ADDIS ABABA UNIVERSITY

SCHOOL OF GRADUATE STUDIES

FACULTY OF TECHNOLOGY

DEPARTMENT OF CIVIL ENGINEERING

RESERVOIR OPERATIONAL PLANNING OF

IRRIGATION DAMS FOR MICRO-HYDROPOWER
DEVELOPMENT

(A Case Study Conducted on Haiba Micro-Irrigation Earth

Dam in Tigray Regional State)

BY MULATU TIRUNEH

NOVEMBER 2005

ADDIS ABABA
 ADDIS ABABA UNIVERSITY

SCHOOL OF GRADUATE STUDIES

FACULTY OF TECHNOLOGY

DEPARTMENT OF CIVIL ENGINEERING

RESERVOIR OPERATIONAL PLANNING OF

IRRIGATION DAMS FOR MICRO-HYDROPOWER
DEVELOPMENT

(A Case Study Conducted on Haiba Micro-Irrigation Earth Dam in

Tigray Regional State)

A Thesis Submitted to the School of Graduate Studies, Addis Ababa

University in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Civil Engineering

BY MULATU TIRUNEH

NOVEMBER 2005
Declaration

I the undersigned, declare that this thesis is my original work and has not
been presented for a degree in any other university. All sources of materials
used for the thesis have been duly acknowledged.

Name: Mulatu Tiruneh

Signature: ____________________

Date: _______________

Place: Civil Engineering Department, Faculty of Technology, AAU.

Addis Ababa
 ACKNOWLEDGMENT

This thesis is prepared under the advisory support of Dr.-Ing. Zelalem Hailu,
Lecturer at Addis Ababa University in the Department of Civil Engineering
and Ato Michael Abebe, Head, Dams and Hydropower Design Department in
the Ministry of Water Resources. I therefore would like to express my sincere
thanks to Dr.-Ing. Zelalem Hailu and Ato Michael Abebe for their intellectual
and valuable contribution, their consistent guidance, feed back and
comments on the various draft reports.

My thanks is also due to the Department of Civil Engineering, Faculty of

Technology, Addis Ababa University, for facilitating my work in so many
ways. I would like to thank all professors who gave me the post- graduate
courses.

I would like to take this opportunity to extend my thanks to Tigray Regional

State Water Development Commission and the respective line managers,
team leaders and staff members who provided me with numerous valuable
data and for their hospitable library service. I had a wonderful time and
received a very keen support from the commission during my data collection,
especially staffs who accompanied me on the field visits.

I would also highly appreciate and acknowledge the following institutions for
providing data and information related to my research.

1. Ministry of Water Resources

2. Rural Energy Development and Promotion Center
3. National Meteorological Service Agency
4. Ethiopian Electric Power Corporation

Finally, I would like to express my indebted thanks to my wife Martha

H/Mariam, my parents, sisters, brothers and friends who were always by my
side and encouraging and helping me in taking off my burden.

i
TABLE OF CONTENTS

Page
Acknowledgment................................................................................i
List of Tables ....................................................................................iv
List of Figures ...................................................................................v
List of Abbreviations and Symbols.....................................................vi
Abstract................................................................................... …. .viii

1. INTRODUCTION
1.1 General….......……………………………………………………….1
1.2 Statement of the Problem...............…………………………….5
1.3 Objectives of the study ……………………………………………6
1.4 Organization of the Thesis.……………………………………….7

2. MICRO-HYDROPOWER DEVELOPMENT IN ETHIOPIA

2.1 Potential and Status of Hydroelectric Energy..........……….8
2.2 Electricity Supply in Rural Areas...………………................9
2.3 Definition of Micro-Hydropower.………………………………..9
2.4 Advantages of Micro-Hydropower Plants…………………….10
2.5 Overview of Energy Policies and Strategies of Ethiopia....13

3. MICRO-EARTH DAM CONSTRUCTION IN ETHIOPIA

AND PARTICULARLY IN TIGRAY REGION.........................15

4. DESCRIPTION OF THE STUDY AREA

4.1 Location, Population and Climate..……………………………19
4.2 Salient Features of Haiba Micro-Irrigation Earth Dam......20
4.3 Assessment of the Project Site during Field Visit………….22
5. DATA AVAILABLITY AND ANALYSIS
5.1 Meteorological Data...…………………………………………….24
5.1.1 Rainfall.........................................................................24
5.1.2 Evaporation......................................................................26

ii
 5.2 Runoff............................................……………………………..27
6. DYNAMIC PROGRAMMING MODEL FOR RESERVOIR
OPERATION
6.1 Reservoir Operation Technique................……………………...29
6.1.1 Guide Curve for Reservoir Operation.................................29
6.1.2 Simulation........................................................................30
6.1.3 Optimization Techniques...................................................30
6.2 Dynamic Programming ..................……………………………...31
6.3 Formulation of Dynamic Programming Model ………………..33
6.3.1 Objective Function and Constraints..................................33
6.3.2 Determination of Maximum Conveyance Capacity
of the Irrigation Outlet....................................................38
6.3.3 Recursive Procedure and its Algorithm.............................41
6.4 Preparation of Input Data for the DP Model …………………..44
6.5 The Visual Basic User Interface for the DP Model................46

7. RESULT AND DISCUSION

7.1 Optimal Releases for Stationary Policy ........................…....49
7.2 Monthly Energy Output .....................................................50
7.3 Reservoir Operation Guide Curve .................................... ...52

8. CONCLUSIONS AND RECOMMENDATIONS

8.1 Conclusions ................................................................ ......54
8.2 Recommendations ..............................................................55
REFERENCES ..................................................................................57
ANNEX
Annex A: Tables ......................................................................60
Annex B: Figures ......................................................................79
Annex C: Visual Basic Code .....................................................84

iii
List of Tables

Table 3.1 Fully Implemented Micro-Irrigation Schemes Existing in

Tigray ……………………………………………………………..16
Table 6.1 Monthly Reservoir Inflows …………………………………….45
Table 6.2 Monthly Evaporation Losses from Haiba Reservoir……...45
Table 6.3 Monthly Irrigation Requirement (MIR) in the
Order of 104 m3………………………………………………….46
Table 7.1 Summary of Stationary Policy Optimal Releases ………..50
Table 7.2 Monthly Energy Production and Other Parameters
From Output of DP (Visual Basic Program)………………52

iv
List of Figures

Figure 1.1 Power House Located at the

Base of the Dam…………………………………………………3
Figure 1.2 Siphon Intake (Generator
Located on Top of the Dam)………………………………….3
Figure 2.1 Cross Flow Turbine Manufacrured by STVC.................12
Figure 3.1 Fullly Implemented Dams and their Distribution in
Tigray Region……………………………………………………17
Figure 4.1 Photographs Showing the Different Views of Haiba
Reservoir…………………………………………………………19
Figure 4.2 Schematic Diagram of Haiba Dam…………………………20
Figure 4.3 Photograph Showing Part of Spillway …………………….22
Figure 4.4 Photograph Showing the Irrigation Outlet ………………22
Figure 5.1 Mean Monthly Rainfalls at Dengolat Station…………… 25
Figure 5.2 Annual Rainfalls at Dengolat Station............................25
Figure 5.3 Double Mass Curve Plot for Dengolat Station……........26
Figure 6.1 Schematic Diagram Representing the System…….…….34
Figure 6.2 Storage-Elevation Curve……………………………………...35
Figure 6.3 Relationship between period t and n at each stage of
reservoir operating problem…………………………………42
Figure 6.4(a) Visual Basic User Interface for the DP Model:
Hydrology and Irrigation Tab.......... …………................47
Figure 6.4(b) Visual Basic User Interface for the DP Model:
Reservoir Characteristics Tab.......... …………...............48
Figure 7.1 Plot of Mean Monthly Energy………………………………..51
Figure 7.2 Energy Output Duration Curve……………………………..51
Figure 7.3 Guide Curve Developed Using
Visual Basic Program………………………………….........53

v
List of Abbreviations and Symbols

Abbreviations
SHP……………...........Small Hydropower
EREDPC………..........Ethiopian Rural Energy Development and
Promotion Centre
EEPCO …………........Ethiopian Electric Power Corporation
DP............................Dynamic Programming
MW ……………..........Mega Watt
MWh ……………........Mega Watt Hour
KW……………….........Kilo Watt
KWh …………….........Kilo Watt Hour
TWh……………….......Tera Watt Hour
MWR………………......Ministry of Water Resources
CSA…………………....Central Statistical Authority
Opt Rls……………......Optimal Release
MaxEnrgPrdcd……....Maximum Energy Produced
TRBIMPP…………......Tekeze River Basin Integrated Development
Master Plan Project
TRSBBWR…………... Tigray Regional State Bureau Water Resources
HHs......................... Households
PH ……………………..Power House
STVC........................Selam Technical and Vocational Centre
DSL..........................Dead Storage Level
NPL..........................Normal Pool Level
UNIDO.....................United Nations Industrial Development
Organization

vi
Symbols
i t= reservoir inflow in a month t in units of 104 m3
r t= release in a month t in units of 104 m3
Irr,t = irrigation water release in a month t
rhp,t = water released for power generation in a month t

S t
= initial storage volume in a month t in units of 104m3

S t +1
= final storage volume at the end of month t in units of 104 m3

S t
= average storage volume during month t in units of 104m3
S= instantaneous storage volume in units of 104m3
h= instantaneous height of water above the turbine in m.

h= t
average height of water above the turbine in month t in m.

EV t
= evaporation from reservoir water in month t in units of 104m3

SP t
= seepage water through the embankment of the dam in month t

in units of 104m3
Tir,t= target water allocation for irrigation in month t in units of
104m3
CCmax= the maximum conveyance capacity of the outlet structure in
units of 104m3
K= reservoir total storage capacity in units of 104m3
K d= dead storage capacity of the reservoir in units of 104m3
Kw= minimum working storage capacity free of vortices in units
of 104m3
T= months in a year
n= months remaining for reservoir operation

vii
 Abstract
Modern forms of energy are simply not available in rural areas of Ethiopia,
while traditional sources such as fuel wood, cow dung, and crop residue are
being depleted rapidly thereby deepening the rural energy crisis. Compared
with other new and renewable sources of energy, micro-hydropower has
been recognized as being a viable and mature technology. It can be applied
immediately on an economic scale in a flexible manner and can
comparatively easily bring benefits to the population in isolated areas, who
are so far not covered by national electricity supply grid.

About 84 percent of the Ethiopian people reside in rural areas and less than
one percent of this population has access to electricity. However, a number
of micro- irrigation dams have been constructed and planned for
implementation, especially in Tigray and Amhara Regional States which
could generate and provide electricity to the local population. A case study
regarding reservoir operational planning had been conducted on Haiba dam,
which is one of the fully implemented micro-irrigation earth dams in Tigray
Regional State so as to integrate it with micro-hydropower. Accordingly a
number of objectives were accomplished like formulation of discrete dynamic
programming model, collecting, processing and analyzing meteorological
data, collecting and preparing input data for the DP model.

A Visual Basic Program was written to solve the DP model. The main results
obtained were monthly energy output, energy output duration curve and
optimal reservoir operation guide curve. The optimal power output of Haiba
reservoir has an electrification capacity of 50 to 650 households each using
one light bulb of 40w each.

The results of this study showed that it is possible to produce a valuable

amount of electric energy that is very useful in electrifying the rural
community, from micro-irrigation dams without affecting its irrigation

viii
service by applying systems engineering as a planning tool. Based on the
findings, conclusions and recommendations for further studies are drawn.

ix
 1. INTRODUCTION

1.1 General

Access to energy is a key element in rural development. However, despite two

decades of attention to energy issues in Ethiopia in response to the
international oil crisis of the 1970s, rural communities continue to be
deprived of basic energy services (EREDPC, 2002). Modern forms of energy
are simply not available in rural areas while traditional sources are being
depleted rapidly thereby deepening the rural energy crisis.

Among the principal manifestations of the rural energy crisis is depletion of

fuel wood resources. This leads to a decline in household welfare caused by
an increased use of inferior fuels, higher fuel wood prices, and a reduction in
the quality and variety of cooked meals. It also leads to a reduction in
agricultural productivity as a result of the use of dung and crop residues for
fuel instead of using them as a soil nutrient and to loss of human availability
for productive work due to time spent on collecting fuels.

A number of factors are responsible for the prevailing rural energy crisis in
the country. The high incidence of rural poverty, the wide geographical
spread of rural settlements and the consequent lack of economies of scale,
poor rural infrastructure and difficulty of access, absence of appropriate
policy, strategy and institutional arrangement are among the key constraints
(EREDPC, 2002).

The UN conference on new and renewable sources of energy spot lighted the
importance of non-traditional forms and sources of energy in the
development process of the developing countries. Compared with other new
and renewable sources of energy, small and micro hydropower has been
recognized as having a viable and mature technology. Unlike the other new
and renewable sources, it can be applied immediately on an economic scale
in a flexible manner, and can comparatively easily (and at reasonable cost)

1
bring benefits to the population in isolated areas, who are so far not covered
by national electricity supply grid (SHP in china, 1985). With the same
source, it is noted that without upgrading the living conditions and
standards of the rural population, very little can be achieved in terms of
development. As one starting point, Micro-Hydropower could play a positive
role towards accelerating a development process in developing countries,
particularly, in the remote areas.

Moreover, according to EREDPC (2002), hydropower resource development

has been given the first priority in the Ethiopian rural energy policy (this has
been discussed in more detail in chapter two).

Despite all the above facts, a number of micro – irrigation earth dams have
been constructed and planned to be constructed in different parts of the
country especially in rural areas of Tigray and Amhara Regional states
without taking into consideration the opportunity to integrate them with
power generation so as to electrify the surrounding rural community.

If a reservoir has already been built for other purposes such as flood control,
irrigation network, water abstraction for a big city, recreation area, etc., it
may be possible to generate electricity using the bottom outlet as a penstock
if the dam already has a bottom outlet as in figure 1.1. Otherwise, provided
the dam is not too high, a siphon intake can be installed. Integral siphon
intakes (Figure 1.2) provide an elegant solution in schemes with heads up to
10 meters and for units of no more than 1,000 MW, although there are
examples of siphon intakes with an installed power up to 11 MW (Sweden)
and heads up to 30.5 meters (USA).

2
Fig. 1.1 Power House Located at the Base of the Dam

The turbine can be located either on top of the dam or on the downstream
side. The unit can be delivered pre-packaged to the works, and installed
without major modifications of the dam (Penche, 1998).

Figure 1.2 Siphon Intake (Generator Located on Top of the Dam)

The main questions are, therefore, how to harmonize the existing operational
plan to the intended power generation and how to assess the optimum
electrical power generation capacity of the dam at a given time (month) in a
year.

3
Hence this thesis aims to assess the hydroelectric potential of Haiba micro-
irrigation earth dam, which is found in Tigray regional state, without
affecting the existing irrigation water supply by applying optimal reservoir
operation techniques.

4
1.2 Statement of the Problem

The rural villages / ‘Gots’ like Adi Awusa (small rural town), Atsgebta and
Endemeskel which are found surrounding the Haiba dam as well as
government institutions like clinics, schools and development agents’ offices
and private flour mills in these villages do not have access to electricity. Due
to this, they totally depend on traditional energy source, that is, biomass
energy, for example, straw, wood, animal waste and so on and on petroleum
products for their flour mills and night time illumination. Moreover, these
traditional resources are being depleted rapidly due to deforestation, soil
erosion and its fertility loss, population growth and so on, thereby deepening
the rural energy crisis in the area.

On the other hand, Haiba dam is continuously releasing irrigation water

through its bottom outlet from September to August and spills during the
rainy season without producing any electric energy. Therefore, the turbine or
the powerhouse can be located on the downstream side of the dam and can
generate electric energy using the bottom outlet as a penstock without major
modifications of the dam.

However, taking away from the reservoir for irrigation will result in the
reduction of water for power generation. In most cases the time table of
irrigation does not coincide with the electricity needs for household
electrification. Moreover, the irrigation takes priority over power generation
and thus a method has to be devised to harmonize power generation and
irrigation.

5
1.3 Objectives of the Study

The overall objective of this study is to assess the potential of Haiba micro-
irrigation earth dam for micro – hydropower development so as to electrify
the surrounding rural community and to maximize the benefit of the stored
water.

Some of the specific objectives adopted to meet the main objective include:

- Collecting, processing and analyzing meteorological data.

- Preparation of the input data for the DP model.

- Establishment of reservoir operation rule curve using the

presently available meteorological information.

- Formulation of a discrete dynamic programming model

which takes into account the release of water for both
irrigation and hydroelectric power generation purposes.

- Writing a Visual Basic Program to solve the DP model.

6
1.4 Organization of the Thesis

The thesis is organized into eight chapters. Chapter one presents the
introduction, statement of the problem and objectives of the study.

The second chapter discusses about micro hydropower development in

Ethiopia. Potential and status of hydroelectric energy in general and its
supply in rural areas is discussed in this chapter. The definition and
advantages of micro hydropower are also dealt. Finally overview of rural
electrification policies and strategies of Ethiopia are discussed in brief.

Chapter three and four deal with micro earth dam construction in Ethiopia
and particularly in Tigray region and description of the study area
respectively. In description of the study area salient features of Haiba micro
irrigation earth dam are presented.

Chapter five deals with the data availability and analysis. The availability of
meteorological and physical data is included along with the methods
employed in the analysis.

Chapter six presents the reservoir operation modeling using dynamic

programming. At the beginning of this chapter the different techniques of
reservoir operation are discussed. The formulation of the dynamic
programming model along with its objective function, constraints and
algorithm are discussed in detail.

The results of dynamic programming model and discussion of the results are
presented in chapter seven while summary, conclusions and
recommendations are incorporated in chapter eight.

7
 2. MICRO-HYDROPOWER DEVELOPMENT IN ETHIOPIA

2.1 Potential and Status of Hydroelectric Energy

Ethiopia, being one of the countries with the lowest GDP per capita has also
the lowest per capita consumption of energy, particularly electric energy. The
per capita electric consumption is 27.1 KWh (EEPCo, 2004).

On the other hand, Ethiopia has vast energy resources. The gross hydro
based energy potential of the country is estimated at 650 TWh per year. It is
estimated that out of this potential about 25 percent (162.5 TWh) could be
exploited for power at economic costs and existing technologies (CESEN,
1986).

There are both traditional and modern energy sources in use in Ethiopia. Of
the total energy produced in the country, 95 percent is from traditional
sources like fuel wood, charcoal, agricultural residues and dry dung. The rest
5 percent is contributed by modern energy sources, notably petroleum
products and electricity. The share of electricity is only 1 percent of the total
national energy consumption in Ethiopia (EREDPC, 2002).

On the other hand, hydroelectricity accounts for about 98 percent of the total
electricity supply in the country. However, it is only about 2 percent of the
total hydropower potential of the country that is currently utilized (EEPCo,
2004).

According to CSA (2004), there are 625,496 domestic consumers connected

to the public electricity system of EEPCo. The population of the country for
2003 was 73.04 million (or 14.6 million HHs). The proportion of the
population with electricity connection from public sector was thus just 4
percent.

8
2.2 Electricity Supply in Rural Areas

According to CSA (2004), 84% of the Ethiopian people reside in rural areas
where grid extension is not feasible and is largely dependent on biomass
fuels for cooking, lighting and heating. Access to electricity in rural areas was
limited to just 0.7 percent of the population or about 61,000 HHs in 1994.
Moreover, even this few house holds consume very small amounts of
electricity, usually lighting in the evenings (EREDPC, 2002).

Due to the vastness of the rural areas, the largely dispersed population and
difficult communications, the total energy requirement is so large that no
single energy component can meet it satisfactorily. Therefore, it is necessary
to have a diversified energy policy based on increasing conventional energy
generation and developing various new and renewable resources.

2.3 Definition of Micro-Hydropower

The capacity range of micro – hydropower plant is coordinated with the

development of the national economy, and is especially related to the
development of the rural economy and the level of rural electricity
consumption. Accordingly different authors put different ranges. Some of the
definitions are given as follows.

According to Harvey (1993), Micro – hydro schemes are small and usually do
not supply electricity to the national grid at all. They are used in remote
areas where the grid does not extend. Typically they provide power to just
one rural industry, or one rural community. They range in size from 200
watts, just enough to provide domestic lighting to a group of houses through
a battery charging arrangement, to 300 KW, which can be used for small
factories and to supply an independent local ‘mini – grid’ which is not part of
the national grid. And scheme ranging from 3 -10 MW is referred to as ‘small
hydro’ power. Scheme with more than 10MW is referred to as ‘full scale
hydro’ power.

9
According to Fritz (1984), small hydro projects are defined as systems of 15 -
30 MW or less capacity. Mini hydro refers to projects of 1MW capacity or less,
and Micro hydro to projects of 100 KW capacities or less.

According to Ethiopian conditions, micro Hydro power is defined as having

power generation capacity of less than 100 KW. This accords with definition
by UNIDO and such countries as Turkey and Peru. Isolated hydropower sites
having production levels higher than 100 KW are termed small hydropower.
But the definition should not convey the wrong idea that an absolute
demarcation is drawn between micro hydropower and upper production
levels (Zelalem, 1992).

2.4 Advantages of Micro-Hydropower Plants

Socio - Economic Advantage

Cases have been identified where micro hydropower stations have acted in
development pole which started with the supply of light to the village
communities, stimulating the positive and active thinking of population to
utilize the available electric power for semi industrial activities, and gradually
acquiring and strengthening the mechanical and metal working capabilities
of the population (SHP in china, 1985).

There is an increasing need in many countries for power supplies to rural

areas, partly to support industries, and partly to provide illumination at
night. Government authorities are faced with very high costs of extending
electricity grids. Often micro-hydro provides an economic alternative to the
grid. This is because independent micro-hydro schemes save on the cost of
grid transmission lines and because of grid extension schemes often have
very expensive equipment and staff costs. In contrast, micro -hydro schemes
can be designed and built by local staff and smaller organizations following

10
less strict regulations and using ‘off-the shelf ‘ components or locally made
machinery (Harvey, 1993).

Although the unit cost per installed kilowatt of generating capacity is higher
for small scale projects, financing is often easier to obtain. These
characteristics make micro hydro particularly attractive for least developed
countries where near term installation of dispersed energy systems is
essential for economic and social development (Fritz, 1984).

Physical and Technical advantages

Since the technology is simple, it has quick response to the needs of the
community. It does not require massive hydraulic structures and has a short
construction period.

As already explained the equipment and structures can be designed and

built by local staff (experts from one's own country) and smaller
organizations. Utilization of local materials for civil work is especially
important in design of civil works and manufacture of electro mechanical
equipment locally will certainly lead to reduced costs thereby making the
project more feasible and sustainable.

One of the technical centers that have good capability in manufacturing

hydro related equipment in Ethiopia is Selam Technical and Vocational
centre (STVC). These are relatively simple cross flow turbines for a power
range of 5-250 KW (STVC, 2000).

Figure 2.1 Cross Flow Turbine Manufactured by STVC (Source: STVC)

11
Most of the systems installed use direct shaft power running a grinding mill.
Only a few are connected to generators (e.g 15 KW in Arbaminch Hospital). A
17 KW unit was also delivered to the international technology development
group and installed the system in Kenya (Michael, 2004).

Environmental Advantages

According to Fritz (1984), when compared to large scale hydro projects micro
hydro can be planned and built in less time and are less likely to create
extensive environmental problems. Moreover, unlike fuel-driven industries,
the water powered plants do not affect the environment with smoke and by
products of fuel oil.

Through the use of local hydropower resources, problems related with

deforestation could be minimized. This needs special attention since Ethiopia
is one of the countries which are in deforestation risk zone. The extremely
backward and wasteful pattern of energy consumption based mostly on
biomass and particularly fuel wood has led to a high degree of ecosystem
imbalance causing wide scale deforestation, environmental degradation, soil
erosion and consequent loss of soil fertility and wide spread climatic changes
with enormously disastrous social and economic consequences (Zelalem,
1992).

12
2.5 Overview of Energy Policies and Strategies of Ethiopia

According to EREDPC (2002), as part of overall programme of economic

reforms, the government of Ethiopia formulated a national energy policy in
1994. The policy states the general objectives and priorities of the
government in the energy sector. These include:

- Development of hydropower resources

- Shift from traditional fuels to modern fuels

- Establish standards and codes for efficient energy use

- Development of human resource and strong institutions

- Promote and support private sector participation in development

of energy resources and

- Incorporate environmental considerations in development of

energy programmers.

Increasing access to electricity in rural areas to significant levels clearly

requires huge resources both from the government and non governmental
sectors. The government in recognition of the above has formulated a rural
electrification strategy with two components.

i. To extend the publicly owned grid system into rural

areas, and

ii. To promote private sector – led off grid rural

electrification i.e. in villages which are found where EEPCO
can’t cover them in the near future by extending its grid.

In implementation of the rural electrification strategy the government

established a rural electrification fund by proclamation in 2003 as a
permanent financial source with the following objectives:
1. To provide loan and technical services for rural electrification project
carried out by private operators, cooperatives, and local communities and

13
 more specifically for those projects operating on renewable energy
resources, and
2. To encourage the utilization of electricity for production and social welfare
purpose in rural areas.

The sources of the fund shall be budget allocated by the government, loans
and grants from international financial institutions, loans and grants from
other governments, grants from non- governmental organizations and income
from other different sources (EEA, 2004). One important point noted in the
same is that the rural electrification program is justified by its potential
contribution for the socio economic development of the rural areas of the
country.

14
 3. MICRO-EARTH DAM CONSTRUCTION IN ETHIOPIA
AND PARTICULARLY IN TIGRAY REGION

Construction of Micro-dams in Ethiopia started in the late seventies to

combat the recurrent drought in the country. Construction of micro dams by
the Cuban engineering team had been carried out from 1978-1982 after the
agreement made on the collaboration between the Ethiopian and the Cuban
Governments, started just after the visit made by the Cuban President Fidel
Castro Ruz to Ethiopian in September 1978. The dams constructed by the
team had been working together with the Ethiopian Water Resource
Authority’s counter parts. During their stay in Ethiopia, they studied,
designed and constructed 4 micro-dams. These are, Belbela, Wedecha, Tolly
and Chichat dam. The first three are in the Awash River basin and the fourth
one is in the Tekeze River basin. In addition to these dams a micro dam
known as Diksis was built to provide drinking water to the Diksis state farm.
It was commissioned in 1979 (Michael, 2004).

Since 1992 the responsibility for the development of small-scale irrigation

schemes has been transferred to the regional governments. Sustainable
Agricultural and Environmental Rehabilitation Commissions (COSAER) have
been established in Tigray, Amhara and Southern Nations Nationalities and
people's regional state. These Commissions have been conducting design and
implementation of small – scale irrigation dams in their respective regions.

In Tigray and Amhara regional states a total of 48 and 6 small dams were
constructed to irrigate 3194. 5 ha land. The detail description of these dams
in Tigray is found in Table 3.1. Their storage capacity ranges from 0.1-3.10
Mm3 and 38 similar projects have been studied in Tigray Regional state for
subsequent implementation (TRSBWR, 2003).

15
 Table 3.1 Fully Implemented Micro-dam Irrigation Schemes
Existing in Tigray (Source: TRSBWR, 2003)
Dam
S/N Site Name Woreda Capacity Height Reservoir Command
3
(Mm ) (m) Area(ha) Area (ha)
1 Mejae H.Wejerat 0.30 13.50 6.00 14.00
2 Gereb-Mihiz H.Wejerat 1.35 17.50 30.00 80.00
3 Mai-Gassa H.Wejerat 1.30 12.70 42.12 70.00
4 Mai-Delle H.Wejerat 1.77 15.00 35.00 90.00
5 Gum-Sellasa H.Wejerat 2.03 11.50 48.00 110.00
6 Adi-Kenafiz H.Wejerat 0.75 15.50 60.00
7 Mai-Haidi H.Wejerat 0.24 9.20 5.65 9.00
8 Gra-Shito H.Wejerat 0.30 10.00 6.72 16.00
9 Fledgling H.Wejerat 0.28 14.00 6.60 20.00
10 Dur-Anbessa H.Wejerat 0.13 18.00 14.00 61.00
11 Gereb-Segen H.Wejerat 0.55 14.86 11.70 24.00
12 Shilant III H.Wejerat 0.15 9.00 7.00
13 Meskebet Laelay Adi 1.34 17.50 52.80 70.00
14 Mai Gundi Laelay Mai 0.80 12.50 46.00
15 Ruba Feleg Ats. Wenbe 0.90 17.50 80.00
16 Felaga Ats. Wenbe 0.90 11.92 21.53 75.00
17 H.W.Cheber Enderta 15.50 80.00
18 Era Quhila Enderta 87.00
19 Haiba H.Wejerat 3.10 16.00 95.00 100.00
20 MwL H.Wejerat 1.40 19.00 31.00 100.00
21 Adi-Amharay Enderta 0.96 14.70 31.50 60.00
22 Era Wenberta 1.96 16.70 100.00
23 Sewhineda Enderta 0.36 14.50 7.80 23.00
24 Teghane Ats. Wenbe 1.08 11.00 60.00
25 Mai-Negus Laelay Mai 2.38 24.00 38.00 150.00
Laelay-
26 Wukro Wukro 0.93 11.00 50.00
27 Korir Tsirea 15.00 32.00 100.00
28 Gereb-Awso Enderta 0.11 10.50 2.12 9.00
29 Adi-Hilo Enderta 0.10 11.40 2.50 9.00
30 Shilanat I Enderta 1.61 23.00 98.00
31 Gindae Wenberta 0.73 19.50 53.00
32 Adi-Shihu Wenberta 1.00 10.80 36.00 40.00
33 Endazeoy Enderta 0.18 12.34 4.05 13.00
34 Hashenge Enderta 2.23 19.00 38.00 120.00
35 Arato Enderta 2.59 20.00 40.00 120.00
36 Mai-Serakit Enderta 0.49 11.00 31.00
37 Adi-Gela Enderta 0.51 18.00 30.00
38 Embagedo H.Wejerat 1.35 22.00 18.50 100.00
Dedba
39 Embagedo Dergeajen 1.78 20.00 36.00 80.00
Zamra
40 Diversion H.Wejerat
41 Gereb-Birki Enderta 1.01 17.80 17.00 88.00

16
 continued
Shilanat IV
42 H.Wejerat 2.86 24.00 31.50 171.00
43 Mai Egam 0.17 13.00 10.00
Gerb
44 Shegalu 1.00 20.00 50.00
45 Higaetcheber N.A 15.50 80.00
46 Lealay Yukro 0.93 11.00 50.00
47 Betiquate 0.61 16.00 70.00
48 Embagedo 1.78 20.00 80.00
Total 3194.50

Figure 3.1 Fully Implemented Dams and their Distribution in Tigray Region

Most of the fully implemented dams (operational dams) are located in

southern part of the region including Haiba dam as shown in figure 3.1.
According to the information obtained from water resource development

17
commission, this relatively high concentration of micro-dam construction in
southern region is due to the food shortage existing in the area.

18
 4. DESCRIPTION OF THE STUDY AREA
4.1 Location, Population and Climate

Haiba Earth Dam is found in Tigray Regional State, southern zone and
Samre Woreda. Geographically it is located 13o17'19.6'' north and
39o16'44.7'' east with an altitude of 2264m amsl. The dam is found at 45km
from Mekelle and at about 17 km from the Woreda town Samre.

It is a single purpose dam designed and constructed (commissioned) in 1996

and 1999 respectively to irrigate 100 hectare of land. Accordingly it is used
for irrigation only. Both design and construction had been carried out by the
Water Development Commission of the region the then SAERT/Sustainable
Agriculture and Environmental Rehabilitation for Tigray.

The dam is one of the successful dams constructed by the commission. It has
stored adequate quantity of water and spills during the rainy season.

Figure 4.1 Photographs Showing the Different Views of Haiba Reservoir

There are about six rural villages namely Assegbta, Astah, Hantebat,
Enegabir, Endameskel and Mai-Senti in addition to one rural town (Adi
Awuso) which do not have access to electricity. Their means of living is totally
dependent on agriculture. According to the information obtained from the
development agents of these villages, the total population living in these
areas is estimated to be 1200 house holds with an average household family

19
size of five. Average household farm size is 1.25 hectare (Sinkneh, 1996).
According to the same source, there is a severe fuel wood shortage in this
area. People collect fuel wood from distant places.
The agro-climate of Samre woreda is 'Dry Woyna Dega' type. The mean
annual rainfall of the woreda is about 427.38mm and there are two main
rainy seasons namely 'Meher' rain covering from June to September and
'Belg' rain covering from March to May. The average temperature of the area
ranges from 9.94 - 22.65oC (Sinkneh, 1996).

4.2 Salient Features of Haiba Micro-Irrigation Earth Dam

Figure 4.2 Schematic Diagram of Haiba Dam

Hydrology

Mean Annual Rainfall..................................................427mm

Mean Annual Runoff.....................................................402*104m3

Catchment Area..........................................................24.7km2

Weighted average runoff coefficient.............................0.26

Expected life of reservoir...........................................48years

20
Dam

Dam type ................................................................Earth fill

Dam height.............................................................16m

Dam crest length....................................................162m

Dam crest width.....................................................5m

Dam crest elevation...............................................2000m amsl

Dam bottom level..................................................1984m amsl

Reservoir

Storage capacity...................................................3.11*106m3

Dead storage capacity......................................... 46995m3

Surface area........................................................95 hectare

Normal pool level.................................................1998m amsl

Dead storage level...............................................1990m amsl

Spillway

Spillway type...................................................Chute & Natural bed rock

Length of crest....................................................50m

Discharge.......................... ................................97m3/s

Irrigation Outlet

Type.................................................................Circular Welded Steel Pipe

Elevation of its centre........................................1989.8m amsl

Command Area................................................100 hectare

Rate of Sedimentation....................................800m3/km2/year

21
4.3 Assessment of the Project Site during the Field Visit

During field visit, which was conducted at the beginning of April, 2005 the
following site conditions were observed.
The structural aspects of the dam, such as the dam body, the spillway and
the irrigation outlet have been found to be at good condition. The reservoir
water level was at about 3.5m below the normal pool level and it was
supplying water for irrigation purpose.

Figure 4.3 Photograph Showing Part of Spillway

Figure 4.4 Photograph Showing the Irrigation Outlet

22
According to the information obtained from the reservoir operator and
development workers of the kebele, the reservoir has no definite rule of
release and totally depends on the operator's personal decision and on the
users' request. Generally the irrigation water is released from September to
August 12 hours a day during day time.
In general, much of the information which was obtained from design
document has been verified during the field visit and from the informal
interview made with the farmers, reservoir operator and development agents.

23
 5. DATA AVAILABLITY AND ANALYSIS

Data on meteorology such as rainfall and evaporation, monthly irrigation

demand, seepage, elevation-area curve, elevation-storage curve, topography,
and hours of operation of the reservoir are among the major ones that have
been collected. Therefore, the meteorological data and runoff data
availability, processing and analysis will be dealt within the following
sections of this chapter. However, preparation of input data for dynamic
programming model such as monthly inflows, monthly evaporation as well as
data and information on irrigation demand, and seepage will be discussed in
the next chapter.

5.1 Meteorological Data

5.1.1 Rainfall

The relevant precipitation in the research area is precipitation in the form of

rainfall. Monthly precipitation data of four stations, namely Dengolat,
Mekelle, Samre and Adigudom, have been collected from the National
Meteorological services Agency of Ethiopia. Dengolat station is located within
the catchment of Haiba reservoir at 6km from the dam. It is a point rainfall.
The monthly inflow data for the reservoir is used from the data obtained from
this meteorological station. However, the data is not processed and quality
control is not made to it. Moreover, there are a number of missing data to
this station. Due to this, rainfall data have been collected from other nearby
stations which are found within the same agro-climatic condition as the
Dengolat station. These are Mekelle, Samre and Adigudom meteorological
stations located at 37km, 20km and 20 km respectively from it. These
stations have been used to infill the missing data and to check its
consistency. The rainfall data from these stations have been checked for their
consistency and the missing data have been calculated. Eventhough the data
collected covers recent period about 20 to 30 years, only the data from the

24
1992 to 2004 is continuous and is used for analysis. For example, for
Dengolat station 9 - years of data that is from 1983 up to 1992 is missed.

In this study the mean monthly values have been determined and the
missing monthly data have been filled using arithmetic average and normal
ratio methods. Both the mean monthly values and the summarized annual
rainfall values in mm are given in Annex – A. The graphs showing the
monthly and yearly variability of rainfall at Dengolat station are given below.
For other stations, the graphs are attached in Annex – B.

250.0

200.0
Rainfall in mm

150.0

100.0

50.0

0.0
Jan Feb m ar Apr May June July Aug Sep Oct Nov Dec
Months

Figure 5.1 Mean Monthly Rainfalls at Dengolat Station

1000.0
Rainfall in mm

800.0
600.0
400.0
200.0
0.0
92

93

94

95

96

97

98

99

00

01

02

03

04
19

19

19

19

19

19

19

19

20

20

20

20

20

Years

Figure 5.2 Annual Rainfalls at Dengolat Station

25
The figures given above indicate that the rainfall characterstics is a bimodal
rainfall pattern. The main rainy season in the Haiba reservoir is from July to
September while the second rainy season is from March to May.

A double mass curve technique is used to test the consistency and accuracy
of rainfall records at all stations. After constructing the double mass curve, it
is found that there is no inconsistency observed for all stations. The double
mass curve constructed for Denogolat stations is presented below. Mekelle,
Adigudom, and Samre are the base stations used in double mass curve
analysis of Dengolat stations. For other stations, refer Annex - B.

Double Mass Curve for Dengolat Station

10000

9000
Cummulative Annual Rainfall at

8000
Dengolat Station in mm

7000

6000

5000

4000

3000

2000

1000

0
0 1000 2000 3000 4000 5000 6000 7000 8000

Cum m ulative Annual Rainfall at Base Stations in m m

Figure 5.3 Double Mass Curve Plot for Dengolat Station

5.1.2 Evaporation

The evaporation loss from Haiba reservoir is estimated using evaporation

data collected at Mekelle meteorological station. Pan evaporation data is
available only for 1966 to 1969 and for 1992 to 1995. In between these two
periods data is missing and not continuous. Therefore, the data between
1992 to 1995 has been used for analysis. The monthly average and annual

26
evaporation have been determined and are given in Annex – A. Hence
evaporation from the reservoir could be computed by the following formula:

EVa = K p EVm ............................................................ (5.1)

Where: EVa = actual evaporaton from the reservoir in mm

EVm = measured evaporation from pan in mm

Kp = pan coefficient.

The annual pan coefficient for surface pan of class A is 0.7. The true
coefficient on seasonal basis may vary from 0.6 to 0.8 (Reddi, 2002).

5.2 Runoff

Haiba catchment is a small catchment which is equipped neither with rain

gauges nor a stream gauge facility at the outlet. Moreover, there is no
information such as type of soil, type of covers, soil classification, infiltration
capacity of soil, and so on. Hence, eventhough there are relatively more
acceptable methods of estimating runoff from ungaged catchment such as
infiltration method and runoff curve numbers method, here runoff coefficient
method has been adopted.

Runoff Coefficient Method

The runoff can be directly computed approximately, by using an equation of

the form.

Q = K.P ......................................................................... (5.2)

Where: Q = runoff in mm or cm,

P = precipitation in mm or cm and

K= run off coefficient.

27
The runoff coefficient K depends upon the characteristics of the catchments.
However, this formula cannot be rational, because the run off not only
depends upon the precipitation, but also depends upon the recharge of the
basin. But the equation gives more and more reliable results, as the
imperviousness of the drainage area increases and the value of k, tends to
approach unity (Garag, 2003). Various values of k, which are commonly
used, are given in Annex -A from the same source.

28
 6. DYNAMIC PROGRAMMING MODEL FOR RESERVOIR
OPERATION

6.1 Reservoir Operation Techniques

6.1.1 Guide Curves for Reservoir Operation

Guide curve also called operating rule (policy) is a time schedule of releases
from reservoirs. The purpose of operating rule for water resource systems is
to specify how water is managed throughout the system. The establishments
of such schedules, which indicate quantities of water to be affected through
the action of the manger at defined points in time, is an important problem
in water resources engineering. The problem is, of course, the selection of the
operating procedure that will best achieve the stated objective (s) of the
development scheme.

A set of operating rules established for the reservoir takes account of inflows,
needs for water withdrawals and releases, storage volumes, and reservoir
elevations.

Buras (1972) stated that it was customary for a long time to establish
operating rules on the basis of personal judgment alone. No alternative
procedures were tested. The rules were generally simple: (1) store all inflow
unless needed to meet a target output; (2) when available, release water from
storage to fulfill immediate needs; (3) study all damaging floods on record in
the flood control analysis. As water resources systems became more complex,
however, it became apparent that operating procedures consist of three (and
possibly four) kinds of decisions. Storage and release of water must be
apportioned among (1) reservoirs; (2) purposes; (3) time periods; and (4)
depth layers from a reservoir to provide water of required quality.
Furthermore, it was recognized that operating procedures are sequential
decision problems and have to be treated as such.

29
There are two basic approaches for solving and analysis of operating
procedures: simulation and optimization.

6.1.2 Simulation

Simulation relies on trial – and –error to identify near optimal solutions. The
search for an optimal alternative is dependent on the engineer’s ability to
manipulate design variables and operating policies in an efficient manner.
There may be no guarantee that a globally optimal alternative is found (Mays
and Tung, 1992).

Simulation is not an optimizing procedure. Rather, for any set of design and
operating policy parameter values, it merely provides a rapid means for
evaluating the anticipated performance of the system. It is necessary for the
analyst to specify the trial design (or, equivalently, to allow the computer to
do so in accordance with some algorithm), where upon the simulation model
yields estimates of the economic, environmental, and other responses
associated with that trial. Simulation methods do not identify the optimal
design and operating policy, but they are an excellent means of evaluating
the expected performance resulting from any design and operating policy.
Hence they are often used to assist water resource planners in evaluating
those designs and operating policies defined by simpler optimization models
(Loucks et al., 1981).

6.1.3 Optimization Techniques

The most commonly used optimization techniques in water resource

problems are Langrange Multipliers, Linear Programming, Non-linear
Programming, Dynamic Programming, Quadratic Programming and
Geometric Programming. Each of these is highly dependent on the
mathematical structure of the model. The choice of techniques usually
depends on the characteristics of the reservoir system, on the availability of
data, on the objective and constraints specified. Due to its primary relevance
for this study only dynamic programming is briefly explained below.

30
6.2 Dynamic Programming

Dynamic programming (DP) is a mathematical technique that is often useful

for making a sequence of interrelated decisions where nonlinearities in the
objective function or constraints are present. It provides a systematic
procedure for determining the combination of decisions which maximizes
overall effectiveness. There is no standard mathematical formulation for
dynamic programming, and general DP computer codes are usually not
available. The procedures are not difficult, however, and a computational
code can be written for each application. Although there are similarities in
the construction of various DP problems, the specific approach and the
necessary equations are tailored to suit the actual situations (Good man,
1984).

Dynamic programming transforms a sequential or multistage decision

problem that may contain many interrelated decision variables into a series
of single – stage problems, each containing only one or a few variables. In
other words, the DP technique decomposes an N – decision problem into a
sequence of N-separate, but interrelated, single decision sub problems.
Decomposition is very useful in solving large, complex problems by
decomposing a problem into a series of smaller sub problems and then
combining the solutions of the smaller problems to obtain the solution of the
entire model composition. The reason for using decomposition is to solve a
problem more efficiently which can lead to significant computational savings.
As a rule of thumb, computations increase exponentially with the number of
variables, but only linearly with the number of sub problems (Mays and
Tung, 1992).

Dynamic programming can overcome the short comings of enumeration of all

combinations of the problem using the following concepts.

31
 1. The problem is decomposed into sub problems and the optimal
alternative is selected for each sub problem sequentially so that it is
never necessary to enumerate all combinations of the problem.

2. Because optimization is applied to each sub problem, non optimal

combinations are automatically eliminated.

3. The problems are linked together in a special way so that it is never

possible to optimize over infeasible combinations.

The basic elements and terminologies in a dynamic programming formulation

are introduced as follows.

1. Stages are the points of the problem where decisions are to be

made. If a decision making problem can be decomposed into N sub
problems, there will be N stages in dynamic programming
formulation.

2. Decision Variables are courses of action to be taken for each stage.

The number of decision variables in each stage is not necessarily
one.

3. State variables are variables describing the state of the system at

any stage. A state variable can be discrete or continuous, finite or
infinite. The state variables of the system in a dynamic
programming model have the function of linking succeeding stages
so that, when each stage is optimized separately, the resulting
decision is automatically feasible for the entire problem.
Furthermore it allows one to make optimal decisions for the
remaining stages without having to check the effect of the future
decisions for decisions previously made.

4. Stage return is a scalar measure of the effectiveness of decision

making in each stage. It is the function of the input state, the
output state, and the decision variables of a particular stage.

32
 5. Stage transformation or state transition is a single valued
transformation which expresses the relationships between the input
state, the output state and the decision.

6.3 Formulation of the Dynamic programming Model

6.3.1 Objective function and constraints

Objective Function

As already explained in chapter one, the objective of this study is to integrate

the irrigation service of Haiba micro- irrigation dam with micro- hydropower
development in the context of rural electrification. Moreover, the dam was
initially constructed as a single purpose reservoir (irrigation), and hence it
had been giving this service only since it was commissioned in 1999.
Therefore, the addition of the new service, that is, production and supply of
electrical energy shouldn’t affect the irrigation as much as possible.

To maintain the above mentioned policy, the objective could be stated as

maximization of return obtained from energy production maintaining the
target allocation of water for irrigation. However, it is better not to relate the
objective function in monetary terms because of the following reasons.

1. The benefits obtained from micro- hydropower development

couldn’t be easily expressed in monetary terms rather it can be
explained in the role it plays in bringing a fast socio-economic
development of the rural community as a whole, as it is already
explained in detail in Chapter Two.
2. According to Rivelle (1999), annual production of electric energy or
annual revenues from the sale of electric energy are really the same
problem, but in the latter, the monthly production is weighted by
its value in the electricity market place in that month.

33
The release for irrigation and hydropower energy production are to be made
through the same conveyance structure. Generally the system can be
represented by the following schematic diagram.

Figure 6.1 Schematic Diagram Representing the System

The electrical power produced in KW is given by the following formula:

P = εγQh ............................................................... (6.1)

Where:

γ = Unit weight of water in (=9.81KN/m3)

h = head of water above the turbines in (m)
Q = discharge through the turbines in(m3/s)
ε = overall efficiency of the hydropower scheme

The overall efficiency of hydropower scheme is equal to the product of the

hydraulic efficiency, the turbine efficiency and the generator efficiency. For
most of the schemes at the optimum conditions, the overall efficiency of the
scheme is usually between 60 to 70 percent (Arora, 2002). For this study, 65
percent is used.

34
Now, if θ is seconds per month, then

rt x10 4
Q= ...................................................... (6.2)
θ

Hence the monthly production of electric energy Et in KWh is given by:

E t
= P∆t ......................................................... (6.3)

where ∆t is hours in a month. Now substituting equations 6.1 and 6.2 into
6.3 and letting α to be (∈ γ∆t / θ ) , we obtain the following equation.

E t
= 10α r t h ........................................................ (6.4)

where Et is in MWh. In Haiba dam all the 16m dam height could not be
utilized for power generation since the irrigation outlet is located 5.8m above
the river bed. According to Rivelle (1999), the head of water above the
turbines, h is obviously a function of the volume of water stored in the
reservoir. The function is represented by the solid curve (storage-elevation
curve) in figure 6.2 and is approximated by the dashed line, which may be
written as linear function h= ho+ms, where m is the slope of the straight line.
Accordingly m is 0.02 and ho=2.18m.

h = 0.02St + 2.18
R2 = 0.8817
10
9
8
Elevation h in m

7
6
5
4
3
2
1
0
0 50 100 150 200 250 300 350
Storage Volume in Units of 10 4 m 3

Figure 6.2 Storage-Elevation Curve

35
In the preparation of Figure 6.2, a minimum operating level which eliminates
the formation of vortices has been calculated by applying a general rule-of-
thumb guideline which is applicable for horizontal intakes. This general rule
of thumb relates the submergence of the intake which is one of the
parameters influencing the occurrence of intake vortices with the diameter of
the pipe and is stated in the following statement. For a horizontal intake with
S/D > 0.7, vortex problems are unlikely, where S is intake submergence and
D is the diameter of the pipe (Gulliver and Arndt, 1991). Therefore, the
minimum working elevation has been obtained by summing bottom level of
intake, the diameter of the pipe and 0.7*D. Accordingly, the minimum
working elevation and its corresponding minimum working storage free of
vortices has been found to be 1990.70m and 8x104m3 respectively.

The average height in month t may be written as a function of average

storage in month t.

__
S +S
t t +1
h t
= ho + m S t where S t
=
2
which implies that:

m m
h =ht o
+ S t
+ S t +1 ........................................ (6.5)
2 2

The final storage is related with the inflow, initial storage, release,
evaporation and seepage by the mass balance equation, which is also used
as a system transformation function, as follows.

S t +1
= S t + it − r t − EV t − SPt ........................... (6.6)

Substituting equation 6.6 into 6.5 and the resulting equation into equation

6.4, and after rearranging, we obtain an expression for E t

in MWh as

follows.

36
  
E t
= 5mα r t  2h0 + 2 S t + it − r t − EV t − SPt  ........ (6. 7)
 m 

Therefore, the management objective is to maximize the total annual energy

production, that is, equation 6.7 maintaining irrigation target allocation
restriction and other constraints enumerated below, that is:

 
T

∑ 2h
Maximize 5 mα r t  m S t it r t EV t SPt  ...... (6.8)
 o
+ 2 + − − −
t =1
 

Subject to the following constraints.

1. The release cannot exceed inflows plus initial storage minus losses due
to evaporation and seepage, or in other words it limits the release to
the water available.

r ≤ S + i − EV − SP ,
t t t t t
for all t .................................... (6.9)

2. After releases, the remaining amount in storage must not exceed the
reservoir capacity. In other words, it should force spill if the available
water exceeds the reservoir capacity.

S + i − r − EV − SP
t t t t t
≤ k − kw

Or r ≥ S + i − EV − SP
t t t t t
− K + K w , for all t................. (6.10)

3. Capacity restriction

S t
≤ k − k w for all t ................................................. (6.11)

4. Irrigation target allocation restriction

r t
≥ Tirr ,t , for all t .................................................... (6.12)

37
5. Restriction of conveyance structures

r ≤ CC t max
, for all t ................................................ (6.13)

6. Non- negativity Constraint

r t
≥ 0, for all t ........................................................... (6.14)

6.3.2 Determination of Maximum Conveyance

Capacity of the Irrigation Outlet

For flow in a pipe, Bernoulli's equation between the water surface behind
the dam and the irrigation outlet can be written as follows.

H T
= h L + ho ............................................................. (6.15)

Where: HT= total head needed to overcome the various head losses to
produce discharge,
hL = cumulative losses of the system, and

h o
= velocity head at the outlet.

Equation 6.15 can be expanded to express the various losses as follows.

HT= ht + he + h f + h g + ho ........................................ (6.16)

Where: ht = trash rack losses,

he = entrance losses,
hf = friction losses,
hg= gate or valve losses, and

Where the various losses are related to the individual components,

equation 6.16 may be written as:

38
  2  2  2  2  2  ............. (6.17)
V V
  L V
  V
  V 
HT = kt  2gt  + Ke  2g  + f D  2g  + K g  2g  + Ko  2g 
         

Where:

Kt = trash rack loss coefficient,

Ke = entrance loss coefficient,
f= friction factor in Darcy- Weisback equation in pipe flow,
Kg= gate loss coefficient, and
Ko= Exit velocity head coefficient at the outlet.

Again from continuity equation, we obtain:

Q= AVt t
= AV P

where: At = area of the trash rack

Ap = cross-sectional area of the pipe

2

V
2
t
/ 2g = (A / A ) V2 g
p t
2
........................................... (6.18)

Then equation 6.16 becomes

 V
2 2
 Ap  L
H T
=  K t   + K e + f + K g + K o  ............. (6.19)
  At  D  2 g

An average approximation of the trash rack loss, ht can be obtained from

 2
the equation ht = K t  V t  : Where
 2g 
 

39
 2
 
K = 1.45 − 0.45 a n −  a n  ...................................... (6.20)
t
a a 
g  g
Where: Kt = trash rack loss coefficient (empirical),
an = net area through the trash rack,
ag = gross area of the racks and supports, and

V t
= velocity through the net trash rack area (USBR, 1987)

According to Yigzaw et al. (1996), an and ag are equal to 2.1m2 and 2.25m2
and assuming that 50 percent of the trash area is clogged for maximum loss
value, we obtain a value of Kt equal to 1.02.

Assuming flow at high Reynold’s number, the Von Karman equation for
friction factor is:

1  D
= 2 log 3.7  .................................................. (6.21)
f  ∈

Where: D= Diameter of the pipe

∈ = average roughness height of the pipe wall in mm.

According to Yigzaw et. al (1996) the diameter of the outlet pipe and its
length are given as 0.40m and 63m respectively and according to Penche
(1998) ∈ for welded steel is given to be 0.60 mm. Substituting these values
into equation 6.21 gives 0.02 for f. According to the same source, the values
of Ke, Kg and Ko could be assumed as 0.1, 0.1 and 1.0 respectively.

Substituting the values of each loss coefficient in equation 6.19 and using
the relation Q=VA one can get the following expression.

Q = 0.27 H T
....................................................... (6.22)

40
The maximum conveyance capacity of the outlet structure can now be
calculated using equation 6.22, taking HT corresponding to the normal water
level. Accordingly, the value of Q will be 0.77m3/s which is equivalent to
200*104 m3 per month.

Discretization of storage and Release Levels

Each recursive equations should be solved for discrete values of storage and
release values as follows.

From capacity restriction constrains, we know that St should be less than

303*104m3. Therefore, if we discretize it with 30*104m3, the values of each
discretization will be 0, 30, 60, 90,120, 150, 180, 210, 240, 270, 300 and
303 in the order of 104m3. And regarding to the discretization of the release
rt, rt>0 and rt< CCmax (200*104m3), should be satisfied. Accordingly the
discretization has been done as follows: 0, 20, 40, 60, 80, 100, 120, 140,
160, 180, and 200 m3 in the order of 104.

6.3.3 Recursive Procedure and its Algorithm

The objective function, that is, equation 6.8 should be solved recursively at
each stage for the reasons already explained at the beginning of this chapter.
The stages are the time periods, and the states are the storage volumes.
Substituting the values of all the constants in equation 6.8, it could be re-
written as:

12
 h 
∑ 8.86 *10
−3
Maximize * m * rt 2 o + 2 S t + it − r t − EV t − SPt 
t =1  m 

and Et in MWh as a function of St, St +1 and rt is expressed as:

41
 −3  h 
E t
= 8.86 *10 * m * rt 2 o + 2 S t + it − r t − EV t − SPt  ..... (6.23)
 m 

Equation 6.23 is also called a return function and should be solved at each
stage for each discretization levels of storage and release.

Either a backward or forward moving sequence of recursive equations can be

formulated, one for each stage of the process. Proceeding backward, a
particular period is selected after which it is assumed that the reservoir no
longer be operated. Let the arbitrary terminal period be period T, only one
period of operation remains, which is the period on the far right of the time
shown in the figure 6.3 below (Loucks et al., 1981).

Figure 6.3 Relationship between periods t and n at each stage of

reservoir operating problem

Let f
1

T
(S )
T
be the maximum electrical energy produced from operating the

reservoir in the last month of that last year, given an initial storage volume of
ST,

f
1

T
(S ) = Maximum
T E (s , s + i − EV
T T T T T
− SPT − r T , rT ) ..... (6.24)

This must be solved for all discrete values of ST and rT subjected to different

constraints. These values of f

1

T
(S )will be needed to solve the next recursive
T

equation.

42
Moving backward in time (from right to left in figure 6.3) the next stage is the
previous period T-1. There are now two periods /months remaining for
2
reservoir operation. In this case the function f (S ) represents the
T −1
T −1

maximum energy with two months to go, given an initial storage of s T −1

in

period T-1. Since S T

= S T −1 + iT −1 − EV T −1 − SPT −1 − r T −1 , f
1

T
(S )
T
can be

expressed in terms of the state variable S T −1 , the decision variable r T −1

, and

the known average evaporation EV T −1

and seepage SPT −1 , the second

recursive equation can be written as:

2  


(
E T −1 S T −1, S T −1 + iT −1 − rT −1 − EV T −1 − SPT −1 , r T −1  )
f 
T −1 S
 = Maximum
+ 1 
 T −1 
(
 f T S T −1 + iT −1 − EV T −1 − SPT −1 − r T −1  )
...... (6.25)

Again this must be solved for all discrete values of S T −1

between 0 and k-Kw subjected to all
constraints.

Continuing backward in time, the general recursive equation for each period
t with n (n>1) periods/ months remaining can be written as

 (
E t S t , S t + it − r t − EV t − SPt , r t ) ............ (6.26)
f (S ) =
n
Maximum 
t t
+ f t +n1−1
 (
S t + it − EV t − SPt − r t ) 


The computations shall be repeated until the stationary solution is obtained,

that is, at the stationary solution the optimal release rt associated with each
initial storage volume st will be the same as the corresponding rt and St in the
previous year (Loucks et al., 1981).

43
Once the stationary policy is obtained, a trace-back procedure is used to
identify the optimal storage trajectory over the entire period of analysis, from
which the optimal releases in each period can be found.

6.4 Preparation of Input Data for DP Model

The calculations of reservoir monthly inflow and monthly evaporation loss

have been dealt within this section. Also monthly irrigation requirement and
the seepage loss calculations made by the Tigray Regional state Water
Development commission for Haiba dam have been discussed briefly.

Reservoir Inflow

Reservoir inflow comprises of storm runoff/catchment yield, direct

precipitation on the reservoir and a very small quantity of base flow. The
runoff is calculated using equation 5.2 and a runoff coefficient of 0.26 and an
average monthly rainfall determined in the previous chapter. A very small
amount of base flow was measured by floating method during the driest
season by the study team when the project was studied. Hence, this flow has
been considered in the calculation of the reservoir inflow. The direct
precipitation on the reservoir has been added to the reservoir inflow after
multiplying the average monthly rainfall by the reservoir area. Finally, as
shown in Table 6.1, the base flow, the direct precipitation and the storm
runoff are added together to get the reservoir inflow. Note that catchment
yield during the driest months (October, November, December, January, and
February) have been set to zero.

Evaporation loss

The monthly evaporation loss from Haiba reservoir is determined using

equation (5.1). The average monthly evaporation obtained from evaporation
pan at Mekelle meteorological station and an average pan coefficient of 0.7
have been used in the calculation and the result is shown in table 6.2.

44
Table 6.1 Monthly Reservoir Inflows
Av.
monthly Base Total
Rainfall Run off Direct PPt Flow Inflow
4 3 4 3 4 3 4 3
Months (mm) ( '10 m ) ( '10 m ) ( '10 m ) ( '10 m )

Jan. 2.8 0.00 0.27 1.31 2

Feb. 5.1 0.00 0.48 1.31 2

Mar. 37.9 23.38 3.60 1.31 28

Apr. 39.4 24.33 3.74 1.31 29

May 24.8 15.31 2.36 1.31 19

Jun. 56.6 34.98 5.38 1.31 42

Jul. 219.4 135.47 20.84 1.31 158

Aug. 231.9 143.22 22.03 1.31 167

Sep. 41.2 25.47 3.92 1.31 31

Oct. 5.7 0.00 0.54 1.31 2

Nov. 7.0 0.00 0.67 1.31 2

Dec. 2.8 0.00 0.26 1.31 2

Annual 674.62 402.15 64.09

Table 6.2 Monthly Evaporation Losses from Haiba Reservoir

Months Sep. Oct Nov. Dec. Jan Feb Mar Apr May June July Augt Annual

Evap.
4 3
( '10 m ) 4 6 4 5 5 6 7 7 8 7 3 2 63

Seepage loss

Due to lack of data the seepage loss from Haiba reservoir is not computed.
However, the seepage loss through the embankment was calculated during

45
its design phase using the principle of Darcy’s law of ground water flow, that
relates the seepage discharge per unit width of the dam with the product of
coefficient of permeability of the dam material and the focal distance
obtained from the dam geometry. Accordingly the result obtained was 3034.6
m3 per year which is equivalent to 0.03*104m3 per month (Yigzaw et al.,
1996).

Since this loss only accounts loss through the dam embankment and doesn’t
include seepage loss under the dam foundation and loss into the ground
water, 50 percent of its value has been added to it for safety factor as an
input for the DP model.

Irrigation Requirement
The values of monthly total irrigation requirement which were computed
according to Sinkneh (1996) have been used as input for the DP model. The
computation of reference crop evapotranspiration was based on Hargrave’s
method. In general this computation had accounted the type of crops grown,
the command area, the cropping calendar, the effective rainfall, the crop
coefficient and other important parameters like project efficiency and hours
of operation of the reservoir. The summary of it is given in Table 6.3.

Table 6.3 Monthly Irrigation Requirements (MIR)

Month Jan Feb mar Apr May June July Aug Sep Oct Nov Dec Total

MIR
4 3
in 10 m 21 25 16 3 4 9 3 8 26 19 17 21 171

6.5 The Visual Basic User Interface for the DP Model

The user interface of the DP Model which is prepared using the Visual Basic
Code is given in Figure 6.4. The interface generally consists of two tabs:
namely Hydrology and Irrigation tab and Reservoir Characteristics tab. It has
a file menu which consists of Open, Save, Run DP and Exit sub-menus. The
Open Sub-menu loads the program with the input data for both tabs. The

46
Run DP sub-menu is used to run the program and the Exit sum-menu is
used to exit the program.

Figure 6.4(a) The Visual Basic User Interface for the DP Model:
Hydrology and Irrigation Tab

It has also a Clear button to clear the hydrology and irrigation data and
makes ready for new data entry. The reservoir characteristics tab consists of
three main frames which are storage capacity, elevation storage curve and
maximum conveyance capacity of the irrigation outlet.

47
The storage capacity frame consists of text boxes for entering the total
storage capacity of the reservoir and the minimum working capacity of the
reservoir free of vortices in units of 104 m3. The elevation-storage curve frame
consists of text boxes for entering the slope and y-intercept of the fitted graph
of the elevation-storage curve. CCmax frame consists of text boxes for entering
diameter of the pipe or penstock, length of the pipe, friction coefficient and
maximum head available above the turbine when the reservoir is full.

Figure 6.4(b) The Visual Basic User Interface for the DP Model: Reservoir
Characteristics Tab

48
 7. RESULT AND DISCUSSION

In chapter six the dynamic programming technique has been used to model
the Haiba reservoir and this has been solved by a visual Basic Program
written to solve this particular problem. The full result of this program is
given in Annex A. Therefore in this chapter only the summary of the output
of the dynamic programming model for stationary policy of Haiba reservoir
operation, monthly energy output, energy duration curve, and reservoir
operation guide curve are given and discussed.

7.1 Optimal Releases for Stationary Policy

After solving the recursive equations given in chapter six through only three
years, the stationary solution for optimal release rt for each month t
associated with each discrete value of the initial storage volume St has been
obtained. This has been particularly obtained in the second month of the
third year. As it has been already explained in chapter six, the output has an
optimal release rt associated with each initial storage volume St the same as
the corresponding rt and St in the previous year. Only summary of it is given
in Table 7.1. Where t=1 refers to the month of September and t=2 refers to
the month of August and a 24-hr mode of energy supply is assumed.

49
 Table 7.1 Summary of Stationary Policy Optimal Releases

Stationary Policy
Optimal Releases in Units of 104m3 per Month
St r1* r2* r3* r4* r5* r6* r7* r8* r9* r10* r11* r12*
0 - - - - - - 20 20 - 20 20 20
30 40 20 20 - - - 20 20 20 20 20 20
60 40 40 40 40 40 40 20 20 20 20 20 20
90 40 80 80 80 40 40 20 20 20 20 20 20
120 40 100 100 100 40 40 20 20 20 20 20 20
150 40 140 140 40 40 40 20 20 20 20 20 20
180 40 20 20 40 40 40 20 20 20 20 40 60
210 40 20 20 40 40 40 20 20 20 20 80 80
240 40 20 20 40 40 40 20 20 20 20 100 120
270 40 20 20 40 40 40 20 20 20 20 140 140
300 40 20 20 40 40 40 20 20 20 40 160 180
303 40 20 20 40 40 40 40 40 20 40 160 180

7.2 Monthly Energy Output

The monthly energy output of the dynamic programming model is given in

Table 7.2. The yearly average energy production for the mean monthly inflow
data is found to be 50.57 MWh.

The plot for monthly variability of the optimum energy output is shown in
Figure 7.1. The variability is very high. The maximum energy output found is
18.81 MWh during the month of August whereas the smallest energy output
is found to be 1.57MWh during the month of May. This is because of the wet
season effect, that is, there is high amount inflow during the months of July
and August as compared to very low amount of inflow during the other
months of the year.

50
 20.000
18.000

Monthly Energy in MWh

16.000
14.000
12.000
10.000
8.000
6.000
4.000
2.000
0.000
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
Time in Months

Figure 7.1 Plot of Mean Monthly Energy

The maximum and minimum monthly energy outputs can be explained in

terms of their capacity to electrify the rural community as follows. The
maximum and minimum monthly energy outputs are equal to 25kw and 2kw
respectively. 25kw electric power is capable enough to illuminate 625 light
bulbs of 40W each. This is enough electricity for about 312 households each
using two light bulbs of 40w each. Whereas the minimum power, that is, 2kw
is enough power for 50 light bulbs of 40w each which is again capable to
illuminate 25 households each using two light bulbs of 40w each. This
minimum energy is the firm energy which is available 100 percent of the time
as shown in the energy output duration curve of Figure 7.2.

20.000
18.000
Monthly Energy in MWh

16.000
14.000
12.000
10.000
8.000
6.000
4.000
2.000
0.000
0.0 20.0 40.0 60.0 80.0 100.0 120.0
Probablity of Excedence in %

Figure 7.2 Energy Output Duration Curve

51
7.3 Reservoir Operation Guide Curve

As already explained in chapter six, reservoir operation guide curve is a guide

line which proposes the status of the reservoir as a function of time for a
period of one year. The major output of the dynamic programming model is
the storage at a monthly time step.

Table 7.2 Monthly Optimal Energy Production and Other Parameters

from Output of DP (Visual Basic Program)

Release in Storage in Water level Energy

4 4
Month 10 m3 10 m3 in m amsl in MWh
Sep 40 303 1998 5.742
Oct 20 290 1997.9 2.740
Nov 20 266 1997.6 2.577
Dec 40 244 1997.3 4.694
Jan 40 201 1996.8 4.085
Feb 40 158 1996.2 3.469
Mar 20 114 1995.5 1.582
Apr 20 115 1995.5 1.593
May 20 117 1995.6 1.568
Jun 20 108 1995.4 1.589
Jul 20 123 1995.7 2.120
Aug 140 258 1997.5 18.807
Sum 440 50.57

As discussed in Section 3.6, a trace-back procedure is used to identify the

optimal storage trajectory over the entire period of analysis. For Haiba
reservoir, the optimum for the whole system for the stationary solution is
found to be 106.325 MWh for the month of September. The corresponding
optimal initial storage and optimal releases for September are 303*104 m3
and 40*104 m3 respectively. Now the initial storage for October is found using
the mass balance equation which is 290*104 m3. Hence, the optimal release
corresponding to this initial storage is read from the output of the DP Model.
This optimal release is 20*104 m3. Therefore the trace-back procedure

52
continues as such until August. Now corresponding to each initial storage
the elevation of water can be found from elevation-Storage curve. The final
result is shown in Table 7.2.

From the water levels for each month (Table 7.2) the guide curve has been
developed as shown in Figure 7.3. This curve can be interpreted as the most
effective operation guide curve for those years of hydro-meteorological
condition. This information will help to give an idea on how the reservoir
should have been operated in the past and will give some information on how
the reservoir operation should be in the future.

The normal pool level of Haiba reservoir is at 1998 m amsl and its dead
storage level is at 1990m amsl. Whereas the optimum guide curve developed
using the dynamic programming for its stationary policy falls in short of the
normal pool level except at the beginning of the month of September, but it
always falls above the dead storage level.

2000.0
Reservoir Water Elevation in m

1998.0

1996.0
amsl

1994.0

1992.0

1990.0
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Time in M onths

Figure 7.3 Guide Curve Developed Using Visual Basic Program

The optimal release obtained from the dynamic programming model for a
particular month is generally much higher than the release required for total
irrigation requirement of the system for the same month.

53
 8. CONCLUSIONS AND RECOMMENDATIONS

8.1 Conclusions

From the results obtained as an output of dynamic programming, literature

review and data processing and analysis made in the previous chapters, the
following conclusions have been made.

1. It is possible and is actually very wise to use micro irrigation dams

for micro- electric energy generation so as to electrify the rural
community without affecting the existing irrigation requirement by
applying systems engineering as a planning tool.
2. Much water has been lost from Haiba reservoir through the
irrigation outlet and over the spillway annually without producing a
valuable amount of electric energy that is very useful to the rural
community.
3. The optimal power output of Haiba reservoir is within the range of
micro-hydropower and has an electrification capacity of 50 to 644
rural households using one light bulb of 40W each.
4. The software developed using Visual Basic code to solve the
dynamic programming model can still be used for any change in its
hydrologic and irrigation as well as reservoir characteristics data.

54
8.2 Recommendations

Based on this study the following recommendations are drawn.

1. The actual inflow into Haiba reservoir is not explicitly known. This
is because of two main reasons. These are firstly, staff gauge
reading at the dam site is not taken and secondly, there are
inadequate meteorological stations in the catchment. And hence
other additional meteorological stations should be installed within
the catchment so that an appropriate and applicable rainfall –runoff
model for the catchment could be developed.
2. In this study the seepage loss from the Haiba reservoir is not as
such included due to absence of data and appropriate method to
estimate it. Only a constant value of seepage loss through its
embankment for each month which was calculated during its
design is considered. However, this value would have varied from
month to month as the reservoir level changes. Moreover, the
seepage loss into the groundwater should have been included.
Therefore, a method has to be found to estimate this loss and
include it in the analysis.
3. The irrigation releases for each month are directly taken from the
agronomy and soil report made during the scheme’s design.
Eventhough some of the parameters used in irrigation requirement
analysis have been verified during field visit, a thorough and careful
revision and determination of it is essential and should be done
during the design of the micro-hydropower plant.
4. Since there is a high variability of monthly energy output, it is
better to include another source of energy such as diesel generator
as a back up to increase the dependable power of the scheme so
that the supply could be better in terms of both amount and
consistency.

55
5. Other planning studies for different scenarios like 12- hour mode of
energy supply versus 24- hr mode of energy supply and inclusion of
night time storage for irrigation use should be conducted and
compared before the implementation of the scheme.
6. The guide curve is established mainly on the basis of the design
report especially data like irrigation requirement, elevation-storage
curve and seepage losses. Therefore, it is recommended to update
the data and run the program to obtain better results. Moreover,
results could further be improved if many years of meteorological
data are used. Since many years of continuous meteorological data
is not available at the present, generating and using synthetic data
series is an alternative in the future.
7. It is recommended to introduce day time loads such as storage
cookers, grain mills, workshops, and pumping water supply
schemes.

56
 REFERENCES

Arora, K.R. (2002). Irrigation, Water Power and Water Resources Engineering.
Standard Publishers Distributors, Delhi.

Buras, N. (1972). Scientific Allocation of Water Resources. American Elsevier

Publishing, inc., New York.

CESEN (1986). Energy Sector Main Report.

CSA (2004). Statistical Abstract, Addis Ababa.

Denardo, E.V. (1982). Dynamic programming: Models and Applications.

Prentice- Hall, inc., Englewood Cliffs, New Jersey 07632.

EEA (2003). Rural Electrification Fund: Final Project Operations Manual.

ESD Ltd., Addis Ababa.

EEPCo (2004). Facts in Brief. Ethiopian Electric Power Corporation Corporate

Relations, Addis Ababa.

EREDPC (2002). Integrated Rural Energy Development Program, Addis

Ababa.

Fritz, J.J. (1984). Small and Mini Hydropower Systems. McGraw-Hill, New
York.

Garg, S.K. (2003). Irrigation Engineering and Hydraulic Structures. Khanna

Publishers, Delhi.

Good Man, A.S. (1984). Principles of Waters Resources Planning. Prentice-

Hall, inc., Englewood Cliffs, New Jersey 07632.

Gulliver, J.S., and Arndt, R.E. (1991). Hydropower Engineering Handbook.

McGraw-Hill,inc., New York.

57
Haan. C.T., Barfield, B.J., and Hayes, J.C (1984). Design Hydrology and
Sedimentology for small catchments. Academic press, San Diego.

Haith, D.A. (1982). Environmental Systems Optimization. Johny Wiley and

Sons, New York.

Harvey, A., et al (2002). Micro-Hydro Design Manual: A Guide to Small –scale

Water power schemes. ITDG Publishing, London.

Linsely, R.K., and Frnazini, J.B. (1979). Water- Resources Engineering.

McGraw-Hill, London.

Loucks, D.P., Stediner, J.R., and Haith, D.A. (1981). Water Resource Systems
Planning and Analysis. Prentice-Hall, inc., Englewood Cliffs, New
Jersey 07632.

Mays, L.W., and Tung, Y.K. (1992). Hydrosystems Engineering and

Management. Mc-Graw-Hill inc., New York.

Michael, A. (2004). Irrigation Dams for Micro-Hydropower Development in the

context of Rural Electrification, Symposium Proceedings on
Renewable Energy in Ethiopia, GTZ and EREDPC, 26th_ 27th April
2004, Addis Ababa.

Michael, A.(2004). Micro-Dams in Ethiopia. Addis Ababa.

MWR (1998). Tekeze River Basin Integrated Development Master Plan Project.
Addis Ababa.

Paulos., S. (1998). Establishing Water Release Rules for Koka Reservoir for
Wet Seasons. Addis Ababa University School of Graduate, Addis
Ababa.

Penche, C. (1998). Layman’s GuideBook on how to develop a small hydro

site, 2d ed. DG XVIII-97/010, Brussels.

58
Reddi, P.J. (2002) A text Book of Hydrology. Laxmi Publications (Pvt.) Ltd.,
New Delhi.

Rivelle, C. (1999). Optimizing Reservoir Resources including A New Model for

Reservoir Reliability. John Willy and Sons, Inc., New York.

Sinkneh, E. (1996). Feasibility study Report of Haiba irrigation Scheme:

Agronomy and soils. CO-SAERT, Mekelle.

SHP in China (1985). A survey prepared by Hanzahan Regional Centre (Asia

Pacific) for small Hydro Power. Intermediate Technology
Publications, 9 king street, London.

STVC(2000). Products Catalogue. STVC, Addis Ababa.

TRSBWR (2003). Fully implemented, studied and Designed Micro Dam

Irrigation schemes, Mekelle.

USBR (1987). Design of Small Dams. U.S. Department of the interior,

Washington. D.C.

Yigzaw, H., Abayneh, M., and Zelalem, A. (1996). Haiba Dam Design Report.
Co-SAERT, Mekelle.

Zelalem, H. (1992). Micro-Hydropower under Ethiopian conditions. Addis

Ababa University School of Graduate, Addis Ababa.

59
Annex-A: Tables

60
Table A.1 Mean Monthly Rainfall at Dengolat Station in mm

Year Jan Feb mar Apr May June July Aug Sep Oct Nov Dec Annual
1992 4.3 1.1 19.2 19.8 27.2 9.8 176.8 214.8 51.3 12.2 35.0 9.6 581.1
1993 7.0 23.4 55.5 88.5 40.6 33.2 131.6 172.3 94.0 10.8 0.0 0.9 657.8
1994 0.0 4.2 5.4 23.7 4.1 103.1 294.8 295.7 49.3 0.0 0.0 0.0 780.3
1995 0.0 2.5 59.2 71.5 30.3 8.9 256.0 193.4 67.8 2.1 0.0 7.8 699.5
1996 0.0 19.0 139.0 78.5 59.7 88.5 115.7 173.1 22.2 0.0 28.7 0.8 725.2
1997 0.0 0.0 27.6 61.1 27.3 45.6 269.5 160.1 12.7 42.5 24.9 0.0 671.3
1998 0.0 0.0 13.0 45.9 30.0 22.5 294.4 348.1 65.9 0.0 0.0 0.0 819.8
1999 18.9 0.0 3.4 14.4 0.0 18.7 311.8 344.9 15.5 0.0 0.0 0.0 727.6
2000 0.0 0.0 6.2 16.0 58.6 33.9 242.5 235.7 49.2 0.0 2.7 11.8 656.6
2001 0.0 0.0 31.0 14.5 34.8 120.5 338.5 273.5 11.6 0.0 0.0 0.0 824.4
2002 0.0 1.3 114.0 5.3 0.0 39.0 153.1 195.3 29.3 0.0 0.0 4.1 541.4
2003 0.0 14.2 6.6 51.1 2.9 123.5 103.8 199.6 65.8 0.0 0.0 0.8 568.3
2004 6.6 0.0 12.1 21.9 6.9 89.2 163.4 208.6 1.6 6.5 0.0 0.0 516.8

Total 36.8 65.7 492.2 512.2 322.4 736.4 2851.9 3015.1 536.2 74.1 91.3 35.8

Average 2.8 5.1 37.9 39.4 24.8 56.6 219.4 231.9 41.2 5.7 7.0 2.8 674.6

Table A.2 Mean Monthly Rainfall at Mekelle Station in mm

Year Jan Feb mar Apr May June July Aug Sep Oct Nov Dec Annual
1992 8.7 2.1 38.5 1.0 30.7 6.2 140.7 233.1 1.3 2.1 54.2 8.3 526.9
1993 11.7 7.7 63.9 125.0 74.4 69.0 217.2 106.5 15.2 20.0 0.0 0.0 710.6
1994 0.0 5.3 0.4 43.8 0.1 67.6 147.9 317.8 70.1 0.0 1.8 2.0 656.8
1995 0.0 5.9 31.2 29.2 27.1 6.8 268.2 237.7 51.4 3.0 0.0 21.7 682.2
1996 1.4 0.0 59.5 12.5 92.2 47.9 109.2 224.0 7.1 0.0 31.4 1.1 586.3
1997 0.0 0.0 19.8 32.6 29.8 32.4 236.1 100.5 16.3 85.9 15.7 0.0 569.1
1998 10.0 1.2 0.0 10.6 22.0 48.0 289.0 318.8 31.7 22.0 0.0 0.0 753.3
1999 22.0 0.3 10.9 0.0 0.0 7.4 293.6 359.2 22.8 0.9 0.0 0.0 717.1
2000 0.0 0.0 0.0 10.4 24.6 5.4 201.4 282.0 15.8 2.2 10.3 3.5 555.6
2001 0.0 0.0 38.1 18.7 4.7 65.5 267.9 226.3 9.2 2.9 0.0 0.0 633.3
2002 12.5 0.0 35.5 4.2 23.0 60.8 95.5 206.6 28.0 0.0 0.0 0.3 466.4
2003 0.0 25.9 18.2 8.4 35.2 87.5 125.6 201.8 22.4 0.7 0.0 0.1 525.8
2004 7.4 3.7 35.2 20.5 7.1 25.4 64.3 221.1 1.4 3.1 0.8 0.0 390.0

Total 73.7 52.1 351.2 316.9 370.9 529.9 2456.6 3035.4 292.7 142.8 114.2 37.0

Average 5.7 4.0 27.0 24.4 28.5 40.8 189.0 233.5 22.5 11.0 8.8 2.8 598.0

61
Table A.3 Mean Monthly Rainfall at Samre Station in mm

Ja
Year n Feb mar Apr May June July Aug Sep Oct Nov Dec Annual
1992 4.8 1.2 21.4 11.6 31.2 4.0 118.5 187.0 17.1 6.9 29.2 5.4 438.2
1993 5.7 10.2 30.3 88.1 39.5 32.1 120.1 93.9 43.6 10.7 0.0 0.2 474.4
1994 0.0 2.4 1.4 21.7 1.0 55.1 158.0 269.3 40.0 0.0 0.5 0.5 549.9
1995 0.0 4.2 32.7 35.6 14.4 3.9 178.5 203.7 33.6 6.3 0.0 7.6 520.6
1996 0.4 4.5 51.4 31.8 57.6 41.8 87.5 140.6 33.6 0.0 17.1 0.5 466.9
1997 0.0 0.0 17.4 35.9 20.9 22.7 181.5 96.7 7.4 47.4 11.1 0.0 441.0
1998 2.7 0.3 3.1 16.2 23.2 21.2 200.4 278.6 52.4 5.9 0.0 0.0 604.0
1999 10.4 0.1 3.7 3.4 0.0 7.4 224.6 262.6 15.8 0.2 0.0 0.0 528.2
2000 0.0 0.0 0.4 12.3 12.8 53.8 204.7 238.0 39.9 20.5 5.4 0.0 587.8
2001 0.0 0.0 0.0 0.0 0.0 115.1 271.3 200.7 30.5 0.0 0.0 0.0 617.6
2002 0.0 0.0 21.0 1.8 0.0 61.4 168.6 133.5 30.0 0.0 0.0 26.1 442.4
2003 0.0 8.7 9.9 6.4 18.2 98.9 142.8 145.0 28.0 0.0 0.0 0.0 457.9
2004 0.0 0.0 7.6 13.8 40.8 40.8 10.8 174.2 2.0 0.0 0.0 0.0 290.0

Total 24.0 31.5 200.3 278.6 259.6 558.4 2067.3 2423.9 373.9 97.9 63.2 40.4
Aver
age 1.8 2.4 15.4 21.4 20.0 43.0 159.0 186.5 28.8 7.5 4.9 3.1 493.8

Table A.4 Mean Monthly Rainfall at Adigudom Station in mm

Ja Fe
Year n b mar Apr May June July Aug Sep Oct Nov Dec Annual
1992 5.0 1.2 22.3 22.3 55.8 0.0 131.6 249.2 15.3 11.6 21.5 3.2 539.0
1993 3.2 8.6 0.0 113.9 33.8 19.6 104.1 82.9 58.3 9.5 0.0 0.0 433.9
1994 0.0 0.0 0.0 14.8 0.0 42.3 163.7 386.1 32.3 0.0 0.0 0.0 639.2
1995 0.0 6.7 34.9 36.4 0.0 0.0 155.8 318.8 12.8 17.0 0.0 0.0 582.4
1996 0.0 0.0 8.3 33.1 63.7 27.1 104.4 134.0 89.4 0.0 6.5 0.0 466.5
1997 0.0 0.0 18.9 43.0 21.7 11.0 184.0 107.7 0.0 48.5 3.2 0.0 438.0
1998 0.0 0.0 0.0 8.4 34.4 10.4 180.0 374.6 95.7 0.0 0.0 0.0 703.5
1999 0.0 0.0 0.0 0.0 0.0 3.4 243.7 286.5 20.3 0.0 0.0 0.0 553.9
2000 0.0 0.0 0.0 19.6 41.5 40.1 166.9 187.3 32.9 0.0 3.2 0.0 491.5
2001 0.0 0.0 40.5 31.6 10.7 65.5 346.6 169.0 0.0 0.0 0.0 0.0 663.9
2002 0.0 0.0 0.0 0.0 0.0 21.8 92.4 113.8 49.7 0.0 0.0 10.2 287.9
2003 0.0 15.9 4.4 17.6 0.0 13.6 128.4 230.0 0.0 0.0 0.0 7.5 417.4
2004 0.0 0.0 7.5 12.5 0.0 27.6 38.3 145.9 0.0 0.0 0.0 0.0 231.8

Total 8.2 32.4 136.8 353.2 261.6 282.4 2039.9 2785.8 406.7 86.6 34.4 20.9
Aver
age 0.6 2.5 10.5 27.2 20.1 21.7 156.9 214.3 31.3 6.7 2.6 1.6 496.1

62
Table A.5 Output of the Dynamic Program

63
 MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=1
St 0 1.286 2.431 3.434 4.295 5.014 5.591 6.027 6.321 - - 6.321 160
St 30 1.499 2.856 4.072 5.145 6.077 6.867 7.516 8.022 8.387 - 8.387 180
St 60 1.712 3.281 4.709 5.996 7.14 8.143 9.004 9.723 10.301 10.737 10.737 200
St 90 1.924 3.707 5.347 6.846 8.203 9.419 10.493 11.424 12.215 12.863 12.863 200
St 120 2.137 4.132 5.985 7.697 9.267 10.695 11.981 13.126 14.128 14.989 14.989 200
St 150 2.349 4.557 6.623 8.547 10.33 11.971 13.47 14.827 16.042 17.116 17.116 200
St 180 - - 7.261 9.398 11.393 13.246 14.958 16.528 17.956 19.242 19.242 200
St 210 - - - 10.249 12.456 14.522 16.446 18.229 19.87 21.369 21.369 200
St 240 - - - - - 15.798 17.935 19.93 21.783 23.495 23.495 200
St 270 - - - - - - 19.423 21.631 23.697 25.621 25.621 200
St 300 - - - - - - - - 25.611 27.748 27.748 200
St 303 - - - - - - - - 25.802 27.96 27.96 200

n=2
St 0 17.303 16.995 16.545 15.953 15.219 14.085 12.951 - - - 17.303 20
St 30 19.642 19.547 19.309 18.93 18.408 17.746 16.941 15.807 14.673 - 19.642 20
St 60 21.981 22.098 22.073 21.907 21.598 21.148 20.556 19.822 18.688 17.554 22.098 40
St 90 24.321 24.65 24.838 24.883 24.788 24.55 24.171 23.649 22.987 22.182 24.883 80
St 120 26.66 27.202 27.602 27.86 27.977 27.952 27.785 27.477 27.027 26.435 27.977 100
St 150 28.999 29.753 30.366 30.837 31.167 31.354 31.4 31.305 31.067 30.687 31.4 140
St 180 - 32.305 33.131 33.814 34.356 34.757 35.015 35.132 35.107 34.94 35.132 160
St 210 - - - 36.791 37.546 38.159 38.63 38.96 39.147 39.193 39.193 200
St 240 - - - - 40.736 41.561 42.245 42.787 43.187 43.446 43.446 200
St 270 - - - - - - 45.86 46.615 47.228 47.699 47.699 200
St 300 - - - - - - - 50.442 51.268 51.951 51.951 200
St 303 - - - - - - - 50.825 51.672 52.377 52.377 200

64
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=3
St 0 19.298 - - - - - - - - - 19.298 20
St 30 21.861 21.187 20.383 - - - - - - - 21.861 20
St 60 24.706 24.033 23.36 22.626 - - - - - - 24.706 20
St 90 27.846 27.173 26.499 25.826 25.152 24.348 - - - - 27.846 20
St 120 31.329 30.656 29.983 29.309 28.636 27.963 27.229 - - - 31.329 20
St 150 35.107 34.434 33.76 33.087 32.414 31.74 31.067 30.394 29.589 - 35.107 20
St 180 39.229 38.555 37.882 37.208 36.535 35.862 35.188 34.515 33.842 33.108 39.229 20
St 210 43.634 42.971 42.297 41.624 40.951 40.277 39.604 38.93 38.257 37.584 43.634 20
St 240 48.099 47.649 47.057 46.383 45.71 45.037 44.363 43.69 43.017 42.343 48.099 20
St 270 52.564 52.327 51.947 51.426 50.763 50.09 49.417 48.743 48.07 47.397 52.564 20
St 300 - 57.005 56.838 56.53 56.079 55.487 54.814 54.141 53.467 52.794 57.005 40
St 303 - 57.473 57.327 57.04 56.611 56.04 55.367 54.693 54.02 53.347 57.473 40
n=4
St 0 - - - - - - - - - - 0 -
St 30 22.038 21.148 - - - - - - - - 22.038 20
St 60 24.979 24.143 23.306 - - - - - - - 24.979 20
St 90 28.257 27.421 26.584 25.748 24.858 - - - - - 28.257 20
St 120 31.836 31 30.163 29.327 28.491 27.654 - - - - 31.836 20
St 150 35.752 34.916 34.079 33.243 32.407 31.57 30.734 29.844 - - 35.752 20
St 180 39.969 39.133 38.296 37.46 36.624 35.787 34.951 34.114 33.278 - 39.969 20
St 210 44.523 43.687 42.85 42.014 41.177 40.341 39.505 38.668 37.832 36.995 44.523 20
St 240 49.201 48.524 47.705 46.869 46.032 45.196 44.36 43.523 42.687 41.85 49.201 20
St 270 53.879 53.415 52.808 52.06 51.224 50.388 49.551 48.715 47.879 47.042 53.879 20
St 300 58.468 58.305 57.912 57.376 56.699 55.881 55.044 54.208 53.371 52.535 58.468 20
St 303 58.957 58.794 58.422 57.908 57.252 56.455 55.618 54.782 53.946 53.109 58.957 20

65
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=5
St 0 0.78 - - - - - - - - - 0.78 20
St 30 23.214 22.222 - - - - - - - - 23.214 20
St 60 26.396 25.443 24.49 3.969 - - - - - - 26.396 20
St 90 29.901 28.948 27.995 27.041 26.049 - - - - - 29.901 20
St 120 33.721 32.768 31.815 30.861 29.908 28.955 8.433 - - - 33.721 20
St 150 37.864 36.911 35.957 35.004 34.051 33.097 32.144 31.152 - - 37.864 20
St 180 42.322 41.369 40.415 39.462 38.509 37.555 36.602 35.649 34.695 14.174 42.322 20
St 210 47.103 46.149 45.196 44.243 43.289 42.336 41.383 40.429 39.476 38.523 47.103 20
St 240 51.993 51.214 50.292 49.339 48.385 47.432 46.479 45.525 44.572 43.619 51.993 20
St 270 56.884 56.317 55.608 54.757 53.804 52.851 51.897 50.944 49.991 49.037 56.884 20
St 300 61.7 61.42 60.924 60.286 59.506 58.584 57.631 56.678 55.724 54.771 61.7 20
St 303 - 61.931 61.456 60.839 60.08 59.18 58.227 57.273 56.32 55.366 61.931 40
n=6
St 0 1.563 - - - - - - - - - 1.563 20
St 30 24.302 23.164 - - - - - - - - 24.302 20
St 60 27.711 26.605 25.5 4.741 - - - - - - 27.711 20
St 90 31.436 30.33 29.224 28.118 26.981 - - - - - 31.436 20
St 120 35.483 34.377 33.271 32.165 31.06 29.954 9.195 - - - 35.483 20
St 150 39.845 38.739 37.634 36.528 35.422 34.316 33.211 32.073 - - 39.845 20
St 180 44.53 43.424 42.318 41.213 40.107 39.001 37.895 36.79 35.684 14.925 44.53 20
St 210 49.53 48.425 47.319 46.213 45.107 44.002 42.896 41.79 40.684 39.579 49.53 20
St 240 54.634 53.708 52.642 51.536 50.43 49.324 48.219 47.113 46.007 44.901 54.634 20
St 270 59.737 59.024 58.17 57.174 56.068 54.963 53.857 52.751 51.645 50.54 59.737 20
St 300 64.773 64.34 63.699 62.915 61.99 60.923 59.818 58.712 57.606 56.5 64.773 20
St 303 - 64.872 64.252 63.49 62.586 61.54 60.434 59.329 58.223 57.117 64.872 40

66
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=7
St 0 - - - - - - - - - - 0 -
St 30 - - - - - - - - - - 0 -
St 60 - 24.897 - - - - - - - - 24.897 40
St 90 - 28.593 27.427 6.098 - - - - - - 28.593 40
St 120 - 32.584 31.418 30.252 29.086 - - - - - 32.584 40
St 150 - 36.918 35.752 34.586 33.42 32.254 10.925 - - - 36.918 40
St 180 - 41.546 40.38 39.214 38.048 36.882 35.716 34.55 - - 41.546 40
St 210 - 46.518 45.352 44.186 43.02 41.854 40.688 39.522 38.356 17.027 46.518 40
St 240 - 51.784 50.618 49.452 48.286 47.12 45.954 44.788 43.622 42.456 51.784 40
St 270 - 57.313 56.228 55.062 53.896 52.73 51.564 50.398 49.232 48.066 57.313 40
St 300 - 62.841 61.969 60.955 59.8 58.634 57.468 56.302 55.136 53.97 62.841 40
St 303 - 63.394 62.543 61.551 60.417 59.251 58.085 56.919 55.753 54.587 63.394 40
n=8
St 0 - - - - - - - - - - 0 -
St 30 - - - - - - - - - - 0 -
St 60 - 2.091 - - - - - - - - 2.091 40
St 90 - 6.02 3.561 4.465 - - - - - - 6.02 40
St 120 - 29.887 28.735 5.315 6.29 - - - - - 29.887 40
St 150 - 34.189 33.048 31.906 10.857 8.398 9.302 - - - 34.189 40
St 180 - 38.785 37.644 36.503 35.362 34.21 10.79 11.765 - - 38.785 40
St 210 - 43.725 42.584 41.443 40.302 39.16 38.019 16.97 14.511 15.415 43.725 40
St 240 - 48.959 47.818 46.677 45.536 44.395 43.254 42.112 40.96 17.541 48.959 40
St 270 - 54.537 53.396 52.255 51.114 49.973 48.832 47.69 46.549 45.408 54.537 40
St 300 - 60.41 59.268 58.127 56.986 55.845 54.704 53.563 52.421 51.28 60.41 40
St 303 - 61.005 59.874 58.733 57.592 56.451 55.31 54.168 53.027 51.886 61.005 40

67
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=9
St 0 - - - - - - - - - - 0 -
St 30 - - - - - - - - - - 0 -
St 60 - 2.091 - - - - - - - - 2.091 40
St 90 - 4.422 3.561 4.465 - - - - - - 4.465 80
St 120 - 6.226 6.247 5.315 6.29 - - - - - 6.29 100
St 150 - 31.502 9.217 9.238 9.259 8.398 9.302 - - - 31.502 40
St 180 - 36.078 34.958 33.806 11.701 11.722 10.79 11.765 - - 36.078 40
St 210 - 40.975 39.855 38.735 37.615 15.33 15.351 15.372 14.511 15.415 40.975 40
St 240 - 46.188 45.068 43.948 42.828 41.708 40.556 18.452 18.473 17.541 46.188 40
St 270 - 51.723 50.604 49.484 48.364 47.244 46.124 45.004 22.718 22.74 51.723 40
St 300 - 57.574 56.455 55.335 54.215 53.095 51.975 50.855 49.735 48.583 57.574 40
St 303 - 58.17 57.05 55.93 54.81 53.69 52.57 51.45 50.33 49.2 58.17 40
n=10
St 0 - - - - - - - - - - 0 -
St 30 0.907 - - - - - - - - - 0.907 20
St 60 1.12 2.098 - - - - - - - - 2.098 40
St 90 4.426 4.443 3.572 4.479 - - - - - - 4.479 80
St 120 6.237 6.254 6.272 5.329 6.307 - - - - - 6.307 100
St 150 9.22 9.238 9.256 9.273 9.291 8.419 9.327 - - - 9.327 140
St 180 34.664 33.515 11.704 11.722 11.74 11.758 10.815 11.793 - - 34.664 20
St 210 39.533 38.43 37.328 15.344 15.361 15.379 15.397 15.415 14.543 15.45 39.533 20
St 240 44.731 43.629 42.527 41.425 40.276 18.466 18.484 18.501 18.519 17.576 44.731 20
St 270 50.239 49.136 48.034 46.932 45.83 44.728 22.743 22.761 22.779 22.796 50.239 20
St 300 56.075 54.973 53.871 52.769 51.667 50.564 49.462 48.314 26.503 26.521 56.075 20
St 303 56.671 55.569 54.466 53.364 52.262 51.16 50.058 48.93 26.801 26.819 56.671 20

68
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=11
St 0 - - - - - - - - - - 0 -
St 30 0.9 - - - - - - - - - 0.9 20
St 60 2.062 2.084 - - - - - - - - 2.084 40
St 90 4.387 4.408 4.429 4.451 - - - - - - 4.451 80
St 120 6.187 6.208 6.229 6.251 6.272 - - - - - 6.272 100
St 150 9.149 9.171 9.192 9.213 9.234 9.256 9.277 - - - 9.277 140
St 180 12.966 11.609 11.63 11.651 11.673 11.694 11.715 11.736 - - 12.966 20
St 210 37.775 36.69 15.23 15.252 15.273 15.294 15.315 15.337 15.358 15.379 37.775 20
St 240 42.942 41.861 40.78 19.706 18.349 18.37 18.391 18.413 18.434 18.455 42.942 20
St 270 48.396 47.315 46.234 45.153 44.068 22.608 22.63 22.651 22.672 22.693 48.396 20
St 300 54.201 53.12 52.039 50.958 49.877 48.796 27.722 26.365 26.386 26.407 54.201 20
St 303 54.796 53.715 52.634 51.553 50.472 49.391 48.257 26.663 26.684 26.705 54.796 20
n=12
St 0 - - - - - - - - - - 0 -
St 30 - 1.878 - - - - - - - - 1.878 40
St 60 - 4.202 4.121 4.039 - - - - - - 4.202 40
St 90 - 6.003 5.921 5.84 5.758 - - - - - 6.003 40
St 120 - 8.965 8.884 8.802 8.721 8.639 8.558 - - - 8.965 40
St 150 - 11.403 11.322 11.24 11.159 11.077 10.996 10.914 - - 11.403 40
St 180 - 15.004 14.922 14.841 14.759 14.677 14.596 14.514 14.433 14.351 15.004 40
St 210 - 40.085 19.185 17.917 17.835 17.753 17.672 17.59 17.509 17.427 40.085 40
St 240 - 45.518 44.356 43.168 22.073 21.992 21.91 21.829 21.747 21.666 45.518 40
St 270 - 51.28 50.118 48.955 47.793 26.893 25.624 25.543 25.461 25.38 51.28 40
St 300 - 57.351 56.189 55.026 53.864 52.701 51.514 30.419 30.337 30.256 57.351 40
St 303 - 57.968 56.805 55.643 54.48 53.318 52.152 30.759 30.678 30.596 57.968 40

69
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=13
St 0 12.406 11.644 10.882 10.12 9.358 8.596 7.834 6.321 - - 12.406 20
St 30 15.496 14.816 14.135 13.455 12.774 12.094 11.413 10.733 10.052 - 15.496 20
St 60 40.911 18.639 17.877 17.115 16.353 15.591 14.83 14.068 13.306 12.544 40.911 20
St 90 46.521 44.678 22.672 20.843 20.163 19.482 18.802 18.122 17.441 16.761 46.521 20
St 120 52.425 50.582 48.739 46.896 24.625 23.863 23.101 22.339 21.577 20.815 52.425 20
St 150 58.673 56.83 54.987 53.144 51.301 29.295 27.467 26.786 26.106 25.425 58.673 20
St 180 - - 61.529 59.686 57.843 56 54.157 31.886 31.124 30.362 61.529 60
St 210 - - - 66.572 64.729 62.886 61.043 59.2 37.194 35.366 66.572 80
St 240 - - - - - 70.066 68.223 66.38 64.537 62.694 70.066 120
St 270 - - - - - - 75.747 73.904 72.061 70.218 75.747 140
St 300 - - - - - - - - 79.879 78.036 79.879 180
St 303 - - - - - - - - 80.687 78.844 80.687 180
n=14
St 0 56.759 53.782 50.805 47.828 44.826 21.917 20.021 - - - 56.759 20
St 30 61.529 60.395 57.418 54.441 51.464 48.487 25.772 22.689 20.793 - 61.529 20
St 60 66.607 65.473 64.339 61.362 58.385 55.408 52.431 49.429 26.52 24.624 66.607 20
St 90 70.172 69.038 67.904 66.77 65.636 62.659 59.682 56.705 53.728 31.013 70.172 20
St 120 75.888 74.754 73.62 72.486 71.352 70.218 67.241 64.264 61.287 58.31 75.888 20
St 150 80.091 78.957 77.823 76.689 75.555 74.421 73.287 72.153 69.176 66.199 80.091 20
St 180 - 85.311 84.177 83.043 81.909 80.775 79.641 78.507 77.373 74.396 85.311 40
St 210 - - - 87.884 86.75 85.616 84.482 83.348 82.214 81.079 87.884 80
St 240 - - - - 93.742 92.608 91.474 90.34 89.206 88.072 93.742 100
St 270 - - - - - - 96.952 95.818 94.684 93.55 96.952 140
St 300 - - - - - - - 103.448 102.314 101.18 103.448 160
St 303 - - - - - - - 104.405 103.271 102.137 104.405 160

70
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=15
St 0 60.855 - - - - - - - - - 60.855 20
St 30 64.137 62.329 60.522 - - - - - - - 64.137 20
St 60 69.605 67.798 65.99 64.183 - - - - - - 69.605 20
St 90 73.524 71.717 69.909 68.102 66.295 64.487 - - - - 73.524 20
St 120 79.63 77.823 76.016 74.208 72.401 70.593 68.786 - - - 79.63 20
St 150 84.188 82.38 80.573 78.765 76.958 75.151 73.343 71.536 69.728 - 84.188 20
St 180 88.664 87.99 87.317 85.509 83.702 81.895 80.087 78.28 76.472 74.665 88.664 20
St 210 92.725 92.051 91.378 90.705 88.897 87.09 85.282 83.475 81.667 79.86 92.725 20
St 240 97.838 97.165 96.492 95.818 95.145 94.472 92.664 90.857 89.049 87.242 97.838 20
St 270 102.537 101.864 101.191 100.517 99.844 99.171 98.497 96.69 94.882 93.075 102.537 20
St 300 - 107.616 106.943 106.269 105.596 104.922 104.249 103.576 102.902 101.095 107.616 40
St 303 - 108.615 107.942 107.269 106.595 105.922 105.248 104.575 103.902 102.094 108.615 40
n=16
St 0 - - - - - - - - - - 0 -
St 30 64.817 62.847 - - - - - - - - 64.817 20
St 60 68.538 66.568 64.597 - - - - - - - 68.538 20
St 90 74.439 72.468 70.498 68.527 66.557 - - - - - 74.439 20
St 120 78.797 76.827 74.857 72.886 70.916 68.945 - - - - 78.797 20
St 150 85.336 83.366 81.395 79.425 77.454 75.484 73.513 71.543 - - 85.336 20
St 180 89.199 88.362 86.392 84.421 82.451 80.48 78.51 76.539 74.569 - 89.199 20
St 210 94.107 93.27 92.434 91.598 89.627 87.657 85.686 83.716 81.745 79.775 94.107 20
St 240 98.607 97.771 96.935 96.098 95.262 93.291 91.321 89.35 87.38 85.41 98.607 20
St 270 104.154 103.317 102.481 101.644 100.808 99.972 99.135 97.165 95.194 93.224 104.154 20
St 300 108.619 108.456 107.619 106.783 105.946 105.11 104.274 103.437 101.467 99.496 108.619 20
St 303 108.505 108.342 107.506 106.669 105.833 104.997 104.16 103.324 102.488 100.517 108.505 20

71
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=17
St 0 0.78 - - - - - - - - - 0.78 20
St 30 66.291 64.204 - - - - - - - - 66.291 20
St 60 70.253 68.166 66.078 3.969 - - - - - - 70.253 20
St 90 76.381 74.293 72.206 70.118 68.031 - - - - - 76.381 20
St 120 80.98 78.893 76.806 74.718 72.631 70.543 8.433 - - - 80.98 20
St 150 87.746 85.658 83.571 81.483 79.396 77.309 75.221 73.134 - - 87.746 20
St 180 91.849 90.896 88.809 86.721 84.634 82.546 80.459 78.371 76.284 14.174 91.849 20
St 210 96.984 96.031 95.078 94.124 92.037 89.95 87.862 85.775 83.687 81.6 96.984 20
St 240 101.726 100.773 99.819 98.866 97.913 95.825 93.738 91.65 89.563 87.475 101.726 20
St 270 107.499 106.546 105.592 104.639 103.686 102.732 101.779 99.691 97.604 95.517 107.499 20
St 300 112.205 111.925 110.972 110.018 109.065 108.112 107.158 106.205 104.118 102.03 112.205 20
St 303 - 111.833 110.88 109.926 108.973 108.02 107.066 106.113 105.16 103.072 111.833 40
n=18
St 0 1.563 - - - - - - - - - 1.563 20
St 30 67.528 65.288 - - - - - - - - 67.528 20
St 60 71.717 69.477 67.237 4.741 - - - - - - 71.717 20
St 90 78.064 75.824 73.584 71.344 69.105 - - - - - 78.064 20
St 120 82.89 80.651 78.411 76.171 73.931 71.691 9.195 - - - 82.89 20
St 150 89.875 87.636 85.396 83.156 80.916 78.676 76.437 74.197 - - 89.875 20
St 180 93.072 91.966 90.86 88.621 86.381 84.141 81.901 79.661 77.422 14.925 93.072 20
St 210 99.561 98.455 97.349 96.244 94.004 91.764 89.524 87.284 85.044 82.805 99.561 20
St 240 103.395 102.289 101.184 100.078 98.972 97.866 95.627 93.387 91.147 88.907 103.395 20
St 270 110.522 109.416 108.31 107.205 106.099 104.993 103.887 101.648 99.408 97.168 110.522 20
St 300 114.321 113.888 112.782 111.677 110.571 109.465 108.36 107.254 106.148 103.908 114.321 20
St 303 - 114.951 113.846 112.74 111.634 110.529 109.423 108.317 107.211 104.972 114.951 40

72
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=19
St 0 - - - - - - - - - - 0 -
St 30 - - - - - - - - - - 0 -
St 60 - 66.039 - - - - - - - - 66.039 40
St 90 - 72.358 70.058 6.098 - - - - - - 72.358 40
St 120 - 77.128 74.828 72.528 70.228 - - - - - 77.128 40
St 150 - 84.085 81.785 79.484 77.184 74.884 10.925 - - - 84.085 40
St 180 - 89.492 87.192 84.892 82.592 80.292 77.992 75.692 - - 89.492 40
St 210 - 95.953 94.787 92.487 90.187 87.887 85.587 83.287 80.987 17.027 95.953 40
St 240 - 100.865 99.699 98.533 96.233 93.932 91.632 89.332 87.032 84.732 100.865 40
St 270 - 106.829 105.663 104.497 103.331 102.165 99.865 97.565 95.265 92.965 106.829 40
St 300 - 112.378 111.212 110.047 108.881 107.715 106.549 104.249 101.948 99.648 112.378 40
St 303 - 113.442 112.276 111.11 109.944 108.778 107.612 105.312 103.012 100.712 113.442 40
n=20
St 0 - - - - - - - - - - 0 -
St 30 - - - - - - - - - - 0 -
St 60 - 2.091 - - - - - - - - 2.091 40
St 90 - 6.02 3.561 4.465 - - - - - - 6.02 40
St 120 - 73.559 71.284 5.315 6.29 - - - - - 73.559 40
St 150 - 78.641 76.366 74.091 10.857 8.398 9.302 - - - 78.641 40
St 180 - 85.86 83.585 81.309 79.034 76.759 10.79 11.765 - - 85.86 40
St 210 - 91.58 89.304 87.029 84.754 82.479 80.203 16.97 14.511 15.415 91.58 40
St 240 - 99.436 97.161 94.886 92.611 90.335 88.06 85.785 83.51 17.541 99.436 40
St 270 - 103.526 102.385 101.244 98.968 96.693 94.418 92.143 89.867 87.592 103.526 40
St 300 - 110.887 109.745 108.604 107.463 105.188 102.913 100.637 98.362 96.087 110.887 40
St 303 - 110.096 108.955 107.814 106.673 104.397 102.122 99.847 97.572 95.296 110.096 40

73
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=21
St 0 - - - - - - - - - - 0 -
St 30 - - - - - - - - - - 0 -
St 60 - 2.091 - - - - - - - - 2.091 40
St 90 - 4.422 3.561 4.465 - - - - - - 4.465 80
St 120 - 6.226 6.247 5.315 6.29 - - - - - 6.29 100
St 150 - 73.24 9.217 9.238 9.259 8.398 9.302 - - - 73.24 40
St 180 - 80.438 78.184 75.93 11.701 11.722 10.79 11.765 - - 80.438 40
St 210 - 86.115 83.861 81.607 79.353 15.33 15.351 15.372 14.511 15.415 86.115 40
St 240 - 93.95 91.696 89.442 87.188 84.934 82.68 18.452 18.473 17.541 93.95 40
St 270 - 100.265 98.011 95.757 93.503 91.249 88.996 86.742 22.718 22.74 100.265 40
St 300 - 107.605 106.485 104.231 101.977 99.723 97.469 95.215 92.961 90.707 107.605 40
St 303 - 108.647 107.527 105.273 103.019 100.765 98.511 96.257 94.003 91.749 108.647 40
n=22
St 0 - - - - - - - - - - 0 -
St 30 0.907 - - - - - - - - - 0.907 20
St 60 1.12 2.098 - - - - - - - - 2.098 40
St 90 4.426 4.443 3.572 4.479 - - - - - - 4.479 80
St 120 6.237 6.254 6.272 5.329 6.307 - - - - - 6.307 100
St 150 9.22 9.238 9.256 9.273 9.291 8.419 9.327 - - - 9.327 140
St 180 77.592 75.356 11.704 11.722 11.74 11.758 10.815 11.793 - - 77.592 20
St 210 83.241 81.004 78.768 15.344 15.361 15.379 15.397 15.415 14.543 15.45 83.241 20
St 240 91.062 88.826 86.589 84.353 82.117 18.466 18.484 18.501 18.519 17.576 91.062 20
St 270 97.349 95.112 92.876 90.64 88.404 86.167 22.743 22.761 22.779 22.796 97.349 20
St 300 105.808 103.572 101.336 99.099 96.863 94.627 92.39 90.154 26.503 26.521 105.808 20
St 303 106.85 104.614 102.377 100.141 97.905 95.669 93.432 91.196 26.801 26.819 106.85 20

74
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=23
St 0 - - - - - - - - - - 0 -
St 30 0.9 - - - - - - - - - 0.9 20
St 60 2.062 2.084 - - - - - - - - 2.084 40
St 90 4.387 4.408 4.429 4.451 - - - - - - 4.451 80
St 120 6.187 6.208 6.229 6.251 6.272 - - - - - 6.272 100
St 150 9.149 9.171 9.192 9.213 9.234 9.256 9.277 - - - 9.277 140
St 180 12.966 11.609 11.63 11.651 11.673 11.694 11.715 11.736 - - 12.966 20
St 210 81.596 79.381 15.23 15.252 15.273 15.294 15.315 15.337 15.358 15.379 81.596 20
St 240 87.543 85.328 83.113 19.706 18.349 18.37 18.391 18.413 18.434 18.455 87.543 20
St 270 95.619 93.404 91.189 88.974 86.759 22.608 22.63 22.651 22.672 22.693 95.619 20
St 300 102.204 99.989 97.774 95.559 93.344 91.129 27.722 26.365 26.386 26.407 102.204 20
St 303 103.246 101.031 98.816 96.601 94.386 92.171 89.956 26.663 26.684 26.705 103.246 20
n=24
St 0 - - - - - - - - - - 0 -
St 30 - 1.878 - - - - - - - - 1.878 40
St 60 - 4.202 4.121 4.039 - - - - - - 4.202 40
St 90 - 6.003 5.921 5.84 5.758 - - - - - 6.003 40
St 120 - 8.965 8.884 8.802 8.721 8.639 8.558 - - - 8.965 40
St 150 - 11.403 11.322 11.24 11.159 11.077 10.996 10.914 - - 11.403 40
St 180 - 15.004 14.922 14.841 14.759 14.677 14.596 14.514 14.433 14.351 15.004 40
St 210 - 81.972 19.185 17.917 17.835 17.753 17.672 17.59 17.509 17.427 81.972 40
St 240 - 90.027 87.73 85.434 22.073 21.992 21.91 21.829 21.747 21.666 90.027 40
St 270 - 96.569 94.272 91.976 89.679 26.893 25.624 25.543 25.461 25.38 96.569 40
St 300 - 105.262 102.966 100.669 98.373 96.076 93.78 30.419 30.337 30.256 105.262 40
St 303 - 106.325 104.029 101.732 99.436 97.139 94.843 30.759 30.678 30.596 106.325 40

75
 continued
MaxEnPrdcd
R20 R40 R60 R80 R100 R120 R140 R160 R180 R200 in MWH Opt Rls
n=25
St 0 12.406 11.644 10.882 10.12 9.358 8.596 7.834 6.321 - - 12.406 20
St 30 15.496 14.816 14.135 13.455 12.774 12.094 11.413 10.733 10.052 - 15.496 20
St 60 82.053 18.639 17.877 17.115 16.353 15.591 14.83 14.068 13.306 12.544 82.053 20
St 90 90.286 87.309 22.672 20.843 20.163 19.482 18.802 18.122 17.441 16.761 90.286 20
St 120 96.969 93.992 91.015 88.038 24.625 23.863 23.101 22.339 21.577 20.815 96.969 20
St 150 105.84 102.863 99.886 96.909 93.932 29.295 27.467 26.786 26.106 25.425 105.84 20
St 180 - - 107.207 104.23 101.253 98.276 95.299 31.886 31.124 30.362 107.207 60
St 210 - - - 113.739 110.762 107.785 104.808 101.831 37.194 35.366 113.739 80
St 240 - - - - - 115.744 112.767 109.79 106.813 103.836 115.744 120
St 270 - - - - - - 122.914 119.937 116.96 113.983 122.914 140
St 300 - - - - - - - - 125.557 122.58 125.557 180
St 303 - - - - - - - - 126.812 123.835 126.812 180
n=26
St 0 103.536 99.425 95.314 91.203 87.092 21.917 20.021 - - - 103.536 20
St 30 107.951 106.817 102.706 98.595 94.484 90.373 25.772 22.689 20.793 - 107.951 20
St 60 114.518 113.384 112.25 108.139 104.028 99.917 95.806 91.695 26.52 24.624 114.518 20
St 90 116.595 115.461 114.327 113.193 112.058 107.947 103.836 99.725 95.614 31.013 116.595 20
St 120 123.8 122.665 121.531 120.397 119.263 118.129 114.018 109.907 105.796 101.685 123.8 20
St 150 126.514 125.38 124.246 123.112 121.978 120.843 119.709 118.575 114.464 110.353 126.514 20
St 180 - 133.223 132.088 130.954 129.82 128.686 127.552 126.418 125.284 121.173 133.223 40
St 210 - - - 134.307 133.173 132.038 130.904 129.77 128.636 127.502 134.307 80
St 240 - - - - 141.653 140.519 139.385 138.251 137.117 135.983 141.653 100
St 270 - - - - - - 143.375 142.241 141.107 139.973 143.375 140
St 300 - - - - - - - 151.36 150.226 149.091 151.36 160
St 303 - - - - - - - 152.763 151.629 150.495 152.763 160

76
Table A.6 Monthly Average and Annual Evaporation in mm for Mekelle Station

Month Sep. Oct Nov. Dec. Jan Feb Mar Apr May June July Augt Annual
1992 118.1 175.9 160.3 170.2 114.6 127.2 179.5 226.1 245.3 251.6 111.5 52.2 1932.5
1993 111.3 160.3 170.1 165.2 112.9 130.8 181.2 223.3 248.9 260.4 117.5 60.2 1942.1
1994 150 311.2 163.1 191 203 299.5 386.9 328.8 321.2 239.4 87.9 77.8 2759.8
1995 146 279.2 120 132 296.4 239.8 262.8 247.9 270.4 253.3 141.5 81.7 2471
Average 131.4 231.7 153.4 164.6 181.7 199.3 252.6 256.5 271.5 251.2 114.6 68.0 2276.4

77
Table A.7 Values of Runoff Coefficient

Value of K
Hilly
Flat Rolling Land
Land 0 Land 5 10 to
to 5% to 10% 30%
S/N Type of Area slope slope slope
Urban areas
30% area impervious(paved) 0.40 0.50 _
50% area impervious(paved) 0.55 0.65 _
(a) 70% area impervious(paved) 0.65 0.80 _
(b) Single family residence in urban areas 0.3
Cultivated Areas
Open Sandy Loam 0.30 0.40 0.52
Clay and Silt Loam 0.50 0.60 0.72
2 Tight Clay 0.60 0.70 0.82
Pastures
Open Sandy Loam 0.10 0.16 0.22
Clay and Silt Loam 0.30 0.36 0.42
3 Tight Clay 0.40 0.55 0.60
Wooded land or Forested Areas
Open Sandy Loam 0.10 0.25 0.30
Clay and Silt Loam 0.30 0.35 0.50
4 Tight Clay 0.40 0.50 0.60

78
Annex-B: Figures

79
Figure B.1 Mean Monthly Rainfall at Mekelle Station in mm

250
Mean Monthly Rainfall in
200

150
mm

100

50

0
Jan Feb mar Apr May June July Aug Sep Oct Nov Dec
Months

Figure B.2 Annual Rainfall at Mekelle Station in mm

800.0
Annual Rainfall in mm

700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
92

93

94

95

96

97

98

99

00

01

02

03

04
19

19

19

19

19

19

19

19

20

20

20

20

Years 20

Figure B.3 Mean Monthly Rainfall at Samre Station in mm

Mean Monthly Rainfall in

200.0

150.0
mm

100.0

50.0

0.0
v
n

ne

ly

c
g

p
r
ar
b

ct
ay
Ap

No
Ja

Au

Se

De
Fe

Ju

O
m

Ju
M

Months

80
Figure B.4 Annual Rainfall at Samre Station in mm
Annual Rainfall in mm

700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
92

93

94

95

96

97

98

99

00

01

02

03

04
19

19

19

19

19

19

19

19

20

20

20

20

20
Years

Figure B.5 Mean Monthly Rainfall at Adigudom Station in mm

250.0
Mean Monthly Rainfall in

200.0

150.0
mm

100.0

50.0

0.0
ov
ne
n

ly

ug

ep

c
pr
eb

ar

ay

ct
Ja

e
Ju

O
A
m

N
Ju

D
M

S
F

Months

Figure B.6 Annual Rainfall at Adigudom Station in mm

Annual Rainfall in mm

800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
92

93

94

95

96

97

98

99

00

01

02

03

04
19

19

19

19

19

19

19

19

20

20

20

20

20

Years

81
Figure B. 7 Double Mass Plot for Mekelle Station

y = 1.065x - 72.391
2

Rainfall at Mekelle Station

Cummulative Annual R = 0.9993
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
0 1000 2000 3000 4000 5000 6000 7000 8000
Cummulative Average Annual Rainfall at Base Stations

Figure B. 8 Double Mass Plot for Samre Station

y = 0.8561x + 80.026
R2 = 0.9985
Rainfall at Samre Station in

7000
Cummulative Annual

6000
5000
4000
mm

3000
2000
1000
0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Cummulative Average Annual Rainfall at Base Stations in mm

Figure B. 9 Double Mass Plot for Adigudom Station

y = 0.8533x - 180.98
R2 = 0.9995
Rainfall at Adigudom Station
Cummulative Annual

7000
6000
5000
in mm

4000
3000
2000
1000
0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Cummulative Average Annual Rainfall at Base Stations in
mm

Figure B. 10 Area- versus- Storage Curve

82
 y = 0.2936x + 56910
R2 = 0.9522
1400
Thousands

1200
Area in m2

1000
800
600
400
200
0
0 1000 2000 3000 4000 5000
Storage in m3 Thousands

83
 Annex-C: Visual Basic Program to Solve DP Problem
(Note that it is also applicable for other micro earth dams other than
Haiba)

84
'Project: MSc Thesis at AAU, Faculty of Technology, Department of Civil Engineering
' Major: Hydraulics Engineeering.
'Date: May 2005
'Programmer: Mulatu Tiruneh
'Description: Reservoir Operational Planning of Haiba Micro-Irrigation Dam for Micro-
' Power Development using Dynamic Programming.

Option Explicit

Dim mvrtInflow(12) As Variant

Dim mvrtEvaporation(12) As Variant
Dim mvrtSeepage(12) As Variant
Dim mvrtEnergyProduced(1000) As Variant
Dim mvrtOptimalReleases(1000) As Variant
Dim mvrtInitialStorages As Double
Dim mvrtPossibleReleases As Double
Dim mvrtIrrTarRelease(12) As Double
Dim f(1000, 1000) As Variant
Dim mvrtStorageCapacity As Double
Dim mvrtMinWorkingStorageCapacity As Double

Private Sub Command3_Click()

Dim i As Integer
For i = 1 To 12
MSFlexGrid1.TextMatrix(i, 1) = ""
MSFlexGrid1.TextMatrix(i, 2) = ""
MSFlexGrid1.TextMatrix(i, 3) = ""
MSFlexGrid1.TextMatrix(i, 4) = ""
Next

End Sub

Private Sub open_Click()

On Error GoTo exitthissub
Dim filename As String
CommonDialog1.Filter = "*.txt|*.txt"
CommonDialog1.ShowOpen
filename = CommonDialog1.filename
Call fLoadFile(filename)
Text1.Text = filename
exitthissub:
End Sub

Private Sub runDP_Click()

'---------------------------------------Read Inputs From GUI------------------------------------
Dim i As Integer
Dim ElevCapintercept As Double
Dim ElevCapSlop As Double
Dim ActiveStorageCapacity As Double
Dim maximumConCapacity As Double
Dim Discrit1 As Integer
Dim Discrit2 As Integer
'read Inflows, Seepage, Evaporation and Irrigation Release
Dim empty_field As Boolean
For i = 1 To 12
If (IsNumeric(MSFlexGrid1.TextMatrix(i, 1)) = False) Then
MsgBox "Invalid value is provided for Inflow Field for " & MSFlexGrid1.TextMatrix(i, 0), vbInformation, "Run Dp"
GoTo exitSub
End If
If (IsNumeric(MSFlexGrid1.TextMatrix(i, 2)) = False) Then
MsgBox "Invalid value is provided for Evaporation Field for " & MSFlexGrid1.TextMatrix(i, 0), vbInformation, "Run Dp"
GoTo exitSub

85
End If
If (IsNumeric(MSFlexGrid1.TextMatrix(i, 3)) = False) Then
MsgBox "Invalid value is provided for Seepage Field for " & MSFlexGrid1.TextMatrix(i, 0), vbInformation, "Run Dp"
GoTo exitSub
End If
If (IsNumeric(MSFlexGrid1.TextMatrix(i, 4)) = False) Then
MsgBox "Invalid value is provided for Irrigation Release Field for " & MSFlexGrid1.TextMatrix(i, 0), vbInformation, "Run
Dp"
GoTo exitSub
End If
Next

For i = 1 To 12
mvrtInflow(i) = MSFlexGrid1.TextMatrix(i, 1)
mvrtEvaporation(i) = MSFlexGrid1.TextMatrix(i, 2)
mvrtSeepage(i) = MSFlexGrid1.TextMatrix(i, 3)
mvrtIrrTarRelease(i) = MSFlexGrid1.TextMatrix(i, 4)
Next
'Read Elevation storage Slope and Y intercept
ElevCapintercept = CDbl(eleinter.Text)
ElevCapSlop = CDbl(elevSlope.Text)
'Read storage Capacity and Minimum Working capacity
mvrtStorageCapacity = CDbl(textStorageCapacity.Text)
mvrtMinWorkingStorageCapacity = CDbl(txtWcapa.Text)
'Calculate Active Storage and Maximum Conveyance Capacity
ActiveStorageCapacity = Abs(textStorageCapacity.Text - txtWcapa.Text)
maximumConCapacity = (((12.4 * CDbl(MaxHeadtxt.Text) * (CDbl(diamtext.Text) ^ 4)) / ((0.14 * (CDbl(diamtext.Text) ^
4)) + 1.2 + (ftxt.Text * lentxt.Text / diamtext.Text))) ^ 0.5) * 259.2
'Discrtization Cooeficients
Discrit1 = Int(ActiveStorageCapacity / 10)
Discrit2 = Int(maximumConCapacity / 10)
'------------------------------------------------------------------------------------------
'-------------------Wrire Heading For the Output file-------------------------------------------------
Open App.Path & "\diba.CSV" For Output As #2
Dim tecc As String
For mvrtPossibleReleases = 20 To Int(maximumConCapacity) Step Discrit2
tecc = tecc & "," & "R" & mvrtPossibleReleases
Next
tecc = tecc & "," & "MaxEnPrdcd in MWH" & "," & "Opt Rls"
Print #2, tecc

Dim Countyears As Integer

Dim theindex As Integer

'================================================================================
theindex = 12
For Countyears = 1 To 30
Print #2, "n=" & Countyears
If (theindex = 0) Then
theindex = 12
End If

'------------------------------------------------------------------------------------------
Dim maxx As Double
Dim j As Integer
For mvrtInitialStorages = 0 To Int(ActiveStorageCapacity)
For mvrtPossibleReleases = 20 To Int(maximumConCapacity) Step Discrit2
If mvrtInitialStorages + mvrtInflow(theindex) - mvrtEvaporation(theindex) - mvrtSeepage(theindex) - mvrtStorageCapacity
+ mvrtMinWorkingStorageCapacity <= mvrtPossibleReleases And mvrtPossibleReleases <= mvrtInitialStorages +
mvrtInflow(theindex) - mvrtEvaporation(theindex) - mvrtSeepage(theindex) And mvrtPossibleReleases >=
mvrtIrrTarRelease(theindex) Then
If (Countyears = 1) Then

86
 f(mvrtInitialStorages, mvrtPossibleReleases) = 8.86 * 10 ^ -3 * ElevCapSlop * (2 * ElevCapintercept / ElevCapSlop + 2 *
mvrtInitialStorages + mvrtInflow(theindex) - mvrtEvaporation(theindex) - mvrtSeepage(theindex) - mvrtPossibleReleases) *
mvrtPossibleReleases
Else
f(mvrtInitialStorages, mvrtPossibleReleases) = 8.86 * 10 ^ -3 * ElevCapSlop * (2 * ElevCapintercept / ElevCapSlop + 2 *
mvrtInitialStorages + mvrtInflow(theindex) - mvrtEvaporation(theindex) - mvrtSeepage(theindex) - mvrtPossibleReleases) *
mvrtPossibleReleases + mvrtEnergyProduced(mvrtInitialStorages + mvrtInflow(theindex) - mvrtEvaporation(theindex) -
mvrtSeepage(theindex) - mvrtPossibleReleases)
End If
Else
f(mvrtInitialStorages, mvrtPossibleReleases) = -100000
End If
Next mvrtPossibleReleases
Next mvrtInitialStorages
'------------------------------------------------------------------------------------
For mvrtInitialStorages = 0 To Int(ActiveStorageCapacity)
'-------------Get the maximum--------------------
maxx = 0
For j = 20 To Int(maximumConCapacity) Step Discrit2
If maxx < f(mvrtInitialStorages, j) Then
maxx = f(mvrtInitialStorages, j)
End If
Next
mvrtEnergyProduced(mvrtInitialStorages) = maxx
'----------------------------------------------------
For mvrtPossibleReleases = 20 To Int(maximumConCapacity) Step Discrit2
If f(mvrtInitialStorages, mvrtPossibleReleases) = -100000 Then
f(mvrtInitialStorages, mvrtPossibleReleases) = "-"
End If
If mvrtEnergyProduced(mvrtInitialStorages) = f(mvrtInitialStorages, mvrtPossibleReleases) Then
mvrtOptimalReleases(mvrtInitialStorages) = mvrtPossibleReleases
End If
Next

If mvrtEnergyProduced(mvrtInitialStorages) = -100000 Then

mvrtEnergyProduced(mvrtInitialStorages) = 0
End If
If mvrtEnergyProduced(mvrtInitialStorages) = 0 Then
mvrtOptimalReleases(mvrtInitialStorages) = "-"
End If
'---------------------------
Dim tex As String
Dim kk As Integer
tex = "St" & mvrtInitialStorages
If (((mvrtInitialStorages Mod Discrit1) = 0) Or (mvrtInitialStorages = Int(ActiveStorageCapacity))) Then
For kk = 0 To 9
If IsNumeric(f(mvrtInitialStorages, 20 + kk * Discrit2)) Then
tex = tex & "," & FormatNumber(f(mvrtInitialStorages, 20 + kk * Discrit2), 3)
Else
tex = tex & "," & f(mvrtInitialStorages, 20 + kk * Discrit2)
End If
Next

If IsNumeric(mvrtEnergyProduced(mvrtInitialStorages)) Then
tex = tex & "," & FormatNumber(mvrtEnergyProduced(mvrtInitialStorages), 3)
Else
tex = tex & "," & mvrtEnergyProduced(mvrtInitialStorages)
End If

If IsNumeric(mvrtOptimalReleases(mvrtInitialStorages)) Then
tex = tex & "," & FormatNumber(mvrtOptimalReleases(mvrtInitialStorages), 3)
Else

87
 tex = tex & "," & mvrtOptimalReleases(mvrtInitialStorages)
End If

Print #2, tex

End If
'---------------------------
Next mvrtInitialStorages

Print #2, vbCr

theindex = theindex - 1
Next
Close #2
exitSub:
End Sub

Private Sub exit_Click()

End
End Sub

Private Sub Form_Load()

MSFlexGrid1.ColWidth(1) = 1000
MSFlexGrid1.ColWidth(2) = 1000
MSFlexGrid1.ColWidth(3) = 1200
MSFlexGrid1.ColWidth(4) = 1200
MSFlexGrid1.TextMatrix(0, 0) = "Month"
MSFlexGrid1.TextMatrix(0, 1) = "Inflow"
MSFlexGrid1.TextMatrix(0, 2) = "Evaporation"
MSFlexGrid1.TextMatrix(0, 3) = "Seepage"
MSFlexGrid1.TextMatrix(0, 4) = "Irrigation "
Dim montt(12) As String
montt(1) = "September"
montt(2) = "October"
montt(3) = "November"
montt(4) = "December"
montt(5) = "January"
montt(6) = "February"
montt(7) = "March"
montt(8) = "April"
montt(9) = "May"
montt(10) = "June"
montt(11) = "July"
montt(12) = "August"

Dim i As Integer
For i = 1 To 12
MSFlexGrid1.TextMatrix(i, 0) = montt(i)
Next
End Sub
Sub fLoadFile(ByVal filename As String)
Dim inputdata As Double
Dim i As Integer
Dim j As Integer

Open filename For Input As #1

For i = 1 To 12
For j = 1 To 4
Input #1, inputdata
MSFlexGrid1.TextMatrix(i, j) = inputdata
Next
Next

Input #1, inputdata: textStorageCapacity.Text = inputdata

88
Input #1, inputdata: txtWcapa.Text = inputdata
Input #1, inputdata: eleinter.Text = inputdata
Input #1, inputdata: elevSlope.Text = inputdata
Input #1, inputdata: diamtext.Text = inputdata
Input #1, inputdata: lentxt.Text = inputdata
Input #1, inputdata: ftxt.Text = inputdata
Input #1, inputdata: MaxHeadtxt.Text = inputdata

Close 1
End Sub
Sub fSaveFile(ByVal filename As String)
Dim inputdata As Double
Dim i As Integer
Dim j As Integer
Dim tex As String

Open filename For Output As #1

For i = 1 To 12
tex = MSFlexGrid1.TextMatrix(i, 1)
For j = 2 To 4
tex = tex & "," & MSFlexGrid1.TextMatrix(i, j)
Next
Print #1, tex
Next
Print #1, textStorageCapacity.Text
Print #1, txtWcapa.Text
Print #1, eleinter.Text
Print #1, elevSlope.Text
Print #1, diamtext.Text
Print #1, lentxt.Text
Print #1, ftxt.Text
Print #1, MaxHeadtxt.Text
Close 1
End Sub
Private Sub MSFlexGrid1_KeyDown(KeyCode As Integer, Shift As Integer)
Dim tex As String
tex = MSFlexGrid1.TextMatrix(MSFlexGrid1.Row, MSFlexGrid1.Col):

Select Case KeyCode

Case 46: MSFlexGrid1.TextMatrix(MSFlexGrid1.Row, MSFlexGrid1.Col) = ""
Case 13: If MSFlexGrid1.Row < 12 Then MSFlexGrid1.Row = MSFlexGrid1.Row + 1
Case 8:
If (Len(tex) >= 1) Then
MSFlexGrid1.TextMatrix(MSFlexGrid1.Row, MSFlexGrid1.Col) = Left(tex, Len(tex) - 1)
End If
Case Else:
If IsNumeric(MSFlexGrid1.TextMatrix(MSFlexGrid1.Row, MSFlexGrid1.Col) & Chr(KeyCode)) Then
MSFlexGrid1.TextMatrix(MSFlexGrid1.Row, MSFlexGrid1.Col) = MSFlexGrid1.TextMatrix(MSFlexGrid1.Row,
MSFlexGrid1.Col) & Chr(KeyCode)
End If
End Select
End Sub
Private Sub save_Click()
On Error GoTo exitthissub
Dim filename As String
CommonDialog1.Filter = "*.txt|*.txt"
CommonDialog1.ShowSave
filename = CommonDialog1.filename
Call fSaveFile(filename)
exitthissub:
End Sub

89
90