LONG TERM HYDROLOGIC TRENDS IN THE NILE BASIN

A Thesis

Presented to the Faculty of the Graduate School

of Cornell University

In Partial Fulfillment of the Requirements for the Degree of

Master of Professional Studies

by

Zelalem Kassahun Tesemma

May 2009

2009 Zelalem Kassahun Tesemma

ABSTRACT

A study has been conducted to examine if and how streamflow in the Nile Basin has

varied over the period of available records. Streamflow records from 13 flow gauging

stations in four major river basins of the Nile and 38 precipitation stations all over the

Nile basin were studied. Monthly measured discharge (1912-1982) and rainfall data

for those selected stations were collected from four different data sources and Global

Hydro Climate Data Network available at http://dss.ucar.edu/datasets/ds553.2/data/

and Global Historical Climatology Network available at http://gpcc.dwd.de were

selected as the main data sources except those Ethiopian stations. The remaining

recent 20 years data were collected from countries. Monthly and annual streamflows

(up to the year 2000, some up to 2007) were extracted and analyzed for each of the 13

station. The raw data were validated thoroughly by comparing different sources,

corrected and augmented if needed.

The Mann-Kendal and Sens T non-parametric test was used to detect significant

trends in the selected years in combination with the Trend Free Pre-Whitening

(TFPW) method for correcting time series data from serial correlation. The slope of

the data set was computed using the Thiel-Sen Approach (TSA). For this study a 5-

percent level of significance was selected to indicate the presence of statistical

significant trends. Rainfall-Runoff Modeling was done on the upper Blue Nile using

the Thronthwait-Mather model to understand the land cover changes on runoff over

the past 30 years.

The mean annual natural streamflow on the Blue Nile Stations (Bahir Dar, Kessie and

El Diem) show no trend. The rainfall over the basin also shows no significant trend.

The Monthly runoff showed moderate variability at El Diem with 19% and 34% at

Bahir Dar and Kessie. This might be a result that more land was cultivated growing of

different crops as shown by rainfall-runoff modeling over the last 30 years. White Nile

Stations (Jinja, Mongalla and Malakal) show a significant increasing trend on both

rainfall and streamflow. The runoff increased 72%, 67% and 20% of the mean annual

flow at Jinja Mongalla and Malakal respectively. Stations of the Main Nile

(Tamaniate, Hassanab and Dongolla) show significant decreasing trend in streamflow

due to abstraction of flow before reach gauging stations. For water resources

management the key conclusion, that Nile natural streamflows have not changed

significantly during the last 100 years.

iii

BIOGRAPHICAL SKETCH

NAME BIRTH DATE

Mr. Zelalem Kassahun Tesemma April, 23, 1984 EDUCATION

INSTITUTION DEGREE YEAR CONFERRED FIELD OF STUDY

Bahir Dar University B.Sc. 2007

MAJOR RESEARCH AREA OF INTEREST

Water- and Environmental-related research

CURRENT RESEARCH

Long term hydrologic trends in the Nile basin

iv

This research is dedicated to my best friend Mebrahtom Gebre Hiwot.

v

ACKNOWLEDGMENTS

I wish to express appreciation to Dr. Yasir Mohamed and Dr. Tammo Steenhuis, for

their support and assistance, constructive comments and corrections throughout the

period of the research. Special thanks to Dr. Yasir Mohamed for providing me helpful

materials and for his patience and persistence in obtaining data from different

countries.

I also appreciate Dr. Amy Collick for her fruitful comment and continuous support in

materials throughout the research period.

The author was granted access to internet, office and financial support for this research

from International Water Management Institute (IWMI) expresses his gratitude for

this privilege.

I would like to acknowledge the Ethiopian Ministry of Water Resources, NBI-ENTRO

and Ethiopian Metrological Agency and Sudan Ministry of Water Resources and

Irrigation.

vi

TABLE OF CONTENTS

BIOGRAPHICAL SKETCH ......................................................................................... iii

ACKNOWLEDGMENTS .............................................................................................. v

TABLE OF CONTENTS .............................................................................................. vi

LIST OF FIGURES ..................................................................................................... viii

LIST OF TABLES ......................................................................................................... x

LIST OF ABBREVIATIONS ....................................................................................... xi

1. INTRODUCTION ...................................................................................................... 1

1.1 Problem Statement .......................................................................................... 2

1.2 Objective of the research ................................................................................ 3

2. LITERATURE REVIEW ........................................................................................... 4

2.1 Step Trend versus Monotonic Trend .................................................................... 4

2.2 Non parametric versus parametric methods ......................................................... 4

2.3 Trend analysis in the Nile Basin. .......................................................................... 6

3. THE NILE BASIN ..................................................................................................... 7

3.1 Climate, Hydrology and Water Resources of the Nile basin ................................ 8

3.2 Description of the main sub basins ..................................................................... 11

3.2.1 White Nile River sub basin .......................................................................... 11

3.2.2 Blue Nile River sub basin ............................................................................ 12

3.2.3 Atbara River sub basin ................................................................................ 12

4. RESEARCH METHODOLOGY ............................................................................. 13

4.1 Selection of variables ......................................................................................... 13

4.2 Data validation and completion .......................................................................... 14

4.3 Mann-Kendall and Seasonal Kendall test for trend detection ............................ 15

vii

4.4 Sens T test ......................................................................................................... 20

4.5 Rainfall-Runoff modeling of Upper Blue Nile ................................................... 21

5. DATA COLLECTION AND VALIDATION ......................................................... 25

5.1 Data collection and Pre-processing .................................................................... 25

5.2 Selection of stations ............................................................................................ 27

5.3 The Nile stream gauging stations and validation of data ................................... 29

6. RESULTS AND DISCUSSION ............................................................................... 40

6.1 Results of statistical analysis .............................................................................. 40

6.1.1 Pettitt test results .......................................................................................... 40

6.1.2 Runoff trend results ..................................................................................... 41

6.1.3 Precipitation trend results ............................................................................ 47

6.2 Model Results ..................................................................................................... 49

7. CONCLUSIONS ...................................................................................................... 52

8. REFERENCES ........................................................................................................ 54

APPENDIX .................................................................................................................. 61

Appendix I: Location of stations and data availability graphs. ............................... 61

Appendix II: Relation Curves and regression equation comparison plots ............... 65

Appendix III Tables and figures of statistical analysis ............................................ 68

viii

LIST OF FIGURES

Figure 3.2 Map (not to scale) of the Nile Basin which shows the major supply

structures (Adopted Alan, 2005, SOAS). ..................................................................... 10

Figure 5.1 Comparison plot between data sources at Jinja, show a 3rd data source ..... 26

Figure 5.2 Comparison plot between data sources at Tamaniate. ................................ 27

Figure 5.3 Comparison plot between data sources at Aswan ....................................... 27

Figure 5.4 Map of the Nile basin showing the locations of the precipitation station and

stream gauges used in this analysis. ............................................................................. 30

Figure 5.5 Comparison of Mean flow and maximum and minimum daily flow at Bahir

Dar. ............................................................................................................................... 34

Figure 5.6 Comparisons between Outflow at Bahir Dar and Lake Level of Tana. ...... 35

Figure 5.7 Relation curve between flow at Tamaniate and Hassanab .......................... 39

Figure 6.1 Natural and Observed mean annual runoff trend at Sennar station of the

Upper Blue Nile River. ................................................................................................. 46

Figure 6.2 Balance between natural and observed mean annual runoff at Sennar

station. .......................................................................................................................... 46

Figure 6.3 Mean monthly areal and average rainfall distribution over Upper Blue Nile

and White Nile. ............................................................................................................. 47

Figure 6.4 Coefficient of variation in rainfall for Upper Blue Nile and White Nile. ... 47

Appendix I.2 Data availability graphs for Streamflow and rainfall stations

(downloaded from internet). ......................................................................................... 62

Appendix I.3 Data availability graphs for precipitation stations. ................................. 62

Appendix II.1 Regression equations for data validation and completion. ................... 65

ix

Appendix III.1 Pettit change point test result for the average precipitation over

Victoria Nile. ................................................................................................................ 68

Appendix III.2 Pettit change point test result for the areal precipitation over upper

Blue Nile. ...................................................................................................................... 69

Appendix III.3 Pettit change point test result for the runoff over Nile basin. .............. 70

AppendixIII.8 Linear trend in monthly streamflow for the Bule Nile River Basin at

Roseires/El Diem from 1912 to 2000.Statistically significant decreases in flow are

highlighted with heavy line. Total and percent changes are expressed relative to the

beginning of the trend. Annual discharge is expressed m3/s ........................................ 78

AppendixIII.7 Linear trend in monthly streamflow for the Nile River Basin at Jinja

from 1912 to 2000.Statistically significant increases in flow are highlighted with

heavy line. Total and percent changes are expressed relative to the beginning of the

trend. Annual discharge is expressed m3/s. .................................................................. 79

AppendixIII.9 Linear trends in average monthly precipitation for the Victoria Nile

Basin from 1912 to 2000. Statistically significant trend in precipitation are highlighted

with heavy line. Total and percent changes are expressed relative to the beginning of

the trend. Annual Precipitation is expressed in mm ..................................................... 80

AppendixIII.10 Linear trends in areal monthly precipitation for the Upper Blue Nile

Basin from 1965 to 2000. Annual Precipitation is expressed in mm ........................... 81

x

LIST OF TABLES

Table 3.1 Major Nile Basin supply-side structures (Adopted from Alan, 2005). .......... 9

Table 5.1 Total number of data corrected during validation and then completed. ....... 26

Table 5.2 List of data availability. ................................................................................ 28

Table 5.3 List of data sources used in the analysis. ...................................................... 29

Table 6.1 Results of change points with pettitt test for runoff and precipitation. ........ 41

Table 6.2 Results of the runoff trend test for Upper White Nile .................................. 42

Table 6.3 Results of the runoff trend test for Upper Blue Nile .................................... 44

Table 6.4 Results of the precipitation trend test for White and Upper Blue Nile. ....... 48

Table 6.5 Calibrated and validated parameters showing land use/cover change during

the past 30 years. .......................................................................................................... 50

Appendix I.1 Names and Location of rainfall stations. ................................................ 61

Appendix III.4 Trend analysis results for flow gauges in the Nile Basin by months for

different time periods. .................................................................................................. 71

Appendix III.5 Trend analysis results for flow gauges in the Nile Basin from 1912-

2000 by months ............................................................................................................ 75

Appendix III.6 Trend analysis results for average and areal precipitation in the

Victoria Nile and Upper Blue Nile Basin by months. .................................................. 77

Appendix III.11 Trend analysis results for Precipitation Station in the Nile Basin by

Months for different time period. ................................................................................. 82

xi

LIST OF ABBREVIATIONS

TSA-Thiel-Sen Approach

GHCDN- Global Hydro-Climate Data Network

GHCN Global Historical Climatology Network

MN- Mann-Kendall

SM-Soil Moisture

WOM- World Meteorological Organization

1

CHAPTER ONE

1. INTRODUCTION

The hydrology of Nile basin has been studied from many perspectives. Several studies

concerned with the long-term climatologic trends and especially precipitation

(Conway, 2000; Yilma and Demarce, 1995 and Sutcliffe and Parks, 1999). Other

studies relate the effect of climate change and spatial variability of precipitation to

streamflow; (Conway and Hulme, 1993) and developing water balance model for

water resource management (Conway, 1997; Kebede and Travi, 2006) and for

sensitivity analysis of lake level and outflows such as Lake Victoria (Lyons, 1906).

Egypt and Sudan almost completely depend on the Nile water as water source, with

water demand in Egypt alone set to increase (Conway, 1993). It is critical that the role

of future climate change on Nile water management is well understood. On the other

hand, other Nile basin countries want to increase their share of the Nile Water for

economic development which may be a cause for a potential conflict between the

riparian Countries (Shiva, 2002). Similarly, the Nile riparian acknowledges the need

for basin scale management of the Nile water resources (e.g., the Nile Basin

Initiative), if they want to achieve maximum benefit of the resource. Because of these

challenges towards utilization of the Nile waters resources there is great anxiety about

reduction in available water due to future climate changes. Since experts do not agree

on how the streamflow will change in the future, one of the few remaining ways is

studying past trends in streamflow and rainfall. This will help a better understanding

of the change in discharge caused by implementing past practices and use this

information in developing strategies for better utilization of water resources in the

future.

2

The particularities of the trend analysis given in this study it cover the entire Nile

basin as a study domain, consider both the effect of serial correlation and cross

correlation effect on the analysis of the statistical Mann-Kendall test, not only

interblock procedure such as the Mann-Kendall test but also will include an aligned

procedure such as Sens T test to obtain extra confidence in the results and this

research will examined trend characteristics for different time interval at the same

time, such as monthly, seasonal and annual bases, to see whether or not a dramatic

change occurs.

1.1 Problem Statement

The hypothesis is that the assessment of long term trend of time series of discharge

and precipitation at selected locations within the Nile basin will lead to insights into

future Nile water availability. The key questions to be asked in this research are then:

What is the long term trend (100 years) of monthly and annual stream

discharge at key locations in the Nile basin?

What is the long term trend (100 years) of monthly and annual precipitation at key location in the Nile basin?

How is the relation between discharge and rainfall changes with time and also land use/cover change during the last 30 years (1961-1970 and 1991-

2000)?

Using discrete statistical indicators can we determine the trends and long term variability of Nile flows and precipitation?

3

1.2 Objective of the research

The main objective of this research is to determine whether there is evidence of long-

term trends in streamflow as well as precipitation over the entire Nile basin, and if so,

to determine the long term mean annual streamflow from the Nile basin. Another

objective is to investigate relationships between streamflow and precipitation.

This research will provide updated information on the effect of climate change and

climate variability on streamflow in the Nile basin. Such knowledge is vital for the

riparian countries, as well as for their joint effort for cooperative management (e.g.,

Nile Basin Initiative).

4

CHAPTER TWO

2. LITERATURE REVIEW

Information has been gathered from both published, grey literature and basin water

resource management plans such as Abbay river basin master plan (Ethiopia).

Hydrologic studies of the whole basin, workshop proceedings and annual reports of

the countries in the Nile Basin were reviewed. Based on this literature, the

methodology has been developed.

2.1 Step Trend versus Monotonic Trend

Two primary types of long-term trends can be considered in hypothesis testing and

trend estimation. One is the Monotonic trend; the other is the step trend. (Hirsch et al.,

1991). Monotonic trend tests are applied in this study. Step trend test is applied for

those stations with naturally broken in to two distinct periods with relatively long time

gap between them (Helsel and Hirsch, 1992). The other is when human influence or

diversion structure which likely result in a change in streamflow. Monotonic trend test

is applied for those stations with no human influence and diversion.

2.2 Non parametric versus parametric methods

The assumption of the parametric approach (i.e. normality, linearity and

independence) is mostly not satisfied by hydro-climatologic data (Huth and Pokorna,

2004; Van Belle and Hughes, 1984; Helsel and Hirsch, 1988). A statistical trend

analysis will be performed to determine if significant time trends existence for mean

5

monthly and annual streamflow and precipitation at each of the representative

locations. For a detection of the statistical signification of a trend, non-parametric

methods have been used in several studies (Zhang et al, 2001; Huth and Pokorna,

2004; Harry et al, 1999; Kahya, and Kalayci, 2004; Xu et al, 2003; Partal and Kalya,

2006 and Yue and Hashimoto, 2003).

Non-parametric methods were found to be suitable for data commonly skewed, and

the sample size is large. (Hirsch et al, 1982) Non-parametric methods not only tend to

be more resistant to a misbehavior of the data (e.g. outliers) but also are give results

close to their parametric counterparts and lay well within the confidence limits even

the distributions are normal (Huth and Pokorna, 2004). Regarding all the point

discusses above it is suitable to use non parametric methods for trend analysis. Some

of the research used non parametric method and the results were satisfactory (Zhang et

al, 2001; Huth and Pokorna, 2004; Harry et al, 1999; Kahya and Kalayci, 2004; Xu,

2003; Partal and Kalya, 2006 and Yue and Hashimoto, 2003).

Non-parametric tests are more robust compared to their parametric counterpart.

Speaking on the power of the test, i.e. ability to distinguish between the null

hypothesis and alternative hypothesis, the Mann-Kendall tests (Mann, 1945 and

Kendall, 1975) for monotonic trends perform well in comparison to the parametric t-

test (Van Belle and Hughes, 1984). Mann-Kendall test will be used to test for trends

over time. This test is non-parametric test, has been widely used to test for randomness

against trend in hydrology and climatology (e.g. Burn and Elnur, 2002; Zhang et al

2000; and Pokorna, 2004; Harry et al, 1999; Kahya and Kalayci, 2004; Xu, 2003;

Partal and Kalya, 2006 and Yue and Hashimoto 2003).

6

The problem in using Mann-Kendall test is the result is affected by serial correlation

of the time series. If there is a positive serial correlation (persistence) in the time

series, the test will suggest a significant trend in a time series which is actually random

more often than specified by the significance level Kulkarni and Von Storch (1995).

To remove the effect of serial correlation Von Storch (1995) suggest that the series be

pre-whitened before applying the Mann-Kendall test.

2.3 Trend analysis in the Nile Basin.

Some literature has considered climate change and variability of Nile flow. Conway

and Hulme (1996) studied the variability in precipitation and streamflow on the whole

Nile and found causes for the historical fluctuation in main Nile runoff was the

fluctuation in precipitation. In addition they found no correlation in precipitation and

runoff between Blue Nile and White Nile and the precipitation and runoff over the

upper Blue Nile basin displayed no significant temporal trend. Sutcliffe and Parks

(1999) work showed Blue Nile and Atbara flow are variable and declined in the 1970s

and 1980s and Main Nile stations showed high flow up to 1990 and the variable flows

until 1970 and low flow since 1970.

7

CHAPTER THREE

3. THE NILE BASIN

The Nile basin lies to the north east of Africa joining the Lake Victoria to the

Mediterranean Sea. The Nile River, with an estimated length of over 6800km is

longest river in the world flowing from south to north over 35 of latitude. The total

area of the Nile basin (3112400 km2), covers 10% of the area of the African continent

and is shared by 10 riparian countries: Ethiopia, Sudan, Egypt, Tanzania, Burundi,

Democratic Republic of Congo, Eritrea, Rwanda and Uganda (Figure 3.1).

Figure 3.1 Location Map of the study area (Map of Nile basin).

8

3.1 Climate, Hydrology and Water Resources of the Nile basin

The climate and vegetation cover in the Nile basin are highly related with the amount

of precipitation. Precipitation increases southward and with altitude. The common area

with high precipitation about 1200-1600mm/years on the highlands of Ethiopia and

the Equatorial lakes plateaus. The potential Evaporation over the basin increases as

one move downstream which show opposite trend to the precipitation (Mohamed et al,

2005).

In addition to the main route of Nile there are so many tributaries and lakes which feed

into the Nile. After the Nile leaves Lake Victoria it receives water from lakes

(Kayoga, Albert and Edward) and streams. North of Mongalla, the White Nile is

known as the Bahr el Jebel and flows into a vast complex of channels, Lakes, and

Swamps in an enclosed basin. Bahr elghazal, coming from west, has very little

contribution to the Nile flow. A comparison of the historical inflow data at Mongalla

(33.332 km3) and outflow data at Malakal (29.714 km3) shows a Negative balance of

3.619 km3. Taking in to account that the Sobat river contributes on average 13.53 km3

of water per year to the flow at Malakal one can easily conclude that more than half of

the river inflow is lost by evaporation, evapotranspiration and ground water losses

(Sutcliffe and Parks, 1999). White Nile joins Blue Nile at Khartoum and name as main

Nile. The source of Blue Nile is the little Abbay River originated in the Ethiopian

highlands. The little Abbay flows in to Lake Tana, which discharges into the Blue Nile

and runs down through the highlands into Sudan. The long term mean annual flow of

Blue Nile measured at Roseires/El Diem is 48.65 billion m3 and contribute about 60%

of the flow of main Nile.

9

The last tributary of main Nile is Atbara which also originated from Ethiopia and

Eritrea highlands is highly seasonal and contributes on average about 11.05 billion m3

per years. Use of water resource of Nile basin needs agreement and cooperation

between the riparian countries for sustainable utilization. The major reservoirs

constructed on the Nile river basin are Angeber and Koka in Ethiopia for irrigation

and hydropower, Roseires, Sennar and Khashm El Girba in Sudan for irrigation and

Aswan High Dam in Egypt for irrigation. Refer to Figure 3.2 and Table 3.1 for further

information on the reservoirs.

Table 3.1 Major Nile Basin supply-side structures (Adopted from Alan, 2005).

Structure and Location Main function

Date completed

Old Aswan Dam (Egypt)

For irrigation in Egypt, saving some 1 bcm of water; heightened in 1912 and later in 1934, increasing storage capacity to 5.1 bcm.

1902

Sennar Dam (Sudan)

On the Blue Nile in Sudan, 350 km from Khartoum. Completed in 1925 to supply the Gezira Scheme. Storage of 0.8 bcm.

1925

Jebel Aulia (Sudan)

On the White Nile 44 km south of Khartoum to store water for summer irrigation in Egypt. 1937

Owen Falls Dam

(Uganda)

Built at the outlet of the White Nile from Lake Victoria to generate hydroelectricity for Uganda. 1954

Aswan High Dam (Egypt)

To capture an entire years Nile flood, thereby allowing Egypt complete control of Nile flows downstream.

1968

Kashem el-Girba (Sudan)

Built to serve the New Halfa irrigation scheme built to its storage was 1.3 bcm, but fell dramatically because of siltation such that by 1971 this was just 0.97 bcm.

1964

Rosaries (Sudan)

Supplies water to the Gezira Managil extensions and the Rahad scheme. Also produces hydropower for the Sudanese network.

1966

Jonglei Canal Construction halted. Anticipated increase in discharge was expected of l 7.6 bcm per year. Early 1980s

10

Figure 3.2 Map (not to scale) of the Nile Basin which shows the major supply structures (Adopted Alan, 2005, SOAS).

11

3.2 Description of the main sub basins

The Nile basin comprises four main river reaches, White Nile, Blue Nile, Atbara and

Main Nile. The Basin shows a great deal of geographic diversity with rugged

mountains, plateaus, deeply incised gorges, meandering river sections and deserts.

Elevation range from over to 4000m above sea level in the highland areas to several

hundred meters below sea level in depressions. The topography is a major controlling

factor in both climate and water distribution.

3.2.1 White Nile River sub basin

The watershed of the White Nile at Khartoum is 1.7 million km2. It contains Lake

Victoria and comprises a complex of channel, lakes, swamps and wetlands. The

streams which feed the White Nile River are seasonal. The average annual

precipitation in Lake Victoria 1221mm with a bimodal seasonal distribution with

peaks in March-May and November-December. After leaving Lake Victoria the White

Nile flows into Equatorial Lakes (Lake Kyoga and Lake Albert) and then northward

into Sudd sub basin and named Bahir el Jebel. The precipitation falls mostly in one

season from April to October. The vegetation that covers in most of the swamps are

Cyperus papyrus, Vossia cuspidate and Typha Australia (Sutcliffe and Parks,

1999).This part of the sub basin there is more evaporation than rainfall and

consequently the total flow in the river is decreased after it leaves the basin exposed to

loss rather than gain due to the topographic nature of the area.

12

3.2.2 Blue Nile River sub basin

The Blue Nile River starts at the outlet of Lake Tana and flows to Khartoum where it

meets the White Nile with basin area of 324,530km2. Blue Nile contributes about 60%

of the flow of Main Nile (Sutcliffe and Parks, 1999). The topography of the Blue Nile

composed of highlands, hills, valleys and occasional rock peaks. Most of the streams

feeding the Blue Nile are perennial and includes the Dinder and Rahad. The average

precipitation over the Blue Nile subbasin is 1394mm and is higher than the other

subbasin of the Nile basin. The precipitation over the Blue Nile basin varies from

1000mm in the north-eastern part to 1450-2100mm over the south-western part of the

sub basin.

3.2.3 Atbara River sub basin

The Atbara River originates in the Northern Ethiopia and Eritrea and joins the Nile

after the lowland in the eastern Sudan with total basin area of 112,400 km2. The

discharge of the river is extremely torrential. The rainfall is unimodall concentrated in

August and September with mean annual rainfall 900mm relatively high value over

the Ethiopian highlands to less downstream at the confluence with the main Nile.

Generally the average annual precipitation is lowest among the other Nile sub-basins

(Sutcliffe and Parks, 1999).

13

CHAPTER FOUR

4. RESEARCH METHODOLOGY

To be able to access the long term trends in streamflow and precipitation in the Nile

basin. As discussed above the whole Nile basin will be divided into three sub basins:

White Nile, Blue Nile and Atbara sub basins. Each of these sub basins will be divided

further in sub basins according to the long term data availability and completeness.

For each sub basin the steps followed are:

1. Selection of variables to be studied. This is precipitation and streamflow variable

are used.

2. Selection and of stations that have sufficient long record of stream discharge and

obtain the required data.

3. Data analysis and interpretation which include checking for the presence of trend

in the data and to determine the significance of the detected trends.

4.1 Selection of variables

A total of 13 streamflow and precipitation variables have been selected for this

research. Theses variables include the annual mean, flow and precipitation monthly

mean flow and monthly precipitation variables were analyzed in order to gain a bread

understanding of the trends.

14

4.2 Data validation and completion

Data validation started with a good representation of the collected data, tabular or

graphical form using the various techniques provided by HYMOS software packages.

The software was provided from IHE UNISCO and used for data management

validation and completion together with EXCEL. The software provides both tabular

and graphical analysis sheet. Missing data was filled in with regression equation signal

between the neighboring stations is weak (Hastie et al., 2001). In addition for those

stations with having preceding or successor neighboring station, sum or difference of

the time series are used to fill in the missing data. In particular the following steps

were taken.

Screening of data series

This step provides checking the data series against the data limits for total at long

duration. To flag a few very unlikely values from the data set using two standard

deviations from the mean as boundary values. Using the suspect value for the station

considered, the suspicion may be dropped or accepted by confirmed using the

comparison plot of the neighboring stations.

Multi-station validation

Comparison of series may also permit the acceptance of the value flagged as suspect

in screening of data. When two or more station show the same behavior there is strong

evidence to suggest that the value are correct.

15

Relation curve (Regression analysis)

The relation curve between neighboring stations which developed also strengthens the

flagged value by looking the relation curve of two sequential station data values. Then

the suspect values previously identified should be removed before deriving the

relationship, which may then be applied to compute corrected values to replace the

suspect ones. The validation was displayed river by river for the four sub basin. Each

sub basins station was compared with other neighboring stations in the sub basin.

4.3 Mann-Kendall and Seasonal Kendall test for trend detection

First of all, test for the trend in annual series was made so as to get an overall view of

the possible changes in streamflow processes. To determine if the trends found are

significant, the Mann-Kendall trend test was used (Mann, 1945 and Kendall, 1975).

This test was chosen over other trend detection tests based on the following factors:

(1) the Mann-Kendall test is a rank based non parametric test. When compare to

parametric test like t-test the Mann-Kendall test has a higher power for non-normally

distributed data which are frequently encountered in hydrological records (Onoz and

Bayazit, 2003; Yue and Pilon, 2004). (2) In comparison to other non-parametric tests,

like Spearmans rho test, the power of the Mann-Kendall test is similar to the point

where both give indistinguishable results in practice (Yue et al, 2002a, b). (3)The

Mann Kendall test has been extensively used to determine trends in similar hydrologic

studies done in the past (Hirsch et al, 1982; Lins and Slack, 1999; Burn et al, 2004;

Abdul Aziz and Burn, 2006).

The Mann-Kendall test is based on the null hypothesis that a sample of data is

independent and identically distributed, which means that there is no trend or serial

16

correlation among the data points. The alternative hypothesis is that a trend exists in

the data. First the statistic defined by variable S was computed which is the sum of the

difference between the data points for a series {x1...xn} come from a population

where the random variables are independent and identically distributed shown in

Esq.(4.1)

( ) == +=

=1

1 1sgn

n

i

n

ijij xxS , Where

+=

10

1)sgn( If

000

(4.1)

Mann (1945) and Kendall (1975) determined that the statistics S is normally

distributed when n 8 allowing for the computation of the standardized test statistics

Z which represent an increasing or decreasing trends respectively. For the statistical

trend test used in this study a 5-percent level of significance was selected. The 5-

percent level of significance indicates that a 5-percent chance for error exists in

concluding that a trend is statistically significant when in fact no trend exists.

+

=

)(1

0)(

1

SVS

SVS

Z If

0

0

0

S

S

S

(4.2)

Where Var(S), the variance of the data point is given by,

( )( ) ( )( )

++= =

m

tiii tttnnnSVar

1521521

181)( (4.3)

Where m is the number of tied (i.e., equal values) groups in the data set and ti is the

number of data points in the ith tied group. Under the null hypothesis, the quantity z

defined in the following equation is approximately standard normally distributed even

17

for the sample size n = 10. The positive values of S indicate upward trends whereas

negative S value indicate downward trend.

The slope of the data set can be estimated using the Thiel-Sen Approach (4.4). This

equation is used instead of a linear regression because it limits the influence that the

outliers have on the slope (Hirsch et al, 1982). To normalize the slopes calculated for

streams of different size, the mean flow value of each parameter and station was used to

find a percent change in flow rate.

=

ijXX

Median ij For all i

18

lag-1 serial coefficient is calculated after the trend was removed in order to preserve the

initial trend (Yue et al, 2002c).

( )( )( )

=

=+

= nt

tt

kn

ttkttt

k

YYn

YYYYknr

1

2

1

1

1

(4.6)

If lag-1 serial correlation coefficient (rk) is not significant at 5% significance level, then

the Mann-Kendall test is applied to the original time series. Otherwise it is removed from Yt

as:

11 = ttt YrYY (4.7) This procedure is known as the Trend-Free Pre-Whitening (TFPW) procedure. The third

step is to add the trend back to Yt by using Equation (4.8) and then the Mann-

Kendall test is conducted on this new series.

tYY t += (4.8) Seasonal Kendall test

The trend test for annual series gives us an overall view of the change in streamflow

volumes. To examine the possible changes occur in smaller time scale, we need to

investigate the monthly or seasonal flow series. Monthly streamflow usually exhibit

strong seasonality. Trend test techniques for dealing with seasonality of univariate

time series fall into three major categories (Helsel and Hirsh, 1992): (1) fully

nonparametric method, i.e., seasonal Kendall test; (2) mixed procedure, i.e., regression

of deseasonalized series on time; (3) parametric method, i.e., regression of original

series on time and seasonal terms. The first approach, namely, seasonal Kendall test

will be used in this research considering the benefit that the seasonal Kendall test

considers the effect of fluctuation in season in both runoff and precipitation.

19

Hirsch et al, (1982) introduced a modification of the MK test, referred to as the

seasonal Kendall test that allows for seasonality in observations collected over time by

computing the Mann-Kendall test on each of m seasons separately, and then

combining the results. Compute the following overall statistic S.

=

=m

jjSS

1, (4.10)

Where Sj is simply the S-statistic in the MK test for season j (j = 1, 2... m) where Sj

(Equation 4.10). When no serial dependence exhibit in the time series, the variance of

S is defined as

)()(1

=

=m

jjSVarSVar (4.11)

Then the quantity z defined in the following equation is approximately standard

normally distributed:

+

=

)(1

0)(

1

SVS

SVS

Z If

0

0

0

S

S

S

(4.12)

The positive values of S indicate upward trends whereas negative S value indicate

downward trend.

20

4.4 Sens T test

This test is an aligned rank method having procedures that first remove the block

(effect of season) from each time series and sum the data over seasons to produce a

statistic from these sums (Sen, 1968a, b). This procedure is distribution free and not

affected by seasonal fluctuations (Van Belle and Hughes, 1984). The computational

steps are as follows:

1. Compute the average for the month j, nxXn

iijj

==

1 and the average for the year i,

1212

1=

=j

iji xX . Subtract the monthly average from each of the corresponding

months in the n years of data to remove seasonal effects, i.e. calculate Xij - X.j for i = 1, 2, ..., n and j = 1, 2, ...,12.

2. Rank all the differences from 1 to nm (number of Months times the number of

years) to obtain the matrix (Rij), where Rij = rank of (Xij - X.j) among the 12n values

of differences. If t ties occur, the average of the next t ranks is assigned to each of the t

tied values.

3. The ranks for each year are averaged, i.e. 12.12

1=

=j

iji RR . Also the rank for each

month nRRn

iijj

==

1. .

4. Calculate the following test statistic.

+

+

+= =

n

ii

jijij

nmRniRRnn

mT1

2

,

2.

2

21.

21

)()1(12 (4.13)

Where m=number of months or seasons for this case m=12.

For the sample size (n) become large the distribution of T tends toward normality with

mean 0 under the null hypothesis of no trend and unit variance. The statistic test is to

21

reject the hypothesis of no trend if T > z exceeds a pre-specified percentile of the

normal distribution. Where z is the standard normal variate is the level of

significance which is 5% in this work. Van Belle and Hughes (1984) showed by

Monte Carlo simulation that the normal approximation for the statistic T was

reasonable even for small samples. Positive value of T indicates an increasing trend

and negative value indicate decreasing trend.

4.5 Rainfall-Runoff modeling of Upper Blue Nile

Change in land use/cover during the past 30 years was detected using modeling over

the Upper Blue Nile basin. From 1961-1970 and after 30 years 1991-2000 was

divided in to two 5 years for calibration and 5 for validation, then compare and discuss

changes between calibration parameters. Comparing the parameter between the two

time period give some insight on both land use and land cover change.

Based on nature of landscape and other land cover condition, topographic index and

saturation index the watershed will be broken up into a series of contributing areas

with different (SM), or maximum soil moisture storage;

a) Wettest area,

b) Intermediate area,

a) Hillslope area,

22

(Steenhuis et al, 2008)

It is reasonable to assume that water stored in the topmost layer of the soil system for

hillslope and runoff contributories area to be estimated as follows (Collick et al, 2008

and Steenhuis et al, 2008)

SM (t)=S (t-t) + (P AET R Perc) t....(4.14)

Where P is rainfall (mm/month), AET the actual evapotranspiration (mm/month), SM

(t-t) is previous time step storage water in the soil system (mm), R is saturation excess

runoff (mm/month), Perc is percolation to the subsoil (mm/month) and t is monthly

time step. When P (t) is less than PET, water is withdrawn from the soil system. This

results into the exponential soil moisture depletion at time step t is defined by the

following formula (Steenhuis, 2008).

=

(max))()(

)(expSM

tPETPSMSM ttt , When P

23

If P (i) is higher than PET, the actual evapotranspiration (AET) equals potential

evapotranspiration (PET) Steenhuis and Van der Molen, 1986. If not, it is computed

as:

=

(max)

)(

SMSM

PETAET t ....(4.16)

The soil moisture deficit is the difference between PET and AET within the same

month, and is given by:

Deficit (t) = PET (t) AET (t) ............................(4.17)

The soil moisture surplus is the difference between the effective rainfall and the sum

of SM and AET. It is the excess rainfall when the soil layer under consideration is

saturated with water. The surface runoff produced from wettest area and intermediate

area can be computed from the following equation.

Saturation excess runoff (SER (t)) = P (t) [SM + AET].........(4.18)

Where, SM is the difference between the soil moisture content in the current and

previous month. It is given by:

SM = SM (t) - SM (t-t) ............(4.19)

For hillside area, area with high infiltration capacity, the water flow as either interflow

or percolates (Perc) and added to ground water reservoir to form a ground water flow

(baseflow). Steenhuis et al, 2008 assumed that the after the baseflow reservoir is filled

first, the interflow reservoir starts filling. It is possible to assume the ground water

reservoir acts as a linear reservoir and its outflow, GWF and ground water storage

(GWS) is less than the maximum ground water storage, GWR (max) (Steenhuis, 2008).

24

tGWFPercGWSGWS ttttt += )( ()()( ..... (4.20)

[ ]t

tGWSGWF tt

= )exp(1)()(

..........(4.21)

When the storage reaches its maximum storage, GWS (max)

GWS (t) = GWS (max)...(4.22)

[ ]t

tGWSGWFt

= )exp(1(max) ..... (4.23)

Storage of a previous month is available for a surplus of a current month and so on.

Finally, the summation of a direct storm runoff (DR), ground water flow (GWF) in

month gives the total watershed runoff of in month.

Q = DR + GWF (t)......(4.24)

The model was tested and validated for the Blue Nile at the Sudanese border by three

students of the Masters Program in Integrated Watershed Management and

Hydrology and by Steenhuis et al (2009) for three SCCP watersheds and found that

this model gave good predictions.

25

CHAPTER FIVE

5. DATA COLLECTION AND VALIDATION

5.1 Data collection and Pre-processing

Monthly river flow data were collected from different sources: Global Hydro Climate

Data Network (GHCDN) operated by UNESCO/IHP which was used as a main

sources for this research, available at http://dss.ucar.edu/datasets/ds553.2/data/,

Shahin (1985) from Cairo University - Massachusetts Institute of Technology, 1977,

Global Runoff Data Center operated by world metrological organization (WOM)

funded by the Federal Government of Germany available at http://grdc.bafg.de/ as per

requested and from Countries like Sudan, Ethiopia and Uganda Ministry of Water

Resources. The monthly precipitation data for this study were downloaded from

Global Historical Climatology Network (GHCN) available at http://gpcc.dwd.de and

recent data for Ethiopian station from Ethiopia Meteorological Agency. Appendix I.3

for data availability graphs for both river flow and precipitation stations.

After all data from different sources were collected comparison between sources were

made to select the best data sources among them. All data from GRDC, GHCDN and

Shahin (1985) agree perfectly for the period 1912-1982 except the three stations

namely Jinja, Tamaniate and Aswan for some period. For example at Jinja the data

from the Global River Data Center shift by two years as compared with that of data

obtained from Uganda Ministry of Water Resources (Figure 5.1). The data from

GHCDN for Tamaniate in month June 1947 and 1948 were not in agreement with the

two other sources of data (Figure 5.2). Also at Aswan for the period 1912-1945 both

GRDC and GHCDN data are from the dam release but that of Shahin book 1985 is the

26

inflow to the dam and agrees then after 1945 (Figure 5.3). After this initial check of

the data, further data analysis and validation took place before conducting any analysis

as described in the next sections (Table 5.1).

Table 5.1 Total number of data corrected during validation and then completed.

Sub basins

Period of validation Stations name

Numbers of data corrected

( % ) of data corrected

Blue Nile

1959-2007 Lake Tana 13 2 1953-2004 Kessie 15 2

1912-2000

Roseires/El diem 12 1.4

Sennar 6 0.7 Khartoum 2 0.23

Main Nile

1912-1982

Tamaniat 11 1.29 Hassanab 7 0.82 Dongola 2 0.9 Aswan 0 0.5

White Nile

1912-2000

Jinja 0 0 Mongolla 5 0.5 Malakal 4 0.45

Jebel Aulia 4 0.9

Figure 5.1 Comparison plot between data sources at Jinja, show a 3rd data source

27

Figure 5.2 Comparison plot between data sources at Tamaniate.

Figure 5.3 Comparison plot between data sources at Aswan

5.2 Selection of stations

The selection of stations is one of the more important steps in hydro-climate time

series analysis. Stations were selected with record length of above 30 years both for

precipitation and streamflow variables. Burn and Elnur (2002) stated that the selection

28

of stations in a hydro-climate change research is substantial at the initial step and that

a minimum record length of 25 years ensures validity of the trend results statistically.

Hence 15 streamflow stations and 38 precipitation stations were selected. The station

names missing data and years uses for analysis are shown in Tables 5.2 and 5.3 The

location of the precipitation station are shown in Figure 5.4 given appendix I.1

entitled: Names and locations of selected station. Collected data include monthly

discharge and precipitation for the available extended period of time. After the data

had been collected, the entire raw data was imported to Excel sheet accordingly. This

step is a base for conducting any further analysis since data in Excel format can easily

be transferred to any of the software package to be used for the analysis. Table 5.2 List of data availability. No. Flow

station GHCDN GRDC Shahin,

1985 Sudan/ Uganda

Ethiopia

1 Bahir Dar 1959-20072 Kessie 1953-20043 Jinja 1912-2000 4 Mongalla 1912-1982 1912-1982 1912-1973 5 Malakal 1912-1995 1912-1982 1912-1973 1965-2000 6 Jebel Aulia 1973-1982 1912-1973 1966-2000 7 Roseires 1912-1995 1912-1982 1912-1973 1980-2000 8 El Diem 1964-1996 9 Sennar 1912-1995 1912-1982 1912-1973 1980-2000 10 Khartoum 1900-1982 1912-1982 1912-1973 1980-2000 11 Tamaniate 1912-1982 1912-1982 1912-1973 1980-2000 12 Hassanab 1912-1982 1912-1982 1912-1973 1980-2000 13 Kilo-3 1912-1982 1912-1982 1912-1973 14 Dongolla 1912- 1995 1912- 1982 1912-1973 1980-2000 15 Aswan 1912 -1984 1912-1982 1912-1973

29

Table 5.3 List of data sources used in the analysis. No. Flow station GHCDN Sudan Uganda Ethiopia From

relation curve

1 Bahir Dar 1959-2007 2 Kessie 1953-2004 3 Jinja 1912-2000 4 Mongalla 1912-1982 1983-20005 Malakal 1912-1995 1996-2000 6 Jebel Aulia 1966-2000 7 Roseires 1912-1995 1996-2000 8 El Diem 1964-1996 9 Sennar 1912-1995 1996-2000 10 Khartoum 1900-1982 1983-2000 11 Tamaniate 1912-1982 1983-2000 12 Hassanab 1912-1982 1983-2000 13 Kilo-3 1912-1982 14 Dongolla 1912- 1995 1983-2000 15 Aswan 1912 -1984

In the next sections the discharge data is validated and prepared for trend analysis by

dividing the river basin in to four sub-river basins as Blue Nile, White Nile and

Atbara.

5.3 The Nile stream gauging stations and validation of data

White Nile stations

Jinja

The discharge at Jinja, hydrological station located at the outlet of Lake Victoria has

been recorded since 1900 (Kite, 1982 and Conway, 1993). The discharge was

originally regulated by Ripon Falls until the construction of Owen Falls, some 3 km

downstream dam in 1954 but which was began in 1951 with construction of coffer

dam (Sutcliffe and Parks, 1999). The lake level/ discharge relationship remained the

same before and after dam construction.

30

Figure 5.4 Map of the Nile basin showing the locations of the precipitation station and stream gauges used in this analysis.

31

Mongalla

Station indicate the outflow White Nile from the Equatorial Lakes where White Nile

before enters to marshes and swamps. This station is confined to a single channel and

next to Jinja has reliable and long term gauging site data. Data from 1983 onwards is

not available because of the civil war in Sudan and measurements have resumed since

2007.

Helit Dolieb

The discharge gauging station which measures the contribution of Sobat River located

upstream of Malakal. Since the station is located 8 km above the White Nile

confluence, the back water effect of the White Nile due to the rise in Lake Victoria has

affected the levels of measurement. During some years 1965-1967 those flows may

have been overestimated because of lack of the dry season flow otherwise the flow

record is reliable for analysis (Sutcliffe and Parks, 1999). Hence adjustments have

been made by comparing with neighborhood stations.

Malakal

Discharge measuring station on the White Nile basin indicating the contribution of

White Nile, Sobat River, and Bahr al-Ghazel basin. The flows measurements at

Malakal are accurate because the numbers of gauging have been sufficient and the

rating curve is good (Sutcliffe and Parks, 1999).

Jebel Aulia

Streamflow measuring station found 44km south of Khartoum. The Jebel Aulia dam

built forty kilometers upstream of Khartoum in 1937 to store water for later use in

Egypt, has added further evaporation losses along this stretch. The rapid silt up of this

32

reservoir and the construction high Aswan dam in Egypt in 1965 stopped its function

(Shahin, 2002).

Validation of White Nile stations

White Nile at Jinja, Mongalla and Malakal

The discharge measurement at Jinja is relatively in good quality because it is

computed from the developed rating curve between outflow and lake level so no

adjustment was made. The monthly flow at Mongalla was compared with flow at Jinja

by using relation curve to see pronounced outliers. The flow at Mongalla after 1982

was inferred from Jinja in monthly bases using the regression equation between them

r2=0.76. Refer appendix II.1,e for the relation curve and equation. The flow records at

Malakal on March 1944 is very high as compared with the other months and also

advance flow of Mongalla on the same month hence it is clearly an outlier. The value

was corrected by the long term monthly mean value for the same months.

Blue Nile gauging stations

This sub basin has five stations namely, Blue Nile at Lake Tana, Blue Nile at Kessie,

Blue Nile at Roseires /Eddiem dam, Blue Nile at Sennar and Blue Nile at Khartoum.

Khartoum

Discharges measuring station for Blue Nile immediately upstream its junction with the

White Nile. The flow at this station contains seasonal flow Dinder and Rahad which

originated in the highlands of Ethiopia in addition to flow from Sennar.

33

Roseires dam/Eddiem

The hydrological station on the Ethiopia-Sudan border indicates the inflow of Blue

Nile from Ethiopia highlands to the Sudanese areas. This station named Roseires

before the construction of the dam until 1965 and Eddiem afterward which was shifted

upstream of the dam. This station is one of the long term records for Blue Nile.

Sennar

Discharge gauging station located downstream of Sennar dam on the reach of Blue

Nile. This is located 350 km from Khartoum which was completed in 1925 to supply

the Gezira irrigation scheme.

Kessie

The gauging station is located at the bridge where the main road to Addis Ababa from

Bahir Dar crosses Abbay river with Bridge. Discharge was measured at Kessie started

from 1953 to present. The records are complete from 1954 to present. The

construction of the new bridge may affect the gauging section which is directly

affected the stage measurements and then the discharge measurements due to the

constructed temporary coffer dam. The data before the construction is valid and good

in quality for analysis. Conway, 2000 also comment on the quality of the records as it

is fear good.

Lake Tana

The outflow of Blue Nile at Lake Tana is recorded in three separate time period, 1921-

1926, 1928-1933, 1959-present. Considering the data quality, before 1990 records

show errors and needed correction. The construction of the Chara-Chara weir, which

was completed in 1996, has affected the natural flow from the lake.

34

Validation of Blue Nile stations

Lake Tana and Kessie

First visual screening showed a few mistyped decimal points. These were corrected

and flagged in the data files. Then data were subjected to a few validations the data for

upper and lower boundary limit to identify outliers. Sometimes, when data for

complete years were missing, the whole year was omitted which needed further

attention when converting the data format. For flow stations at Bahir Dar (Lake Tana)

and Kessie for which the minimum and maximum of flow data available, three time

series were created, mean monthly discharge (m3/s), maximum daily discharge (m3/s)

and minimum daily discharge (m3/s). The mean monthly series was validated against

the maximum and minimum daily discharges (Figure 5.5). Whenever mean monthly

discharge > maximum daily discharge or mean monthly discharge was smaller than

the minimum daily discharge the record for that month was flagged and further

investigated. Sometimes, errors resulted from mistyping one number or misplacing

the decimal point and were fixed accordingly.

Figure 5.5 Comparison of Mean flow and maximum and minimum daily flow at Bahir Dar.

35

Those records violated the data limit was flagged and wait to be confirmed by the next

step which was comparison of plot between adjoining stations. Comparison was made

between each others and with Roseires/El Diem. For the case of Bahir Dar flow

measuring station has two periods of records, before (1959-1995) and after (1996-

2007) the construction of chara-chara weir. Those streamflow records after the weir

become functional have taken as it is. Those suspect value identified in screening was

further validated using comparison plot between the lake levels records of Lake Tana

at Bahir Dar which is in relatively good quality (Figure 5.6). The streamflow record in

some months of 1984 and 1985 are problematic and hence corrected using average

flow and those missed value was completed using the developed power equation

between lake level and outflow from the lake. Since the lake level records are

completed, the missed monthly discharge for 1984 estimated by regression with lake

level r2=0.95. Refer appendix II.1, a for relation curve and regression equation.

Figure 5.6 Comparisons between Outflow at Bahir Dar and Lake Level of Tana.

36

Blue Nile at Roseires / El Diem, Sennar and Khartoum

These three stations had relatively longer records of streamflow data. The main data

source of this research (GHCDN) compared with two other data sources (GRDC and

Shahin, 1985) has encountered error of using month February as 28 days in leap years

in both Roseires and Sennar stations and corrected by multiplying with 28 first and

divided it by 29. To have a natural flow for after 1963 El Diem station which is

located upstream of Roseires station data was used from Ministry of Irrigation Sudan

and ENTRO since GRCDN data after 1964 shows the release from the Roseires dam.

When the two data plotted together the low flow data from the GRCDN source was

higher the data from Ministry of Irrigation Sudan and ENTRO. Screening was done

independently for the three stations. Value out of the boundary limits flagged and

further validated by comparison of plots between the remaining two stations. The

comparison plot was made in 5 years period to easily identify problematic records.

The suspect values flagged was removed and relation curve developed between the

adjoining stations for correcting the suspect values. The missed data from 1997 to

2000 were filled using the relation curve with Sennar with correlation coefficient

r2=0.99. Refer to appendix II.1 b, c for relation Roseires/Ediem and Sennar.

Main Nile Station

Tamaniat

It is located downstream of confluence of the White Nile and the Blue Nile River. This

station records illustrate the history of flows of both White and Blue Nile. The flow

records are based on gauge-discharge curve from gauging and some few years are base

on a general curve or on interpolation between measurements. From October 1928 to

September 1929 gauging was unreliable hence needed adjustment.

37

Hassanab

Discharge measuring station just above the confluence of Atbara river with the Main

Nile. This station is located downstream of Tamaniat. In between these stations there

is abstraction by Khartoum Hassanab scheme.

Kilo 3

The flow gauging station located at the outlet of the Atbara River immediately

upstream of its Junction with the Main Nile. It joins the main Nile about 320 km

downstream from Khartoum. There is a constructed dam on the river to serve for

irrigation.

Dongola

Discharge measuring station of the Main Nile above Aswan dam. To avoid the

backwater effect of the Aswan dam the station first moved from Wadi-Halfa to

Kajnarty and then to Dongola. The flow records contribution of these stations

expressed as Wadi-Halfa 1912-1939, Kajnarty 1931-1964 and Dongola 1962 to

present. These stations combined to form a single long term records as Dongola 1912

to present by taking into consideration the evaporation loss from Wadi-Halfa/ Kajnarty

to Dongola with its width and length of 450km.

Aswan

The final outlet of the Nile is measured at Aswan. This station provides the longest

historical records. The records are moreover completed and with good quality but it

should be naturalized because there has been considerable usage of water for irrigation

in Sudan.

38

Validation of Main Nile station

Tamaniat and Hassanab

Error of using February as 28 days in leaps years in both Tamaniat and Hassanab

stations and corrected in the same manner like those stations in Blue Nile. Data entry

error such as instead of putting 1169 and 1223 put 116.9 and 122.3 for June 1947 and

1984. These were clearly confirmed by comparing with other data sources Shahin

1985 and GRDC. After correcting the above errors screening was continued to identify

those suspect value violating the data limits. Time series graph of the two stations as

well as sum of time series of Khartoum and Jebel Aulia in five years interval was

plotted in the same graph for comparison. To develop more confidence on the flagged

value relation curve between the two stations was plotted as shown below. The

outliers shown were some of them were problems of Tamaniate and some of them

were problems of Hassanab based on the results from the above comparison analysis

from the three stations (Figure 5.7). All the suspect values were then removed and

completed using regression without including those flagged value with strong

regression coefficients 0.98 for the period 1912-1982. The data after 1982 was

validated and completed using relation curve between Tamaniate and Hassanab from

1980 to 2000 with correlation coefficient 0.967. Refer appendix II.2, d for the relation

curve and equation.

39

Figure 5.7 Relation curve between flow at Tamaniate and Hassanab

Kilo-3, Dongola and Aswan

After screening of each station independently was processed, time series graph of all

the three stations were plotted to see the problematic months together with the

screening results. During plotting the graph, Dongola and Aswan as it is but Kilo-3

plus Hassanab as one series was plotted. Any station value in different behavior with

the remaining two was identified as suspect value. Adjustment of that suspect values

were adjusted using regression analysis between neighboring stations. With respect to

discharge the number of values corrected as a total or a percentage noted for

individual stations.

40

CHAPTER SIX

6. RESULTS AND DISCUSSION

6.1 Results of statistical analysis

The first step in time series analysis is visually inspecting the data. Significant changes

in level or slope usually are obvious. From the visual inspection, it seems that the

annual flow series of the upper Blue Nile at Kessie which exhibits obvious upward

trend but the other Blue Nile station showed no significant change over the period

under consideration. This is also supported from the 5-year moving average curve of

the annual mean discharge at station Kessie exhibits fluctuation in recent 50 years, and

the discharge reached higher record values started from 1995. This procedure also

conducted on the Upper White Nile basin, the visual detection also supported by the

statistical analysis result.

6.1.1 Pettitt test results

Using pettitt the possible change points were examined for the monthly and mean

annual runoff and precipitation and the possible change-points were indicated as upper

values of the probability curve. The change point year is consistent with the monthly

and mean annual runoff and precipitation. Change point years were identified for all

flow gauging stations; 1965 for Upper Blue Nile and 1962 for White Nile basin (Table

6.1). As Sutcliff and Park (1999) stated clearly the phenomena of heavy rainfall in

October-December season in 1961and 1962 showed over the Lake Victoria basin in

the rainfall records. Changes in rainfall over Lake Victoria basin cause for change in

streamflow of White Nile basin stations (Jinja, Mongolla and Malakal). Considering

the validity of the discharge data series, it is divided into two and three stages to have

Mann-Kendall trend test in this research, the results of the computation of pettitt test

41

are given in the next section. Refer also the change point test with probability curves

appendix III.1, 2, and 3. Table 6.1 Results of change points with pettitt test for runoff and precipitation.

Station of River flow series Years Probability Lake Tana (Bahir Dar) 1994 0.98 Kessie 1992 0.99 Roseires/Eddiem 1965 0.9 Jinja 1962 1 Mongolla 1962 1 Malakal 1962 1 Upper Blue Nile Arial precipitation - - Victoria Nile Average precipitation 1961 0.9

6.1.2 Runoff trend results

Significance of serial correlation

Prior to conducting the Mann-Kendall test, the Trend Free Pre-Whitening procedure

was applied to the series in which there was a significant serial correlation. The lag-1

serial correlation test was applied to all the time series data. The majority of the

monthly time series in the data set appear to have a significant lag-1 serial correlation

coefficient at 5% significant level. These indicate the data series violating the

assumption of independence. The White Nile station (Jinja, Mongalla and Malakal)

showed a significant lag-1 serial correlation in all months and annual series. In

addition the station on the Blue Nile (Lake Tana, Kessie and El Diem) showed a

significant lag-1 serial correlation in low flow season and no significant serial

correlation in Flood season.

Seasonal Mann-Kendall and Sens T tests on Upper White Nile

Generally, the Mann-Kendall and Sens T tests confirmed the findings of each other.

According to Sens T test in all White Nile stations (Jinja, Mongalla and Malakal)

there is a upward trend at 5% significant level (T= 22.10, 18.83 and 13.98) indicating

42

a tendency towards much more water on annual basis (Table 6.2). According to Mann-

Kendall test in all White Nile station (Jinja, Mongalla and Malakal) there is a upward

trend at 5% significant level (z = 8.6, 8.11 and 6.02) indicating a tendency towards

much more water on annual basis (Table 6.2). Among the findings of seasonal trend

analysis, the result of Flood and Low flow series showed upward trend indicating an

increasing in amount of flow at 5 % significant levels according to both the Sens and

Seasonal Mann- Kendall test.

Table 6.2 Results of the runoff trend test for Upper White Nile

BN station Season T values of Sens T test

z values of Mann-Kendall test

Jinja Flood 15.57 19.64 Low flow 15.73 20.23 Annual 22.10 8.6 Mongalla Flood 13.92 18.0 Low flow 12.67 16.29 Annual 18.83 8.11 Malakal Flood 9.22 12.86 Low flow 11.13 10.87 Annual 13.98 6.02

Note: Bold figures are significant at 5% significance level.

White Nile Stations

Summary of the Mann-Kendall test for monotonic trend and non-Parametric Sens

slope estimates for the streamflow series are given in Table 6.2. An increase of 7.12

m3/s/year (P

43

Jinja and Malakal the higher increasing slope were estimated and runoff changed at

the rate of 7.67 m3/s/year and 3.71 m3/s/year respectively since 1912. Also in the

month of April at Mongalla the highest runoff changed was exhibited and runoff

changed at the rate of 10.37 m3/s/year since 1912. However, monotonically decreasing

trend in all months was estimated for the analysis period 1962-2000 after the change

point at Jinja, Mongalla and Malakal. For example Annual mean runoff decreased at a

rate 13.13 m3/s/year, 25.72 m3/s/year and 8.96 m3/s/year at Jinja and Mongalla in the

month of September and at Malakal in the month of November respectively. Refer

appendix III.4 for result of the trend analysis.

Seasonal Mann-Kendall and Sens T tests on Upper Blue Nile

Upper Blue Nile stations (Lake Tana and El Diem) there are no trend at 5% significant

level in flood season (T= 0.34 and -1.77), low flow season (T= 0.01 and 0.4) and

annually (T= 1.04 and -1.24) and indicating flow stay the same but at station Kessie

there is an upward trend at 5% significant level in flood season (T= 2.67), low flow

season (T= 0.72) and annually (T= 3.37) indicating the amount of flow increasing on

annual basis . According to Mann-Kendall test low flow season in all Upper Blue Nile

stations (Lake Tana, Kessie and El Diem) displayed no trend at 5% significant level (z

= 0.31, 0.79 and 0.72) indicating no additional flow added to the basin, but for flood

season the lake Tana and Kessie displayed an upward trend at the same significance

level (z = 2.34 and 2.87). Among the findings of seasonal trend analysis from the

Sens T test and seasonal Mann-Kendall test at Lake Tana and Kessie. This is due to

the trend heterogeneity between months imply that there is an upward and downward

trend with in the season. For such case the result from Sens T test seems better to

explain the trend since it is not affected by seasonal blocks.

44

Table 6.3 Results of the runoff trend test for Upper Blue Nile BN station Season T values of

Sens T test z values of

Mann-Kendall test Lake Tana Flood -0.75 0.47 Low flow 1.74 3.45 Annual 1.04 0.4 Kessie Flood 2.02 1.5 Low flow 2.56 3.23 Annual 3.37 1.45 El Diem Flood -1.77 -1.78 Low flow 0.40 0.72 Annual -1.24 -1.10

Note: Bold figures are significant at 5% significance level.

Upper Blue Nile Stations

Summary of the Mann-Kendall test for monotonic trend and non-Parametric Sens T

test for the streamflow series for both Low and Flood flow season are given in Table

6.3. In general no mean annual runoff trend was recorded at those stations in the

Upper Blue Nile. Lake Tana flow station showed increasing trend for months April,

May, June and July at a rate of 0.37, 0.31, 0.4 and 1.14 m3/s/yr respectively during

1959-2007. In Upper Blue Nile river, at Kessie significantly increasing trend in

monthly flow from May to June at a rate of 0.99 and 1.18 m3/s/yr respectively.

But the down stream station from Kessie which is Roseires/El Diem no trend in all

months except September with significantly decreasing trend at 9.78 m3/s/yr (appendix

III.5) which also true for the natural monthly flow at Sennar down stream of

Roseires/El Diem with 13.52 m3/s/yr. The observed monthly flow at Sennar decreased

significantly between September to February and June but increased significantly

during April and May. The trend physically explained by comparing with the flow to

Roseires and its release, the dam hold water during high flow period and release

during low flow period. The same pattern of trend with observed flow at Sennar was

estimated at Khartoum station. Both stations exhibited significantly decreasing trend

45

in mean annual runoff at a rate of 5.77 m3/s/yr and 5.87 m3/s/yr at Sennar and

Khartoum (Figure 6.1). When comparing the Natural mean annual runoff (appendix

III.5) with the observed annual runoff, the slopes of the trend line for the Sennar

station are noticeably reduced. In other word, the runoff reduction directly due to

human activities has distinctly increased during the last four decades, especially after

1964. The results of Mann-Kendalls trend test for natural monthly runoff (appendix

III.5) show some differences with that of observed monthly runoff. The increasing

trend in observed flow for April and June at Sennar disappeared, which means this

increasing trend resulted from regulated stream flow due to dam control if the

estimation of natural runoff is accurate. The decreasing trend is still exists, but the

magnitude is remarkably reduced, which is reflected by the smaller z values and fewer

months with a significant decreasing trend observable when comparing natural and

observed flow in (appendix III.5) This indicates that the Upper Blue Nile runoff has a

decreasing trend even after deducting the water withdrawn for human uses (Figure

6.2).

Main Nile Stations

The trend analysis was made on observed runoff at three sequential stations on the

main rout of the Main Nile Basin, at Tamaniate, Hassanab and Dongolla. The mean

annual runoff display a strong significant decreasing trend at a rate of 5, 5.6 and 7.6

m3/s / yr respectively. The result also showed that the rate increased as downstream

from Tamaniate to Dongolla. Monthly runoff from July to January significantly

decreased, while it significantly increased from March to May. The increasing trend

exhibited from March to May do not imply the actual trend patter instead due to the

regulation effect of dams upstream of the stations. But the peak flow period display

significantly decreasing trend also identified in those unregulated upstream station.

46

Figure 6.1 Natural and Observed mean annual runoff trend at Sennar station of the Upper Blue Nile River.

Figure 6.2 Balance between natural and observed mean annual runoff at Sennar station.

47

6.1.3 Precipitation trend results

The pattern of the mean monthly rainfall on Upper Blue Nile and Upper White Nile

differ due to seasonal variation (Figure 6.3). Upper Blue Nile has shorter wet season

and highly variable during dry season than wet season as compared with Upper White

Nile. The rainfall in upper White Nile have two rainfall season one having high

amount of rainfall than the other. The seasonal coefficients of variation are displayed

in Figure 6.4 for both the Upper Blue Nile and White Nile.

Figure 6.3 Mean monthly areal and average rainfall distribution over Upper Blue Nile and White Nile.

Figure 6.4 Coefficient of variation in rainfall for Upper Blue Nile and White Nile.

48

To give insight on the rainfall trend over the Upper White Nile and Upper Blue Nile

basin the average rainfall over the lake Victoria Nile and the areal Rainfall were taken

and the seasonal Mann-Kendall and Sens T statistical test were conducted. It is self

evident that the results of the Mann-Kendall and Sens T test are in good agreement

about the existence of significant trends over the Upper White Nile basin and also no

significant trend over the Upper Blue Nile basin (Table 6.4). For the Upper White Nile

basin, there is a positive trend indicating a tendency towards wet condition on seasonal

basis at 5% significance level according to the Sens T test (T = 2.58) and Mann-

Kendall test (z = 2.61), respectively. Significantly increasing trend in monthly rainfall

wet season was display. Out of ten stations selected for rainfall trend analysis over the

Upper White Nile basin seven of them were displayed significantly increasing trend in

November, three in October, three in January and two in mean annual. These indicate

much of the wet season month showed a significant increasing trend. Refer appendix

III.11 for the full results of the trend analysis. According to Sens T test on Upper

Blue Nile basin rainfall there is a no trend at 5% significant level on both wet and dry

season as well in annual basis (T= -0.42, 0.27 and 0.40) indicating a no tendency

towards wet condition. According to Mann-Kendall test on Upper Blue Nile basin

there is no trend at 5% significant level on both wet and dry season as well in annual

basis (z = -1.14, 0.14 and -0.57) indicating no change in rainfall on both seasonal and

annual basis.

Table 6.4 Results of the precipitation trend test for White and Upper Blue Nile.

Basin Season T values of Sens T test

z values of Mann-Kendall test

White Nile Wet 2.58 2.61Dry -1.2 -0.9Annual 1.35 1.37

Upper Blue Nile Wet -0.42 -1.14Dry 0.27 0.14Annual 0.4 -0.57

Note: Bold figures are significant at 5% significance level.

49

6.2 Model Results

Model running started before the beginning of the rainfall period; January was

selected. The calibration parameters were selected according to suggestions from

Steenhuis, 2008 and Collick, 2008. The data from 1961 to 1970 used for calibration

and validation then after 30 years from 1991 to 2000 used for calibration and

validation (Figure 6.4 A and B). The parameters calibrated are the soil moisture and

the percentage of contributing areas. The root mean square error and coefficient of

regression were used as a relation criterion between the observed and simulated runoff

and the efficiency criterion by Nash and Sutcliffe (1970) represented as E is used for

the model efficiency. The evapotranspiration was set according to Steenhuis, 2008

recommendation 3.3mm/ day for wet season (June to September) and 5mm/day for dry

season (October to May) the = 0.21month-1. These all parameters have been used to

analyze the relationship between different land uses/covers with the soil physical

properties and crop properties.

A

50

Figure 6.5 Calibration (A) from 1991 1995 and validation (B) from 1996 - 2000 of the model. Table 6.5 Calibrated and validated parameters showing land use/cover change during the past 30 years. Parameters 1961-1965 1966-1970 1991-1995 1996-2000

Calibration Validation Calibration Validation Nash-Sutcliffe model eff. (e) 0.8 0.7 0.78 0.75 Root mean square error (RMSE) 13.52 15.5 12.67 15.06 Correlation coefficient (R2) 0.79 0.8 0.87 0.83 AR (Saturated zone) 0.10 0.10 0.15 0.15 AR (Intermediate zone) 0.20 0.20 0.20 0.20 AR ( Hillslope zone) 0.70 0.70 0.65 0.65 SM (Saturated zone) 400.00 400.00 300.00 300.00 SM (Intermediate zone) 40.00 40.00 10.00 10.00 SM (Hillslope zone) 600.00 600.00 450.00 450.00 PET wet season 3.30 3.30 3.50 3.50 PET dry season 4.50 4.50 5.00 5.00 Note: AR-area ratio, SM-soil moisture.

The result from the model is good indicator for land use/land cover change over the

past 30 years. The soil moisture and percent of contributing area is a good indicator of

land use change and the potential evapotranspiration is good indicator of the land

cover changes. The soil moisture in forest is very high as compared with the other land

cultivated or plantation. Hence the model result indicates clearly that more land

B

51

changed from forest and forest related land to plantations. More land from forest and

related land use type changed to cultivation land. In addition to the land use change

the land cover change from low to high water consuming crops. This may also be due

to temperature change over the past 30 years.

52

CHAPTER SEVEN

7. CONCLUSIONS

Based on the above analysis, some conclusions are drawn as follows:

1) Most data sources for the Nile basin shows a good agreement between them

especially the Global Hydro Climate Data Network have good data for most of

the streamflow considering some errors.

2) Years through 1960 are the period experiencing abrupt changes in runoff in the

White Nile Basin stations. Pettit test proves that years are periods most

changes in runoff were observed. The reason for the change is due to abrupt

changes in Rainfall over the region.

3) All the stations located in White Nile presents an obviously increasing trend in

the annual mean runoff, and also an increasing trend to different degrees in

monthly mean runoff from 1912-2000. But in the last four decades all of that

station showed a significant decreasing trend in both annual and monthly flow

series.

4) The Upper Blue Nile Basin presents no trend in the mean annual and the flood

season runoff but increasing trend in the low flow season at two of the station

located on the route of the river. In recent years saw a significant increasing

trend in low flow period April to May due to regulation in the upstream.

5) The trends of the runoff variation are basically consistent with the changing

trend of precipitation in the Victoria Nile even though no correlation was

found between the average precipitation and streamflow due regulation and

large area lake effects.

6) No temporal trend in precipitation over the Upper Blue Nile was determined;

hence the display in no trend in the streamflow was expected.

53

7) Trends of low flow significantly increase after chara-chara weir started

operation. The reason for the increment is not due to an actual increase in

runoff but regulation effect.

8) There was land use/cover change during the past 30 years in the upper Blue

Nile basin. More land went for cultivation and also change in crop type which

has high water consumption. The model is useful for evaluating the effect of

land cover/land use scenarios on discharge and soil moisture regimes.

54

CHAPTER EIGHT

8. REFERENCES

Abdul Aziz, O., Burn, D., 2006. Trends and variability in the hydrological regime of

the Mackenzie river basin. Journal of Hydrology 319, 282-294.

Alan Nicol, 2005. The Nile: Moving beyond cooperation. Water policy program, ODI