Joint GWP CEE/DMCSEE training: From Drought Management Strategies to Drought Management Policies by Christos Karavitis

1. Agricultural University of Athens, Hellas
Christos A. Karavitis
Ass. Professor, Water Resources Management
Sector of Water Resources Management
Dep. of Agricultural Engineering
AUA
From Drought Management Strategies
to Drought Management Policies

2. From Drought Management Strategies to Drought Management Policies
THE TRANSFORMING ΕNVIRONMENT
TRENDS AND DEVELOPMENTS
PREPARING THE SYSTEM OF THE 21ST CENTURY
SHIFTING PARADIGMS IN THEORY & PRACTICE
SPECULATING ABOUT THE FUTURE

3. Definition of Drought
A creeping phenomenon, a “non-event”
A source of confusion in devising an objective definition may be that drought implies a variety of things to various professionals according to the specialized field of study (meteorology, hydrology, water resources, agriculture etc.).
•Operational definitions attempt to demarcate the severity, onset and termination point of droughts
•Conceptual definitions attempt to identify the boundaries of the drought event

5. Drought
•a usually unexpected and unpredicted time period of abnormal dryness which affects water supply" (Grigg, N.S., 1988).
•The state of adverse and wide spread hydrological, environmental, social and economic impacts due to less than generally anticipated water quantities (Karavitis, 1992)

6. Social and Economic Drought / Water resources Engineering
•Gap between supply and demand of economic goods such as
–water,
–food,
–raw materials,
–hydroelectric power,
–transportation
•depends on the time and space processes of supply and demand
•Social Stresses – Economic impacts

7. From Drought Management Strategies to Drought Management Policies
THE TRANSFORMING ΕNVIRONMENT
TRENDS AND DEVELOPMENTS
PREPARING THE SYSTEM OF THE 21ST CENTURY
SHIFTING PARADIGMS IN THEORY & PRACTICE
SPECULATING ABOUT THE FUTURE

10. Cook et al., 2004
Percent area affected by drought (PDSI) Western States, USA.

11. J Sheffield et al. Nature 491, 435-438 (2012) doi:10.1038/nature11575
Global average time series of the PDSI and area in drought. Little change in global drought over the past 60 years
a, PDSI_Th (blue line) and PDSI_PM (red line). b, Area in drought (PDSI <−3.0) for the PDSI_Th (blue line) and PDSI_PM (red line). The shading represents the range derived from uncertainties in precipitation (PDSI_Th and PDSI_PM) and net radiation (PDSI_PM only). Uncertainty in precipitation is estimated by forcing the PDSI_Th and PDSI_PM by four alternative global precipitation data sets. Uncertainty from net radiation is estimated by forcing the PDSI_PM with a hybrid empirical–satellite data set and an empirical estimate. The other near- surface meteorological data are from a hybrid reanalysis–observational data set. The thick lines are the mean values of the different PDSI data sets. The time series are averaged over global land areas excluding Greenland, Antarctica and desert regions with a mean annual precipitation of less than 0.5 mm d−1

12. The Greenland temperature proxy during the Holocene
Reconstructed from the GISP2 Ice Core (Alley, 2000, 2004). Data from:
ftp.ncdc.noaa.gov/pub/data/paleo/icecore/greenland/summit/gisp2/isotopes/gisp2_temp_accum_alley2000.txt
-1.5-0.50.51.52.53.501000200030004000500060007000800090001000011000Years before present Temperature departure (oC) 20-year scale (interpolated) 500-year average2000-year averageMedieval warm periodLittle ice ageRoman climate optimumMinoan climate optimum

13. ANTARCTIC ICE CORE

17. From Drought Management Strategies to Drought Management Policies
THE TRANSFORMING ΕNVIRONMENT
TRENDS AND DEVELOPMENTS
PREPARING THE SYSTEM OF THE 21ST CENTURY
SHIFTING PARADIGMS IN THEORY & PRACTICE
SPECULATING ABOUT THE FUTURE

18. •Prevailing crisis management attitude
•Natural Hazards Emergency Response Procedures
•Protocols for Processes and Procedures
•Create a wider menu of options and alternatives
Why are Drought Contingency Policies Needed?

19. Crisis
Management
Risk/Adaptive
Management
NMMC, 2004; Karavitis, C. A., . E. C. Vlachos and N. S. Grigg, 2005
Preparedness
Mitigation
Reconstruction
Recovery
Response
Impact
Assessment
Disaster
Prediction
Warning

21. Analysis for a Risk Management Framework

22. Adaptation Planning
Source: IPCC Dr. Thomas E. Downing (SEI)

23. First Plenary meeting 22-25 November - Nauplion 2011
FP7
Tomorrow’s answers start today
Vulnerability
The degree to which a system, subsystem, or system component is likely to experience harm due to exposure to a hazard, either a perturbation or stress/stressor (Turner et.al, 2003)
Institution
Logo
Vulnerability = F(Hazard , Impacts)
UN-ISDR, 2004

24. The vulnerability term is composed of two basic elements: hazard and impacts. Thus, without a hazard or something to be affected, no vulnerability is implied.
Exposure is also considered to be part of vulnerability though sometimes it is conceived as the relation that connects the system to the concerning hazard.
Generally, vulnerability refers to the factors that affect both the systems likelihood to be harmed and the system’s ability to cope.
Drought vulnerability depends both on the sector of concern where drought is applied on (agriculture, industry, etc.) and the system of interest.

25. Drought contingency Plan

30. From Drought Management Strategies to Drought Management Policies
THE TRANSFORMING ΕNVIRONMENT
TRENDS AND DEVELOPMENTS
PREPARING THE SYSTEM OF THE 21ST CENTURY
SHIFTING PARADIGMS IN THEORY & PRACTICE
SPECULATING ABOUT THE FUTURE

32. Primary Meteorological, Agricultural or Hydrological Drought Indices
•Percent of Normal
•Deciles
•Palmer Drought Index
–PDSI, PHDI, CMI
•Surface Water Supply Index
•Standardized Precipitation Index (SPI)
•Reconnaissance Drought Index (RDI)
•Vegetation indices (NDVI, VCI, SVI)
•U.S. Drought Monitor
–Composite index approach

33. Relation between SDVI & drought aspects (DMCSEE, 2011)

34. Methodological Framework
•Drought Impact database
•Impact assessment-Resilience
•Vulnerability assessment
•Vulnerability visualization

35. Steps
1. SPI 6 and SPI 12 calculation and Kriging
2. Supply and Demand Data
3. Impact Data
4. Infrastructure Data on the Level of deficiency
5. Index Calculation
6. Index Visualization

36. Drought Vulnerability Index
The SDVI is composed of six components in four categories:
1. cSPI-12 representing the non-agricultural water availability (hydropower, households and tourism) and cSPI-6 portraying the agricultural (irrigation) use, respectively. Their values are calculated on local (meteorological station) scale.
2. Supply and Demand that describe the deficits in supplying capacity (network operation and losses) and in demand coverage. Their magnitude depends on the available and delivered amount of water.
3. Impacts that describe the losses (transformed into monetary units) that might have been caused due to the supply – demand deficiencies.

37. Drought Vulnerability Index
4. Infrastructure that describes the existing infrastructure level of development regarding the level of deficiency (divergence from the designed supply capacity).
The values of the components included in the last three categories are calculated on a basin or sub-basin scale (in the case of infrastructure, average values are used).
The assessment is based on a synthetic SPI-based Drought Vulnerability Index (SDVI) that was developed by Agricultural University of Athens (2011), in the context of Drought Management Centre in South-eastern Europe (DMCSEE Project-eu).

38. Components Classification
(DMCSEE, 2011)

39. Drought Vulnerability Index
The assessment is based on a synthetic SPI-based Drought Vulnerability Index (SDVI) that was developed by Agricultural University of Athens, in the context of Drought Management Centre in South-eastern Europe (DMCSEE Project , 2011).
.
푆퐷푉퐼= 푆푐푎푙푒푑 푉푎푙푢푒 표푓 푡ℎ푒 퐶표푚푝표푛푒푛푡푠 푁 푁 푖=1
The equation implies that all the components are equally weighted.

40. Lower Upper Relative Cumulative Cum. Rel.
Class Limit Limit Midpoint Frequency Frequency Frequency Frequency
at or below 0 0 0.0000 0 0.0000
1 0 0.5 0.25 24 0.0563 24 0.0563
2 0.5 1.0 0.75 64 0.1502 88 0.2066
3 1.0 1.5 1.25 189 0.4437 277 0.6502
4 1.5 2.0 1.75 125 0.2934 402 0.9437
5 2.0 2.5 2.25 23 0.0540 425 0.9977
6 2.5 3.0 2.75 1 0.0023 426 1.0000
above 3.0 0 0.0000 426 1.0000
SENSITIVITY ANALYSIS & CLASSIFICATION

41. Index Classification

42. Methodology used
7 Case studies were selected located in:
1.Bulgaria
2.F.Y.R.O.M.
3.Hellas
4.Hungary
5.Montenegro
6.Serbia
7.Slovenia

47. •Water Stress Vulnerability Index (WStVI)
•Water Scarcity Vulnerability Index (WScVI)
•Drought Vulnerability Index (DVI)
For each point (sub-area), the web tool can automatically calculate:
1.3 - Calculation + Visualization
47

48. Water Changing Conditions

49. Selection & Classification of Indicators for DVI
Non VulnerableLess VulnerableMedium VulnerableHighly VulnerableExtremely VulnerableExceptionally Vulnerable012345Coefficient of variation of precipitation (%)< 2020 to 3030.1 to 3535.1 to 4040.1 to 50> 50Dryness Ratio (%)< 2020 to 3030.1 to 4040.1 to 5050.1 to 65> 65SPI 1> 0.50-0.49 to 0.49-0.50 to -0.99-1.00 to -1.49-1.50 to -1.99< -2.00SPI 3> 0.50-0.49 to 0.49-0.50 to -0.99-1.00 to -1.49-1.50 to -1.99< -2.00SPI 6> 0.50-0.49 to 0.49-0.50 to -0.99-1.00 to -1.49-1.50 to -1.99< -2.00SPI 9> 0.50-0.49 to 0.49-0.50 to -0.99-1.00 to -1.49-1.50 to -1.99< -2.00SPI 12> 0.50-0.49 to 0.49-0.50 to -0.99-1.00 to -1.49-1.50 to -1.99< -2.00SPI 24> 0.50-0.49 to 0.49-0.50 to -0.99-1.00 to -1.49-1.50 to -1.99< -2.00Vegetation cover of basin area (%)> 8079.9 to 7069.9 to 6059.9 to 5049.9 to 35.0< 35Total Rate of Coverage (%)10099.9 to 95.0094.9 to 9089.9 to 8584.9 to 80< 80Domestic Coverage (%)10099.9 to 95.0094.9 to 9089.9 to 8584.9 to 80< 80Industrial Coverage (%)10099.9 to 95.0094.9 to 9089.9 to 8584.9 to 80< 80Agricultural Coverage (%)10099.9 to 95.0094.9 to 9089.9 to 8584.9 to 80< 80Coverage of Other Uses (%)10099.9 to 95.0094.9 to 9089.9 to 8584.9 to 80< 80Change in Total Coverage (%)> 33 to 0.10-0.1 to -3-3.1 to -5.0> -5Change in Domestic Coverage (%)> 33 to 0.10-0.1 to -3-3.1 to -5.0> -5Change in Industrial Coverage (%)> 33 to 0.10-0.1 to -3-3.1 to -5.0> -5Change in Agricultural Coverage (%)> 33 to 0.10-0.1 to -3-3.1 to -5.0> -5Change in Coverage for Other Uses (%)> 33 to 0.10-0.1 to -3-3.1 to -5.0> -5Population Density (No/km2)< 19.119.1 to 50.050.1 to 90.090.1 to 150.0150.1 to 250.0> 250Population Growth (%)< 00 to 0.60.61 to 1.101.11 to 1.60 1.61 to 2.00>2.00....... ....... ....... Drought Vulnerability Indicators (WStVI)

50. Calculation of Indicators (75 Indicators)
RSPI1 RSPI3 RSPI6 RSPI9 RSPI12 RSPI24 AGRGDP AGRGROW AcSan ChAcSan AgrPr ChAgrPr CosWatExTr . . .
2 1 1 1 1 1 5 1 4 0 0 3 5 .
1 0 2 1 1 1 5 1 4 0 0 3 5 .
0 1 1 1 1 1 5 1 4 0 0 3 5 .
0 1 1 1 1 1 5 1 4 0 0 3 5 .
2 4 2 2 1 2 5 1 4 0 0 3 5 .
2 2 1 1 1 2 5 1 4 0 0 3 5 .
3 1 1 1 1 1 5 1 4 0 0 3 5 .
0 1 1 1 1 1 5 1 4 0 0 3 5 .
1 1 1 1 1 2 5 1 4 0 0 3 5 .
1 1 1 1 0 3 5 1 4 0 0 3 5 .
1 1 1 1 1 3 5 1 4 0 0 3 5 .
0 1 1 1 1 3 5 1 4 0 0 3 5 .

51. Correlation matrix & Descr. Stats for DVI (52 Indicators)
SPI 1
SPI 3
SPI 6
SPI 9
SPI 12
SPI 24
Population Density (No/km2)
Population Growth (%)
Agricultural Growth (%)
Industrial Productivity ($/m3)
Change in Industrial Productivity (%)
Agricultural Productivity ($/m3)
Total Use of Reclaimed Water (Ratio)
Change in Agricultural Productivity (%)
Total Stored Water (hm3)
Change in Stored Water (%)
Gross Domestic Product ($/capita)
Water Supplied Population (%)
Change in Supplied Population (%)
Tourists Accomodation (Days)
Tourists Density (No/km2)
Population under Poverty (%)
Change in Population Under Poverty (%)
Water Governance (Q)
Wastewater Governance (Q)
Degree of Preparedness (Q)
Status of Water Supply Infrastructure (Q)
Status of Wastewater Treatment and Supply Infrastructure (Q)
Change in Use of Reclaimed Water (%)
Legal Framework (Q)
Financial Framework (Q)
Institutional Framework (Q)
Policy Framework (Q)
Index of Inflow (%)
Storage Capacity per capita (m3/capita)
Distribution Network Efficiency (%)
Share of water use by agriculture adjusted by the sector’s share of GDP
Share of water use by industry adjusted by the sector’s share of GDP
Irrigated land as % of cultivated land (%)
Hydropower Dependence (%)
Water charges as percentage of per capita income (%)
Literacy rate (%)
Economically active population (%)
Investments (%GDP)
Cost of Water Extraction and Treatment ($/m3)
Groundwater development stress indicator (GDSI)
Share of domestic supply from conventional freshwater sources (surface & groundwater)
Share of agricultural supply from conventional freshwater sources (surface & groundwater)
Share of industrial supply from conventional freshwater sources (surface & groundwater)
Share of domestic supply from alternative freshwater sources (e.g. reclaimed water)
Share of agricultural supply from alternative freshwater sources (e.g. reclaimed water)
Share of industrial supply from alternative freshwater sources (e.g. reclaimed water)
Change in Use of Reclaimed Water (%)

52. Factor Analysis for Drought Vul. Ind.
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 3.187 45.522 45.522 3.187 45.522 45.522
2 1.494 21.349 66.871 1.494 21.349 66.871
3 1.091 15.589 82.461 1.091 15.589 82.461
4 .614 8.777 91.238
5 .358 5.119 96.357
6 .185 2.644 99.001
7 .070 .999 100.000
DVI Indicators weights
Index of Flow 0.092
RSPI6 0.166
RSPI12 0.191
Change in Agricultural Productivity (%) 0.078
Change in Industrial Productivity (%) 0.075
Status of Wastewater Treatment and Supply
Infrastructure
0.202
Status of Water Supply Infrastructure 0.196

53. SENSITIVITY ANALYSIS & CLASSIFICATION
Lower Upper Relative Cumulative Cum. Rel.
Class Limit Limit Midpoint Frequency Frequency Frequency Frequency
at or below 0 0 0.0000 0 0.0000
1 0 0.714286 0.357143 64 0.0064 64 0.0064
2 0.714286 1.42857 1.07143 1000 0.1000 1064 0.1064
3 1.42857 2.14286 1.78571 3050 0.3050 4114 0.4114
4 2.14286 2.85714 2.5 3630 0.3630 7744 0.7744
5 2.85714 3.57143 3.21429 1808 0.1808 9552 0.9552
6 3.57143 4.28571 3.92857 412 0.0412 9964 0.9964
7 4.28571 5.0 4.64286 36 0.0036 10000 1.0000
above 5.0 0 0.0000 10000 1.0000
Classes
No Vulnerability 0
Very Low Vulnerability 0.72 1.42 1
Low Vulnerability 1.43 2.14 2
Medium Vulnerability (At Risk) 2.15 2.86 3
High Vulnerability 2.87 3.57 4
Very High Vulnerability 3.58 4.28 5
Extreme Vulnerability 6
Drought Vulnerability Index
<0.71
>4.29

54. The results for the whole study area can then be represented on the visualization Platform (eg: using Kriging - regression)
1.3 - Calculation + Visualization
54

55. After this initial analysis, the web tool will allow the end- -user to deeply analyze/improve the vulnerable subareas, towards possible WR&R:
2 - WR&R Evaluation and Policy options
55

56. From Drought Management Strategies to Drought Management Policies
THE TRANSFORMING ΕNVIRONMENT
TRENDS AND DEVELOPMENTS
PREPARING THE SYSTEM OF THE 21ST CENTURY
SHIFTING PARADIGMS IN THEORY & PRACTICE
SPECULATING ABOUT THE FUTURE

57. Nevertheless, the major challenge for any drought related research may be the development of comprehensive and effective drought management and decision making schemes. In such quests, forecasting may provide some help…

58. •Traditional Planning
– Assumes stationary climate
–Uses recorded weather and hydrology times series
–Cylinder of Certainty
Cone of Uncertainty
58
Adapted from Malcolm Pirnie,2009
Present
Recorded
Variability
Future
 Updated Planning Methods
Hundreds of possible climate scenarios
Multi-outcome planning
Robust over optimal

59. Scenario Planning
•Small number of equally likely scenarios [A, B, C, D]
•Common strategies (no regrets)
•Sign posts
59
A
B
C
D
Sign Post
Present
Future

60. Robust Decision Making
–CIS analysis of many plausible likely scenarios
–Iteration and hedging
60
Present
Future

61. DROUGHT FORECASTING
•Statistical predictions: based on lagged relationships between regional meteorological patterns (i.e. rainfall) and actual data.
•Probabilistic predictions: by trying to estimate the mathematical probability of a given phenomenon in a certain time frame based usually on analysis of time series data.
•Deterministic predictions: which attempt to model important features of the existing system and provide a basis for contingency analysis through simulation

62. Greece is highly dependent on the annual rainfall patterns resulting that any precipitation deficit may cause most of the time significant impacts on the economy, societal activities and the environment.
A series of such deficits have occurred during the last decades (e.g. 1989-90, 1993, 2000, 2003, 2007) characterizing Greece as drought prone, exposing economy to threats and leaving it vulnerable to loses.

63. Mean Annual Precipitation 1947-2011
ΕΜΙΤ, AUA, 2011

66. THE ATHENIAN 1989-90 DROUGHT

67. TECHNICAL SUPPORT TO THE CENTRAL WATER AGENCY FOR THE DEVELOPMENT OF A DROUGHT MASTER FOR GREECE AND AN IMMEDIATE DROUGHT MITIGATION PLAN
Contract No. 10889/11/07 /2007 Ministry of Planning, Public Works and the Environment To Water Resources Management Sector, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens Co-ordinator Christos A. Karavitis Submitted on October 2008

68. the Seasonal ARIMA Model (Auto Regressive /Integrated/Moving Average) has been applied for forecasting

69. Forecasting Methodology
52 stations
24 station at Central
Greece for January
2007
Application for all Greece
33 stations

70. Validation model (Seasonal ARIMA 15 years)

71. Station
X
Y
Period
Average
Standard deviation
Minimum
Maximum
Stnd. skewness
Stnd. kurtosis
Agia Triada
405136
4244800
1981-2010
986.56
220.27
534.3
1382.7
-1.10
-0.30
Amfissa
358886
4265827
1981-2010
681.94
161.66
328.4
1025.7
0.34
0.42
Gravia
363497
4280548
1981-2010
822.31
143.23
431.3
1048.9
-1.87
0.80
Davlia
389166
4248703
1981-2010
770.83
153.20
463.5
1062.1
-0.63
-0.89
Distomo
383406
4253888
1981-2010
605.78
121.66
329.4
821.7
-0.78
-0.45
Eptalofos
367725
4273077
1981-2010
972.64
221.66
612.1
1420.1
0.41
-1.22
Zilefto
349557
4310404
1981-2010
394.19
166.89
96.9
835.7
0.93
0.22
Thisvi
409381
4233654
1981-2010
458.00
149.83
185.6
874.9
0.93
0.90
Itea
363056
4254654
1981-2010
377.45
119.48
187.7
660.9
0.80
-0.53
Kalithea
451708
4238840
1981-2010
468.85
157.18
0
753.6
-1.35
1.61
Kaloskopi
354830
4282551
1981-2010
879.01
235.07
568.0
1740.1
4.01
5.87
K.Tirothea
388071
4274616
1981-2010
655.60
129.32
388.8
991.9
0.31
0.84
Lamia
361050
4306493
1981-2010
538.38
115.08
328.0
870.5
1.12
1.27
Livadia
400880
4254100
1981-2010
737.21
156.38
414.1
1037.8
0.51
-0.43
Lilea
369237
4276752
1981-2010
848.55
335.74
0
1272.4
-3.59
2.44
Pavlos
421355
4264972
1981-2010
479.07
160.67
82.5
741.5
-0.86
-0.23
Trilofo
345367
4317887
1981-2010
598.79
126.42
373.0
826.5
0.72
-0.85
Tymfristos
319174
4309189
1981-2010
1022.99
382.17
141.0
1713.3
-1.55
0.09
Elliniko
475537
4194336
1981-2010
363.63
96.39
155.4
546.6
-0.82
0.06
Asteroskopio
475089
4202597
1981-2010
397.06
138.72
150.6
896.0
3.30
5.36
Elefsina
440916
4212390
1981-2010
344.17
91.06
117.4
468.3
-1.48
-0.16
Tatoi
480783
4218209
1981-2010
435.09
153.46
169.6
812.7
-0.03
-0.15
Filladelphia
477479
4210345
1981-2010
452.17
177.52
180.5
1015.5
2.95
3.35
Ypati
346524
4303061
1981-2010
790.74
293.18
327.6
1431.0
0.98
-0.57

73. Adopted Methodology
Drought event in 2007…
1.Based on the available raw data obtained from twenty-four meteorological stations and for a calibration period of thirty years (1981 – 2010), the SPI (3, 6, 9, 12 and 24) values were calculated on a monthly scale.
2.The SPI values from January, 1981 to December, 2006 were used as input data in the ARIMA (Auto Regressive /Integrated/Moving Average) model for a forecasting period of three months (January – March, 2007) to be developed.
3.Comparative analysis of Steps 1 and 2

77. Divergence of the projected SPI 6 values compared to the observed ones

78. Application Period (2013-2020)

79. Station
X
Y
Period
Average
St. Deviation
Coeff. of variation
Minimum
Maximum
Stnd. skewness
Stnd. kurtosis
Agrinio
268875.16
4275530.28
1947-2012
891.31
225.80
0.25
388.60
1463.90
0.25
-0.74
Aktion
219283.86
4312709.01
1971-2012
873.47
179.94
0.21
504.40
1251.40
0.21
-0.87
Alexandroupolis
662821.24
4523618.93
1947-2012
540.44
134.46
0.25
275.70
905.90
0.91
-0.60
Anchialos
396082.70
4342228.76
1956-2012
491.37
116.95
0.24
288.30
847.00
2.29
0.61
Andravida
261465.38
4199167.76
1959-2012
778.55
178.68
0.23
325.60
1160.00
-0.73
-0.10
Arta
240224.50
4338878.73
1976-2012
1082.74
232.05
0.21
493.70
1509.90
-0.51
-0.26
Asteroskopio
475089.38
4202597.25
1858-2012
394.97
111.63
0.28
115.70
896.00
5.72
9.20
Chania
512901.68
3932269.85
1959-2012
633.32
171.80
0.27
287.50
977.20
1.12
-1.12
Chios
687028.85
4245693.86
1974-2012
562.24
158.66
0.28
110.30
857.80
-0.98
0.69
Elefsina
440915.88
4212389.83
1951-2012
377.89
111.36
0.29
117.40
740.40
2.00
2.66
Hellenicon
476989.70
4193354.66
1948-2012
367.81
92.76
0.25
155.40
560.30
0.17
-0.30
Heraclion
607395.45
3910363.07
1947-2012
478.71
117.08
0.24
261.80
793.90
0.78
-0.35
Ioannina
227392.02
4398141.29
1950-2012
1096.80
247.13
0.23
490.70
1708.20
0.18
-0.22
Kalamata
324360.97
4102811.24
1971-2012
766.62
167.77
0.22
288.00
1082.30
-1.90
0.82
Kastoria
269220.67
4480918.49
1981-2012
554.86
138.64
0.25
281.47
882.94
0.53
0.11
Kerkyra
147783.91
4391112.67
1947-2012
1119.05
246.07
0.22
620.80
1636.30
0.29
-1.04
Konitsa
256042.84
4462236.91
1955-2012
922.17
304.21
0.33
209.80
1642.00
-0.12
-0.08
Lamia
360383.21
4300968.99
1956-2012
571.76
151.69
0.27
159.28
973.80
0.93
1.69
Larisa
366008.76
4389788.05
1949-2012
431.64
109.36
0.25
211.30
772.50
1.68
1.44
Lesvos
725179.25
4325645.06
1952-2012
642.64
158.24
0.25
246.30
931.30
-0.86
-0.30
Lymnos
605166.73
4419248.05
1968-2012
513.19
175.49
0.34
211.86
970.01
1.32
0.13
Melos
538541.95
4065090.13
1950-2012
407.68
125.18
0.31
69.10
774.80
0.86
1.56
Methone
295151.20
4077583.50
1967-2012
647.01
133.53
0.21
389.66
969.79
0.84
0.03
Rhodes
866517.69
4035914.60
1955-2012
670.42
180.79
0.27
310.70
1112.50
1.11
-0.38
Samos
756986.43
4175249.62
1978-2012
701.20
189.14
0.27
353.00
1064.50
0.08
-1.04
Serres
463452.85
4547812.89
1956-2012
483.42
121.38
0.25
137.30
858.40
1.46
3.10
Skyros
541787.38
4312174.03
1931-2012
471.83
163.61
0.35
159.80
879.50
0.41
-1.13
Tanagra
461951.48
4243016.32
1960-2012
471.21
123.20
0.26
175.60
775.60
0.20
0.10
Tatoi
480783.38
4218208.89
1952-2012
461.30
132.27
0.29
169.60
812.70
-0.18
0.76
Thera
632478.91
4029392.55
1961-2012
324.92
124.81
0.38
16.40
562.90
-0.46
-0.14
Thessalonike
412412.02
4485367.97
1959-2012
454.77
97.97
0.22
248.94
722.87
0.82
-0.09
Tripolis
358423.25
4154541.74
1949-2012
733.92
185.95
0.25
251.98
1192.80
0.04
0.21

80. Station
Model
RMSE
RUNS
RUNM
AUTO
MEAN
VAR
Agrinio
ARIMA(2,0,1)x(2,1,2)180
0.101624
OK
OK
***
OK
*
Aktion
ARIMA(1,1,2)x(1,2,0)180
0.201477
***
OK
Alexandroupolis
ARIMA(2,0,2)x(1,1,2)180
0.186691
OK
OK
***
**
OK
Anchialos
ARIMA(1,1,2)x(2,2,0)180
0.431217
***
OK
OK
Andravida
ARIMA(1,1,2)x(2,1,0)180
0.135341
***
OK
OK
Arta
ARIMA(2,0,2)x(2,1,2)180
0.249207
OK
*
***
OK
OK
Asteroskopio
ARIMA(2,0,1)x(2,1,2)180
0.343600
OK
OK
***
OK
**
Chania
ARIMA(1,1,1)x(2,2,0)180
0.058688
***
OK
OK
Chios
ARIMA(1,1,2)x(2,0,1)180
0.035345
***
OK
OK
***
Elefsina
ARIMA(2,0,2)x(2,2,1)180
0.138900
OK
***
***
OK
Hellenicon
ARIMA(2,0,2)x(2,2,1)180
0.295673
OK
***
***
Heraclion
ARIMA(1,1,1)x(1,2,0)180
0.026318
***
OK
OK
Ioannina
ARIMA(1,0,2)x(2,2,1)180
0.322977
***
**
***
Kalamata
ARIMA(1,1,2)x(2,1,0)180
0.0000510
***
OK
OK
Kastoria
ARIMA(1,1,1)x(1,1,0)180
0.0014500
*
OK
OK
Kerkyra
ARIMA(2,0,1)x(2,2,2)180
0.3864025
OK
OK
***
Konitsa
ARIMA(1,1,1)x(2,1,1)180
0.0020666
***
OK
***
Lamia
ARIMA(1,1,1)x(2,1,0)180
0.0043118
***
OK
OK
Larisa
ARIMA(2,0,2)x(2,2,0)180
0.02018869
***
***
***
Lesvos
ARIMA(1,0,1)x(2,2,2)180
0.00440373
***
***
***
Lymnos
ARIMA(1,1,0)x(2,1,0)180
0.0003154
***
OK
OK
Melos
ARIMA(2,1,1)x(2,2,2)180
0.3947320
OK
OK
***
Methone
ARIMA(2,0,2)x(2,2,1)180
0.0023547
***
***
***
Naxos
ARIMA(1,1,2)x(2,2,0)180
0.0000037
***
OK
OK
Rhodes
ARIMA(0,1,0)x(2,2,2)180
0.0001758
***
OK
OK
Samos
ARIMA(1,1,1)x(1,2,0)180
0.0002198
***
OK
OK
Serres
ARIMA(1,1,1)x(2,1,0)180
0.0028237
***
OK
OK
Skyros
ARIMA(2,1,2)x(2,1,2)180
0.4102889
OK
OK
***
OK
**
Tanagra
ARIMA(1,1,1)x(2,1,0)180
0.0170188
***
OK
Tatoi
ARIMA(2,1,1)x(2,2,2)180
0.0011950
OK
OK
OK
Thera
ARIMA(1,1,1)x(2,2,0)180
0.00054447
***
OK
OK
Thessalonike
ARIMA(1,2,2)x(2,2,2)180
0.00002195
OK
*
***
Tripolis
ARIMA(1,1,1)x(2,1,1)180
0.00001420
***
OK
OK
RMSE = Root Mean Squared Error RUNS = Test for excessive runs up and down RUNM = Test for excessive runs above and below median AUTO = Box-Pierce test for excessive autocorrelation MEAN = Test for difference in mean 1st half to 2nd half VAR = Test for difference in variance 1st half to 2nd half OK = not significant (p >= 0.05) * = marginally significant (0.01 < p <= 0.05) ** = significant (0.001 < p <= 0.01) *** = highly significant (p <= 0.001)

81. Residual Autocorrelations for adjusted Col_1
ARIMA(2,1,1)x(2,2,2)180
0 5 10 15 20 25
lag
-1
-0.6
-0.2
0.2
0.6
1
Autocorrelations
Residual Autocorrelations for adjusted SPI 12
ARIMA(0,1,1)x(2,2,0)180
0 5 10 15 20 25
lag
-1
-0.6
-0.2
0.2
0.6
1
Autocorrelations
Residual Normal Probability Plot
ARIMA(0,1,1)x(2,2,0)180
-8 -4 0 4 8
(X 1.E-18)
Residual
0.1
1
5
20
50
80
95
99
99.9
percentage
Residual Normal Probability Plot
ARIMA(0,0,0)x(2,2,2)180
-0.1 0.3 0.7 1.1 1.5 1.9 2.3
Residual
0.1
1
5
20
50
80
95
99
99.9
percentage

83. Will be Drought year the 2015?

92. NORMAL – WET CONDITIONS IN AUTUMN OF 2017

97. Conclusions
•Seasonal ARIMA (15 years seasonal length) suggests:
Extreme drought conditions the year 2015 (Cyclades islands, Dodecanese islands & Northern Greece)
Normal – Wet condition the year 2017
•The present effort, as part of other pertinent research, point towards that certain drought events may be anticipated with some certainty.
•The results produced by ARIMA model may be trusted up to a degree, since forecasting of complex and random variables seems generally elusive.
• This may be of great value since the forecasting processes can be used for early warning mechanisms developed as parts of drought contingency planning, monitoring and integrated management.
• Such mechanisms may be able to minimize the impacts of the various drought events while increasing the area’s absorbing capacity.