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Optimizing Intensified Runoff from Roads for Supplemental Irrigation:

Tigray Region, Ethiopia

Meseret Dawit Teweldebrihan

MSc Thesis 14.22

April 2014

Optimizing Intensified Runoff from Roads for

Supplemental Irrigation: Tigray Region, Ethiopia

Master of Science Thesis

by

Meseret Dawit Teweldebrihan

Supervisors Prof.Charlotte de Fraiture. PhD, MSc (UNESCO-IHE)

Mentors Abraham Mehari Haile PhD, MSc (UNESCO-IHE)

Examination committee

Prof.Charlotte de Fraiture. PhD, MSc (UNESCO-IHE)

Abraham Mehari Haile PhD, MSc (UNESCO-IHE)

Eyasu Yazew Hagos Phd, MSc (Mekelle University)

This research is done for the partial fulfilment of requirements for the Master of Science degree at the UNESCO-IHE Institute for Water Education, Delft, the Netherlands

Delft

April 2014

©2014by Meseret Dawit Teweldebrihan . All rights reserved. No part of this publication or the information

contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any

means, electronic, mechanical, by photocopying, recording or otherwise, without the prior permission of

the author. Although the author and UNESCO-IHE Institute for Water Education have made every effort to

ensure that the information in this thesis was correct at press time, the author and UNESCO-IHE do not

assume and hereby disclaim any liability to any party for any loss, damage, or disruption caused by errors

or omissions, whether such errors or omissions result from negligence, accident, or any other cause.

i

Dedicated to my Family

ii

Abstract

The Ethiopia irrigation strategy highlights rain water harvesting from various surface conditions as a main

source of irrigation water for small scale irrigation development at farmer's level. While ponds, dams, and

in-situ water harvesting systems have been implemented, roads have primarily been built for transportation

purpose – the additional benefits: rain water harvesting for supplemental irrigation, groundwater recharge

have not yet been explored. As is the case in the study area of this MSc. research, lack of proper integration

of road construction into the broader rural agricultural livelihoods has resulted in various negative impacts:

soil erosion and gully formation in cultivated land, flooding of agricultural and inhabited areas, and reduced

recharge of groundwater.

Piloting on the Sinkata (Freweyni) - Hawzen - Abreha we Atsbaha 52 Km road in the Tigaray Region,

Ethiopia. This research aimed at minimizing the negative impacts of road development and maximizing the

benefits. It employed both quantitative methods - modelling (HBV and Aqua Crop) in combination with

field observation and interviews as well as discussions with diverse stakeholders. The runoff generated was

estimated from the roads using HBV model. The crop yields that correspond to different rainfall regimes

were assessed using Aqua Crop. The contributions of supplemental rainfall to enhancing productivity were

investigated with the same model. Field observation and interviews resulted in a better insight on how

significant the negative impact of roads could be when they are not properly integrated into the overall

agricultural and rural development programs.

From the model simulation in every catchment, the calibration results of Calculated or simulated discharge

for Agula and Sulluh are 326 MCM/year from 1994 - 2001 and 426 MCM/year from 1994 - 2002

respectively. Simulated result for Validation period for catchment Agula and sulluh is 499 MCM/year from

2002 - 2006 and 806 MCM/year from 2003 - 2006 respectively.

The simulation result of the aqua crop showed that due to poor rainfall distribution, yield and biomass

productions were reduced by 1.2 and 4.6 ton/ha. In some years, when rainfall shortage and distribution was

extremely limiting, farmers were left empty handled - with no production to feed themselves and their

household members. With supplementary rainfall the water scarcity and distribution inefficiency of the

rainfall could be improved.

The SPSS analyses of the interviews have reveled that 70% of farmers living on the study area were

affected by the road side runoff as follows: 45 % of their farm land was exposed to temporary water

logging and around 65% of the cultivable land was affected by erosion.

This research has demonstrated that the road in the study area is having significant negative impact to the

agricultural livelihoods, but that also it has a huge potential to be a key contributor to the enhancement of

the livelihoods. The three major recommendations are :( 1) for the betterment of the impacts, it is suggested

that Roads for water harvesting and multiple uses be mainstreamed in educational systems (2) There should

be integration between relevant institutions and authorities (ERA, MoA as well as regional and zonal line

offices) in making future road development plans. And (3) Awareness generation should be done to

encourage farmers utilize the runoff from roads for productive purposes. Moreover, technical assistance

and training's needs to be delivered at grass-root level.

Key words: Rainfall runoff modeling, HBV, Crop water requirement, Aqua Crop.

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Acknowledgements

I would like to express my deepest and sincere gratitude to, my supervisor Prof. Charlotte De Fraiture, for

her critical review of my work and constructive comments and overall guidance. My special thanks goes to

my mentor, Abraham Mehari Haile (PhD), for his valuable supervision, guidance, critical comments in the

whole process of the research work.

This research would not have been realized without the financial support from the Netherlands Fellowship

Program (NFP). I also thank DUPC and Rain foundation (from the IFAD project) for providing me with

supplementary research fund and grateful to UNESCO-IHE for the convenient study environment with all

the required facilities.

I also would like to thank Adey Nigatu and Dawit Tadesse for their great help in proof reading and their

constant encouragement in all my stay here.

Dr. Frank van Steenberg has been consistently encouraging me and giving me advice during my thesis

work, especially in the initial phase and the field work. He deserves my sincere thanks and appreciation.

I appreciate the assistance from Mr. Berihun and Atakilti Hailu, local community and agricultural extension

experts in the study sites in guiding, organizing and facilitating discussions with farmers during the data

collection process.

I am grateful to Dr, Kifle Woldearegay for his great help and technical support in coordinating and

facilitating the field work. I cannot forget to thank my dear friends, Tsiyon yinesulih and Freweyini Kidane.

I am indebted to my extended family and friends back home for their constant care and encouragement all

the time.

Above all, glory be to the Almighty God for His presence to make my life meaningful in every aspect.

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Table of Contents

Abstract ii

Acknowledgements iii

List of Figures vii

List of Tables viii

Abbreviations ix

1. Introduction 1 1.1. Background 1 1.2. Statement of Problem 2 1.3. Research Questions 3 1.4. Research Objectives 3

1.4.1. Overall objectives 3 1.4.2. Specific objective 3

1.5. Thesis Structure 3

2. Literature Review 4 2.1. Importance of Water Harvesting 4 2.2. History of Water Harvesting 6 2.3. Types of Water Harvesting Techniques 6

2.3.1. In situ rainwater harvesting (soil and water conservation) 7 2.3.2. Micro-catchment water harvesting 7 2.3.3. Macro-catchment water harvesting 7

2.4. Impact of Climate Variability on Agriculture 7 2.5. How Road Construction Links with Poverty Alleviation 7

2.5.1. Water from roads 8 2.6. Current Road Construction Development in Ethiopia 8

2.6.1. Road construction development in rural of Ethiopia 8 2.7. Water Harvest from Road Construction 8

3. Methodology 9 3.1. Description of Study Area 9

3.1.1. Topography 10 3.1.2. Climate 10 3.1.3. Water Source 11 3.1.4. Vegetation and Land Use 12 3.1.5. Geology 13

3.2. Road section of the study area 13 3.2.1. Assessment of slope stability 14 3.2.2. Drainage 14 3.2.3. Pipe and Slab Culverts 16 3.2.4. Bridge Widths 16 3.2.5. Location, Accessibility and Existing Road Conditions 17

vi

3.3. Research Methodology 18 3.3.1. Field work and Data Collection 19 3.3.2. HBV and Hydrological Modelling 21 Data required for HBV model 24 3.3.3. HBV Model performance 24 3.3.4. Rational and the SCS Unit Hydrograph for Runoff Estimation from constructed

road 25 3.3.5. Aqua Crop Model 29

4. Result and Discussion 36 4.1. Runoff from gauged catchment 36

4.1.1. Model calibration 37 4.1.2. Model Validation 39 4.1.3. Results of runoff from road 43

4.2. Crop water requirement and its potential 45 4.3. Result from Statistical Package for the Social Sciences (SPSS) 54

5. Conclusion and Recommendation 58 5.1. Conclusion 58 5.2. Recommendation 59

References 61

Appendices 64 Appendix A : Laboratory Analyses and Data used 64 Appendix B : Monthly dekade and GPS readings 67 Appendix C : Monthly areal rainfall map (2001- 2012) 72

vii

List of Figures

Figure 2.1 Agro hydrological flows indicating ”green” and ”blue” water flows and the two

partitioning points determining the amount of plant available soil water in the root zone. ...... 4 Figure 2.2 General overview of rainfall partitioning in farming systems in the semi-arid tropics of

sub-Saharan Africa. .................................................................................................................. 5 Figure 2.3 Classification of the aforementioned water harvesting systems. OWB: Open water basins;

FWH: Flood water harvesting (Beckers et al., 2013). .............................................................. 6 Figure 3.1 Location of the study area ........................................................................................................ 9 Figure 3.2 Digital elevation model of the research area .......................................................................... 10 Figure 3.3 Major rivers, towns and DEM map Suluh, Agulae and Genfel Watersheds .......................... 11 Figure 3.4 Land cover of the study area. .................................................................................................. 12 Figure 3.5 Complete road section Sinkata – Hawzen – Abraha we Atsbaha ........................................... 13 Figure 3.6 Erosion from alongside farmlands ....................................................................................... 14 Figure 3.7 Slope and drainage map .......................................................................................................... 15 Figure 3.8 Before road construction (left), after road construction (right) ............................................. 17 Figure 3.9 Simplified flow chart of the methodology adopted in the research ........................................ 18 Figure 3.10 Field sample collection ........................................................................................................... 19 Figure 3.11 Laboratory work. .................................................................................................................... 20 Figure 3.12 Schematic presentation of the HBV model for one sub basin (IHMS, 2006) ......................... 22 Figure 3.13 Aqua Crop flow chart (FAO, 2012) ........................................................................................ 31 Figure 4.1 Model calibration result of Sulluh catchment (1994-2002) .................................................... 38 Figure 4.2 Model calibration result of Agula catchment (1994-2001) .................................................... 39 Figure 4.3 Model Validation result of Sulluh catchment (2003-2006) .................................................... 40 Figure 4.4 Model Validation result of Agula catchment (2002-2006) ..................................................... 41 Figure 4.5 Relation between runoff and rainfall for Genfel River ........................................................... 42 Figure 4.6 Observed flow and rainfall of Genfel catchment .................................................................... 42 Figure 4.7 Rain fall distribution during the growing period for good yield ............................................ 47 Figure 4.8 Rainfall distribution during the growing period for minimum yield. ..................................... 48 Figure 4.9 Simulation barely crop result with supplemental irrigation .................................................... 49 Figure 4.10 Simulation of barely crop result without supplemental irrigation .......................................... 50 Figure 4.11 Dekadal Crop water requirement vs Rainfall for Wheat and Barely ...................................... 53 Figure C.1 Long - term monthly areal rainfall for Jan and Feb (2001 -2012) ........................ 72

Figure C.2 Long - term monthly areal rainfall for March - June (2001 -2012) ......................... 73 Figure C.3 Long - term monthly areal rainfall for July - October (2001 -2012) ....................... 74 Figure C.4 Long - term monthly areal rainfall for Nov and Dec (2001 -2012) ......................... 75

viii

List of Tables

Table 3.1 Summary of Water Sources .................................................................................................... 12 Table 3.2 Targeted farmers ..................................................................................................................... 21 Table 3.3 Model parameter space in SHMS HBV model (IHMS, 2006) ............................................... 24 Table 3.4 Frequency Factors for Rational Formula ................................................................................ 26 Table 3.5 Recommended value for r (Hydrology manual) ..................................................................... 27 Table 3.6 Calibrations parameter for Wheat crop (Aqua crop manual).................................................. 32 Table 3.7 Calibrations parameter for Barely crop (Aqua crop manual) ................................................. 34 Table 4.1 Calibrated model parameters for gauged catchments ............................................................. 37 Table 4.2 Model validation from year 2003-2006 for Agula and Sulluh. .............................................. 40 Table 4.3 Estimated discharge from the road using rational method ..................................................... 43 Table 4.4 Estimated discharge from the road using SCS Unit Hydrograph method .............................. 44 Table 4.5 Crop and water productivity under different scenarious ........................................................ 45 Table 4.6 Irrigation schedule .................................................................................................................. 45 Table 4.7 Hawzen barley crop simulation result .................................................................................... 46 Table 4.8 Sinkata barely crop simulation result ..................................................................................... 48 Table 4.9 Irrigation schedule in addition to rainfall ............................................................................... 51 Table 4.10 Aqua crop result of Hawzen from 2002 to 2012 for wheat crop ............................................ 52 Table 4.11 Aqua crop result from 2001 to 2012 for wheat crop............................................................... 52 Table A.1 Laboratory result for permanet wilting point .......................................................................... 64 Table A.2 Laboratory result for Field capacity ....................................................................................... 65 Table A.3 Laboratory result for Soil texture analysis.............................................................................. 66 Table B.1 Standard meteorological dekad .............................................................................................. 67 Table B.2 Records of GPS reading ......................................................................................................... 68

ix

Abbreviations

BD Bulk density

CN Curve Number

DEM Digital Elevation Model

EA Actual evapotranspiration in the HBV model

EMA Ethiopia Meteorological Agency

EMWR Ethiopian Ministry of Water Resource

EP Potential evapotranspiration

ERA Ethiopian Road Authority

FAO Food and Agriculture Organization

FC Field Capacity

GDP Gross Domestic Product

GPS Global Positioning System

ha Hectare

HBV Hydologiska Byrans Vattenbalansavdelning (Hydrological Bureau Water balance

section)

Hq Parameter representing the high flow rate in the HBV model

HTS Hunting Technical Services

IDF Intensity-Duration-Frequency

IFAD International Fund for Agricultural Development

ITCZ Inter-Tropical Coverage Zone

KHQ Parameter representing a recession coefficient at a corresponding reservior volume in

the HBV model

K4 Recession coefficient for lower response box

LHS Left hand side

LP Parameter defining a limit where above the actual evapotranspiration reaches the

measured potentail evapotranspiration in the HBV model

m.a.s.l Metres above sea level

MCM Million cubic meters

MFL Maximum flow length

MoA Ministry of Agriculture

NFP Netherlands Fellowship Program

NS Nash Sutcliffe coefficient

PERC Percolation from upper to the lower response box [mm/day]

PWP Permanent Wilting point

x

RHS Right hand side

RVE Relative Volume error

RWH Rain Water Harvesting

SANRAL South African National Roads Agency Limited

SCS Soil Conservation Service

SMHI Swedish Meteorological and Hydrological Institute

SPSS Statistical Package for the Social Sciences

SWC Soil Water Content

UTM Universal Tranverse Mercator Coordinate System

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List of Symbols

Actaul vapour pressure

Saturation vapour pressure

Slope vapour pressure curve

A Catchment area

C Runoff Coefficient

Cf Frequency Factor

g Acceleration due to gravity

G Soil heat flux density

I Runoff intencity

n number of observation

Pcorr general precipitation correction factor.

Q Rate of Runoff, Discharge

r roughness coefficient

R2 Coefficient of determination

rfcf rainfall correction factor,

Rn Net radiation at the crop surface

sfcf snow fall correction factor

Tc or Tc Time of concentration

β Parameter in soil moisture routine in the HBV model

Psychrometric constant

.

Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 1

1.1. Background

Agriculture in Ethiopia is the foundation of the country’s economy which accounts for half of gross

domestic product (GDP), 84% of export with 80% of total 90 million populations engaged in this sector.

According to (Conway and Schipper, 2011) the dependency of farming system on rain fed agriculture has

made the Ethiopia’s agricultural economy extremely exposed to weather and climate effects. The failure of

rain and the occurrence of drought or consecutive dry spells during the growing season lead to crop failure.

This in turn results in food shortage and contributes to food insecurity and reduced income generation from

agricultural products sale (Teshome et. al., 2010).

According to World Bank report (2010), the climatic zone of Ethiopian weather has been divided into four

seasons. These are winter (Kremet), summer (Bega), spring (Belg) and lent (Tseday). The rainfalls for these

seasons are very varying caused by the migration of inter-tropical convergence zone. For instance, the main

wet season called Kiremt starts from mid-June to mid-September of which the rainfall is up to 350 mm per

month; this is rainy season. On the other hand, the period from October to December is a lesser rainfall

season of which the rainfall amount is 100 mm per month; which is called Bega. Likewise, the month

between Decembers to February is a dry season; which is called Tseday. Moreover, the secondary wet

season called Belg which is from February to May and counts to the rain fall amount of 100 to 200 mm per

month. These shows that Ethiopia receives different rainfall amount in the year changing from minimum of

100 mm per month to relatively maximum 350 mm per month which are distributed to different parts of the

country depending on the inter tropical convergence zone. Although getting the amounts of these rainfall

amounts, it was reported that, Ethiopia receives very little and variable rainfall at any season of a year.

Moreover, the reasons for the fluctuation of rainfall in Ethiopia are the movements of the Inter-Tropical

Coverage Zone (ITCZ) which are sensitive to variations in Indian Ocean sea-surface temperatures and vary

from year to year, hence the onset and duration of the rainfall seasons vary considerably inter-annually,

causing frequent drought.

As reported in World Bank (2006), achieving water security in Ethiopia is very challenging. This will

need very large investment in water infrastructure and management capacity. As suggested by Marizai and

Tumbo (2010), implementing Rain Water Harvesting (RWH) could make substantial contribution towards

better water security. RWH was defined as the process of interception and concentration of runoff and its

subsequent storage either in soil for direct use by plants or in reservoirs for later use (Marizai and Tumbo,

2010). Unmitigated hydrological variability increases poverty rates by about 25 percent and costs the

Ethiopian economy about 40 percent of its growth potential, leaving growth rates hostage to hydrology

(Awulachew, 2011).A good knowledge of the amount of available runoff and its effective utilization will

CHAPTER 1

Introduction

Introduction 2

help supplement rainfed agriculture and hence minimize the risk of crop failure due to the erratic nature of

rainfall in Ethiopia. RWH will also have an important contribution to the success of irrigated agriculture. It

is the policy of the Government of Ethiopia to expand and increase the productivity of irrigated agriculture.

The Government in particular focuses on supporting and promoting community level small-scale irrigation

practices. To this end, the irrigation strategy of the country highlights rain water harvesting from various

surface conditions as a main source of irrigation water for small scale irrigation developments at farmers

level.

Integrating water harvesting in road design is a new concept that has recently attracted a lot of attention.

Roads have major but little understood impact on soil as a result of the quite significant runoff they

generate to flow over downside farmlands. In addition, roads also change existing run-off patterns. In

Ethiopia, existing and planned road design & development is insensitive to water and this constitutes a

major missed opportunity for water harvesting (rainwater) in support of local agriculture and water supply.

According to World Bank (2010), the volume of road network construction was estimated in Ethiopia at

approximately 38,000 km of which around 6,000 km are paved. An estimated 55% of the roads are in flat

terrain. In addition, the road network comprises approximately 4,400 bridges and more than 40,000

culverts. Hence, integrating road construction plans in rural areas with managed water harvesting systems

could have a major impact on supplementing rain fed agriculture at an affordable additional cost as well as

in reducing the negative effects of the runoff flow on farmlands, mainly flooding and erosion. Therefore,

this research was initiated with the objective of assessing the impacts of runoff from roads on farmlands

and optimizing their use as a supplement to rain-fed agriculture. This is believed to contribute towards

poverty reduction and socio-economic development efforts of the country.

Therefore, a clear Knowledge of the available natural resources such as runoff will assist in short- and

long-term agricultural planning (Elewa et al., 2012).Moreover, rainwater is known to be the mother of

surface and groundwater sources; nevertheless, both global and local experiences indicate that its effective

utilization has by and large been unnoticed. However, a wide range of climatic, ecological and

topographical diversities influences rainwater (Awulachew, Loulseged et al., 2007) .

1.2. Statement of Problem

In rural Ethiopia, farmers have been unable to escape from the poverty trap as they have relied on rainfed

agriculture that is relies on erratic rainfall and is prone to risks in crop failures. The poor productivity from

rainfed agriculture has been further exacerbated by the extensive road construction under way throughout

the rural country-side. In Ethiopian, road development is still viewed as a single-function ‘technical’

infrastructure with improved access being the primary goal without taking into account the possible

additional side effects and differentiated impacts on communities that live alongside the road (Kordrzycki,

2013; Ericson, 2008; World Bank, 1997).

As is the case in the study area of this MSc research, the lack of proper integration of road construction into

the rural agricultural livelihoods has resulted in a several negative impacts: soil erosion and gully

formation in cultivated land, flooding of agricultural and inhabited areas, ; reduced recharge of

groundwater.These research aims at minimizing the negative impacts of road development by effectively

harvesting the road-generated runoff and optimizing its use for agricultural production.

There are several water harvesting technologies like roof water harvesting ranging from individual farm

household to community level are increasing expansion of irrigation land. However, little study has been

conducted with regard to runoff harvesting from rural and urban road surfaces for agricultural use - rainfed

and irrigated.


1.3. Research Questions

What is the impact of unreliable and erratic rainfall on yield of the major crops?

How significant is the number of farmers negatively affected by poorly managed runoff water and

how large is the impact (erosion, flooding) on the agricultural land?

How much runoff can be generated from the whole catchment in general and the Sinkata

(Freweyni) - Hawzen - Abraha we Atsbaha 52 Km long road in particular?

To what extent can the runoff water generated from the road contribute to increasing the crop yield

of the major crops?

How is the perception of stakeholders in utilizing roadside runoff for agriculture?

1.4. Research Objectives

1.4.1. Overall objectives

Contribute to improving rural livelihoods through generation of knowledge useful for minimizing

the negative impacts of runoff water generated from roads (erosion, flooding) and optimizing its

benefits as supplementary source for addressing inherent crop failures under the rainfed agriculture

due to mainly erratic rainfall.

1.4.2. Specific objective

To model rainfall-runoff relationship with in the whole catchment and from the total road surface.

To address crop failure that may result due to rain fall that is either insufficient in amount or poor

in distribution.

To quantify the contribution of runoff water generated from roads with regard to improving crop

productivity.

To know the perception of stakeholders in utilizing roadside runoff for agriculture.

1.5. Thesis Structure

The main issues addressed in this thesis are: (a) hydrological modelling, (b) Aqua crop modelling, and (c)

assessment of farmers/stakeholders opinion about road water harvesting. The thesis has been divided into

five chapters. The first chapter begins by giving a brief overview of the general background. It will then go

on to research objectives and problem statement. Chapter 2 presents a literature review on the history, art,

and approaches of water harvesting technology. It gives the descriptions of harvesting structures. The

review of the water harvesting structures and impact of road runoff and its method of estimations are also

presented at the end of this chapter. Chapter 3 describes the study area and data used for the hydrological

modelling and the assessment study stakeholder's opinion about road water harvesting and discusses the

methodologies for building up of models, parameter derivation, calibration, validation, and Aqua crop

model construction. Chapter 4 presents the results of the thesis finding followed by discussion. The last

chapter summarizes the research finding and recommendation for further study.

4

2.1. Importance of Water Harvesting

Rainwater harvesting (RWH) is a method of inducing, collecting, storing and conserving local surface

runoff for agricultural production (Hatibu and Mahoo, 2000). Dry lands are typically defined as areas of the

world where potential average yearly moisture loss (evapo-transpiration) exceeds average yearly moisture

gain, precipitation (Lancaster and Marshall, 2008). In arid and semi-arid regions, annual precipitation is

always poorly distributed over the crop growing season and hence in these regions, precipitation alone is

generally not enough to support low-risk crop production (Oweis, Prinz et al., 2012). The non-uniform

distribution of precipitation in these areas usually results in frequent drought periods that cause severe

moisture stress on growing crops and reduce yields. Since the intensity of most storms is greater than the

soil infiltration rate, runoff occurs. Runoff greatly reduces the amount of water that infiltrates into the soil

and hence less water is available to the crop. Furthermore, as indicated in figure 2.1 and 2.2 below, the

rainwater could be going through different systems at certain percentage of which some could be used and

some lost with no use for agriculture/irrigation (Falkenmark et al., 2001).

Figure 2.1 Agro hydrological flows indicating ”green” and ”blue” water flows and the two partitioning points determining the amount of plant available soil water in the root zone.

CHAPTER 2

Literature Review


The first point divides the rainfall and run-on between surface runoff, infiltration and direct evaporation

losses from the soil surface; the second point divides the soil water between plant water uptake, soil

evaporation and drainage. (Falkenmark et al., 2001)

Figure 2.2 General overview of rainfall partitioning in farming systems in the semi-arid tropics of sub-Saharan Africa.

Where: R = Rainfall, Ec = Plant transpiration, Es = Evaporation from soil and through interception, Roff =

Surface runoff, D = Deep percolation. (Falkenmark et al., 2001)

Water harvesting makes to a large extent use of water, that otherwise would have been lost to atmosphere

through evapo-transpiration or as runoff without any benefit. The basic principle, in particular of

agricultural water harvesting, is to capture precipitation falling on one part of the land and transfer it to

another part thereby increasing the amount of water available to the latter part. Hence, it is considered as

one option for increasing the availability of water to crops in dry areas. It increases the amount of water per

unit cropping area, reduces the impact of drought and uses runoff beneficially (Barrow, 1999; Oweis,

Hachum et al., 1999).

Water harvesting systems in dry areas can provide water for domestic consumption, including drinking

water, production of agricultural crops, and livestock (Falkenmark et al., 2001). Moreover, water

harvesting in dry areas also offers a number of environmental benefits, including reducing flooding risk,

reducing soil erosion, reducing demand for surface water and groundwater, and recharging groundwater

(Barrow, 1987; Nilsson, 1988).

6

2.2. History of Water Harvesting

According to Prinz (1996), a wide range of indigenous water harvesting techniques can be found in areas of

very low annual precipitation and with higher population densities. These traditional methods played a

much greater role in the past and were the backbone of ancient civilizations in arid and semi-arid areas

worldwide. The earliest water harvesting structures are believed to have been built 9000 years ago in the

Edom Mountains in southern Jordan to supply drinking water for people and animals. Moreover, in other

parts of the Middle East archaeological evidence of water harvesting structures appears in Israel, Palestine,

Syria, Iraq, the Negev desert (Evenari, 1982) and the Arabian Peninsula. Similarly, the traditional

techniques of water harvesting have been reported from many regions of Sub-Saharan Africa (Critchley,

Reij et al. ,1992) like the "Caag" and the "Gawan" systems in Somalia; various types of "Hafirs" in Sudan

(Oweis, Prinz et al., 2012) and the ‘Zay’ system in West Africa.

Recently, there has been a new kind of interest in water harvesting, particularly in arid and semi-arid areas,

as a result of limited supply of water resources caused by increasing standards of living and higher

population pressure in the dry regions of the world. This interest has also led to increases in the

understanding, implementation, and management of water harvesting (Falkenmark ,2001; Mechlia, Oweis

et al., 2009).

2.3. Types of Water Harvesting Techniques

According to Beckers et al. (2013), water harvesting has been practiced in different ways to solve the

various water needs of people living in dry lands ever since antiquity. Some of the techniques are used

mainly to provide water for plant production, while others are used to provide water for human and animal

consumption or for groundwater recharge (Malin et al, 2001). There are several classifications of water

harvesting methods for agricultural use; - the most commonly used types in the literature are presented in

figure 2.3 below.

Figure 2.3 Classification of the aforementioned water harvesting systems. OWB: Open water basins;

FWH: Flood water harvesting (Beckers et al., 2013).


2.3.1. In situ rainwater harvesting (soil and water conservation)

The first step in any RWH system involves methods to enhance the amount of water stored in the soil

profile, and hence, these types of water harvesting systems include methods that will increase the amount

of moisture in the soil profile by holding rainwater where it falls; there is no separation between the area

where rainfall is collected and where it is stored. This kind of water harvesting is important in areas where

the soil water capacity is large enough and the rainfall amount is equal to or greater than the amount

required by crops (Hatibu and Mahoo, 1999).

2.3.2. Micro-catchment water harvesting

This technique involves the collection of runoff from small catchments and its conveyance over a short

distance to cultivation land or to a detention basin where it can be stored temporarily. It is characterized by

sheet or rill erosion. The method is simple in design and the construction cost is usually minimal; hence

micro - catchments are easily replicable and adaptable (Oweis et al., 2012).

Road runoff harvesting is one of the micro-catchment water harvesting methods. It is the diversion of

runoff water from the road and the surrounding catchment into road - side ditches and distribution into

farmland or retention basins for fruit tree or crop production. Water is stored in the retention basins for

future use (Malin et al., 2001).

2.3.3. Macro-catchment water harvesting

In this type of water harvesting runoff from large (slope of mountain or hill) is collected and taken to farm

land located a considerable distance from the collection catchment. This type of RWH is characterized by

predominance of turbulent runoff and channelized flow of the catchment rainfall (Hatibu and Mahoo,

1999).

2.4. Impact of Climate Variability on Agriculture

The rainfall intensities are influenced by weather condition. Ethiopia has four different weather conditions

called 'belge, tsaday, cremit and bega' and during all these seasons there are changes in precipitation, both

in terms of overall levels and rainfall intensities. These weather conditions impact on runoff in the future.

Moreover, the probability of the impacts mentioned on the increased runoff and erosion effects on land uses

and ecological resources are high. Increased runoff, with large volumes and higher intensities , typically

happens in June to August (Meyer, Flood et al.).

2.5. How Road Construction Links with Poverty

Alleviation

There is a vital impact of the development of road construction on the growth of the economy and poverty

alleviation, even though there is a considerable danger that misdirected road construction might favour

development at the expense of the poor people (Hook and Howe, 2005). Ever since 1960s, the involvement

of international institution almost did not prevail with respect to the investment in road construction which

was dependent by the direct inter-relation between transportation development and poverty alleviation.

8

2.5.1. Water from roads

Water, which is a valuable natural resource, can also be trouble. There is a need for practical measures for

water harvesting or conservation as the West grows in population with increasing demand for water and

shrinking available supply. Roads can be managed as tools for saving water, improving vegetative cover

and increasing crop yields while they also protect valuable soils from erosion. In addition, rainwater run-

off from rural roads usually creates gullies and causes other damages as it finds its way into the bush or

farmers’ fields, before it ends up in the sea or in underground aquifers (Nissen-Petersen ,2006).

2.6. Current Road Construction Development in Ethiopia

As stated in the report by World Bank (2010), Ethiopia has a classified road network of about 38,000 km of

which approximately 6,000 km are paved. An estimated 55% of the roads are in flat topography. The road

network also consists of about 4,400 bridges and more than 40,000 culverts. Furthermore, as reported by

Ethiopian Road Authority (ERA), 30% of the 2955 bridges that are registered in the federal road network

require some form of rehabilitation, and 3.6% are already due for replacement.

2.6.1. Road construction development in rural of Ethiopia

Ethiopia’s road construction is still in an early stage of development; however, many efforts have been

taken so far depending on the availability of finance to build road connections between different regions of

the country through a modern infrastructural system. Nevertheless, there is still more to be done in the road

construction sector, especially because in the rural regions roads are not constructed based on modern

drainage design and land topology. Currently efforts have been taken to connect the country's main regions

with a standard road network that consists of a classified road network system. Roads in rural regions are

either almost neglected or poorly constructed without following the right drainage system; this in itself

plays a significant role in contributing to high volume intensified runoff during the rainy season. Designing

road construction or optimizing systems to use this runoff as a potential source of water harvest to improve

rain fed irrigation through supplementary irrigation would be the main objective of this research, which

also serve to alleviate the poverty of local farmers.

2.7. Water Harvest from Road Construction

Water is the main source of life and it directly or indirectly affects the daily existence of societies in all

aspects of their social and economic interrelationships. Food production is directly related to the

availability of water and water harvest especially for agricultural systems. Irrigation is one of the main

technical systems for food production in developing nations like Ethiopia. Designing systems to maximize

the usage of this technique is one of the main objectives of the country's economic policies. In a developing

country such as Ethiopia, gullies from the surrounding construction site and runoff alongside the

constructed roads are serious problems, especially during the rainy season. This water has to date been

ignorantly wasted without any economic benefit for the local farmers and instead causes serious damage to

the surrounding environment including the constructed roads. Therefore, engineers have to design systems

to collect this water using culverts to the nearby ponds, which will be later used as a potential source of

water harvest to increase food productivity by rainfed irrigation through a supplementary irrigational

system.


3.1. Description of Study Area

The study was conducted in three Districts, in areas along the main roads connecting Senkata through

Hawzen to Abreha-we-Atsbeha woreda towns in the Tigray region, Northern Ethiopia. The area is bounded

by N13038’ and N13

058’ latitude and E38

058’ and E39

025’ longitudes. Hawzen E39

025’ 21” N13

058’21”

and Fireweyni E390 34’33” N14

0 03’11”.Hawzen, with a population of 8,494(Male 3,982 and Female

4,512), is the second largest control point of the project roads. Fireweyni town is located on the Mekelle –

Adigrat trunk road, 60km away from Mekelle. Fireweyni (Sinkata), with a population of 5, 350, (Male 2,

662, Female 2,688).

Figure 3.1 Location of the study area

CHAPTER 3

Methodology

Methodology 10

3.1.1. Topography The research area road spans between ground elevations of 940 m and 2243 m above sea level. The lowest

point of the roads is located at Tekezze crossing and the highest point is Hawzen town. Following the high

elevation variation along the alignment the road traverses through mountainous to escarpment terrain. The

Abiy Adi – Hawzen section is relatively gentler with rolling terrains being dominant.

Figure 3.2 Digital elevation model of the research area

3.1.2. Climate Tigray is one of the regions of the country to have a tropical mountain climate. The rain fall in the area

stops before the expected time and experienced a lengthy dry period from nine to ten months (HTS, 1976)

The whole study area road route lies in an area with climate characteristics varying between semi arid and

arid around Hawzen and Senkata. In general, the road falls in semi – arid climate. The semi – arid climate

is characterized by lowlands between 500 – 1500 m elevations above sea level (Atlas of the Ethiopian

Rural Economy, 2006).

The minimum and maximum temperatures are 14 °C and 29.5 °C at Abrahawe Astbeha and 10.5 °C and

25.9 °C at Hawzen.


3.1.3. Water Source Water samples were collected from most of the rivers that cross the roads. Most of these rivers are seasonal

though there are two perennial rivers that cross the road. There are three rivers available in the selected

study area, namely Agula, Genfel and Sulluh.

Figure 3.3 Major rivers, towns and DEM map Suluh, Agulae and Genfel Watersheds

Methodology 12

Water quality tests were performed on the samples taken and the results are listed in Table 3.1. below.

Table 3.1 Summary of Water Sources

Chainage,

km*

GPS Location RHS /

LHS

Offset,

m**

Material

Description

Remark

Easting Northing

35+400 513264 1527838 RHS/

LHS

0 Perennial

river

Good discharge

river

90+000 552927 1549159 RHS/

LHS

200 Perennial

river

Moderate

discharge river

3.1.4. Vegetation and Land Use Few scattered trees with poor undergrowth cover the majority of the areas surrounding the roads. Small

cultivation lands are also located in settlement areas, notably in Hawzen and Fireweyni. The underlying

rocks, being at shallow depth, can also be spotted as rock outcrops all over the surface.

The settlement areas on the road route are small and are not developed enough to cause significant

alteration in the quantity of rainfall converted to runoff in the road basins as compared to the natural

ground.

Figure 3.4 Land cover of the study area.


3.1.5. Geology The geology of Agula and Genfel catchment is prevailed predominantly limestone and Suluh are

predominantly sandstone. The Abiy Adi - Hawzen - Fireweyni section is influenced by colluvial deposits,

tertiary volcanic rocks, Mesozoic sedimentary rocks and Precambrian metamorphic rocks. Tertiary volcanic

units are exposed from km 66+000 to 84+000 (chainage is considered from Abiy Adi town). Mesozoic

sandstone units are the most dominant geological formation in this section of the road. Sandstone, sand and

silt deposits are observed around the first 10km.

3.2. Road section of the study area

The roads section of the study area starts from Sinkata (Freweyni) – Hawzen – Abreha we Atsbaha. This

road section covers around 52km. Construction of new road links or improvement of existing ones has

become a precondition for overcoming numerous economic and social problems. The study area is

characterized by a wide range of landforms that include plateaus, mountains, rolling hills, steep hill slopes

and deeply incised valleys.

Figure 3.5 Complete road section Sinkata – Hawzen – Abraha we Atsbaha

Methodology 14

3.2.1. Assessment of slope stability The Abiy Adi – Hawzen - Fireweyni section of the roads has varying topographic features. The first 10km

of the section (new alignment) is characterized by flat terrain with sand deposits and on the RHS (right

hand side) of the alignment at around 1km or less distance from the roads centreline there exist a sandstone

formation, which forms a continuous ridge. In this part there is a great potential for erosion during rainy

seasons. Thus it is useful to consider a gabion or other erosion controlling measures in order to prevent the

erosion. Following this up to km 60+000 there is no slope stability problem due to the stable rock

formations except for a potential for minor sliding of shattered cobbles down steep side slopes. Between

km 60+000 and 65+000 there is a problem of slope stability due to alluvial deposits and shale. Therefore, in

this part of the road it is necessary to construct retaining walls or other slide preventing mechanisms. From

km 66+000 to the end of the road (Fireweyni), there is no slope stability problem.

Figure 3.6 Erosion from alongside farmlands

3.2.2. Drainage The streams and rivers crossing the Abiy Adi – Hawzen – Fireweyni road are direct or indirect tributaries

of Tekeze River. They drain an elevated flat to rolling terrain surrounding the roads area and the majority

of them cross the road from right to left. The streams and rivers at the crossings sites have generally flat

slopes and are also characterized by wide channels and banks lacking clear outlines.

Drains

To avoid accumulation of rainwater on the bridge surface, deck drains are provided in each span of the

bridges. Rectangular steel plate openings with equally spaced bars covering the top are provided. The

distances between consecutive drains shall be kept between 5 and 10 meters.


Figure 3.7 Slope and drainage map

Methodology 16

3.2.3. Pipe and Slab Culverts Culvert hydraulics is influenced by the location of flow control section. Flow control is the point in a

channel or culvert, which has the lowest capacity. In the context of culverts, both the inlets and outlets can

control the flow. To assess the capacity of a proposed culvert, one has to determine the flow rate, which

will pass through the culvert without exceeding the permissible headwater elevation.

The two most important flow types that occur in culverts are:

a) When culvert inlet is submerged and the culvert itself flows partly full (the entrance will not admit

water fast enough to fill the barrel). In this case the inlet controls the flow.

b) With outlet control the culvert with maximum discharge flows fully or partly, wall friction

becomes also important and the critical depth is at the outlet. Thus the outlet controls the flow.

The design equation for the culverts' hydraulics calculation is given in ERA Design manual:

HW + DZ + (Vu2/2g) =TW + (Vd

2/2g) + HL

Where: HW - Head water depth above the inlet invert (m)

DZ - Elevation difference between inlet and outlet invert (m)

Vu - Approach velocity (m/s)

TW – Tail water depth above the outlet invert (m)

Vd – Downstream velocity (m/s)

HL – Sum of all losses

g - Acceleration due to gravity (9.8 m/ s^2)

The following considerations have been adopted in evaluating the adequacy of the existing structures and

also design of new culverts.

• For culverts, it is not possible to find small catchment areas corresponding to all the individual culverts

from topographic maps of 1:50,000 scale and contour interval of 20 m. Consequently some of the

catchments, which cannot be identified, are estimated using history of flooding, site visit data and

information.

3.2.4. Bridge Widths The determination of the width of the bridge depended on the length of the total carriageway width adopted

for the approach roads and the pedestrian width required, which depends on traffic and proximity to

townships. In this project case, all the bridges are located in rural areas, at least 10kms away from towns,

and therefore the same width is adopted for all. The geometric design of the approach roads width adopted

in the design is 7m. An additional 0.32m is allowed for safety. A pedestrian width of 0.8m including

guardrails on each side is provided as the pedestrian traffic is low and the areas of the bridge sites are

located in rural countryside. A total width of 8.92m is thus provided for all bridges.


3.2.5. Location, Accessibility and Existing Road Conditions Abiy Adi – Hawzen - Fireweyni section of the project road is located in the northern part of Ethiopia,

within the bounds of Tigray National Region State. Geographically the area is bounded by N13038’ and

N13058’ latitude and E38

058’ and E39

025’ longitudes. This road section links Abiy Adi with Werk Amba,

Hawezen and Fireweyni, and also crosses many small villages and rivers. Abiy Adi town is around 100km

away from Mekelle and can be accessed via the Mekelle – Abiy Adi – Adwa link road. Fireweyni town is

located on the Mekelle – Adigrat trunk road, 60km away from Mekelle.

The Abiy Adi – Hawzen - Fireweyni road section comprises a new road alignment for the bulk of its

stretch. The Hawzen - Fireweyni segment, however, has a proper road alignment and pavement structures.

Figure 3.8 Before road construction (left), after road construction (right)

Methodology 18

3.3. Research Methodology

The methodology employed includes modelling and field observation as well as consulting advisory

literature formal and informal communication with respective organizations, stakeholders' interview, and

focal group discussions.

The overall research methodology approach along with the data collected and models used is as presented

in figure 3.9.

In the following sections, description of the general methodology applied including field visit and data

collection, the description including calibration and validation of the models used namely Aqua Crop and

HBV, is presented in detail.

Literature

review

Preparation of

field set up

Laboratory

work

Rainfall run

off modeling

(HBV)

Calibartaion

Validation

Data

Collection

Metrological

data

Hydrological

dataLand cover

Topographical

data (DEM)

No

yesRainfall run

off modeling

Run off

Aqua crop

Identification and quantification

of demand and supply

Figure 3.9 Simplified flow chart of the methodology adopted in the research


3.3.1. Field work and Data Collection A field work was conducted from October to December for a period of 60 days. The objective was to

collect relevant data, understand the perception of stakeholders in utilizing roadside runoff for agriculture,

the hydraulogic system of the area, and to become familiar with landscape and land cover of the research

area.

The data collected includes farmers' opinions, meteorological data, land cover GPS coordinates and GPS

reading of every culvert along the roads. Laboratory work was done to assess the soil physical

characteristics.

Soil Data

In order to know the textural class, bulk density, field capacity and permanent wilting point of soil in the

research area, 60 disturbed and undisturbed samples were collected from the whole study area - 20 each

from the three main study area villages, namely Sinkata/Freweyni, Hawzen and Abreha we Atsbeha.

Laboratory analysis was conducted using the hydrometer method to determine the soil texture and the

corresponding physical characteristics - soil moisture at field capacity and permanent wilting point, and

bulk density were obtained using Gravimetric sampling tachnique. The hydraulic conductivity was referred

from De laat, (2002).

Figure 3.10 Field sample collection

Methodology 20

Figure 3.11 Laboratory work.

Meteorological Data

Meteorological data were used as input for both hydrological modelling to assess the amount/quantity of

surface runoff and Aqua crop modelling for analysis of crop yield under different rainfall regions and

supplemental runoff water applications. The meteorological data used were temperature, precipitation, and

potential evaporation, daily sunshine hours.

The temperature and precipitation data were obtained from three different stations around the study area:

Hawzen, Wukro, and Sinkata/Freweyni. The temperature data used were obtained from three stations: the

potential evapotrabspiration and daily sunshine hours and discharge data were obtained from Ethiopia

Meteorological Agency (EMA) and Ethiopian Ministry of Resource (EMWR).

GIS data

The physical properties of the basin are described by the Digital Elevation Model (DEM): land use and soil

maps. These GIS data are resembled to a grid resolution of 20 x 20 m. Also the GPS reading was taken

from the study area along the road, the reading point is from every culvert and bridge from the road starting

from Sinkata (Freweyni) - Hawzen - Abraha we Atsbeha.

Questionnaire was prepared and survey was conducted to find out the willingness and opinions of the

farmers living along the roadside to utilize the roadside runoff.

Interview and discussions were held with individual households. The targeted population comprised of all

the communal farmers as detailed in Table 3.2.


Table 3.2 Targeted farmers

Village No of households in the

Village

No. of households interviewed from

the Village

Sinkata(Freweyini) 1150 20

Hawzen 929 20

Abraha we Atsbeha 910 20

A total of 60 households were interviewed; - to find out their willingness and their opinions on utilizing the

road side runoff for supplemental irrigation and other purposes.

Focus group discussions were done with Kebele leaders, farmer's representatives, representative of

agricultural development workers and stakeholders. The focal group discussion was held in the three

villages (Sinkata, Hawzen and Abraha we Atsbeha).

3.3.2. HBV and Hydrological Modelling

Runoff from a given rainfall can be estimated using hydrological models. There is a range of models that

can be employed which are simplified representations of the real world. Hydrological models can either be

physical or mathematical and they have various functions. They can also be used in flow predictions at

ungauged sites, in infilling of gaps in incomplete flow records or when extending flow records on the basis

of longer rainfall records. In this study, the HBV model is applied to all runoff simulations for both gauged

and ungauged catchments.

HBV Model Structure

The HBV model is a conceptual hydrological model for continuous estimation of runoff. It was first

developed at the Swedish Meteorological and Hydrological Institute (SMHI) in the early 70's to help

hydropower operations (Bergström and Forsman, 1973) by means of hydrological forecasting.

Daily rainfall, air temperature, vapour pressure, wind speed and potential evaporation are used as model

input. Long-term monthly average values for evaporation are used while the other parameters are used on

daily time basis. To calibrate the model, daily discharges are used in order to correct and verify the model

before making runoff predictions.

The HBV model uses daily rainfall, air temperature, potential evapotranspiration and snow accumulation to

simulate daily discharge, for soil moisture accounting where recharge to groundwater and actual

evaporation are combined. It also has a response routine, a transformation function as well as a simple

procedure for routing.

Methodology 22

Figure 3.12 Schematic presentation of the HBV model for one sub basin (IHMS, 2006)

Where:

RF: Rainfall, SF: Snow fall, IN: Infiltration, EI: Evapotranspiration, SM: Compound soil moisture routine,

FC: Maximum soil moisture storage, PERC: Percolation capacity, CF: Capillary rise, EA: Actual

evaporation, Qo: Direct runoff from upper reservoir, UZ: Upper zone reservoir, R: Seepage, EL: Lake

evaporation, LZ: Lower zone reservoir and Q1: Base flow lower reservoir. It is noted that all units are in

mm.

Preciptation and snow accumulation routine

Daily precipitation, daily air temperature and long-term monthly potential evaporation are the requirements

of HBV model. Precipitation is computed separately for each elevation/vegetation zone with a sub basin

(IHMS, 2006). A threshold temperature is needed to separate between rainfall and snow.


RF = Pcorr * rfcf *P If T > tt [3.1]

RF = Pcorr * sfsf *P If T < tt [3.2]

Where:

RF: Rainfall, P: Observed precipitation [mm], T: Observed temperature [◦c], SF: Snowfall, tt: threshold

temperature[c], rfcf: rainfall correction factor, sfcf: snow fall correction factor and Pcorr: general

precipitation correction factor.

Soil routine

Soil moisture routine is based on three empirical parameters: Beta, FC and LP (Equation [3.3] and [3.4]).

Beta controls the contribution to the response function ( or the increase in soil moisture storage (1-

) from every millimetre of rainfall or snow melt. This ratio and are frequently called runoff

coefficient and effective precipitation respectively (HBV manual, 2006).

FC is referred to as the maximum soil moisture storage (mm). In the soil moisture routine, actual

evapotranspiration is related to the measured potential evapotranspiration, the soil moisture state and the

parameter value LP. If SM/FC is above LP, actual evapotranspiration from the soil box equals the potential

evaporation. The linear reduction is used when SM/FC is below LP and Beta which controls the

contribution of soil moisture storage, SM, to the response function (IHMS, 2006).

[3.3]

[3.4]

Where: response function, SM: Compound soil moisture routine, FC: Maximum soil moisture

storage, EA: Actaul evapotranspiration, EP: Potentail evapotranspiration and LP: Limit for potential

evaporation.

Calibration and parameter in HBV model to estimate the rainfall-runoff

Four principal phases are involved when estimating rainfall-runoff in the HBV modelling process. These

are; model set-up, calibration, validation and utilization of the model in actual solution. The goodness-of-fit

of the model is based on model calibration as well as a good overall agreement of the shape of hydrograph

by comparing the observed and simulated hydrographs (Wale, 2008).

Model parameters in HBV are sorted into volume controlling parameters (FC, LP and Beta) which

determine the entire shape and volume controlling parameters (K4, PERC, KHQ, HQ and Alfa) which

distribute the computed discharge in time. HQ is computed of the mean annual flow and/or mean annual

peak flow (Equation [3.5]).

[3.5]

Where: MQ: mean annual flow, MHQ: is the mean annual peak flow and A: area of catchment.

The calibrated of quick flow is done using KHQ and Alfa. KHQ gives a hydrograph with higher peaks and

more dynamic response. Alfa is used to facilitate fitting the higher peaks to the hydrograph. High Alfa

produces higher peaks and the quicker recession (HBV manual, 2006). Table 3.3 shows recommended start

values and parameter space for a new basin/sub basin to be calibrated.

Methodology 24

Table 3.3 Model parameter space in SHMS HBV model (IHMS, 2006)

Parameter Starting value Approximate

Interval

Comment

FC Use a value for the

region

100-1500 Maximum soil moisture storage[mm]

LP 1 < = 1 Limit for potential evaporation

Beta 1 1 - 4 Exponent in the equation for discharge from the

zone of soil water

K4 0.01 0.001 – 0.1 Recession coefficient for lower response box

PERC 0.5 0.01 - 6 Percolation from upper to the lower response box

[mm]

KHQ 0.09 0.005 - 2 Recession coefficient for upper response box

Alfa 0.9 0.5 – 1.1 Measure of non-linearity to the response of upper

reservoir.

Data required for HBV model

The data required for estimating the rainfall-runoff in the HBV model are; - daily precipitation, daily

temperature, long- term monthly potential evapotranspiration and runoff data for model calibration.

.

Potential evapotranspiration

The long-term mean values of evapotranspiration recorded at a certain time of the year are used in the HBV

model. It is established that the inter-annual variation in actual evaporation depends more on the soil

moisture condition than on the inter-annual variation in potential evaporation (IHMS, 2006). In this study,

Penman-Monteith formula (Equation [3.6]) was used to compute potential evaporation.

[3.6]

Where: ETo = Reference evapotranspiration [mm day-1]

Rn = Net radiation at the crop surface [MJ m-2 day-1]

G = Soil heat flux density [MJ m-2 day-1]

T = Mean daily air temperature at 2 m height [◦C]

U2 = Wind speed at 2 m height [m s-1]

= Saturation vapour pressure [kPa]

= Actaul vapour pressure [kPa]

= Slope vapour pressure curve [kPa oC-1]

= Psychrometric constant [kPa oC-1]

3.3.3. HBV Model performance The evaluation of the HBV model performance is usually done using the traditional R

2 value, the volume

error (VE) and the R2 computed for logarithmic discharge values (R

2log) (IHMS, 2006).


Relative volume error

There are different functions believed as a measure for the performance of the model. Relative volume

error is one among the functions and can vary between ∞ and -∞.The relative volume error performs well

when the value of 0 is generated;-it shows there is no difference between simulated and observed discharge.

As such, this objective function should always be used in combination with another objective function that

considers the overall shape agreement. The formula used to calculate the relative volume error is shown

below in equation [3.7].

) 100% [3.7]

Where: : Relative volume error, : Simulated flow and : Observed flow.

Nash-Sutcliffe coefficient

The Nash-Sutcliffe coefficient (with values ranging from -∞ to 1) measures the efficiency of the model by

finding the relationship between the goodness-of-fit of the model and the variance of the measured data. A

Nash-Sutcliffe efficiency of 1 implies that the modelled discharge is perfectly similar to the observed data.

Owing to the frequent use of this coefficient, it is generally accepted that when values between 0.6 and 0.8

are generated, the model performance is reasonable. Values between 0.8 and 0.9 mean that the model

performs well and values between 0.9 and 1 imply that the performance of the model is extremely good

(Deckers, 2006).

[3.8]

Where: NS: Nash-Sutcliffe coefficient, : Simulated flow, : Observed flow and

:

Average of observed flow.

3.3.4. Rational and the SCS Unit Hydrograph for Runoff Estimation from constructed road

To estimate the road runoff, the rational and SCS Unit Hydrograph methods were used.

Rational Method

The rational method is a simplified and widely used method of runoff estimation with an assumption of a

uniform rainfall over an entire basin while it is contributing to the discharge at the outlet point.

The rational formula estimates runoff as a function of runoff coefficient, frequency factor, rainfall intensity

and area. To estimate the discharge from road by using rational method is shown below in equation [3.9]

[3.9]

Where: Q; Discharge in m3/sec

Cf; Frequency factor

C; Runoff coefficient, unit less

I; Intensity of rainfall, mm/hr

A; Area of the basin in hectares

Uniform rainfalls occur for a short period of time and over small catchments. This has led to limitation in

usage of the rational method. The method is most accurate for estimating discharge for areas smaller than

0.5 m2.

Methodology 26

1. Frequency Factors, Cf

Infiltration and other losses have lesser effect on runoff for less frequent higher intensity storm. Depending

on the return period selected for design, a frequency factor to account for increase in runoff with higher

frequency storms is given in the following table [3.4](Hydrology manual)

Table 3.4 Frequency Factors for Rational Formula

Recurrent Interval(years) Cf

5 1.00

10 1.00

25 1.10

50 1.20

100 1.25

2. Runoff Coefficient, C

The method takes into consideration the different runoff affecting parameters such as infiltration, slopes,

land use and cover through a runoff coefficient, C. The runoff coefficient is determined based on ERA’s

recommendations.

The major soil on the entire route corridor and the catchments for the streams and rivers is Soil Group B.

Based on the land use and nature of the land cover, conservative values of C ranging from 0.35 to 0.52 are

adopted for this study.

3. Rainfall Intensity, I

Rainfall intensity is determined from IDF curves for a selected return period and duration equalling time it

takes water to reach the outlet point from the most remote point on the catchment, time of concentration.

The discharge at the outlet point is high when the entire basin is contributing to the flow, i.e. after the time

of concentration has elapsed. For larger catchments with longer time of concentrations, the rainfalls would

not have similar intensities in time and space, hence the limitation of the area for rational method

applicability.

4. Time of Concentration, Tc

ERA recommends three formulas for three components of flow in basins: sheet flow that would occur in

plane surfaces with depths of 3cms, overland flow that builds up on sheet flow after a distance of 100ms

and open channel flow. With the project road section basins located on mountainous areas with steep

slopes, uneven ground is expected for the overland flow to occur.

The terrain additionally does not incur occurrence of distinctly separate sheet flow and shallow

concentrated flow as stipulated in ERA. Hence, overland flow with a single time of concentration equation,

taken from SANRAL Drainage Manual, is used. A formula developed by US SCS, similarly presented in

SANRAL Drainage Manual, is also used in place of ERA’s recommended equation for time of

concentration in open channel flows, as use of Manning's equation is unsuitable for channels with

significantly varying cross sections, changing slopes and also varying roughness coefficient.


For overland flows at the upper lengths of each stream, the following formula has been used to compute

time of concentration shown in equation [3.10].

[3.10]

Where:

Tc; time of concentration, hours

r; roughness coefficient (shown in table 3.5)

L; hydraulic length of catchment measured along flow path, km

S; slope of the catchment S= (H/ (100*L)), m/m

H; height of most remote point above outlet of catchment, m

Table 3.5 Recommended value for r (Hydrology manual)

Surface Description Recommended Value of r

Paved Areas 0.02

Clean compacted soil, no stones 0.1

Sparse grass over fairly rough surface 0.3

Medium grass cover 0.4

Thick grass cover 0.8

The SCS Unit Hydrograph Method

This method of runoff assessment is based on physical considerations of runoff generated by rainfall and

takes into account specific catchment parameters such as slopes, area, infiltration rates and catchment shape

factors. These physical characteristics are combined with rainfall depth-duration-frequencies to yield

estimates of peak runoff.

The method enables the generation of different return periods flood by introducing parameter called the

Curve Number (CN), which is estimated from the hydrological soil group together with the classification of

land use of each catchment area.

The SCS runoff equation is used for estimating direct runoff from 24-hour or 1-day storm rainfall by the

equation [3.11] given below:

[3.11]

Where:

Q; accumulated direct runoff, mm

P; accumulated rainfall (potential maximum runoff), mm

Ia; initial abstraction including surface storage, interception, and infiltration prior to runoff, mm

S; potential maximum retention, mm.

Methodology 28

The relationship between Ia and S was developed from experimental catchment area data. It removes the

necessity for estimating Ia for common usage. The empirical relationship used in the SCS runoff equation

is:

Ia = 0.2 * S [3.12]

Substituting 0.2 * S for Ia in equation above, the SCS rainfall-runoff equation becomes:

[3.13]

S is related to the soil and cover conditions of the catchment area through the CN. CN has a range of 0 to

100, and S is related to CN by:

[3.14]

1. Curve Number

The curve numbers of the catchments were derived from topographic maps of 1:50,000, and 250,000

scales, satellite imagery, soil and geological maps and site reconnaissance data. The curve numbers for the

drainage areas for crossings have been estimated based on ERA’s recommendations. Values ranging from

82 to 85, as very conservative values for lands of mixture of small trees and brush with hydrologic soil

group B needed to be adopted for the fact that the surface or the underlying rocky layers in the areas would

result in higher runoffs.

Antecedent Moisture Conditions for the catchment areas have been adopted from ERA’s recommendations

on antecedent moisture conditions for the different hydrologic regions in Ethiopia. The antecedent moisture

condition for region A1 is accordingly assumed average and this will be adopted for the design.

Conversions of CNs are only required for antecedent moisture conditions different from average.

Consequently, no conversions have been made on the original assumed values of CNs.

2. Daily Maximum rainfalls

The calculation results from rainfall data analysis for daily maximum rainfall are taken in directly.

3. Time of Concentration

Time of concentration shall be computed following the procedure specified in the rational method

computation.


3.3.5. Aqua Crop Model

Aqua Crop model is normally used to examine the yield response of crops to water and is widely used for

the design and management of irrigation schemes (FAO I&D 33). By altering the input, the expected crop

production and yield can be simulated for different environmental conditions and the crop responses to

environment changes can be understood from the simulated result of the Aqua Crop model.

This model is applicable to all major herbaceous crop types: fruit or grain crops; root and tuber or storage-

stem crops; leafy or floral vegetable crops, and forage crops typically subjected to several cuttings per

season. For all but forage crops, the key developmental stages are: emergence, start of flowering or

root/tuber/storage-stem initiation, time when maximum rooting depth is reached, start of canopy

senescence, and physiological maturity. For forage cops, the list may be shortened to only emergence or

start of re-growth in spring, time of cuttings, and start of senescence.

In this study the main focus in simulating Aqua Crop is to address the crop water requirement and crop

yield production in response to water. In the research area, most of the agriculture is rain fed. To deal with

the relation between crop yield and water use, the suggested equation [3.20] was relative yield reduction

related to the corresponding relative reduction in evapotranspiration (ET). In equation [3.20] the yield

response to ET is expressed (FAO, 2012).

[3.20]

Where:

Yx and Ya are the maximum and actual yields

ETx and ETa are the maximum and actual evapotranspiration

Ky is a yield response factor representing the effect of a reduction in evapotranspiration on yield losses.

The yield response factor (Ky) captures the essence of the complex linkages between production and water

use by a crop, where many biological, physical and chemical processes are involved. The relationship has

shown a remarkable validity and allowed a workable procedure to quantify the effects of water deficits on

yield. The procedures used to quantify the yield response to water deficits using the Equation 3.20 above

are briefly described below

Calculation Procedures

The calculation procedure for Equation 3.20 to determine actual yield Ya has four steps:

i. Estimate maximum yield (Yx) of an adapted crop variety, as determined by its genetic makeup and

climate, assuming agronomic factors (e.g. water, fertilizers, pest and diseases) are not limiting.

ii. Calculate maximum evapotranspiration (ETx) according to established methodologies and considering

that crop-water requirements are fully met.

iii. Determine actual crop evapotranspiration (ETa) under the specific situation, as determined by the

available water supply to the crop.

iv. Evaluate actual yield (Ya) through the proper selection of the response factor (Ky) for the full growing

season or over the different growing stages.

Methodology 30

Evolving concepts in yield response to water

Intercepted solar radiation is the driving force for both crop transpiration and photosynthesis. A direct

relation exists therefore between biomass production and water consumed through transpiration. Water

stress and reduced transpiration result in a reduced biomass production that normally also reduces yields.

The yield response to water approach adopted in the FAO Irrigation and Drainage Paper No. 33

(Doorenbos and Kassam, 1979)

The management

Aqua Crop encompasses two categories of management practices: the irrigation management, which is

quite complete in its various features, and the field management, which is limited to selected aspects and is

relatively simple in approaches.

Irrigation management

Here options are provided to assess and analyze crop production as well as water management and use,

under either rainfed or irrigated conditions. Management options include the selection of water application

methods (sprinkler, surface, or drip either surface or underground), defining the schedule by specifying the

time, depth and quality of the irrigation water of each application, or let the model automatically generate

the schedule based on fixed time interval, fixed depth per application, or fixed percentage of allowable

water depletion. An additional feature is the estimation of full water requirement of a crop in a given

climate.

Model Input data

The input for Aqua Crop model comprises daily climatic (sunshine hour, daily minimum and maximum

temperature, daily rain fall) crop phenology related to diverse characteristics of the canopy; rooting depth

as well as response to water, salinity and fertility stress by the crop. Figure 3.14 provides details.


Figure 3.13 Aqua Crop flow chart (FAO, 2012)

Where: I, irrigation;

Tn, minimum air temperature;

Tx, maximum air temperature;

ETo, reference evapotranspiration;

E, soil evaporation;

Tr, canopy transpiration;

gs, stomatal conductance;

WP, water productivity;

HI, harvest index;

CO2, atmospheric carbon dioxide concentration;

(1), (2), (3), (4), water stress response functions for leaf expansion, senescence, stomatal

conductance and harvest index, respectively

Calibration:

Aqua crop was calibrated for the major crops in the study area using the extensive data input presented in

Table 3.6 for wheat crop and in Table 3.7 for Barely Crop. The model was set-up in such a way that there is

no any water, fertility, salinity or any other stress so as to obtain the maximum possible yield and biomass.

These are compared with the maximum potential yields from advisory literature to assess the level of the

model.

Methodology 32

Table 3.6 Calibrations parameter for Wheat crop (Aqua crop manual)

1.Crop Penology

Symbol Description Type(1),(2),(3),(4)

Values/ranges

1.1 Threshold air temperature

Tbase Base temperatures(OC) Conservative

(1) 0.0

Tbase Upper temperatures(OC) Conservative

(1) 26.0

1.2 Development of green canopy cover

CCo Soil surface covered by an individual seedling at

90% emergence(cm2/plant)

Conservative(2)

1.50

Number of plants per hectare Management(3)

2,000,000 -

7,000,000

Time from sowing to emergence(growing degree

day)

Management(3)

100 - 250

CGC Canopy growth coefficient (fraction per growing

degree day)

Conservative(1)

0.005 - 0.007

CCx Maximum canopy cover (%) Management(3)

80 - 99 %

Time from sowing to start senescence(growing

degree day)

Cultivar(4)

Time to

emergence +

1000 - 2000

CDC Canopy decline coefficient(fraction per growing

degree day)

Conservative(1)

0.004

Time from sowing to maturity, i.e., length of crop

cycle (growing degree day)

Cultivar(4)

Time to

emergence

+1500 - 2900

1.3 Flowering

Time from sowing to flowering(growing degree day) Cultivar(4)

Time to

emergence +

1500 - 2900

Length of the flowering stage(growing degree day) Cultivar(4)

150 - 280

Crop determinacy linked with flowering Conservative(1)

Yes

1.4 Development of root zone

Zn Minimum effective rooting depth(m) Management(3)

0.30

Zx Maximum effective rooting depth(m) Management(3)

Up to 2.40

Shape factor describing root zone expansion Conservative(1)

1.5

2. Crop transpiration

KcTr,x Crop coefficient when canopy is complete but prior

to senescence

Conservative(1)

1.10

Decline of crop coefficient (%/day) as a result of

ageing, nitrogen deficiency, etc

Conservative(1)

0.15

Effect of canopy cover on reducing soil evaporation

in late season stage

Conservative(1)

50

3. Biomass production and yield formation

3.1 Crop water productivity

WP* Water productivity normalized for ETo &

CO2(gram/m2)

Conservative(1)

15.0

Water productivity normalized for ETo and

CO2during yield formation (as percent WP* before

yield formation)

Conservative(1)

100


3.2 Harvest Index

HIo Reference harvest index (%) Cultivar (4)

45 - 50

Possible increase (%) of HI due to water stress

before flowering

Conservative(1)

Small

Excess of potential fruits (%) Conservative(2)

Medium

Coefficient describing positive impact of restricted

vegetative growth during yield formation on HI

Conservative(1)

Small

Coefficient describing negative impact of stomatal

closure during yield formation on HI

Conservative(1)

Moderate

Allowable maximum increase (%) of specified HI Conservative(1)

15

4. Stresses

4.1 Soil water stresses

Pexp,lower Soil water depletion threshold for canopy expansion

- Upper threshold

Conservative(1)

0.20

Pexp,upper Soil water depletion threshold for canopy expansion

- Lower threshold

Conservative(1)

0.65

Shape factor for Water stress coefficient for canopy

expansion

Conservative(1)

5.0

psto Soil water depletion threshold for stomatal control -

Upper threshold

Conservative(1)

0.65

Shape factor for Water stress coefficient for stomatal

control

Conservative(1)

2.5

psen Soil water depletion threshold for canopy

senescence - Upper threshold

Conservative(1)

0.70


senescence

Conservative(1)

2.5

Ppol Soil water depletion threshold for failure of

pollination - Upper threshold

Conservative(1)

0.85

(Estimate)

Vol% at anaerobiotic point (with reference to

saturation)

Cultivar(4)

Environment (3)

Moderately

tolerant to

water logging

4.2 Air temperature stress

Minimum air temperature below which pollination

starts to fail (cold stress)(oC)

Conservative(1)

5.0 (Estimate)

Maximum air temperature above which pollination

starts to fail (heat stress) (°C)

Conservative(1)

35.0

(Estimate)

Minimum growing degrees required for full biomass

production (°C - day)

Conservative(1)

13.0-15.0

(Estimated)

4.3 Salinity stress

ECen Electrical conductivity of the saturated soil-paste

extract: lower threshold (at which soil salinity stress

starts to occur)

Conservative(1)

6.0

ECex Electrical conductivity of the saturated soil-paste

extract: upper threshold (at which soil salinity stress

has reached its maximum effect)

Conservative(1)

20.1

1) Conservative generally applicable

(2) Conservative for given specie but can or may be cultivar specific

(3) Dependent on environment and/or management

(4) Cultivar specific

Methodology 34

Table 3.7 Calibrations parameter for Barely crop (Aqua crop manual)

1.Crop Penology

Symbol Description Type(1),(2),(3),(4)

Values/ranges

1.1 Threshold air temperature

Tbase Base temperatures(OC) Conservative

(1) 0.0

Tbase Upper temperatures(OC) Conservative

(1) 15

1.2 Development of green canopy cover

CCo Soil surface covered by an individual seedling at 90%

emergence(cm2/plant)

Conservative(2)

1.50

Number of plants per hectare Management(3)

1,500,000 -

3,000,000

Time from sowing to emergence(growing degree day) Management(3)

90 - 200

CGC Canopy growth coefficient (fraction per growing degree

day)

Conservative(1)

0.008

CCx Maximum canopy cover (%) Management(3)

50 - 99

Time from sowing to start senescence(growing degree

day)

Cultivar(4)

900 - 2, 000

CDC Canopy decline coefficient(fraction per growing degree

day)

Conservative(1)

0.006

Time from sowing to maturity, i.e., length of crop cycle

(growing degree day)

Cultivar(4)

1296

1.3 Flowering

Time from sowing to flowering(growing degree day) Cultivar(4)

700 - 1,300

Length of the flowering stage(growing degree day) Cultivar(4)

150 - 250

Crop determinacy linked with flowering Conservative(1)

Yes

1.4 Development of root zone

Zn Minimum effective rooting depth(m) Management(3)

0.30

Zx Maximum effective rooting depth(m) Management(3)

Up to 2.50m

Shape factor describing root zone expansion Conservative(1)

15

2. Crop transpiration

KcTr,x Crop coefficient when canopy is complete but prior to

senescence

Conservative(1)

1.10

Decline of crop coefficient (%/day) as a result of ageing,

nitrogen deficiency, etc

Conservative(1)

0.15

Effect of canopy cover on reducing soil evaporation in

late season stage

Conservative(1)

50

3. Biomass production and yield formation

3.1 Crop water productivity

WP* Water productivity normalized for ETo& CO2(gram/m2) Conservative

(1) 15.0

Water productivity normalized for ETo and CO2during

yield formation (as percent WP* before yield formation)

Conservative(1)

100

3.2 Harvest Index

HIo Reference harvest index (%) Cultivar (4)

30 - 50

Possible increase (%) of HI due to water stress before

flowering

Conservative(1)

Small

Excess of potential fruits (%) Conservative(2)

Medium

Coefficient describing positive impact of restricted

vegetative growth during yield formation on HI

Conservative(1)

Small


Coefficient describing negative impact of stomatal

closure during yield formation on HI

Conservative(1)

Moderate

Allowable maximum increase (%) of specified HI Conservative(1)

15

4. Stresses

4.1 Soil water stresses

Pexp,lower Soil water depletion threshold for canopy expansion -

Upper threshold

Conservative(1)

0.20

Pexp,upper Soil water depletion threshold for canopy expansion -

Lower threshold

Conservative(1)

0.65


expansion

Conservative(1)

3.0

psto Soil water depletion threshold for stomatal control -

Upper threshold

Conservative(1)

0.60

Shape factor for Water stress coefficient for stomatal

control

Conservative(1)

3.0

psen Soil water depletion threshold for canopy senescence -

Upper threshold

Conservative(1)

0.55


senescence

Conservative(1)

3.0

Ppol Soil water depletion threshold for failure of pollination -

Upper threshold

Conservative(1)

0.85

Vol% at anaerobiotic point (with reference to saturation) Cultivar(4)

Environment (3)

15

4.2 Air temperature stress

Minimum air temperature below which pollination starts

to fail (cold stress)(oC)

Conservative(1)

5.0

Maximum air temperature above which pollination starts

to fail (heat stress) (°C)

Conservative(1)

35.0

Minimum growing degrees required for full biomass

production (°C - day)

Conservative(1)

14

4.3 Salinity stress

ECen Electrical conductivity of the saturated soil-paste extract:

lower threshold (at which soil salinity stress starts to

occur)

Conservative(1)

6.0

ECex Electrical conductivity of the saturated soil-paste extract:

upper threshold (at which soil salinity stress has reached

its maximum effect)

Conservative(1)

20.1

(1) Conservative generally applicable

(2) Conservative for given specie but can or may be cultivar specific

(3) Dependent on environment and/or management

(4) Cultivar specific

36

Areal rainfall

The areal rainfall throughout the station was calculated by using the Thiessen polygon method and weight

is given for the stations. The calculated weight is used as an input for the model.

Thiessen polygon

The Thiessen polygon approach used for this case is; - the area is separated into N number of sub regions,

more or less every sub regions centered to the rainfall station. For this study all the sub regions are

described as a manner of every sub regions are nearer to their central gauges than to any other gauges.

Later on, describing the number of sub- regions and their respective areas (As), the weight is determined as

Ws= AS/A and the spatial average rainfall is calculated below in equation [4.1]

[4.1]

Where : Areal average rainfall, P: Rainfall measured at sub regions, As: Area of sub regions and A:

Total area of the sub regions.

4.1. Runoff from gauged catchment

In the study area three sub catchments have measured daily runoff records for a period of 13 years between

1994 and 2007 for Genfel, for Agulea and Sulluh it is for period of 12 years during the 1994 - 2006.

Using the DEM 20 by 20 meter resolution the area of the gauged catchment for Genfel River was

characterised by 42% cultivated land, 31.3% bush land and 12.5% forest and plantation. For Agulea the

corresponding percentages were 49%, 28.6% and 12.8% in the same order, the percentages Sulluh River are

48.5%, 20% and 12.1%. The daily measured data of runoff from the gauged station was analyzed for

consistency and result of the analysis shows some of the records are non-dependable.

CHAPTER 4

Result and Discussion


4.1.1. Model calibration For this study HBV model was used to simulate the runoff. Before validation, the model should be

calibrated because models are placing from parsimonious lumped to complex distributed physically based,

since it is hard to figure out all the parametric values needed through field measurement. Measured

discharges were used for calibration, it proposed at the water balance and the overall shape agreement of

the observed discharge NS and RVE respectively .The aim of NS and RVE functions shown in equation

[3.8] and [3.7].

To simulate the runoff, measured values from the set of data of 1994 to 2007 was separated into three

categories: the first 1994 data was used to warm up the model, the second set of data from 1995-2000 was

used for the calibration of the model and the last category from 2001 to 2007 was used to validate the

model. The results of analysis from the model calibrations as demonstrated in Table 4.1 shown that the

model performance of Agula and Sulluh is found to be satisfactory with R2 greater than 0.7 and RVE

smaller than +5% or -5%.

Table 4.1 Calibrated model parameters for gauged catchments

Parameter Agula Sulluh

Alfa 0.5 1

Beta 1.5 1.7

FC 250 300

Hq 5.27 2.75

K4 0.005 0.007

KHQ 0.037 0.052

LP 1 0.95

PERC 0.41 0.5

NS[ - ] 0.86 0.91

RVE[ % ] 0.52 1.2

As it could be depicted from the output of the model calibration the result was found to be good. As it was

assumed that rainfall – runoff time series of those catchments was satisfactory and that the model

parameters of those catchments could be used for regionalization. Some of the gauging stations have easy

road access.

Time of concentration which is defined as the duration of time it takes for a drop of rain water to travel

from hydrologically most remote point to the outlet of a catchment was computed to see the effect of

manual daily gauging stations.

[4.2]

Where: TC: Time of concentration [hr], LC: Distance from the outlet to the center of the catchment [km], L:

Length of the main stream [km] and S: Slope of the maximum flow distance path (Dingman, 2002).

38

The calibration result as shown in figure 4.1 for Sulluh catchment the Nash Sutcliffe coefficient and

relative volume errors are 0.91 and 1.2 respectively. The Nash Sutcliffe coefficient measures the efficiency

of the model by finding the relationship between the goodness-of-fit of the model and the variance of the

measured data. Values between 0.8 and 0.9 mean that the model performs well, and then the calibration

result of the Sulluh catchment was found to be good.

Figure 4.1 Model calibration result of Sulluh catchment (1994-2002)

0

10

20

30

40

50

01-0

1-9

4

01-0

5-9

4

29-0

8-9

4

27-1

2-9

4

26-0

4-9

5

24-0

8-9

5

22-1

2-9

5

20-0

4-9

6

18-0

8-9

6

16-1

2-9

6

15-0

4-9

7

13-0

8-9

7

11-1

2-9

7

10-0

4-9

8

08-0

8-9

8

06-1

2-9

8

05-0

4-9

9

03-0

8-9

9

01-1

2-9

9

30-0

3-0

0

28-0

7-0

0

25-1

1-0

0

25-0

3-0

1

23-0

7-0

1

20-1

1-0

1

20-0

3-0

2

18-0

7-0

2

15-1

1-0

2

Date

Rainfall-runoff

Observed [mm] Simulated


Figure 4.2 Model calibration result of Agula catchment (1994-2001)

4.1.2. Model Validation

The model approach may not be as such accurate because of the unreliability of rainfall, climate change

and various parameters; it is difficult to exactly represent the real world with a model. When there is only

one field situation simulated, models are considered to be uncertain and are not reliable. Obviously, model

doesn’t accurately represent the real world under which different complex hydrological stress conditions

are existing despite the fact that the behaviour of the world system the optimal and calibrated model

parameters are used to minimize the uncertainty(Rientjes,2007). For this particular study to validate the

model, model parameters had been tasted against another independent set of stress conditions; in this case

study data from 2002 to 2006 was used for validation for Agula catchment, validation data from 2003 to

2006 was selected for and Sulluh.

The validation duration of the model was intended to be used after making sure that the calibration model

parameter sets are not failed and or otherwise the model should be calibrated again with a new set of model

parameters until the model validation meets calibration directs by the set of model parameter values. The

model parameters used for the catchments satisfying the objective function values of calibration period and

the result is shown in table [4.3] below.

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70 01-0

1-9

4

11-0

4-9

4

20-0

7-9

4

28-1

0-9

4

05-0

2-9

5

16-0

5-9

5

24-0

8-9

5

02-1

2-9

5

11-0

3-9

6

19-0

6-9

6

27-0

9-9

6

05-0

1-9

7

15-0

4-9

7

24-0

7-9

7

01-1

1-9

7

09-0

2-9

8

20-0

5-9

8

28-0

8-9

8

06-1

2-9

8

16-0

3-9

9

24-0

6-9

9

02-1

0-9

9

10-0

1-0

0

19-0

4-0

0

28-0

7-0

0

05-1

1-0

0

13-0

2-0

1

24-0

5-0

1

01-0

9-0

1

10-1

2-0

1

Date

Rainfall-runoff

Observed [mm] simulated

40

Table 4.2 Model validation from year 2003-2006 for Agula and Sulluh.

Catchments NS [ - ] RVE [ % ]

Agula 0.81 -2.57

Sulluh 0.79 1.1

The result of model validation has revealed a good performance was found for Agulla as compared to the

calibration period for Sulluh which was found to be moderate. Generally the model validation performance

showed that for all catchments, the result of NS is greater than 0.7 which is considered to be reasonable

performance and RVE less than – 5% and + 5%

Figure 4.3 Model Validation result of Sulluh catchment (2003-2006)

0

2

4

6

8

10

12

14

01-0

1-0

3

11-0

4-0

3

20-0

7-0

3

28-1

0-0

3

05-0

2-0

4

15-0

5-0

4

23-0

8-0

4

01-1

2-0

4

11-0

3-0

5

19-0

6-0

5

27-0

9-0

5

05-0

1-0

6

15-0

4-0

6

24-0

7-0

6

Date

Rainfall-runoff



Figure 4.4 Model Validation result of Agula catchment (2002-2006)

Overall result from the model simulation from every catchment: the calibration result of Calculated or

simulated discharge for Agula and Sulluh are 326 MCM/year from 1994 to 2001 and 426 MCM/year from

1994 to 2002 respectively. Simulated result for Validation period for catchment Agula and sulluh is 499

MCM/year from 2002 to 2006 and 806 MCM/year from 2003 to 2006 respectively.

The result obtained from the model shows for the Genfel catchment there is a big difference on the

discharge from the calibration period and validation period. For the other catchment the result for

calibration and validation period doesn't have a big variation that means comparatively well.

For Genfel catchment from 1994 to 1999, there was a consistently large runoff, this decreased significantly

to almost zero from 2000 to 2007. As it can be observed from Figure 4.5, the correlation between rainfall

and observed runoff- The R-square is 0.5% meaning only 0.5% of the runoff generated is explained by the

rainfall. However, there was not significantly less amount of rainfall from 2000 to 2007 as compared to the

previous 5 years. In other words as displayed in Figure 4.6 the rainfall data appears very reasonable, but the

runoff data is extremely small and suggests that the river is dried out.

0

10

20

30

40

50

01-0

1-0

2

11-0

4-0

2

20-0

7-0

2

28-1

0-0

2

05-0

2-0

3

16-0

5-0

3

24-0

8-0

3

02-1

2-0

3

11-0

3-0

4

19-0

6-0

4

27-0

9-0

4

05-0

1-0

5

15-0

4-0

5

24-0

7-0

5

01-1

1-0

5

09-0

2-0

6

20-0

5-0

6

28-0

8-0

6

06-1

2-0

6

Date

Rainfall-runoff


42

Figure 4.5 Relation between runoff and rainfall for Genfel River

Long-term average monthly runoff and rainfall for the study period, as shown in the figure 4.5 the rainfall

data it is a uni-modal rainfall. But the runoff is seems like controlled flow, it shows a runoff in the dry

season the correlation for the monthly rainfall and runoff data is poor, the rainfall is only capturing 43% of

the runoff variation.

Figure 4.6 Observed flow and rainfall of Genfel catchment

y = 0.365x + 12.33 R² = 0.005

0

50

100

150

200

250

300

350

400

450

0 10 20 30 40 50 60

Ru

no

ff m

3/s

Rainfall mm

Genfel catchment

0

300

600

900

1200

1500

0

5000

10000

15000

20000

25000

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Runoff m3/s

Rainfall mm/day


Based on the information obtained from discussions with the responsible authorities to verify the runoff

data obtained for calibration, though there are no as such written information or quantitative analyses done,

one possible reasons for the very small runoff and river base flow for Genfel catchment could be the dam

construction after the year 1999 that is abstracting significant amount of the flow as well as soil

conservation measures that contributed measurably to reducing the runoff generated and the other could be

error in data recording.

4.1.3. Results of runoff from road The estimated quantity of peak discharge from the 10 Km length of road by using rational method was

found to be around 35m3/s and using SCS Unit Hydrograph method the amount of peak discharge

contributed was 100 m3/s from 42 Km. The detailed results are presented in Tables 4.3 and 4.4.

Table 4.3 Estimated discharge from the road using rational method

No

Flow

Dir.

BasinA.

(km^2)

MFL

(km)

S.

(m/m) C CN

Tc.

(min) Cf

I.

(mm/hr)

24hr

rainfall

depth

Q.

(m^3/sec)

1 Left 0.3 1.08 0.07 0.36 82 30.69 1 65.62 68.75 1.22

2 Left 0.38 1.65 0.03 0.35 82 47.88 1 51.36 68.75 1.47

3 Right 0.17 0.75 0.12 0.36 82 18.62 1 89.93 68.75 0.62

4 Right 0.16 0.92 0.18 0.42 82 23.75 1 77.95 68.75 0.75

5 Right 0.45 1.71 0.18 0.42 82 29.24 1 80.17 78.52 1.67

6 Right 0.39 1.61 0.16 0.42 82 30.44 1 78.07 78.52 1.49

7 Right 0.61 0.79 0.07 0.36 82 23.72 1 78.01 68.75 1.06

8 Right 0.37 1.13 0.05 0.35 82 38.24 1 59.36 68.75 1.43

9 Right 0.4 1.03 0.05 0.35 82 37.01 1 71.59 78.52 1.52

10 Right 0.21 0.83 0.02 0.35 82 41.3 1 56.82 68.75 0.92

11 Right 0.29 1.28 0.05 0.35 82 39.93 1 57.96 68.75 1.2

12 Right 1.14 0.9 0.05 0.35 82 28.7 1 68.64 68.75 1.7

13 Left 0.41 1.04 0.03 0.35 82 45.96 1 52.95 68.75 1.55

14 Left 0.18 0.71 0.04 0.35 82 30.45 1 65.82 68.75 0.85

15 Left 0.37 2.05 0.05 0.35 82 34.86 1 73.71 78.52 1.44

16 Left 0.34 1.23 0.03 0.35 82 40.52 1 57.47 68.75 1.14

17 Left 0.23 1.5 0.32 0.42 82 16.11 1 117.08 78.52 1

18 Left 0.34 1.6 0.28 0.42 82 16.9 1 113.73 78.52 1.33

19 Left 0.34 1.17 0.51 0.42 82 15.47 1 119.79 78.52 1.34

20 Left 0.36 1.3 0.4 0.42 82 15.03 1 121.66 78.52 1.4

21 Left 0.25 0.81 0.05 0.35 82 30.72 1 65.6 68.75 1.01

22 Right 0.28 1.45 0.03 0.35 82 42.73 1 55.63 68.75 1.15

23 Right 0.45 1.43 0.04 0.35 82 41.2 1 67.45 78.52 1.66

24 Right 0.19 0.78 0.04 0.35 82 31.9 1 64.62 68.75 0.86

25 Left 0.47 1.23 0.05 0.35 82 38.68 1 69.93 78.52 1.71

26 Right 0.31 0.78 0.04 0.35 82 33.36 1 63.41 68.75 0.88

27 Right 0.13 0.65 0.03 0.35 82 33.63 1 63.19 68.75 0.65

28 Left 0.17 0.7 0.03 0.35 82 38.22 1 59.38 68.75 0.78

29 Left 0.16 0.76 0.02 0.35 82 54.08 1 46.22 68.75 0.77

30 Right 0.15 0.54 0.03 0.35 82 37.96 1 59.59 68.75 0.74

44

Table 4.4 Estimated discharge from the road using SCS Unit Hydrograph method

No

Flow

Dir.

BasinA.

(km^2)

MFL

(km)

S.

(m/m) C CN

Tc.

(min) Cf

I.

(mm/hr)

24hr

rainfall

depth

Q.

(m^3/sec)

1 Right 0.72 1.22 0.08 0.36 82 33.39 1 78.11 90.45 2.37

2 Right 5.10 5.6 0.19 0.42 82 67.16 1.1 55.4 89.93 10.61

3 Right 1.27 1.88 0.06 0.35 82 47.67 1 61.07 78.52 3.6

4 Right 0.76 1.71 0.06 0.36 82 36.67 1 71.92 78.52 2.46

5 Right 2.83 3.32 0.09 0.36 82 45.37 1 63.33 78.52 6.58

6 Left 0.96 1.91 0.14 0.36 82 30.99 1 77.52 78.52 2.92

7 Left 3.63 3.65 0.25 0.42 82 34.73 1.1 88.98 89.69 8.17

8 Left 0.83 1.78 0.04 0.35 82 42.04 1 66.62 78.52 2.63

9 Left 3.07 3.89 0.24 0.42 82 30 1.1 94.6 89.69 7.2

10 Left 2.88 3.61 0.35 0.42 82 28.38 1.1 98.93 89.69 6.87

11 Left 0.96 1.94 0.1 0.36 82 28.78 1 81.19 78.52 2.93

12 Left 0.82 2.03 0.22 0.42 82 23.03 1 93.91 78.52 2.61

13 Left 1.79 2.12 0.26 0.42 82 23.52 1 92.83 78.52 4.67

14 Right 0.79 1.92 0.04 0.35 82 49.26 1 59.5 78.52 2.53

15 Right 1.01 1.72 0.03 0.35 82 58.89 1 50 78.52 3.05

16 Left 4.38 4.31 0.03 0.42 82 91.79 1 35.98 78.52 9.13

17 Left 0.98 1.53 0.03 0.35 82 50.89 1 57.88 78.52 2.98

18 Right 7.97 5.76 0.03 0.35 82 105.39 1.1 39.54 89.69 14.74

19 Left 1.25 2.1 0.03 0.35 82 69.94 1 44.76 78.52 3.57

1 Right 0.72 1.22 0.08 0.36 82 33.39 1 78.11 90.45 2.37

2 Right 5.22 5.6 0.19 0.42 82 67.16 1.1 55.4 89.93 10.73

3 Right 1.27 1.88 0.06 0.35 82 47.67 1 61.07 78.52 3.6

4 Right 0.76 1.71 0.06 0.36 82 36.67 1 71.92 78.52 2.46

5 Right 2.83 3.32 0.09 0.36 82 45.37 1 63.33 78.52 6.58

6 Left 0.96 1.91 0.14 0.36 82 30.99 1 77.52 78.52 2.92

7 Left 3.63 3.65 0.25 0.42 82 34.73 1.1 88.98 89.69 8.17

8 Left 0.83 1.78 0.04 0.35 82 42.04 1 66.62 78.52 2.63

9 Left 3.07 3.89 0.24 0.42 82 30 1.1 94.6 89.69 7.2

10 Left 2.88 3.61 0.35 0.42 82 28.38 1.1 98.93 89.69 6.87

11 Left 0.96 1.94 0.1 0.36 82 28.78 1 81.19 78.52 2.93


4.2. Crop water requirement and its potential

The yield of the major crops, wheat and barley was obtained from Aqua crop model under three scenarios:

the results are given in Table 4.5.

Table 4.5 Crop and water productivity under different scenarious

Scenario description Crop yield in ton/ha Water productivity in kg/m3

Wheat Barely Wheat Barley

Scenario 1: good rainfall season

with a uniform distribution of

water

2.5 2.4 0.6 0.5

Scenario 2: rainfall stops in

August 10,in the middle of the

cropping season

1.2 1.3 0.47 0.41

Scenario 3: testing the impacts

of supplementary irrigation

from runoff water- see schedule

in Table 4.6.

3.1 2.8 0.7 0.76

Table 4.6 Irrigation schedule

Event Date Day No. Application depth(mm) EC(ds/m)

1 4 July 1 20 0

2 8 July 5 20 0

3 13 July 10 50 0

4 23 July 20 50 0

5 2 August 30 50 0

6 12 August 40 50 0

7 22 August 50 50 0

8 1 September 60 50 0

9 11 September 70 50 0

Here discuss comparing the results from the three scenarios

Scenario one:

From the simulation result of the Aqua crop model Evapotranspiration (mm), Biomass production (ton/ha)

and Yield (ton/ha), respectively, was found to be: 342.5, 7.89 and 2.6 are. Biomass produced since the start

of simulation: actual produced biomass is 7.89 ton/ha and the potential biomass 8.34 ton/ha. The ET water

productivity is 0.76 kg (yield) per m3 water evapotranspired.

Scenario two:

Based on the climatic data and the information gathered from farmers, rainfall starts in the month of June

and ends on August 10. The rainfall stops almost in the middle of the growing season and normally this

happens before yield formation. This brings about water stress and significantly reduced the yield of barely.

The response for barely, as it was seen from the Aqua crop simulation, indicates that the yield in terms of

production per unit area decreases from 2.6 ton/ha to 1.1 ton/ha, biomass production from 8ton/ha to 4

ton/ha and evapotranspiration from 342.5mm to 292.1mm.

46

From the simulation result there is a reduction of yield by1.5ton/ha, biomass production by 4 ton/ha and

Evapotranspiration by 50.4mm. This result implies a decrease in household income generation as well as

consumption. Due to the unreliability of rainfall the farmers face difficulties in fulfilling their consumption

requirements and other household needs. A 1.5 ton/ha reduction means that the household loses

15quintal/ha of yield because the rain stops before crop maturity (Note: one quintal =100kg).

Scenario three:

In this scenario the Aqua crop simulation was done by taking into consideration the actual rainfall during

the season with uniform rainfall in addition to supplemental irrigation. The simulation result of the aqua

crop model for the third scenario was: 2.74 ton/ha yield production, 8.31 ton/ha biomass production and

357.3mm Evapotranspiration. According to (FAO, 2012) the average yield response of barley is 2.8

tone/ha. The third scenario in which supplemental irrigation is used results in comparatively good

production and crop water requirements.

The crop yield and water productivity were also analysed for the period 2002 to 2012 considering the

rainfall as the only variable input.

For barely in Hawzen (Table 4.7), the simulated crop yield ranges from 1.3 - 2.7 ton/ha and the crop water

productivity from 0.4 - 0.7 kg/m3. This difference is mainly brought about by poor distribution of rainfall,

not necessarily because there was no sufficient amount of total rainfall. This point is illustrated by Figure 4.

7 and 4.8

Table 4.7 Hawzen barley crop simulation result

Year Yield

(Ton/ha)

Biomass

(Ton/ha)

CropWater

requirement(actual

Evapotranspiration)

ETwater

productivity

(kg/m3)

2002 2.145 7.364 320.4 0.67

2003 1.786 6.340 339.2 0.53

2004 1.985 7.008 354.9 0.56

2005 1.756 6.255 347.6 0.51

2006 2.469 8.710 422.9 0.58

2007 2.611 9.024 443.5 0.59

2008 1.315 4.978 323.8 0.41

2009 2.439 8.575 355.1 0.69

2010 2.691 9.119 391.9 0.69

2011 2.146 7.339 379.5 0.57

2012 2.336 8.320 422.1 0.55


Figure 4.7 Rain fall distribution during the growing period for good yield

48

Figure 4.8 Rainfall distribution during the growing period for minimum yield.

In Sinkata (Freweyni), similar rainfall variability impact on crop and water productivity is observed (Table

4.8). What is unique is that the yield in 2011 was zero and this is caused by extended dry period during the

cropping season.

Table 4.8 Sinkata barely crop simulation result

Year Yield

(Ton/ha)

Biomass

(Ton/ha)

Crop Water requirement(actual

Evapotranspiration)

ETwater productivity

(kg/m3)

2001 2.151 7.654 374.0 0.58

2002 1.651 5.894 310.2 0.53

2003 2.063 7.351 348.5 0.59

2004 2.068 7.187 335.1 0.62

2005 2.371 8.360 372.1 0.64

2006 2.504 8.801 388.1 0.65

2007 1.991 6.994 325.8 0.61

2008 2.085 7.359 346.9 0.60

2009 1.509 5.569 294.3 0.51

2010 2.564 9.004 396.1 0.65

2011 0.00 0.656 53.2 0.0

2012 2.228 7.573 359.6 0.62


Applying supplemental irrigation and hence a no stress condition, the simulated yield reaches up to 2.8

ton/ha for barely crop (see figures 4.9 and 4.10). This corresponds well with the maximum barely yield of

around 3 ton/ha response in FAO, (2012).

Figure 4.9 Simulation barely crop result with supplemental irrigation

50

Figure 4.10 Simulation of barely crop result without supplemental irrigation


When irrigation (schedule is in Table 4.9) is applied in addition to rainfall.The irrigation schedule using

supplemental runoff water that led to the 2.8 ton/ha yield of barely is portrayed in Table 4.9.

Table 4.9 Irrigation schedule in addition to rainfall

Result of Aqua crop Simulation for Hawzen district for wheat crop during the growing season starting from

sowing date, June 16, for every year since 2002 to 2012 is shown below in table [4.10].From the simulation

result, there is a difference in yield and biomass production every year during the cropping season starting

from 2002 to 2012, this shows the unreliability of rainfall from time to time.

The yield in 2002 is very low and in 2007 is good compared to the other years; it shows farmers are facing

problems due to uneven distribution of rainfall. The assessment results are close to the responses gathered

from the farmers by interview and discussion.

52

Table 4.10 Aqua crop result of Hawzen from 2002 to 2012 for wheat crop

Year Yield

(Ton/ha)

Biomass

(Ton/ha)

Crop Water

requirement

ET water productivity

(kg/m3)

2002 1.161 3.869 378.4 0.47

2003 1.607 5.355 406.1 0.52

2004 1.816 6.053 403.7 0.54

2005 1.873 6.237 406.3 0.58

2006 2.014 6.713 396.5 0.60

2007 2.606 8.425 407.9 0.65

2008 1.385 4.615 425.5 0.43

2009 2.517 7.185 338.2 0.72

2010 1.770 5.899 397.1 0.57

2011 1.985 6.618 439.5 0.56

2012 2.277 7.527 420.0 0.62

Similarly, results f rom simulation by Aqua crop model for wheat crop in Sinkata district for the wheat

crop during the growing season since sowing date, from June 16, every year from 2001 to 2012 are shown

below in table [4.11].The simulation result of the model indicates that there is no yield in 2001, 2006 and

2011 at all, the model considered the uneven distribution of rainfall and due to this rainfall distribution it

difficult to cultivate wheat that is why the simulation result comes out to be zero yield for the mentioned

years. In reality, according to the information obtained from the farmers during discussion and interviews,

it happens sometimes that the rain doesn't come on time and it stops during the critical plant germination

time, and hence, it is difficult for the crop to grow. But in practice when this kind of problems happen the

farmers do sowing and cultivation again or shift to other crops that are less moisture demanding or drought

resistant.

The overall result from the model and according to the interview/discussion with farmers living in the area

shows there is often uneven distribution of rain fall and the rain stops before the plant growth period.

Sometimes it happens that there is a large amount of rain that brings water logging and it has influence for

the crop growth/yield production.

Table 4.11 Aqua crop result from 2001 to 2012 for wheat crop

Year Yield(Ton/ha) Biomass(Ton/ha) CropWater

requirement

ETwater

productivity(kg/m3)

2001 0.00 0.821 68.10 0.0

2002 1.011 3.371 241.3 0.42

2003 2.532 7.604 329.4 0.71

2004 1.759 5.863 298.2 0.59

2005 2.282 7.474 332.1 0.69

2006 0.00 0.796 67.50 0.0

2007 2.614 8.704 368.9 0.71

2008 1.675 5.584 305.9 0.55

2009 0.982 3.263 230.2 0.42

2010 1.231 4.147 300.6 0.48

2011 0.00 0.814 94.7 0.00

2012 1.415 7.755 335.4 0.42


Figure 4.11 Dekadal Crop water requirement vs Rainfall for Wheat and Barely

Wheat

As far as crop water requirement is concerned for wheat in Hawzen, it is a very important parameter for all

crops to suffice their water demand for a consumptive use. As it is very well known every crop has got

different growing stages throughout their growing seasons which are known as initial stage, development

stage, mid stage and late or maturity stage. Each stage has got vital role in the overall yield of biomass. So,

as the analysis indicated in figure [4.11], during initial sage of wheat its dekadal crop water requirement

was found to be less than the dekadal rainfall. Therefore, from this result it can be depicted that due to lack

of sufficient amount of water there could not be good germination and emergence which is very crucial part

of yield or production. Not only at the initial stage but also during mid and late stages there was deficiency

of water. Since, these stages are so critical stages, application of supplementary irrigation is a must in order

to obtain a reasonable amount of yield, and hence, here is the siginificance of runoff harvested from roads

becomes evident. For instance, the dekadal crop water requirement at dekad 27 (September 21-30) was

found to be 2.5 mm while the dekadal raifall was found to be 0.05(≈ 0 ) mm which shows that the dekadal

crop water requirement is about 50 times more than the dekadal rainfall which dictates that supplemental

irrigation is a mandatory.

0

1

2

3

4

5

6

7

8

9

10

0 5 10 15 20 25 30 35

CWR of Wheat crop for Hawzen

CWR of Wheat crop for Sinkata

RF of Wheat crop for Hawzen

RF of Wheat crop for Sinkata

CWR of Barely crop for Hawzen

CWR of Barely crop for Sinkata

RF of Barely crop for Hawzen

RF of Barely crop forSinkata

54

Analysis for wheat at Sinkata woreda showed that there is deficit in moisture as in figure [4.11], since the

dekadal rainfall is lower as compared to dekadal crop water requirement of the crop during its mid stage

and late stage while both stages are very critical for the crop. Therefore, at these stages there should be

supplementary irrigation inorder to support proper growth of the crop so that an optimum yield and

biomass are obtained. For example, at dekad 29 (October 11-20), the dekadal crop water requirement is

about three times more than the dekadal rainfall distribution which signified that an additional application

of water is required to meet the crop water requirement.

Barley

The analysis, figure [4.11], for Barley at Hawzen shows that there was relatively a good distribution of

dekadal rainfall against dekadal crop water requirement except for some dekads at the end of the season

(i.e. late stage). So, this implies that there is a need for additional amount of water to be applied to suffice

the seasonal crop water requirement for good production of yield. . For instance, at dekad 28 (October 1-

10), the dekadal crop water requirement is 2.28 mm which is about nineteen times more than the dekadal

rainfall distribution (0.12 mm) which revealed that an application of supplemental irrigation water is

needed starting from dekad 24 (August21-31) until dekad 28 (October 1-10).

Analysis for Barley at Sinkata woreda has also shown that there is shortage of moisture figure [4.11],

since the dekadal rainfall is lower as compared to dekadal crop water requirement of the crop during its

maturity stages (mid and late stages) in which both stages are very critical for the crop. Though, there is no

as such a pronounced problem of moisture deficit during early growing stages (initial and development

stages) of the crop. Therefore, at these stages there must be a supplementary irrigation for the proper

growth of the crop so that an optimum yield can be expected. For example, at dekad 28 (October 1-10), the

dekadal crop water requirement is about seven times greater than the dekadal rainfall amount which

revealed that an additional application of water is a must to obtain a good yield.

An overall analysis for dekadal distribution of crop water requirement against dekadal rainfall revealed that

the need for the use of supplmentary irrigation with the water harvested from roads in the study area is

unquestionable as far as sufficing the existing moisture deficit for optimum production is concerned.On top

of that, harvesting road water for supplemental irrigation is affordable by most Ethiopian farmers as

compared to other conventional types of irrigation systems.

4.3. Result from Statistical Package for the Social Sciences (SPSS)

Analysis of the results of the interviews in the research area was done using SPSS. The results from SPSS

show that 70% of farmers living in the study area were affected by the road side runoff. The results

indicated that this roadside runoff results in water logging on 45 % of the farm lands of farmers living

along the road sides: and around 65% of the farmlands are affected by erosion. Results from households'

interview and discussions showed that more than 95% of the farmers are willing to use roadside runoff for

their agricultural production as a supplemental water source. The detailed results of the interviews and

discussion with key informants, stakeholders and households living in the study area are shown in the

following tables.


Farmers' classification by gender

Frequency Percent Valid Percent

Cumulative

Percent

Valid female 16 27 27 27

male 44 73 73 100.0

Total 60 100.0 100.0

Farmers' classification by willingness to use runoff water as

supplemental irrigation


Cumulative

Percent

Valid yes 59 98 98 98

no 2 2 2 100.0

Total 60 100.0 100.0

Farmers' classification by willingness to pay if there is the possibility to have

harvesting structures


Cumulative

Percent

Valid yes 47 78 78 78

no 13 22 22 100.0

Total 60 100.0 100.0

Happening of Temporary Water Logging on Farm Lands due to runoff

comes from roads


Cumulative

Percent

Valid yes 27 45.0 45.0 45.0

no 33 55.0 55.0 100.0

Total 60 100.0 100.0

56

Happening of Erosion on the Farm Lands caused by runoff that comes

from roads


Cumulative

Percent

Valid yes 39 65.0 65.0 65.0

no 21 35.0 35.0 100.0

Total 60 100.0 100.0

Number of Farmers lost their farm farm lands Due to the constructed

roads


Cumulative

Percent

Valid yes 36 60.0 60.0 60.0

no 24 40.0 40.0 100.0

Total 60 100.0 100.0

Number of Farmers got compensation for their farm lands taken due to

road construction


Cumulative

Percent

Valid yes 1 2 2 2

no 59 98 98 100.0

Total 60 100.0 100.0


Number of Farmers that did not get compensation for their farm lands

taken due to constructed roads


Cumulative

Percent

Valid yes 35 58 58 58.3

no 25 42 42 100.0

Total 60 100 100

Number of Farmers affected by road side runoff


Cumulative

Percent

Valid yes 42 70.0 70.0 70.0

no 18 30.0 30.0 100.0

Total 60 100.0 100.0

Number of Farmers that do not use road side runoff Currently


Cumulative

Percent

Valid yes 48 80.0 80.0 80.0

no 12 20.0 20.0 100.0

Total 60 100.0 100.0

58

5.1. Conclusion

In this study daily rainfall - runoff relationship was simulated by using the daily measured data from the

three catchments Agula, Genfel and Sulluh for calibration and validation of HBV model. Although, the

runoff generated from each and every catchment is so considerable, more emphasis was given to the runoff

coming from the catchments to the roadsides including the runoff which is generated from the road itself.

For the available or measured daily rainfall between 2001-2012 simulations analysis was made for the crop

water requirement, yield and biomass production using Aqua crop model to assess whether a supplemental

irrigation is needed or not during the growing season.

It is oblivious that there are various factors that negatively affect agricultural productivity and sustainability

of farmer's income as well as their consumptions. As this research was conducted aiming to somehow

contribute a solution to the aforementioned problems, based on the results obtained the following

concluding remarks are drawn out.

The climate is changing and drought is getting prevalent in most regions of the country, and also

Tigray is among the Arid and Semi Arid region of the country characterized by having uneven

distribution of rainfall. Due to the erratic nature of rainfall distribution in the three districts of the

study area, crops have been suffering from failures during the growing season as a result of

insufficient moisture to support the full growing season of the crops since it is totally dependent on

rainfall ( i.e. when rainfall fails to occur, crop fails).Therefore, supplementary irrigation can rescue

crops from failure caused due to the uneven distribution of rainfall, resulting in a better production

as well as income

Harvesting runoff from roads cannot only be used as additional water source for supplementary

irrigation but also minimizes the damage caused by flood on farms along the road side as well as

on the rural roads which in turn reduces the cost of maintenance of the road itself for damage that

is caused by excess runoff.

Apart from using the harvested runoff from roads for supplementary irrigation, the collected water

can also be used for other alternative purposes such as for domestic consumption and livestock

watering, most importantly, in areas such as the present study area, where there is severe water

shortage to satisfy the various water needs of the local community.

CHAPTER 5

Conclusion and Recommendation


5.2. Recommendation

The development of rainfall harvesting from roads, ground water recharge including moisture conservation

should be considered. To promote enhancements of the result of the simulation the following suggestions

are developed.

Mainstreaming in educational system: Roads for water harvesting and multiple use

Filling the knowledge gap

There should be integration between relevant institutions and authorities (ERA, MoA as well as

regional and zonal line offices) in making future road development plans.

Operationalzing the knowledge acquired

Awareness generation should be done to encourage farmers utilize the runoff from roads for

productive purposes. Moreover, technical assistance and trainings needs to be delivered at grass-

root level.


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Appendices 64

Appendices

Appendix A : Laboratory Analyses and Data used

Table A.1 Laboratory result for permanet wilting point

Permanent Wilting point Culculation

Core

w(gm)

Before

Oven Dry

After Oven

Dry

Volume

(cm3)

M

wet

Soil Ms Mw

PWP

BD

(g/cm3)

Φm

(g/g)

Core+Wet

W(gm)

Core+Dry

W(gm)

Φv

(cm3/cm3)

96 271 256 98 176 160 16 0.11 1.63 0.10

96 273 257 98 177 161 16 0.12 1.65 0.10

96 289 267 98 194 171 23 0.22 1.74 0.13

96 252 234 98 156 138 18 0.13 1.41 0.13

96 239 225 98 143 129 14 0.12 1.32 0.11

96 265 253 98 169 157 12 0.08 1.60 0.08

96 292 261 98 196 165 31 0.25 1.68 0.19

96 297 267 98 202 171 31 0.25 1.74 0.18

96 291 262 98 195 167 29 0.26 1.70 0.17

96 272 248 98 177 152 24 0.16 1.55 0.16

96 273 244 98 178 149 29 0.17 1.51 0.20

96 286 256 98 190 161 29 0.20 1.64 0.18

96 252 229 98 156 133 23 0.11 1.36 0.17

96 260 230 98 164 135 30 0.15 1.37 0.22

96 277 245 98 181 150 32 0.19 1.53 0.21

96 234 218 98 139 122 17 0.09 1.24 0.14

96 251 232 98 155 136 19 0.13 1.38 0.14

96 239 221 98 144 125 19 0.13 1.27 0.15

96 256 235 98 160 140 20 0.03 1.42 0.15

96 255 231 98 159 135 24 0.07 1.38 0.18

96 247 231 98 151 135 16 0.06 1.38 0.12

96 263 244 98 168 148 20 0.06 1.51 0.13

96 251 204 98 156 108 48 0.05 1.10 0.44


Table A.2 Laboratory result for Field capacity

Field capacity Culculation

Before Oven

Dry

After Oven

Dry M

wet

Soil Ms Mw

BD

(g/cm3)

Φm

(g/g)

FC

Core

w(gm)

Core+ Wet

W(gm)

Core+Dry

W(gm)

Volume

(cm3)

Φv (cm3/cm3)

96 271 256 98 176 160 16 1.63 0.10 0.16

96 273 257 98 177 161 16 1.65 0.10 0.16

96 289 267 98 194 171 23 1.74 0.13 0.23

96 252 234 98 156 138 18 1.41 0.13 0.18

96 239 225 98 143 129 14 1.32 0.11 0.14

96 265 253 98 169 157 12 1.60 0.08 0.12

96 292 261 98 196 165 31 1.68 0.19 0.32

96 297 267 98 202 171 31 1.74 0.18 0.31

96 291 262 98 195 167 29 1.70 0.17 0.29

96 272 248 98 177 152 24 1.55 0.16 0.25

96 273 244 98 178 149 29 1.51 0.20 0.30

96 286 256 98 190 161 29 1.64 0.18 0.30

96 252 229 98 156 133 23 1.36 0.17 0.24

96 260 230 98 164 135 30 1.37 0.22 0.30

96 277 245 98 181 150 32 1.53 0.21 0.32

96 234 218 98 139 122 17 1.24 0.14 0.17

96 251 232 98 155 136 19 1.38 0.14 0.20

96 239 221 98 144 125 19 1.27 0.15 0.19

96 256 235 98 160 140 20 1.42 0.15 0.21

96 255 231 98 159 135 24 1.38 0.18 0.24

96 247 231 98 151 135 16 1.38 0.12 0.16

96 263 244 98 168 148 20 1.51 0.13 0.20

96 251 204 98 156 108 48 1.10 0.44 0.49

96 240 226 98 144 130 14 1.33 0.11 0.14

Appendices 66

Table A.3 Laboratory result for Soil texture analysis

Soil texture analysis

S. No

Samp. taken (gm)

40 Second reading After 2 hr reading

Hydrometer reading

T. reading

Hydrometer reading

T. reading

%of Sand

%of Clay

%of Silt

1 50 14.5 21.5 5 20.5 73 8 19

2 50 12 21.5 6.5 20.5 78 11 11

3 50 19.5 21.5 12 20.5 63 22 15

4 50 14.5 20.5 8.5 20.5 74 14 12

5 50 13.5 20.5 9.5 20.5 76 16 8

6 50 11.5 20.5 7 20.5 80 11 9

7 50 18.5 21 11 20.5 66 19 15

8 50 17.5 21 11.5 20.5 68 20 12

9 50 18.5 21 12 20.5 66 21 13

10 50 14.5 20.5 11 20.5 74 19 7

11 50 15.5 20.5 11.5 20.5 72 20 8

12 50 17 20.5 12 20.5 69 21 10

13 50 18.5 20.5 9.5 20.5 66 16 18

14 50 20.5 20.5 11 20.5 62 19 19

15 50 24.5 20.5 12.5 20.5 54 22 24

16 50 15.5 20.5 8 20.5 72 13 15

17 50 17.5 20.5 9.5 20.5 68 16 16

18 50 19 20.5 10 20.5 65 17 18

19 50 7 20.5 4 20.5 89 5 6

20 50 9.5 20 6 20.5 84 9 7

21 50 7.5 20 5.5 20.5 88 8 4

22 50 8.5 20 4 20.5 86 5 9

23 50 10.5 20 6 20.5 82 9 9

24 50 14.5 20 6.5 20.5 74 10 16


Appendix B : Monthly dekade and GPS readings

Table B.1 Standard meteorological dekad

Dekade no Month Date Dekade no Month Date

1 January 1-10 19 July 1-10

2 11-20 20 11-20

3 21-30 21 21-30

4 February 1-10 22 August 1-10

5 11-20 23 11-20

6 21-30 24 21-30

7 March 1-10 25 September 1-10

8 11-20 26 11-20

9 21-30 27 21-30

10 April 1-10 28 October 1-10

11 11-20 29 11-20

12 21-30 30 21-30

13 May 1-10 31 November 1-10

14 11-20 32 11-20

15 21-30 33 21-30

16 June 1-10 34 December 1-10

17 11-20 35 11-20

18 21-30 36 21-30

Appendices 68

Table B.2 Records of GPS reading

GPS READING UTM

Site No East North Elivation Sinkata 1 561603 1553360 2396

Sinkata 2 Culvert 561558 1553227 2361

Sinkata 3 Culvert 558885 1551992 2305

Sinkata 4 Culvert 557865 1548990 2273

Sinkata 5 Culvert 557707 1548889 2280

Sinkata 6 Culvert 557253 1548602 2282

Sinkata 7 Culvert 556221 1548110 2276

Sinkata 8 Culvert 555925 1548084 2273

Sinkata 9 Culvert 555187 1548118 2271

Sinkata 10 Bridge 554113 1548383 2272

Sinkata 11 Culvert 554058 1548442 2275

Sinkata 12 Bridge 552911 1549172 2221

Sinkata 13 Culvert 551981 1548309 2239

Sinkata 14 Bridge 551639 1547948 2225

Sinkata 15 Culvert 551625 1547943 2225

Sinkata 16 Culvert 551479 1547892 2226

Sinkata 17 Bridge 551200 1547807 2231

Sinkata 18 Culvert 549766 1546772 2254

Sinkata 19 Bridge 548808 1546091 2261

Sinkata 20 Culvert 548547 1545945 2261

Sinkata 21 Culvert 547495 1545570 2266

Hawzen 22 Culvert 546548 1545371 2250

Hawzen 23 Bridge 546213 1544755 2225

Hawzen 24 Culvert 544269 1543565 2104

Hawzen 25 Bridge 543439 1543440 2095

Hawzen 26 Culvert 543598 1543212 2096

Hawzen 27 Culvert 542717 1542620 2090

Hawzen 28 Culvert 540772 1540990 2096

Hawzen 29 Culvert 540514 1540789 2090

Hawzen 30 Culvert 540256 1540315 2068

Hawzen 31 Culvert 541002 1539714 2050

Hawzen 32 Culvert 541152 1539484 2044

Hawzen 33 Culvert 541294 1539262 2036


Hawzen 34 Culvert 541524 1538903 2032

Hawzen 35 Irish Bridge 541819 1538790 2026

Hawzen 36 Culvert 542059 1538720 2029

Hawzen 37 Culvert 542147 1538522 2029

Hawzen 38 Culvert 542291 1538255 2024

Hawzen 39 Culvert 542429 1538209 2024

Hawzen 40 Culvert 542619 1538193 2025

Hawzen 41 Culvert 542836 1537996 2020

Hawzen 42 Culvert 543037 1537827 2017

Hawzen 43 Culvert 543277 1537628 2017

Hawzen 44 Culvert 543419 1537451 2017

Hawzen 45 Culvert 543659 1537060 2015

Hawzen 46 Bridge 543917 1536851 1993

Hawzen 47 Culvert 544028 1536854 1997

Hawzen 48 Culvert 544503 1536522 2044

Hawzen 49 Culvert 545015 1535886 2072

Hawzen 50 Culvert 545281 1535577 2069

Hawzen 51 BrIdge 545315 1535421 2070

Hawzen 52 Culvert 545632 1534813 2083

Hawzen 53 Culvert 545872 1534541 2095

Hawzen 54 Culvert 545920 1534351 2094

Hawzen 55 Culvert 545847 1534257 2103

Hawzen 56 Culvert 545963 1533827 2114

Hawzen 57 Culvert 547173 1533237 2143

Hawzen 58 Culvert 547993 1533013 2142

Hawzen 59 Culvert 548584 1532732 2134

Hawzen 60 Culvert 549049 1532428 2124

Hawzen 61 Culvert 549324 1532210 2118

Hawzen 62 Culvert 549838 1531656 2116

Hawzen 63 Culvert 549876 1531589 2117

Hawzen 64 Culvert 550126 1531497 2115

Hawzen 65 Culvert 550172 1531494 2113

Hawzen 66 Culvert 550253 1531487 2111

Hawzen 67 Culvert 550358 1531474 2110

Hawzen 68 Culvert 550608 1531487 2105

Hawzen 69 Culvert 551134 1531360 2100

Hawzen 70 Culvert 551598 1531325 2071

Hawzen 71 Culvert 551636 1531305 2069

Hawzen 72 Culvert 551768 1531151 2067

Hawzen 73 Culvert 552395 1531162 2039

Abraha wa Atsbeha 74 Culvert 552842 1531092 1997


Appendices 70






Abraha wa Atsbeha 81 Bridge 555325 1530672 1961










Abraha wa Atsbeha 91 Irish Bridge 564406 1525579 2046









Abraha wa Atsbeha 100 Irish Bridge 561618 1526037 2073


















Appendices 72

Appendix C : Monthly areal rainfall map (2001- 2012)

Figure C.1 Long - term monthly areal rainfall for Jan and Feb (2001 -2012)


Figure C.2 Long - term monthly areal rainfall for March - June (2001 -2012)

Appendices 74

Figure C.3 Long - term monthly areal rainfall for July - October (2001 -2012)


Figure C.4 Long - term monthly areal rainfall for Nov and Dec (2001 -2012)

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