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Presentation of 5.5 Demonstration Project
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Presentation of the 5.5
Demonstration Project
Prof. Dr. János Tamás
1st Workshop
Integrated Drought Management Programme in Central and
Eastern Europe, Slovakia
Date: 15 – 16 October 2013
Policy oriented study on remote sensing
agricultural drought monitoring methods
• Partners of Activity
• GWP HUNGARY
• University of Debrecen
• University of Oradea
• Institute of Hydrology of the Slovak Academy of
Sciences
Key qualifications of partners
• Hungary (University of Debrecen and GWP HU):
– Applied hydrological remote sensing and GIS;
– Spatial Decision Supporting Systems
• Romania (University of Oradea):
- Geography and Integrated watershed management
• Slovakia (Institute of Hydrology of the Slovak Academy of Sciences):
- Agricultural water management, Soil hydrology
Task definition
• The drought types: meteorological, hydrological and
agricultural
• The drought indexes of meteorological and hydrological
drought parameters well-measurable and widely tested
(temperature, precipitation, humidity, water level etc.)
• The agricultural drought least quantified in soil-water-
plant environment, the most uncertain drought type.
• The main objective of this case study is to formulate
concrete practical agricultural drought monitoring method
and intervention levels with calibrating for the important
crops and fruits (wheat, corn and apple)
2. RS tools for
vegetation indices
3. Agricultural
drought decision
support parameters
Finalize OUTPUT 1:
An analysis report on
the role of soil and
crop water content
status in
waterbalance within
different agricultural,
landuse and water
management
practices at rain fed
and irrigated systems
for the most important
crops and fruit (wheat,
corn and apple)
Finalize OUTPUT 2:
Toolbox with the concrete
identification of remote
sensing and GIS data
tools for agricultural
drought monitoring and
forecast
Finalize OUTPUT 3:
Report on integration of
RS and GIS tools and
intervention levels into
drought monitoring
system
June 2013-Dec 2013
Sept 2013 – Jun 2014 May 2014 – Jan 2015
2. RS tools for
vegetation
indices
1. Analysis of
green and
brown water
status
3. Agricultural
drought decision
support
parameters
Process flow of RS agricultural drought monitoring methods
NDVI
Time
Series
Land
use
mask
Calibration
with Yield
statistical
data
Meteorological
Data
Calibration
with Drought
Index
Soil Physical
Data
Calibration
with available
water content
SDSS
Classification
Watch Varning Alert
Plant Specific
Drought Risk
Evaluation
STUDY AREA-SITE SELECTION
HU Ro
Sk
The Tisa River Basin is the largest sub-
basin in the Danube River Basin, covering
157,186 km² (19.5%) of the Danube Basin.
Drought Risk on Hungarian Great Plain
6 Y LONG TIME SERIES of WHEAT NDVI
DROUGHT IMPACT
Modis Terra/Aqua
Ground res. From 250 m
36 band, Cycle: 1 d
DROUGHT IMPACT ON NDVI BIOMASS NDVI
Relation of NDVI Biomass and Drought YIELD LOSS
Potential yield loss is changing in time
End result is depend on climatic, soil
condition
If we calibrate the NDVI TS with real
yield loss data and combined with soil
data, and meteorological Drought
Index, we can estimate the expected
different crops yield loss by region by
region.
Relative Yield
Loss Biomass -NDVI
Database Building
The case study will utilize the available database prepared for the Tisza River
Basin. Crop data – Remote sensing time series
Selection of training sites
Spectral data noise filtering
Rectification (UTM system)
Cropping and mosaicing of reference area
Indexing
Statistical time series data of yields
Soil data- digital soil map
Common soil physical database of reference area
Common topology and coordinate system of reference area
Calculation of available water capacity
Calculation of water balance on watersheds
Meteorological data – Drought Index SPI,
fAPAR
Sources: USGS, ESA, Literature, Scientific reports, Publications, Media, Statistical
reports, Owner data,
Winter Wheat - Yield data sources
Source: AKI, Hungary
Winter wheat yield T/County- Tisza Hungarian region
Winter wheat –yield/ area- Hungary
Same Growing Area
Hectically yield
(Drought effect)
Automatic sensors to control soil water on East-Slovakian plan
Tisza river north watershed
Slovakian reference site
Agrometeorological data
Implemented sensor:
Groundwater level
Soil temperature
Available water content
Precipitation
Measurements of field soil sensors
Monitoring panel of soil water content
Available soil water content on remote controlled panel
Water content in different soil layers
CRISURILOR PLAIN – Romanian reference site
• The Crisuri Plain is situated
in the mid part of the
Western Plain (between
Barcau and Mures rivers).
• The total surface area of
the plain is 3600 sqkm.
• Altitudes vary between 90-
180 m.
• Along Barcau, Crisul Alb,
Crisul Negru, Cigher rivers.
CRISURILOR PLAIN – Meteorological data
AIR TEMPERATURE
at Oradea (up left),
Holod (up right),
Săcueni (down left),
Chişineu Criş (down
right) meteorological
Station from 1975-1980
to present.
0C
y = 0,032x + 9,7834
R2 = 0,2155
8
8,5
9
9,5
10
10,5
11
11,5
12
12,5
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Oradea
Linear (Oradea)
0C
y = 0,0386x + 9,6294
R2 = 0,306
8
8,5
9
9,5
10
10,5
11
11,5
12
12,5
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Holod
Linear (Holod)
0C
y = 0,0354x + 9,7966
R2 = 0,2504
8
8,5
9
9,5
10
10,5
11
11,5
12
12,5
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Săcueni
Linear (Săcueni)
0C
y = 0,0164x + 9,9476
R2 = 0,0809
8
8,5
9
9,5
10
10,5
11
11,5
12
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Chişineu Criş
Linear (Chişineu Criş)
CRISURILOR PLAIN –Meteorological Data
MEAN ANNUAL
RAINFALL at Oradea (up
left), Holod (up right),
Săcueni (down left),
Chişineu Criş (down right)
meteorological Station from
1994-
mm
y = 1,5343x + 588,36
R2 = 0,0181
350
450
550
650
750
850
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Oradea
Linear (Oradea)
mm
y = 6,2134x + 488,47
R2 = 0,2042
350
450
550
650
750
850
950
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Chișineu Criş
Linear (Chișineu Criş)
mm
y = 0,2548x + 687,37
R2 = 0,0005
350
450
550
650
750
850
950
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Holod
Linear (Holod)
mm
y = 2,4334x + 536
R2 = 0,0528
350
400
450
500
550
600
650
700
750
800
850
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Săcueni
Linear (Săcueni)
a
b
c
d
mm
y = 2,4334x + 536
R2 = 0,0528
350
400
450
500
550
600
650
700
750
800
850
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Săcueni
Linear (Săcueni)
CRISURILOR PLAIN LANDUSE
(source :Institutul Naţional de Cercetare-Dezvoltare „Delta Dunării”: http://www.indd.tim.ro) )
Nr.
crt. Type of land use Surface (ha) Percentage (%)
1 Unirrigated agricultural land 193633,017 55,784
2 Secondary pastures 69458,290 20,010
3 Discontinuous urban and rural space 18086,764 5,211
4 Deciduous forests 17770,277 5,119
5 Swamps 15760,909 4,541
6 Predominant agricultural land mixed with natural vegetation 12894,911 3,715
7 Complex agricultural crops 6057,103 1,745
8 Industrial and commercial bodies 4340,424 1,250
9 Vineyards 3004,937 0,866
10 Water bodies 2367,766 0,682
11 Rivers 1246,070 0,359
12 Natural pastures 563,606 0,162
13 Transition shrub areas 548,625 0,158
14 Rice fields 372,402 0,107
15 Orchards 243,782 0,070
16 Continuous urban space 222,456 0,064
17 Airfields 136,557 0,039
18 Waste dumps 132,569 0,038
19 Recreational areas 131,535 0,038
20 Coniferous forests 76,446 0,022
21 Green urban areas 61,998 0,018
347110,444 100,000
CRISURILOR PLAIN - SOIL DATA
Main soil classes
(SourceOSPA Bihor)
Main soil types
CRISURILOR PLAIN
The distribution of cernoziom soil within
Crisurilor Plain
(source, Harta Solurilor României, scale 1:
200.000, I.G.F.C.O.T., Bucureşti)
Physical Implementation of different stake holder
intervention points
- Watch: When a plant water stress is observed in sensitive
phenological phases
- Early Warning: When relevant a plant water stress is observed,
available soil moisture is close to critical, Predicted potential yield loss
<10%- Preparation to intervention
Warning: When this plant stress translates into significant biomass
damage
Potential yield loss <20%
Alert: when these two conditions are accompanied by an anomaly in
the irreversible vegetation damage Potential yield loss <30%
Catastrophe: When have to mitigate serious damages. Potential yield
loss <40%
SUMMARY
• The status of 5.5 activity based on Gantt table of
IDMP is correct
• Partners almost done data acquisition
• Further work focus on data coherency to integrate all
data
• End of this year we start the 10 years long time series
analysis (green and brown water) on reference site
THANK YOU FOR ATTENTION
1st WORKSHOP IDMP, SLOVAKIA