REPUBLIC OF SLOVENIA MINISTRY OF AGRICULTURE AND THE ENVIRONMENT

SLOVENIAN ENVIRONMENT AGENCY

Drought management Case of Slovenia & SE Europe

Gregor Gregorič

Andreja Sušnik

Slovenian Environmental Agency

DMCSEE

REPUBLIC OF SLOVENIA MINISTRY OF AGRICULTURE AND THE ENVIRONMENT

SLOVENIAN ENVIRONMENT AGENCY

Drought monitoring & DMCSEE

Remote sensing & NWP

Drought impacts and risk

Conclusions

www.dmcsee.org

Implementation of standardized precipitation index

Maps of SPI, percentiles and precipitation for the SEE region

Historical maps (record 1951-2000)

Data origin: GPCC data/ update once per month

Drought monitor – focus on meteorological drought

Drought monitor – meteorological drought: SPI

Monitoring of meteorological drought

Implementation of Standardized Precipitation Index - International collaboration

DMCSEE mapping service

included in EuroGEOSS drought

catalogue and in European

Drought Observatory

SPI3 in SPI4 Application for crop insurance

SPI-4 in the vegetatio period of summer crops (SPI4, calculated on September 1)

2012 2013 2003

2003 2012 2013

SPI-3 in the vegetation period of spring crops (SPI3, calculated on June 15) in the year 2003, 2012 and 2013 for cadastrial municipalities.

Extremely wet

Eytremely dry

Surface water balance (RR-ETo) Slovenia, 1961 – 2013

Average cumulative SUMMER meteorological water balance (mm) in the period 196–2013 (red line represents 75. percentile – dry) in Slovenia. Drought extent: national drought (more than 5 regions), regional drought (3 or 4 regions) and local drought (1 or 2 regions).

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vo

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nacionalna

regionalna

lokalna

suficit

deficit

national regional local

Model presents real continous space in equidistant discrete grid form.

Space resolution defines the smallest structures seen by a NWP:

• Low resolution (~200 km) – simulation of basic structures (planetary waves, big frontal systems) – used for climate modeling and studies of global mechanisems;

• Medium resolution (50 - 10 km) – simulation of sinoptic and mesoscale systems –

used for general weather forecast;

• High resolution (< 10 km) – simulation of local systems (wind, fog, tunderstorms, etc.)

Regardless the resolution, there are

• Global models covering entire globe and

• Limited Area Models simulating weather over choosed smaller area

Numerical Weather Prediction Models - NWP

(Non)Predictability:

• Physical Laws

• Co-existence of various scales

• Interaction of all variables

• Exchange of energy among various scales

• Discretization of continous space

• Limited computing power

• Incomplete knowledge of initial state

Numerical Weather Prediction Models - NWP

Limited Area Model

Main Idea:

•Take data from global archive

•Choose area and grid points

•Re-simulate weather patterns from global model to obtain more details on a regional scale

Numerical Weather Prediction Models - NWP

Limited Area Model

NMM (NCEP)

Non-Hydrostatic

Meso-scale

Model

• Area: 461 x 289 x 92 = 12.257.068 points

(133.229 points “on ground”)

• Top Level: 1 hPa (~ 60 km)

• Horizontal resolution: ~8.5 km

• Time Step: 30 sec.

• Integration Time: 36 h starting at 12

UTC to get simulation for the entire

next day

Numerical Weather Prediction Models - NWP

Application of NWP for drought monitoring

ERA-Interim

LAM downscaling over SE Europe -> “model climatology” for interpretation

ECMWF ERA – Interim

1989 - 2012

Model Integration

Area (SE Europe)

Limited Area Model

NNM (NCEP)

Application of NWP for drought monitoring

DROUGHT RELATED VARIABLES

Water Balance anomaly

Soil moisture

Temperature (degree days)

DROUGHT RELATED TIME SCALE

Decade (10-day)

DROUGHT RELATED INTERPRETATION

Deviation from normals, percentiles

Application of NWP for drought monitoring

DROUGHT RELATED VARIABLES

Water Balance anomaly

Soil moisture

Temperature (degree days)

DROUGHT RELATED TIME SCALE

Decade (10-day)

DROUGHT RELATED INTERPRETATION

Deviation from normals, percentiles

Drought monitoring application of remote sensing data Monitoring response of vegetation (not meteorological conditions) Vegetation indices are composed from multi-channel measurements: - NDVI (Normalized Difference Vegetation Index) – most basic, 2 channels - FVC (Fraction of Vegetation Cover) – derived, 3 channels - LAI (Leaf Area Index) – derived, 3 channels - FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) derived

Drought monitoring application of remote sensing data Fraction of Vegetation Cover [0 – 1]: fraction of the surface within satellite pixel covered by green vegetation

Leaf Area Index

total area occupied by the leaves

per unit area [m2/m2]

It provides complementary information

to the FVC, accounting for the surface

of leaves contained in a vertical column

normalized by its cross-sectional area.

Drought monitoring application of remote sensing data Fraction of Vegetation Cover [0 – 1]: fraction of the surface within satellite pixel covered by green vegetation

Leaf Area Index

total area occupied by the leaves

per unit area [m2/m2]

It provides complementary information

to the FVC, accounting for the surface

of leaves contained in a vertical column

normalized by its cross-sectional area.

FVC=0.8 LAI=5 FVC=0.8 LAI=7

Drought monitoring application of remote sensing data geostationary satellites + virtually above same location on Earth + large temporal frequency - poor spatial resolution polar-orbiters + high spatial resolution - long return time

Drought monitoring application of remote sensing data

Drought monitoring application of remote sensing data

Drought monitoring application of remote sensing data Implementation in Slovenian Environmental Agency: FVC –data provided by EUMETSAT (satellite MSG, processing done by LSA-SAF) Spatial resolution is limiting factor– homogenious surface ~ 1500 ha -> vineyards around Gorica (W Slovenia)

Drought monitoring application of remote sensing data Implementation in Slovenian Environmental Agency: FVC –data provided by EUMETSAT (satellite MSG, processing done by LSA-SAF)

WMO-IPA project: Building resilience to disasters in

Western Balkans and Turkey

Secondment of experts to DMCSEE (slovenian environmental agency office)

Topics:

Application of remote sensing data

- 9 agricultural locations identified (3 in FYROM, 1 in MNE, 2 in SRB

and 3 in BiH-RS)

- FVC and LAI indices compared to meteorological records (SPI, ET, …)

Drought monitoring application of remote sensing data

Secondment of experts to DMCSEE

Application of remote sensing data

Drought monitoring application of remote sensing data

Hot spot - short summary, short insight of possible circumstances of drought at the time of issue.

Additional and auxiliary information (more detailed information on water balance or temperature situation, additional NWP or RS maps)

Report on impacts (more about agricultural drought impacts is missing!)

Outlook

Drought Bulletin for SE Europe

Recent developments - The SatDroughtMon project funded by ESA

project was launched in February 2013 by Slovenian Centre of Excellence SPACE-SI in cooperation with Slovenian Environment Agency, DMCSEE and University of Primorska, funded by ESA.

The main aim of the research is to develop an automatic system for satellite drought monitoring.

This is to be done with machine learning for building classification and prediction systems and will be based on satellite data as well as ground measurements collected by different authorities in Slovenia.

The results of the project will have an impact on drought monitoring with a particular applicability in diverse landscapes.

Work will be focused on Slovenia, but its implications are much broader and could be transferred to any other region of the world.

Satellite data: MERIS full resolution 250 m

(usually one image daily) VITO/VEGETATION in 2006-2012

Drought detection

Recent developments - The SatDroughtMon project funded by ESA

Most important parameter for monitoring agricultural drought

is soil moisture.

Natural tools for monitoring soil moisture:

- Local measurements

- Irrigation sheduling models

WinISAREG:

water balance model for simulating crop

irrigation schedules.

Developed in Technical University of Lisbon (prof. L.-S.

Pereira).

Applied by prof. Zornitsa Popova, ISSNP, Sofia

Approach to risk assessment Use of agrometeorological models

WinIsareg:

• soils divided into several layers;

• large selection of irrigation methods;

• results: variety of data

- required data

CROP DATA

dates of phenological stages

SOIL DATA

data for different soil layers

CLIMATOLOGICAL DATA

humidity, wind, sunshine…

Use of agrometeorological models for drought risk assessment

WinIsareg outputs:

Net irrigation requirement (NIR):

Amount of water needed that plants don’t experience water strees (prescribed moisture level).

- Daily irrigation values

- Annual sums

Use of agrometeorological models for drought risk assessment

WinIsareg outputs:

Shortage of water causes yield decrease.

Plants in stress transpirate less than optimally irrigated plants:

RYD = Ky x (1 - AET/PET)

RYD – relative yield decrease (1 - Ya/Ym)

AET – actual evapotranspiration

PET – potential evapotranspiration

Ky – linear coeficcient in range 1.2 – 1.8

Use of agrometeorological models for drought risk assessment

Use of agrometeorological models for drought risk assessment

Definition of drought indicators

Application of system of surveillance of indicator values

Definition of tresholds and

alarm levels

Decision-making body

Defined mitigation measures and reallocation of resources

Planning

Monitoring

Management