701
702
703
704
705
706
707
Agha OMM, Şarlak N (2016) Spatial and temporal patterns of climate variables in Iraq. Arab
J Geosci 9:302 doi:10.1007/s12517-016-2324-y
Al-Ansari N (2013) Management of water resources in Iraq: perspectives and prognoses.
Engineering 5:667-684 doi:10.4236/eng.2013.58080
Al-Khayat BYT, Al-Sulaiman MSS (2013) Forecasting of rainy conditions in Mosul city. Iraqi
Journal of Statistical Sciences 13:19-32
31
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
708
Al-Najafee EH, Rashad EM (2012) Rainfall levels and their impact on wheat productivity: a
709
comparative study between rainfall assured and semi-assured regions in Nineveh.
710
Tanmyat al-Rafidain 34:161-170
711
Anagnostopoulou C, Maheras P, Karacostas T, Vafiadis M (2003) Spatial and temporal
712
analysis of dry spells in Greece. Theor Appl Climatol 74:77-91 doi:10.1007/s00704-
713
002-0713-5
714
Angelidis P, Maris F, Kotsovinos N, Hrissanthou V (2012) Computation of drought index SPI
715
with alternative distribution functions. Water Resour Manage 26:2453-2473
716
doi:10.1007/s11269-012-0026-0
717
718
719
720
Awchi TA, Kalyana MM (2017) Meteorological drought analysis in northern Iraq using SPI
and GIS. Sustain Water Resour Manag 3:451-463 doi:10.1007/s40899-017-0111-x
Azooz A, Talal S (2015) Evidence of climate change in Iraq. Journal of Environment Protection
and Sustainable Development 1:66-73
721
Barron J, Rockstrom J, Gichuki F, Hatibu N (2003) Dry spell analysis and maize yields for two
722
semi-arid locations in east Africa. Agr Forest Meteorol 117:23-37 doi:10.1016/S0168-
723
1923(03)00037-6
724
725
726
727
728
729
730
731
Cabrera BL, Odening M, Ritter M (2013) Pricing rainfall futures at the CME. J Bank Financ
37:4286-4298 doi:10.1016/j.jbankfin.2013.07.042
Cavus Y, Aksoy H (2019) Spatial drought characterization for Seyhan River basin in the
Mediterranean region of Turkey. Water 11 doi:10.3390/w11071331
Cavus Y, Aksoy H (2020) Critical drought severity/intensity-duration-frequency curves based
on precipitation deficit. J Hydrol 584 doi:10.1016/j.jhydrol.2019.124312
Evans JP (2009) 21st century climate change in the Middle East. Clim Change 92:417-432
doi:10.1007/s10584-008-9438-5
32
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
732
Fadhil RM Markovian properties and distribution of daily rainfall in northern Iraq. In: Al-
733
Zyoud F, Abdel-Ghani, A. (ed) The Eighth Scientific Agricultural Conference ESAC-
734
2018, Karak, Jordan, 2018. National Agricultural Research Center, Baqa', Jordan, p 57
735
Farr TG et al. (2007) The shuttle radar topography mission. Rev Geophys 45:RG2004
736
doi:10.1029/2005RG000183
737
Fischer BMC, Mul ML, Savenije HHG (2013) Determining spatial variability of dry spells: a
738
Markov-based method, applied to the Makanya catchment, Tanzania. Hydrol Earth Syst
739
Sci 17:2161-2170 doi:10.5194/hess-17-2161-2013
740
Gao C, Booij MJ, Xu YP (2020) Development and hydrometeorological evaluation of a new
741
stochastic daily rainfall model: Coupling Markov chain with rainfall event model. J
742
Hydrol 589 doi:10.1016/j.jhydrol.2020.125337
743
Giga Y, Muszkieta M, Rybka P (2019) A duality based approach to the minimizing total
744
variation flow in the space H-s. Jpn J Ind Appl Math 36:261-286 doi:10.1007/s13160-
745
018-00340-4
746
Hajim AY, Al-Dabagh AY, Yaseen HI, Dawud AF, Shayth AH (1996) Analysis of irrigation
747
in fields and orchards in Nineveh. Department of Irrigation and Drainage, College of
748
Engineering, University of Mosul, Mosul, Iraq (in Arabic)
749
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working
750
Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
751
Change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.
752
Tignor, and H.L. Miller (eds.).
753
Jimoh OD, Webster P (1999) Stochastic modelling of daily rainfall in Nigeria: intra-annual
754
variation of model parameters. J Hydrol 222:1-17 doi:10.1016/S0022-1694(99)00088-
755
33
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
756
Kadim AA (2013) The negative and positive divergence of the heat and the rain refraction from
757
their common average at Mosul, Baghdad and Basra stations. Adab al-Basrah 67:309-
758
334
759
760
761
762
Kalyan MM, Awchi TA (2015) Investigating the meteorological drought in Northern Iraq using
deciles method. Al-Rafdain Engineering 23:12-21
Leobacher G, Ngare P (2011) On modelling and pricing rainfall derivatives with seasonality.
Appl Math Finance 18:71-91 doi:10.1080/13504861003795167
763
Loucks DP, van Beek E (2005) Water Resource Systems Planning and Management: An
764
Introduction to Methods, Models and Applications. Studies and Reports in Hydrology.
765
UNESCO PUBLISHING, Paris
766
Martin-Vide J, Gomez L (1999) Regionalization of peninsular Spain based on the length of dry
767
spells. Int J Climatol 19:537-555 doi:10.1002/(Sici)1097-0088(199904)19:5<537::Aid-
768
Joc371>3.0.Co;2-X
769
Masala G (2014) Rainfall derivatives pricing with an underlying semi-Markov model for
770
precipitation
771
doi:10.1007/s00477-013-0784-0
772
773
occurrences.
Stoch
Environ
Res
Risk
Assess
28:717-727
Mustafa LMF (2012) Spatial and Temporal Variation of Rainfall in Ninava Governorate.
Journal of Education and Science 25:98-114 doi:10.33899/edusj.2012.66773
774
Nop C, Fadhil RM, Unami K (2021) A multi-state Markov chain model for rainfall to be used
775
in optimal operation of rainwater harvesting systems. J Clean Prod 285:124912
776
doi:10.1016/j.jclepro.2020.124912
777
Ojara MA, Lou YS, Aribo L, Namumbya S, Uddin MJ (2020) Dry spells and probability of
778
rainfall occurrence for Lake Kyoga Basin in Uganda, East Africa. Nat Hazards
779
100:493-514 doi:10.1007/s11069-019-03822-x
34
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
780
Onof C, Chandler RE, Kakou A, Northrop P, Wheater HS, Isham V (2000) Rainfall modelling
781
using Poisson-cluster processes: a review of developments. Stoch Environ Res Risk
782
Assess 14:384-411 doi:DOI 10.1007/s004770000043
783
Osher S, Burger M, Goldfarb D, Xu JJ, Yin WT (2005) An iterative regularization method for
784
total
785
doi:10.1137/040605412
786
787
variation-based
image
restoration.
Multiscale
Model
Sim
4:460-489
Rasheed AMM (2010) Analysis of rainfall drought periods in the North of Iraq using standard
precipitation index (SPI). Al-Rafdain Engineering 18 60-72
788
Richardson CW, Wright DA (1984) WGEN: A model for generating daily weather variables.
789
US Department of Agriculture, Agricultural Research Service Washington, DC, USA,
790
Robaa SM, AL-Barazanji ZJ (2013) Trends of annual mean surface air temperature over Iraq.
791
792
793
794
795
Nature and Science 11:138-145
Rockström J et al. (2010) Managing water in rainfed agriculture—The need for a paradigm
shift. Agric Water Manage 97:543-550 doi:10.1016/j.agwat.2009.09.009
Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms.
Physica D 60:259-268 doi:10.1016/0167-2789(92)90242-F
796
Salman SA, Shahid S, Ismail T, Ahmed K, Wang XJ (2018a) Selection of climate models for
797
projection of spatiotemporal changes in temperature of Iraq with uncertainties. Atmos
798
Res 213:509-522 doi:10.1016/j.atmosres.2018.07.008
799
Salman SA, Shahid S, Ismail T, bin Abd Rahman N, Wang XJ, Chung ES (2018b)
800
Unidirectional trends in daily rainfall extremes of Iraq. Theor Appl Climatol 134:1165-
801
1177 doi:10.1007/s00704-017-2336-x
802
Sharifi E, Unami K, Yangyuoru M, Fujihara M (2016) Verifying optimality of rainfed
803
agriculture using a stochastic model for drought occurrence. Stoch Environ Res Risk
804
Assess 30:1503-1514 doi:10.1007/s00477-015-1129-y
35
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
805
Sirangelo B, Caloiero T, Coscarelli R, Ferrari E (2015) A stochastic model for the analysis of
806
the temporal change of dry spells. Stoch Environ Res Risk Assess 29:143-155
807
doi:10.1007/s00477-014-0904-5
808
Sirangelo B, Caloiero T, Coscarelli R, Ferrari E (2017) Stochastic analysis of long dry spells
809
in Calabria (Southern Italy). Theor Appl Climatol 127:711-724 doi:10.1007/s00704-
810
015-1662-0
811
Taha MAQ (2014) Selecting the best models in calculating the amount of rainfall in Sinjar and
812
Mosul stations. Journal of University of Babylon for Pure and Applied Sciences
813
22:2015-2022
814
Tatano H, Okada N, Kawai H (1992) Optimal operation model of a single reservoir with
815
drought
816
doi:10.1007/BF01591334
817
818
819
820
duration
explicitly
concerned.
Stoch
Hydrol
Hydraul
6:123-134
Tong Z, Liu A (2021) A censored Ornstein-Uhlenbeck process for rainfall modeling and
derivatives pricing. Physica A 566:125619 doi:10.1016/j.physa.2020.125619
Turvey CG (2001) Weather derivatives for specific event risks in agriculture. Rev Agric Econ
23:333-351
821
Unami K, Abagale F, Yangyuoru M, Alam A, Kranjac-Berisavljevic G (2010) A stochastic
822
differential equation model for assessing drought and flood risks. Stoch Environ Res
823
Risk Assess 24:725-733 doi:10.1007/s00477-009-0359-2
824
Unami K, Mohawesh O (2018) A unique value function for an optimal control problem of
825
irrigation water intake from a reservoir harvesting flash floods. Stoch Environ Res Risk
826
Assess 32:3169-3182 doi:10.1007/s00477-018-1527-z
827
Unami K, Mohawesh O, Fadhil RM (2019) Time periodic optimal policy for operation of a
828
water storage tank using the dynamic programming approach. Appl Math Comput
829
353:418-431 doi:10.1016/j.amc.2019.02.005
36
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
830
Vicente-Serrano SM, Beguería-Portugués S (2003) Estimating extreme dry‐spell risk in the
831
middle Ebro valley (northeastern Spain): a comparative analysis of partial duration
832
series with a general Pareto distribution and annual maxima series with a Gumbel
833
distribution. International Journal of Climatology: A Journal of the Royal
834
Meteorological Society 23:1103-1118 doi:10.1002/joc.934
835
Wilby RL, Prudhomme C, Parry S, Muchan K (2015) Persistence of hydrometeorological
836
droughts in the United Kingdom: A regional analysis of multi-season rainfall and river
837
flow anomalies. J Extreme Events 2:1550006 doi:10.1142/S2345737615500062
838
Williams D (1991) Probability with Martingales. Cambridge Mathematical Textbooks.
839
840
841
Cambridge University Press, Cambridge, UK
World Meteorological Organization (2012) Standardized Precipitation Index User Guide vol
WMO-No.1090. WMO, Geneva
842
Yadeta D, Kebede A, Tessema N (2020) Climate change posed agricultural drought and
843
potential of rainy season for effective agricultural water management, Kesem sub-
844
basin, Awash Basin, Ethiopia. Theor Appl Climatol 140:653-666 doi:10.1007/s00704-
845
020-03113-7
846
Yang LC, Franzke CLE, Fu ZT (2020) Power-law behaviour of hourly precipitation intensity
847
and dry spell duration over the United States. Int J Climatol 40:2429-2444
848
doi:10.1002/joc.6343
849
Zakaria S, Al-Ansari N, Knutsson S (2013) Historical and future climatic change scenarios for
850
temperature and rainfall for Iraq. Journal of Civil Engineering and Architecture 7:1574-
851
1594
852
37
Fig. 1
Click here to access/download;colour figure;Fig1-NINEVEHmap.eps
37N
fold mountains
Mo
Rabea
sul
Da
Ri
ve
Nineveh Plains
Za
Telafer
Sinjar
re
at
Mosul
Tigris R
36N
iver
41E
1000
2000
3000
Elevation above sea level (m)
42E
10
20
Distance (km)
30
43E
44E
Fig. 2
Click here to access/download;colour figure;Fig2-RainChart.eps
700
600
Accumulated rainfall depth (mm)
500
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1990
1991
1992
1994
1995
1996
1997
1998
1999
2000
2001
2002
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2018
400
300
200
100
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 3
Click here to access/download;colour figure;Fig3-FlowChart.eps
Time series data of daily rainfall
Multi-state Markov chain: definition by (9)
Positive rainfall depths
Transition probabilities for DSL
Extraction of sub-periods by K-S tests
Empirical estimation
Parameters of gamma distributions
Regularization by MTVF
Drought risk assessment
Hazard futures
E[S ], SD[S]
E[L], SD[L]
λt
Measures of risk aversion
Crop management
Supplementary irrigation
Fig. 4
Click here to access/download;colour figure;Fig4-alphaL.eps
1974-1989
1975-1990
1976-1991
1977-1992
1978-1993
1979-1994
1980-1995
1981-1996
1982-1997
1983-1998
1984-1999
1985-2000
1986-2001
1987-2002
1988-2003
1989-2004
1990-2005
1991-2006
1992-2007
1993-2008
1994-2009
1995-2010
1996-2011
1997-2012
1998-2013
1999-2014
2000-2015
2001-2016
2002-2017
2003-2018
2004-2019
α L -value
0.0
0.5
1.0
2004-2019
2003-2018
2002-2017
2001-2016
2000-2015
1999-2014
1998-2013
1997-2012
1996-2011
1995-2010
1994-2009
1993-2008
1992-2007
1991-2006
1990-2005
1989-2004
gamma
2009-2019
2008-2018
2007-2017
2006-2016
2005-2015
2004-2014
2003-2013
2002-2012
2001-2011
2000-2010
1999-2009
1998-2008
1997-2007
1996-2006
1995-2005
1994-2004
1993-2003
1992-2002
1991-2001
1990-2000
1989-1999
1988-1998
1987-1997
1986-1996
1985-1995
1984-1994
gamma
1974-1984
1975-1985
1976-1986
1977-1987
1978-1988
1979-1989
1980-1990
1981-1991
1982-1992
1983-1993
1984-1994
1985-1995
1986-1996
1987-1997
1988-1998
1989-1999
1990-2000
1991-2001
1992-2002
1993-2003
1994-2004
1995-2005
1996-2006
1997-2007
1998-2008
1999-2009
2000-2010
2001-2011
2002-2012
2003-2013
2004-2014
2005-2015
2006-2016
2007-2017
2008-2018
2009-2019
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Fig. 5
Click here to access/download;colour figure;Fig5-mosulCDF.eps
CDF
1.0
0.8
0.6
0.4
: 1977-1992, gamma
: 1977-1992, empirical
: 2004-2019, gamma
: 2004-2019, empirical
0.2
0.0
20
40
60
r (mm)
80
100
Fig. 6
Click here to access/download;colour figure;Fig6-empP5mm7792.eps
200
Pi0
1.0
State x (DSL up to t ) (day)
150
0.8
0.6
100
0.4
0.2
50
0.0
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 7
Click here to access/download;colour figure;Fig7-regP5mm7792.eps
200
Pi0
1.0
State x (DSL up to t ) (day)
150
0.8
0.6
100
0.4
0.2
50
0.0
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 8
Click here to access/download;colour figure;Fig8-regP5mm0419.eps
200
Pi0
1.0
State x (DSL up to t ) (day)
150
0.8
0.6
100
0.4
0.2
50
0.0
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 9
Click here to access/download;colour figure;Fig9-E[L]7792.eps
200
E[L] (day)
400
State x (DSL up to t ) (day)
150
300
100
200
100
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 10
Click here to access/download;colour figure;Fig10-SD[L]7792.eps
200
SD[L] (day)
160
State x (DSL up to t ) (day)
150
120
100
80
40
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 11
Click here to access/download;colour figure;Fig11-E[L]0419.eps
200
E[L] (day)
400
State x (DSL up to t ) (day)
150
300
100
200
100
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 12
Click here to access/download;colour figure;Fig12-SD[L]0419.eps
200
SD[L] (day)
160
State x (DSL up to t ) (day)
150
120
100
80
40
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 13
Click here to access/download;colour figure;Fig13-lambda.eps
State x (DSL up to t ) (day)
50
Sub-period 1977-1992
40
Sub-period 2004-2019
30
20
10
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 14
Click here to access/download;colour figure;Fig14-E[S]7792.eps
200
E[S ] (mm)
400
State x (DSL up to t ) (day)
150
300
100
200
100
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 15
Click here to access/download;colour figure;Fig15-SD[S]7792.eps
200
SD[S] (mm)
160
State x (DSL up to t ) (day)
150
120
100
80
40
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 16
Click here to access/download;colour figure;Fig16-E[S]0419.eps
200
E[S ] (mm)
400
State x (DSL up to t ) (day)
150
300
100
200
100
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
Fig. 17
Click here to access/download;colour figure;Fig17-SD[S]0419.eps
200
SD[S] (mm)
160
State x (DSL up to t ) (day)
150
120
100
80
40
50
JAN
FEB
MAR
APR
MAY
JUN
Time t
JUL
AUG
SEP
OCT
NOV
DEC
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Table 1
Click here to access/download;table;NinevehSERR_Table1.docx
Table 1: Basic statistics of the data sets for the whole period and for the disjoint sub-periods including the selected ones.
Water years
Total number of
days in data set
Number of
observation days
Number of wet
days
Number of dry
days
Mean of positive
rainfall depths
(mm)
Unbiassed
sample variance
of positive
rainfall depths
(mm2)
1974-2019
(The whole period)
16071
15191
2988 (19.7 %)
12203 (80.3 %)
4.809
66.03
1974-1977
943
943
221 (23.4 %)
722 (76.6 %)
4.335
45.41
1977-1992
(The Selected sub-period)
5479
4901
974 (19.9 %)
3927 (80.1 %)
5.018
68.08
1992-2004
4383
4234
827 (19.5 %)
3407 (80.5 %)
4.980
70.73
2004-2019
(The Selected sub-period)
5266
5113
966 (18.9 %)
4147 (81.1 %)
4.560
64.67
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Table 2
Click here to access/download;table;NinevehSERR_Table2.docx
Table 2: The depths and numbers of irrigation (Depth (mm) : Number (times)) in a water year for major annual crops in Nineveh Governorate, adapted
from Hajim et al. (1996).
Crop
Date of
planting
Date of
harvesting
AUG
SEP
OCT
NOV
DEC
JAN
FEB
MAR
APR
MAY
JUN
JUL
Total
Wheat
Early NOV
Mid-MAY
60 : 1
40 : 2
0:0
0:0
0:0
0:0
20 : 1
40 : 1
200
Barley
Early NOV
Early MAY
60 : 1
40 : 2
0:0
0:0
0:0
0:0
0:0
0:0
140
Clover
Early OCT
Late MAY
40 : 1
40 : 3
20 : 1
0:0
0:0
0:0
0:0
20 : 1
40 : 3
340
Flax
Early NOV
Late MAY
60 : 1
60 : 1
0:0
0:0
0:0
0:0
30 : 1
60 : 1
210
Sugar beet
Mid-OCT
Mid-JUN
60 : 1
40 : 2
20 : 1
0:0
0:0
0:0
0:0
40 : 1
60 : 3
60 : 2
500
Potato
Mid-AUG
Mid-DEC
30 : 5
30 : 5
30 : 4
30 : 2
0:0
480
Onion
SEP
MAY
20 : 2
20 : 2
20 : 1
0:0
0:0
0:0
0:0
20 : 2
20 : 3
200
Cabbage
SEP
MAY
25 : 1
25 : 6
25 : 3
25 : 1
0:0
0:0
0:0
0:0
0:0
0:0
275
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Table 3
Click here to access/download;table;NinevehSERR_Table3.docx
Table 3: Statistical analysis of each dry season during the sub-period 1977-1992, assuming the Gumbel distribution for the length L.
Year
Onset
E[L]
SD[L]
βGum
μGum =
Mode
Observed L
CDF
Return
period
Significance
level
1978
March 15th
150.027
123.183
96.045
94.588
262
0.839
6.229
0.481
1979
March 25th
163.262
114.272
89.098
111.834
217
0.736
3.781
0.652
1980
April 29th
193.766
55.999
43.663
168.563
193
0.565
2.297
0.907
1981
April 29th
193.766
55.999
43.663
168.563
165
0.338
1.510
0.773
1982
May 7th
186.542
53.645
41.827
162.399
146
0.228
1.295
0.590
1983
May 15th
191.327
12.614
9.835
185.650
182
0.235
1.307
0.602
1984
May 10th
183.770
52.874
41.226
159.974
160
0.368
1.583
0.819
1985
April 25th
196.654
58.331
45.480
170.402
200
0.594
2.460
0.873
1986
May 1st
191.918
55.481
43.258
166.949
153
0.251
1.336
0.630
1987
March 28th
171.751
108.753
84.794
122.806
205
0.684
3.168
0.737
1989
July 2nd
139.314
13.514
10.537
133.232
131
0.291
1.410
0.695
1990
April 12th
199.231
73.914
57.630
165.966
211
0.633
2.723
0.818
1991
April 11th
201.770
74.269
57.907
168.345
207
0.599
2.492
0.866
1992
May 11th
182.847
52.616
41.025
159.167
179
0.540
2.173
0.933
No data was available to determine the dry season in the year 1988 due to the Iran-Iraq War
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
Table 4
Click here to access/download;table;NinevehSERR_Table4.docx
Table 4: Statistical analysis of each dry season during the sub-period 2004-2019, assuming the Gumbel distribution for the length L.
Year
Onset
E[L]
SD[L]
βGum
μGum =
Mode
Observed L
CDF
Return
period
Significance
level
2004
April 20th
202.671
86.211
67.219
163.872
198
0.548
2.211
0.925
2005
May 3rd
192.300
80.997
63.153
155.847
202
0.618
2.617
0.840
2006
April 27th
196.861
83.616
65.195
159.229
181
0.489
1.956
0.956
2007
May 16th
212.723
14.753
11.503
206.083
258
0.989
91.735
0.282
2008
March 14th
225.342
106.851
83.311
177.253
224
0.565
2.300
0.907
2009
April 18th
204.357
86.930
67.779
165.233
194
0.520
2.083
0.950
2010
May 4th
198.859
72.803
56.764
166.094
221
0.684
3.162
0.738
2011
April 23rd
200.190
85.092
66.346
161.894
207
0.602
2.516
0.861
2012
March 29th
221.844
93.526
72.922
179.752
207
0.502
2.010
0.962
2013
May 28th
178.835
15.172
11.830
172.007
164
0.140
1.163
0.450
2014
April 18th
204.357
86.930
67.779
165.233
181
0.453
1.827
0.926
2015
May 11th
198.239
63.570
49.565
169.630
118
0.059
1.062
0.338
2017
April 15th
206.877
88.014
68.624
167.266
207
0.571
2.331
0.900
2018
May 13th
215.723
14.752
11.502
209.084
161
0.000
1.000
0.270
No data was available to determine the dry season in the year 2016 due to the Iraqi Civil War
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