Effects of Oceanic and Atmospheric Phenomena on Precipitation
and Flooding in the Manafwa River Basin
by
MASSACHUSETS INSThfrTE
OF TECHNOLOGY
William W. Finney III
JUN 13 20M
B.S. Civil Engineering
The Pennsylvania State University, 2013
IBRARIES
SUBMITTED TO THE DEPARTMENT OF CIVIL AND ENVIRONMENTAL
ENGINEERING IN PARTIAL
FULTFILLMENT OF THE REQUIRMENTS FOR THE DEGREE OF
MASTER OF ENGINEERING IN CIVIL AND ENVIRONMENTAL ENINGEERING
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2014
C2014 William W. Finney III. All rights reserved.
The author hereby grants to MIT permission to reproduce and distribute publicly paper and
electronic copies of this thesis document in whole or in part in any medium now known or
hereafter created.
Signature of Author:
Signature redacted
-X
1YWilliam
4
W. Finney III
Department of Civil and Environmental Engineering
May 12, 2014
Certified by:
Signature redacted
Senior Lecturer of Civil
Accepted by:
Richard Schuhmann, Ph.D.
d Environmental Engineering
The Supervisof
Signature redacted
Heidi M. Nep
Chair, Departmental Committee for Graduate Students
Effects of Oceanic and Atmospheric Phenomena on Precipitation
and Flooding in the Manafwa River Basin
by
William W. Finney III
Submitted to the Department of Civil and Environmental Engineering
on May 12, 2014 in Partial Fulfillment of the
Requirements for the Degree of Master of Engineering in
Civil and Environmental Engineering
ABSTRACT
An investigation was performed to determine the relationship between certain oceanic and
atmospheric phenomena and the precipitation patterns in the Manafwa River Basin of eastern
Uganda. Such phenomena are the El Niino Southern Oscillation (ENSO) and the Indian Ocean
Dipole (IOD). The indices that describe ENSO and IOD are measurements of sea surface
pressure and sea surface temperature conditions of the Pacific and Indian Oceans. This study
compares the historical precipitation of the Manafwa River Basin with the corresponding values
for the Southern Oscillation Index (SOI), Oceanic Ninio Index (ONI), and Dipole Mode Index
(DMI). This investigation shows a correlation between the magnitude of such indices, and the
total monthly and seasonal precipitation and the occurrence of heavy precipitation events. The
strongest signal detected was a positive correlation between precipitation from December to
February and the Oceanic Nifio Index (ONI) of preceding seasons. A delay in precipitation
response was observed from the measured climate indices. This precipitation delay indicates that
current index conditions can be monitored to assess the nature of future precipitation. Additional
correlations between precipitation and climate indices were identified for many of the remaining
months.
The characteristics of precipitation in the Manafwa River Basin during consecutive seasons of
ENSO phases were also explored. It was discovered that, on average, consecutive El Niiio
seasons are associated with a trend of increasing amplitude of seasonal precipitation.
Conversely, consecutive La Niia seasons are associated with a trend of decreasing amplitude of
seasonal precipitation. The investigation was expanded to Houston, Texas and the Bou Regreg
Watershed in northern Morocco to inquire if a similar response could be detected in other areas
of the world. The results of this expanded study generally support the proposition that, on
average, consecutive ENSO seasons are associated with trends of precipitation amplification or
reduction. Although individual ENSO events do not always behave with the same precipitation
patterns shown by the averages, there is value in understanding the average long-term seasonal
effects of El Niio and La Nina events on watersheds. Applications of this study include, but are
not limited to: reservoir and water management, flood prediction, and agricultural planning.
Thesis Supervisor: Richard Schuhmann, Ph.D.
Title: Senior Lecturer of Civil and Environmental Engineering
Acknowledgements
I would like to express my sincere appreciation to all those who offered guidance and
encouragement throughout the writing of this thesis. I am extremely grateful for the opportunity
to have pursued a Master of Engineering at MIT while surrounded by such amazing individuals.
Although there are many people who provided support, I would like to extend my gratitude to
the following:
To my advisor, Dr. Rick Schuhmann, for his guidance and advice over the last five years at MIT
and Penn State. He constantly challenges me to think critically and motivates me to produce the
best work I am capable of. I appreciate all that he has done for me as a teacher, mentor, advisor,
and friend.
To my teammates, Joel Kaatz and Joyce Cheung, for their friendship and support throughout this
year at MIT. We have certainly shared crazy adventures both in Cambridge and Uganda.
To my new friends in Uganda: Julie, Tumwa, Geoffrey, Nasa, and Frank. Their companionship
and support made for a rewarding, productive, and memorable trip to Uganda.
To my fellow Course 1 M.Eng. classmates, for making this a fun and unforgettable year.
To all of the M.Eng. and MIT professors that have offered their assistance and expertise in any
way possible.
And finally, to my mother, Sue; my father, Bill; my sisters, Kristen and Juli; and my entire
family. The guidance they have provided throughout this process has been unmatched. I am
extremely grateful for their continuous encouragement and support.
5
6
1
Table of Contents
Acknowledgements.........................................................................................................................
5
L ist o f F igures ...............................................................................................................................
11
L ist o f T ab les ................................................................................................................................
13
1
15
Intro ductio n ...........................................................................................................................
1.1
Purpose of Study ...................................................................................................
15
1.2
Study Area: The Manafwa River Basin ...............................................................
15
1.3
Community Impact of Flooding...........................................................................
18
1.4
Regional Climate Description...............................................................................
19
2
Observed Precipitation Patterns in the M anafwa River Basin ..........................................
23
2.1
Precipitation Data Used for this Study..................................................................
23
2.2
Historical Precipitation Conditions ......................................................................
25
3
2.2.1
Annual Precipitation ............................................................................................
25
2.2.2
Monthly and Seasonal Total Precipitation...........................................................
25
2.2.3
Heavy Precipitation Events.................................................................................
28
2.2.4
Precipitation by Geographical Location ............................................................
33
Oceanic and Atmospheric Phenomena...............................................................................
36
3.1
Teleconnections in Equatorial East Africa and Uganda.......................................
36
3.2
The El Nino Southern Oscillation ........................................................................
36
3.2.1
Indices for Measuring ENSO...............................................................................
39
3.2.2
The Effects of ENSO in Equatorial East Africa .................................................
41
3.3
The Indian Ocean Dipole .....................................................................................
41
3.3.1
The Index for Measuring IOD ............................................................................
42
3.3.2
The Effects of IOD in Equatorial East Africa....................................................
43
The Combined Effect of ENSO and IOD.............................................................
3.4
43
44
W eather in the Manafwa River Basin and Climate Indices................................................
4
M eth o d .....................................................................................................................
4 .1
44
4.1.1
Normalization of Precipitation Data ....................................................................
44
4.1.2
Organization of Heavy Precipitation Events......................................................
45
4.1.3
Determining the General Relationship to ENSO and IOD .................................
46
7
4.1.4
Comparing Precipitation to the Strength of Indices.............................................
46
4.1.5
Classifying the Strength of Observed Trends .....................................................
47
4.2
Observed Effect of ENSO in the Manafwa River Basin......................................
4.2.1
General Relationship Between ENSO and Precipitation .....................................
48
4.2.2
Monthly Precipitation Compared with SOI Strength...........................................
50
4.2.3
Seasonal Precipitation Compared with ONI Strength .........................................
59
4.3
Observed Effect of IOD in the Manafwa River Basin ..........................................
66
4.3.1
General Relationship Between IOD and Precipitation ........................................
66
4.3.2
Monthly Precipitation Compared with DMI Strength ........................................
68
4.4
5
48
Relationship Between Past Flood Events and Climate Indices.............................
78
Precipitation Trends for Consecutive ENSO Seasons........................................................
79
5 .1
M eth o d .....................................................................................................................
79
5.2
Classifying Precipitation Characteristics of El Ninio Phases.................................
79
5.2.1
Oceanic Nifio Index of Consecutive El Ninio Seasons........................................
80
5.2.2
Normalized Seasonal Precipitation of Consecutive El Ninio Seasons .................
81
5.2.3
Normalized Monthly Precipitation of Consecutive El Nifio Months................... 83
5.2.4
Heavy Precipitation Events of Consecutive El Nifio Seasons ............................
84
5.2.5
Precipitation Variability of Consecutive El Nifio Seasons .................................
84
5.2.6
Behavior of Consecutive Seasons in Individual El Nifno Events ........................
85
5.3
87
5.3.1
Oceanic Nifio Index of Consecutive La Nifia Seasons .......................................
88
5.3.2
Normalized Seasonal Precipitation of Consecutive La Nifia Seasons .................
88
5.3.3
Normalized Monthly Precipitation of Consecutive La Ninia Months .................
91
5.3.4
Heavy Precipitation Events of Consecutive La Ninia Seasons.............................
92
5.3.5
Precipitation Variability of Consecutive La Ninia Seasons.................................
93
5.3.6
Behavior of Consecutive Seasons in Individual La Ninia Events ........................
93
5.4
6
Classifying Precipitation Characteristics of La Nilia Phases ................................
Analysis of Results from Investigation of Consecutive ENSO Seasons............... 96
Exploring the Effect of ENSO in Additional Locations...................................................
98
6 .1
M eth o d .....................................................................................................................
6.2
Bou Regreg Watershed, Morocco - Precipitation Response to ENSO.................. 100
6.2.1
Investigation of Consecutive El Ninio Seasons .....................................................
8
98
101
6.2.2
6.3
7
8
Investigation of Consecutive La Niia Seasons.....................................................
Houston, Texas - Precipitation Response to ENSO ..............................................
104
107
6.3.1
Investigation of Consecutive El Niio Seasons .....................................................
107
6.3.2
Investigation of Consecutive La Niia Seasons.....................................................
111
Recommendations and Conclusion .....................................................................................
113
7.1
Value of Study for the Red Cross...........................................................................
113
7.2
Recommendations for Future Flood Prognostication Strategies............................
119
7.3
Continuing Analysis of Precipitation in Consecutive ENSO Seasons...................
120
12 1
Work s C ited .........................................................................................................................
12 7
A pp endices..................................................................................................................................
Appendix A - TRMM Precipitation Data Extraction Code ..............................................
127
Appendix B - Monthly and Seasonal Precipitation ..........................................................
130
Appendix C - Heavy Precipitation Events........................................................................
131
Appendix D - Example Equations ....................................................................................
132
Appendix E - Normalized Precipitation Values ...............................................................
133
Appendix F - Climate Index Values .................................................................................
134
Appendix G - Months with No Correlation between Precipitation and SOI.................... 136
Appendix H - Seasons with No Correlation between Precipitation and ONI .................. 138
Appendix I - Months with No Correlation between Precipitation and DMI.................... 139
Appendix J - Historical Flood Events...............................................................................
9
141
10
List of Figures
Figure 1-1: The Manafwa River Basin in Eastern Uganda........................................................
Figure 1-2: Elevation Changes of the Manafwa River Basin ...................................................
Figure 1-3: Tributaries of the Manafwa River and Sub-basins that Contribute to Flooding........
Figure 1-4: Typical Homes of the Butaleja District..................................................................
Figure 1-5: The ITCZ off the Western Coast of Central and South America...........................
Figure 1-6: Tim e-Series Movement of the ITCZ......................................................................
Figure 2-1: The Six TRMM Grid Cells of the Regional Watershed....................
Figure 2-2: The Surrounding Region Encompassing the Regional Watershed ........................
Figure 2-3: Total Annual Precipitation of the Regional Watershed ..........................................
Figure 2-4: Average Monthly Precipitation of the Regional Watershed ...................................
Figure 2-5: Average Seasonal Precipitation of the Regional Watershed..................................
Figure 2-6: Total January Precipitation of the Regional Watershed.........................................
Figure 2-7: Total September Precipitation of the Regional Watershed ....................................
Figure 2-8: Number of Heavy Precipitation Events Occurring Annually ................................
Figure 2-9: Number of Heavy Precipitation Events Occurring in January................................
Figure 2-10: Maximum One-Day Precipitation Event Each Year................................................
Figure 2-11: Maximum One-Day Precipitation Event for Each Month ..................
Figure 2-12: Average Daily Precipitation of the Surrounding Region......................................
Figure 2-13: Surrounding Region with Average Daily Precipitation Circles...........................
Figure 2-14: Precipitation Elevation Comparison, West to East at 1.125' N...........................
Figure 3-1: Oceanic and Atmospheric Deviations of ENSO ...................................................
Figure 3-2: Example Locations with Teleconnections to ENSO...............................................
Figure 3-3: Time-Series of Standardized SOI Values ..............................................................
Figure 3-4: Tim e-Series of ON I V alues....................................................................................
Figure 3-5: Sea Surface Temperature and Precipitation Response to IOD ...............................
Figure 4-1: All M onths - NM P vs. SOI (No Delay)..................................................................
Figure 4-2: All Months - Heavy Precipitation vs. SOI (No Delay) ..........................................
Figure 4-3: February NMP vs. October SOI (4-Month Delay) .................................................
Figure 4-4: May NMP vs. February SOI (3-Month Delay)......................................................
Figure 4-5: December NMP vs. October SOI (2-Month Delay) ..............................................
Figure 4-6: August Heavy Precipitation vs. April SOI (4-Month Delay)..................................
Figure 4-7: February Heavy Precipitation vs. October SOI (4-Month Delay) ..........................
Figure 4-8: December Heavy Precipitation vs. October SOI (2-Month Delay) ........................
Figure 4-9: All Seasons - NSP vs. ONI (No Delay).................................................................
Figure 4-10: All Seasons - NSP vs. ONI (No Delay) - By ENSO Phase .................................
Figure 4-11: All Seasons - Heavy Precipitation vs. ONI (No Delay)......................................
Figure 4-12: DJF NSP vs. ASO ONI (4-Season Delay)............................................................
Figure 4-13: DJF NSP vs. SON ONI (3-Season Delay) ............................................................
11
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Figure 4-14: DJF NSP vs. OND ONI (2-Season Delay) ..........................................................
Figure 4-15: DJF Heavy Precipitation vs. SON ONI (3-Season Delay)....................................
Figure 4-16: JJA Heavy Precipitation vs. AMJ ONI (2-Season Delay) ...................................
Figure 4-17: All Months - NMP vs. DMI (No Delay) ..............................................................
Figure 4-18: All Months - Heavy Precipitation vs. DMI (No Delay).....................................
Figure 4-19: January NMP vs. December DMI (1-Month Delay).............................................
Figure 4-20: May NMP vs. March DMI (2-Month Delay)........................................................
Figure 4-21: July NMP vs. May DMI (2-Month Delay) ..........................................................
Figure 4-22: November NMP vs. October DMI (1-Month Delay)..........................................
Figure 4-23: January Heavy Precipitation vs. December DMI (1 -Month Delay) ....................
Figure 4-24: May Heavy Precipitation vs. March DMI (2-Month Delay) ................................
Figure 4-25: November Heavy Precipitation vs. October DMI (1-Month Delay) ...................
Figure 5-1: Strength of ONI for Consecutive El Niho Seasons.................................................
Figure 5-2: Average NSP for Consecutive El Nifno Seasons...................................................
Figure 5-3: Average NMP for Consecutive El Niiio Months ...................................................
Figure 5-4: Heavy Precipitation for Consecutive El Nino Seasons..........................................
Figure 5-5: Standard Deviation of NSP for Consecutive El Nifio Seasons ...............................
Figure 5-6: ONI of Consecutive Seasons in Each El Niiio Event.............................................
Figure 5-7: NSP of Consecutive Seasons in Each El Niiio Event ............................................
Figure 5-8: Strength of ONI for Consecutive La Ninia Seasons ..............................................
Figure 5-9: Average NSP for Consecutive La Nifia Seasons ...................................................
Figure 5-10: Average NMP for Consecutive La Ninia Months.................................................
Figure 5-11: Heavy Precipitation for Consecutive La Nifia Seasons .......................................
Figure 5-12: Standard Deviation of NSP for Consecutive La Niia Seasons.............................
Figure 5-13: ONI of Consecutive Seasons in Each La Nifia Event ..........................................
Figure 5-14: NSP of Consecutive Seasons in Each La Ninia Event..........................................
Figure 5-15: Average NSP for Consecutive La Nifia Seasons - Events 1, 3, 5, & 6 ................
Figure 6-1: Average Monthly Precipitation in the Bou Regreg Watershed - Meknes...............
Figure 6-2: Average NSP for 10 Consecutive El Nifio Seasons - Meknes ................................
Figure 6-3: Average NSP for 15 Consecutive El Niio Seasons - Meknes ................................
Figure 6-4: NSP of Consecutive Seasons in Each El Nifio Event - Meknes..............................
Figure 6-5: Average NSP for 10 Consecutive La Nifia Seasons - Meknes................................
Figure 6-6: Average NSP for Consecutive La Nifia Seasons (5-10) - Meknes..........................
Figure 6-7: NSP of Consecutive Seasons in Each La Ninia Event - Meknes .............................
Figure 6-8: Average NSP for 7 Consecutive El Ninio Seasons - Houston .................................
Figure 6-9: Average NSP for 16 Consecutive El Ninio Seasons - Houston ...............................
Figure 6-10: NSP of Consecutive Seasons in Each El Ninio Event - Houston...........................
Figure 6-11: Average NSP for 5 Consecutive La Nifia Seasons - Houston...............................
Figure 6-12: Average NSP for 19 Consecutive La Nifia Seasons - Houston.............................
12
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List of Tables
Table 1: Classification of a Heavy Precipitation Event by Season...........................................
Table 2: Classification of Linear Correlation Strength used in this Study ...............................
Table 3: General Precipitation Characteristics of ENSO Phases.............................................
Table 4: Months with a Correlation between Total Precipitation and SOI................................
Table 5: Months with a Correlation between Heavy Precipitation and 50I.............................
Table 6: Seasons with a Correlation between Total Precipitation and ONI .............................
Table 7: Seasons with a Correlation between Heavy Precipitation and ONI ............................
Table 8: General Precipitation Characteristics of IOD Modes .................................................
Table 9: Months with a Correlation between Total Precipitation and DMI .............................
Table 10: Months with a Correlation between Heavy Precipitation and DMI .........................
Table 11: Normalized Seasonal Precipitation (NSP) Values of El Niio Events......................
Table 12: Observed Trend Line Parameters for Individual El Niino Events.............................
Table 13: Normalized Seasonal Precipitation (NSP) Values of La Nina Events ......................
Table 14: Observed Trend Line Parameters for Individual La Nifia Events ............................
Table 15: List of all ENSO Events used in this Study (1950-2013)........................................
Table 16: ENSO Events Not Included in Study because of Data Deficiencies ..........................
Table 17: Observed Trend Line Parameters for Individual El Nifio Events - Meknes ..............
Table 18: Observed Trend Line Parameters for Individual La Nifia Events - Meknes..............
Table 19: Observed Trend Line Parameters for Individual El Nuio Events - Houston .............
Table 20: Sample Guidance for Relating Current SOI Conditions to Future Precipitation........
Table 21: Sample Guidance for Relating Current DMI Conditions to Future Precipitation ......
Table 22: Sample Guidance for Relating Current ONI Conditions to Future Precipitation.......
Table 23: Sample Guidance for Actions Taken in Consecutive El Niio Seasons......................
13
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1
1.1
Introduction
Purpose of Study
The Manafwa River Basin, located in eastern Uganda, is often afflicted by several floods
each year causing hardship for the tens of thousands of residents who live in the flooded
downstream districts. The American Red Cross (ARC), in collaboration with the Uganda Red
Cross Society (URCS), has requested that Massachusetts Institute of Technology (MIT) provide
technical support for the development of a flood early warning system. The Department of Civil
and Environmental Engineering at MIT aims to assist in the creation of two distinct flood
warning methods: a short-term warning system, and the identification of a long-term flood
indicator. The short-term warning system will monitor upstream river stage and 'trigger' an
alarm hours before imminent flooding. Given hours of advanced warning, the Red Cross will
have supplementary time for the preparation and mobilization of disaster relief. The foundation
of the short-term warning system is a combination of hydrologic and hydraulic models
developed by Kaatz (2014) and Cheung (2014). The function of a long-term flood indicator is to
communicate a possible heightened risk of flooding in a future month or season. The long-term
flood information strategy can be used by the Red Cross to better organize disaster relief through
the stocking of necessary supplies in local offices. The search for a long-term flood indicator
involves the examination of historical precipitation patterns to identify if significant trends exist.
The investigation also involves the comparison of local weather conditions with oceanic and
atmospheric phenomena: the El Niino Southern Oscillation (ENSO), and the Indian Ocean Dipole
(IOD).
This study focuses on historical precipitation trends, the relationship between
precipitation and global climate indices, and the development of a long-term flood information
strategy for the Manafwa River Basin.
1.2
Study Area: The Manafwa River Basin
The boundary of the Manafwa River Basin was defined by Cecinati (2013), Bingwa
(2013), and Ma (2013) with the use of a 30-meter resolution digital elevation map (DEM). The
western most point of the basin was selected as the "pour point" and the boundary was delineated
with respect to the topography of the territory. The basin covers an area of 2,280 square
kilometers (km) and falls mainly within the districts of Bududa, Mbale, Manafwa, Budaka,
Butaleja, and Tororo (Cecinati, 2013). Minor sections of the basin also fall within the districts of
Sironko, Kibuku, and Pallisa. The basin is located entirely within Uganda; however, sections of
the basin are within 1 km of the Uganda-Kenya border. The distance from the most eastern point
to the most western point of the basin is 84 km. The only urban area in the basin is the city of
Mbale, which is estimated to have a population of 91,800 residents (City Population, 2011).
15
Mbale is not positioned in the flood zone of concern.
Manafwa River Basin in eastern Uganda.
Figure 1-1 shows the outline of the
Figure 1-1: The Manafwa River Basin in Eastern Uganda
To the east of the basin is Mt. Elgon, a large extinct shield volcano that rises to an
elevation of over 4,000 meters above sea level (ASL). On the western side of the basin the
terrain transforms into level plains that are positioned at an elevation of approximately 1,000
meters ASL. The topography of the basin makes it prone to landslides in the upstream
mountains and floods in the downstream plains. As precipitation falls in the upstream
mountainous districts of Manafwa, Bududa, and Mbale runoff gathers into the Manafwa River
and flows downhill (west) towards the plains of Butaleja. Over time agricultural land has moved
closer to the river resulting in deforestation and removal of natural vegetation, which has caused
deterioration of the natural strength of river banks (URCS, n.d.). The shallow and weakened
banks of the Manafwa River cannot contain the increased river stage and flow rate, which occur
following precipitation events in the upstream districts. This results in overbank flow and
chronic flashfloods.
The downstream topography is not only flat, but is also swampy. This landscape has
made the downstream districts an ideal setting for large rice growing schemes that support the
social and economic activities of the region (URCS, n.d.). When floods occur, the already
saturated soil prevents the flood waters from infiltrating into the ground resulting in floods of
increased duration. Figure 1-2 shows the elevation changes of the basin and the surrounding
area. Mt. Elgon is represented by the red coloring on the eastern half of the image.
16
Max
= 4296 m ASL
Min
=938 m ASL
I-A
laawl
Figure 1-2: Elevation Changes of the Manafwa River Basin
Water flows from points of higher elevation to lower elevation, thus, the general flow of
water in the Manafwa River Basin is from east to west. Theoretically, all precipitation that
enters the watershed and transforms into surface water exits the watershed through the "pour
point" in the far west. The Manafwa River Basin was divided into multiple sub-basins based on
its topography, which dictates how runoff flows over land. Bingwa originally defined the
watershed as containing eleven sub-basins (Bingwa, 2013). Kaatz redefined the region of
interest within the watershed, selected sub-basin w-150 from Bingwa's analysis and subdivided
it into six sub-basins that directly contribute to flooding in the redefined downstream area of
interest (Kaatz, 2014). Figure 1-3 shows the sub-basins defined by Bingwa and excluded from
the current analysis in white; the sub-basins that are the focus of the current analysis are shown
in color. The precipitation that falls over the excluded white sub-basins flows into tributaries,
which do not join the Manafwa River until after the current flood zone of interest.
Flood Zone
Figure
1-3: Tributaries of the Manafwa River and Sub-basins that Contribute to Flooding
17
The Manafwa River Basin encompasses an area large enough to permit the existence of
microclimates; therefore, various parts of the basin experience different weather conditions at the
same time. One area of the basin may experience severe rains while another area is
simultaneously experiencing clear skies. Flashfloods may occur because of rain events
upstream, not because of rain in the location of flooding. This causes the unexpected arrival of
floods, which have no logical or instinctual warning. Local residents of Butaleja recounted
instances of going to sleep during dry weather conditions and waking up because of water
flowing into their homes (CAO, 2014).
1.3 Community Impact of Flooding
Floods threaten food and economic security of the Butaleja region and cause risks to
human health. The sub counties in Butaleja District that are most affected by floods are
Mazimasa, Himutu, Kachonga, Butaleja Rural, and Butaleja Town Council. In 2010 the
combined residency of these five sub counties was estimated to be 38,780 people living in 7,756
households (URCS, 2010).
Flood waters enter homes and dampen the floors creating a breeding ground for bacteria.
Many local residents sleep on the ground and the damp floors increase the risk of contracting
illnesses such as pneumonia. The invasion of the flood waters also increases the risk of diseases
such as malaria and cholera. Latrines overflow and flood waters carry sewage to residential and
agricultural areas. The stagnant pools of water that form after flow stops become a breeding
ground for mosquitoes. Boreholes where many local residents draw their drinking water can
become contaminated with turbidity levels that are too high for safe human consumption
(BDLG, 2010).
The infrastructure in the region is often not constructed to withstand the force of flood
water. Houses and other buildings are customarily assembled with resources that are available
locally such as branches and mud bricks. Figure 1-4 shows a typical home in Butaleja District.
The flood waters cause cracks to develop in the walls of buildings compromising the structural
integrity and often leading to complete collapse (BDLG, 2010). Damage to homes causes the
displacement of families, which then rely on the government and humanitarian organizations for
shelter and other necessary services. The challenge of caring for these people is intensified by
the loss of additional community infrastructure such as roads, bridges, health centers and schools
(URCS, n.d.). The collapse of bridges causes transportation challenges for the local people and
for relief agencies trying to carry supplies into the area. The destruction of other community
hubs such as schools and health centers causes further difficulty in securing a place to gather and
begin recovery after the disaster (URCS, n.d.).
18
Figure 1-4: Typical Homes of the Butaleja District
Floods typically occur two times per year: either in the months of April to June, or
September to October (URCS, n.d.). The floods coincide with the two main crop growing
seasons of March to June, and August to November (BDLG, 2010). The onset of such floods is
particularly devastating for an area where 95% of the population depends on agriculture for their
livelihoods (URCS, 2010). In the past, flood waters have lingered for one and a half to three
weeks before receding. The strong force of the flowing water and prolonged duration of
standing water can cause complete destruction of any crops that were not harvested or properly
stored before a flood (BDLG, 2010).
When floods occur the communities affected often experience displacement from their
homes, loss of crops and livestock, destruction of infrastructure, and even face the risk of
fatalities. The Red Cross aims to build community resilience to such disasters through the
development of community action plans (URCS, n.d.), and a flood early warning system. The
objective of this project is to develop a flood early warning system that can be owned and
operated by the local communities, not just the Red Cross. Once operational, the short-term
warning system will notify the Red Cross and local communities of imminent flooding allowing
them to begin early evacuation of people and belongings, and expedite disaster response. The
identification of a long-term flood indicator may even help prevent the loss of crops by adjusting
cultivation and harvesting patterns to the expected precipitation conditions of future seasons.
1.4 Regional Climate Description
The nation of Uganda is located in equatorial East Africa. It is positioned mostly between
the latitudes of 1.50 S and 40 N with sections of the country on either side of the equator. The
typical rainfall patterns of Uganda are predominately driven by the movement of the InterTropical Convergence Zone (ITCZ). The ITCZ is a low-pressure region characterized by clouds
19
and precipitation that oscillates across the equator on a predictable schedule two times every year
(McSweeney, et al., 2008). This meteorological phenomenon is caused by the joining of the
northeasterly and southeasterly trade winds along the equator. At times the ITCZ may circle the
entire globe along the equator; at other times it exists in broken segments (Lu, 2013). Figure 1-5
is a satellite image provided by NASA showing the ITCZ as a belt of increased cloud cover off
the western coast of Central and South America.
Figure 1-5: The ITCZ off the Western Coast of Central and South America
Image Source: (NASA, 2000)
The movement of the ITCZ back and forth across the equator results in two distinct rainy
seasons in Uganda. The first rainy season occurs from March to May (MAM) and is commonly
referred to as the 'long rains.' The MAM rainy season is caused by the northern migration of the
ITCZ across the equator. The second rainy season occurs from September to November (SON)
and is commonly referred to as the 'short rains.' The SON rainy season is the result of the ITCZ
returning south across the equator (Camberlin & Philippon, 2001; Breytenbach, 2013). The SON
rainy season in Uganda typically experiences greater year-to-year variability than the MAM
rainy season (Behera, et al., 2005). Certain sources suggest that the 'short rains' in East Africa
last from October through December (Kizza, et al., 2012; McSweeney, et al., 2008). This study
will consider the 'short rains' in Uganda to occur during the SON season. Precipitation is also
experienced during the other six months of the year that do not fall within a rainy season;
however, total precipitation is highest during the MAM and SON seasons.
The location of the ITCZ at various times throughout the year is shown in Figure 1-6.
The images were generated using a time-series animation of rainfall depth on NASA's Earth
Observatory website. The rainfall data used for this animation are monthly derived satellite
precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) product 3B43.
The dense blue belt spanning horizontally across the globe represents the high levels of
precipitation caused by the ITCZ (NASA, 2013).
20
mm
Total Rainfall
1.0
10
100
Figure 1-6: Time-Series Movement of the ITCZ
Image Source: (NASA, 2013)
21
2000
As shown by Figure 1-6, in July 2002, the majority of the ITCZ is north of Uganda (top image).
In October 2002, while the ITCZ is on its migration south, Uganda is directly beneath of the
ITCZ (middle image). By January 2003, the ITCZ has traveled far enough south that the heavy
band of rainfall has passed through Uganda (bottom image).
Although the main precipitation patterns of Uganda are driven by the ITCZ, the weather
is affected by the interaction of multiple atmospheric and oceanic conditions. The ITCZ
interacts with the quasi-biennial oscillation, monsoon winds, and other tropical weather systems
to dictate the precipitation conditions of East Africa (Kizza, et al., 2012). The Uganda
Department of Meteorology participates in annual meetings with the countries of the Greater
Horn of Africa to develop seasonal weather predictions. The countries work together using
statistical methods to predict how global climate conditions will affect the weather of the region.
The Uganda Department of Meteorology then down scales the analysis to produce individual
forecasts for the various regions of Uganda (Bamanya, 2014). The final published weather
forecasts specify the following elements: an overall seasonal precipitation projection (below
normal, normal, or above normal), when the rains will start, when the rains will peak, when the
rains will cease, warnings of flash floods, and indications of poor rainfall distribution for specific
areas (NECJOGHA, 2011). The seasonal weather forecasts are the result of statistical models
interpreting the interaction of multiple inputs. Such inputs are sea surface temperatures, the
strength of various trade winds, the Indian Ocean Dipole (IOD), and the El Niino Southern
Oscillation (ENSO) (Bamanya, 2014). The affect that ENSO and IOD have on precipitation
characteristics in Uganda is further discussed in this study.
22
2
2.1
Observed Precipitation Patterns in the Manafwa River Basin
Precipitation Data Used for this Study
The historical precipitation data used for this study were daily derived satellite
precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) product 3B42
V7. Using the NASA Goddard Earth Sciences Data and Information Services Center (GES
DISC) website, daily data were accessed for January 1, 1998 through December 31, 2013. The
study period covers a timeframe of 16 years. The TRMM 3B42 V7 system gathers a
precipitation estimate every day on a 0.250 by 0.250 gridded resolution. The range of the data
collection spans from latitudes of 50* S to 50* N and covers all 3600 around the equator. A
binary file corresponding to global daily precipitation was downloaded from GES DISC for each
day of the 16-year study period (NASA, 2014). The data downloaded are an estimate of the
average total daily precipitation in millimeters (mm) that falls over an entire (0.250 by 0.250)
gridded area. Code was written in MATLAB to extract the precipitation data from the binary
files for the TRMM grid cells associated with the Manafwa River Basin. A sample of this code
is included in Appendix A. The number of functioning rain gauges in Uganda has been
decreasing since the 1960's making it difficult to gather ground-based precipitation
measurements for the country in general (Kigobe, et al., 2011). The Manafwa River Basin is
located in one of the most data-scarce regions in Uganda. TRMM precipitation estimates were
Previous studies have
used because of the lack of reliable rain gauge data in the study area.
determined that the TRMM 3B42 system is among the most reliable products for estimating
precipitation in the region (Asadullah, et al., 2010).
The Manafwa River Basin is contained mainly within the six TRMM grid cells spanning
an area between longitudes of 33.75' E and 34.5' E, and between latitudes of 0.75' N and 1.50
N. The positioning of the Manafwa River Basin within these six TRMM grid cells is depicted in
Figure 2-1.
1.250 N
1.000
N
0.750 N
33.750 E
34.250 E
34.000 E
0
34.50 E
Figure 2-1: The Six TRMM Grid Cells of the Regional Watershed
23
The combined area of the six TRMM grid cells, which are shaded in Figure 2-1, is approximately
4,620 square km and will be referred to as the "Regional Watershed." The cells in Figure 2-1 are
identified using the following naming scheme: NW, N, NE, SW, S, and SE. The daily
precipitation recorded by each of these six TRMM grid cells was used to calculate the average
daily precipitation for the Regional Watershed.
The "Surrounding Region" is defined as the area between the longitudes of 330 E to
35.250 E, and between the latitudes of 0' N to 2' N. The long-term average daily precipitation
estimates for the Surrounding Region were processed to allow for a geographical comparison of
precipitation rates. The location of the Regional Watershed within this Surrounding Region is
shown in Figure 2-2.
33.OOP*E
2.00* N
0.000
N
35.250 E
Figure 2-2: The Surrounding Region Encompassing the Regional Watershed
Image Source: Google Earth
The Surrounding Region covers an area of approximately 55,400 square km and encompasses
the northern coast of Lake Victoria in the south, Lake Kyoga in the west, and Kenya in the east.
The precipitation data for the Regional Watershed and Surrounding Region have been organized
in multiple ways to observe monthly, seasonal, and annual trends.
24
2.2
Historical Precipitation Conditions
2.2.1 Annual Precipitation
The average annual precipitation for the 16-year observation period (1998-2013) was
determined to be 1.52 meters per year for the Regional Watershed and 1.46 meters per year for
the Surrounding Region. These values indicate that, on average, the Manafwa River Basin may
experience slightly more precipitation than adjacent regions. The annual precipitation for the
Regional Watershed fluctuated between 1.30 meters in 2000 and 1.92 meters in 2006. The
precipitation experienced each year over the 16-year period is shown in Figure 2-3.
2
Sy
=-0.0043x + 10.1
R2 =
0.02
tf
1.25
1997
1999
2001
2003
2005
2007
2009
2011
2013
Figure 2-3: Total Annual Precipitation of the Regional Watershed
The linear trend line illustrates a slight decreasing trend in annual precipitation over time;
however, the coefficient of determination (R2) is extremely weak and prevents this data set from
independently supporting a claim that precipitation is decreasing with time. Given the short 16year period of this analysis, long-term annual trends cannot be commented on with a high level
of confidence. A study by the United Nations Development Programme (UNDP) concludes that
the annual precipitation in Uganda may be decreasing with time (McSweeney, et al., 2008). The
results shown in Figure 2-3 representing annual precipitation of the Regional Watershed do not
contradict the UNDP conclusion that annual precipitation is decreasing.
2.2.2 Monthly and Seasonal Total Precipitation
As stated in Section 1.4, the weather patterns of equatorial East Africa are predominately
driven by the ITCZ, which results in two main rainy seasons in Uganda. The first rainy season
lasts from March through May (MAM) and the second lasts from September through November
25
(SON). The Uganda Department of Meteorology states that the districts of Bududa and
Manafwa, located on the western slopes of Mt. Elgon, experience an additional rainy season
from July to August (Bamanya, 2014). Figure 2-4 shows the calculated average monthly
precipitations for the Regional Watershed.
200
C
150
0
100
-
50
0
Figure 2-4: Average Monthly Precipitation of the Regional Watershed
The month of June is not specified as falling within any of the known rainy seasons.
However, June was determined to have similar precipitation to the months of March and July,
which both fall in rainy seasons. Therefore, June was grouped into the regionally unique, July to
August, rainy season. The monthly data were then transformed into average seasonal data and
are depicted in Figure 2-5.
500
400
-
300
200
100
Dec-Jan-Feb
Mar-Apr-May
Jun-Jul-Aug
Sept-Oct-Nov
Figure 2-5: Average Seasonal Precipitation of the Regional Watershed
26
The 'long rains' from March to May (MAM) have the greatest precipitation with an average of
485 mm. The 'short rains' from September to November (SON) receive slightly less
precipitation at 455 mm. Following the two main rainy seasons in amount of precipitation is the
rainy season specific to the region, lasting from June to August (JJA), which receives an average
of 384 mm. The season spanning from December to February (DJF) receives the least amount of
precipitation at 195 mm.
All monthly and seasonal total precipitation data for the Regional Watershed during the
16-year study period can be found in Appendix B.
2.2.2.1
Monthly and Seasonal Total Precipitation Trends
The total precipitation experienced in each month of the 16-year study period was
analyzed to determine if monthly precipitation is changing with time. The results were
unremarkable for ten of the twelve months. The two months that showed a trend with time were
January and September. The plot in Figure 2-6 shows that January total precipitation has a
decreasing trend with time.
200
y =-5.1869x + 10462
R2= 0.39
o
150
100
0
50
1997
1999
2001
2003
2005
2007
2009
2011
2013
Figure 2-6: Total January Precipitation of the Regional Watershed
The appearance of a decreasing trend in Figure 2-6 is influenced by the unusually high
precipitation total of January 1998, and low precipitation total of January 2012.
27
A trend for total precipitation in September was also detected.
September precipitation may have increasing trend with time.
250
Figure 2-7 shows that
-
y =4.7037x - 9278.7
R2 = 0.38
200f
Cd
150
1
.0
50
1997
1999
2001
2003
2005
2007
2009
2011
2013
Figure 2-7: Total September Precipitation of the Regional Watershed
If the observed trend of increasing total precipitation in September is accurate (as shown in
Figure 2-7), then there may be a future heightened risk of flooding in the Manafwa River Basin
during the month of September.
The R2 values for the trends of January (0.39) and September (0.38) total precipitation,
are considerably higher than the R2 value that was observed for annual precipitation (0.02) in
Figure 2-3. Although a stronger trend may be present for January and September than for annual
precipitation, analysis of precipitation data spanning farther back in time would be necessary to
draw conclusions on monthly trends.
2.2.3 Heavy Precipitation Events
2.2.3.1
Classification of Heavy Precipitation Events
The classification of a "heavy precipitation" event is somewhat subjective. Some studies
define heavy precipitation as a daily rainfall event, which falls in the top 10% and/or 5% of all
precipitation events (Groisman, et al., 2005). Other sources indicate that a heavy precipitation
event occurs when the daily precipitation total exceeds a defined threshold value that is specific
for that season and region (McSweeney, et al., 2008). This paper defines "heavy precipitation"
events as the greatest 10% by depth of all daily precipitation events for each of the four main
seasons. The four seasons have been previously defined as: December to February (DJF), March
28
to May (MAM), June to August (JJA), and September to November (SON). If precipitation was
not experienced in any of the six TRMM grid cells that compose the Regional Watershed on any
given day (all cells reading a precipitation value of 0.00 mm), then that day was classified as a
"non-precipitation" event. If precipitation was experienced in any of the six TRMM grid cells
(any cell reading a precipitation value greater than 0.00 mm), then that day was classified as a
"precipitation" event. Once classified as a "precipitation event," each day was assigned the
precipitation value associated with the TRMM grid cell that received the greatest amount of
precipitation that day. The calculation of the threshold value for a "heavy precipitation" event
was dependent on the number of "precipitation events" that were experienced in each season
over the 16-year study period. The number of days considered to be "precipitation events"
within each season was calculated and the "threshold" value that separated the top 10% from the
bottom 90% of "precipitation events" was then determined for each season.
2.2.3.2
Monthly and Seasonal Heavy Precipitation Events
The method detailed in Section 2.2.3.1 was carried out to determine the daily
precipitation threshold value for a heavy precipitation event in each season. The results from this
process are shown in Table 1.
Table 1: Classification of a Heavy Precipitation Event by Season
Season
Daily Precipitation Threshold
(mm/day)
December to February
(DJF)
16.9
March to May
(MAM)
26.8
June to August
(JJA)
23.0
September to November
(SON)
26.9
The seasonal heavy precipitation thresholds are similar in relative scale to the total
seasonal precipitation results shown in Figure 2-5. The two main rainy seasons, MAM and SON,
have the highest threshold values for the classification of a heavy precipitation event. The
regionally unique JJA rainy season has the third highest heavy precipitation threshold, followed
by the DJF season with the lowest threshold value.
29
2.2.3.3
Heavy Precipitation Trends
Based on the seasonally unique heavy precipitation threshold values detailed in Table 1,
the number of heavy precipitation events that occurred in each month, season, and year over the
16-year study period was determined. The number of heavy precipitation events that occurred
each year is plotted in Figure 2-8.
65
y= -0.6029x + 1244.3
R2 =0.12
OL
50
Ce
20
20
1997
1999
2001
2003
2005
2007
2009
2011
2013
Figure 2-8: Number of Heavy Precipitation Events Occurring Annually
Similar to the total annual precipitation plot shown in Figure 2-3, there is a decreasing trend of
heavy precipitation events. The weak R2 value (0.12) does not permit this decreasing trend to be
commented on with high level of confidence. The two plots (Figures 2-3 and 2-8) show that
2006 received the greatest total annual precipitation, as well as the highest number of heavy
precipitation events.
30
The number of heavy precipitation events that occurred in each month and season over the
16-year time period was also plotted with respect to time. A small correlation was detected for
heavy precipitation events occurring in one out of the twelve months. Figure 2-9 shows a slight
decreasing trend (with R2 =0.31) of heavy precipitation events for the month of January.
8
*
y= -0.2074x + 418.16
R 2= 0.31
6
>
-~0
4
0
1999
1997
2001
2003
2005
2007
2009
2011
2013
Figure 2-9: Number of Heavy Precipitation Events Occurring in January
The decreasing trend of January heavy precipitation events (shown in Figure 2-9) corresponds
with the simultaneous decreasing trend of total precipitation for January (shown in Figure 2-6).
The presence of decreasing trends is primarily driven by abnormally high total and heavy
precipitation in January 1998, and abnormally low total and heavy precipitation in January 2012.
It is likely that the precipitation values for these two months cause the appearance of a declining
precipitation trend when a long-term trend may not actually exist.
31
The maximum one-day precipitation event that occurred each month, season, and year
was then examined to determine if extreme precipitation events have been gaining intensity. The
maximum one-day precipitation event that occurred each year is plotted in Figure 2-10.
110
y =0.5587x - 1049.7
R 2 = 0.04
e
90
70
rZ
70
50
50
-
1997
1999
2001
2003
2005
2007
2009
--
2011
2013
Figure 2-10: Maximum One-Day Precipitation Event Each Year
Although the trend line in Figure 2-10 has a positive slope, indicating the maximum one-day
event is increasing with time, the weak R2 value (0.04) does not support the existence of an
increasing annual trend. The maximum one-day precipitation event that occurred in each month
over the 16-year time period is displayed in Figure 2-11.
120
-
E80
--
0
04
Figure 2-11: Maximum One-Day Precipitation Event for Each Month
Figure 2-11 provides an indication as to which months have historically experienced the most
extreme rainfall events. The highest one-day precipitation events were experienced during the
32
last two months of both main rainy seasons (April and May for the MAM season, and October
and November for the SON season).
Tables in Appendix C provide detailed information on the number of heavy precipitation
events and the maximum one-day heavy precipitation event for each month, season, and year.
2.2.4
Precipitation by Geographical Location
The average daily precipitation for each TRMM grid cell that is contained in the
Surrounding Region was plotted with respect to latitude and longitude. The average daily
precipitation determined for each cell accounts for every day of the entire 16-year study period.
This analysis was performed to recognize how the precipitation conditions of the Manafwa River
Basin differ from adjacent areas and if microclimates exist.
Figure 2-12 shows the
geographically unique average daily precipitation of the Surrounding Region.
6-
1.875 N
-
5.5
1.6250 N
C4
4.5
4
335
4
0
1.3750 N
-- W
1.1250 N
-Al+0.3750
-
N
0.1250 N
2.5
33
33.5
34
Longitude ('E)
34.5
35
Figure 2-12: Average Daily Precipitation of the Surrounding Region
Each latitude was assigned a different colored circle to make the precipitation patterns easier to
visually detect. As seen in Figure 2-12, the data points representing daily precipitation in the
west are positioned tightly together, indicating low variability in average precipitation when
moving from north to south. Average daily precipitation becomes more variable when moving
east across the Surrounding Region. This is shown by the wider spread of precipitation data
points on the right side of Figure 2-12.
33
Figure 2-13 provides a different depiction of the data shown in Figure 2-12. Below,
Figure 2-13 overlays a satellite image of the Surrounding Region with TRMM grid cells, each
cell containing a circle with a diameter corresponding to the magnitude of precipitation estimated
for that grid cell (a larger circle indicates greater average daily precipitation).
Figure 2-13: Surrounding Region with Average Daily Precipitation Circles
Note: The size of the circle contained within each TRMM grid cell represents magnitude of daily precipitation
The same feature of low precipitation variability in the west can also be observed in Figure 2-13;
the circles on the west side are of similar diameter. The topography of the land to the west is
predominantly flat plains with minor changes in elevation. The precipitation values become
variable when moving eastward across the Surrounding Region, which is illustrated by circles of
varying diameters on the right side of Figure 2-13. The presence of Mt. Elgon (4,000 meter
ASL) causes orographic lifting and increased precipitation because of the substantial rise in
elevation. Ground elevation and increased precipitation because of orographic lifting can be
observed in Figure 2-14.
34
*
Precipitation
-
Ground Elevation
4.5
9000
4S
o
L7000
E
3c
5000
>
C2.5
W
3000
2
1000
33
33.5
34
34.5
Latitude
(
35
E)
Figure 2-14: Precipitation Elevation Comparison, West to East at 1.1250 N
Figure 2-14 indicates a relationship between ground elevation and precipitation. An increase in
precipitation is observed when approaching Mt. Elgon on the western slope.
The change in ground elevation causes a wider range of the daily average precipitation
values to be experienced near Mt. Elgon. The larger circles in the southeast quadrant of Figure
2-13 indicate that the highest precipitation occurs in that region. This is likely because of the
combined effect of Lake Victoria and Mt. Elgon. The close proximity to a massive body of
water, Lake Victoria, causes "lake-effect" precipitation, which interacts with the orographic
lifting produced by Mt. Elgon and results in greater precipitation than adjacent regions.
The analysis performed in this section indicates that geographic location and topography
have an impact on the amount of precipitation that falls within the Surrounding Region. These
findings support a previous study that details the precipitation experienced in the Mt. Elgon
region. This study found the greatest precipitation to be on the western slopes of Mt. Elgon,
followed by the southern slopes, and then by the northern and eastern slopes. The study also
mentions that the mid-slopes of Mt. Elgon receive the highest precipitation, as opposed to the
summit or lower slopes (Mugagga, et al., 2012). The upstream extents of the Manafwa River
Basin are located on the western mid-slopes of Mt. Elgon, which has been shown as a high
precipitation area. This helps to provide explanation for why the Manafwa River Basin may be
prone to precipitation events that cause flooding.
35
3
Oceanic and Atmospheric Phenomena
3.1 Teleconnections in Equatorial East Africa and Uganda
There have been numerous studies that have observed teleconnections between certain
oceanic and atmospheric phenomena and the precipitation conditions of East Africa. Two such
phenomena are the El Nifio Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD).
The Uganda Department of Meteorology takes into account the presence and strength of ENSO
and IOD when developing seasonal weather forecasts for the Greater Horn of Africa and for the
specific regions of Uganda (Bamanya, 2014). Studies have shown that ENSO and IOD impact
the total precipitation and the variability of precipitation in the region (Behera, et al., 2005;
Black, et al., 2002; Breytenbach, 2013; Nicholson & Kim, 1997).
3.2 The El Niuo Southern Oscillation
The El Nifto Southern Oscillation (ENSO) is a global climate phenomenon driven by the
interactions of atmospheric and oceanic conditions across the equatorial Pacific Ocean (NOAA
a, 2012). The Southern Oscillation is an interruption of the typical movements of the Walker
Circulation, which is a convective circulation of moist air rising over the western Pacific and dry
descending air over the eastern Pacific. The disturbance of this circulation is also characterized
by deviations in sea surface temperatures and stronger than normal currents (Reid, 2000). The
presence of ENSO conditions across the equatorial Pacific Ocean are recognized to alter weather
patterns at various locations around the world. There is both a "warm" and a "cool" phase to
ENSO, which are respectively referred to as El Ninio and La Niia. An ENSO episode typically
occurs every three to five years; however, historical records show that the episodes may occur on
a two to seven year interval. An El Nifno episode is expected to last in the range of nine to
twelve months, whereas La Niia episodes have the capability of lasting up to three years (NOAA
a, 2012).
The El Niio component of ENSO is considered to be the "warm" phase because of higher
than normal sea surface and sub-surface temperatures of the central and eastern equatorial
Pacific Ocean.
The atmospheric conditions associated with El Ninio are above average sea
surface air pressures over the western Pacific Ocean and below average sea surface air pressures
in the east. The typical easterly trade winds are reversed, which then move east across the
Pacific towards South America. El Niino has been linked with a decrease in convective rainfall
over the western equatorial Pacific, and an increase in convective rainfall in the east. The
atmospheric conditions push ocean water to the east resulting in a higher than normal sea surface
elevation near South America, and a lower than normal sea surface elevation near Indonesia. In
addition to above average seas surface temperatures, the movement of ocean water causes the
36
deepening of the Pacific Ocean thermocline in the east, and rising of the thermocline in the west
(NOAA a, 2012).
The La Nina component of ENSO is considered to be the "cool" phase because of below
normal sea surface and sub-surface temperatures of the central and eastern equatorial Pacific
Ocean. The atmospheric conditions associated with La Nifia are above average sea surface air
pressures over the eastern Pacific, with below average air pressure in the west. The easterly
trade winds move across the equatorial Pacific towards the Indonesian region. La Niia has been
linked with an increase in convective rainfall over the western Pacific and a decrease in
convective rainfall in the east. The atmospheric conditions push ocean water to the west
resulting in a higher than normal sea surface elevation near Indonesia, and a lower than normal
sea surface near South America. In addition to below average sea surface temperatures, the
movement of ocean water causes a shallower than normal Pacific Ocean thermocline in the east
and the deepening of the thermocline in the west (NOAA a, 2012).
The disruption that ENSO causes to the convective Walker Circulation, sea surface
temperatures, and thermocline depth can be seen in Figure 3-1. Images were found on the
website for the NOAA Pacific Marine Environmental Laboratory.
Normal Conditions
---------------------- 0----I
El Nilo Conditions
12"E
WOW
La N 11a Conditions
12 "
80W
Figure 3-1: Oceanic and Atmospheric Deviations of ENSO
Image Source: (NOAA e, 2014)
37
The weather implications of ENSO expand far beyond the limits of the Pacific Ocean.
During El Niino and La Nifia episodes many areas of the world get warmer or cooler, respectively
(NOAA a, 2012). The teleconnections of ENSO extend around the globe, affecting certain
locations greatly, and other locations not at all. Figure 3-2 shows locations around the world,
which are affected by ENSO conditions during the northern hemisphere winter (DJF).
Figure 3-2: Example Locations with Teleconnections to ENSO
Image Source: (NOAA c, 2012)
As seen in Figure 3-2, the impact of El Ninio (warm phase) and La Nifia (cool phase) are opposite
for the specific location that is affected. The location and effects of teleconnections change with
the different seasons.
38
3.2.1
3.2.1.1
Indices for Measuring ENSO
Southern Oscillation Index (SOI)
As discussed in Section 3.2, the Southern Oscillation is the deviation from the typical
convection of the Walker Circulation. The "strength" of the departure from the typical Walker
Circulation is measured by the Southern Oscillation Index (SOI) (Reid, 2000). The SOI is a
measure of sea surface air pressure differences across the equatorial Pacific Ocean. The
reference points for measuring sea surface air pressure are Darwin, Australia and Tahiti. The
SOI is calculated by measuring the sea surface air pressure at both locations, standardizing the
air pressure measurements, taking the difference of the standardized values, and dividing by the
monthly standard deviation of the two locations. The SOI is ultimately, a simple representation
of the departure from the average sea surface air pressure differences that were experienced
during the 30-year base period from 1951 to 1980 (NOAA g, 2014).
A negative SOI is associated with El Nifio, the warm phase of ENSO, which occurs when
the sea surface air pressures are below normal at Tahiti (central Pacific) and above normal at
Darwin (western Pacific). A positive SOI is associated with La Nifia, the cold phase of ENSO,
which occurs when the sea surface air pressures are above normal at Tahiti and below normal at
Darwin. An SOI that remains either positive or negative for an extended period of time typically
indicates the occurrence of a La Niia or El Ninio event (NOAA g, 2014). Sea surface air
pressures at these locations are continuously monitored and are usually recorded as monthly
averages. Figure 3-3 shows a time-series of historical SOI values since 1950.
Southern Oscillation Indicies
S01: Tahiti - Dan
4.0
~2.0
N
a
0.0
-2.0
-4.0
1950
1960
1970
1980
1990
2000
2010
Year
Figure 3-3: Time-Series of Standardized SOT Values
Image Source: (NCAR, 2012)
In Figure 3-3 positive SOI values (blue) represent La Nifia conditions and negative SOI values
(red) represent El Nifno conditions.
39
3.2.1.2
Oceanic Nino Index (ONI)
The Oceanic Ninio Index (ONI) is a measurement related to sea surface temperatures of the
equatorial Pacific Ocean. The index is measured on a seasonal basis, where every three
consecutive months represents one season. The ONI is the departure of the current season
(three-month period) average sea surface temperature (0 Celsius) from the average sea surface
temperature for that same season over the 30-year base period of 1971 through 2000. The sea
surface temperature measurements are associated with the Ninio 3.4 region (between the latitudes
of 50 S and 5' N, and longitudes of 1200 W and 1700 W) of the central equatorial Pacific Ocean.
A positive ONI indicates that the Ninio 3.4 region is warmer than normal, whereas a negative
ONI indicates that it is cooler than normal (NOAA d, 2014).
The ONI is the index that NOAA uses to categorize the existence of a warm phase (El
Nifio) or a cool phase (La Ninla). The warm phase of ENSO, El Ninio, is represented by ONI
values of 0.5 and above. The cool phase of ENSO, La Ninia, is represented by ONI values of -0.5
and below. ENSO is only considered to be in a warm or cool phase when the ONI is above 0.5
or below -0.5 for five consecutive three-month seasons (NOAA a, 2012). Figure 3-3 shows a
time-series of historical ONI values since 1955.
3
0
S0C
2
-2
U
1960
1970
1980
1990
2000
2010
Year
Figure 3-4: Time-Series of ONI Values
Image Source: (NOAA Fisheries, n.d.)
In Figure 3-4 positive ONI values (red) represent El Nifio conditions and negative SOI values
(blue) represents La Niia conditions. It should be noted that the relationship between index
values and ENSO conditions is the opposite for SOI and ONI. This can be observed by the
reversal of colors on either side of the axes in Figures 3-3 and 3-4.
40
3.2.2
The Effects of ENSO in Equatorial East Africa
The two phases of ENSO, El Niio and La Nifia, have opposite effects on weather
conditions in East Africa; however, the affect is only observed during certain seasons. When the
impact is measurable, El Niio is typically associated with above average precipitation and La
Niia is associated with below average precipitation. ENSO affects each season differently and
there is not a scientific consensus on the specifics of its impact. One study indicates that ENSO
has a strong connection with the short rains (SON) in East Africa but that the long rains (MAM)
are unaffected (Phillips & McIntyre, 2000). Other sources have identified a delayed reaction
between ENSO conditions and the MAM rainy season; a report from the United States Agency
for International Development (USAID) indicates that the presence of La Nifia conditions from
October through December may lead to less rainfall during the following MAM rainy season.
The report also indicates that La Niia conditions lead to below average rainfall from October
through December, while El Niio conditions lead to above average rainfall during this same time
period (USAID, 2010). Additional studies indicate that there is a positive correlation between
ENSO and precipitation in November and December, but a negative correlation for precipitation
in August and September (Phillips & McIntyre, 2000). NOAA indicates that there is also a link
between ENSO and the DJF season in equatorial East Africa; La Nifia events result in decreased
rainfall while El Niio results in increased rainfall (NOAA b, 2012). La Nifia conditions have
also been recognized to delay the onset of the rains in East Africa, reduce the total precipitation
received, and make the precipitation patterns more erratic (USAID, 2010).
ENSO has complicated interactions with the weather patterns of equatorial East Africa and
Uganda. The possible connections between ENSO and precipitation become even more complex
when analyzing the weather patterns on a sub-regional scale. One study, which focused on the
impact that ENSO has on the various regions of Uganda, found that ENSO affects the regions
differently. The study recommended that local level decisions regarding climate must be made
on a sub-regional level (Phillips & McIntyre, 2000). Such decisions could involve agricultural
planting schedules, drought mitigation, and flood preparedness.
3.3
The Indian Ocean Dipole
Similar to ENSO, the Indian Ocean Dipole (IOD) is a coupled oceanic and atmospheric
phenomenon that has weather implications far beyond the Indian Ocean. The IOD is the
irregular fluctuation of sea surface temperatures across the western and the southeast equatorial
Indian Ocean (ICDC, n.d.). The two phases to IOD are referred to as "positive" and "negative,"
with neutral periods occurring in between. The positive phase of IOD is associated with greater
than average sea surface temperatures in the western Indian Ocean, and the negative phase is
41
associated with below average sea surface temperatures in the western Indian Ocean
(JAMSTEC, 2012).
3.3.1
The Index for Measuring IOD
3.3.1.1
Dipole Mode Index (DMI)
The strength of the IOD is determined through the use of the Dipole Mode Index (DMI),
which is a measurement of the east (100 S to 00 N, 900 E to 1100 E) to west (500 E to 700 E, 100
S to 100 N) temperature difference across the equatorial Indian Ocean (Australia Bureau of
Meteorology, 2014). The anomaly is the departure of Indian Ocean sea surface temperatures
differences from the base period of 1982 to 2005 (NOAA h, 2014).
A positive DMI is
characterized by cooler than normal sea surface temperatures in the southeast Indian Ocean and
warmer than normal temperatures in the western Indian Ocean. The meteorological conditions
associated with a positive DMI are increased precipitation over East Africa and drought over the
Australian and Indonesian region. Conversely, a negative DMI is characterized by warmer than
normal sea surface temperatures in the southeast Indian Ocean, and cooler than normal
temperatures in the western Indian Ocean (JAMSTEC, 2012). The meteorological conditions
associated with a negative DMI are increased precipitation in the Australian and Indonesian
region and decreased precipitation over East Africa. (Cai, et al., 2013) (Australia Bureau of
Meteorology, 2014). The sea surface temperature and precipitation effects of IOD are depicted
by Figure 3-5.
Positive Dipole Mode
Negative Dipole Mode
Figure 3-5: Sea Surface Temperature and Precipitation Response to IOD
Image Source: (JAMSTEC, 2012)
In Figure 3-5 the regions shaded with blue and red are respectively associated with below or
above average sea surface temperatures during IOD events. Cloud cover indicates areas of
enhanced convective systems and increased precipitation (JAMSTEC, 2012).
42
3.3.2
The Effects of 10D in Equatorial East Africa
Previous studies have demonstrated that IOD has an impact on the weather patterns
experienced in East Africa. Precipitation in the region typically increases during positive IOD
events and decreases during negative IOD events (JAMSTEC, 2012).
Although the
teleconnection has been observed across seasons, certain studies suggest that IOD has the most
significant impact on precipitation in East Africa during the short SON rainy season. It has also
been theorized that only extreme IOD events impact the precipitation in the region (Behera, et
al., 2005).
3.4 The Combined Effect of ENSO and IOD
It is generally accepted by the scientific community that ENSO has a substantial effect on
precipitation in East Africa. There are a greater number of studies documenting the effect of the
ENSO than studies documenting the effect of the IOD. Certain studies have more recently called
for the need to observe the combined effect of ENSO and IOD on precipitation in Africa. The
two climate phenomena cannot be viewed in isolation of each other when studying precipitation
patterns (Williams & Hanan, 2011; Black, et al., 2002). A study by USAID indicates that the
precipitation impact of ENSO on East Africa is dependent on the strength of ENSO conditions,
as well as the sea surface temperature anomaly of the Indian Ocean (USAID, 2010). Another
study suggests that the influence of IOD on the short SON rainy season in East Africa is
"overwhelming" when compared to the concurrent influence of ENSO. By isolating ENSO from
IOD, it appears that ENSO has a significant impact, when in reality the greater impact is caused
by IOD. That study also indicates that the removal of IOD conditions can show the opposite of
what would be expected for precipitation in East Africa given the ENSO conditions (Behera, et
al., 2005). For example, precipitation in Africa is consistently associated with IOD, despite the
concurrent phase of ENSO (Saji & Yamagata, 2003). Severe flooding in East Africa during
1961 occurred without the existence of El Niio conditions; however, anomalous Indian Ocean
sea surface temperatures were present (Behera, et al., 2005; Reverdin, et al., 1986).
Certain studies also encourage further investigation of the way in which ENSO drives
IOD. This will contribute to an overall enhanced understanding of how the entire system
disturbs the weather patterns of equatorial East Africa and various regions around the world. It
has been acknowledged that ENSO and IOD conditions effect precipitation in the region;
however, there is still a poor correlation between the strength of the climate indices (e.g. SOI,
ONI, and DMI) and the historical precipitation experienced (Black, et al., 2002). An
investigation was performed to further understand the effect that the two oceanic and
atmospheric climate systems have on the microclimate of the Manafwa River Basin. The
findings are presented and discussed in subsequent sections of this paper.
43
4 Weather in the Manafwa River Basin and Climate Indices
4.1 Method
A correlation between certain global climate indices and the local weather in the Manafwa
River Basin would provide a macro-level tool with which to gauge the relative probability of
extreme precipitation events and floods. This macro-level tool allows the issuance of
information to the Red Cross that an increased amount of precipitation might be expected with
an associated increased risk of flooding. A method was developed to determine if there exists a
relationship between global climate indices and characteristics of precipitation experienced in the
Manafwa River Basin. The climate indices of concern (SOI, ONI, and DMI) are measurements
of ENSO and IOD and are recognized to impact weather patterns of equatorial East Africa.
These teleconnections were discussed in Section 3.
This analysis attempts to confirm whether a correlation exists between the occurrence of an
ENSO or IOD event, and how the strength of the associated indices affects the microclimate of
the Manafwa River Basin. If such a correlation exists, it is essential to understand the impact
that the indices have on various precipitation characteristics for specific months and seasons.
Precipitation characteristics of concern in this analysis included: total precipitation, occurrence
of heavy precipitation events, extreme precipitation events, and overall variability. If a variation
from the expected precipitation was observed, the indices were further examined to determine if
ENSO or IOD was the driving force behind the divergence. Total precipitation and heavy
precipitation events were compared to the various climate indices to determine existing
relationships. The data inputs for this method and their treatment are discussed in subsequent
sections.
4.1.1
Normalization of Precipitation Data
The total precipitation experienced by the regional watershed for every month over the
16-year period (1998-2013) was calculated and the average monthly precipitation values were
determined. The monthly average results were previously discussed in Section 2.2.2. The
monthly precipitation data were normalized by the average monthly precipitation value for that
specific month. The data normalization process resulted in a Normalized Monthly Precipitation
(NMP) value for every month of the 16-year period. NMP values are a dimensionless
representation of the amount of precipitation that falls in a given month, compared to the average
precipitation for that month. NMP values greater than 1.0 correspond to a monthly precipitation
that is greater than average while NMP values less than 1.0 correspond to a monthly precipitation
that is below average.
44
NMP values are useful because they provide the ability to simultaneously compare the
total monthly precipitation of any month to the strength of a global climate index. Precipitation
experienced in two different months (e.g. June and December) cannot be compared unless they
are first normalized. For example, a total monthly precipitation of 100 mm in both June and
December is not equivalent because June and December have different expected monthly
precipitation totals. Normalizing the precipitation data allows for the comparison of the total
precipitation experienced in separate months to determine the magnitude of effect that a climate
index may have on precipitation. For example, in December 1999 and August 2012 the Regional
Watershed experienced, respectively, 62.2 mm and 126.4 mm of precipitation. August 2002
received more than twice as much precipitation as December 1999; however, both months have a
NMP value of 0.85 (indicating 85% of average precipitation for that specific month). This
investigation aims to find if there is a climate index, series of indices, or pattern of events that
leads to an increase or decrease in expected monthly precipitation.
The process of normalizing precipitation data was repeated for the seasonal precipitation
data. This study follows the seasonal specification set forth by the NOAA, where each year has
12 three-month running seasons: starting with December-January-February (DJF) and ending
with November-December-January (NDJ). Each season has a two-month overlap with the
season before it and after it. The first step in the process of normalizing seasonal data was to
calculate the total seasonal precipitation experienced in each season for the entire 16-year time
period. Total precipitation for each season was normalized to the average precipitation for that
season. Normalized Seasonal Precipitation (NSP) values are important when comparing
precipitation to the ONI, which is measured on a running three-month seasonal basis.
Example calculations for the normalization of monthly and seasonal precipitation data is
found in Appendix D. The resultant NMP and NSP values for the 16-year study period in the
Manafwa River Basin are found in Appendix E.
4.1.2
Organization of Heavy Precipitation Events
The classification of heavy precipitation events and the determination of daily threshold
values for each season were previously discussed in Section 2.2.3. This study compares the
number of heavy precipitation events experienced in a month or season to the SOI, ONI, and
DMI to determine if a certain index or series of indices leads to an increased number of heavy
events, which may also increase the risk of flooding. The analysis of heavy precipitation events
with respect to climate indices is discussed in subsequent sections.
45
4.1.3
Determining the General Relationship to ENSO and IOD
The monthly and seasonal precipitation data were analyzed to determine if the phases of
ENSO (El Ninio or La Ninia) and IOD (positive or negative) affect the amount of precipitation
received in the Manafwa River Basin. The average of the NMP and NSP values corresponding
to the each phase was calculated to observe if a greater or lesser amount of precipitation is
typically experienced in one phase or another. Additionally, the number of months and seasons
within each phase that received above average precipitation (i.e. NMP > 1.0) and below average
precipitation (i.e. NMP < 1.0) was determined. Each phase of all ENSO and IOD events was
then examined for the minimum, maximum, and range of NMP and NSP values experienced.
This analysis determined what phase received the most extreme precipitation events (i.e. both
high and low precipitation extremes) and the range between the two most extreme values. The
greater the range between the maximum and minimum values of each phase, the more variability
of precipitation was experienced in that phase. This definition of precipitation extremes allows
for the determination of whether precipitation is more variable during an ENSO and IOD event,
or during the neutral phase.
4.1.4
Comparing Precipitation to the Strength of Indices
Monthly and seasonal precipitation was compared to the strength of climate indices (SOI,
ONI, and DMI) to determine the effect of Pacific and Indian Ocean atmospheric and oceanic
conditions on precipitation in the Manafwa River Basin. The analysis performed in subsequent
sections involves plotting NMP, NSP, and the number of heavy precipitation events against the
corresponding climate index values. This investigation aims to determine if the magnitude of a
global climate index yields a significant correlation with the precipitation characteristics of the
Manafwa River Basin. Plots were generated showing a series of delays of monthly and seasonal
precipitation from the index of previous months and seasons. For example, the NMP value for
May 2011 was plotted against the SOI values of: May 2011 (No Delay), April 2011 (1-Month
Delay), March 2011 (2-Month Delay), February 2011 (3-Month Delay), and January 2011 (4Month Delay). NMP values and the number of heavy precipitation events were compared to the
DMI in an identical manner. NSP values were compared to the ONI using the same structure of
delays for ENSO seasons, not months. For example, the NSP for AMJ 2011 was plotted against
the ONI values of: AMJ 2011 (No Delay), MAM 2011 (1-Season Delay), FMA 2011 (2-Season
Delay), JFM (3-Season Delay), and DJF (4-Season Delay). Delays were plotted to determine if
precipitation is most affected by the current, or by the preceding oceanic and atmospheric
conditions; and if previous conditions affect precipitation, to define how far in advance that
signal starts.
46
The evaluation of the relationship between precipitation and climate indices was
performed by observing the correlation in all months collectively, followed by observing all
months (and seasons) independently. The study was conducted with this method to determine if
the relationship between indices and precipitation is uniform or changes throughout the year.
Furthermore, each season may have a different time interval in which it takes precipitation in the
basin to respond to the index, and thus needs to be viewed in isolation of the other seasons.
The monthly and seasonal climate index values used in this study are displayed in
Appendix F. Monthly SOI and seasonal ONI values were accessed through the National Oceanic
and Atmospheric Administration (NOAA). Monthly DMI values were accessed through the
Japan Agency for Marine-Earth Science and Technology (JAMSTEC, 2014). The monthly index
values for SOI and DMI varied slightly among the different climate agencies that record and
publish the indices; therefore, the analyses performed in subsequent sections of this report are
valid for the specific indices that were accessed from NOAA and JAMSTEC.
4.1.5
Classifying the Strength of Observed Trends
The precipitation data used in this study covered a timeframe of 16 years; therefore, each
month and each season occurred 16 times. In this study, the occurrence of every month or
season will be represented by a data point that symbolizes a precipitation characteristic (e.g. total
precipitation or number of heavy precipitation events) and the strength of a climate index. When
observing each month independently, the data set will consist of 16 points. There are two
exceptions with regards to seasonal data. The DJF and NDJ seasons each have 15 precipitation
data points because the precipitation values for December 1997 and January 2014 were not
included this study. It will be neglected that DJF and NDJ have a sample size of one less than all
other months and seasons. A system for classifying the strength of trends was developed
assuming that all plots comparing precipitation to indices had a sample size of 16 data points.
A directional trend, with a sample size of 16, is significant at the 5% level (95%
confidence) if the correlation coefficient (R) is greater than or equal to 0.43. This corresponds to
a coefficient of determination (R2 ) of 0.185 or greater to indicate significance. The source used
to define this "significance" level (Lowry, 2013) is not peer reviewed literature. It is an internet
textbook (VassarStats) that was provided as a link on a NOAA website (Linear Correlations in
Atmospheric/Seasonal Monthly Averages) when seeking guidance on the significance of
correlations. Additional sources were consulted for the development of a classification system
for the strength of the trend (Taylor, 1990; Harrington, n.d.; Jost, n.d.; Quinnipiac, n.d.). It was
determined that the classification of correlation strength is specific to the science or discipline in
which it is being used, and even within a discipline, there is still subjectivity in classifying
significance.
47
Table 2 shows the classification system that was developed to compare the trends within
this study.
Table 2: Classification of Linear Correlation Strength used in this Study
Range of R2 Values
Strength Classification
0.0-0.185
None (-)
0.185 -0.25
Very Weak
0.25-0.375
Weak
0.375 - 0.575
Moderate
0.575 - 0.725
Strong
0.725-1.0
Very Strong
The classification system specified in Table 2 will be used for the remainder of this study. Any
linear trend line that is observed to have and R2 value less than 0.185 will be considered
insignificant, and will be denoted with a dash mark (-) in any following table. Trends with R2
values ranging from 0.185 to 1.0 will be classified using the terminology of "Very Weak" to
"Very Strong" as indicated in Table 2.
4.2
Observed Effect of ENSO in the Manafwa River Basin
4.2.1
General Relationship Between ENSO and Precipitation
The total seasonal precipitation data were analyzed to determine what impact the phase of
ENSO has on total precipitation in the Manafwa River Basin. The average NSP value
corresponding to each ENSO phase was calculated to observe if a greater or lesser amount of
precipitation is experienced during El Nifto or La Nifia. Additionally, the number of seasons in
each phase receiving above average precipitation (NSP > 1.0) and below average precipitation
(NSP < 1.0) was determined. The results of this analysis are shown in Table 3.
48
Table 3: General Precipitation Characteristics of ENSO Phases
Phase of ENSO
Description of Seasonal Precipitation Parameters
Number of Seasons
in Each Phase
Normalized
Seasonal
Precipitation
(NSP)
El Nifno
La Nifia
Neutral
Overall Total
35
71
84
With Above Average Precipitation
(NSP > 1.0)
18
24
46
With Below Average Precipitation
(NSP < 1.0)
17
47
38
Average
1.15
0.91
1.02
Minimum
0.58
0.25
0.66
Maximum
2.08
1.47
1.34
Range (Max - Min)
1.50
1.22
0.68
As previously discussed in Section 3.2, the classification of an ENSO episode is
contingent upon the event lasting at least five seasons. El Niio events typically last less than a
year, while La Nilia events can last up to three years (NOAA a, 2012). The total number of
seasons over the 16-year study period that were associated with El Niio, La Niia, and neutral
phases were totaled to determine how often each phase occurs. There were approximately twice
as many La Nifia seasons (71) in the study period as there were El Niio seasons (35). An ENSO
event was present approximately 56% of the study period, 106 out of 190 seasons.
El Niino and La Ninia ENSO phases were determined to have average NSP values of 1.15
and 0.91, respectively. The ENSO neutral phase was found to have an average NSP value of
1.02, falling approximately half way between El Niio and La Nifia. These values indicate that
the average El Ninio season receives 12.8% more precipitation than the average neutral season,
and the average La Niia season receive 10.9% less precipitation than the average neutral season.
For El Niio episodes, the total number of seasons that received above average precipitation (18)
was similar to the number of seasons that received below average precipitation (17). These
numbers suggest that the above average precipitation seasons were more extreme than the below
average precipitation seasons in terms of deviation from the average; therefore, driving the
average NSP value higher. The same comparison yielded different results for La Niia, which
had nearly twice as many seasons with below average precipitation (47) as compared to above
average precipitation (24). This indicates that in the Manafwa River Basin, La Niia is more
likely to result in below average seasonal precipitation than El Nifio is to result in above average
49
seasonal precipitation; however, when an El Nifio season is above average, it has a greater
potential to far exceed the average precipitation.
As expected, El Ninto phases had the highest maximum NSP (2.08) indicating that the
most extreme precipitation occurred during an El Nuio event. Similarly, La Nifia had the lowest
minimum NSP (0.25) indicating that the most extreme minimum precipitation occurred during a
La Niia event. The range between maximum and minimum NSP values experienced in each
phase was much greater for El Nifno (1.5) and La Nin-a (1.22) than for the ENSO neutral phase
(0.68). This indicates that variability of total seasonal precipitation is greater for ENSO seasons
than for neutral seasons. These findings show that, overall, El Niio and La Nifia have distinct
effects on the seasonal precipitation conditions in the Manafwa River Basin.
4.2.2 Monthly Precipitation Compared with SOT Strength
Monthly precipitation was compared to the strength of the SOI to determine the effect
that the gradient of Pacific Ocean sea surface air pressures between Darwin and Tahiti have on
precipitation in the Manafwa River Basin. NMP values and the number of heavy precipitation
events each month are plotted against the measured SOI in a series of delays.
4.2.2.1
Comparison of Precipitation and SOI for all Months Collectively
The NMP values for all months over the 16-year period were plotted against the
corresponding SOI for that month. Figure 4-1 shows that by viewing all months simultaneously,
no correlation exists between the precipitation of a given month and the corresponding SOI.
3
-0..1264x + 1.0438
y
R2=0.09
2
0.0
7
8
se %0
00
-4
9
-3
-2
9,
-1
0
1
Southern Oscillation Index (SOI)
2
Figure 4-1: All Months - NMP vs. SOI (No Delay)
50
3
4
The weak R2 value (0.09) in Figure 4-1 prevents the confirmation of a relationship between total
precipitation in any month and the strength of the SOI. The number of heavy precipitation
events occurring each month was also compared to the strength of the SOI. The results are
shown in Figure 4-2.
12
y =-0.5047x + 3.1022
R2 =
8*
*
00
9
99
0
m0~
e ------.-
S----,-4
0.06
-3
-2
-1
,--t----3
4
Southern Oscillation Index (SOI)
Figure 4-2: All Months - Heavy Precipitation vs. SOL (No Delay)
Similarly, by viewing all months at once in Figure 4-2, a weak 1R2 value (0.06) exists for the trend
of heavy precipitation events against the S0I. The plots representing the S0I's effect on total
monthly precipitation (Figure 4-1) and the number of heavy precipitation events per month
(Figure 4-2) both have linear trendlines with negative slopes. The negative slopes indicate that a
positive SOI (La Nijia conditions) is associated with below average total precipitation and fewer
heavy precipitation events. Conversely, a negative 501 (El Nii'o conditions) is associated with
above average precipitation and more heavy precipitation events. Previous studies have found
that, although a relationship exists between precipitation and ENSO, the precipitation response to
ENSO is non-linear and requires a non-linear approach (Black, et al., 2002) (Slingo &
Annamalai, 2000). The weak R2 values observed in Figures 4-1 and 4-2 show that there is no
correlation between precipitation and 501 when viewing all months collectively. Plots were also
generated to determine the relationship between SOI and the delayed monthly total and heavy
precipitation. Similarly, no correlation was detected between SOI and the delayed monthly
precipitation response when observing all months collectively.
4.2.2.2
Comparison of Total Precipitation and SOT for Independent Months
The NMP values were sorted by month to observe how the SOI might uniquely affect the
precipitation of each month. Previous studies have shown that certain months are affected by
ENSO and certain months are not. To enhance the observed effect of the SOI, the NMP values
51
for all twelve months of the year were plotted (No-Delay through 4-Month Delay) specific to the
individual month. It was determined that seven out of twelve months had no observable
correlation to the SOI (all plots had R2 values less than 0.185). The seven months with no
correlation to the SOI are March, April, July, August, September, October, and November. The
linear trend line parameters corresponding to the months with no correlation between SOI and
total precipitation can be found in Appendix G.
The five months that had a correlation between total precipitation and SOI are January,
February, May, June, and December. The strength of the correlations was determined to range
from "very weak" to "moderate." The linear trend line parameters and the correlation details for
these five months are shown in Table 4. It is apparent that by observing the precipitation of
individual months, correlations between precipitation and SOI begin to emerge. This confirms
that months must be observed independently of each other to understand the true effect of SOI on
local precipitation.
Table 4: Months with a Correlation between Total Precipitation and SOT
Precipitation
Month
Index
Month
Length of
Delay
Normalized Monthly
Precipitation
(NMP)(NP)(Sol)
Southern
Oscillation
Index
Index
-toPrecipitation
January
December
1-Month Delay
November
2-Month Delay
October
3-Month Delay
September
4-Month Delay
February
January
December
November
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
October
4-Month Delay
May
April
March
February
January
June
May
April
March
February
December
November
October
September
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Dela
3-Month Dela
= -0.4657 + 1.1368
= -0.2057x + 1.0103
August
4-Month Delay
January
February
May
June
December
No Delay
Linear Trend Line
Parameters
Equation
Characteristics of
Trend
R2
y= NMP x = S01
Strength
only if
R 2 > 0.185
Positive
or
Negative
Negative
Negative
I______
y = -0.3068x + 1.1151
y = -0.2388x + 1.1176
y = -0.3056x + 1.1143
y = -0.36x + 1.1055
0.392
0.224
0.166
0.292
Moderate
Very Weak
-
-
Weak
Negative
1.1217
0.315
Weak
Negative
y= -0.1638 + 1.0707
= -0.2649 + 1.0993
y -0.2757 + 1.1361
= -0.4729x + 1.1773
0.159
0.280
0.285
0.379
-
-
Weak
Weak
Moderate
Negative
Negative
Negative
0.468
Moderate
Negative
0.317
0.190
0.221
0.405
Weak
Very Weak
Very Weak
Moderate
Negative
Negative
Negative
Negative
0.060
-
-
0.077
0.199
-
-
Very Weak
Negative
0.004
-
-
0.004
-
-
0.024
-
-
0.299
0.253
0.428
0.208
Weak
Weak
Moderate
Very Weak
Negative
Negative
Negative
Negative
0.274
Weak
Negative
= -0.4339x +
= -0.0987x + 1.0321
y = -0.0838x + 1.0477
y = -0.0795x + 1.0343
y -0.0374 + 1.014
y= -0.1357x + 1.022
= -0.2117x + 1.0106
= 0.019lx + 0.9938
y= 0.0141x + 0.992
v = -0.0252x + 1.0109
y = -0.3403x + 1.1914
y = -0.5057x +1.2497
= -0.5802x + 1.2212
y = -0.4882x + 1.1892
y = -.5395x +
52
1.0944
Every correlation shown in Table 4 between SOI and total monthly precipitation is negative,
indicating that a greater SOI is associated with less precipitation. The strongest correlation in
four of the five months, shown in Table 4 was determined to be between total precipitation and
the SOI of a preceding month (all months except January). This is significant because it implies
that there is a delayed response between Pacific Ocean air pressure conditions and the resultant
weather in the Manafwa River Basin. This allows for the possibility of monitoring SOI to
anticipate the nature of future precipitation. The delay from the SOI that yielded the highest
correlation was different for each month, which means that each month is unique in its local
precipitation response to SOI, and its reaction time.
The results for February precipitation show that the response to the SOI becomes stronger
as the delay is lengthened; the slope of the trend line becomes steeper and the R 2 becomes larger,
which both indicate that the impact on precipitation is greater. The plot for February total
precipitation and October SOI is shown in Figure 4-3.
3
-y =-0.4657x + 1.1368
R2 =0.468
.2
~
11
0
-
-3
--
-
---
-2
_
__
_______
0
-l
1
2
3
October - Southern Oscillation Index (SOI)
Figure 4-3: February NMP vs. October SOI (4-Month Delay)
It is demonstrated in Figure 4-3 that there exists a moderate negative correlation between the SOI
of October and the precipitation in February of the following year. The three points located
farthest left in the plot all had SOI values less than -1 and NMP values of 1.5 or greater. This
indicates that the three lowest SOI values during October led to precipitation in February that
was at least 50% greater than average. Conversely, the October with the highest SOI value
(point farthest to the right) resulted in a February precipitation that was 64% below average.
53
The correlation between the SOI of February and the precipitation received three months
later, in May, is shown in Figure 4-4.
2
y
=
-0.0795x + 1.0343
R2 0.405
2
1.5
S
1
__
0.5
0
-4
-3
-2
-1
0
1
2
3
4
February - Southern Oscillation Index (SOI)
Figure 4-4: May NMP vs. February SOI (3-Month Delay)
Although the trend displayed in Figure 4-4 has a moderate correlation between February SOI and
May precipitation, the slope of the trend line (-0.0795) is far smaller in magnitude than the slope
in Figure 4-3 (-0.4657). This indicates that February precipitation is more impacted by October
SOI, than May precipitation is impacted by February SOI. The range of May NMP values stays
with in 0.63 and 1.42 despite a wide range of February SOI values. The smaller range of May
NMP values indicates that total precipitation in May is less variable than in February, despite the
magnitude of the preceding months' SOI.
54
Figure 4-5 shows the moderate negative correlation between October SOI and
precipitation in December.
3
-0.5802x + 1.2212
R2 = 0.428
.y
.t
0 1+-3
9
-2
-l
0
1
October - Southern Oscillation Index (SOI)
2
3
Figure 4-5: December NMP vs. October SOI (2-Month Delay)
The cluster of data points in the bottom right of Figure 4-5 shows that October SOI values above
1.0 typically lead to December NMP values of 1.0 (average) or below. The results in Figure 4-3
and 4-5 show that October SOI may be an indicator for total precipitation in both December and
February.
4.2.2.3
Comparison of Heavy Precipitation and SOI for Independent Months
An analysis comparing heavy precipitation events with the strength of the SOI was
performed to determine the effect of SOI on heavy precipitation events in specific months. Six
out of twelve months were determined to have a significant correlation between the occurrence
of heavy precipitation events and the S01 of previous months. These six months are January,
February, April, May, August, and December. The information pertaining to correlations
between SOI and the precipitation of these six months is found in Table 5.
55
Table 5: Months with a Correlation between Heavy Precipitation and SOT
Precipitation
Month
Heavy Precipitation
Events
January
February
April
May
Index
Month
Oscillatio
Index
(Sol)
atIndex
Precipitation
y = # heavy x = So
No Delay
Characteristics of
Trend
2
S nifint
Positive
R2 > 0.185
Negative
y = -0.7022x + 2.5758
y = -0.5219x + 2.5702
0.285
Weak
Negative
1-Month Delay
0.148
-
-
November
2-Month Delay
y = -0.4009x + 2.4662
0.041
-
-
October
3-Month Delay
y = -0.6722x + 2.51
-
-
September
4-Month Delay
y = -0.9565x + 2.5815
0.141
0.212
Very Weak
Negative
February
January
December
November
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
-
-
Weak
Moderate
Weak
Negative
Negative
Negative
October
4-Month Delay
Moderate
Negative
April
March
February
January
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
y= -0.4801x + 2.957
= -0.8561x + 3.071
= -1.0703x + 3.2785
y -1.4163x + 3.2811
y= -l.5796x + 3.214
y= -0.0221x + 4.1947
y= -0.5344x + 4.4914
= -0.586x + 4.4402
= -0.2413x + 4.278
-
-
December
May
4-Month Delay
No Delay
= -0.2937x + 4.3325
= - 1.208x + 2.8729
April
March
February
1-Month Delay
2-Month Delay
3-Month Delay
-0.8789x + 3.0981
= -0.6735x + 3.1956
y = -0.2724x + 2.93
Janua
4-Month Dela
August
December
November
October
September
No Delay
1-Month Delay
2-Month Dela
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Dela
3-Month Delay
y = -0.3015x + 2.9255
= -0.223 1x + 3.7265
y = -0.8528x + 3.9007
y = -0.9843x + 3.8474
y = 0.7248x + 3.6513
y = 1.2088x + 3.2947
y = -1.545x + 4.119
y = -2.2515x + 4.3617
= -2.8791x + 4.3476
= -2.0871x + 4.0588
August
4-Month Delay
y = -2.4102x + 3.6718
June
May
April
December
Linear Trend Line
Parameters
January
December
July
August
Length of
Delay
=
0.122
0.261
0.384
0.306
0.481
0.000
0.118
0.289
-
-
Weak
Negative
0.033
0.046
-
-
-
-
0.263
0.363
0.344
Weak
Negative
Weak
Weak
-
Ne ative
Negative
-
0.114
0.094
0.007
0.076
0.066
0.038
-
-
-
-
0.309
0.251
0.527
0.190
Weak
Weak
Weak
Moderate
Very Weak
Positive
Negative
Negative
Negative
Negative
0.274
Weak
Negative
0.278
The findings presented in Table 5 are similar to the results discussed in Section 4.2.2.2, which
compared total monthly precipitation and the strength of the SOI. The precipitation of four
months was determined to be impacted by S01, with respect to both, total precipitation and the
number of heavy precipitations events. Those four months are January, February, May, and
December.
The results in this section differ from the results of Section 4.2.2.2 because one month
(August) was identified to have a positive correlation between the strength of the SOI and the
number of heavy precipitation events experienced. This indicates the effect of S0I on the
number of heavy precipitation events is the opposite of what was observed from the negative
56
correlations between SOI and total precipitation in Section 4.2.2.2. A positive correlation
between SOI and heavy precipitation events means that a higher SOI is associated with a greater
number of heavy precipitation events. The positive correlation between April SOI and August
heavy precipitation can be observed in Figure 4-6.
8
y= 1.2088x + 3.2947
R2
=
0.278
6
0
U
E
'E
41
2
00
-2
-1
0
April - Southern Oscillation Index (SOI)
12
Figure 4-6: August Heavy Precipitation vs. April SOI (4-Month Delay)
It seems unusual that the number of heavy precipitation events in August only has a correlation
with April SOI and not any of the months following April. Furthermore, it is unique that August
total precipitation was seemingly unaffected by the SOI of any preceding months. The positive
correlation detected may indicate a trend between April SOI and heavy precipitation events in
August, or it may be the result of a separate climate system, such as IOD, having a greater
impact on precipitation in the basin during August. However, one study did identify a negative
correlation between El Nifio conditions and precipitation in the months of August and September
in Uganda (Phillips & McIntyre, 2000).
The number of heavy precipitation events in both December and February was most
affected by the state of the SOI in October (a 2-month delay to December and a 4-month delay to
February); however, October SOI was not found to have a correlation with heavy precipitation in
January. Although no correlation was detected between October SOI and heavy precipitation in
January, total precipitation in January did appear to be affected by the October SOI. Figure 4-7
shows the effect that October SOI has on heavy precipitation the following February.
57
8
y =-1.5796x + 3.214
R 2= 0.481
4
0
00
-2
0
-l
2
1
October - Southern Oscillation Index (SOT)
Figure 4-7: February Heavy Precipitation vs. October SOI (4-Month Delay)
It is shown in Figure 4-7 that the three lowest October SOI values (points farthest to the left) are
associated with greatest number of heavy precipitation events experienced in February. The
positive October SOI values on the right side of the plot are shown to result in zero heavy
precipitation events in February three different times.
The plot representing the relationship between October SOI and heavy precipitation
events in December is shown in Figure 4-8.
12
y= -2.8791x + 4.3476
R2= 0.527
9
6
00
3e
0~
-2
-
-1
0
October - Southern Oscilliation Index (SOl)
1
2
Figure 4-8: December Heavy Precipitation vs. October SOI (2-Month Delay)
The results depicted in Figure 4-8 for October SOI and December heavy precipitation are similar
to what was observed in Figure 4-7 for October SOI and February heavy precipitation. In both
58
plots the lowest SOI values are associated with the highest number of heavy precipitation events
while the occurrence of no heavy precipitation events is associated with positive SOI values.
The analysis performed in this section shows that of the six main rainy season months
(MAM and SON) only April and May were affected by the SOI. The precipitation experienced
during the long rains of SON was found to have to no correlation with SOI of any preceding
months. SOI will not be a reliable indicator for the prognostication of increased total or heavy
precipitation during the SON season. The linear trend line parameters for the six months with no
correlation between SOI and heavy precipitation events are found in Appendix G.
4.2.3
Seasonal Precipitation Compared with ONI Strength
Seasonal precipitation was compared to the strength of the ONI to determine what effect
Pacific Ocean sea surface temperatures have on precipitation in the Manafwa River Basin. NSP
values and the number of heavy precipitation events each season were plotted against the ONI in
a series of delays.
Comparison of Precipitation and ONI of all Seasons Collectively
4.2.3.1
NSP values for every season over the 16-year study period were plotted against the
corresponding seasonal ONI value for all seasons. Figure 4-9 shows NSP compared to the
strength of the ONI.
2.4
y =0.1338x + 1.0268
R2 =0.16
0
0.8
0
-2
-1.5
-1
0.5
0
-0.5
Oceanic Ninio Index (ONI)
Figure 4-9: All Seasons - NSP vs. ONI (No Delay)
59
1
1.5
2
The R2 value of 0.16 in Figure 4-9 is stronger than what was observed when simultaneously
plotting precipitation of all months against the corresponding SOI in Figure 4-1. A series of
seasonal precipitation delays from the ONI were also plotted but no significant correlations were
determined when observing all months collectively.
The data points plotted in Figure 4-9 were then separated into the three separate data sets
according to the ENSO phase in which they fell: El Nifno, La Ninia, and Neutral. A linear trend
line was plotted for both the El Nifio and La Niia events to observe if a stronger trend exists in a
specific ENSO phase. The results of this analysis are shown in Figure 4-10.
2.4
9 El Nino
* La Nifia
* Nuetral
R2 =0.33
Z-
1.6
R2=
-z
0.09
0.8
* *;
9
9
00
0 -- _
-2
-1.5
-1
-0.5
0
0.5
Oceanic Niho Index (ONI)
l
1.5
2
Figure 4-10: All Seasons - NSP vs. ONI (No Delay) - By ENSO Phase
The R 2 value (0.33) for the El Nifno data set increased substantially from the all-phase data set (in
Figure 4-9) indicating that when ENSO is in the El Niino phase, total precipitation may have a
stronger correlation to the magnitude of the ONI.
60
The number of heavy precipitation events occurring each season was then compared to
the strength of the ONI. The results are shown in Figure 4-11.
30
8
4
W
y= 1.8434x + 9.1003
R2 = 0.12
-~i)
* *
20
0
-2
-1.5
-1
0.5
0
-0.5
Oceanic Nifio Index (ONI)
1
1.5
2
Figure 4-11: All Seasons - Heavy Precipitation vs. ONI (No Delay)
The plots representing the ONI's effect on total seasonal precipitation (Figures 4-9 and 4-10) and
the number of heavy precipitation events per season (Figure 4-11) all have linear trend lines with
positive slopes. This indicates that a negative ONI (La Nifia conditions) is associated with
below-average precipitation, and less heavy precipitation events. Conversely, a positive ONI (El
Nifio conditions) is associated with above-average precipitation and more heavy precipitation
events. Although the correlations observed in these plots are too weak to confirm the
relationship between seasonal precipitation and the ONI, it is evident that the ONI may be a
better indicator than the SOI for precipitation in the Manafwa River Basin. This hypothesis is
further explored by observing the effect of the ONI on specific seasons.
4.2.3.2 Comparison of Total Precipitation and ONI for Independent Seasons
Total precipitation of specific seasons was then evaluated against the strength of the ONI.
The analysis of individual seasons allowed strong trends to emerge for certain seasons, while
other seasons were relatively unaffected. The DJF season was determined to be more affected by
the ONI in terms of total precipitation than any other season, and was the only season deemed to
have a significant correlation with the ONI. Furthermore, the trend between DJF precipitation
and the strength of the ONI was the strongest observed thus far in this study. The correlation
parameters for DJF total precipitation and ONI are provided in Table 6.
61
Table 6: Seasons with a Correlation between Total Precipitation and ONI
Precipitation
Season
Index
Season
Length of
Delay
Normalized Seasonal
Precipitation
Oceanic Niio
Index
Index
(NSP)
(ONI)
Precipitation
December
to
February
DJF
NDJ
OND
SON
_________
_
F
ASO
Linear Trend Line
Parameters
Characteristics of
Trend
on
-to-
No Delay_
=NSP
x=ON!
2-Season Delay
3-Season Delay
= 0.4072x
y = 0.4002x
y = 0.4262x
y = 0.4458x
4-Season Delay
y=
_1-Season Delay
REqua
Significant
only if
R2 > 0.185
Positive
or
Negative
+ 1.1602
+ 1.1361
+ 1.1051
+ 1.0713
0.618
0.658
0.662
0.591
Strong
Strong
Strong
Strong
Positive
Positive
Positive
Positive
0.471lx + 1.0628
0.471
Moderate
Positive
The results displayed in Table 6 show that DJF precipitation has "strong" and "moderate"
positive correlations with the ONI of all preceding seasons that were investigated. This analysis
supports the results produced in 4.2.2 from studying the impact of the SOI on monthly
precipitation. The three months of December, January, and February were all determined to
experience precipitation impacts (total and heavy) based on the strength of the SOI. It is
reasonable that a second ENSO related index, ONI, also impacts precipitation in these months.
The trends observed between DJF precipitation and ONI were all positive correlations,
indicating that a higher ONI (El Nifto conditions) is associated with greater precipitation. The
observed R2 values resulted in the classification of trends as "moderate" and "strong." The SOI
was not found to have a "strong" correlation with precipitation of any month. The results shown
in Table 6 indicate that the ONI can be monitored during the ASO and SON seasons, to gauge a
forecast of what the precipitation may be like from December to February. The relationship
between the ONI of ASO and the precipitation of DJF is shown in Figure 4-12.
2.4 T
y =0.4711x + 1.0628
71
R 2 =_0.4
1.6
0
--
-2
-l
0
ASO - Oceanic Niio Index (ONI)
Figure 4-12: DJF NSP vs. ASO ONI (4-Season Delay)
62
1
2
As depicted in Figure 4-12, positive ONI values during ASO are associated with above average
total precipitation during DJF.
Figure 4-13 shows the strong positive correlation between DJF precipitation and the ONI
of SON.
24A
v3o
y = 0.4458x + 1.0713
R2 = 0.591
1.6
p
-
_________________________
9
CL
p
U
0.
9
0.8
I
9,
9
9
0
- ---
-
- -
-1
-2
0
SON - Oceanic Niho Index (ONI)
2
I
Figure 4-13: DJF NSP vs. SON ONI (3-Season Delay)
When observing DJF total precipitation from the 4-season delay (Figure 4-12), to the 3-season
delay (Figure 4-13) from the ONI, the correlation increases in strength from "moderate" to
"strong." This indicates that the ONI signal for precipitation response in DJF becomes more
reliable as the season gets closer in time. The correlations shown in Figures 4-12 and 4-13
would provide sufficient lead-time with which to aid in the forecasting of future total
precipitation.
Figure 4-14 shows the strong positive relationship between the ONI of OND and the total
precipitation of DJF.
2.4
y=y
9
0.4262x + 1.1051
R =0.662
1.6
-o>
0.8
0
-2
-1
0
OND - Oceanic Nifio Index (ONI)
Figure 4-14: DJF NSP vs. OND ONI (2-Season Delay)
63
1
2
The 2-season delay of DJF precipitation plotted in Figure 4-14 shows an even stronger
precipitation response than what was observed for the 3-season delay (Figure 4-13) and 4-season
delay (Figure 4-12). The R2 value (0.662) in Figure 4-14 indicates the correlation between the
ONI of OND and the precipitation of DJF to be the strongest trend that has been observed in this
study.
4.2.3.3
Comparison of Heavy Precipitation and ONI for Independent Seasons
The seasonal precipitation analysis was then expanded to search for a relationship
between heavy precipitation events and the strength of the ONI. Two seasons were detected to
have a correlation between heavy precipitation and the ONI, while two seasons had no
correlation. The two seasons with a correlation were DJF and JJA. The two main rainy seasons,
MAM and SON, are essentially unaffected by the ONI according to this study. The trend line
parameters corresponding to the seasons with no correlation between heavy precipitation and
ONI can be found in Appendix H.
Table 7 shows the trend line information for the two seasons (DJF and JJA), which have
a correlation between heavy precipitation and ONI.
Table 7: Seasons with a Correlation between Heavy Precipitation and ONI
Precipitation Season
Index Season
________________________
Length of
Linear Trend Line
Delay
Characteristics of Trend
Parameters
_____
2
Heavy Precipitation
Events
Oceanic Nifio
Index
Equation
y # heavy x = ONI
R
(ONI)
Index
-toPrecipitation
December
to
February
DJF
NDJ
OND
SON
No Delay
I-Season Delay
2-Season Delay
3-Season Delay
= 4.0105x + 9.4441
y = 3.9534x + 9.2108
= 4.232x + 8.9106
y = 4.4695x + 8.5818
0.526
0.562
0.572
0.521
ASO
4-Season Delay
JJA
MJJ
AMJ
MAM
No Delay
I-Season Delay
2-Season Delay
3-Season Delay
4-Season Delay
y 4.6302x + 8.484
y= -1.645x + 8.7422
y = -3.222x + 8.5347
= -3.712x + 8.450
= -2.3514x + 8.541
y = -1.0834x + 8.701
0.399
0.072
(DJF)
June
Jo
to
August
I JFMA
Significant
only if
R 2 > 0.185
Positive
or
Negative
Moderate
Moderate
Moderate
Moderate
____________
Moderate
Positive
Positive
Positive
Positive
Positive
-
-
0.188
0.234
Very Weak
Very Weak
Negative
Negative
0.150
0.064
-
-
-
The results in Table 7 indicate that DJF heavy precipitation has a moderate positive correlation
with ONI for all seasons investigated. The heavy precipitation events experienced in JJA was
found to have a very weak negative correlation with ONI of two preceding seasons. As seen in
Tables 6 and 7 the DJF season was found to have similar correlations for heavy precipitation and
total precipitation. The R2 values fell slightly from Table 6 to Table 7 for all JJA precipitation
delays plotted, which resulted in the reclassification of the correlation strength as "moderate."
64
The moderate positive correlations, which exist for DJF heavy precipitation compared to the
strength of the ONI, indicate that the correlation is well established. This suggests the ONI of
previous seasons can be used to predict the possibility of heavy precipitation events in an
upcoming DJF season.
Figure 4-15 shows the correlation between the ONI of SON and the heavy precipitation
of DJF.
20
y =4.4695x + 8.5818
R2 =0.521
15
z
S*.
10
-
5
_
_
_
__
_
_
_
_
---
0
-2
-1
0
SON - Oceanic Niio Index (ONI)
1
2
Figure 4-15: DJF Heavy Precipitation vs. SON ONI (3-Season Delay)
Figure 4-15 shows that the highest three ONI values for SON were associated with the DJF
seasons with the greatest number of heavy precipitation events. High ONI values during SON
can be used to anticipate the occurrence of heavy precipitation events during DJF.
As discussed in Section 4.2.3.2, the JJA season was not detected to have a relationship
between total precipitation and ONI. When the month of August was observed independently, in
Figure 4-6, total precipitation in August had a weak positive correlation between heavy
precipitation and the SOI. That positive correlation indicates that El Niio conditions lead to
decreased heavy precipitation events (the opposite of expected). Similar results were observed
for this seasonal analysis using ONI as the indicator. JJA was found to have a very weak
negative correlation between heavy precipitation events and the ONI of the two preceding
seasons (AMJ and MJJ). The negative correlation, when observing the ONI, indicates that El
Nio conditions lead to a decreased number of heavy precipitation events during JJA. Figure
4-16 shows the very weak negative correlation between the ONI of AMJ and the heavy
precipitation events in JJA.
65
15
0
y =-3.7124x + 8.4503
R = 0.234
V0)
>e
0
S5T
0
-1
-0.5
0
AMJ - Oceanic Nifio Index (ONI)
0.5
1
Figure 4-16: JJA Heavy Precipitation vs. AMJ ONI (2-Season Delay)
The 2-season delay correlation detected in Figure 4-16 is not as useful, from a practical
standpoint, as a 3 or 4-season delay would be for predicting the occurrence of future heavy
precipitation events. Although this 2-season delay correlation was detected, it does not provide
sufficient advanced warning because the ONI measurement of AMJ is not complete until after
the JJA season has already started. The 2-season delay plots have a one-month overlap between
the seasons of ONI and precipitation. For example, by the time the ONI for AMJ is calculated,
the JJA season would already be one full month (June) into the season. The correlation is still
helpful because it can aid in understanding what precipitation may be like for the remainder of
the season. There are also strategies, used by agencies such as NOAA, for predicting the
movement of the indices based on the current climate activity; the prediction of such indices can
then translate into the prognostication of future precipitation conditions in the basin.
4.3 Observed Effect of IOD in the Manafwa River Basin
4.3.1
General Relationship Between IOD and Precipitation
An initial investigation of the impact that the mode of IOD has on precipitation in
Manafwa River Basin was conducted. This analysis serves as an indicator of the general effect
that Indian Ocean sea surface temperatures have on precipitation in the basin. The results from
this analysis are detailed in Table 8.
66
Table 8: General Precipitation Characteristics of IOD Modes
Description
Mode of IOD
of Monthly Precipitation Parameters
Number of Months in
Each Mode
Normalized Monthly
Precipitation
(NSP)
Positive
Negative
Neutral
Overall Total
161
31
0
With Above Average Precipitation
(NMP > 1.0)
66
13
0
With Below Average Precipitation
(NMP < 1.0)
95
18
0
Average
1.00
0.98
-
Minimum
0.01
0.19
-
Maximum
2.91
1.73
-
Range (Max - Min)
2.9
1.55
-
The 16-year study period consists of 192 months. The majority of the study period, 161 of 192
months (84%), was characterized by a positive monthly DMI. Considering the poor distribution
of negative and positive IOD events during the 16-year period, a longer study period with a
wider spread of IOD events is needed to more clearly define any trends.
There are 161 months that are classified as a positive IOD event, 58% of which (95) had
below average precipitation for that month (NMP < 1.0). The distribution of below to aboveaverage months was similar for the 31 months when the IOD was negative; 58% of these months
(18) had precipitation that was below average for that month. Given the near identical
proportion of months with above-average precipitation to months with below-average
precipitation for both positive and negative IOD events, it does not appear that the mode of IOD
has a strong impact on precipitation in the basin.
The average NMP for the positive and negative modes were 1.00 and 0.98, respectively.
An NMP value of 1.0 indicates average precipitation, meaning that both positive and negative
IOD modes received within 2% of average monthly precipitation overall. The maximum NMP
observed for the positive IOD was 2.91, much higher than the maximum NMP of 1.73 for
negative IOD. The results also show that the range of NMP values was much greater for the
positive mode than for the negative. This could indicate that positive IOD events have greater
variability in total precipitation; however, these numbers may be skewed by the existence of two
extreme outliers in monthly data. January 1998 (NMP=2.91) and January 2012 (NMP=0.1)
67
received abnormally high and low levels of precipitation, respectively, and both corresponded to
positive IOD events. These are extreme outliers and do not properly represent the distribution of
the monthly precipitation data set.
This analysis does not provide conclusive results of the overall effect of DMI on
precipitation in the Manafwa River Basin. There are multiple explanations for why no effect was
observed: only extreme IOD events may affect precipitation, an index to precipitation delay was
not accounted for, and precipitation is likely to be effected in only certain months.
4.3.2
Monthly Precipitation Compared with DMI Strength
Monthly precipitation was plotted against corresponding DMI values to determine if a
relationship exists between the two. Delays were plotted to determine if precipitation is most
affected by the Indian Ocean conditions of current months or previous months.
4.3.2.1
Comparison of Precipitation and DMI for all Months Collectively
The NMP values and the number of heavy precipitation events for all months over the
16-year study period were plotted against the corresponding monthly DMI. The results are
shown in Figures 4-17 and 4-18, respectively.
y =0.3384x + 0.923
R2=0.04
0
0
2
*
0
00
..
.
0
-1
-0.5
0
Dipole Mode Index (DMI)
Figure 4-17: All Months - NMP vs. DMI (No Delay)
68
0.5
1
12
0
y
1.7016x + 2.54
R2 = 0.04
8
*0
e
-
0~
0
e
. e
e
@
Oft
0 00
so
m.e
@
es
.
*
@
4
-1
0
-0.5
0.5
1
Dipole Mode Index (DMI)
Figure 4-18: All Months - Heavy Precipitation vs. DMI (No Delay)
The weak R2 values for both plots indicate that no significant correlation exists between
precipitation and DMI when observing all months collectively. The total precipitation and
number of heavy precipitation events was also plotted against the ONI of previous months.
Similarly, no correlations were detected for the series of delays between DMI and precipitation
when observing all months collectively.
4.3.2.2
Comparison of Total Precipitation and DMI for Months Independently
The analysis performed in Section 4.2 showed that ENSO indices only impact the
precipitation of certain months, and that viewing all months collectively in Section 4.2.2.2
prevented the detection of a correlation between indices and precipitation. It was expected that
the analysis of individual months would also allow trends to emerge between total precipitation
and the DMI. The results from this analysis are shown in Table 9.
69
Table 9: Months with a Correlation between Total Precipitation and DMI
Precipitation
Index
Length of
Month
Month
Delay
Normalized Monthly
Precipitation
(NMP)
Dipole Mode
Index
(DMI)
Index
-toPrecipitation
January
No Delay
y = 0.8508x + 0.8364
1-Month Delay
January
December
November
October
2-Month Delay
3-Month Delay
y = 1.4177x + 0.7
y = 0.8584x + 0.7908
y = 0.7166x + 0.7305
September
February
4-Month Delay
No Delay
y = 0.8727x + 0.6258
y = 0.2476x + 0.9514
January
December
November
October
March
February
January
December
I-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
y = 1.3314x + 0.7439
y = 0.8506x + 0.82
y= 0.5945x + 0.8551
y 0.5725x + 0.784
y = -0.0516x + 1.0115
y = -0.5221x + 1.1024
y = -0.4686x + 1.0901
y = -0.3999x + 1.0846
November
4-Month Delay
April
March
February
January
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
y = -0.4618x + 1.1126
y = -0.4281 x + 1.0853
December
4-Month Delay
May
April
March
February
January
July
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
June
4-Month Delay
November
October
September
August
February
March
April
May
October
November
EquationR
NMP x = DMI
Significant
only if
R2 > 0.185
y=
Positive
or
Negative
0.086
0.401
0.326
0.202
Moderate
Weak
Very Weak
Positive
Positive
Positive
0.208
Very Weak
Positive
0.007
0.201
0.138
0.150
-
-
0.124
-
0.001
0.082
0.069
0.084
-
0.248
Very Weak
Negative
y = -0.1632 + 1.0362
y = -0.065x + 1.0128
y = 0.0089x + 0.9983
0.239
0.040
0.008
0.000
Very Weak
-
Negative
-
0.1358x + 0.9713
y = 0.3376x + 0.9521
0.057
0.182
0.010
0.373
0.045
0.011
0.001
0.191
0.297
December
November
October
September
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
y = 0.1048x + 0.9791
y = -0.6127x + 1.1361
y = -0.1942x + 1.0381
y = 0.0961 x +0.9815
y= 0.0377x + 0.9891
y = 0.7413x + 0.8761
y = 0.7043x + 0.9
y = 0.5023x + 0.9
y = -0.481x + 1.1069
y = 0.0813x + 0.9746
y = 0.1 385x + 0.9498
y = -0.068 1x + 1.0206
y = 0.0704x + 0.9796
y = 0.5808x + 0.9029
y= 0.8637x + 0.8475
y 0.7121x + 0.7779
y = 0.729lx + 0.736
y = 0.4724x + 0.8568
y= 0.5019x + 0.8544
y= 2.136 1x + 0.6437
y = 0.9215x + 0.8373
y = 1.1741 x + 0.6338
y = 1.3666x + 0.5051
August
4-Month Delay
y=
June
May
April
March
October
September
August
July
December
Classification of Trend
Parameters
Positive
-
July
July
Linear Trend Line
y
70
0.6621 x
+ 0.7994
0.082
0.086
Very Weak
-
-
-
-
Weak
Negative
-
Very Weak
Weak
-
0.011
0.024
0.006
-
0.005
0.190
-
0.368
0.369
0.285
0.121
-
-
Positive
Positive
-
-
Very Weak
Positive
Weak
Weak
Weak
Positive
Positive
Positive
-
0.100
-
0.267
0.112
0.266
0.267
Weak
0.064
-
Positive
-
Weak
Weak
Positive
Positive
Table 9 shows that nine months were determined to have a correlation between total precipitation
and the magnitude of the DMI. June, August, and September were the only months with no
observed correlation and are excluded from discussion and inclusion in tables and figures in this
section. The linear trend line parameters corresponding to months with no correlation between
total precipitation and DMI can be found in Appendix I.
In this study, it has been shown that ENSO indices have the greatest correlation with
precipitation in December, January and February. These months were also shown to be impacted
by DMI. Total precipitation in December, January, and February all had correlations with the
DMI of at least one preceding month. January precipitation was observed to have the greatest
correlation with the magnitude of the DMI in December. Figure 4-19 shows a moderate positive
correlation between the DMI of December and the total precipitation of January.
3
y
1.4177x + 0.7
R2 =0.401
2
0
-1.2
-0.6
0
December - Dipole Mode Index (DMI)
0.6
1.2
Figure 4-19: January NMP vs. December DMI (1-Month Delay)
The trend shown in Figure 4-19 has an R2 value of 0.401, which is greater than what was
observed for January precipitation against SOI in Tables 4 and 5. This indicates that DMI has a
stronger correlation than SOI for total precipitation in January. The trend line in Figure 4-19 has
the steepest slope of any correlation shown in Table 9, which indicates that variations in DMI
have an even greater impact on total precipitation in January than for any other month. The
outlying point in the upper right of Figure 4-19 represents an unusually high total precipitation
coupled by and unusually high DMI. The presence of this one point is very responsible for
magnifying the slope and R2 value associated with the linear trend line. The other points on the
plot all appear in the same vicinity in comparison to the far right point; therefore, Figure 4-19
shows that DMI may only affect precipitation when the DMI is sufficiently high (or low) and the
resultant precipitation response may be severe.
71
The three months of the long rainy season (March, April and May) are the only months
that were identified to have a negative correlation between total precipitation and the strength of
the DMI. Figure 4-20 shows the weak negative correlation between March DMI and total
precipitation in May.
1.8
-
-0.612 7 x + 1.1361
R 2 =0.373
Sy
-
1.2
0>0
N
0.6
0
-1
0
-0.5
0.5
1
March - Dipole Mode Index (DMI)
Figure 4-20: May NMP vs. March DMI (2-Month Delay)
The negative correlation in Figure 4-20 indicates that a lower (negative) DMI in March is
associated with greater precipitation in May. As shown in Table 9, low DMI conditions between
November and April may lead to increased precipitation during the long rainy season of MAM;
however, the "weak" and "very weak" correlations between DMI and total precipitation do not
permit a high level of confidence in this trend.
72
The relationship between DMI and total precipitation transitions back to a positive
correlation when observing the total precipitation of July. Figure 4-21 shows the weak positive
correlation between May DMI and total precipitation in July.
2
+ 0.9
y =0.7043x
R2 =0.297
1.5
0
00
0
-1
-0.5
0
0.5
1
May - Dipole Mode Index (DMI)
Figure 4-21: July NMP vs. May DMI (2-Month Delay)
The positive correlation shown in Figure 4-21 demonstrates that a high DMI in May can lead to a
greater amount of precipitation in July. As shown in Table 9, a high DMI in June may
additionally indicate greater total precipitation in July. In Section 4.2.3.3 the JJA season was
determined to have a very weak negative correlation between heavy precipitation and ONI, and
Section 4.2.2 shows that precipitation in July was unaffected by the SOI. This indicates that
precipitation in July may be more affected by DMI than by either ENSO index.
The analysis of ENSO indices in Section 4.2 shows that SOI and ONI do not impact
precipitation during the short rains of September, October, and November (SON); however, the
investigation performed in this section shows precipitation in October and November is impacted
by DMI. The relationship between June DMI and October precipitation is a very weak positive
correlation; however, the DMI correlations with November total precipitation get stronger. The
plot shown in Figure 4-22 demonstrates the positive correlation between October DMI and total
precipitation in November.
73
2.4
y
.
b
-
0.6996x + 0.799
R'2=0.369
1.6
0
-1.0
-0.5
0.0
October - Dipole Mode Index (DMI)
0.5
1.0
Figure 4-22: November NMP vs. October DMI (1-Month Delay)
Figure 4-22 shows that high DMI values in October may indicate a greater amount of total
precipitation in November. As specified in Table 9, the DMI values of September may also be
an indicator for total precipitation in November, though the correlation is weaker than for
October DMI values.
4.3.2.3
Comparison of Heavy Precipitation and DMI for Months Independently
The analysis of correlations between monthly heavy precipitation events and the strength
of DMI yielded correlations for seven months: January, February, March, May, August,
November, and December. These findings are slightly different than the analysis of DMI and
total precipitation, which was discussed in Section 4.3.2.2, because three of the months that had a
correlation between DMI and total precipitation (April, July, and October) were not identified as
having a correlation between DMI and heavy precipitation.
Conversely, August total
precipitation was not determined to have a correlation with DMI, while heavy precipitation in
August does have a correlation with DMI. Table 10 shows the seven months, which were
determined to have a correlation between heavy precipitation events and DMI.
74
Table 10: Months with a Correlation between Heavy Precipitation and DMI
Index
Month
Length of
Delay
Heavy Precipitation
HayIndex
Events
Dipole Mode
(DMI)
Index
-toPrecipitation
y
January
No Delay
y=
1-Month Delay
January
December
November
October
2-Month Delay
3-Month Delay
September
4-Month Delay
February
January
December
November
No Delay
Delay
y = 3.8089x +2.0175
2-Month Delay
3-Month Delay
y= 2.9376x + 2.1285
y = 2.258x +2.1997
October
4-Month Delay
March
No Delay
February
February
March
May
August
November
December
1-Month
1-Month
Delay
Characteristics of
Trend
Linear Trend Line
Parameters
Precipitation Month
=
quation
Eqato
# heavy x = DMI
1.2287x +
2
ignificant
only if
R2 > 0.185
Positive
or
Negative
0.025
0.359
0.280
0.204
-
-
y = 3.5985 + 1.5511
y 2.1342x + 1.7923
y = 1.933x + 1.5855
Weak
Weak
Very Weak
Positive
Positive
Positive
1.1195
0.293
Weak
Positive
0.011
0.147
0.147
0.193
-
-
-
-
-
-
Very Weak
Positive
1.9662
0.146
-
-
y =-l.135x + 2.3147
0.017
0.068
0.189
0.103
-
-
y = 2.782x +
2.0762
y =1.0606x + 2.541 9
y = 2.084x +
y
=
-2.0948x + 2.4735
January
December
2-Month Delay
3-Month Delay
y = -3.4383x + 2.7238
y =-1.9617x + 2.4776
November
4-Month Delay
y
May
April
March
No Delay
I-Month Delay
2-Month Delay
y 0.3122x + 2.7682
y= -2.929x + 3.3959
y = -3.5786x + 3.6076
February
January
3-Month Delay
4-Month Delay
y
y
August
July
June
May
April
November
October
September
August
July
December
November
October
September
August
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
=
-1.9195x + 2.5303
-0.6239x + 2.9349
-0.4439x + 2.8979
y = 3.1528x + 2.7321
y =2.9715x + 2.8255
=
y =-l.6235x + 3.959
y = -1.6398x + 3.9203
y = 0.0222x + 3.6831
y 4.2091 x + 2.3194
y = 3.3085x + 2.0306
y= 2.921x + 2.0047
y= 1.7364x + 2.5363
y= 1.7441x + 2.5566
y= 10.804x + 1.4478
y =4.1331x + 2.5203
y = 4.8553x + 1.7356
y = 5.3465 + 1.3138
y = 2.5551 x + 2.4757
-
-
Very Weak
Negative
-
-
0.219
0.004
Very Weak
Negative
-
-
0.179
0.306
0.011
-
-
Weak
Negative
-
-
0.006
-
-
0.202
0.131
0.024
Very Weak
-
Positive
-
-
-
0.042
Weak
Weak
Positive
Positive
-
-
-
-
0.000
0.292
0.266
0.153
0.055
0.040
0.342
0.112
0.228
0.205
-
-
Very Weak
Very Weak
Positive
Positive
0.047
-
-
-
-
Weak
Positive
As seen in Table 10, the strongest correlation classification observed between DMI and heavy
precipitation in any month was "weak". Although DMI may impact heavy precipitation events
in seven months, the small R2 values indicate that DMI may not be a reliable gauge for
predicting heavy precipitation events.
75
The number of heavy precipitation events in January had the greatest correlation to the
magnitude of the DMI. Figure 4-23 depicts the weak positive correlation between December
DMI and heavy precipitation events in January.
8
y =3.5985x + 1.5511
R2 =0.359
61
>
:j41
0
2*
0I
-1.2
_
0
-0.6
0.6
1.2
December - Dipole Mode Index (DMI)
Figure 4-23: January Heavy Precipitation vs. December DMI (1-Month Delay)
As seen in Figure 4-23, a high DMI in December may lead to an increase in the number of heavy
precipitation events in January. This is similar to the results shown in Figure 4-19, which depicts
the correlation between December DMI and total precipitation in January. The outlying point
in the top right of both plots is associated with a high DMI and precipitation (total and heavy)
that is well above average. The presence of this one point is a strong contributor to creating the
observed positive trend. As previously stated in Section 4.3.2.2, an extreme IOD event may be
required to actually impact total precipitation and heavy precipitation, but the impact from the
extreme IOD event may be severe.
The analysis of total precipitation showed that February was most impacted by the
January DMI. This analysis of heavy precipitation shows that February is most impacted by
November DMI. It is possible that total and heavy precipitation in February may be influenced
by the window of DMI values between November and January. The positive relationship
indicates that high DMI values from November to January may lead to a greater amount of total
and heavy precipitation in February.
In Section 4.3.2.2 the three months that constitute the long MAM rainy season (March,
April, and May) were shown to have a negative correlation between total precipitation and DMI.
Similarly, the analysis performed in this section determined that a negative correlation exists
between DMI and the heavy precipitation events of March and May, indicating that a higher
DMI is accompanied by less heavy precipitation events. For the number of heavy precipitation
76
events in March, the correlations extend back to the DMI of November, a 4-month delay. From
the correlations shown in Tables 9 and 10, increased total and heavy precipitation would be
expected during March through May following a period of November through February that is
characterized by low (negative) DMI values. Figure 4-24 shows the negative correlation between
March DMI and the number of heavy precipitation events in May.
6
y
0
-3.5786x + 3.6076
R 2=0.306
0@
4
2
0
-1
-0.5
0
March - Dipole Mode Index (DMI)
0.5
1
Figure 4-24: May Heavy Precipitation vs. March DMI (2-Month Delay)
Figure 4-24 shows that the highest two DMI values during March were associated with the
smallest number of heavy precipitation events experienced in May.
The number of heavy precipitation events in November and December had the strongest
correlation with the DMI of the current month (no-delay). The no-delay relationship is not as
valuable, from a precipitation forecasting perspective, as a relationship that has an index to
precipitation delay. Both months, November and December, were also determined to have
correlations with the DMI of previous months. For example, Figure 4-25 shows the positive
correlation between October DMI and heavy precipitation events in November.
77
10
y = 3.3085x + 2.0306
R2 = 0.266
8
6
4
0
0
2
0
0
00
-_
__
_
__
_
0
-1.2
0
-0.6
0.6
1.2
October - Dipole Mode Index (DMI)
Figure 4-25: November Heavy Precipitation vs. October DMI (1-Month Delay)
As indicated in Figure 4-25, high DMI values in October can lead to an increased number of
heavy precipitation events in December. The point on the plot, farthest to the right, has the
highest October DMI and the greatest number of heavy precipitation events in November.
4.4 Relationship Between Past Flood Events and Climate Indices
A list of historical flood events in the Manafwa River Basin was compiled from records
accessed through the Red Cross, Emergency Events Database (EM-DAT), Dartmouth Flood
Observatory, and news agencies. An investigation was performed to determine if a certain
pattern of climate index values is most likely to precede a flood. The study took note of the SOI,
ONI, and DMI of the month of flooding and of the months prior to flooding. It was further
examined in which phase of ENSO (El Nifio, La Nifia, and neutral) the flood occurred, and how
many consecutive seasons into that phase it was. By identifying the climate conditions that gave
way to historical floods, it is possible that monitoring for similar conditions would help act as an
indication of heightened risk of future floods. The results of this study are shown in tables found
in Appendix J. A more extensive list of historical floods is necessary to perform an in depth
analysis on the climate conditions that typically precede floods.
78
5
5.1
Precipitation Trends for Consecutive ENSO Seasons
Method
The analysis in Section 4.2 shows correlations between the strength of ENSO climate
indices (SOI and ONI) and the precipitation experienced in the Manafwa River Basin during
certain months. The examination performed in this section aims to determine if there is a
specific trend of precipitation that develops through each successive season of an ENSO event.
The individual seasons of El Niio and La Nifia episodes were analyzed to determine if any
patterns exist. This study will explore the precipitation patterns of the first five consecutive
seasons of an ENSO event. The five-season study duration was selected for two reasons: (1) the
ONI must be either above 0.5 or below -0.5 for five consecutive three-month over-lapping
seasons to be classified as an ENSO event, and (2) using five seasons permitted no less than four
data points to be factored into any average. It will be determined if the progression of an ENSO
event leads to specific set of precipitation patterns. The analysis of consecutive ENSO seasons
will attempt to explain the relationship between the ONI, total monthly and seasonal
precipitation, the number of heavy precipitation events, and the variability of experienced
precipitation.
5.2
Classifying Precipitation Characteristics of El Ninto Phases
The 16-year study period from January 1998 through December 2013 experienced four
complete El Nifno events. There was an additional El Niio event, which started in AMJ 1997
and ended in MAM 1998; however, the entire duration of the El Nifio event is not within the 16year study period and is not included in the results discussed in this section. The El Nifno events
that occurred during this period had durations between five and ten seasons. It has been
previously established in this paper that El Niio conditions typically lead to above average
precipitation in the Manafwa River Basin (with the exception of JJA having a "very weak"
negative correlation between ONI and heavy precipitation). It was hypothesized that consecutive
El Nifio seasons may lead to a heightened effect on precipitation.
79
Oceanic Nifto Index of Consecutive El Niuo Seasons
5.2.1
The average ONI value for each consecutive season of an El Niio event is shown in
Figure 5-1. The positive trend line indicates that the average ONI increases as an El Nino event
progresses through the first five seasons.
y =0. 105x + 0.46
R2= 0.88
0.8
XS
con2
0
goz
>
0.6
0.4
0
1
3
2
4
5
Consecutive El Niio Season Number
Figure 5-1: Strength of ONI for Consecutive El Nifio Seasons
The strong R2 value (0.88) allows the increasing trend to be commented on with a high level of
confidence. The increasing magnitude of the ONI indicates that the Nifio 3.4 region in the
central equatorial Pacific Ocean is a undergoing a greater departure (warmer) from the average
seasonal temperature of the base period. This paper has previously shown that the strength of
ENSO indices (ONI and SOI) may have an impact on precipitation experienced in the basin;
therefore, it is possible that precipitation may follow the increasing trend of ONI with
consecutive El Ninto seasons.
80
5.2.2
Normalized Seasonal Precipitation of Consecutive El Nifto Seasons
The NSP values for every season of all four El Nino events, which occurred during the
16-year study period, are shown in Table 11.
Table 11: Normalized Seasonal Precipitation (NSP) Values of El Nifto Events
Consecutive
El Nuo
Seasons
Normalized Seasonal Precipitation
(Seasonal Precip / Avg 19 98-2013 Seasonal Precip)
El Nifio
Event 1
El Nifio
Event 2
El Nifio
Event 3
El Nifo
Event 4
1
2
3
4
5
6
0.99
0.83
0.79
0.74
0.85
1.13
1.46
1.61
1.76
1.71
0.58
0.73
0.95
1.00
1.27
1.28
7
8
1.33
0.84
0.96
1.00
0.96
0.85
0.83
0.68
1.54
-
9
10
1.94
-
-
1.06
-
Duration
10
(Season-Y ar)
End Date
(Season-Year)
A
A
Er
AllEvents
(1998-2013)
2.08
0.88
0.99
1.09
1.12
1.17
1.00
1.36
-
1.53
1.41
1.06
1.54
1.67
1.06
7
5
10
8
AMJ-2002
JJA-2004
ASO-2006
JJA-2009
-
JFM-2003
DJF-2005
DJF-2007
MAM-2010
-
0.89
-
(Seasons)
-
The column to the far right shows the average NSP for each consecutive season. The average
NSP value for the first five seasons of El Nifio events was plotted against the corresponding
consecutive El Ninto season number (1 through' 5) and is shown in Figure 5-2. The average NSP
values for consecutive El Ninto seasons, six through ten, are also shown in Table 11; however,
these values were not factored into the analysis because their average was based on too few
events. The fields highlighted in gray in Table 11 are the data, which were factored into the plot
in Figure 5-2.
81
1.4
I
y
=
u.uitx -r- u.OJnu
R2 = 0.95
7
1.2
1.1211
1.09
0.99
08
1.0
0.8
0.8
10
0
1
3
2
4
5
Consecutive El Ninio Season Number
Figure 5-2: Average NSP for Consecutive El Nifio Seasons
The positive slope of the linear trend line shows the increasing amplitude of NSP with each
consecutive season of an El Niino event. The high coefficient of determination (R2=0.95) permits
this trend to be accepted with a high level of confidence. Although only four El Ninio events
were used to generate this plot, it is remarkable to see such a strong increasing trend of
normalized precipitation as El Nifto seasons progress. The author of this paper is not aware of
any other sources that have commented on the amplified precipitation trend of consecutive
ENSO seasons.
The plot shown in Figure 5-2 does not indicate that a particular season will receive more
precipitation than the previous El Nifio season; instead, it shows that each consecutive season
will receive more based on what is average for the particular time of year. For example, the third
consecutive El Nifio season may occur during DJF of one El Ni5to event, and then during SON of
another El Nifio event. The slope of the trendline implies that, on average, each consecutive El
Nifio season receives 7% greater normalized precipitation than the previous season. These
findings do not indicate or suggest that the trend will continue past the fifth El Ninio season.
There was not sufficient precipitation data available to include additional El Nifio events and
involve a greater number of consecutive seasons.
82
5.2.3 Normalized Monthly Precipitation of Consecutive El Nin-o Months
A similar analysis was then performed to compare the average NMP values of the
consecutive months of El Nifio events. Monthly precipitation was explored for two main
reasons: (1) to separate the overlap in the seasonal precipitation data, and (2) to confirm that that
the increasing amplitude of precipitation still exists when observing consecutive months. Each
season shares precipitation data for two of its three months with the season preceding it, and the
season following it. For example, the three consecutive seasons AMJ, MJJ, and JJA all have a
two-month overlap with the adjacent seasons. Plotting monthly data shows the unique monthly
precipitation, uninterrupted by the precipitation that occurred in the month before and after it.
The minimum of five seasons that constitutes an ENSO event translates into a minimum of seven
months.
Figure 5-3 shows the increasing trend in amplitude of monthly precipitation for
consecutive months of the average El Ninio event.
1.6
-
-y
_
0.0888x + 0.6962
R2 =0.61
CZ
e
e
0.7
0
1
2
4
5
3
Consecutive El Nifio Month Number
6
7
Figure 5-3: Average NMP for Consecutive El Niffo Months
The correlation is interrupted by a low average NMP for the sixth month; however, the R2 value
(0.61) demonstrates that the increasing trend still exists. It appears that when plotting the
seasonal data the deviation of the sixth month was hidden by the very high precipitation during
the seventh month. The trend of seasonal precipitation likely has a stronger correlation than
monthly because the total seasonal precipitation makes up for variations or discrepancies in the
monthly data.
83
5.2.4 Heavy Precipitation Events of Consecutive El Nino Seasons
The average number of heavy precipitation events experienced in each consecutive
season of an El Nifio event was then calculated and plotted to determine if a trend exists. The
results are shown in Figure 5-4.
12
y =0.2x + 10.15
R2=0.2
0
0
9
0
1
2
3
Consecutive El Nifio Season Number
4
5
Figure 5-4: Heavy Precipitation for Consecutive El Nbio Seasons
The positive slope of the trend line indicates that, on average, more heavy precipitation events
occur as an El Nifio event progresses. The R2 value (0.2) corresponding to the relationship of
heavy precipitation with consecutive El Ninio seasons is much smaller than what was observed
for the trend of total seasonal precipitation (R2 =0.95) shown in Figure 5-2. Although a positive
slope exists in Figure 5-4, the trend cannot be commented on with confidence. Figure 5-4 shows
that El Nifio seasons 2, 3, and 5 all receive a similar number of heavy precipitation events,
indicating that there is not an actual increase in heavy precipitation events with each season.
5.2.5
Precipitation Variability of Consecutive El Nio Seasons
The standard deviation of the NSP for each consecutive El Nilo season was then
calculated and plotted. The results, seen in Figure 5-5, clearly show the increasing trend of
standard deviation with each consecutive season of an El Nifio event.
84
0.6
-
y =0.0468x + 0.2145
R2 = 0.83
C4'-
0.4
0.2
0
0
1
2
3
4
5
Consecutive El Ninio Season Number
Figure 5-5: Standard Deviation of NSP for Consecutive El Niuo Seasons
The R2 value (0.83) shows that this positive linear correlation can be accepted with confidence.
The plot shows that deviation of NSP becomes greater as the El Niio event progresses,
indicating that precipitation in consecutive seasons becomes more variable, and thus, more
unpredictable (for at least the first five seasons).
The findings in this section have shown that as an El Nifio event progresses, there is an
increasing amplitude of average NSP (Figure 5-2), coupled with an increasing variability in
precipitation (Figure 5-5).
5.2.6
Behavior of Consecutive Seasons in Individual El Nifno Events
The El Ninio behavior detailed in the previous sections is the result of averaging four El
Nifto events together. The characteristics of these four El Niino events were then observed
independently to determine if each event acts consistent with the behavior of the average El Nifto
event.
The ONI for the first five consecutive seasons of each El Nifto event was plotted for
comparison to the increasing trend of average ONI shown in Figure 5-1. The trend of ONI
through consecutive seasons for each individual El Nifto event is shown in Figure 5-6.
85
1.5
1.1
* El Nifio - Event 1
* El Nifio - Event 2
z
0.7
0.7
A
l
Niiio --
vent
* El Nifio - Event 3
+ El Nifio - Event 4
0.3
0
1
2
3
4
Consecutive El Nino Season Number
5
Figure 5-6: ONI of Consecutive Seasons in Each El Nbio Event
All four events yielded linear trend lines with positive slopes, indicating that the ONI is
increasing with consecutive seasons (i.e. the temperature departure of the Nifio 3.4 region is
becoming greater).
The NSP values for the first five consecutive seasons of each El Nifio event were then
plotted to determine if the trend would mimic that of the ONI for the specific individual events.
The plots of NSP values for each El Ninio event are shown in Figure 5-7.
2.0
0
1.5 +* El Nifio - Event I
1.0
-
M
U
M
* El Nifio - Event 2
A El Nifio - Event 3
* El Nifio - Event 4
0.5
0
I
2
4
3
5
Consecutive El Niho Season Number
Figure 5-7: NSP of Consecutive Seasons in Each El Nifio Event
Figure 5-7 shows that only two of the four trend lines for NSP with consecutive El Nifto seasons
have a positive slope, indicating that the increasing trend of NSP does not exist for all El Nifio
events.
86
The slope and R2 values associated with all trend lines in Figures 5-6 and 5-7 are shown
in Table 12.
Table 12: Observed Trend Line Parameters for Individual El Nino Events
El Nio Event
Duration
(seasons)
Oceanic Nbio Index
(Figure 5-6)
Normalized Seasonal Precipitation
(Figure 5-7)
Linear Trend Line
Slope
R2
Linear Trend Line
Slope
R2
1
10
0.09
0.88
-0.0359
0.37
2
7
0.04
0.33
0.003
3
5
0.06
0.20
0.146
0.00
0.82
4
10
0.23
0.97
0.1668
0.97
The R2 values associated with the ONI (Figure 5-6) for El Nifno Events 2 and 3 are weak,
implying that a strong increasing ONI trend does not actually exist. El Ninio Events I and 4 have
the greatest slopes for the ONI trend lines and correspondingly high R2 values. The stronger
correlation observed for El Nino events 1 and 4 is likely because of the longer duration of these
El Ninio events (10 seasons); whereas, the El Ninio Events 2 and 3 have poor correlations and
only lasted seven and five seasons, respectively.
When observing the average NSP for each of the first five consecutive El Ninio seasons in
Figure 5-2, there is a clear trend of increasing amplitude of precipitation. By viewing each El
Niino event individually in Figure 5-7, the increasing trend of precipitation amplitude is not as
evident. The two most recent El Niio events (3 and 4) have positive slopes with high R2 values,
as shown in Table 12, indicating a strong trend of increasing precipitation amplitude with
consecutive seasons. The two earliest El Nino events (I and 2) have near-zero or negative slopes
with much weaker R2 values; therefore, these events did not experience the expected trend of
increasing precipitation amplitude. As shown in Table 12, El Nino Event 4 was observed to have
the greatest slope and R2 value for both the ONI and NSP trends. El Nifno Event 2 provides an
example of a poor ONI correlation translating into a poor NSP correlation.
Although Figure 5-2 shows that average NSP has a strong tendency to increase with
consecutive El Nifio seasons, the same trend does not carry over into individual El Niio events.
Additional El Nino events should be further observed to explore whether the precipitation
behavior is similar to the average in a majority of El Nifno events.
5.3
Classifying Precipitation Characteristics of La Ninia Phases
The 16-year study period from January 1998 through December 2013 experienced six
complete La Niia events. The La Niia events that occurred during this timeframe had durations
87
between 5 and 33 seasons. It was shown in Section 5.2 that consecutive El Nifno seasons are
characterized by trends of increasing precipitation amplitude and variability. It is hypothesized
that consecutive La Nifia seasons will be characterized by decreasing trends of normalized
precipitation.
5.3.1
Oceanic Nin-o Index of Consecutive La Nin-a Seasons
The average ONI for each consecutive season of a La Ninia event was plotted and the
results are shown in Figure 5-8.
-0.3
y
-0.1862x - 0.249
R2 = 0.747
X -0.6
.5-0.9
-1.2
0
1
2
3
4
5
Consecutive La Niia Season Number
Figure 5-8: Strength of ONI for Consecutive La Nifia Seasons
The negative trend line indicates that the average ONI decreases as a La Ninia progresses through
the first five seasons, meaning that the Nifio 3.4 region is getting cooler, experiencing a greater
departure from the average temperature of the base period. The strong R2 value (0.75) permits
this decreasing trend to be accepted with a high level of confidence.
5.3.2
Normalized Seasonal Precipitation of Consecutive La Nin-a Seasons
The process of averaging the NSP value for each consecutive season of a La Ninia event was then
performed. Table 13 shows the NSP values for each season of all six La Nifia events used in this
study.
88
Table 13: Normalized Seasonal Precipitation (NSP) Values of La Niia Events
Normalized Seasonal Precipitation
Consecutive
(Seasonal Precip / Avg998-20
3
- Average
All La Nia
Seasonal Precip)
La Nifia
Seasons
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
La Nifia
La Nifia
La Niia
La Nifla
Event 1
Event 2
Event 3
La Nifia
Event 4
Event 5
1.03
0.94
0.87
0.90
0.85
0.88
0.57
0.46
0.88
1.31
1.47
1.18
0.97
0.69
14
0.97
0.96
0.93
0.87
0.78
1.31
-
0.67
0.60
-
1.26
1.07
1.08
-
0.67
0.72
0.89
0.75
La Nia
(1998-201 3)
1.18
1.27
1.11
1.04
0.25
0.32
0.85
-
1.06
0.96
0.89
0.91
0.79
0.68
0.79
1.00
0.93
0.94
Event 6
-
-
0.88
1.15
1.08
1.03
0.93
0.90
-
1.08
-
-
-
0.99
0.89
-
-
-
-
-
0.89
-
-
-
-
-
1.06
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
1.03
0.98
1.06
0.97
0.99
0.81
0.61
0.46
0.63
0.76
0.85
0.89
0.98
0.96
1.03
0.96
1.02
1.08
1.11
-
-
-
0.63
0.57
0.66
0.70
0.82
-
1.03
0.98
0.97
0.99
0.81
0.61
0.46
0.63
0.76
0.85
0.89
0.98
0.96
1.03
0.96
-
-
-
-
-
-
-
-
-
-
1.02
1.08
1.11
32
33
1.22
0.95
-
-
-
-
-
1.22
-
-
-
-
0.95
Duration
33
5
11
5
10
7
11.8
(Season-Y ar)
JJA-1998
OND-2005
JAS-2007
OND-2008
JJA-2010
ASO-2011
-
(Season-Year)
FMA-2001
FMA-2006
MJJ-2008
FMA-2009
MAM-2011
FMA-2012
-
(Seasons)
89
-
The column to the far right in Table 13 shows the average NSP for each consecutive season of
La Nifia events. The average NSP value for the first five seasons of a La Nifla event was plotted
against its corresponding consecutive La Nifia season number (1 through 5). The fields
highlighted in gray in Table 13 show the data, which was factored into creating the resultant plot
in Figure 5-9.
1.2
y= -0.0594x + 1.0994
R2 =0.89
1.06
0
0.96
1.0
9
e
091
0.79
0.8
z
0.6
0
1
3
2
4
5
Consecutive La Nifia Season Number
Figure 5-9: Average NSP for Consecutive La Nifia Seasons
The negative slope of the linear trend line in Figure 5-9 shows the decreasing amplitude
of the total precipitation received with each consecutive season of a La Nifia event. The R2 value
(0.89) indicates that a very strong relationship exists for the value of the average NSP with each
consecutive season of a La Nifta event. The decreasing trend shown in Figure 5-9 does not
indicate that a particular season will receive less precipitation than the previous La Nifia season;
instead, it indicates that a season will receive less precipitation based on what is average for the
specific time of year in which that season falls (e.g. in some cases the seasonal precipitation
The decrease in NSP is
might decrease while absolute precipitation might increase).
approximately 6% with each consecutive La Nifia season for the first five seasons. These
findings do not indicate that the decreasing trend will continue past the fifth season.
90
5.3.3 Normalized Monthly Precipitation of Consecutive La Nin-a Months
The average NMP values for each consecutive month of La Nifia events were then plotted
to confirm that the decreasing trend still exists when observing months individually. The results
are seen in Figure 5-10.
1.3
S
y =-0.0775x + 1.219
R2= 0.69
>
1.0
0.8
Ze
0.7
0
1
2
4
3
5
6
7
Consecutive La Nifia Month Number
Figure 5-10: Average NMP for Consecutive La Nifia Months
The R 2 value (0.69) decreased from the plot of average NSP (0.89) in Figure 5-9; however, the
decreasing trend of normalized precipitation can still be seen in the monthly data. Similar to the
results of plotting NMP in consecutive months of El Nifio, there appears to be one outlying
month. The average NMP for the third month of La Ninia drops below what it is expected
considering the trend of the other six months. Adding additional La Nifna events to this study
may improve the observed correlation by having a larger sample size that minimizes the effect of
outlying precipitation data.
91
5.3.4 Heavy Precipitation Events of Consecutive La Ninla Seasons
The average number of heavy precipitation events experienced in each consecutive
season of a La Nifia event was calculated and plotted to determine if a decreasing trend exists.
The negative slope of the trend line in Figure 5-11 indicates that fewer heavy precipitation events
occur as a La Nifia event progresses.
1
10 1
y = -0.8x + 9.3
R2 = 0.80
0
0>
8
0
z
6
4 t
I
' 2
3
Consecutive La Nifia Season Number
4
5
Figure 5-11: Heavy Precipitation for Consecutive La Nifia Seasons
The R2 value (0.80) is very high indicating a strong linear correlation may exist. Consecutive El
Nifno seasons are not necessarily associated with an increase in heavy precipitation events;
however, the results of this study show that consecutive La Nifia seasons are associated with a
decline in heavy precipitation events. The decreasing trends of average NSP (Figure 5-9) and
number of heavy precipitation events (Figure 5-11) suggest that precipitation may become more
suppressed with each consecutive La Nifia season.
92
5.3.5 Precipitation Variability of Consecutive La Nin-a Seasons
The standard deviation of NSP values for each consecutive La Ninia season was
calculated and plotted. The results, shown in Figure 5-12, indicate no correlation between
standard deviation of NSP and the consecutive La Nifia seasons.
0.5
y=
0.005x + 0.2458
R2 =0.01
0.4
0.3
-
o
0.2
z
-
0.1
0
0
1
2
3
Consecutive La Nifia Season Number
4
5
Figure 5-12: Standard Deviation of NSP for Consecutive La Nifia Seasons
The lack of a correlation between standard deviation of NSP and consecutive La Nijia seasons
differs from the results shown in Figure 5-5, which represents increasing standard deviation of
NSP for consecutive El Nifno seasons. There is no observed trend in variability of precipitation
for consecutive La Nifia seasons.
5.3.6 Behavior of Consecutive Seasons in Individual La Nin-a Events
The La Ninia behavior detailed in the previous sections is the result of averaging
These six events were then observed
parameters for six individual La Nifia events.
independently to determine if the behavior of separate events is similar to that of the average.
The ONI for the first five consecutive seasons of each La Nifia event was plotted to
compare with the decreasing trend of average ONI shown in Figure 5-8. The plot of seasonal
ONI values for all six La Nifia events is shown in Figure 5-13.
93
-0.2
U
0
*
-0.6
* La Ninia - Event I
-o
U
-1
0
p
*
~e
-
-.-------
A La Nifia - Event 3
z0
0
0
A La Nifia - Event 2
~
* La Nifia - Event 4
1.4 +
* La Nilia - Event 5
* La Nilna - Event 6
-1.8
1
0
2
3
4
Consecutive La Niia Season Number
5
Figure 5-13: ONI of Consecutive Seasons in Each La Nifia Event
Figure 5-13 indicates that four of the six La Nifia events generated a linear trend line with a
negative slope, indicating the ONI was decreasing with consecutive seasons.
The NSP values for the first five consecutive seasons of each La Nifia event were then
plotted to determine if the NSP trends are linked to the ONI trends for the specific events. The
results of plotting all NSP values for each La Nifia event are shown in Figure 5-14.
[
1.8
1
A
1.4
U
0
U
I
0
0
1.0
N
0.6
* La Ninia -
Event I
E La Nifia -
Event 2
A La Ninia -
Event 3
~0
*LaNiia+ La Nifia - Event
Event 4
4
U
X La Ninia - Event 5
* La Ninia - Event 6
0.2
0
1
2
3
4
5
Consecutive La Niia Season Number
Figure 5-14: NSP of Consecutive Seasons in Each La Nifia Event
Figure 5-14 indicates that most of the La Nina events appear to exhibit a trend of decreasing
NSP. La Ninia Event 2 is clearly an exception to the decreasing trend.
94
The slope and R2 values associated with all trend lines in Figures 5-13 and 5-14 are
shown in Table 14.
Table 14: Observed Trend Line Parameters for Individual La Nifia Events
La Nijia Event
Number
1
2
3
4
5
6
Duration
(Seasons)
33
5
11
5
10
7
Normalized Seasonal Precipitation
Oceanic Niuo Index
(Figure 5-13)
(Figure 5-14)
Linear Trend Line
Linear Trend Line
Slp2_________
Slope
-0.17
0.01
-0.2
0.0
-0.15
-0.08
R
2
0.94
0.01
0.98
0.00
0.87
0.57
Slope
R2
-0.0392
0.2338
-0.2103
-0.0763
-0.0566
-0.208
0.79
0.85
0.96
0.39
0.98
0.64
As seen in Table 14 and Figure 5-13, four of the six La Ninia events had ONI trend lines with
negative slopes and significant R2 values (0.94, 0.98, 0.87, and 0.57), indicating that a trend in
decreasing ONI exists. The referenced events are La Nifia Events 1, 3, 5, and 6. It is likely that
La Nifia Events 2 and 4 did not behave similarly because they each had short, five-season
durations, and the magnitude of the ONI was weakened by the fifth season.
The four La Niia events, which had decreasing ONI trends (events 1, 3, 5, and 6), also
yielded decreasing trends of NSP with consecutive seasons. The decreasing trend of NSP for
each of these four La Niia events was supported by strong R2 values (0.79, 0.96, 0.98, and 0.64,
respectively). La Nifia Events 2 and 4 were characterized by poor correlations for ONI, thus
resulting in poor correlations for NSP as well.
The decreasing trend of average NSP shown in Figure 5-9 is consistent with the behavior
of at least four of the six individual La Nifna events observed. The analysis performed in this
section indicates the effect that each consecutive season of a La Niia event (for the first five
seasons) has on the expected precipitation characteristics of that season. As a La Niia event
progresses, the average ONI decreases leading to subsequent decreases in the amplitude of total
seasonal precipitation, and the number of heavy precipitation events that occur.
5.3.6.1
Accounting for Minimum-Length La Ninia Events
The analysis in Section 5.3.6 suggests that a decreasing trend of ONI in consecutive La
Niia seasons leads to a subsequent decreasing trend of NSP. La Niia Events 2 and 4 only lasted
for five seasons each, and thus, did not demonstrate the decreasing trend of ONI and NSP
observed in the other four, longer, La Ninia events. La Niia Events 2 and 4 were then removed
from the data set to help differentiate between the behavior of short and long-lasting La Niia
95
events. The average NSP was then calculated for the first five consecutive La Ninia seasons, only
factoring in La Nifia Events 1, 3, 5 and 6. The results of this process are shown in Figure 5-15.
1.4
y = -0.1285x + 1.3212
R2=0.96
0
0.6~
0.
zJ~
0.2
0
1
3
2
Consecutive La Niia Season Numbe
4
5
Figure 5-15: Average NSP for Consecutive La Nifia Seasons - Events 1, 3, 5, & 6
The removal of the short La Nifia Events 2 and 4 leads to a strengthening of the linear trend line
slope and of the R2 value (when compared to Figure 5-9). The decrease in average NSP between
consecutive seasons rises from to 6 to 13%, and the R2 value rises from 0.89 to 0.96. The trend
of decreasing amplitude of NSP with consecutive La Nifia seasons is enhanced by the removal of
the two minimum-length La Nifia events. This indicates that the behavior explained for the first
five seasons is only applicable to La Nifia events that last longer than five seasons.
5.4 Analysis of Results from Investigation of Consecutive ENSO Seasons
The analysis performed in this section attempts to explain trends in precipitation behavior
of consecutive ENSO seasons. With respect to weather in the Manafwa River Basin of eastern
Uganda, it is shown that El Ninho is correlated with increasing amplitude of local precipitation
with consecutive seasons, while decreasing amplitude of local precipitation is observed for La
Nifia. These trends were observed for only the first five seasons of an ENSO event. This
observation is not supported by any other study, and is believed to be a new and unique remark
on the precipitation impact of ENSO.
The behavior of consecutive El Nifio and La Nifia seasons was found to exhibit opposite
properties. As an El Nifio event progresses, the average ONI increases leading to subsequent
increases in the amplitude of seasonal precipitation and the variability of precipitation
96
experienced.
The trend in increasing occurrence of heavy precipitation events was not
sufficiently significant to suggest that heavy precipitation events increase with consecutive El
Nifio seasons. As a La Nina event progresses the average ONI decreases leading to subsequent
decreases in the amplitude of seasonal precipitation and in the occurrence of heavy precipitation
events.
This study provides a method of forecasting the expected precipitation of an upcoming
month or season within an ENSO event. Given that the ONI begins yielding ENSO conditions,
these findings provide a way of anticipating precipitation in upcoming ENSO seasons. The
individual ENSO events were not always found to behave in the same manner as the average
event describes; however, knowledge of the long-term average behavior is a valuable tool for
responsible adaptation to weather conditions.
97
6 Exploring the Effect of ENSO in Additional Locations
6.1
Method
The teleconnections of ENSO extend far beyond Uganda and equatorial East Africa.
Every location that experiences weather impacts is affected in a specific way, and has a unique
response to ENSO.
The precipitation characteristics that one location exhibits cannot be
translated to describe the simultaneous precipitation response of a separate location. The
investigation detailed in Section 4.4 shows notable trends for the precipitation experienced in
consecutive seasons of ENSO events. Presently, the observed trends can only be associated with
the weather conditions of the Manafwa River Basin in eastern Uganda. This study intends to
apply the same methods of consecutive ENSO season comparison to other locations around the
world that are impacted by ENSO. The results of this examination will indicate if various
regions around the world exhibit the systematic precipitation trends that the Manafwa River
Basin does. The identification of such precipitation tendencies would provide a tool with which
to better forecast extreme events such as floods and drought.
To support to validity of the precipitation trends observed in the Manafwa River Basin,
two additional locations have been selected for analysis of historical weather conditions related
to ENSO. The locations are Houston, Texas and the Bou Regreg Watershed in northern
Morocco. Monthly precipitation measurements were accessed for Houston from 1950 through
2013 (NOAA f, 2014), and for Morocco from 1950 through 2009 (ONEE, 2009). The study
performed in this section has multiple advantages over the study performed on the effect of
ENSO for the Manafwa River Basin: (1) the precipitation data available extend farther back in
time, (2) the precipitation data are measurements, not satellite estimates, (3) the study includes
many additional El Niio and La Nifia events, and (4) trends can be explored past the first five
consecutive seasons.
98
The ENSO events that are reflected in this study, which occurred between 1950 and 2012,
are listed in Table 15. During this time-period there were 19 complete El Nifno events, and 16
complete La Niia events.
Table 15: List of all ENSO Events used in this Study (1950-2013)
El Nifio Events
La Ninia Events
Number
Start
Season
End
Season
Duration
(Seasons)
Number
Start
Season
End
Season
Duration
(Seasons)
1
JJA-1951
DJF-1953
MAM-1957
OND-1958
MJJ-1963
AMJ-1965
JAS-1968
AMJ-1972
ASO-1976
ASO-1977
AMJ-1982
JAS-1986
AMJ-1991
ASO-1994
AMJ-1997
AMJ-2002
JJA-2004
ASO-2006
JJA-2009
DJF-1952
JFM-1954
JJA-1958
FMA-1959
JFM-1964
MAM-1966
DJF-1970
FMA-1973
JFM-1977
JFM-1978
MJJ-1983
JFM-1988
MJJ-1992
FMA-1995
MAM-1998
JFM-2003
DJF-2005
DJF-2007
MAM-2010
7
14
16
5
9
12
18
1
SON-1950
AMJ-1954
AMJ-1964
JJA-1970
AMJ-1973
SON-1974
ASO-1983
SON-1984
AMJ-1988
ASO-1995
JJA-1998
OND-2005
JAS-2007
OND-2008
JJA-2010
ASO-2011
JFM-1951
NDJ-1956
DJF-1965
DJF-1972
JJA-1974
MAM-1976
DJF-1984
ASO-1985
AMJ-1989
FMA-1996
FMA-2001
FMA-2006
MJJ-2008
FMA-2009
MAM-2011
FMA-2012
5
32
9
19
15
19
5
12
13
7
33
5
11
5
10
7
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
11
6
6
14
19
14
7
12
10
7
5
10
The average NSP for consecutive seasons of El Niino and La Nifia events will be calculated and
plotted to determine if a correlation exists. If a precipitation trend is evident it will be interesting
to observe how far into the ENSO event the trend lasts. The NSP for each season of individual
ENSO events will then be plotted to observe if the individual events exhibit similar behavior as
the average NSP values illustrate.
99
6.2 Bou Regreg Watershed, Morocco - Precipitation Response to ENSO
The Bou Regreg Watershed, in northern Morocco, often endures periods of extreme
drought. The precipitation data used for this study were recorded at a meteorological station in
the city of Meknes, just northeast of the watershed. The average monthly precipitation for
Meknes is shown in Figure 6-1.
1
11
II0
r-
0
80
60
o
40
Co
20
PU~UU7I
--
Figure 6-1: Average Monthly Precipitation in the Bou Regreg Watershed - Meknes
The monthly precipitation data show that June through September is characterized by very little
precipitation, while July and August receive almost no precipitation. Although the remaining
eight months of the year experience greater precipitation, all twelve months were found to have
years when a total of 1 mm of precipitation, or less, was received during that month.
The development of a strategy to better predict future precipitation conditions in the Bou
Regreg Watershed would be invaluable for implementing responsible water management
practices during times of anticipated drought conditions. The monthly precipitation data is used
to calculate NSP values for the analysis of precipitation response in consecutive ENSO seasons.
The monthly precipitation data set provided by the Office National de l'Electricit6 et de l'Eau
Potable was missing approximately 5% of the monthly precipitation measurements from the
nearly 60-year timeframe. Any ENSO event that occurred during this 60-year time period,
which was missing at least one month of precipitation data, was omitted from the study. The
analysis of ENSO precipitation trends in the Bou Regreg Watershed did not consider the ENSO
events shown in Table 16.
100
Table 16: ENSO Events Not Included in Study because of Data Deficiencies
ENSO Events Omitted from
Bou Regreg Watershed Study
(Event Numbers defined in Table 15)
El Niho Events
La Nifia Events
12
13
14
18
19
-
8
9
10
12
15
16
The ENSO events listed in Table 16 did not have complete monthly precipitation data and are
not used in the analysis in subsequent sections.
Investigation of Consecutive El Nin-o Seasons
6.2.1
Normalized Seasonal Precipitation of Consecutive El Niuo Seasons
6.2.1.1
The average NSP was plotted for consecutive El Niiio seasons to determine if a trend
exists. Figure 6-2 shows that there is a strong decreasing trend of average NSP for the first 10
seasons of an El Ninio event.
1.6
y =-0.0436x + 1.4455
R2 =0.59
.o
1.4
t o
1.2
>
N
1
0.8
0
2
6
4
Consecutive El Nifio Season Number
8
Figure 6-2: Average NSP for 10 Consecutive El Nio Seasons - Meknes
101
10
The ten-season NSP trend shown in Figure 6-2 indicates that the five-season NSP trend,
observed in Section 4 for the Manafwa River Basin, may in fact last longer into the ENSO event.
The study duration of consecutive El Niflo seasons was then extended to determine if the
decreasing NSP trend continues past the tenth season. The results of this investigation are shown
in Figure 6-3.
1.5
y
-0.0491x + 1.4611
R 2 = 0.83
S1.2
eoe
0.
0.6
-
0
3
6
9
12
-
15
Consecutive El Nifio Season Number
Figure 6-3: Average NSP for 15 Consecutive El Nifio Seasons - Meknes
Figure 6-3 shows that the decreasing NSP trend continues further into consecutive La
Nina seasons. When prolonging the precipitation analysis from 10 to 15 seasons, the slope of the
trend line becomes steeper, and the R2 value becomes considerably higher. The transformations
of these parameters can be observed by viewing Figure 6-2, followed by Figure 6-3. The steeper
trend line slope indicates that the percent decrease of normalized precipitation between seasons
becomes greater, and the larger R 2 indicates a very strong correlation. It is evident that a trend of
decreasing amplification of seasonal precipitation associated with consecutive El Niio seasons
exists.
Not all El Nifio events considered in this study lasted for 15 seasons; therefore, the NSP
values for each El Nifio event were only factored into the consecutive season average NSP until
that specific event ended. Each progressive season, after the fifth, season may have less El Nifio
events that are used to calculate the average NSP.
6.2.1.2
Behavior of Consecutive Seasons in Individual El Niiio Events
The NSP values for each El Niho event were then plotted for the first ten consecutive seasons.
The parameters for the resultant trend lines are shown in Table 17 and the plots are displayed in
Figure 6-4.
102
Table 17: Observed Trend Line Parameters for Individual El Nifto Events - Meknes
El Niuo - NSP Linear Trend Line Parameters
(Figure 6-4)
Event
Duration
Slope
R2
1
2
3
7
14
16
5
9
12
18
11
6
6
14
-0.1383
0.0867
-0.1383
-0.1817
-0.1518
-0.1191
0.0803
0.0089
0.00218
-0.1339
-0.0785
0.184
0.158
0.184
0.830
0.299
0.443
0.180
0.006
0.008
0.597
0.325
-
4
5
6
7
8
9
10
11
12
(seasons)
19
-
13
14
-
-
14
7
-
-
15
12
10
7
-0.0916
-0.2469
0.0195
0.094
0.212
0.012
18
5
-
-
19
10
-
-
16
17
4
* El Nifio - Event I
* El Nifio - Event 2
Z
* El Nifio - Event 3
3
x El Nifo - Event 4
El Nifio - Event 5
* El Nifio - Event 6
0
2--
+ El Nifio - Event 7
CA
-
03
El Nifio - Event 8
El Niuo - Event 9
+ El Nifio - Event 10
U
U
U
* El Nifmo - Event
+
m El Nino
U
0
11
A El Nifio - Event 15
2
6
4
Consecutive El Nifio Season Number
8
10
- Event
16
, El Nifo - Event 17
Figure 6-4: NSP of Consecutive Seasons in Each El Nifto Event - Meknes
It is evident that the consecutive season decreasing trend of average NSP does not exist within
every El Nifno event. Negative trend line slopes were detected in 9 of the 14 El Nifno events,
103
indicating similar behavior to the deceasing trend of average NSP; however, not all of the nine
events with negative slopes have sufficiently high R2 values to confirm the decreasing trend. The
R2 values observed range from 0.094 for El Nifio Event 15, up to 0.83 for El Nifio Event 4. Not
one of the five El Nifio events with positive slopes has a significant R 2 value (maximum of 0.18),
indicating these are anomalous events and there is no significant trend of increasing NSP with
consecutive El Nifio seasons.
6.2.2 Investigation of Consecutive La Nina Seasons
6.2.2.1
Normalized Seasonal Precipitation of Consecutive La Nifia Seasons
The average NSP for each La Nifia season was then calculated, and the first ten seasons
were plotted as shown in Figure 6-5.
1.5
_
_
_
y =0.061x + 0.5354
R2 = 0.61
-S
1.2
-
0.9
0
-
0.6
-
-
-
0.30
2
4
6
8
10
Consecutive La Ninia Season Number
Figure 6-5: Average NSP for 10 Consecutive La Nifia Seasons - Meknes
Although a strong increasing precipitation trend was observed over the first ten seasons, it
appears that the trend does not begin immediately. Instead, the increasing tendency of average
NSP starts in the fifth season, and has a strong increasing trend through the tenth season.
104
The average NSP for the fifth through tenth seasons were then plotted in isolation of the
first four seasons. This plot is shown in Figure 6-6.
1.5
y
0.1437x - 0.1186
R. 0.99
R2=
0.9
0.6
0.3
4
5
6
7
8
9
10
Consecutive La Ninia Season Number
Figure 6-6: Average NSP
for Consecutive La Nifia Seasons (5-10) - Meknes
The trend line slope and R 2 value increased considerably from observing the trend of the first ten
seasons (Figure 6-5) to observing the trend of the fifth through tenth seasons (Figure 6-6). The
two plots show that the amplitude of precipitation in the Bou Regreg Watershed increases for a
subset (fifth through tenth) of consecutive La Nifia seasons.
6.2.2.2
Behavior of Consecutive Seasons in Individual La Ni*na Events
The NSP of consecutive seasons in all La Nifia events were then plotted to determine if
individual events exhibit the same trend of increasing precipitation amplitude for average NSP
that is shown in Figure 6-5. The parameters for the resultant trend lines are shown in Table 18
and the plots are displayed in Figure 6-7.
105
Table 18: Observed Trend Line Parameters for Individual La Nifia Events - Meknes
La Nijia - NSP Linear Trend Line Parameters
(Figure 6-7)
Event
Duration
Slope
R2
1
2
3
4
5
6
7
5
32
9
19
15
19
5
0.2819
0.1437
0.0597
0.2098
0.0654
0.2509
0.0278
0.693
0.464
0.222
0.932
0.465
0.827
0.007
8
12
13
-
7
33
5
11
5
-0.1882
0.033
-0.1514
10
-
7
-
(seasons)
9
10
11
12
13
14
15
16
-
0.683
0.080
0.710
-
2.4
4
x
* La Nifia - Event I
1.6
* La Nifia - Event 2
ALa Nifia
c2
- Event 3
XLa Nijia - Event
A
N
A
0.8
~
X
*
4
La Nifia - Event 5
* La Nifia - Event 6
A
La Niha - Event 7
-
z
La Nifia - Event
11
La Nifia - Event 13
AA
+La Nifia - Event 14
x
..-w
U
0
2
4
6
8
10
Consecutive La Nina Season Number
Figure 6-7: NSP of Consecutive Seasons in Each La Nifia Event - Meknes
Positive trend line slopes were observed for eight of the ten La Ninia events, indicating that many
of the events exhibit an increasing behavior similar to what was observed for average NSP. La
Ninia Events 3, 7, and 13 had positive slopes, but were accompanied by R2 values too small to
106
indicate a significant increasing trend. The remaining five events with positive slopes had R2
values of varying strength, between 0.464 (Event 2) and 0.932 (Event 4); therefore, five of the
ten La Nifna events studied exhibit an increasing trend of NSP in consecutive seasons.
The duration of the La Niia events were then analyzed to determine if duration had an
influence on the trend observed. La Nifia Events 1 and 7 both had durations of 5 seasons, yet
Event 1 had a strong NSP correlation and Event 7 had no correlation. La Nifia Events 4 and 6
each had long durations of 19 seasons and both exhibited a strong increasing trend of NSP;
however, La Nifia Event 11 was also long (33 seasons), but proved to have a negative trend of
NSP. The duration of the La Ninia events does not immediately appear to have a strong influence
on whether the consecutive season NSP values display the expected increasing trend.
6.3
Houston, Texas - Precipitation Response to ENSO
The city of Houston is located in the southern United States approximately 40 miles from
the Gulf of Mexico. Considerable portions of Texas are considered to be water scarce and a
fundamental understanding of the anticipated hydrologic cycle is essential for responsible water
management. Houston has been associated with experiencing temperature and precipitation
impacts from ENSO. Certain studies show that Texas is impacted by ENSO in similar ways to
Uganda; El Ninio phases are associated with above-average precipitation, and La Ninia phases
with below-average precipitation. The teleconnection is most recognized to affect the weather
conditions of December through February (NOAA a, 2012). Developing a better understanding
of how consecutive ENSO seasons affect precipitation in the region would help the city improve
its water management practices.
6.3.1
Investigation of Consecutive El Nifto Seasons
The average NSP values for consecutive El Ninio seasons were calculated and plotted
over two different durations. A strong correlation between consecutive El Ninio seasons and
increasing amplitude of precipitation was observed for the first seven seasons, which is seen in
Figure 6-8.
107
1.4
y
0
N
0.0471x + 0.8556
R2 = 0.624
1.2
M-
~0
0.8
0.6
0
2
1
4
3
6
5
7
Consecutive El Nifio Season Number
Figure 6-8: Average NSP for 7 Consecutive El Nifio Seasons - Houston
The average NSP values were then plotted for the first 16 consecutive El Nifio seasons to explore
if the trend exists in the later seasons of El Nifno events. The results of this investigation appear
in Figure 6-9.
1.4
0
eS
1.2
0
S
0e
eS
e0
0.8
0.6
0
2
4
6
8
10
12
Consecutive El Nino Season Number
14
16
Figure 6-9: Average NSP for 16 Consecutive El Niuo Seasons - Houston
After the seventh season, the NSP appears to decrease through the sixteenth season. Figure 6-9
indicates that consecutive El Niiio seasons in Houston are characterized by an increasing trend in
NSP, followed by a decreasing trend. After the thirteenth season the average NSP value falls
108
below 1.0 (indicating below average precipitation). The average NSP stays below 1.0 for the
remaining El Niino seasons. This suggests that although El Niio is characterized by an
increasing trend of normalized precipitation for the first seven seasons, the later seasons are more
unpredictable and can even result in below-average seasonal precipitation.
6.3.1.1
Behavior of Consecutive Seasons in Individual El Ninto Events
The NSP values for the first seven consecutive seasons of individual El Nifno events were then
plotted to identify if an increasing NSP trend exists. The parameters for the resultant trend lines
are shown in Table 19 and the plots are displayed in Figure 6-10.
Table 19: Observed Trend Line Parameters for Individual El Nifno Events - Houston
El Nifno - NSP Linear Trend Line Parameters
(Figure 6-10)
Event
Duration
Slope
R2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
7
14
16
5
9
12
18
11
6
6
14
19
14
7
12
10
7
5
10
0.0267
0.0608
0.0853
0.4727
0.0481
0.0475
0.0017
0.0324
-0.1652
0.1339
0.1282
0.0244
-0.0334
-0.0263
0.019
0.239
0.0493
-0.2199
0.0828
0.084
0.286
0.086
0.938
0.120
0.267
0.000
0.246
0.895
0.667
0.478
0.026
0.043
0.112
0.072
0.863
0.042
0.831
0.151
(Seasons)
109
2.5
* El Niuo - Event 1
* El Niuo - Event 2
AEl Nijo - Event 3
2
x El Nifto - Event 4
El Niho - Event 5
0
* El Niho - Event 6
4")
I.-
+ El Nifto - Event 7
1.5
A
*
0
4)
C,)
-o
4")
N
-
El Nifio - Event 8
-
El Niino - Event 9
+ El Nifio - Event 10
1
* El Ni'o - Event 11
a
X
*El Nifo - Event 12
x
z
AEl Niuo - Event 13
0.5
+ El Nio - Event
14
a El Niho
15
- Event
* El Niho - Event 16
- El Nifio - Event 17
0
El Nifio - Event 18
0
1
2
3
5
4
6
7
El Niio - Event 19
Consecutive El Ninio Season Number
Figure 6-10: NSP of Consecutive Seasons in Each El Niio Event - Houston
This study encompassed analyses of 19 individual El Nifno events. Trend lines with positive
slopes were detected for 15 of the 19 El Ninio events; however, 8 of those 15 events were
associated with R2 values that made the trend insignificant. Even though only seven events have
increasing NSP trends with strong linear correlations, the majority of El Ninio events appear to
exhibit increasing NSP tendencies with consecutive seasons.
110
6.3.2 Investigation of Consecutive La Nin-a Seasons
The average NSP for the first five consecutive seasons of a La Niia event was plotted and
is shown in Figure 6-11.
1.2
y= 0.0276x + 0.9052
R2 = 0.683
.1.1--
N
0.8 4-
0
1
3
2
Consecutive La Nifia Season Number
5
4
Figure 6-11: Average NSP for 5 Consecutive La Nifia Seasons - Houston
The increasing trend was the opposite of the expected trend because Texas typically receives
below-average precipitation during La Ninha events. The increasing trend of the first five seasons
is insignificant because the slope is small, only increasing NSP by approximately 2.8% each
season. The increasing NSP trend disappears when expanding the plot to encompass the average
NSP of the first 19 seasons, as shown in Figure 6-12.
1.2
-
C
0.8
.N
z
0.6
0
4
12
8
Consecutive La Nina Season Number
16
Figure 6-12: Average NSP for 19 Consecutive La Nifia Seasons - Houston
111
20
As seen in Figure 6-12, any linear trend ceases to exist after the fifth consecutive season and the
average NSP values oscillate, while staying below 1.0 (average precipitation). This indicates
that the seasonal precipitation is below average for the sixth through the nineteenth seasons.
Although Houston is expected to have dry conditions during La Nifia, consistent with the state of
Texas, Figure 6-12 shows that the precipitation response to La Nifia may take approximately six
seasons before the precipitation declines to below-average for the remainder of the La Niia
seasons. The delayed precipitation decline in Houston may be influenced by conditions from the
Gulf of Mexico and, therefore, deviate from the expected La Niia conditions of the greater state
of Texas. The NSP trend for individual La Nifia events was not explored further because there
was no significant trend observed for average NSP with which to compare the behavior of the
individual events.
112
7 Recommendations and Conclusion
7.1
Value of Study for the Red Cross
The Red Cross aims to better serve the residents of the Manafwa River Basin in eastern
Uganda by improving disaster response following the devastating floods that frequently affect
the region. The development a long-term flood information strategy would provide the Red
Cross with an advanced warning that the risk of flooding is heightened, allowing for early
preparation and mobilization activities. A process or method that helps inform the Red Cross of
an increased potential for extreme weather events would permit the collection of necessary
resources, scheduling of volunteers, and the formulation of a unique response plan. The findings
discussed in Sections 4 and 5 of this study, which explain correlations between precipitation and
climate indices, provide the Red Cross with the framework to begin implementing a long-term
flood information system for the Manafwa River Basin.
Daily precipitation estimates for the Manafwa River Basin from 1998 through 2013 were
analyzed and compared to three climate indices: the Southern Oscillation Index (SOI), the
Oceanic Nifno Index (ONI), and the Dipole Mode Index (DMI). This investigation showed that
the strength of indices in preceding months and seasons may function as an indicator for future
precipitation conditions. Certain indices were demonstrated to have correlations with the amount
of total precipitation and the occurrence of heavy precipitation events for specific months. The
Red Cross could implement a system of monitoring these indices on a weekly basis to aid in the
identification of precipitation patterns of concern. If the value of a current index indicates that a
future month will receive enhanced precipitation, proper flood recovery planning can begin.
Continued monitoring of these indices will show if the threat of enhanced precipitation
conditions diminishes or becomes even more prevalent. Given the strength of a correlation
between the magnitude of an index and the subsequent expected precipitation, different levels of
preparation can be initiated that are appropriate for the specific threat.
A series of tables was prepared that provide sample guidance for relating current climate
index conditions to the anticipated precipitation conditions of the Manafwa River Basin. Tables
20, 21, and 22 provide a simplified account of the precipitation and index correlation results
discussed in Section 4. The term "Total" indicates that the total precipitation is expected to be
greater for that month or season if the corresponding index meets the specified condition. The
term "Heavy" indicates that the number of heavy precipitation events is expected to be greater
for that month or season if the corresponding index meets the specified condition.
113
Table 20 below provides sample guidance for relating the current SOI conditions to
anticipated precipitation conditions in the Manafwa River Basin. The information in this table is
a summary of the correlations detected between SOI and precipitation in the basin, which is
presented in further detail in Tables 4 and 5 of Section 4.2.2.
Table 20: Sample Guidance for Relating Current SOI Conditions to Future Precipitation
Climate Index Conditions
Current Month
January
SOI Status
Current Month
Next Month
Low Sol
Total (M)
Heavy (W)
-
Total (W)
Heavy (W)
-
Low Sol
-
-
Heavy (W)
Total (M)
-
High SOT
-
-
-
-
-
Low SOI
-
-
SOI
-
-
Total (VW)
Heavy (W)
-
-
Low SoI
-
Total (VW)
High SOl
-
-
-
Heavy (W)
Low SOl
Hea (W)
-
-
-
High
February
Anticipated Precipitation Conditions
SOI
In 2 Months
April
May
June
July
Heavy (W)
Heavy_____
-
Total (VW)
________
(W)_____
High SOl
-
-
-
-
-
SOI
High SOI
Low SOI
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
High SOl
-
-
Low SOI
-
-
-
Total (W)
Heavy (W)
SOI
-
-
-
Low SOI
-
-
-
High SOT
-
-
-
Low SOl
-
-
Total (M)
Heavy (M)
High SOI
-
-
Low SOI
-
Total (W)
Total (M)
-
Heavy (W)
-
Heavy (W)
-
Total (W)
Total (VW)
Low
August
High
September
October
In 4 Months
-
-
March
High
In 3 Months
November
High SOI
Low SoI
December
High SOI
Total (VW)
Total (W)
Heavy (VW)
Heavy (VW)
-
Total (W)
-
-
-
Total (M)
Heavy (M)
Total (W)
Heavy (M)
Heavy (W)
-
--
-
-
-
Precipitation Type:
Total - Indicates that increased Total Monthly Precipitation may occur
Heavy - Indicates that a greater number of Heavy Precipitation Events may occur
Strength of Trend:
(VW) - Very Weak (W) - Weak (M) - Moderate
114
(S) - Strong (VS) - Very Strong
-
Table 21 below provides sample guidance for relating the current DMI conditions to
anticipated precipitation conditions in the Manafwa River Basin. The information in this table is
a summary of the correlations detected between DMI and precipitation in the basin, which is
presented in further detail in Tables 9 and 10 of Sections 4.3.2
Table 21: Sample Guidance for Relating Current DMI Conditions to Future Precipitation
Anticipated Precipitation Conditions
Climate Index Conditions
Current Month
___________
January
February
DMI Status
__________
Current
Month
Next Month
In 2 Months
In 3 Months
In 4 Months
-
_____________________________
Low DMI
High DMI
-
-
Heavy (VW)
-
-
Total (VW)
-
-
Low DMI
-
-
-
-
-
-
-
H ig h D M I
-
-
-
Low DMI
-
-
Total (W)
Heavy (W)
High DMI
-
-
-
-
-
Low DMI
Total (VW)
-
-
-
-
High DM1
-
-
MayHigh Low DMI
DM I
M
Hg
May
--
--
--oa
--W-
-
-
Total (W)
-
-
F eb ruary
March
April
High DMI
Low DMI
-
-
-
-
-
High DMI
-
Total (VW)
-
-
Total (VW)
Low DMI
-
-
-
-
-
______
High DMI
-
-
-
--
Low DMI
High DM I
-
-
-
-
-
August
Heavy (VW)
-
-
-
-
-
-
-
September
Low DMI
High DMI
Total (W)
Total (VW)
Heavy (VW)
Heavy (W)
June
July
Total (W)
Low DMI
-
-
October
Total (VW)
Heavy (W)
Total (W)
Heavy (VW)
Low DM1
-
-
-
Total (VW)
Total (W)
Total (W)
High DM1
Heavy (W)
Low DMI
High DMI
Total (W)
Total (M)
Heavy (W)
Heavy (W)
Ig
D
-
Heavy (VW)
High DM1
November
December
-
Total (W)
-
-Heavy
Heavy (W)
Heavy (VW)
-
-
-
-
Precipitation Type:
Total - Indicates that increased Total Monthly Precipitation may occur
Heavy - Indicates that a greater number of Heavy Precipitation Events may occur
Strength of Trend:
(VW) - Very Weak
(W) - Weak
(M) - Moderate
115
(VW)
(S) - Strong (VS) - Very Strong
Table 22 below provides sample guidance for relating current ONI conditions to
anticipated precipitation conditions in the Manafwa River Basin. The information in this table is
a summary of the correlations between ONI and precipitation in the basin, which is presented in
detail in Tables 6 and 7 of Sections 4.2.3
Table 22: Sample Guidance for Relating Current ONI Conditions to Future Precipitation
Anticipated Precipitation Conditions
Climate Index Conditions
Current Season
DJF
JFM
ONI Status
Current Season
Low ONI
-
High ONI
Total (S)
Heavy (S)
Low
Next Season
ONI
In 2 Seasons
In 3 Seasons
In 4 Seasons
-
High ONI
Low ONI
FMA
MAM
AMJ
MJJ
High ONI
Low ONI
-
High ONI
-
Low
ONI
Heavy (VW)
High
ONI
-
Low
ONI
Heavy (VW)
High
ONI
-
Low ONI
JJA
-
ONI
-
High ONILow
ASO
ONI
-_-
Total (M)
High ONI
High
SON
-
High ONI-Low
JAS
-
Low ONI
High ONI
NI
Heavy (M)
-
High-Heavy
Total (S)
(S)
NI
Low ONI
ONDHigh ONI
High__N_
Low
ONI
High
ONI
NDJ
Total (S)
(S)
_Heavy
-_-
Total (S)
Heavy (S)
Precipitation Type:
Total - Indicates that increased Total Monthly Precipitation may occur
Heavy - Indicates that a greater number of Heavy Precipitation Events may occur
Strength of Trend:
(VW) - Very Weak (W) - Weak (M) - Moderate (S) - Strong (VS) - Very Strong
116
Although Tables 20, 21, and 22 do not provide a guarantee of precipitation conditions in the
Manafwa River Basin, they provide a framework for the Red Cross to begin monitoring current
climate indices as part of a long-term flood information strategy.
The analysis of the precipitation behavior of consecutive seasons in ENSO events also
provides valuable guidance to the Red Cross for use in a long-term flood information strategy in
the Manafwa River Basin. It was shown in Figure 5-2 that the first five seasons of the average El
Niio event are characterized by increasing amplitude of normalized precipitation. It was further
revealed in Figure 5-5 that variability of precipitation becomes greater with consecutive El Nifio
seasons. Various climate agencies (such as NOAA's Climate Prediction Center) provide updates
and predictions regarding anticipated ENSO conditions. The Red Cross should stay informed
about the state of El Ninio to compliment the flood information strategy based upon monitoring
the strength of climate indices as presented in Tables 20, 21, and 22. When an El Niio event
begins the Red Cross can anticipate the continuation of heightened precipitation conditions for,
at least, the first five consecutive seasons of the event. A summary of how the Red Cross can
adapt their actions and emergency preparedness to such heightened precipitation conditions is
provided in Table 23 on the following page.
117
Table 23: Sample Guidance for Actions Taken in Consecutive El Ninio Seasons
Expected Precipitation Conditions
El NioE
Season
Number
Normalized
Seasonal
Precipitation
Departure from
Average Seasonal
Precipitation
Example Action for Red Cross
1
0.91
9% Less
No Action
2
0.98
2% Less
No Action
3
1.05
5% More
No Action
4
1.12
12% More
Begin monitoring ONI for El Niio conditions (ONI
above 0.5). If El Niio conditions persist, future
seasons may have increased rainfall. Monitor index
conditions closely.
5
1.19
19% More
The ONI has been above 0.5 for five consecutive
seasons and is officially classified as an El Nifio
episode. Seasonal precipitation is well above
average. Begin preparing for possibility of flooding.
6
1.26
26% More
7
1.33
33% More
8
1.40
40% More
9
1.47
47% More
10
1.54
54% More
As El Nifio progresses the amplitude of seasonal
precipitation increases. A rise in total precipitation
may enhance the risk of flooding. Red Cross staff
should stock necessary resources and be prepared for
disaster relief.
Notes: The example actions are not suggested, they are merely provided as an example of what in-country experts at
the Red Cross in Uganda might decide best fits the system that they develop as a long-term information strategy.
The NSP values for El Niho seasons I through 5 were calculated from the linear trend line equation in Figure 5-2,
which presents the average NSP in the Manafwa River Basin for the first five consecutive El Niio seasons. The NSP
values for El Nino seasons 6 through 10 were calculated through extrapolation of this trend into further seasons.
Table 23 shows the increasing amplitude of Normalized Seasonal Precipitation (NSP) in
successive El Ni5o seasons. The NSP values in this table are based off of the analysis performed
in Section 5.2.2 and are calculated from the linear trend line for average NSP shown in Figure
5-2, which depicts the increasing trend of precipitation amplitude over the first five El Nifno
seasons. The trend in Figure 5-2 was only plotted for five seasons because the 16-year study
period had a limited number of El Nifio events with which the calculate the average season NSP.
The sample guidance in Table 23 has projections for NSP through the tenth season, which means
that the NSP values for El Nifio seasons 6 through 10 were calculated through extrapolation of
the linear trend line in Figure 5-2. The trend was extended to the tenth season because the
analysis in Section 6.2.1, which considers nearly 60 years of precipitation data in the Bou Regreg
Watershed, shows that precipitation trends in consecutive ENSO seasons can last well beyond
the fifth season. When using precipitation in the Bou Regreg Watershed as an example, Figure
6-3 shows an El Ninio trend lasting 15 seasons, and Figure 6-5 shows a La Nina trend lasting 10
seasons; therefore, Table 23 may provide a realistic portrayal of what total seasonal precipitation
118
in the Manafwa River Basin could be like during an El Nifio event. The example actions listed
in Table 23 are not suggestions, they are merely provided as an example of what in-country
experts at the Red Cross in Uganda might decide best fits the system that they choose to develop
as a long-term information strategy.
The analyses discussed in this report have shown correlations between climate indices
(SOI, DMI, and ONI) and the precipitation experienced in the Manafwa River Basin of eastern
Uganda. The precipitation trends presented throughout this study, and summarized in Tables 20,
21, 22, and 23, can be utilized by the Red Cross to determine if there is a heightened risk of
flooding in an upcoming season.
7.2 Recommendations for Future Flood Prognostication Strategies
The investigation of historical precipitation conditions in the Manafwa River Basin and the
analysis of the correlation to various oceanic and atmospheric phenomena presented many
Weather can act in unpredictable ways and contradict previously defined
challenges.
correlations. Precipitation is a result of the intricate interactions of many oceanic and
atmospheric conditions, which should not be viewed in isolation of each other. A long-term
flood information strategy could be enhanced by exploring additional weather systems that may
impact the basin, such as large-scale monsoonal winds and the ITCZ (Kizza, et al., 2012), to
determine how the interaction of all systems will alter precipitation. Future analyses should
account for the interactions of many systems, to determine if the effect of one system out-weighs
another, or if the resultant precipitation patterns are a mixed response of multiple systems
working in concert.
The method relied upon in this study for determining precipitation in the Manafwa River
Basin used daily satellite precipitation estimates with a gridded resolution of 0.25'. Higher
precision products are available that can provide precipitation estimates on a three-hour basis
(e.g. TRMM 3B42 V6), or with an enhanced resolution of 0.10 (e.g. ARC2). Such products may
offer a more accurate representation the precipitation conditions of the basin. Defining
precipitation rates on an interval of less than one day would allow for a better understanding of
the nature of the heavy precipitation events experienced in the basin. Distinguishing the
difference between a one-day heavy precipitation event and a multi-hour extreme event would
allow for the comparison of how climate indices uniquely affect a specific type of precipitation
event. The historical climate indices used in this study were monthly and seasonal averages;
however, real-time running measurements and weekly averages can also be accessed. Focusing
the study on weekly average indices and their corresponding weekly total and heavy
precipitation values may help better define the response time with which oceanic and
atmospheric conditions impact precipitation characteristics.
119
7.3
Continuing Analysis of Precipitation in Consecutive ENSO Seasons
The analysis performed in this thesis indicates the existence of a trend in precipitation
through consecutive seasons of ESNO events. The investigation began by exploring the
precipitation response to ENSO for the Manafwa River Basin in eastern Uganda. It was
discovered that the first five seasons of El Nifio events have a trend of increasing Normalized
Seasonal Precipitation (NSP), indicating that each consecutive season receives a greater
proportion of precipitation based on what is average for the specific time of year. Conversely,
the first five seasons of a La Nifia event exhibited a decreasing trend of normalized precipitation.
The analysis was then expanded to observe the precipitation response in Houston, Texas and
Meknes, Morocco.
The results of the investigation for ENSO precipitation conditions in Houston and Meknes
generally supported the findings initially presented for the Manafwa River Basin. The
normalized precipitation in Meknes was determined to have an increasing trend for the first 15
seasons of an El Ninio event, and a decreasing trend for the first 10 seasons of a La Niia event.
Houston was found to have an increasing trend in amplitude of precipitation for the first seven
seasons of an El Niinio event, but no distinct trend for consecutive seasons of La Nifia. Although
individual ENSO events do not always behave with the same precipitation patterns as shown by
the averages, there is value in understanding the average long-term seasonal effects of El Nifio
and La Nifia events on watersheds.
The author of this thesis believes the observation made on the trend of increasing and
decreasing amplitude of precipitation with consecutive ENSO seasons to be an original remark
for explaining the effect of ENSO. These precipitation trends are discussed in detail in Sections
5 and 6 of this report. An example trend can be observed in Figure 5-2, which shows increasing
amplitude of seasonal precipitation in the Manafwa River Basin through the first five
consecutive El Ninio seasons. Conversely, Figure 5-9 provides an example of consecutive La
Niia seasons experiencing a decreasing trend of total seasonal precipitation. The trends
discussed in this study may be valuable tools for predicting total precipitation and the occurrence
of heavy precipitation events in future seasons of ENSO events. The application of such
knowledge could provide more educated decision-making for water management, agricultural
planning, flood prediction, storm-water management, construction scheduling, and numerous
other strategic requirements. The investigation of precipitation response in consecutive seasons
of ENSO events should be continued in order to better define precipitation characteristics, and
further examine how each ENSO season behaves given the time of year in which it occurs.
120
8
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126
Appendices
Appendix A - TRMM Precipitation Data Extraction Code
%
%
%
%
%
%
%
This code will extract daily precipitation estimates (in units of mm/day)
Global precipitation data can be
from the TRMM Product 3B42(V7).
downloaded from at the following URL: http://mirador.gsfc.nasa.gov/
corresponding to each day of any given month into
Place the .bin files
This code will then extract precipitation data from
the same folder.
the specified TRMM grid cells, and will create and save a .xls table
containing daily precipitation data at specific latitudes and longitudes.
clc
clear
specify the month and year of the data
% The user must first
;
year = 1998
month = 12
The
"if"
%Enter the year (####) of the data you wish to extract
%Enter the month (##) of the data you wish to extract
below will
statements
determine
automatically
the
number of
days
% in the month based on the user entered information above
mon=num2str(month,'%02.0f');
if
strcmp(mon, '04')==llstrcmp(mon,'06')==llstrcmp(mon,
=1
dayspermon
=
'09')==llstrcmp(mon,
30;
end
if strcmp(mon,'01')==llstrcmp(mon,'03')==llstrcmp(mon,'05')==l1
strcmp(mon,'07')==lj
strcmp (mon, '08') ==1 I strcmp (mon, '10') ==l Istrcmp (mon, '12') ==1
dayspermon =
31;
strcmp(mon,'02')==l
dayspermon =
29;
end
if
&& mod(year,4)
==
0
end
strcmp(mon, '02')==1 && mod(year,4)>
dayspermon = 28;
if
0
end
% Creates a matrix with 1 row and "dayspermon"
dates =(1:1:dayspermon);
columns
Begins replication of file naming format of the .bin files from NASA
filenames=repmat({'3B42_daily.'},dayspermon,1);
127
'l')=
Creates a stacked
matrix for the month containing
matrix = zeros(8,8,dayspermon);
This
m X n
grid
cells
loop will cut out a region of each daily .bin file and creates a
% stacked matrix for the month
(dimensions m
x n x days permonth)
for i = 1 : dayspermon
% Formats the name of file to match the name of downloaded .bin files
% The
file name includes the name of the TRMM product
% the date as a 4 digit
% suffix
2 digit month,
2 digit
(3B42 daily),
day,
then
followed by the
(.7.bin)
filenames{i}
=
,num2str(mon,'%02d')
% This
year,
[ filenames{i} , num2str(year)
num2str(dates(i),'%02d'),
,'.',
'.7.bin'];
section of code was provided with TRMM data manual
fid =
fopen(filenames{i},
a = fread(fid,
'r');
'float','b');
fclose (fid);
data = a';
count = 1;
%
The variable h defines the number of rows of the matrix.
% number of latitudes at which data will be extracted.
It
is
the
h=8;
j=1;
% This
code was provided with the TRMM data manual
for i lat = 1:400
for j_lon = 1:1440
lat = -49.875 + 0.25*(ilat - 1);
if j lon <= 720
lon = 0.125 + 0.25*(jlon - 1);
else
lon
0.125 + 0.25*(j lon
=
end
dailyraintotal
-
1)
-
360.0;
= data(count);
% This section extracts the TRMM data from the coordinates that the user
% instructs.
The longitudes and latitudes entered here create a box for
% which all data inside of its limits will be extracted.
if lon>33.0 && lon<35.00 && lat>0.0 && lat<2.0
matrix(h,j,i)= daily rain total;
j=j+l;
if
j>8
h=h-1;
j=1;
end
128
end
count = count + 1;
end
end
end
count=0;
% The stacked matrix created in the loop above is loaded and its dimensions
% are defined.
%
file=['matrix.mat'];
name=['matrix'];
load(file)
str=['A=',name,';'];
eval(str)
% Redefines "count" as zero
count = 0 ;
% This section will cycle through each day pulling out specific data points
% from the previously created stacked monthly matrix. The desired points
% can be changed at any time.
for day = 1:
dayspermon
count =
count +
Grid A(count)
GridB(count)
GridC(count)
GridD(count)
GridE(count)
GridF(count)
1 ;
= A(4,4,day) ;
= A(4,5,day) ;
= A(4,6,day)
= A(5,4,day)
= A(5,5,day)
= A(5,6,day)
;
;
;
;
end
% This command creates a table of monthly precipitation data with each
% column corresponding to a different latitude and longitude.
ManafwaGrid =[dates',GridA',GridB',GridC',GridD',GridE',GridF']
clear A
clear(name)
% The
"ManafwaGrid"
table will be saved as an Excel file
monthlyfile = ['MonthlyPrecip','.',
xlswrite(monthlyfile, ManafwaGrid)
mon,'.',
num2str(year),
'.xls']
;
Note: Sections of this code were taken from the NASA TRMM Data Manual and Cecinati, 2013
129
Appendix B - Monthly and Seasonal Precipitation
Total Monthly and Annual Precipitation in the Manafwa River Basin
Year
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Avg.
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
173.0
84.9
31.0
89.5
46.9
83.9
62.7
35.1
39.6
83.6
38.8
62.5
52.1
21.8
0.6
43.3
59.3
94.3
12.7
19.8
41.7
57.5
81.4
72.0
39.0
107.8
119.8
73.8
50.1
159.3
22.3
21.0
23.0
62.2
85.4
202.5
57.8
157.8
120.9
85.8
77.4
126.5
165.2
70.5
160.7
58.7
151.8
112.6
55.2
169.8
116.2
190.9
179.9
154.1
152.4
222.0
206.3
224.5
181.2
213.3
175.1
166.4
219.9
210.2
122.7
239.5
217.8
192.3
244.5
141.9
155.3
178.7
162.9
220.5
110.5
249.9
181.9
188.9
174.2
168.6
152.8
163.3
174.5
151.4
176.2
104.1
113.2
102.1
165.3
94.3
177.5
89.5
116.7
169.2
126.8
108.0
69.1
115.5
115.3
118.1
76.6
116.3
156.8
110.1
108.7
151.6
82.4
121.8
70.5
161.2
136.7
194.8
163.0
48.6
120.8
98.4
91.2
79.4
118.5
133.8
170.5
164.3
139.7
126.4
147.6
160.7
127.5
133.2
193.7
181.5
104.0
133.8
223.9
102.5
139.4
148.9
104.3
134.2
130.7
151.4
102.8
129.0
172.3
144.5
175.1
233.6
133.9
153.8
136.1
187.1
171.0
213.7
154.6
166.1
184.8
180.7
238.1
166.6
97.7
129.7
129.5
214.0
120.2
230.7
184.3
133.8
135.6
145.3
110.6
160.5
138.2
122.8
126.3
159.7
134.9
118.8
135.9
68.9
275.4
89.1
185.8
117.6
85.6
253.5
130.1
96.0
13.6
62.2
73.9
29.5
194.5
71.8
51.1
14.8
112.5
50.3
11.0
174.2
62.3
24.8
152.8
72.9
1604.9
1519.9
1304.9
1655.4
1512.2
1542.1
1356.9
1394.8
1923.8
1646.2
1628.0
1411.5
1513.9
1481.4
1401.8
1393.8
139.9
73.3
1518.2
Total Seasonal Precipitation in the Manafwa River Basin
NDJ
JFM
FMA
MAM
AMJ
MJJ
JJA
JAS
ASO
SON
OND
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
111.2
113.1
205.2
133.9
359.9
206.5
125.2
162.1
315.9
162.9
123.7
385.6
106.4
46.5
219.2
352.7
300.1
108.7
289.0
225.3
251.2
212.2
200.5
312.6
273.9
273.4
171.3
363.2
156.7
76.8
236.1
370.6
395.2
231.7
351.9
400.4
373.6
373.9
346.6
486.3
365.4
401.0
328.7
521.2
257.6
315.7
410.5
520.7
524.4
367.2
488.8
505.8
512.6
412.4
557.6
560.5
434.5
501.4
447.1
514.7
398.6
469.1
538.9
539.4
435.0
411.5
496.4
479.2
604.3
424.5
547.9
564.4
490.7
448.7
457.6
478.4
401.3
532.0
445.8
505.3
365.2
366.2
495.6
339.7
519.8
270.5
527.9
487.8
510.4
445.2
286.3
389.0
377.0
383.8
307.4
394.7
393.8
375.2
456.6
303.2
446.9
320.8
405.5
439.1
515.2
452.5
221.8
370.1
437.6
311.8
295.4
394.9
414.8
403.7
442.7
311.6
398.4
403.5
433.3
445.0
622.1
478.4
306.5
390.7
509.4
364.7
432.5
404.2
489.6
475.7
529.3
395.8
374.2
462.7
401.5
522.2
547.5
546.1
442.2
403.7
546.6
418.8
463.6
408.6
441.8
437.6
549.3
404.2
345.4
437.9
342.9
664.4
442.9
550.4
455.7
355.5
576.2
446.4
420.3
318.0
369.8
380.9
427.3
496.0
288.2
316.7
213.1
601.8
259.5
427.6
476.1
281.7
413.9
428.2
279.5
236.7
216.0
289.8
236.1
413.3
253.3
222.1
123.2
471.5
178.2
259.4
343.9
169.7
278.9
326.2
-
Avg.
185.2
237.7
370.6
484.7
484.8
411.1
383.8
422.0
464.0
455.0
373.7
267.9
Year
DJF
130
Appendix C - Heavy Precipitation Events
Number of Heavy Precipitation Events in the Manafwa River Basin
Year
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
7
3
2
4
2
4
2
5
0
1
2
4
4
2
3
1
3
1
4
5
4
1
3
6
4
6
5
3
4
4
8
5
2
3
2
5
5
2
4
2
2
3
3
1
3
1
3
3
5
6
4
2
7
1
7
1
4
2
1
1
2
3
0
3
1
2
0
2
4
1
9
4
1
0
7
1
0
0
0
2
1
1
6
0
3
1
1
4
6
2
3
2
0
1
3
3
3
3
1
4
4
2
7
4
1
2
1
5
1
6
4
4
3
2
2
1
2
1
9
1
5
4
1
1
0
0
7
5
3
3
3
6
2
5
5
5
1
2
7
2
3
4
3
3
5
3
0
1
1
7
2
7
3
0
2011
7
2
3
6
2
2
1
3
4
3
2
4
3
3
4
2
2
2
4
3
3
4
0
3
1
1
2
2
7
3
40
40
35
34
36
41
29
26
60
32
39
35
31
28
26
30
2.3
2.8
2.1
4.2
2.8
2.8
2.4
3.7
3.1
2.6
3.1
3.3
35.1
2012
2013
Avg.
1
0
1
0
1
0
11
2
Maximum One-Day Precipitation Event (mm/day) in the Manafwa River Basin
Annual
Max.
Year
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
61.1
39.0
19.0
46.1
24.4
52.8
62.8
16.4
18.8
41.8
43.1
43.4
23.7
25.2
1.7
24.1
47.3
11.8
20.3
18.6
37.8
42.1
43.0
30.5
39.2
36.6
29.9
19.5
47.3
11.4
13.0
17.2
44.7
54.4
27.1
45.9
35.2
32.8
27.8
46.4
66.5
26.2
44.7
27.2
42.3
21.3
34.7
50.8
89.6
41.9
43.4
36.3
55.3
45.8
62.4
52.0
33.0
74.2
48.0
102.7
63.4
32.0
65.3
41.5
84.1
33.5
37.9
49.2
56.4
43.8
35.3
60.0
36.2
42.5
44.5
68.1
35.5
27.8
37.6
38.4
34.1
32.3
38.9
57.4
19.7
45.2
28.0
34.8
51.2
44.5
25.7
31.5
35.6
35.3
39.8
53.4
51.9
34.7
40.7
62.6
39.6
29.4
34.1
47.6
28.4
68.3
38.9
18.9
38.2
26.1
21.3
26.0
20.8
37.3
32.8
60.9
26.5
38.8
46.2
42.1
43.4
35.8
49.7
34.8
38.0
73.0
35.7
35.6
39.2
44.8
46.0
50.6
31.9
45.8
48.7
72.2
51.9
53.8
28.9
35.3
34.6
52.6
29.3
68.3
49.1
39.5
47.6
83.2
50.1
21.0
29.4
32.7
50.5
38.6
47.4
46.2
24.5
44.2
58.1
33.4
43.8
41.3
58.3
50.9
49.1
28.1
38.5
27.0
55.2
46.3
83.2
61.2
32.2
79.4
34.4
33.1
11.6
28.2
25.7
29.0
48.3
39.2
18.2
7.8
25.9
29.0
10.5
38.3
25.1
14.3
52.6
36.5
89.6
54.4
58.3
83.2
56.4
52.8
62.8
72.2
66.5
74.2
83.2
102.7
63.4
79.4
65.3
68.3
Max.
62.8
47.3
66.5
102.7
84.1
57.4
68.3
73.0
72.2
83.2
83.2
52.6
-
131
Appendix D - Example Equations
Calculation of Normalized Monthly Precipitation (NMP):
Total Precip.February201 1 (mm)
Average Precip.February 99 8 - 20 13 (mm)
NMP February2 01 1 =
NMP February201 1 =
41.7 mm
62.2 mm
= 0.67 (dimensionless)
0.67 x 100% = 67% of Average PrecipitationFebruary 9 98
2
01 1
Calculation of Seasonal Precipitation and Normalized Seasonal Precipitation (NSP):
Total Precip.MAM 2 0 06 (mm)
=
March2 0 06 (mm) + April2 0 06 (mm) + May 2 0 0 6 (mM)
Total Precip.MAM 2 00 6 = 165.2 mm + 213.3 mm + 181.9 mm = 560.5 mm
NSP MAM 2 00 6
NSP MAM 2 0 06
=
Total Precip.MAM 2 0 0 6
Average Precip.MAM
1 9 9 8 - 2 0 13
560.5 mm
5
= 1.16 (dimensionless)
484.7 mm
132
Appendix E - Normalized Precipitation Values
Normalized Monthly Precipitation (NMP) Values for the Manafwa River Basin
Year
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2.91
1.43
0.52
1.51
0.79
1.41
1.52
0.20
0.32
0.67
0.92
1.31
1.16
0.63
1.73
1.92
1.19
0.81
2.56
0.36
0.34
0.37
0.74
1.74
0.50
1.36
1.04
0.74
0.67
1.09
1.42
0.61
1.38
0.50
1.31
0.97
0.48
1.46
0.99
0.94
0.80
0.79
1.15
1.07
1.17
0.94
1.11
0.91
0.87
1.14
1.09
0.64
1.25
1.13
1.39
0.81
0.88
1.01
0.92
1.25
0.63
1.42
1.03
1.07
0.99
0.96
0.87
0.93
0.99
0.86
0.89
0.97
0.88
1.42
0.81
1.53
0.77
1.00
1.45
1.09
0.93
0.59
0.99
0.99
1.02
0.66
1.32
0.93
0.92
1.28
0.70
1.03
0.59
1.36
1.15
1.64
1.38
0.41
1.02
0.83
0.77
0.67
0.90
1.15
1.10
0.94
0.85
0.99
1.08
0.86
0.89
1.30
1.22
0.70
0.90
1.50
0.69
0.94
0.67
0.87
0.85
0.98
0.66
0.83
1.11
0.93
1.13
1.51
0.87
0.99
0.88
1.21
1.11
1.38
1.04
1.15
1.13
1.48
1.04
0.61
0.81
0.81
1.33
0.75
1.44
1.15
0.83
0.85
0.91
0.69
0.99
0.88
0.90
1.14
0.96
0.85
0.97
0.49
1.97
0.64
1.33
0.84
0.61
1.81
0.93
0.69
0.19
0.85
1.01
0.40
2.65
0.98
0.70
0.20
1.54
0.69
0.15
2.38
0.85
0.34
2.09
0.99
1.06
0.59
0.67
1.41
0.65
1.05
0.88
0.37
0.01
0.73
Normalized Seasonal Precipitation (NSP) Values for the Manafwa River Basin
Year
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
DJF
JFM
FMA
MAM
AMJ
MJJ
JJA
JAS
ASO
SON
OND
NDJ
-
1.48
1.26
0.46
1.22
0.95
1.00
1.07
0.63
0.95
1.08
1.01
1.01
0.94
1.31
0.99
1.08
0.89
1.41
0.70
0.85
1.11
1.07
1.08
0.76
1.01
1.04
1.06
0.85
1.15
1.16
0.90
1.03
0.92
1.06
0.82
0.97
1.11
1.11
0.90
0.85
1.02
0.99
1.25
0.88
1.13
1.16
1.01
0.93
0.94
0.99
0.83
1.10
0.92
1.23
0.89
0.89
1.21
0.83
1.26
0.66
1.28
1.19
1.24
1.08
0.70
0.95
0.92
0.93
0.75
1.03
1.03
0.98
1.19
0.79
1.16
0.84
1.06
1.14
1.34
1.18
0.58
0.96
1.14
0.81
0.77
0.94
0.98
0.96
1.05
0.74
0.94
0.96
1.03
1.05
1.47
1.13
0.73
0.93
1.21
0.86
1.02
0.87
1.06
1.03
1.14
0.85
0.81
1.00
0.87
1.13
1.18
1.18
0.95
0.87
1.18
0.90
1.00
0.90
0.97
0.96
1.21
0.89
0.76
0.96
0.75
1.46
0.97
1.21
1.00
0.78
1.27
0.98
0.92
0.85
0.99
1.02
1.14
1.33
0.77
0.85
0.57
1.61
0.69
1.14
1.27
0.75
1.11
1.15
0.75
0.88
0.81
1.08
0.88
1.54
0.95
0.83
0.46
1.76
0.67
0.97
1.28
0.63
1.04
1.22
0.60
0.61
1.11
0.72
1.94
1.06
1.12
0.68
0.88
1.71
0.88
0.67
2.08
0.57
0.25
1.18
0.89
0.84
1.31
1.15
1.15
0.72
1.53
0.66
0.32
0.99
133
-
Appendix F - Climate Index Values
Monthly Southern Oscillation Index (SOI) Values used for this Study
Data Source: (NOAA g, 2014)
Year
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
-2.7
1.8
0.7
1
0.4
-0.2
-1.3
0.3
1.7
-0.8
1.8
-2
1
1.7
1.7
1.1
-0.7
1.2
-3.1
0.1
-0.1
2.6
1.1
1.9
-1.1
2.3
-1.5
2.7
0.5
-0.2
-2.4
1.3
1.3
0.9
-0.2
-0.3
0.4
0.3
1.8
0.2
1.4
0.4
-0.7
2.5
0.7
1.5
-1.4
1.4
1.2
0.2
-0.1
-0.1
-0.9
-0.6
1.1
-0.1
0.7
0.8
1.2
1.9
-0.3
0.2
0.3
0.2
0.4
-0.5
-0.8
-0.3
1
-0.8
-0.5
-0.1
-0.1
-0.1
0.9
0.4
0
0.8
1
0.3
-0.2
0.3
-0.2
-0.6
-0.8
0.4
-0.2
0.5
0.6
0.1
0.4
0.2
-0.4
1.2
1.2
0.5
-0.2
-0.2
-0.5
0.3
-0.5
0.2
-0.6
-0.3
0.3
0.2
1.8
1
0
0.8
1.2
0.4
0.7
-0.4
-1
0.1
-0.3
-0.3
-1
0.4
1
-0.2
1.8
0.4
-0.2
0.2
-1.4
1
-0.1
0.9
0.2
-0.6
-0.1
-0.3
0.4
-0.6
0.2
1.2
0.3
2.2
1
0.2
0.3
-1.5
1.1
1
1.1
0
-0.4
0
-0.1
1.2
-1.3
0.7
1.3
-1.2
1.7
0.8
0.3
-0.1
-1.2
1
1
1.8
0.7
-0.5
-0.3
-0.7
-0.2
0.1
0.9
1.3
-0.6
1.3
1.1
0.3
0.7
-1
1.4
1.4
0.8
-0.8
-1.1
1.1
-0.8
0
-0.3
1.7
1.4
-0.7
2.9
2.5
-0.6
0.1
1.1
-0.1
Seasonal Oceanic Nifio Index (ONI) Values used for this Study
Data Source: (NOAA, 2014)
Year
DJF
JFM
FMA
MAM
AMJ
MJJ
JJA
JAS
ASO
SON
OND
NDJ
1997
1998
1999
2000
2001
2002
2003
2.2
-1.5
-1.7
-0.7
-0.2
1.1
1.8
-1.3
-1.5
-0.6
0
0.8
1.4
-1
-1.2
-0.5
0.1
0.4
0.9
-0.9
-0.9
-0.4
0.3
0
0.4
-0.2
-0.7
-
-1
2.1
-1.2
2.3
-1.3
2.4
-1.4
2.3
-1.5
-0.9
-0.8
-0.2
0.5
-0.2
-1
-0.7
-0.1
0.7
-0.1
-1
-0.6
0
0.8
0.2
-1.1
-0.5
0
-1.1
-0.6
-0.1
-1.3
-0.6
-0.2
-1.5
-0.8
-0.3
-1.7
-0.8
-0.3
0.8
0.4
0.9
0.4
1.2
0.4
1.3
0.4
1.3
0.3
2004
2005
2006
0.2
0.4
0.1
0.3
0.1
0.3
0.2
0.3
0.3
0.3
0.5
0.2
0.7
0.1
0.8
0
0.7
-0.2
0.7
-0.5
0.7
-0.8
2007
2008
2009
2010
2011
2012
0.3
0.6
-0.9
0.7
-1.5
-0.8
1.6
-1.4
-0.9
-0.7
0.3
-1.5
-0.7
1.3
-1.2
-0.6
-0.5
-0.1
-1.2
-0.5
1
-0.9
-0.5
-0.3
-0.2
-0.9
-0.2
0.6
-0.6
-0.3
0
-0.3
-0.7
0.2
0.1
-0.3
-0.2
0.1
-0.3
-0.5
0.4
-0.4
-0.2
0
0.2
-0.4
-0.3
0.5
-0.9
-0.2
0.1
2013
-0.6
-0.6
-0.4
-0.2
-0.2
-0.3
-0.3
0.3
-0.6
-0.2
0.6
-1.2
-0.4
0.4
-0.3
0.5
-0.8
-0.1
0.8
-1.4
-0.6
0.5
-0.3
0.8
-1.1
-0.2
1.1
-1.5
-0.8
0.6
-0.2
1
-1.2
-0.5
1.4
-1.5
-1
0.2
-0.3
1
-1.4
-0.7
1.6
-1.5
-1
-0.3
-0.4
134
Monthly Dipole Mode Index (DMI) Values used for this Study
Source: (JAMSTEC, 2014)
Year
Jan
Feb
Mar
Apr
May
June
Jul
Aug
Sep
Oct
Nov
Dec
1997
-
-
-
-
-
-
-
-
1.158
1.259
1.542
1.092
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0.701
0.607
0.121
0.259
0.368
0.369
0.067
0.067
-0.241
0.045
-0.059
0.230
0.059
0.060
0.407
0.308
0.223
0.477
0.378
0.231
0.123
0.157
0.178
0.167
0.091
0.204
0.298
-0.372
-0.115
0.335
0.117
0.352
0.213
0.423
0.105
0.377
0.280
0.314
0.155
0.177
0.125
0.253
-0.263
-0.055
0.280
0.274
0.269
0.633
0.542
0.200
0.251
0.204
0.354
0.343
-0.146
0.104
0.126
0.288
0.191
0.301
0.051
0.328
0.569
0.362
-0.055
-0.093
0.044
0.342
0.363
-0.102
0.063
-0.335
0.193
0.015
0.495
0.407
0.481
0.193
0.136
-0.112
-0.281
0.080
0.199
0.344
0.029
0.354
-0.172
0.039
0.155
0.249
0.431
0.313
0.073
0.269
0.226
-0.284
-0.056
0.425
0.398
0.179
0.058
0.444
0.015
-0.065
0.352
0.366
0.559
0.121
0.315
0.539
0.862
0.128
-0.254
0.326
0.445
0.010
0.095
0.412
0.171
-0.037
0.532
0.545
0.429
0.205
0.253
0.654
0.953
0.109
-0.084
0.347
0.308
0.171
0.680
0.335
0.289
-0.136
0.815
0.632
0.483
0.288
0.132
0.595
0.848
0.091
-0.337
0.202
0.247
-0.056
0.788
0.149
0.384
-0.042
0.957
0.464
0.422
0.374
-0.043
0.747
0.502
0.232
-0.366
0.166
-0.026
0.035
0.368
0.114
0.114
0.005
0.769
0.255
0.154
0.197
-0.207
0.607
0.174
0.467
-0.096
0.089
-0.010
0.178
0.073
0.415
0.098
-0.061
0.399
0.011
0.181
0.389
0.029
0.104
0.495
0.375
135
Appendix G - Months with No Correlation between Precipitation and SOI
Months with No Correlation between Total Precipitation and SOT
Precipitation
Month
Index
Month
Length of
Delay
Normalized Monthly
Precipitation
(NMP)
Southern
Oscillation
Index
-toPrecipitation
Index
Linear Trend Line
Parameters
y
Equation
NMP x = SOI
R2
(Sol)
March
April
July
August
September
October
November
r
March
February
I-Month Delay
No Delay
January
2-Month Delay
December
3-Month Delay
November
4-Month Delay
April
March
February
January
December
July
June
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
I-Month Delay
May
2-Month Delay
April
3-Month Delay
March
4-Month Delay
August
July
No Delay
1-Month Delay
June
2-Month Delay
May
April
September
August
July
June
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
= 0.1 143x + 0.935
y = -0.0043x + 1.0019
= 0.0997x + 0.9626
= -0.0429x + 1.0212
y = 0.0272x + 0.9898
y = -0.0781 x + 1.0254
= -0.0531 x + 1.0302
= -0.0323x + 1.0139
y =-0.0413x + 1.0155
y= -0.0419x + 1.0207
= 0.0085x + 0.9979
= 0.2337x + 0.962
y = -0.2064x + 1.0103
= -0.066x + 1.0214
0.037
-0.0448x + 1.0171
= -0.0 109x + 1.0042
= -0.0457x + 1.008
= -0.1093x + 1.0273
0.022
0.001
0.017
0.078
May
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
=
=
June
4-Month Delay
No Delay
1-Month Delay
September
August
2-Month Delay
3-Month Delay
July
4-Month Delay
136
0.003
0.179
0.133
0.101
0.111
0.107
0.000
0.143
0.119
0.032
y = -0.0561 x + 1.0319
= 0.0899x + 0.9843
y = 0.0284x + 0.9929
= 0.0444x + 0.9928
= 0.1005x + 0.995
y = 0.0941 x + 0.9694
y= -0.0445x + 1.0172
y = -0.0436x + 1.0076
y = -0.0506x + 1.0126
y = 0.06x + 0.9902
y = 0.1025x + 0.9949
October
Se tember
August
July
November
October
0.105
0.000
0.109
0.019
0.0352x
0.100
0.008
0.013
0.069
0.157
0.019
0.020
0.021
0.020
0.061
0.9943
0.005
0.0458x + 0.9774
y -0.1485x + 1.0566
= -0.0846x + 1.0328
y = -0.1 37x + 1.024
y= -0.1082x + 1.0271
0.008
0.105
0.024
0.066
=
+
0.033
Months with no Correlation between Heavy Precipitation and SOT
Precipitation
Month
Heavy Precipitation
Events
March
June
July
September
October
November
Index
Month
Southern
Oscillation
Index
(Sol)
Length of
Delay
Ito
Precipitation
March
February
January
December
2-Month Delay
3-Month Delay
November
June
May
April
March
February
July
June
May
April
March
September
August
July
June
May
October
September
August
July
June
November
October
September
August
July
Linear Trend Line
Parameters
No Delay
Equation
R2
y = # heavy x = SOI
y=
y=
y=
y=
0.4208x + 1.8231
0.0605x + 2.0364
0.4454x + 1.8955
-0.0346x + 2.0796
4-Month Delay
y=
0.4571x
1.8911
0.050
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
y= -0.9002x + 2.9588
y= -0.3256x + 2.8288
y = 0.1428x + 2.7661
y = 1.051 x + 2.7527
0.078
0.011
0.006
0.005
4-Month Delay
No Delay
y = -0.27724x + 2.93
y= 0.0364x + 2.4284
0.065
1-Month Delay
y = 0.7768x +2.3113
y = -0.5567x + 2.4653
y = -0.2233x + 2.5 101
y = -0.2936x + 2.6045
y = -0.8994x + 3.4735
y = -0.6315x + 3.2355
y = -0.379x + 3.2198
y = 0.1514x + 3.1004
y = 0.5462x + 3.0977
y = -0.5679x + 2.8415
y = -0.4481 x + 2.7987
y = -0.4925x + 2.7112
y = -1.1516x + 2.9129
y = 0.6898x + 2.5129
y = 0.3875x + 2.8712
y = -0.7885x + 3.3631
y = -0.3243x + 3.1882
y = -0.449x + 3.1411
y = -0.2843x + 3.1336
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
137
+
0.073
0.003
0.111
0.001
0.000
0.096
0.053
0.022
0.062
0.130
0.069
0.020
0.002
0.029
0.051
0.022
0.028
0.123
0.029
0.019
0.099
0.012
0.024
0.008
Appendix H - Seasons with No Correlation between Precipitation and ONI
Seasons with No Correlation between Total Precipitation and ONI
Precipitation
Month
Index
Month
Length of
Delay
Normalized Seasonal
Precipitation
Oceanic Ninio
Index
Index-toPrecipitation
(NSP)
(Sol)
MAM
FMA
1-Season Delay
JFM
2-Season Delay
No Delay
Linear Trend Line
Parameters
y
=
Equation
NSP x = ONI
R
DJF
3-Season Delay
= 0.0819x + 1.0138
y = 0.0587x + 1.0128
= 0.039x + 1.0095
= 0.0308x + 1.0071
NDJ
4-Season Delay
y=
1.0055
0.106
JJA
MJJ
No Delay
I -Season Delay
AMJ
2-Season Delay
MAM
3-Season Delay
0.127
0.165
0.137
0.038
FMA
SON
4-Season Delay
No Delay
September to
ASO
November
(SON)
JAS
JJA
I-Season Delay
2-Season Delay
3-Season Delay
= -0.1364x + 0.9838
y = -0.1 886x + 0.9764
y = -0.1 776x + 0.9767
y = -0.0742x + 0.9875
y = -0.01 15x + 0.9975
= 0.0354x + 1.0057
y = 0.0377x + 1.0054
y = 0.0377x + 1.011
= 0.03x + 1.0036
MJJ
4-Season Delay
y
-0.0279x + 0.9965
0.004
March to May
June to August
I_
=
0.0313x
+
0.131
0.135
0.105
0.095
0.002
0.026
0.021
0.014
0.007
Seasons with NO Correlation between Heavy Precipitation and ONI
Precipitation
Month
Normalized Seasonal
Precipitation
(NSP)
March to May
(MaAM)
September to
November
(SON)
Index
Month
Length of
Delay
Linear Trend Line
Parameters
Southern
Index-to-
Oscillation
Index
Precipitation
MAM
No Delay
FMA
JFM
DJF
_1-Season Delay
2-Season Delay
3-Season Delay
NDJ
SON
4-Season Delay
No Delay
ASO
JAS
JJA
MJJ
1-Season Delay_
(SOI)
Equation
y = # heavy x = ONI
= 0.9072x + 9.2156
y = 0.8792x + 9.2548
y = 0.5852x + 9.2051
y = 0.4356x + 9.1632
y=
0.4089x
+ 9.134
R2
0.030
0.056
0.044
0.035
0.033
3-Season Dela
0.6325x + 8.9153
= 0.4434x + 8.8762
y = 0.0684x + 8.8211
y = -0.0872x + 8.8021
0.013
0.005
0.000
0.000
4-Season Delay
y = -1.2545x + 8.6557
0.012
2-Season Delay
138
=
Appendix I - Months with No Correlation between Precipitation and DMI
Months with No Correlation between Total Precipitation and DMI
Precipitation
Month
Index
Month
Length of
Delay
Normalized Monthly
Precipitation
(NMP)
Dipole Mode
Index
(DMI)
Index
-toPrecipitation
June
May
No Delay
1-Month Delay
y =0.5693x + 0. 9 05 7
y= 0. 1 18x + 0.9833
0.175
0.013
0.2237x + 0.9554
82x + 1.0849
y = -0.4048x + 1.0794
y= 0.1951 x + 0.9409
+ 0.935
y =0.2239x
y = 0.1383x + 0.9769
y = 0.1336x + 0.981
y 0.2515x + 0.9499
y= 0.22x + 0.9203
0.026
June
August
April
2-Month Delay
March
3-Month Delay
February
4-Month Delay
August
July
No Delay
1-Month Delay
June
2-Month Delay
May
3-Month Delay
April
4-Month Delay
No Delay
September
September
__________
August
July
June
May
Linear Trend Line
Parameters
Equation
Equation
NMP
y
y
R
x = DM1
=-0.3
0.086
0.116
0.072
0.069
0.016
0.026
0.050
2-Month Delay
3-Month Delay
y = 0.3267x + 0.901
y = 0.1909x + 0.9446
y = -0.3516x + 1.0588
0.078
0.173
0.043
0.089
4-Month Delay
y -0. 1267x±+1.018
0.020
1-Month Delay
139
Months with No Correlation between Heavy Precipitation and DMI
Precipitation
Month
Index
Month
Length of
Delay
Heavy Precipitation
Dipole Mode
(DM)
Index
Preciptation
April
March
February
January
Events
April
June
July
September
October
Linear Trend Line
Parameters
=
Equation
# heavy x = DMI
R
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
y
y
y=
y
1.8593x + 3.8172
-0.4658x + 4.291
-3.1009x + 4.7976
1.5252x+3.8942
0.039
0.003
0.151
0.038
December
4-Month Delay
y = -0.2157x + 4.2331
0.001
June
May
April
March
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
0.088
0.001
0.034
0.045
February
4-Month Delay
July
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
I-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
No Delay
1-Month Delay
2-Month Delay
3-Month Delay
4-Month Delay
y =2.6351x + 2.3719
y = -0.1 665x + 2.8361
y = I.7005x + 2.4738
y = -1.8141x + 3.2156
y = -1.2432x + 3.0564
y= -l.6258x + 2.9091
y 1.5143x+2.1843
y = 2.1305x + 2.1315
y = 2.3413x + 1.9712
y = -I.9937x + 2.8805
y= 0.6247x + 2.8988
y = 4959x + 2.9747
y = -0.5429x + 3.2825
y = -2.4862x + 3.5407
y = -1.9822x + 3.4064
y=.4212x+2.1817
y = 1.6912x + 2.0126
= -0.6105x + 2.81
y = 0.0892x + 2.5991
y = 4.6153x + 1.8533
June
May
April
March
September
August
July
June
May
October
September
August
July
June
140
y
0.025
0.092
0.048
0.165
0.108
0.090
0.010
0.007
0.006
0.073
0.080
0.049
0.051
0.007
0.000
0.170
Appendix J - Historical Flood Events
Historical Flood Events in the Manafwa River Basin
#
Year
Month
Source of Flood Record
1
1997
November
EM-DAT
April
EM-DAT
May
EM-DAT
November
Dartmouth Flood Observatory
July
EM-DAT
October
EM-DAT
November
EM-DAT
8
December
EM-DAT
9
July
IFRC Appeals
August
EM-DAT
11
September
EM-DAT
12
October
EM-DAT
13
Feb
IFRC Appeals
March
IFRC Appeals
15
April
IFRC Appeals
16
May
Dartmouth Flood Observatory
17
July
IFRC Appeals
August
EM-DAT
September
EM-DAT
May
Dartmouth Flood Observatory
June
Disaster Report
2
3
2002
4
5
2003
6
7
10
14
18
2006
2007
2010
2011
19
20
21
2012
141
Occurrence of Floods With Respect to DMI and SOI
Southern Oscillation Index
(So)
Dipole Mode Index
(DMI)
Date of Past Floods
#
Year
Month
2 Months
Before
Flood
1 Month
Before
Flood
1
1997
November
1.158
April
Month
of
Flood
2 Months
Before
Flood
1 Month
Before
Flood
1.259
1.542
-1.4
-1.5
-1.2
0.091
0.177
-0.146
1.1
-0.2
-0.1
May
0.177
-0.146
-0.102
-0.2
-0.1
-0.8
November
0.680
0.788
0.368
-0.6
-0.4
-0.5
July
0.063
0.354
0.444
-0.3
-0.6
0.3
October
0.532
0.815
0.857
-1.0
-0.6
-1.3
November
0.815
0.857
0.769
-0.6
-1.3
0.1
8
December
0.857
0.769
0.399
-1.3
0.1
-0.3
9
July
0.495
0.249
0.366
-0.1
0.5
-0.3
August
September
0.249
0.366
0.366
0.545
0.545
0.632
0.5
-0.3
0.4
-0.3
0.4
0.2
12
October
0.545
0.632
0.464
0.4
0.2
0.7
13
Feb
0.389
0.477
0.213
-0.7
-1.1
-1.5
March
0.477
0.213
0.633
-1.1
-1.5
-0.7
15
April
0.213
0.633
0.569
-1.5
-0.7
1.2
16
May
0.633
0.569
0.193
-0.7
1.2
0.9
17
July
0.136
0.269
0.539
0.4
0.2
1.0
August
0.269
0.539
0.654
0.2
1.0
0.4
September
0.539
0.654
0.595
1.0
0.4
1.0
May
June
0.200
-0.055
-0.055
-0.112
-0.112
0.226
0.7
-0.3
0.0
-0.3
0.0
-0.4
20
18
18
Months with Negative DMI
1
3
3
Months with Neutral DM1
0
0
0
2
3
2002
4
5
2003
6
7
10
11
14
18
2006
2007
2010
2011
19
20
21
2012
Months with Positive DMI
Month
of
Flood
-
Months with Positive SO
7
8
10
Months with Negative Sol
14
12
10
Months with Neutral SOI
0
1
1
142
Occurrence of Floods With Respect to ENSO Phase
Date of Past Floods
Oceanic Niflo Index
(ONI)
Phase of ENSO
Seasons
into
2 Seasons
Before
1 Season
Before
Season
of
Flood
Flood
Flood
November
1.8
2.1
2.3
April
-0.2
0.0
0.1
X
12
May
0.0
0.1
0.3
X
13
November
0.8
0.9
1.2
July
0.0
-0.2
-0.1
October
0.2
0.3
0.5
X
November
0.3
0.5
0.8
X
2
8
December
0.5
0.8
1.0
X
3
9
July
-0.2
-0.3
-0.3
X
5
August
-0.3
-0.3
-0.4
X
6
11
September
-0.3
-0.4
-0.6
X
1
12
October
-0.4
-0.6
-0.8
X
2
13
Feb
1.4
1.6
1.6
X
7
March
1.6
1.6
1.3
X
8
15
April
1.6
1.3
1.0
X
9
16
May
1.3
1.0
0.6
X
10
17
July
-0.6
-0.3
-0.2
X
2
August
-0.3
-0.2
-0.2
X
3
September
-0.2
-0.2
-0.4
X
4
May
-0.6
-0.5
-0.3
X
June
-0.5
-0.3
-0.2
X
Seasons with Positive ONI
9
10
11
Seasons with Negative ONI
10
10
10
Seasons with Neutral ONI
2
1
0
#
1
Year
1997
2
3
2002
4
5
2003
6
7
2006
10
Month
El Niiio
La Ninia
Neutral
X
iNtO
ENSO
6
X
6
X
4
2007
14
18
2010
2011
19
20
2012
21
Total Floods in
-
-
9
Each ENSO Phase
143
2
2
10
-
Total Precipitation and Heavy Precipitation Events in Month of Flooding
-Month of FloodNormalized Values
Date of Past Floods
Precipitation
Heavy
Events
November
-
-
April
1.15
0.72
May
0.92
1.78
November
0.96
0.65
July
1.03
0.82
October
1.33
2.67
November
1.97
2.94
8
December
1.54
2.15
9
July
1.64
1.23
1.3
1.36
11
August
Ags
September
1.51
1.28
12
October
0.75
0.76
13
Feb
2.56
2.55
March
1.31
0.97
15
April
1.09
1.91
16
May
0.87
0.36
17
July
0.83
0.41
August
1.5
1.90
September
1.21
1.60
May
0.99
1.07
June
1.02
0.71
Above Average
Monthly Precipitation
(> 1.0 )
14
12
Below Average
Monthly Precipitation
( < 1.0 )
6
8
#
#
Year
1
1997
2
3
2002
4
5
2003
6
7
10
14
18
2006
2007
2010
2011
19
20
21
2012
er
MnhTotal
Month
144