Third IDMP CEE workshop: Drought Risk Management Scheme: a decision support system by Tamara Tokaczyk

1. Drought Risk Management Scheme:
a decision support system
Activity 5.4
3rd IDMP CEE Workshop
Budapest, 2 – 4 October 2014
Tamara Tokarczyk, Wiwiana Szalińska Institute of Meteorology and Water Management, National Research
Institute, Wroclaw Branch(IMGW-PIB), Poland
Leszek Łabędzki, Bogdan Bąk, Institute of Technology and Life Sciences (ITP), Poland
Edvinas Stonevicius, Gintautas Stankunavicius Vilnius University, Department of Hydrology and
Climatology (VU), Lithuania
Elena Mateescu, Daniel Aleksandru, Gheorghe Stancalie, National Meteorological Administration (NMA),
Romania

2. • What has been done since the 2nd IDMP CEE workshop
till now (April 2014 – October 2014)?
The goal of the Output 2 was to develop a concept of drought hazard and vulnerability
mapping as a tool for drought risk management for selected regional contexts including:
(i) selection of drought hazard indices that can be use for the drought detection and
monitoring,
(ii) development of drought hazard assessment methods taking into account drought
frequency and severity analysis,
(iii) identification of drought impacts within the given regional and sectoral context and
vulnerability estimation methods,
(iv) integration of the resultant drought hazard assessment with the drought vulnerability
analysis in order to categorize the areas subject to drought risk.
Progress Report
Milestone 2.1 - drought hazard assessment methodology based upon the indices
applicable to the participating countries (LT, PL, RO) for the need of drought hazard
map generation.
Milestone 2.2 - insights for the development of the methodology for vulnerability
assessment for the particular sector of economy in the participating countries.

3. The selected indices were investigated in terms of providing information
on drought hazard for different regional context:
- SPI and EDI indices with respect to detection of agricultural drought in Lithuania
- SPI with respect to detection of agricultural drought in Romania
- SPI, SRI, EDI and FI with respect to detection of hydrological drought in Lithuania
- SPI, SRI with respect to detection of hydrological drought in Poland.
Progress Report
(i) selection of drought hazard indices that can be use for the drought
detection and monitoring
MILESTONE 2.1

4. Progress Report Agricultural drought in Lithuania
06.04.01
06.04.08
06.04.15
06.04.22
06.04.29
06.05.06
06.05.13
06.05.20
06.05.27
06.06.03
06.06.10
06.06.17
06.06.24
06.07.01
06.07.08
06.07.15
06.07.22
06.07.29
06.08.05
06.08.12
06.08.19
06.08.26
06.09.02
06.09.09
06.09.16
06.09.23
06.09.30
06.10.07
06.10.14
06.10.21
06.10.28
-3
-2
-1
0
1
2
3
06.04.01
06.04.08
06.04.15
06.04.22
06.04.29
06.05.06
06.05.13
06.05.20
06.05.27
06.06.03
06.06.10
06.06.17
06.06.24
06.07.01
06.07.08
06.07.15
06.07.22
06.07.29
06.08.05
06.08.12
06.08.19
06.08.26
06.09.02
06.09.09
06.09.16
06.09.23
06.09.30
06.10.07
06.10.14
06.10.21
06.10.28
BIRŽA I
KYBA RTA I
LA UKUV A
LA ZDIJA I
ŠILUTĖ
TELŠIA I
UTENA
V A RĖNA
Mod. drought threshold
EDI365
warm season 2006
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
2006-03-III
2006-04-I
2006-04-II
2006-04-III
2006-05-I
2006-05-II
2006-05-III
2006-06-I
2006-06-II
2006-06-III
2006-07-I
2006-07-II
2006-07-III
2006-08-I
2006-08-II
2006-08-III
2006-09-I
2006-09-II
2006-09-III
2006-10-I
2006-10-II
2006-10-III
fAPAR
Joniškis
Biržai
Prienai
Molėtai
Varėna
Šilutė
Telšiai
Dotnuva
fAPAR warm season 2006
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
2006-03-III
2006-04-I
2006-04-II
2006-04-III
2006-05-I
2006-05-II
2006-05-III
2006-06-I
2006-06-II
2006-06-III
2006-07-I
2006-07-II
2006-07-III
2006-08-I
2006-08-II
2006-08-III
2006-09-I
2006-09-II
2006-09-III
2006-10-I
2006-10-II
2006-10-III
Soil moisture
anomaly
Joniškis
Biržai
Prienai
Molėtai
Varėna
Šilutė
Telšiai
Dotnuva
SMA warm season 2006
distort in typical fAPAR seasonal course SMA shows negative anomaly in places with heavy
soils (loam, clay) while places with sandy soils
seem do not suffer from the drought

5. Progress Report Agricultural drought in Romania
The 3 – month SPI values Soil water reserve in the critical period for
maize crop over 0-100 cm
Zoning of the soil moisture reserves shows good correspondence with the 3-months SPI
spatial distributions for all analyzed periods. Identified extremely dry areas according to SPI
indicator were corresponding to extreme pedological drought estimated from soil moisture
reserves.

6. Progress Report Hydrological drought in Lithuania
The multiannual correlation coefficients
between EDI and daily discharge
Seasonal variation of correlation coefficient
between EDI and daily discharge
The EDI indexes, calculated with the
accumulation of effective precipitation of 30,
90 and 365 days, have statistically
significant relationship with daily discharges

7. Progress Report Hydrological drought in Poland
The SPI vs. SRI correlation plots
0
10
20
30
40
50
60
70
80
90
100
class 0 class 1 class 2 class 3 class 4
frequencydistribution[%]
NIZOWKA: big deficit volume and short duration
Miedzylesie Klodzko Ladek Zdroj
Frequency distribution of the % of
months belonging to each SPI-
SRI class from the population of
months categorized according to
NIZOWKA model outputs.
0
10
20
30
40
50
60
70
80
90
100
class 0 class 1 class 2 class 3 class 4
frequencydistribution[%]
NIZOWKA: big deficit volume and long duration
Miedzylesie Klodzko Ladek Zdroj
-4
-3
-2
-1
0
1
2
3
4
-4 -3 -2 -1 0 1 2 3 4
SRI
SPI
Międzylesie-Międzylesie
class 0 class 1 class 2 class 3 class 4

8. Drought frequency and severity analysis, hazard assessment and mapping exercise
was performed for the study basin - the Odra River. Drought hazard maps were representing
spatial distribution of the probability of occurrence drought of different severity.
Progress Report
(ii) development of drought hazard assessment methods taking into account
drought frequency and severity analysis,
MILESTONE 2.1
Application of Markov chains:
(a) transition probabilities of different
drought severity classes,
(b) the expected time in each class of
severity,
(c) the recurrence time to a particular
drought class.
Drought severity states according to SPI-1:
Non-drought (N),
Moderate drought (1),
Severe drought (2)
Extreme drought (3).

9. Progress Report
The index of proneness to drought (DP)
DP = PNN + P1N + P2N + P3N
Higher the value of DP, lower will be the degree of drought proneness

10. Progress Report
EXPECTED RESIDENCE TIME [months]
severe drought extreme drought

11. Progress Report
EXPECTED RETURN PERIOD [months]
severe drought extreme drought

12. Vulnerability analysis were aimed at building vulnerability functions that represents the
relationship between potential damage or loss to a given element at risk against a specified
event intensity.
Progress Report
Vulnerability assessment for agricultural sector in Poland
Vulnerability assessment for agricultural sector in Romania
Vulnerability assessment for water resources sector in Lithuania
(iii) identification of drought impacts within the given regional and sectoral
context and vulnerability estimation methods,
MILESTONE 2.2

13. Poland, the vulnerability function was describing the relation between drought intensity
(SPI) and reduction in the crop yield: late potato, sugar beet, winter wheat, winter rape
and maize with the distinction of two classes of total available soil water (TASW)
Progress ReportExtremedroughtSPI≤-2.00
120 mm
TASW
200 mm
Reduction
[%]
late potato winter rape

14. Drought Vulnerability Index
(DVI)
Romania, the vulnerability functions were built for maize and the sunflower. State of the crop
vegetation was assessed with the use of satellite-derived indicators: NDVI, NDDI and NDWI,
drought intensity was expressed by indicators: heat stress (HS), Standardized Precipitation
Evapotranspiration Index (SPEI) and available water content of the soil (%AWC) during the
critical period for water needs crops (summer season).
Progress Report
Vulnerability
level
Scales
Heat stress (HS) SPEI Soil Moisture (SM)
No 0 No stress <10 0 No deficit <-0.99 0 No deficit 100% AWC
Low 1 Low stress 11-30 1 Low deficit -1.99 to -1 1 Low deficit 65-100 % AWC
High 2 Moderate stress 31 -50 2 Moderate dry -2.99 to -2 2 Moderate deficit 35-65 % AWC
Extreme 3 Strong stress >51 3 Very dry <-3 3 Strong deficit 0-35 % AWC
Vulnerable drought
areas for maize crop
during the critical
period for water plant
needs (August)

15. Progress Report
Lithuania, the
vulnerability function were
developed for the losses
described as the ratio of
surface water resources to
surface water
consumption. Drought
intensity was expressed in
terms of value of SRI and
FI (FDC).

16. • At what stage of the final output(s) are you at the
moment?
Developing an integrated framework that constitute a systematic
approach for building drought management systems for different sectoral
context.
The final output will profit from the obtained results in order to formulate
and detail a concept of operational decision support systems for drought
risk management in the Odra River study basin for agricultural sector.
The framework should contains concept of:
• components of the system required to support decisions – done
• drought hazard assessment methods – done
• drought vulnerability analysis with the use of impact assessment –
done
• drought risk visualization and mapping – to be done
Progress Report

17. • What are your plans for the final period
(October 2014 – March 2015)?
development of concept of drought risk visualization and
mapping
• What will be your final output(s)?
framework for drought risk management scheme -
recomendation for operational support system in
drought risk management for the Odra River basin
Plans

18. • What kind of challenges/problems do you expect?
not expect
• Will there by any change from the original plan?
Why?
no
• In what aspects would you like to continue your
activity ? Do you have any concrete proposals for
follow-up projects and funding sources?
Drought risk assessment and management for various users.
Recently we have cooperation with insurance company
concerning estimation of risk assessment
Plans

19. THANK YOU!