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