Joint GWP CEE/DMCSEE training: Drought management by Gregor Gregorič and Andrea Sušnik

1. 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

2. REPUBLIC OF SLOVENIA
MINISTRY OF AGRICULTURE AND THE ENVIRONMENT
SLOVENIAN ENVIRONMENT AGENCY
Drought monitoring & DMCSEE
Remote sensing & NWP
Drought impacts and risk
Conclusions

3. www.dmcsee.org

4. 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

5. Drought monitor – meteorological drought: SPI

6. Monitoring of meteorological drought
Implementation of Standardized Precipitation Index -
International collaboration
DMCSEE mapping service included in EuroGEOSS drought catalogue and in European Drought Observatory

7. 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

8. 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).
–300
–250
–200
–150
–100
–50
0
50
100
150
200
–300
–250
–200
–150
–100
–50
0
50
100
150
200
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
vodna bilanca [mm]
nacionalna
regionalna
lokalna
suficit
deficit
national
regional
local

9. 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

10. (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

11. 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

12. 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

13. 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)

14. 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

15. 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

16. 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

17. 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.

18. 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

19. 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

20. Drought monitoring application of remote sensing data

21. Drought monitoring application of remote sensing data

22. 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)

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

24. 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

25. Secondment of experts to DMCSEE
Application of remote sensing data
Drought monitoring application of remote sensing data

26. 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

27. 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.

28. 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

29. 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

30. 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

31. 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

32. 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

33. Use of agrometeorological models for drought risk assessment

34. 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