Post on 06-Feb-2017
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.
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Dedicated to my Family
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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
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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 farm- lands ....................................................................................... 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
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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
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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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 3
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 5
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).
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 7
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 rain- fed irrigation through a supplementary irrigational
system.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 9
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 11
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 13
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 farm- lands
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 15
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 17
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 19
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 21
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 23
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).
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 25
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 27
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 29
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 31
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 33
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
Shape factor for Water stress coefficient for canopy
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 35
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)
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
Shape factor for Water stress coefficient for canopy
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 37
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 39
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
Observed [mm] Simulated
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 41
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
Observed [mm] Simulated
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 43
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 45
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 47
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 49
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 51
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 53
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 55
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
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
Cumulative
Percent
Valid yes 1 2 2 2
no 59 98 98 100.0
Total 60 100.0 100.0
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 57
Number of Farmers that did not get compensation for their farm lands
taken due to constructed roads
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
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
Frequency Percent Valid Percent
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 59
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.
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 61
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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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 65
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 67
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
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 69
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
Abraha wa Atsbeha 75 Culvert 552843 1531067 1999
Appendices 70
Abraha wa Atsbeha 76 Culvert 553104 1531003 1996
Abraha wa Atsbeha 77 Culvert 553715 1530713 1988
Abraha wa Atsbeha 78 Culvert 554088 1530670 1985
Abraha wa Atsbeha 79 Culvert 554772 1530578 1983
Abraha wa Atsbeha 80 Culvert 555104 1530480 1978
Abraha wa Atsbeha 81 Bridge 555325 1530672 1961
Abraha wa Atsbeha 82 Culvert 555486 1530823 1962
Abraha wa Atsbeha 83 Culvert 555601 1530865 1965
Abraha wa Atsbeha 84 Culvert 555924 1530733 1978
Abraha wa Atsbeha 85 Culvert 556323 1530756 1982
Abraha wa Atsbeha 86 Culvert 556779 1530834 1987
Abraha wa Atsbeha 87 Culvert 557158 1530876 1996
Abraha wa Atsbeha 88 Bridge 557619 1530714 2006
Abraha wa Atsbeha 89 Culvert 564857 1525508 2056
Abraha wa Atsbeha 90 Culvert 564641 1525515 2052
Abraha wa Atsbeha 91 Irish Bridge 564406 1525579 2046
Abraha wa Atsbeha 92 Culvert 564055 1525638 2047
Abraha wa Atsbeha 93 Culvert 563678 1525495 2053
Abraha wa Atsbeha 94 Culvert 563392 1525421 2047
Abraha wa Atsbeha 95 Culvert 563015 1525455 2041
Abraha wa Atsbeha 96 Bridge 562876 1525695 2039
Abraha wa Atsbeha 97 Culvert 562620 1525914 2047
Abraha wa Atsbeha 98 Culvert 562534 1525984 2043
Abraha wa Atsbeha 99 Culvert 561947 1525997 2060
Abraha wa Atsbeha 100 Irish Bridge 561618 1526037 2073
Abraha wa Atsbeha 101 Culvert 561575 1526216 2070
Abraha wa Atsbeha 102 Culvert 561467 1526364 2071
Abraha wa Atsbeha 103 Culvert 561120 1526544 2087
Abraha wa Atsbeha 104 Culvert 560788 1526682 2100
Abraha wa Atsbeha 105 Culvert 560672 1526734 2102
Abraha wa Atsbeha 106 Culvert 560350 1526844 2123
Abraha wa Atsbeha 107 Culvert 560138 1526959 2138
Abraha wa Atsbeha 108 Culvert 559803 1527087 2151
Abraha wa Atsbeha 109 Culvert 559474 1527347 2182
Abraha wa Atsbeha 110 Culvert 558929 1527331 2178
Abraha wa Atsbeha 111 Culvert 558648 1527479 2149
Abraha wa Atsbeha 112 Culvert 558445 1527341 2124
Abraha wa Atsbeha 113 Culvert 558037 1527437 2060
Abraha wa Atsbeha 114 Bridge 558023 1527543 2058
Abraha wa Atsbeha 115 Culvert 557770 1527627 2066
Abraha wa Atsbeha 116 Culvert 557192 1527569 2063
Abraha wa Atsbeha 117 Culvert 556716 1527836 2075
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 71
Abraha wa Atsbeha 118 Culvert 556609 1528254 2042
Abraha wa Atsbeha 119 Bridge 556656 1528688 2005
Abraha wa Atsbeha 120 Culvert 556606 1528805 1998
Abraha wa Atsbeha 121 Culvert 556592 1528869 1996
Abraha wa Atsbeha 122 Culvert 556572 1529087 2001
Abraha wa Atsbeha 123 Culvert 556564 1529496 2000
Abraha wa Atsbeha 124 Culvert 556551 1529725 1990
Abraha wa Atsbeha 125 Culvert 556556 1529803 1989
Abraha wa Atsbeha 126 Culvert 556766 1530013 1999
Abraha wa Atsbeha 127 Culvert 556979 1530101 1998
Abraha wa Atsbeha 128 Culvert 557049 1530153 1999
Abraha wa Atsbeha 129 Culvert 557086 1530186 1997
Abraha wa Atsbeha 130 Culvert 557162 1530268 2000
Abraha wa Atsbeha 131 Culvert 557485 1530486 2012
Abraha wa Atsbeha 132 Culvert 557579 1530578 2011
Appendices 72
Appendix C : Monthly areal rainfall map (2001- 2012)
Figure C.1 Long - term monthly areal rainfall for Jan and Feb (2001 -2012)
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 73
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)
Optimizing Intensified Runoff from Roads for Supplemental Irrigation: Tigray Region, Ethiopia 75
Figure C.4 Long - term monthly areal rainfall for Nov and Dec (2001 -2012)