)

National Forest Services

National Forest Fire and civil protection

services

Member States

European Forest Data Centre(EFDAC)

Food and Agriculture Organization (FAO)

Ministerial Conference for the Protection of Forests in Europe

(MCPFE)

International Data Providers

Fire Danger forecast

Forest fire emissions

MODELS

Burnt area mapping algorithms

Image segmentation algorithms

Forest spatial pattern analysis

Forest growth models

Climate change modelling


SoilInternational organisations:Food and Agriculture Organisation of the United Nations (FAO) United States Department of Agriculture – Soil Conservation ServiceAustralia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) United Nations Environment Programme (UNEP) United Nations Convention on Biological Diversity (UNCBD)African Soil Science Society (ASSS)European Confederation of Soil Science Societies (ECSSS) European Environment Agency (EEA) European Society for Soil Conservation (ESSC) International Soil Reference and Information Centre (ISRIC) International Union of Soil Sciences (IUSS) International Soil Conservation Organisation (ISCO)Soil and Water Conservation Society (SWCS)

Research organisations:Within Europe, collaboration with national soil survey organisations, leading universities and research institutes, soil science experts is carried out through the JRC’s European Soil Bureau Network. Currently around 80 organisations are members of the foremost network of soil experts in Europe


Reference Data

European soil map


Topsoil Organic Carbon Content

Soil Erosion Risk

Soil Crusting

Wind Erosion Risk

Organic Carbon (%) No Data 0 - 1 1 - 2 2 - 5 5 - 10 10 - 25 25 - 35 > 35

Applications


Management and Coordination

Data Centers

JRC

European Soil Data Centre(ESDAC)

European Soil Bureau Network

(ESBN)

Soil data from other sources (research

projects, etc.)

Data from related JRC actions

European Forest Data Center

Ancillary data from other data centers (EEA,

EUROSTAT)

National Soil Data Centers

Regional Soil Data Centers

EIONET EuroGeoSurveys

Member States

European Soil Data Centre(ESDAC)

Soil Erosion

Fertiliser Application Rates

MODELS

Urbanisation

Greenhouse Gas Emissions

Microbial Activity

Fertility

Soil Water Retention


Natural Hazards

International organisations:European Centre for Medium Range Weather Forecast (ECMWF)International River Basin Commissions (IKSO - Oder, IKSE - Elbe, IKSD/ICPDR - Danube)World Meteorological Organisation (WMO)

Research organisations:Bristol University (UK)Bundesanstalt für Gewässerkunde (BfG, DE)Delft Hydraulics (NL)University of Utrecht (NL)University of Washington State, Seattle (USA)


ForecastingEuropean Forest Fire Information System

(EFFIS)

European Flood Alert System(EFAS)

European Drought Observatory(EDO)

Platform for drought detection, monitoring, forecasting, and information exchange

• Commonly agreed products • Multi-scale approach,

integrating • Subsidiarity principle


FIRE RISK CLASSIFICATIONRisk Class

LOW

MEDIUM

HIGH

NO FIRE DATA

Risk Mapping

Floods

Forest Fires

Risk = hazard * exposure * vulnerability


Future risk under climate change conditions

Research:Estimated change in potential flood damage of a 100-year flood for SRES* A2 scenario

Underlying data/models:- 12 km A2 scenario DMI- LISFLOOD model setup- SRTM DEM** - CORINE*** landcover- damage curves countries

* Special Report on Emissions Scenarios, ** Shuttle Radar Topography Mission Digital Elevation Database, ***Coordination of the Information on the Environment


Damage assessment

Forest Fires and NATURA 2000 sitesFloods and industrial installations


Desertification

International organisations:

• United Nations Convention to Combat Desertification (UNCCD)• United Nations Environment Programme/Division of Global Environment Facility Coordination (UNEP/GEF)• United Nations University – International Network on Water, Environment & Health (UNU- INWEH)• Consultative Group on International Agricultural Research (CGIAR, ICARDA/ICRISAT Centres)

Research organisations:

• DesertNet International (Network of Global Desertification Research Institutions, 283 members from 47 countries)• Nucleo di Ricerca di Desertificazione (NRD) (Sassari, Italy)• Estación Experimental de Zonas Aridas (CSIC) (Almeria, Spain)• More than 30 universities and research centres in Europe, NW-Africa, Asia and S-America as partners incompetitive research projects


Examples of basic input data

Vegetation Cover

Bio-physical &

socio-economic

datasets

Land Surface Albedo

Dominant Soil Types

Global Land Cover

Population & Trends (WRI)

Cattle Densities (FAO)


Application Examples

Analysis of the Status and Trend of Degradation Indicators

ExampleRain Use Efficiency (RUE)

IncreasingLikeliness

Combining bio-physical and socio-economic models and data to analyse probabilities of land

degradation (Syndrome Models)

ExampleRural Exodus Syndrome


Management and coordination

Land degradation and desertification

Food Security

Climate Change

Marine Resources& Fisheries

Crisis Response& Humanitarian AidBiodiversity

Protection

Renewable Energies

Vulnerability Assessment

Natural RisksReduction

Illegal Mining and Logging

Land degradation and desertificationLand degradation and desertification

Food SecurityFood Security

Climate Change



Crisis Response& Humanitarian Aid

Crisis Response& Humanitarian AidBiodiversity

ProtectionBiodiversity Protection

Renewable Energies

Renewable Energies

Vulnerability AssessmentVulnerability Assessment





Assessment and mapping

EU – Africa Partnerships

UNCCD

Desertification, Land Degradation, Drought

ONLINE PORTAL

Data & Information

DLD-IS*

* DLD-IS: Desertification, Land Degradation, and Drought Information System

Benchmarks &Indicators

Global Monitoring and Assessment


Scientific Output

JRC Ispra - IES1

Monitoraggio dei suoli in Europa e Metalli Pesanti

Parma, 30 settembre 2008

Ciro Gardi, Luca Montanarella, Luis Rodríguez-Lado

Sistemi informativi – Analisi e Gestione del Territorio

JRC Ispra - IES2

La contaminazione dei suoli

• La mobilizzazione di inquinanti dalle loro riserve naturali e la successiva dispersione nell’atmosfera, el suolo e nelle acque, costituisce uno dei principali impatti delle attivita’ antropichesull’ambiente.

• Gran parte dei suoli di vaste aree dei paesi industrializzati sonocaratterizzati da contenuti di elementi e composti, consideratitossici, in concentrazioni notevolmente superiori a valori difondo naturali.

• I metalli pesanti rappresentano una importante fonte dicontaminazione dei suoli. In molti casi tuttavia, sopratutto in Europa, risulta difficile definire dei valori di fondo riferibili allacondizione originaria del suolo.

JRC Ispra - IES3

La Strategia Tematica per il Suolo

• Il suolo è praticamente una risorsa naturale non rinnovabile, e svolge funzioni cruciali per le attività umane e gli ecosistemi

• Proposta di direttiva quadro sul suolo (COM(2006)232 definitivo,22.9.2006)

• La protezione del suolo e la conservazione delle sue capacità di svolgere una qualsiasi delle seguenti funzioni ambientali, economiche, sociali e culturali:

– produzione di biomassa, in particolare nei settori dell’agricoltura e della silvicoltura;

– stoccaggio, filtrazione e trasformazione di nutrienti, sostanze e acqua;

– riserva di biodiversità, ad esempio habitat, specie e geni;

– ambiente fisico e culturale per le persone e le attività umane;

– fonte di materie prime;

– stoccaggio di carbonio;

– sede del patrimonio geologico e archeologico.

JRC Ispra - IES4

La proposta di Direttivae la contaminazione del suolo

• Prevenzione della contaminazione

• Definizione di Sito Contaminato: “un sito nel quale sia stata confermata la presenza di sostanze pericolose di origine antropica ad un livello tale che gli Stati membri ritengono possa comportare un rischio significativo per la salute umana o per l’ambiente. Il rischio è valutato alla luce dell’utilizzo attuale e dell’utilizzo futuro approvato del terreno”

• Inventario dei Siti Contaminati

JRC Ispra - IES5

Le concentrazioni di riferimento di metallipesanti nei suoli Europei• Geochemical baseline ≠ background value

• Attivita’ iniziata nel 1997 da 26 Stati Membri, con ilcoordinamento del FOREGS (Forum of European Geological Surveys)

• Il prodotto finale e’ costituito dall’Atlante Geochimicod’Europa

JRC Ispra - IES6

Elaborazioni sul Dataset FOREGS

• 1558 punti relativi a campioni di suolo

• Concentrazioni di As, Cd, Cr, Cu, Hg, Ni, Pb e Zn

• Estrazione con Acqua Regia (tranne mercurio)

• Analisi con ICP-AES (tranne mercurio)

• Utilizzazione di variabili ausiliarie nel processo diinterpolazione

JRC Ispra - IES7

Carte distribuzione dei metalli pesanti

JRC Ispra - IES8

Perche’ la geostatistica?

• Consente di ottenere stime obiettive:– della concentrazione dei metalli– della incertezza associata

• Offre la possibilita’ di utilizzare un numeroelevato di variabili ausiliarie (predittive)

• Consente di effettuare stime accurate delleconcentrazioni di metalli pesanti nel suolo.

• Le procedure possono essere automatizzate (R scripts)

JRC Ispra - IES9

FOREGS soil database

JRC Ispra - IES10

Variabili obiettivo

• 8 metalli pesanti nel suolo:– As, Cd, Cr, Cu, Hg, Ni, Pb, Zn

JRC Ispra - IES11

FOREGS soil database: Analisi esplorativaAS

foregs$AS

Freq

uenc

y

0 100 300

050

010

0015

00

CD

foregs$CD

Freq

uenc

y

0 5 10 15 20

050

010

0015

00

CR

foregs$CRFr

eque

ncy

0 500 1500

050

010

0015

00

CU

foregs$CU

Freq

uenc

y

0 100 300

050

010

0015

00

HG

foregs$HG

Freq

uenc

y

0 1 2 3 4

050

010

0015

00

NI

foregs$NI

Freq

uenc

y

0 1000 2000

050

010

0015

00

PB

foregs$PB

Freq

uenc

y

0 2000 4000

050

010

0015

00

ZN

foregs$ZN

Freq

uenc

y

0 1000 2000 3000

050

010

0015

00

logAS

log(foregs$AS)

Freq

uenc

y

1 2 3 4 5 6

020

040

060

0

logCD

log(foregs$CD)

Freq

uenc

y

0.0 1.0 2.0 3.0

050

100

150

logCR

log(foregs$CR)

Freq

uenc

y

0 2 4 6 8

010

020

030

040

0logCU

log(foregs$CU)

Freq

uenc

y

0 1 2 3 4 5 6

010

020

030

040

0

logHG

log(foregs$HG)

Freq

uenc

y

0.0 0.4 0.8 1.2

05

1015

logNI

log(foregs$NI)

Freq

uenc

y

0 2 4 6 8

010

020

030

0

logPB

log(foregs$PB)

Freq

uenc

y

2 4 6 8

010

020

030

040

0

logZN

log(foregs$ZN)

Freq

uenc

y

1 2 3 4 5 6 7 8

010

020

030

040

0

TAS

foregs$TAS

Freq

uenc

y

-13 -11 -9 -8

020

040

060

0

TCD

foregs$TCD

Freq

uenc

y

-18 -16 -14 -12

020

040

060

0

TCR

foregs$TCR

Freq

uenc

y

-14 -12 -10 -8 -6

020

040

060

0

TCU

foregs$TCU

Freq

uenc

y

-14 -12 -10 -8

020

040

060

0

THG

foregs$THG

Freq

uenc

y

-20 -18 -16 -14 -12

020

040

060

0

TNI

foregs$TNIFr

eque

ncy

-14 -12 -10 -8 -6

020

040

060

0

TPB

foregs$TPB

Freq

uenc

y

-12 -10 -8 -6

020

040

060

0

TZN

foregs$TZN

Freq

uenc

y

-12 -10 -8 -6

020

040

060

080

0

JRC Ispra - IES12

FOREGS soil database: Analisi esplorativa

logAS

log(foregs$AS)

Freq

uenc

y

1 2 3 4 5 6

020

040

060

0

logCD

log(foregs$CD)

Freq

uenc

y

0.0 1.0 2.0 3.0

050

100

150

logCR

log(foregs$CR)

Freq

uenc

y

0 2 4 6 8

010

020

030

040

0

logCU

log(foregs$CU)

Freq

uenc

y

0 1 2 3 4 5 6

010

020

030

040

0

logHG

log(foregs$HG)

Freq

uenc

y

0.0 0.4 0.8 1.2

05

1015

logNI

log(foregs$NI)

Freq

uenc

y

0 2 4 6 8

010

020

030

0

logPB

log(foregs$PB)

Freq

uenc

y

2 4 6 8

010

020

030

040

0

logZN

log(foregs$ZN)

Freq

uenc

y

1 2 3 4 5 6 7 8

010

020

030

040

0

TAS

foregs$TAS

Freq

uenc

y

-13 -11 -9 -8

020

040

060

0

TCD

foregs$TCD

Freq

uenc

y

-18 -16 -14 -12

020

040

060

0

TCR

foregs$TCR

Freq

uenc

y-14 -12 -10 -8 -6

020

040

060

0

TCU

foregs$TCU

Freq

uenc

y

-14 -12 -10 -8

020

040

060

0

THG

foregs$THG

Freq

uenc

y

-20 -18 -16 -14 -12

020

040

060

0

TNI

foregs$TNI

Freq

uenc

y

-14 -12 -10 -8 -6

020

040

060

0

TPB

foregs$TPB

Freq

uenc

y

-12 -10 -8 -6

020

040

060

0

TZN

foregs$TZN

Freq

uenc

y

-12 -10 -8 -6

020

040

060

080

0

TAS = ln(ASstand/1-ASstand)

ASstand= (AS-ASmin)/(ASmax-ASmin)

JRC Ispra - IES13

-0.10 -0.05 0.00 0.05 0.10-0

.10

-0.0

50.

000.

050.

10

Principal Component Analysis

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113111321133 1134

113511361137

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1153115411561157

11581159 11601161

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1173

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118311851186

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1235 12361237

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12511252 12531257

12581259

12601261

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1267 1268

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1271127212731274

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13531355

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1371137213731374

13751376

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13801381

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138813891390

13911392

13931394

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14101411

14121413

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14201421

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14281429

143014311432

14331434

1435143614371438

1439

144014411442

1443

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14461447

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1450145114521453

1454

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1463 146614671471147214731474

14751476 1477

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14861487

14881489

14901491

1492

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15081509

1510151115121513

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1531 1532

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155315541555

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15611562

15631564

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156815691570

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157915801581

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-30 -20 -10 0 10 20 30

-30

-20

-10

010

2030

TASTCD

TCR

TCU

THG

TNI

TPB

TZN

FOREGS soil database: Analisi esplorativa

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6


PC1

PC

2

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342

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625

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830

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-40 -20 0 20

-40

-20

020

ASCD

CR

CU

HG

NI

PBZN

JRC Ispra - IES14

Regression-kriging

Regressione multipla lineare

Yj = a1 X1 + a2X2 + … + an Xn + εj

Variabileincognita j

residui j

Kriging

Yj

...

... ..... ...

.

. .. ..

..

∑ aiXii

.

..

...

. .. .. .. ..

γεj

distanza (m)

Sem

i-var

ianz

a

(processo di interpolazione realizzatosulla base dell’autocorrelazione tra le varibili)

Dati ausiliari i

Continue Puntuali

Somma tra le due carte

regressione

kriging

regression-kriging

Dati ausiliari

residui

Variabiliincognite

JRC Ispra - IES15

Variabili ausiliarie

JRC Ispra - IES16


PCA delle variabili ausiliarie

JRC Ispra - IES17

Multiple Regression Analysis

PCA delle variabili ausiliarie

JRC Ispra - IES18

Analisi della struttura spaziale: il semivariogramma

JRC Ispra - IES19

Risultati

JRC Ispra - IES20

Risultati: Pb

JRC Ispra - IES21

Confronto tra Kriging Ordinario e Regression Kriging

JRC Ispra - IES22

Risultati: Ni

JRC Ispra - IES23

Correlazioni Ni

3DPlot of Ni content over PC2, PC4 & PC14

JRC Ispra - IES24

Risultati: Cd

JRC Ispra - IES25

Concentrazione complessiva di metalli in Europa

(1) Liege (Arrondissement) (BE), Attiki (GR), Darlington (UK), Coventry (UK), Sunderland (UK), Kozani (GR), Grevena (GR), Hartlepool & Stockton (UK), Huy (BE), Aachen (DE) (As, Cd, Cu, Hg and Pb)

(2) central Greece and Liguria region in Italy (Cr and Ni).

JRC Ispra - IES26

Stima dell’errore

JRC Ispra - IES27

Disponibilita’ dei dati

http://eusoils.jrc.it/foregshmc/

JRC Ispra - IES28

Validazione

• Confronto tra Kriging Ordinario e Regression Kriging

• Abbastanza buono: Ni, Pb• Medio: As, Cd, Hg• Debole: Cr, Cu, Zn

JRC Ispra - IES29

Conclusioni• FOREGS e’ un esempio di dataset pan-

europeo idoneo all’applicazione di metodigeostatistici.

• In molti casi la distribuzione spaziale deimetalli pesanti e’ strettamente correlata ad altri fattori, quali geologia, urbanizzazione, copertura vegetale.

JRC Ispra - IES30

Conclusioni

• La disponibilita’ di cartografia di maggiordettaglio, relativa alle variabili ausiliarie, e di ulteriori dati, consentira’ di ottenererisultati piu’ accurati nel processo diinterpolazione spaziale:– Dati puntuali aggiuntivi– Basi di dati GIS

Luis Rodrguez Lado, Tomislav Hengl, Hannes I. Reuter.Heavy metals in European soils: a geostatistical analysis of the FOREGSGeochemical database, Geoderma (In Press)

JRC Ispra - IES31

Grazie per l’attenzione

land management and natural hazards unit - unipr.it · land management and natural hazards unit 20...

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