Final Thesis

ASSESSING THE ENVIRONMENTAL IMPACTS ON THE WATER QUALITY OF ANGEREB RESERVOIR USING

REMOTE SENSING AND GIS

Dissertation Submitted for the Partial Fulfillment of the Requirements for

the Award of the Degree of

Masters of Science in

Remote Sensing and Geographic Information Systems(GIS) Of Addis Ababa University, Addis Ababa, Ethiopia

BY

Abebe Ejigu Gessesse

Under the guidance of Dr.K.V. Suryabhagavan

Assistant Professor, Department of Earth Sciences Addis Ababa University, Addis Ababa

July 2008

ASSESSING THE ENVIRONMENTAL IMPACTS

ON THE WATER QUALITY OF ANGEREB RESERVOIR USING REMOTE SENSING AND GIS

Dissertation Submitted for the Partial Fulfillment of the Requirements for

the Award of the Degree of

Masters of Science in

Remote Sensing and Geographic Information Systems(GIS) Of Addis Ababa University, Addis Ababa, Ethiopia

BY

Abebe Ejigu Gessesse

Under the guidance of Dr.K.V. Suryabhagavan

Assistant Professor, Department of Earth Sciences Addis Ababa University, Addis Ababa

July 2008

ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES

ASSESSING THE ENVIRONMENTAL IMPACTS ON THE WATER QUALITY ANGEREB RESERVOIR USING REMOTE

SENSING AND GIS

By

Abebe Ejigu Gessesse

Faculty of Science

Department of Earth Sciences

Remote Sensing and GIS

Approval by Board of Examiners

Dr. Balemual Atnafu ____________________________

Chairman, Department Graduate Committee

Dr.K.V. Suryabhagavan ____________________________ Advisor

____________________________ ________________________________

Examiner

____________________________ __________________________________

Examiner

D E C L A R A T I O N

I hereby declare that the dissertation entitled “ASSESSING THE ENVIRONMENTAL IMPACTS ON

THE WATER QUALITY OF ANGEREB RESERVOIR USING REMOTE SENSING AND GIS” has been

carried out by me under the supervision of Dr. K.V. Suryabhagavan, Department of Earth Sciences,

Addis Ababa University, Addis Ababa during the year 2008 as a part of Master of Science

programme in Remote Sensing and GIS. I further declare that this work has not been submitted to

any other University or Institution for the award of any degree or diploma.

Place: Addis Ababa

Date: July 16, 2008

(Abebe Ejigu)

C E R T I F I C A T E

This is certified that the dissertation entitled “ ASSESSING THE ENVIRONMENTAL IMPACTS ON THE

WATER QUALITY OF ANGEREB RESERVOIR USING REMOTE SENSING AND GIS ” is a bonafied work

carried out by under my guidance and supervision. This is the actual work done by Abebe Ejigu

Gessesse for the partial fulfillment of the award of the Degree of Master of Science in Remote

Sensing and GIS from Addis Ababa University, Addis Ababa.

Dr. K.V. Suryabhagavan Assistant professor Department of Earth Sciences Addis Ababa University Addis Ababa

Table Of Content

Table Of Content .................................................................................................................................... vi

List of Tables ......................................................................................................................................... viii

List of Figures.......................................................................................................................................... ix

Acknowledgments ................................................................................................................................... x

Acronyms ............................................................................................................................................... xi

Abstract ................................................................................................................................................... ii

1. Introduction .................................................................................................................................... iii

1.1 Background Information .......................................................................................................... iii

1.1.1 Ethiopia ............................................................................................................................ iii

1.1.1 Amhara Region ................................................................................................................. iii

1.1.2 The city of Gondar and Angereb Reservoir........................................................................ vi

1.2 Statement of the Problem and Justification.............................................................................viii

1.3 Objective ................................................................................................................................. ix

1.3.1 General Objective............................................................................................................. ix

1.3.2 Specific Objectives ........................................................................................................... ix

1.4 Research Questions ................................................................................................................. ix

1.5 Chapter Scheme........................................................................................................................ x

2. Literature Review ............................................................................................................................ xi

2.1 Land Use/ Cover Change Analysis Using Remote Sensing and GIS: ............................................ xi

2.2 Water Quality Modeling .......................................................................................................... xiv

2.3 Pollutants ............................................................................................................................. xviii

2.4 Environmental Impact Assessment ..........................................................................................xx

2.5 Better Assessment Science Integrating Point and Non point Sources (BASINS) ........................ xxi

2.6 PLOAD .................................................................................................................................. xxiii

2.7 Best Management Practice (BMP) ......................................................................................... xxiv

3 MATERIALS AND METHODS .......................................................................................................... xxvi

3.1 Description of the Study Area ........................................................................................... xxvi

3.1.1 Location ........................................................................................................................ xxvi

3.1.2 Topography .................................................................................................................. xxvii

3.1.3 Climate......................................................................................................................... xxvii

3.1.4 Soils ............................................................................................................................. xxvii

3.1.5 Vegetation .................................................................................................................. xxviii

3.1.6 Hydrology ..................................................................................................................... xxix

3.1.7 Socio economic setting ................................................................................................... xxx

3.1.8 Water supply of Gondar city .......................................................................................... xxxi

3.2 Data and Acquisition Methods ........................................................................................ xxxiii

3.2.1 Remote sensing Data................................................................................................... xxxiii

3.2.2 Pollutant loading rate data .......................................................................................... xxxiv

3.2.3 BMP data ................................................................................................................... xxxvi

3.3 Methods ........................................................................................................................... xxxvii

4 ANALYSIS AND RESULTS ........................................................................................................... xxxviii

4.1 Satellite Image Analysis and its Results .......................................................................xxxviii

4.2 Pollutant Load Assessment and its Results ...................................................................... xliii

4.3 BMP Computation and its Results ..................................................................................... xlix

5 DISCUSSIONS .................................................................................................................................. lii

5.1 Land use/ Land cover Dynamics of Angereb Watershed .................................................. lii

5.1.1 Forest Land ...................................................................................................................... lii

5.1.2 Cultivated Land ............................................................................................................... liv

5.1.3 Grazing Land ................................................................................................................... lvi

5.2 Land use change and its impact on the Pollutant load of Angereb Watershed .........................lvii

5.3 Prediction of Annual Pollutant Load of Angereb Watershed .................................................... lix

6 Conclusion and Recommendations................................................................................................. lxi

Annexes ................................................................................................................................................ lxv

Annex 1: Chemical Test Results of Row water at Angereb Treatment plant ......................................... lxv

Annex 2: Monthly and Annual Rainfall Data of Gondar Area .............................................................. lxix

Annex 3: Summary of Hydrometric discharge data ............................................................................. lxx

Annex 4: Catchment Pollution Calculator: EMC to EXPORT converter ................................................ lxxi

Annex 5: KGHAYR EXPORT Calculator ............................................................................................... lxxii

References .......................................................................................................................................... lxxiii

List of Tables Table 1: Surface Water Resources of Major River Basins of Ethiopia-----------------14 Table 2: Major AEZs of Amhara Region------------------------------------------------------16 Table 3 Export coefficient values from Reference 1--------------------------------------26 Table 4: Export coefficient values from Reference 2-------------------------------------27 Table 5: Pollutants source and impact -------------------------------------------------------31 Table 6: Models of BASINS and Their Brief Descriptions---------------------------------33 Table 7: Efficiency of Agricultural BMP-------------------------------------------------------35 Table 8: Mean Monthly Temperature, wind speed and Relative humidity-----------37 Table 9: EMC Values-------------------------------------------------------------------------------44 Table 10: Export coefficient value--------------------------------------------------------------45 Table 10: Catchment Pollution Calculator: EMC to EXPORT converter---------------46 Table 11: Export Coefficient value per land use--------------------------------------------46 Table 12: Land use /cover of Angereb Watershed (1986) ------------------------------50 Table 13: Land use /cover of Angereb Watershed (1999) -------------------------------51 Table 14: Land use /cover of Angereb Watershed (2002) -------------------------------52 Table 15: Annual Pollutant Load (Kg/Year) for the year 1986----------------------------53 Table 16: Annual Pollutant Load (Kg/Year) for the year 1999----------------------------55 Table 17: Annual Pollutant Load (Kg/Year) for the year 2002----------------------------57 Table 18: Annual Pollutant Load with BMP (Scenario 1) ----------------------------------61 Table 19: Annual Pollutant Load with BMP (Scenario 2) ----------------------------------62 Table 20: Forest Land use/cover change of Angereb watershed-----------------------60 Table 21: Rate of Change of Forest Land in Angereb watershed------------------------64 Table 22: Cultivated Land use/cover change of Angereb watershed-------------------65 Table 23: Rate of Change of Cultivated Land in Angereb watershed--------------------64 Table 24: Grass Land use/cover change of Angereb watershed--------------------------66 Table 25: Rate of Change of Grass Land in Angereb watershed---------------------------66 Table 26: Pollutant Load change in the Cultivated Land of Angereb watershed-------69 Table 27: Pollutant Load change in the Cultivated Land of Angereb watershed-------68 Table 28: Pollutant Load change in the Grass Land of Angereb watershed-------------69 Table 29: Pollutant Load with and without BMP (Scenario 1) ------------------------------70 Table 30: Pollutant Load with and without BMP (Scenario 2) ------------------------------71

List of Figures Figure1: Location Map of Angereb watershed-------------------------------------------------36 Figure2: Plantation Forest around Angereb Dam and Reservoir--------------------------39 Figure3: Angereb Dam and Treatment Plant---------------------------------------------------43 Figure 4: Flow chart for the Methodology -----------------------------------------------------47 Figure 5: Land use/cover map of Angereb watershed in 1986----------------------------50 Figure 6: Land use/cover map of Angereb watershed in 1999----------------------------51 Figure 7: Land use/cover map of Angereb watershed in 2002----------------------------52 Figure 8 spatial distribution maps of Pollutant Load for the year 1986-----------------55 Figure 9 Map for the weighted sum of Pollutant Load for the year 1986---------------56 Figure 10 spatial distribution Maps of Pollutant Load for the year 1999-----------------57 Figure 11 Map for the weighted sum of Pollutant Load for the year 1999--------------58 Figure 12 spatial distribution Maps of Pollutant Load for the year 2002----------------59 Figure 13 Map for the weighted sum of Pollutant Load for the year 1922--------------60 Figure 14 Chart for Forest Land coverage of Angereb watershed ------------------------63 Figure 15 Chart for Cultivated Land coverage of Angereb watershed -------------------65 Figure 16 Chart for the Annual Pollutant Loads of Angereb watershed -----------------67

Acknowledgments I wish to acknowledge my supervisor Dr K.V.Suryabhagavan for his valuable advice, comments and

companionship approach during my thesis work. His patience and friendship sprit will be real

forever.

This research work in particular and my MSc education program in general would not have been

possible without support from my employer organization: Organization for Rehabilitation and

Development in Amhara (ORDA). Hence I would like to express my heartfelt gratitude to its staff

that permitted to continue my MSc study and assisted me during my study period.

I am very indebted to the staff of the department of Earth Science of AAU for their support during

my research thesis work and study period. Besides AAU, I want to acknowledge the following

institutes: Bahirdar University, Ethiopian Metrology Agency, water supply and sewerage office of

Gondar town and other institutes.

I would like to extend my sincere appreciation and thanks to Hana Ejigu and all my other

families for their support in every aspect. In addition to my family, I want to acknowldge

Alemseged Tesema, Admasu Amare, Antenh Zewdie, Muluken Emagnu, Addis Hailemicheal, Essayas

from Bahirdar University and others who provide me data and advice during my thesis work.

At last but not least, I wish to acknowledge my collogues in the Department of Earth Science, GIS

and Remote sensing unit for their technical assistance and advise.

(Abebe Ejigu)

Acronyms ac Acre

AFAP Amhara Forestry Action Program

AEZ Agro-ecological Zones

ANRS Amhara National Regional State

BASINS Better Assessment Science Integrating Point and Nonpoint Sources

BMP Best Management practice

Ca Calcium

CSA Central Statistics Agency

EEC European Economic Commission

EIA Environmental Impact Assessment

EMC Event Mean concentration

ENVI Environment for Visualizing Images

EPA Environmental Protection Agency

ETM Enhanced thematic Mapper

Fe Iron

GIS Geographic Information System

ha Hectare

HSPF Hydrological Simulation Program- FORTRAN

lb/yr Pound per Year

LD Load

m.a.s.l Meter above sea level

Mg Magnesium

mg/lt Milligrams per Liter

Mm3 Million Cubic Meter

MSS Multispectral scanner

NGO Non Governmental Organization

NPS Non Point Source

NH3 Ammonia

NO2 Nitrite

NO3 Nitrate

PLOAD Pollutant load

PO4 Phosphate

ROI Region of Interest

TDS Total Dissolved Solids

TLU Total Livestock Unit

TM Thematic Mapper

TN Total Nitrogen

TP Total Phosphorus

USEPA United States Environmental Protection Agency

USGS United States Geological Survey

USLE Universal soil Loss Equations

UTM Universal Transverse Mercator

Abstract Angereb watershed is the source of drinking water supply for the town of Gondar dwellers and the

basis for the livelihood of the rural inhabitants of the watershed. It is obvious that the agricultural

activities carried out around the Angereb dam and reservoir increase the pollutant loads. This

research studies the long term impacts of the upstream land use management which involve the

gradual buildup of pollutants on the downstream reservoir and predicts the impacts of land use

management of Angereb watershed on the pollutant load status by using remote sensing and GIS

techniques.

This study uses the BASINS software and its extension PLOAD in addition to the ENVI and ArcGIS

softwares. The LANDSAT images for the year 1986, 1999, and 2002 of the study area are used to

obtain the respective land use/ cover maps of the watershed. In the modeling process the inputs

used are three years land use/ cover data obtained from the image analysis, Average annual

Rainfall, hydrometric discharge data and Water quality test results of Angereb Dam. The outputs

from this model are annual pollutant load of TDS, Nitrite, Nitrate, Ammonia, Phosphate, Calcium,

Magnesium and Iron for the three years (1986, 1999 and 2002).

Moreover the future pollutant load has been predicted by assuming two scenarios. The first

scenario is based on the past land use management trends and the second one by considering the

different BMP’s recommended in this study. Finally in the GIS analysis environment the results of

the remote sensing data and the biophysical modeling is changed to raster dataset and reclassified

to give the weighted sum outputs of annual pollutant load. The weighted sum result of the year

2002 shows that the highest pollutant load is in the Western part of the watershed in which the

intensively cultivated land is also highly concentrated in this part of the watershed.

Key words: Remote sensing, GIS, PLOAD, BASINS, BMP, weighted sum

1. Introduction

1.1 Background Information

1.1.1 Ethiopia Ethiopia is part of East Africa region commonly referred to as the “Horn of Africa”. Ethiopia is

situated between 3030’ and 14050’ North latitudes and 32042’ and 48012’ East longitudes. It has a

surface area of about 1.13 million square kilometer, of which 1,119,683 square kilometer land and

7,444 square kilometer water area. The country is bordered by Somalia and Djibouti to the east,

the Sudan on the west, Eritrea to the north and Kenya to the south.

Despite Ethiopia’s proximity to the equator, the central and the western highlands enjoy a

temperate climate due to the moderating influence of high altitudes, with a mean annual

temperature rarely exceeding 200C. Rainfall generally occurs in a five month unimodal rainy season

from May to September in western parts of the country and averages around 1000mm annually.

The eastern and southern parts, on the other hand, have bimodal rainfall averaging annually from

less than 200mm in the semi-desert to 1,000mm in the high lands. Rainfall can sometimes be

erratic, especially in the eastern Ethiopia and drought is a common feature.

Ethiopia is endowed with one of the largest surface fresh water resources in Sub- Saharan Africa.

The distribution and quantity of water has strong relationship with the topography and rainfall

distribution. Based on topography, Ethiopia is subdivided in to twelve major drainage basins and

their respective surface water resources are shown on Table 1.

1.1.1 Amhara Region The Amhara National Regional State (ANRS) is located in the North central and North-Western

parts of Ethiopia, approximately between 90 21' to 140 0' N latitude and 360 20' to 400 20'S

longitude. ANRS occupies about 170,152 square kilometers of land with an altitudinal zone,

ranging from 600 to 4620 meters above sea level. The recorded annual mean temperature of the

region ranges from 12.40c in Mehal Meda to 27.80c in Metema.

The physiography of the Amhara Region reflects its geology and geological history. A general

uplifting of the highland plateau, followed by tilting, provided an initial highland altitude range

between 1,500 and 3,000 m.a.s.l. Volcanic activity produced upstanding picks (volcanoes) rising to

over 4,000 m.

Table 1 Surface Water Resources of major river basins of Ethiopia

S.No Basin Area in Km2 Annual Runoff

(billion m3)

Area

coverage

1 Blue Nile 199,812 52.60 17.58

2 Afar Dankil 74,000 0.86 6.51

3 Awash 112,700 4.60 9.91

4 Aysha 2,200 0.001 0.19

5 Baro – Akobo 74,100 23.60 6.52

6 Genale Dawa 171,050 5.80 15.05

7 Mereb 5,700 0.26 0.50

8 Ogaden 77,100 0.001 6.78

9 Omo – Ghibe 78,200 17.90 6.88

10 Rift valley 52,740 5.60 4.64

11 Tekeze 89,000 7.63 7.83

12 Wabi shebele 20,0214 3.15 17.61

Total 1,136,816 122 100%

Source: - (Abebe Sine, 2004)

The region has diverse topography consisting of low lands, extensive plateaus, numerous

mountains, river valleys and gorges. The low lands (500-1,500 m.a.s.l) mainly cover the eastern

and northwestern areas bordering the Afar region and the Sudan. These areas are largely plain and

constitute big river drainage basins. The highlands, which are above 1,500 m.a.s.l. comprise the

largest part of the Northern and Eastern parts of the region. The highland areas are rugged and

mountainous with peaks rising to 4,230 m and 4,640 m at the summit of mount Guna and Ras

Dashin respectively which the associated out pouring of lava left the plateau protected by a thick

basalt cap.

Broadly five major land-forms occur in the Region; the mountains, the scarps and the adjoining

lowlands, nearly flat plateaus, major river gorges and valleys. High relief volcanic mountains such

as Mt. Ras Dashin, Guna, Choke and Adama are scattered throughout the Region. The steep scarps

extend along the main Debre Sina - Desse road which is the result of the major rift system. The

diverse in altitude range makes the region able to grow various agricultural food crops and

livestock development in different places.

The nearly flat to undulating plateaus are the major agricultural potential areas of the Region.

Most of these areas are concentrated in the whole Gojam zones. Major soil types occurring include

Vertisols, Acrisols, Luvisols and Nitosols. The scarps are generally with no vegetation cover with

shallow and stony soils (Leptosols). The adjoining lowlands (part of the "Afar lowlands") are

predominated with Vertisols, Solonchacks, Fluvisols and Gleysols. While in the flat and depression

Tana plain, Vertisols and Luvisols predominate, however, drainage is the major limitation for crop

production uses.

Climate, topography and human settlement are the main factors that have influenced both the

land use and the natural vegetation cover type of the Region. As it has been one of the early

settlement areas in the country, the Region has been subjected to prolonged agricultural use.

Results of land use and cover analysis based upon LANDSAT scenes covering the period 1981

through 1985 highlighted that the Region is composed of eleven major land use/cover classes,

which are further sub-divided into 24 different land use/land cover types (AFAP, 1999).

More than half (56.22%) of the Region is cultivated land indicating that sedentary crop cultivation

is the major land use activity. Of the total land mass about 27.3 percent is under cultivation, 30%

under grazing, 9.1 percent represents settlement sites, swampy areas, lakes, etc.

The second major land use/cover component is shrub land covering some 19.9 % of the Region's

land mass. High forests account for 81,047 ha, or (only 0.5%), the woodlands 716,915 ha (4.2%),

grassland 881,835 ha (5.2 %,) swamps and marshes 23,958 ha (0.1%), bush land with 1,986,870 ha

is 11.6%, afro-alpine vegetation with 93,626 ha is 0.5%, highland bamboo 52,298 ha (0.3%), bare

land 78,835 ha 0.5% and water body 340,960 ha 2% of the Regions total surface area. Given the

dynamic nature of land-use activities, these proportions are constantly changing.

The rugged terrain, high relief and steepness of slope have resulted in many different agro-

ecological zones (AEZ) of ANRS. Analysis of climatic parameters (temperature and length of

growing period) superimposed onto major physiographic units yielded 62 agro-ecological zones

throughout the country. According to the revised Agro-ecological zones (AEZ) of Ethiopia (MoA,

1998), the Amhara Region is categorized into 9 major and 17 sub-AEZs. The area and extent of the

major AEZs are shown in table 2.

Table 2: Major AEZs of Amhara Region Major Agro-ecological Zones % of the total

1 Hot to warm semi-arid 0.08

2 Hot to warm sub-moist 11.02

3 Tepid to cool sub-moist 22.80

4 Cold to very cold sub-moist 1.70

5 Hot to warm moist 14.60

6 Tepid to cool moist 40.70

7 Cold to very cold moist 4.70

8 Hot to warm sub-humid 1.20

9 Tepid to cool sub-humid 3.20

The ANRS is divided into eleven administrative zones, which are further subdivided into 115

Woredas.

1.1.2 The city of Gondar and Angereb Reservoir North Gondar is one among the eleven administrative zones in the ANRS. The city of Gondar,

founded by Emperor Fasiledas in 1636 A.D, is also the current capital of the administrative zone. It

was once the Capital of Ethiopia for more than 200 years. Gondar city is located 748km by road

North of Addis Ababa. The main highway connects Addis Ababa with Gondar via Bahir Dar. It is

situated in the foothills of Seimen Mountains at an average elevation of 2300 m.a.s.l.

Gondar city, with the leading population in the Region, has a residence of about 200,000 and is

currently divided into 21 urban kebeles.

The rainfall of Gondar area is generally erratic with the annual mean rainfall of 1159.22 mm

(Annex 2). The mean annual ambient temperature of Gondar is between 16OC and 20OC.

According to Ethiopian temperature zoning, Gondar belongs to Woina Dega zone. Diurnal

temperature variation is large during the dry season and it often exceeds 15oc. The absolute

maximum temperature usually occurs from March to May and minimum temperature also occurs

from November to February.

The soils of Gondar city can be classified as silty clay, silty clay loam, and black clay and the depth

of the soil in Gondar city ranges from 20 to 70 cm. The color of the soil is identified to be brown

on the slopy areas and dark to gray on the flatter parts.

Topography can be described as fairly mature, with rounded hills and gentle slopes except at the

higher elevations where outcrops of resistant basalt may be seen. The main part of the city is

located on a ridge between two rivers, the Angereb and the Kahai. The city is largely on the slope

facing towards the Kahai, which is less steep than the slope facing the Angereb. The topography of

the majority part of the city that includes the Air Port and Azezo is also gentle slope.

Angereb watershed is found in North Gondar Zone of Amhara Regional State. It comprises 7 rural

kebeles, 5 from Lai-Armachoho and 2 from Gondar Zuria woredas and 3 urban kebeles from

Gondar city. The total population residing in the watershed area is estimated to be 5,279

households with a total family size of 29,148. The average family size is about 5.52 persons with

the male ratio of 50.9%.

The Angereb Reservoir is incited near Gonder town on the Angerb River having the main objective

of supplying water for the residents of Gonder town. The water supply reservoir, with the

objective of Water supply to the population of the town was commissioned in 1987 with an

original volume storage capacity of the reservoir at full Supply level of 1909 meter above sea level,

was 5.50Mm3 as the regional water and mining bureau information.

The Angereb reservoir is found on the downstream of the watershed and the upstream area is

used for agricultural development. Obviously the agricultural activities carried out on the upstream

have a negative impact on the downstream reservoir. On the other hand the reservoir is mainly

used for the town dwellers as a source of drinking water supply. Hence as it has been stated by

Peter Morris and Riki Therivel, (1995), the question of who will be affected is of crucial importance

in Environmental impact assessment.

1.2 Statement of the Problem and Justification Though the type and extent of problem area varies from watershed-to-watershed; low crop

production, Low livestock production, land resource degradation, shortage of wood and water and

lack of adequate infrastructure are the commonest core problem areas across the country. Earlier

studies of ANRS reveal that environmental degradation, poor agricultural production, and poor

water and sanitation management are the major problem areas of the Angereb watershed.

In relation to the Angereb reservoir, two major problems are expected. These are siltation and

pollution of the reservoir. Siltation reduces the expected life volume while pollution increases

treatment cost of the potable water. On the other hand land degradation and fertility losses are

the major problems of the rural inhabitants of Angereb watershed.

To remain healthy, human beings need an adequate supply of high-quality water throughout the

year. Many debilitating or even fatal diseases are transmitted by the contamination of the water

supply with human fecal matter containing disease-causing viruses, bacteria, and parasites.

Unfortunately, over one-third of the world’s population, nearly 2.5 billion people, have inadequate

access to sanitation and over 1 billion people do not have access to enough safe water. These

conditions, combined with poor hygiene, are largely responsible for the fact that 50 percent of the

world’s population suffers from debilitating diarrheal diseases (e.g., typhoid, cholera, dysentery) at

any given time. Of those affected by such diarrhea diseases, three million die every year.

(Environmental Guidelines for Small-Scale Activities in Africa, 2nd Edition, June 2001.)

Overall, polluted water affects the health of 1.2 billion people every year, and contributes to the

death of 15 million children under five every year. Vector-borne diseases, such as malaria, kill

another 1.5 to 2.7 million people per year, with inadequate water management a key cause of

such diseases (UNEP Global Environmental Outlook Report 2000). Sub-Saharan Africa is by no

means exempt from the problem: In Africa alone, over 300 million people lack either sanitation or

adequate water and frequently both. (Environmental Guidelines for Small-Scale Activities in Africa,

2nd Edition, June 2001.)

Disease and mortality are not the only consequences of polluted and scarce water. Less attention

is paid to the fact that women and children bear much of the cost of dirty water and water

shortages. Children are more likely to become ill, and women have to look after them. Women and

girls carry out most water collection, and many spend hours doing so. Hours spent collecting water

could be spent in more productive activity, such as food production or, especially in the case of

children, education. As a result, there is a high opportunity cost to the lack of clean water. When

people are sick, they and their caregivers cannot carry out other tasks, so there are opportunity

costs there as well. (Environmental Guidelines for Small-Scale Activities in Africa, 2nd Edition, June

2001.)

Thus to resolve the problem of Land degradation which is the problem of the rural community of

the Angereb watershed and the problems related to the drinking water supply of the town of

Gondar; deep knowledge of the environmental impacts plays a great role. It is a well known fact

that long-term agricultural sustainability can be achieved by increasing knowledge regarding the

spatial and temporal interactions between environmental processes. Thus this research studies the

long term impacts of the upstream land use management which involve the gradual buildup of

pollutants on the downstream reservoir and predicts the impacts of land use management of

Angereb watershed on the pollutant load status.

1.3 Objective

1.3.1 General Objective The main objective of this study is to assess the environmental impacts in Angereb watershed for

the last two decades and to predict impacts of land use management on the water quality of the

Angereb reservoir.

1.3.2 Specific Objectives The specific objectives include the following.

To develop the past and current land use/cover maps of Angereb watershed

To assess the relative impacts of changes in land use management practice on pollutant

load of Angereb watershed.

To forecast the annual pollutant load considering the past trends of land use management

of Angereb watershed and other assumptions.

1.4 Research Questions The following research questions will be answered in this study.

1. What looks like the land use pattern of Angereb watershed in the past and currently?

2. What are the impacts of the changes in land use management on the pollutant load of

Angereb watershed?

3. What will happen on the pollutant load of Angereb watershed if the change in the land use

pattern continues with the past management trends? What will happen if new

management system (BMP) is installed?

1.5 Chapter Scheme This study consists of 6 chapters. Chapter one which is an introduction tries to give a general

background about Angereb watershed and this study for the reader. Chapter two is about the

literature review and chapter three describes the methodology and materials. The methodology

and material describes in detail the study area, data and acquisition methods, and the

methodology used.

Chapter 4 gives explanation about the data processing and results. It’s concentrated on how the

image interpretation is carried out and the BASIN software is used for impact assessment and what

results are found. Chapter 5 is about the discussion of the results of this study and tries to answer

the research questions. Finally chapter 6 provides conclusion and recommendations.

2. Literature Review

2.1 Land Use/ Cover Change Analysis Using Remote Sensing and GIS: Land is an area of the earth's surface, the characteristics of which embrace all reasonably stable or

predictably cyclic attributes of the biosphere, the soil and the underlying geology, hydrology, the

plant and animal populations and the results of past and present human activity to the extent that

these attributes exert a significant influence on present and future uses of the land by man (FAO,

1976; Beck, 1978; FAO, 1989).

The previous works showed that developing method for land-use information extraction using

digital remotely sensed data is important. Land-use information should contain information

related to ‘uses’, or socio-economic function, rather than land-cover types and the use of

appropriate classification scheme is also critical to the success of such efforts (Projo Danoedoro,

2006)

Land-cover/land-use information is recognized as an important input to planning (Lindgren, 1985;

Lein, 2003). Land-use and land-cover are different. Clear differentiation between land-cover and

land-use concepts has been made by several authors. Campbell (1983), for example, showed the

difference in concrete-abstract dichotomy, where land-cover is concrete and land-use is abstract.

That is, land-cover can be mapped directly from images, while land-use requires land-cover and

additional information on how the land is used. However, both concepts were sometimes mixed in

use (Anderson et al., 1976; Malingreau and Christiani, 1981), although land-use information

normally contains attributes of land-cover.

Visual image interpretation could normally generate land-use information by combining a set of

interpretation elements including colour/tone, texture, shape, shadow, size, pattern, site and

association (Lillesand et al., 2004). With digital multspectral classification, only land-cover could

usually be extracted, as the land-cover types are related to their spectral responses recorded by

the remote sensors (Jensen, 2004; Mather, 2004).

Most digital classification methods were used for land-cover/land-use with limited number of

classes, i.e. equal or less than 10 (Aplin and Atkinson, 2000; Sawaya et al., 2003; Wang et al., 2004;

Puissant et al., 2005), while many applications related to planning require more detailed

categorisation. Detailed categorisations used in remote sensing projects were performed by some

authors, e.g. Loveland and Belward (1997), which dealt with global land cover mapping. Detailed

FAO land-cover classification was prepared for visual interpretation (Jansen and Gregorio, 2003).

Detailed categorisation was also found in the USGS land-cover/land-use classification system

(Anderson et al., 1976). But in general detailed classification of land-use based on digital

processing of remotely sensed imagery was rarely available.

The software used for the image analysis in this study is ENVI 4.3. ENVI is the software for the

visualization, analysis, and presentation of all types of digital imagery. ENVI’s complete image-

processing package includes advanced, yet easy-to-use, spectral tools, geometric correction,

terrain analysis, radar analysis, raster and vector GIS capabilities, extensive support for images

from a wide variety of sources, and much more.

In this study unsupervised and supervised classification of images are carried out to produce the

present and past land use/ cover of Angereb watershed. Unsupervised classification is used to

cluster pixels in a data set based on statistics only, without any user-defined training classes. The

unsupervised classification techniques available are Isodata and K-Means. The Isodata was used in

this study.

The Isodata unsupervised classification calculates class means evenly distributed in the data space

then iteratively clusters the remaining pixels using minimum distance techniques. Each iteration

recalculates means and reclassifies pixels with respect to the new means. Iterative class splitting,

merging, and deleting are done based on input threshold parameters. All pixels are classified to

the nearest class unless a standard deviation or distance threshold is specified, in which case some

pixels may be unclassified if they do not meet the selected criteria. This process continues until the

number of pixels in each class changes by less than the selected pixel change threshold or the

maximum number of iterations is reached.

Supervised classification can be used to cluster pixels in a data set into classes corresponding to

user-defined training classes. This classification type requires that selecting training areas for use

as the basis for classification. Various comparison methods are then used to determine if a specific

pixel qualifies as a class member. Before the supervised classification carried out ROIs should be

selected. ROIs typically used to extract statistics for classification, masking, and other operations.

ENVI provides a broad range of different classification methods, including Parallelepiped,

Minimum Distance, Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Binary

Encoding, and Neural Net. The Maximum likelihood method assumes that the statistics for each

class in each band are normally distributed and calculates the probability that a given pixel belongs

to a specific class. Unless a probability threshold is selected, all pixels are classified. Each pixel is

assigned to the class that has the highest probability (i.e., the maximum likelihood).

Classified images require post-processing to evaluate classification accuracy and to generalize

classes for export to image-maps and vector GIS. The Post Classification processes include clump,

sieve, combine classes, confusion matrices, converting to vector layers and shape files and others.

Clump and Sieve provide means for generalizing classification images. Sieve is usually run first to

remove the isolated pixels based on a size (number of pixels) threshold, then clump is run to add

spatial coherency to existing classes by combining adjacent similar classified areas. The Combine

Classes function provides an alternative method for classification generalization. Similar classes

can be combined to form one or more generalized classes.

One of the most common means of expressing classification accuracy is the preparation of a

classification error matrix (sometimes called a confusion matrix or a contingency table). Error

matrices compare, on a category by category basis, the relationship between known reference

data (ground truth) and the corresponding results of an automated classification. Such matrices

are square, with the number of rows and columns equal to the number of categories whose

classification accuracy is being assessed (Lillesand and Kiefer, 1994).

Change detection involves the use of multitemporal data sets to discriminate areas of land cover

change between dates of imaging (Lillesand and Kiefer, 1994). Change Detection Analysis

encompasses a broad range of methods used to identify, describe, and quantify differences

between images of the same scene at different times or under different conditions.

A key benefit of geographic information systems (GIS) is the ability to apply spatial operators to

GIS data to derive new information. These tools form the foundation for all spatial modeling and

geo-processing. Of the three main types of GIS data—raster, vector, and tin—the raster data

structure provides the richest modeling environment and operators for spatial analysis. Spatial

Analyst extension adds a comprehensive, wide range of cell-based GIS operators to ArcGIS. Among

these extensions the overlay tool explores relationships between layers and combinations (ESRI,

2001).

The Weighted Sum overlays several rasters multiplying each by their given weight and summing

them together. The Weighted Sum tool provides the ability to weight and combine multiple inputs

to create an integrated analysis. It is similar to the Weighted Overlay tool in that multiple raster

inputs, representing multiple factors, can be easily combined incorporating weights or relative

importance. One major difference between the weighted overlay tool and the Weighted Sum tool

is the Weighted Sum tool allows for floating point values whereas the Weighted Overlay tool only

accepts integer rasters as inputs (ESRI, 2006)

2.2 Water Quality Modeling Water can be contaminated by both natural and anthropogenic causes. Human activities, or the

results of human activities, are the principal source of pollutants that affect water quality. These

sources are classified as either point or nonpoint. Point Source means any discernible, confined

and discrete conveyance, including but not limited to, any pipe, ditch, channel, tunnel, conduit,

well, discrete fissure, container, rolling stock, concentrated animal feeding operation, vessel or

other floating craft from which pollutants are or may be discharged (USEPA, 1991a). Non point

sources of surface water pollution include overland and ground water flows to lakes, streams, wet

lands, or other surface water bodies from agricultural, mining, industrial, urban, developed,

undeveloped, or construction areas. These effluents enter surface water bodies along relatively

large reaches or areas (USEPA, 1991a).

It is reasonable to expect that the successful management of reservoirs, in regards to nutrient

enrichment, must be based on a complete understanding of the inherent complexity and

interactions of aquatic and terrestrial systems (Randtke and denoyelles, 1985). Appropriate

strategies to mitigate the adverse impacts of water pollution are critically important in the era of

sustainable development (Goonetilleke et al., 2005). In this context, the use of water quality

assessment models as decision making tools and as evaluation tool are highly appreciated.

The hydrological cycle is a continuous process that describes the circulation and storage of water

in the Earth (Maidment, 1993a), is influenced by humans from the local to the planetary scales

(Committee on Opportunities in the Hydrologic Sciences, 1991). Land cover change, associated

with the intensification of agriculture, cattle raising and urbanization, has had a profound influence

on the hydrological processes in small watersheds and at the regional level (Sahagian, 2000).

Water quality modeling can be performed on a lumped time basis or on a continuous time basis.

Lumped time basis modeling is relatively easier and produce estimates of pollutant load generated

from a catchment over a relatively long period of time (Deletic. A and Fletcher. T, 2006).

Continuous time basis models are relatively complex and produce estimates for pollutant

concentration in relatively shorter time steps (Akan and Houghtalen, 2003).

As it has been explained by Deletic A and Fletcher T, 2006; in management decision making and

evaluation of existing systems, the use of lumped time basis models is common. These models are

based on a simplified form of pollutant export equations. This equation is parameterised by a

representative rainfall-runoff parameter, commonly, runoff volume (Akan and Houghtalen, 2003).

Essentially, the use of single parameter in a water quality model is questionable as storm water

quality can be influenced by a range of rainfall, runoff, climatic and land-use parameters (Deletic A

and Fletcher T, 2006).

There have been many empirical studies over the years that examine the relationship between the

nutrient loads in exiting catchments and the characteristics of the catchments. Land use has been

the most consistently good predictor of nutrient loads from these studies, i.e. there are good

correlations between broad land use type and observed nutrient loads (Frances Marston et.al,

1995). Several water quality models used to estimate non-point water pollution into watersheds

require the input of either export coefficients (typically for rural areas) or event mean

concentrations (typically for urban areas). EMCs represent the concentration of a specific pollutant

contained in storm water runoff coming from a particular land use type within a watershed. Export

coefficients represent the average total amount of pollutant loaded annually into a system from a

defined area, and are reported as mass of pollutant per unit area per year (e.g. kg/ha/yr). EMCs

are reported as a mass of pollutant per unit volume of water (usually mg/L).These numbers are

generally calculated from local storm water monitoring data.

Since collecting the data necessary for calculating site-specific EMCs or export coefficients can be

cost-prohibitive, researchers or regulators will often use values that are already available in the

literature. If site-specific numbers are not available, regional or national averages can be used,

although the accuracy of using these numbers is questionable. Due to the specific climatological

and physiographic characteristics of individual watersheds, agricultural and urban land uses can

exhibit a wide range of variability in nutrient export (Beaulac and Reckhow 1982).

Wanielista M.P., Yousef Y.A. and McLellon W.M. (1977) summarize export coefficients from

previous studies and these values are presented (Table 3) for comparison.

Table 3: Export coefficient values from Reference 1

Source: Frances Marston, William Young, Richard Davis ,1995

A paper by Sonzogni W.C. et.al (1980) provides a brief summary of the "PLUARG" project

undertaken by the International Reference Group on Great Lakes Pollution from Land Use

Activities. This project was one of the most extensive studies of nonpoint source pollution and a

brief description of the study areas, the effects of ‘land form', land use, fertilizers and climate are

discussed. Summary ranges of nutrient export rates are given in Table 4.

There is some question as to the applicability of applying export coefficients or event mean

concentrations for different land uses developed in one part of the country to another region. As

seen in the tables and reports presented above, wide variation can exist not only regionally, but on

a local scale as well.

Furthermore, national mean concentrations or coefficients obtained by averaging numbers from a

variety of geographically disbursed studies can still yield differing results. For example, Rast and

Lee (1983) suggest using a national TP export coefficient of 0.5 kg/ha/yr for agricultural land use,

and 0.05-0.1 kg/ha/yr for forested land use. However, mean values for TP coefficients as reported

in Reckhow et al. (1980) are much higher: 0.236 kg/ha/yr for forested land use and 1.08-4.46

kg/ha/yr for agricultural land use (row and non-row crops).

Range Mean Range Mean Residential/commercial 3.7 5.7

Mixed: 74% non-urban, 17%urban, 9% surface water

2.21.1

Urban - literature summary 1.0 - 5.0 2 3.2 - 18 8.5Pasture - Litrature summary 0.24 - 0.66 0.3 2.5-8.5 5.3Cultivated - literature summary 0.18 - 1.62 1.05 15 - 37 26Woodland - literature summary 0.01 - 0.86 0.1 2.4 - 5.1 3.1

Total Phosphorus(kg/ha/yr) Total Nitrogen(kg/ha/yr)Land Use

Table4: Export coefficient values from Reference 2

Source: Frances Marston, William Young, Richard Davis, 1995.

Omernik (1976, 1977) conducted an extensive study looking at relationships between regionality

(as well as a variety of other factors) and nutrient export and concentrations in streams. The 1976

report examined regional relationships of TN and TP concentrations among four general regions

within the Eastern United States. Although differences exist in concentrations among the regions,

Omernik cautions that use of these distinctions is limited due largely to small sample sizes. The

1977 report includes several maps depicting ranges of nitrogen and phosphorus concentrations

across the entire United States. A few noticeable trends are evident in these maps. Total

phosphorus concentrations are generally higher in the West than they are in the East. Also, total

nitrogen concentrations are higher in the eastern region than in the central or western regions.

Some of these differences are attributable to differing regional land use (for instance, areas in the

far northeast and northwest corners of the country have relatively low TP and TN concentrations,

due to those areas containing watersheds that are largely forested). Conversely, TP and TN

concentrations are relatively high in large portions of the Midwest containing primarily agricultural

watersheds.

The suitability of applying regional coefficients may depend largely on the goals of the particular

study. If the study is conducted primarily for local comparative purposes (comparing/ranking

nutrient loads into local watersheds or catchments), then using regional or national values may

suffice. However, if accurate pollutant estimates are required, researchers should look into

obtaining (or generating their own) local coefficients/concentrations for their area of interest

(Wetlands Regulatory Assistance Program, 2004).

Total Phosphorus Total NitrogenGeneral agriculture 0.1 - 9.1 0.6 - 42Cropland 0.2 - 4.6 4.3 - 31Improved pasture 0.1 - 0.5 3.2 - 14Forest 0.02 - 0.7 1.0 - 6.3Idle 0.07 - 0.7 0.5 - 6.0General urban 0.3 - 4.8 6.2 - 18Residential 0.4 - 1.3 5.0 - 7.3Commercial 0.1 - 0.9 1.9 - 11Industrial 0.9 - 4.1 1.9 - 14Developing urban 23 63

Generation Rates (kg/ha/yr)Land Use

2.3 Pollutants

A pollutant is a man-made or naturally occurring constituent that creates an undesirable effect

when introduced to a specific environment. Elements such as nutrients, sediment, organic matter,

organic compounds, and metals are naturally occurring constituents that do not create adverse

effects when introduced to an aquatic system in balanced proportions. In fact, many of these

constituents are essential for the propagation of aquatic life. However, the introduction of

excessive, unbalanced quantities can create an undesirable effect and result in their acting as

pollutants. Constituents that provide no beneficial use in an aquatic system are also termed

pollutants. Water quality control is the balancing of required constituent masses with the

elimination of pollutants to provide a desirable aquatic system. The major chemical impurities

included in this study are described as follows:

Total Dissolved Solids Total Dissolved solids (TDS) in natural waters consist mainly of carbonates, bicarbonates, chloride,

sulphate, calcium, magnesium, sodium and potassium. Dissolved metals, dissolved organics and

other substances account for a small portion of the dissolved residue. TDS in drinking water tends

to change the waters physical and chemical nature. It is generally agreed that the TDS

concentration of palatable water should not exceed 500mg/lt.

Ammonia NH3 Ammonia occurs as break down product of nitrogenous material in natural waters. It is also found

in domestic effluents and certain industrial waste water. Ammonia is harmful to fish and other

forms of aquatic life and the ammonia level must be carefully controlled in water used. Ammonia

tests are routinely applied for pollution control on effluents and waste waters, and for the

monitoring of drinking supplies.

Nitrate NO3

Nitrates are normally present in natural drinking and waste waters. Nitrate enters water supplies

from the breakdown of natural vegetation, the use of chemical fertilizers in modern agriculture

and from the oxidation of nitrogen compounds in sewage effluents and industrial wastes.

Nitrate is an important control test for water supplies.

Drinking water containing excessive amounts of Nitrates can cause methaemoglobinaemia in

bottle fed infants (Blue babies). The EEC has set a recommended maximum of 5.7 mg/lt N (25mg/lt

NO3) and an absolute maximum of 11.3mg/lt N (50mg/lt NO3) for Nitrate in drinking water.

Nitrite NO2

Nitrite are found in natural waters as an intermediate product in the Nitrogen cycle. Nitrite is

harmful to fish and other forms of aquatic life and the Nitrite level must be carefully controlled in

water used for fish farms and aquariums. The Nitrite test is also applied for pollution control in

waste waters, and for the monitoring of drinking water.

Calcium Hardness

Calcium hardness is caused by the presence of calcium ions in the water. Calcium salts can be

readily precipitated from water and high levels of calcium hardness tend to promote scale

formation in water systems. Calcium hardness is an important control test in industrial water

systems such as boilers and steam raising plant; and for swimming pool waters.

Magnesium Hardness Magnesium is a widely occurring natural element and is found in most water supplies. Magnesium

salts contribute to the hardness of water and higher levels of magnesium will be found therefore

in hard water areas. Scale formation in heating and steam raising equipment is promoted by the

presence of magnesium salts do however have a lower scale forming tendency than calcium salts.

Phosphate PO4 Phosphates are extensively used in detergent formulation and washing powder. Phosphates also

find wide spread application in the food processing industry and industrial water treatment

processes. Agricultural fertilizers normally contain phosphate minerals and phosphates also arise

from the breakdown of plant materials and in animal wastes.

Phosphates can therefore enter water courses through a variety of routes – particularly domestic

and industrial effluents and runoff from agricultural land. Phosphate is an important control test

for natural and drinking water.

Iron Fe Iron occurs naturally in rocks and soils and is one of the most abundant of all elements. It exists in

two form “ferrous” (Fe+2) and ferric (Fe+3) iron. Ferrous iron is found in well waters without much

dissolved oxygen. Iron in solution in water is derived naturally from soils and rocks. It may also

result from the corrosive action of water on unprotected iron or steel mains steel well casings, and

pumps.

Water quality limits on allowable concentrations on iron in water supplies are based on aesthetic

and test problems rather than health concerns. Iron concentration above 0.3mg/lt can cause “Red

water” and staining of plumbing fixtures. Iron also provides a nutrient source for some bacteria

that grow in distribution systems and wells. Iron bacteria such as gallionella, cause red water,

testes and odour, clogged pipes and pump failure.

Earlier studies by the Regional professionals using USLE; reveal that the total soil loss of Angereb

watershed is 546,454.24ton/yr. Based on the same study the annual sedimentation rate is

69,491.51m3/yr. Moreover Angereb technical committee, 2004 and Shawel, 1999 reveal that the

sedimentation rate of Angereb dam is 88,389.81m3/year and 124,000m3/year respectively. Both

studies also estimated the sediment accumulated in the dam and the result was 1.364Mm3

(Shawel, 1999) and 0.9173Mm3 (Angereb Technical Committee, 2004)

2.4 Environmental Impact Assessment

In order to predict environmental impacts of any development activity and to provide an

opportunity to mitigate against negative impacts and enhance positive impacts, the environmental

impact assessment (EIA) procedure was developed in the 1970s. An EIA may be defined as: A

formal process to predict the environmental consequences of human development activities and

to plan appropriate measures to eliminate or reduce adverse effects and to augment positive

effects. (FAO Irrigation and Drainage Paper 53, 1995). In the case of environmental impact

assessment related to water quality assessment the type of pollutant, source and impact that can

be considered are summarized in table 5.

Once the major impacts to be studied have been identified, prediction work can start. This stage

forms the central part of an EIA. Realistic and affordable mitigating measures cannot be proposed

without first estimating the scope of the impacts, which should be in monetary terms wherever

possible. It then becomes important to quantify the impact of the suggested improvements by

further prediction work. It is also important to test the “without project” scenario. An important

outcome of this stage will be recommendations for mitigating measures. This would be contained

in the Environmental Impact Statement. Clearly the aim will be to introduce measures which

minimize any identified adverse impacts and enhance positive impacts.

Table 5: Pollutants source and impacts

Source: Minnesota urban small sites BMP manual, 2001

2.5 Better Assessment Science Integrating Point and Non point Sources (BASINS)

BASINS is a multipurpose environmental analysis system for use by regional, state, and local

agencies in performing watershed- and water-quality-based studies. It was developed by the U.S.

Environmental Protection Agency's (EPA's) Office of Water to address three objectives:

To facilitate examination of environmental information

To support analysis of environmental systems

To provide a framework for examining management alternatives

Because many states and local agencies are moving toward a watershed-based approach, the

BASINS system is configured to support environmental and ecological studies in a watershed

context. The system is designed to be flexible. It can support analysis at a variety of scales using

tools that range from simple to sophisticated.

Stormwater Pollutant Examples of Sources Related ImpactsNutrients: Nitrogen, Phosphorus Animal waste, fertilizers, falling

septic sytemsAlgal growth, reduced clarity, other problems associated witheutrification(oxygen deficit, relese of nutrients and metals fromsediments)

Sedimiments: Suspended and deposited Construction sites, other disturbedand/or non-vegetated lands,eroding banks, road sanding

Increased turbidity, reduced clarity, lower dissolved oxygen,deposition of sediments, smothering of aquatic habitat includingspawning sites, sediment and benthic toxicity

Organic Materials Leaves, grass clippings Oxygen deficit in receiving water Organic Materials Leaves, grassclippings

Pathogens: Bacteria, Viruses Animal waste, falling septic sytems Human health risksvia drinking water supplies, contaminatedswimming beaches

Hydrocarbons: Oil and greases,PAHs(Napthalenes, Pyrenes)

Industrial processes; automobile Toxisity of water column and sediment, biaccumulation in aquaticspecies and through the food chain, fish kill

Metals: Lead, Copper, Cadmium, Zinc,Mercury, Chromium, Aluminium, others

Industrial processes; normal wearof auto brake linings and tires,automobile emissions and fluidleaks, metal roofs

Toxisity of water column and sediment, biaccumulation in aquaticspecies and through the food chain, fish kill

Pesticides: synthetic chemicals Pesticides(herbicides, insecticides,fungicides, rodenticides, etc.),industrial processes

Toxisity of water column and sediment

Chlorides Road salting and uncovered saltstorage

Degradation of the beauty of surface Trash and Debris Litterwashed through storm drain

Traditional approaches to watershed-based assessments typically involve many separate steps

preparing data, summarizing information, developing maps and tables, and applying and

interpreting models. Each individual step is performed using a variety of tools and computer

systems. The isolated implementation of steps can result in a lack of integration, limited

coordination, and time-intensive execution. BASINS makes watershed and water quality studies

easier by bringing key data and analytical components "under one roof". Using the familiar

Windows environment, analysts can efficiently access national environmental information, apply

assessment and planning tools, and run a variety of proven, robust nonpoint loading and water

quality models. With many of the necessary components together in one system, the analysis time

is significantly reduced, a greater variety of questions can be answered, and data and management

needs can be more efficiently identified. BASINS take advantage of recent developments in

software, data management technologies, and computer capabilities to provide the user with a

fully comprehensive watershed management tool.

A geographic information system (GIS) provides the integrating framework for BASINS. GIS

organizes spatial information so it can be displayed as maps, tables, or graphics. GIS provides

techniques for analyzing landscape information and displaying relationships. Through the use of

GIS, BASINS has the flexibility to display and integrate a wide range of information (e.g., land use,

point source discharges, and water supply withdrawals) at a scale chosen by the user.

The watershed characterization component, working under the GIS umbrella, allows users to

quickly evaluate selected areas, organize information, and display results. The modeling

component module allows users to examine the impacts of pollutant loadings from point and

nonpoint sources.

The Models menu of BASINS contains three models that can be set up based on information in the

BASINS project, HSPF, AQUATOX, and PLOAD. The HSPF and AQUATOX options aid the user in

setting up powerful external, yet linked simulation models.

The PLOAD option provides a very simple watershed model for estimating pollutant loads on an

average annual basis. This study uses the PLOAD model and the details are explained in section

2.6.

Table 6: Models of BASINS and Their Brief Descriptions

Model Description

HSPF Sophisticated, high-level watershed model able to perform continuous simulation of surface and subsurface flow and associated physical, chemical, and biologic processes at a tributary level.

AQUATOX Simulation model for aquatic systems that predicts the fate of various pollutants, such as nutrients and organic chemicals, and their effects on the ecosystem, including fish, invertebrates, and aquatic plants.

PLOAD Simple watershed model that computes nonpoint source loads from different sub watersheds and land uses based on annual precipitation, land uses and BMPs.

Source: BASINS 4.0 User Manual

2.6 PLOAD The BASINS Pollutant Loading Estimator (PLOAD) is a simplified, GIS-based model to calculate

pollutant loads for watersheds. Based on the PLOAD extension developed for BASINS 3.0 by CH2M

HILL in Herndon, Virginia, PLOAD estimates nonpoint sources (NPS) of pollution on an annual

average basis, for any user-specified pollutant. The user may calculate the NPS loads using either

of two approaches, using Export Coefficients or the EPA's Simple Method.

Optionally, best management practices (BMPs), which serve to reduce NPS loads, point source

loads, and loads from stream bank erosion, may also be included in computing total watershed

loads. PLOAD produces maps and tables showing the NPS pollution results, and the tool can be run

multiple times to compare results under various scenarios.

PLOAD calculates loads for any sub basin polygon shape file, which may be user-supplied or the

output of one of the BASINS watershed delineation tools. In addition to this sub basin shape file,

the PLOAD application requires pre-processed GIS and tabular input data as listed below:

GIS land use data

Pollutant loading rate data tables

BMP site and area data (optional)

Impervious terrain factor data tables (for the Simple Method only)

Pollutant reduction BMP data tables (optional)

Point source facility locations and loads (optional)

Bank Erosion data tables (optional)

Annual pollutant loads may be calculated for each watershed using either areal export coefficients

or EPA's Simple Method approach. The Simple Method is an empirical approach developed for

estimating pollutant export from urban development sites and its application is limited to small

drainage areas of less than one square mile.

The areal export coefficient model is a similarly empirical approach that provides total loads based

on factors containing mass pollutant per unit area, per year. This option is provided for agricultural

and undeveloped land uses or larger watersheds for which the Simple Method may not apply.

Since Angereb watershed comprises majorly rural land use and it is greater than one square mile,

the export coefficient methods is used in this study.

2.7 Best Management Practice (BMP) BMPs are any measure, practice, or control implemented to protect water quality and reduce the

pollutant content in storm water runoff.

Research in the Catskills has shown that undisturbed forests can remove as much as 90 percent of

the nitrogen from rainwater before it can reach nearby streams (Lovett et al., 1999). However,

activities that produce NPS pollution also cause changes in vegetative cover, disturbance of soil, or

alteration of the path and rate of water flow.

These physical changes may prevent the land from naturally removing pollutants in storm water.

Thus, there are two interacting effects of NPS activities: (1) production of a pollutant and (2)

alteration of the land surface in a way that increases pollutant loading to receiving waters.

The goals of NPS pollution best management practice (BMPs) are to maintain or restore the ability

of the land to remove pollutants and to limit production of the pollutant. In the Chesapeake Bay

program Watershed Model of Nonpoint Source Best Management Practices that have been Peer-

Reviewed and revised 1/18/06 and the results are given in Table 6.

Table 6 Efficiency of Agricultural BMPs

Source: Chesapeake Bay Program Watershed Model Revised 1/18/06

Agricultural BMPs How Credited TN Reduction Efficiency (%) TP Reduction Efficiency (%) Sed Reduction Efficiency(%)

Coastal Plain Lowlands Efficiency 25 75 75Coastal Plain dissected Ulands Efficiency 40 75 75Coastal Plain Ulands Efficiency 83 69 69Piedmont Crystalline Efficiency 60 60 60Blue Ridge Efficiency 45 50 50Mesozoic low lands Efficiency 70 70 70Piedmont Carbonate Efficiency 45 50 50Valley and Ridge Carbonate Efficiency 45 50 50Valley and Ridge Siliciclastic Efficiency 55 65 65Appalachian Plateaus Efficiency 60 60 60

Coastal Plain Lowlands Efficiency 17 75 75Coastal Plain dissected Ulands Efficiency 27 75 75Coastal Plain Ulands Efficiency 57 69 69Piedmont Crystalline Efficiency 41 60 60Blue Ridge Efficiency 31 50 50Mesozoic low lands Efficiency 48 70 70Piedmont Carbonate Efficiency 31 50 50Valley and Ridge Carbonate Efficiency 31 50 50Valley and Ridge Siliciclastic Efficiency 37 65 65Appalachian Plateaus Efficiency 41 60 60

Land use conversion + efficiency

Efficiency applied to 4 upland acres

Efficiency applied to 4 upland acres

Efficiency applied to 4 upland acres

Riparian Forest Buffers and Wetland Restoration - Agriculture1:

Riparian Grass Buffers and Wetland Restoration - Agriculture1:

Land use conversion + efficiency

Efficiency applied to 4 upland acres

Efficiency applied to 4 upland acres

Efficiency applied to 4 upland acres

3 MATERIALS AND METHODS

3.1 Description of the Study Area

3.1.1 Location Administratively Angereb watershed is found in North Gondar Zone of Amhara Regional State. It

comprises 7 rural kebeles, 5 from Lai-Armachoho and 2 from Gondar Zuria woredas and 3 urban

kebeles from Gondar city. This watershed is located at about 748 km away from Addis Ababa

which is the capital of the country.

Geographically the study area lies between UTM coordinate of N 1394096 - N 1407336, and E

329033 - E 338981 with an approximate altitude range of 2,100 to 2,870 m.a.s.l. Two all weather

roads, Gondar-Humera and Gondar-Ambagiorgis, cross the watershed in the northwest and

northeast direction respectively. Fig 1 shows the location map of Angereb watershed.

Fig 1: Location map of Angereb Watershed

3.1.2 Topography The major landform of the Angereb watershed comprises chains of hills with mountainous ridge,

which include most of what is designated in the North central massif. This watershed can briefly be

expressed by mountainous rugged south facing topography. Angereb watershed is almost oval in

shape with dendritic drainage pattern, steep ridges at the boundary, numerous convex hills inside

the watershed and steep gorges.

The altitude range of Angereb watershed varies from 2100 - 2870 m.a.s.l, while the slope is less

than 8% for 11.5% of the land, 8-30% slope for 43.1% of the land and over 30% slope for 45.4% of

the land area.

3.1.3 Climate Similar to other parts of the ANRS, the rainfall of Gondar area is erratic. The annual rainfall varies

from 711.8 to 1822.42 mm with a mean annual value of 1159.22 mm. Based on the long-term

rainfall data (1952 - 2000) most of the rain occurs in July followed by August (Annex 2). The

rainfall in May is also quite significant. The annual rainfall is generally decreased from year to year

except in 1999, which has the second highest extreme value in the history of 45 years rainfall data.

Mean monthly maximum and minimum temperatures, wind speed and relative humidity of

Gondar area are indicated in table 8.

Table 8. Mean monthly Temperature (0C), wind speed (m/s) and Relative humidity (%)

Source: Regional technical committee (December2004)

3.1.4 Soils In most of the sub watersheds, the soils are shallow cambisol underlain by unconsolidated

medium sized gravels with loose joints, which in turn underlain by watertight rocky layers.

These layers are easily visible in some healing gullies and steeper part of the riverbeds. The

dominant color of soil for this watershed is brown. Of course, there are some black soils on

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecTemperature(max) 27.9 28.5 29.4 29.4 28.1 25.3 22.4 22.6 24.5 25.9 26.5 26.7Temperature(min) 10.8 12.2 14.2 15.1 14.9 13.7 13.2 12.8 12.4 12.1 11.5 10.8Mean Temp 19.4 20.4 21.8 22.3 21.5 19.5 17.8 17.7 18.5 19.0 19.0 18.8Wind speed(m/s) 1.5 1.8 1.9 1.8 1.9 1.7 1.4 1.5 1.5 1.5 1.6 1.9Wind speed(km/hr) 5.5 6.3 6.8 6.6 6.8 5.9 5.1 5.2 5.3 5.5 5.8 6.8Relative humidity(%) 43.3 39.2 38.7 40.2 51.4 66.1 78.6 79.3 72.1 59.3 50.2 45.9

lower flat lands and foot of hills and patch of red soil. The color and the texture of the soils of

the area characterized by moderate acidity, high available potassium, calcium and magnesium

contents (DEVECON study).

Based on the technical Committee for Gondar water supply and Angereb watershed report the

dominant textures identified in Angereb watershed are silty clay and loam and the dominant

soil depth classes are between 25 and 100cm.

3.1.5 Vegetation The original vegetation cover of the Angereb watershed has been so severely destroyed that

the landscape, over wide areas, are virtually bare. Some scattered trees left in the farm fields,

churchyards and open riverine forest along the streams are important indicators of the climax

vegetation.

Based on the technical Committee for Gondar water supply and Angereb watershed report

Dodoma viscosa, Olea africana, Croton macrostachys, Apodytes dividiata, Carisa edulis,

Combretum collinum, Acanthus arobresus are some of the dominant vegetation species, in

this watershed. In addition, there are a number of tributaries of Angereb River whose courses

are marked by scattered tree and woody vegetation types as an indicator of riparian forest.

It is common to see homestead plantations, farm boundary planting and woodlots (block

plantations) in the watershed. Plantation forests are either privately, communally or state

owned. Earlier studies reveal that the management and status of privately owned forests is

very good whereas that of state and community forests are regrettable. Most of the state and

community owned forests in the area were established during the previous Dergue regime,

some of these plantations were established twenty years back and yet not utilized even once.

The plantation forests dominantly comprise Eucalyptus globules, Eucalyptus camaldulensis and

to some extent Cuppresus lusitanica respectively in their order of abundance. One of the

plantations near the Reservoir is shown on fig 2.

Fig 2: Plantation forest around the Angereb Dam and Reservoir

3.1.6 Hydrology There are several small streams and springs that feed Angereb River and finally joins Megech

River. Some of the major tributaries of Angereb are Korebreb, Key Bahir, Embuaymesk and

Kokoch. Inspections of streambeds indicate that runoff is high during times of heavy

precipitation. Streambeds are covered with large boulders. During the dry season, flows

frequently disappear into the permeable streambed gravels. Apparently the major streams

maintain a small flow throughout the dry season. This is the base flow maintained by

groundwater discharge from springs and from seepage into the beds of the streams.

Based on the hydrogeological map of Ethiopia (1: 2million); Angereb watershed is classified

under the extensive Aquifers with fracture permeability of Volcanic rocks with low

productivity. Based on the same source the Angereb watershed is under the stratographic unit

of Miocene to Pliocene. More over two fault lines having North-south direction are found in

the watershed.

Experiences of drilling wells in Gondar area show that ground water can be obtained from the

volcanic rocks. However, the rocks of the area are not permeable. But water is able to move

through fractures and particularly along the horizontal contacts between the flows. The total

amount of water which can be obtained from volcanic rocks by wells, assuming that recharge

is adequate, depends on the number of interflow zones cut by the well and permeability of

such zones. The deeper the rocks wells are drilled the more zones are penetrated and the

higher water yield can be obtained.

3.1.7 Socio economic setting

3.1.7.1 Population and settlement The total population residing in the study area is estimated to be 5,279 households with a total

family size of 29,148. Out of the total population about 57% are urban and 43% are rural

inhabitants. The average family size is about 5.52 persons with the male ratio of 50.9%. The

overall population density of the project area is 295 persons /km2.

Most of the inhabitants live on the hill and mountainsides, and the houses are moderately

scattered all over the watershed. Similar to housing condition of Northern rural Ethiopia wood

and mud are the major materials commonly used for the construction of house in the

watershed.

3.1.7.2 Agriculture The estimated present population of the project area is 29,148 of which 13,685 (2,407 HHs)

are engaged in traditional agriculture mainly in crop production. The average farm per

household is 2.2 ha. It seems larger when it is compared to the regional average landholding

but this is due to the fact that very steep sloped of mountain ridges and hillside of the

watershed are intensively cultivated. The cultivated lands are not only being on steeper slopes

but also they are losing their depth and fertility.

The main crops grown in the area are dominated by Wheat 37.9%, Barley 27.9% Teff 21.3% ,

Horse Bean 13.5%. Besides to crops grown in rain fed, they also practice traditional irrigation

and used to cultivate potato, onion, and pepper with it.

Agronomic practices used by the farmers are mainly traditional, which includes plowing with

pair of oxen and hand weeding. A total of 890.5 Dap and Urea have been used in the Angereb

watershed in the year 2004. However, the uses of these inputs are not reflected in crop yields,

which are generally low. Also, the farmers use their own local crop varieties in the area for

longer periods. This mismanagement of land and its consequence on land resources

substantially contributed to low production, which is below the national average.

Livestock production is a principal source of income to the farmers after crop production.

Farmers in the study area keep their livestock for the production of draft power, for the supply

of human nutrition, for social prestige and as a capital asset. In the project area, there are

7013 cattle, 6320 sheep, 1111 goats, 6540 donkeys, 125 horses, 8 mules, 3571 chickens and

431 hives. The total number of tropical livestock unit as it has been revealed by earlier studies

is about 8270.8 TLU.

3.1.8 Water supply of Gondar city Modern water supply system for the city of Gondar was introduced in 1940’s during the Italian

invasion. The first modern water supply was constructed from Korebreb River in 1946 without

any treatment plant and, therefore, was of poor quality. Because of its age, the “Korebreb

system” has contributed to high rate of water loss as well. After two decades, the so-called

Yugoslavia system was constructed in 1969. It added one deep well to the existing system and

contributed its share to improve the service.

The government, from 1975 to 1994, constructed a total of eleven deep wells. The continued

effort has contributed to the increased water supply coverage of the city. All, except the two

deep wells constructed in 1994, were interconnected with the old Korebreb system. The

average discharge rate of each water source except, Angereb reservoir was on the average 6.6

liters per second.

With increased population and the shortfall in water supply, the Ethiopian government in 1982

conducted feasibility study on different alternatives to improve the water supply for Gondar

city. Associated Engineers, an international Consulting firm from Canada, conducted this

study. During the study different options were identified. Of these, the first option, which was

found to be the most feasible one in terms of cost, was digging deep wells. This option was not

selected because earlier deep wells were less than 120 meters deep and there was shortage of

reliable data about ground water potential (discharge of wells).

The other option was the prospect of using water from Lake Tana as a source. This option was

found to be the second feasible one. This would have meant the installation of 39 km pipeline

and a power requirement of about 4000 kW to pump water up an elevation of 215 meters.

Even though it has the prospect of a reliable water source, it was rejected because it

demanded a total budget estimated of 58 million Ethiopian Birr with substantial foreign

currency and high electric power requirement. Both were in short supply and even not

available during that time.

The third feasible option was the construction of Megech dam across Megech River. This was

also rejected because it required over 77 million Eth. Birr. The fourth feasible options were the

construction of Fenter and Angereb dams across Fenter and Angereb rivers, respectively.

These options were rejected because the proposed dam sites were located down streams and

closer to the city. Thus, the reservoirs could be potentially polluted from both solid and liquid

waste disposal from Gondar and thereby increase the cost of water treatment, beside their

construction cost.

At the end, water supply from the construction of a dam on the Angereb River was chosen,

though it was one of the fourth feasible options. The exact reason, why the government had

chosen this option is not known.

Hence, the construction of Angereb dam, across Angereb River within the Angereb watershed

to alleviate the potable water deficiency of Gondar city, was approved. Thus, with a total

budget of 77 million Ethiopian Birr, the construction of the huge Angereb dam that commands

a total watershed area of 9,869 hectares was begun in 1986 to serve about 275,000 people of

Gondar for at least 20 years. Moreover, the scheme was expected to fulfill the demand of

water for enhancing other economic activities of the city including the development of

industries.

The existing treatment plant of the system has a capacity of 90 Lit/sec, the treatment plant has

the following major parts and processes: - Balancing chamber, Pre chlorination, Lime dosing

unit, Rapid mixer, Sedimentation, Rapid Sand Filter, Post chlorination, Water pumps. The dam

and the treatment plant is shown on fig 3.

Fig3: Angereb Dam and Treatment plant

3.2 Data and Acquisition Methods In principle, there are two main categories of spatial data acquisition: the first one is ground

based methods such as making field observations, taking in situ measurements and

performing land surveying. The other one is remote sensing methods, which are based on the

use of image data acquired by a sensor such as aerial cameras, scanners or radar. In this study

the following data and acquisition methods are used.

3.2.1 Remote sensing Data In this study Land sat images for the year 1986, 1999 and 2002 are used. These images are

obtained from different sources. The 1986 image is obtained by downloading from the site:

http://glcf.umiacs.umd.edu/index.shtml using Path number 170 and row number 51. The 1999 and

2002 landsat images are obtained from Bahirdar University. These satellite images are analyzed to

give the land use /cover data which is used as one of the input data for the modeling using PLOAD

3.2.2 Pollutant loading rate data The PLOAD application requires pre-processed GIS and tabular input data. One of the tabular

input data is the pollutant loading rate data. This tabular input data is provided in comma-

delimited text format.

The pollutant loading tables consist of the event mean concentration (EMC) and the export

coefficient. The export coefficient table is developed from the EMC values. And the EMC

values are obtained from sample chemical test results that have been collected in the Angereb

treatment plant by the office of Gondar town water supply and sewerage office. (Annex 1)

The hourly data collected by the office of Gondar town water supply and sewerage include the

pollutant concentration in the raw, filtered, clarified and potable water samples. But in this

study only major pollutants and the raw samples which can represent the pollutant coming

from the watershed are considered. Pollutants evaluated in this study include: TDS, Nitrite,

Nitrate, Magnesium Iron, Calcium, Ammonia and phosphate. In this research hourly data for

the last two years are considered and the mean concentration is calculated to give the

following EMC values (Table 9).

Table 9: EMC Values

TDS Nitrite Nitrate Ammonia Magnesium Calcium Iron Phosphate

Concentration

in mg/l

146.83 0.47 0.51 0.10 0.21 0.28 0.23 3.63

Since the coefficient method is used in this study, EMC is converted to export coefficient value

using a catchment pollution calculator (Annex 4). This spreadsheet calculator is obtained from

UW Urban water.info and uses the following input data.

1. EMC value in mg/l: As explained above this is obtained by taking the mean value of the

hourly concentration data of Gondar Town water supply and sewerage office.

2. The annual rainfall for the Angereb watershed: This is calculated by using the monthly

rainfall data (Annex 4) and the result is 1159.22 mm.

3. Entering the runoff co-efficient for the drainage area. This co-efficient can be calculated in

a coarse way using the equation:

= ( ) ( 2)

( 3) 1000

The measured runoff in the above equation is obtained from the SUMMARY OF HYDROMETRIC

DISCHARGE DATA (Annex 3) of the Ministry of water resource development. Based on this data the

measured runoff is 1.197 X108 m3. The drainage area of Angereb watershed is 98.69km2 (9.869 X

107 m2) as it has been indicated in the Discharge data. Therefore using the above equation the

runoff coefficient is calculated to be 0.96.

4. Entering the catchment area in hectares: As it has been indicated in the land use data the

Angereb watershed is 9869ha.

Finally the following Export coefficient value is obtained for each pollutant as shown in table 11.

Table11: Export coefficient value

Pollutant type Load/Basin Area (kg/ha/year)

TDS 1633.7

Nitrite 5.2

Nitrate 5.7

Ammonia 1.1

Iron 2.3

Magnesium 3.1

Calcium 2.6

Phosphate 40.4

But it is obvious that this pollutant load could not be contributed uniformly from all of the land

uses. Since cultivated land is exposed for erosion and it is also the source for chemical pollutants

due to fertilizer applications; more pollutant is expected from cultivated land than forest or grass

land. Since most of the large extraordinary EMC values are excluded during the development of

EMC values, the export coefficient values (table 9) are considered the lowest amounts and 15% is

added for cultivated land. Based on this assumption the export coefficient value for each land use

is given in Table 12.

Table 12: Export Coefficient value per land use

Pollutant type Load/Basin Area (kg/ha/year) Forest Land Grazing Land Cultivated Land

TDS 1633.7 1633.7 1878.7 Nitrite 5.2 5.2 5.98 Nitrate 5.7 5.7 6.555

Ammonia 1.1 1.1 1.265 Iron 2.3 2.3 2.645

Magnesium 3.1 3.1 3.565 Calcium 2.6 2.6 2.99

Phosphate 40.4 40.4 46.46

3.2.3 BMP data BMP pollutant removal efficiencies are used for each pollutant type in the study area. The data can

be in the format of percentage removal (0 to 100) or removal fraction (0.00 to 1.00). If the case is

percentage removal as it has been used in this study, then PLOAD will automatically divide each

value by 100 prior to processing. In this study the pollutant removal efficiency data is put in two

scenarios. The first one is estimated to be 15% based on the past trends of land use management

practice of Angereb watershed and the development of EMC values for each land use. In the

second scenario 45% efficiency is taken based on Literature values.

Of course in most of previous studies carried out abroad the BMP efficiency percentage is different

for the different BMPs (Table 6). But it is too difficult to use those efficiency percentages in this

study since most of the estimates based on controlled research studies that are highly managed and

maintained by a BMP expert. This approach is not reflective of the variability of effectiveness

estimates in real-world conditions where farmers, not BMP scientists, are implementing and

maintaining a BMP across wide spatial and temporal scales with various hydrologic flow regimes, soil

conditions, climates, management intensities, vegetation, and BMP designs. By assigning effectiveness

estimates that more closely align with operational, average conditions modeling scenarios and

watershed plans will better reflect monitored data (Mid-Atlantic Water Quality Program housed at the

University of Maryland, 2006). Thus based on the above justification a15 and 45% BMP efficiency is

used for the first and second scenarios.

3.3 Methods Multi temporal images of Angereb watershed is used to obtain the land use/ cover dynamics

of the watershed by applying the remote sensing techniques. The multi temporal land use

classifications resulted from the images and other inputs will be used in PLOAD modeling tool.

Finally the output data of the PLOAD model is processed in the GIS environment. In general

the following process has been carried out in this study.

Digital Image

Processing

Landsat Images of Year 1986, 1999, 2002

BASINSPLOAD

GIS Analysis Environment

OutputAssess the environmental

impacts and Predict the effects of land use management

Land use/Cover

BMP Value

EMC Value

Rainfall Data

Pollutant Coefficient Value

Discharge data

Water Quality

Test Data

Fig 4: Flow chart of the Methodology

4 ANALYSIS AND RESULTS

4.1 Satellite Image Analysis and its Results The Landsat images for the year 1986, 1999, and 2002 of Angereb watershed are used to obtain

the respective land use/ cover maps of the watershed by applying the remote sensing techniques.

The following satellite image processing activities are carried out in this study.

The first step carried out after downloading the 1986 Land sat image is layer stacking. Layer

Stacking is used to build a new multiband file from georeferenced images of various pixel sizes,

extents, and projections. The input bands are re-sampled and re-projected to a common user-

selected output projection and pixel size.

After stacking the different bands for the 1986 image; unsupervised classification was carried out

for all the three years images (1986, 1999 and 2002). As it has been discussed in the literature

review unsupervised classification is used to cluster pixels in a data set based on statistics only,

without any user-defined training classes. The unsupervised classification techniques available are

Isodata and K-Means. The Isodata was used in this study. The unsupervised classification result is

used as benchmark during the supervised classification.

Before the supervised classification carried out ROIs have been selected. ENVI provides a broad

range of different supervised classification methods, including Parallelepiped, Minimum Distance,

Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Binary Encoding, and Neural

Net. The Maximum likelihood method has been used in this study after comparing it with some of

the other methods using the accuracy assessment results.

Classified images require post-processing to evaluate classification accuracy and to generalize

classes for export to image-maps and vector GIS. The Post Classification carried out in this study

includes clump, sieve, and combine classes, confusion matrices and converting to vector layers and

shape files. In this study, the images after post classification and ROIs have been compared to give

out the accuracy assessment results.

The training set pixels that are classified in to the proper land cover categories are located along

the major diagonal of the error matrix (running from upper left to lower right). All diagonal

elements of the matrix represent errors of omission or commission. Omission errors correspond to

non diagonal column elements. Commission errors are represented by non diagonal row elements.

The overall accuracy is computed by dividing the total number of correctly classified pixels (i.e. the

sum of the elements along the major diagonal) by the total number of reference pixels.

The Ќ statistics is a measure of the difference between the actual agreement between reference

data and an automated classifier and the chance agreement between the reference data and a

random classifier. Conceptually, Ќ can be defined as

= − ℎ 1 − ℎ

This statistics serves as an indicator of the extent to which the percentage correct values of an

error matrix are due to “true” agreement versus “chance” agreement. As true agreement

(observed) approaches 1 and chance agreement approaches 0, K approaches 1.

The Ќ statistics is computed as:

= ∑ −∑ ( . )

−∑ ( . )

Where r = number of rows in the error matrix

xii = the number of observations in row i and column j(on the major diagonal)

xi+ = total of observations in row i (shown as marginal total to right of the matrix)

x+I = total of observations in column i (shown as marginal total at the bottom of the matrix)

N = total number of observations included in matrix

In reality Ќ usually ranges between o and 1. In this study a Ќ value of 0.99 for the year 1986 is

found and it can be thought of as an indication that an observed classification is 99% better than

one resulting from chance. In the same manner K values for the year 1999 and 2002 are calculated

and are shown in table 12, 13 and 14.

The overall accuracy only includes the data along the major diagonal and excludes the errors of

omission and commission. On the other hand, K value incorporates the non diagonal elements of

the error matrix as a product of the row and column marginal. Normally it is advisable to compute

and analyze both of these values.

Finally the shape files obtained from the image analysis using ENVI have been scrutinized using the

ArcGIS 9.2 software to give the Land use/cover attribute data and maps for the year 1986, 1999,

and 2002 (Table 12, 13, 14 and Fig 5,6,7 ).

Table 12: Land use /cover of Angereb Watershed (1986) Overall Accuracy = 99.4253% Kappa Coefficient = 0.9927

S/n Land use/ Cover 1986 % 1 Forest 765 7 2 Shrub 506 5 3 Scrub 2164 22

Sub Total 3435 34 4 Grazing 454 5

Sub Total 454 5 5 Intensively Cultivated 3066 31 6 Moderately Cultivated 1952 20 7 Sparsely Cultivated 962 10

Sub Total 5980 61

Total 9869 100

Fig 5: Land use/Cover map of Angereb watershed in 1986

Table 13: Land use /cover of Angereb Watershed (1999) Overall Accuracy = 97.9263% Kappa Coefficient = 0.9748

S/n Land use/ Cover Area (Ha) % 1 Forest 647 7 2 Shrub 1422 14 3 Scrub 971 10

Sub Total 3041 31 4 Grazing 1102 11

Sub Total 1102 11 5 Intensively Cultivated 3351 34 6 Moderately Cultivated 1259 13 7 Sparsely Cultivated 1116 11

Sub Total 5726 58 Total 9869 100

Fig 6: Land use/cover map of Angereb watershed in 1999

Table 14: Land use /cover of Angereb Watershed (2002) Overall Accuracy = 97.2441% Kappa Coefficient = 0.9653

S/n Land use/ Cover Area (Ha) % 1 Forest 1059 11 2 Shrub 1258 12 3 Scrub 1014 10

Sub Total 3332 33 4 Grazing 754 8

Sub Total 754 8 5 Intensively Cultivated 3708 38 6 Moderately Cultivated 1130 11 7 Sparsely Cultivated 946 10

Sub Total 5783 59 Total 9869 100

Fig 7 Land use/cover map of Angereb watershed in 2002

4.2 Pollutant Load Assessment and its Results To sustain the supply of water to the city of Gondar at the required level and prolong the useful life of

the dam, it is crucial to estimate the annual pollutant load and identify spots in the watershed with the

huge amount of pollutant. This study tries to assess the biophysical environment of Angereb

watershed and its impact on the Angereb Dam using the PLOAD model.

As it has been discussed earlier the export coefficient method is used for calculating the annual

pollutant loads. In this method of PLOAD, the loads are calculated for each specified pollutant type

by watershed using the following equation:

= ( ∗ )

Where Lp = Pollutant Load

Lpu = Pollutant loading rate for land use type u

Au = Area of land use type u

Based on the above equation PLOAD has given the annual pollutant load for the year 1986, 1999

and 2002 in the form of tables and their spatial distribution in maps (fig 8, 9, 10). The attribute

data obtained from the PLOAD model is in lb/year so that the KGHAYR EXPORT CALCULATOR which

is modified for the pollutants and land use of this study has been used instead which has kg/year

unit of measurement. Annex 5 shows the modified KGHAYR EXPORT Calculator. Using this

spreadsheet calculator the annual pollutant load results are shown in table 15, 16 and 17.

Finally the data obtained from remote sensing and pollutant load analysis is taken to the GIS

environment for further analysis. The GIS environment analysis include rasterization of the shape

file obtained from the BASINS software, Reclassifying each pollutant load based on its annual

pollutant load value, and finally a weighted sum analysis is carried out to identify the spatial

distribution of the pollutant loads.

Table 15: Annual Pollutant Load (Kg/Year) for the year 1986

TDS Nitrite Nitrate Magnesium Iron Calcium Ammonia Phosphate1 Forest 1,249,781 3,978 4,361 842 1,760 2,372 1,989 30,906 2 Grazing 741,700 2,361 2,588 499 1,044 1,407 1,180 18,342 3 Intensively_Cultivated 5,760,094 18,335 20,098 3,878 8,110 10,930 9,167 142,446 4 Moderately Cultivated 3,667,222 11,673 12,795 2,469 5,163 6,959 5,836 90,690 5 Scrub 3,535,327 11,253 12,335 2,380 4,977 6,708 5,626 87,426 6 Shrub 826,652 2,631 2,884 557 1,164 1,569 1,316 20,442 7 Sparsely_Cultivated 1,807,309 5,753 6,306 1,217 2,544 3,430 2,876 44,695

Total 17,588,085 55,983 61,366 11,843 24,762 33,375 27,992 434,946

S/N Land use/CoverAnnual Polutant Load (Kg/Year)

Fig 8 spatial distribution Maps of Pollutant Load for the year 1986

Fig 9 Map for the weighted sum of Pollutant Load for the year 1986 Table 16: Annual Pollutant Load (Kg/Year) for the year 1999

TDS Nitrite Nitrate Magnesium Iron Calcium Ammonia Phosphate1 Forest 1,057,004 3,364 3,688 712 1,488 2,006 1,682 26,139 2 Grazing 1,800,337 5,730 6,281 1,212 2,535 3,416 2,865 44,521 3 Intensively_Cultivated 6,295,524 20,039 21,966 4,239 8,863 11,946 10,019 155,687 4 Moderately Cultivated 2,365,283 7,529 8,253 1,593 3,330 4,488 3,764 58,493 5 Scrub 1,586,323 5,049 5,535 1,068 2,233 3,010 2,525 39,228 6 Shrub 2,323,121 7,394 8,105 1,564 3,271 4,408 3,697 57,449 7 Sparsely_Cultivated 2,096,629 6,674 7,315 1,412 2,952 3,979 3,337 51,849

Total 17,524,222 55,780 61,143 11,800 24,672 33,253 27,890 433,367

S/N Land use/CoverAnnual Polutant Load (Kg/Year)

Fig 10 spatial distribution Maps of Pollutant Load for the year 1999

Fig 11 Map for the weighted sum of Pollutant Load for the year 1999

Table 17: Annual Pollutant Load (Kg/Year) for the year 2002

TDS Nitrite Nitrate Magnesium Iron Calcium Ammonia Phosphate1 Forest 1,730,088 5,507 6,036 1,165 2,436 3,283 2,753 42,784 2 Grazing 1,231,810 3,921 4,298 829 1,734 2,337 1,960 30,462 3 Intensively_Cultivated 6,966,220 22,174 24,306 4,691 9,808 13,219 11,087 172,274 4 Moderately Cultivated 2,122,931 6,757 7,407 1,429 2,989 4,028 3,379 52,500 5 Scrub 1,656,572 5,273 5,780 1,115 2,332 3,143 2,636 40,966 6 Shrub 2,055,195 6,542 7,171 1,384 2,893 3,900 3,271 50,823 7 Sparsely_Cultivated 1,777,250 5,657 6,201 1,197 2,502 3,372 2,829 43,951

Total 17,540,065 55,830 61,199 11,810 24,694 33,283 27,915 433,759

S/N Land use/CoverAnnual Polutant Load (Kg/Year)

Fig 12 spatial distribution Maps of Pollutant Load for the year 2002

Fig 13 Map for the weighted sum of Pollutant Load for the year 2002

4.3 BMP Computation and its Results All the above results are without considering BMP. The amount of pollutant load declines if BMP

are applied in and the PLOAD model has an option for this purpose. After the raw pollutant loads

are calculated using the export coefficient method, three equations are used to recalculate the

pollutant loads.

First, the percent of the watershed area serviced by BMPs are determined using the following

equation

The BMP and watershed areas are derived from the BMP and watershed GIS data. Next, the

pollutant loads remaining after removal by each BMP are calculated:

The raw watershed pollutant loads are derived from the results of the export coefficient methods,

while the percent load reduction comes from the BMP efficiency tables. Finally, the total pollutant

loads accounting for BMPs are computed by watershed. Each watershed load is a cumulative total

of areas which are and are not influenced by BMPs.

As it has been discussed in the Data and acquisition methods the efficiency for the BMP in this

study are in two scenarios.

Scenario 1: By assuming an efficiency value of 15%. This assumption is by considering the past

land use management practices which is tree planting activities carried out in the watershed and

their efficiency value is estimated to be 15%. Based on this assumption the results obtained are

shown in Table 18.

Table 18: Annual Pollutant Load with BMP (Scenario 1)

Scenario 2: By assuming efficiency value of 45%. This assumption is by considering the different

BMP techniques (Riparian forest buffers, Riparian Grass buffers, land retirement, row cropping,

and cereal cover crops) and referring their efficiency values from literatures (Table 7) and taking a

median value. The results for the second scenario are shown in Table 19.

TDS Nitrite Nitrate Magnesium Iron Calcium Ammonia Phosphate1 Forest 1,133,738.93 3,601 3,961 1,620 1,800 2,160 810 27,995 2 Grazing 807,213.55 2,564 2,820 1,154 1,282 1,538 577 19,932 3 Intensively_Cultivated 4,565,145.92 14,498 15,948 6,524 7,249 8,699 3,262 112,724 4 Moderately Cultivated 1,391,212.21 4,418 4,860 1,988 2,209 2,651 994 34,352 5 Scrub 1,085,563.05 3,448 3,792 1,551 1,724 2,069 776 26,805 6 Shrub 1,346,783.35 4,277 4,705 1,925 2,139 2,566 962 33,255 7 Sparsely_Cultivated 1,164,678.54 3,699 4,069 1,664 1,849 2,219 832 28,759

S/N Land use/CoverAnnual Polutant Load (Kg/Year)

Table 19: Annual Pollutant Load with BMP (Scenario 2)

TDS Nitrite Nitrate Magnesium Iron Calcium Ammonia Phosphate1 Forest 700,250.51 2,224 2,446 1,001 1,112 1,334 500 17,291 2 Grazing 498,573.08 1,583 1,742 713 792 950 356 12,311 3 Intensively_Cultivated 2,819,648.95 8,955 9,850 4,030 4,477 5,373 2,015 69,624 4 Moderately Cultivated 859,278.13 2,729 3,002 1,228 1,364 1,637 614 21,218 5 Scrub 670,494.83 2,129 2,342 958 1,065 1,278 479 16,556 6 Shrub 911,059.33 2,893 3,183 1,302 1,447 1,736 651 22,496 7 Sparsely_Cultivated 719,360.28 2,285 2,513 1,028 1,142 1,371 514 17,763

Total 7,178,665 22,798 25,078 10,259 11,399 13,679 5,130 177,258

S/N Land use/CoverAnnual Polutant Load (Kg/Year)

5 DISCUSSIONS

5.1 Land use/ Land cover Dynamics of Angereb Watershed

As it has been discussed in the literature review one of the drawback in land use classification is

the limited number of classes used for classification. Even though an ample amount of classes are

not used; this study tries to reclassify the major land uses of the Angereb watershed in to different

sub classes depending on the spectral response values. The various land use classes and their

respective assumptions used for this study includes the following.

1. Cultivated land: In terms of cultivated land, three subunits are used in this study based

upon the proportion of cultivated land within the unit. These are:

Intensively cultivated: Almost all part of the land is under annual crops.

Moderately cultivated land: Most part is under annual crop

Sparsely cultivated land: some part is under annual crops.

2. Grazing Land

3. Forest Land: In this study the forest land is subdivided in to three subunits based upon

the coverage intensity. These are:

Forest: Vegetation with the highest coverage Shrub: Vegetation with Medium coverage Scrub: Vegetation with low coverage

In this study the land use / cover for the year 1986, 1999 and 2002 has been considered and its rate of

change is calculated as follows:

ℎ = −

5.1.1 Forest Land

As it is shown in the Land use change Table (Table 18 and Fig 11) the forest land coverage declined

in the year 1999 and revived during 2002. The forest land declined by 11.5% in the year between

1986 and 1999; and it increased by 9.6% in the year between 1999 and 2002. Of course it declined

by 3% when the change between 1986 and 2002 is considered. The increment in forest coverage in

the year between 1999 and 2002 may be resulted from the effort to rehabilitate the Angereb

watershed by the government, Local NGOs and the community and majorly the expansion of

privately owned plantation sites.

Table 20: Forest Land use/cover change of Angereb watershed

Fig 11: Forest Land coverage chart for the three years

The rate of forest land use change is summarized in Table 21 and the results show that in the year

1986 - 1999 and 1986 – 2002; the forest land declined by 30 and 6.4 ha per year respectively. But

in the year between 1999 and 2002 the forest land increased by 97 ha per year.

When the forest extent of Angereb watershed is considered; it is also possible to classify as Natural

Vegetation and Plantation (Artificial) forest. Currently there are no significant tracts of natural

vegetations seen in the area, only remnants of it is observed in old churchyards, in a scattered

form on farmlands, in rocky cliffs and more commonly around the most upper watershed divide

(Around “Kedeste” and “Janhoy bata” localities).

Table 21: Rate of change of forest land in Angereb watershed

Change (Ha) Change (%) Change (Ha) Change (%) Change (Ha) Change (%)1 Forest 765 647 1059 -117 -15 412 64 295 392 Shrub 506 1422 1258 915 181 -163 -11 752 1493 Scrub 2164 971 1014 -1193 -55 43 4 -1150 -53

Sub Total 3435 3040 3332 -395 -11.5 291.5 9.6 -103.1 -3.0

2002-1986Landuse/ CoverS/n 1986 1999 2002

Landuse use ChangesArea (Ha)1999-1986 2002-1999

0

500

1000

1500

2000

2500

Forest Shrub Scrub

Area

(

Ha)

Type of Land use

1986

1999

2002

Due to the considerable increase of human population and its increased pressure on the natural

forest, the natural forest coverage of the area depleted greatly. The existent of illegal fuel wood

and charcoal vendors in the watershed due to the geographical proximity of the watershed to

Gondar town contribute for its depletion.

In contrary to the natural forest, the trend of the plantation forest in Angereb watershed showed

increment in 2002 as compared to 1999. There are homestead plantations, farmsteads, farm

boundary planting and woodlots (block plantations) in the watershed. The reasons for wide

adoption of artificial plantations include the following:

The decline in the natural forest coverage is creating a great gap between the demand and

supply for wood and its products. Hence to fill these gap farmers of the Angereb

watershed show a great effort on individual tree planting activities.

Most of the exotic tree species used for the artificial plantation grows more rapidly and

vigorously than the natural forest species.

High price of fuel and construction wood due to the geographical proximity of the

watershed area to Gondar town.

Decline of agricultural land productivity from time to time in the area due to severe land

degradation problems

5.1.2 Cultivated Land As any other rural areas of the Amhara region, the major source of livelihood in Angereb

watershed is crop production. Thus cultivated land has a dominant coverage as compared to the

other land uses of the Angereb watershed. The image analysis result shows that from the total

area of the watershed; cultivated land covers 61%, 58%, and 59% in the year 1986, 1999 and 2002

respectively.

1986 1999 2002 1999-1986 2002-1999 2002-19861 Forest 765 647 1059 -9 137 182 Shrub 506 1422 1258 70 -54 473 Scrub 2164 971 1014 -92 14 -72

Sub Total 3435 3040 3332 -30 97.2 -6.4

Rate of Change (Ha/Year)S/n Landuse/ Cover

Area (Ha)

From the cultivated land coverage it is observed that there was no any considerable fluctuation

but there is still variation from time to time. The cultivated land declined from 61% in 1986 to 59%

in 2002 considering the total watershed area (Fig 12).

Fig 12: Chart for Crop Land coverage of Angereb watershed

The cultivated land declined by 4.2% and 3.3% in the year 1986 - 1999 and 1986 - 2002,

respectively. But there was a 1% increment in the year between 1999 and 2002 (Table 22).

Table 22 Cultivated Land use/cover change of Angereb watershed

The rate of cultivated land use change is summarized in Table 20. The results show that in the year

1986 - 1999 and 1986 - 2002 the cultivated land declined by 20 and 12.3 ha per year respectively.

But in the year between 1999 and 2002 the cultivated land increased by 19 ha per year.

The decline in size of cultivated land and increment on forest coverage is not a common

observable fact in most parts of the rural areas since encroachment to forest land is a familiar

phenomenon along with population pressure. This extra ordinary condition in Angereb watershed

may be due to

0

1000

2000

3000

4000Area

(

Ha)

Type of Land use/Cover

1986

1999

2002

Change (Ha) Change (%) Change (Ha) Change (%) Change (Ha) Change (%)1 Intensivly_Cultivated 3066 3351 3708 285 9 357 11 641 212 Moderatly_Cultivated 1952 1259 1130 -693 -35 -129 -10 -822 -423 Sparsly_Cultivated 962 1116 946 154 16 -171 -15 -16 -2

Sub Total 5980 5726 5783 -254 -4.2 56.9 1.0 -196.7 -3.3

S/n Landuse/ Cover

Area (Ha) Landuse use Changes

1986 1999 20021999-1986 2002-1999 2002-1986

The enhancement of plantation sites by the individual farmers.

Degradation problems: prolonged agricultural use without appropriate land husbandry

result in poor productivity which in turn forced farmers to change it to other land uses.

Table 23: Rate of change of cultivated land in Angereb watershed

5.1.3 Grazing Land Feed for livestock in the watershed is derived mainly from grazing and browsing. Crop residues are

also used as reserve fodder to feed the animals over the long dry season. It is a common

phenomenon that the rapid increase of human population results to reserve more land for crop

production, and the area available for animal grazing is diminishing from time to time. Unlike this

the Angereb watershed grazing land coverage showed an increment by 143 and 66% in the years

1986 to 1999 and 1986 to 2002 respectively. But it declined in the year between 2002 and 1999 by

32% (Table 24).

Table 24 Grass Land use/cover change of Angereb watershed

The rate of Grazing land use change results (Table 25) show that in the year 1986 - 1999 and 1986 -

2002 the grazing land increased by 50 and 19 ha per year respectively. But in the year between

1999 and 2002 the grazing land decreased by 116 ha per year.

Table 25: Rate of change of Grass land in Angereb watershed

From the change in grazing land use we can scrutinize that the encroachment to forest land is due

to the expansion of grazing land in the years between 1986 and 1999. That is why the forest land

1986 1999 2002 1999-1986 2002-1999 2002-19861 Intensivly_Cultivated 3066 3351 3708 22 119 402 Moderatly_Cultivated 1952 1259 1130 -53 -43 -513 Sparsly_Cultivated 962 1116 946 12 -57 -1

Sub Total 5980 5726 5783 -20 19.0 -12.3

S/n Landuse/ CoverArea (Ha) Rate of Change (Ha/Year)

Change (Ha) Change (%) Change (Ha) Change (%) Change (Ha) Change (%)1 Grazing Land 454 1102 754 648 143 -348 -32 300 66

S/n Landuse/ Cover

Area (Ha) Landuse use Changes

1986 1999 20021999-1986 2002-1999 2002-1986

1986 1999 2002 1999-1986 2002-1999 2002-19861 Grazing Land 454 1102 754 50 -116 19

S/n Landuse/ CoverArea (Ha) Rate of Change (Ha/Year)

declined in the year 1999 as compared to its coverage in the year 1986 and the grazing land

started to decline in the year 2002 as the forest land increased.

5.2 Land use change and its impact on the Pollutant load of Angereb Watershed

The pollutant load estimation is based on the land use area and export coefficient values. The

major land uses considered in this study are Forest, Cultivated Land and Grazing. The forest and

grazing land are considered as a measure to decline the annual pollutant load where as the

cultivated land is assumed to play a major role in increasing the amount of pollutant load. Of

course as a land use cover all of the three can contribute the pollutants based on their area of

coverage. But the cultivated land contributes greater as it is the major land area used for fertilizer

applications and it is more susceptible to soil erosion in addition to its dominant coverage (Fig 13).

Fig 13 Chart for the Annual Pollutant Loads of Angereb watershed

When the Forest land is considered, it declined in size and its pollutant load contribution also

decreased in the year 1986 – 1999 and 1986 - 2002. In contrary to this; the forest land size

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

19861999

2002

Pollu

tant

Load

(Ton

)

Year

Forest Land

Cultivated Land

Grazing Land

increased and its pollutant loads contribution raised in the year between 1999 and 2002 (Table

26).

When the cultivated land of Angereb watershed is considered, it declined in size and its pollutant

load contribution decreased in the year between 1986 – 1999 and 1986-2002. In contrary to this;

the cultivated land size increased along with its pollutant loads contribution in the year between

1999 and 2002 (Table 27).

Table 26: Pollutant Load change in the forest Land of Angereb watershed

Table 27: Pollutant Load change in the Cultivated Land of Angereb watershed

When the Grazing land is considered, it increased in size and its pollutant load contribution also

increased in the year 1986 – 1999 and 1986-2002. In contrary to this; the Grazing land size

decreased along with its pollutant loads contribution in the year between 1999 and 2002. This is

shown in table 28.

1986 1999 2002 1999-1986 2002-1999 2002-1986TDS 4,326,383 3,830,140 4,195,395 (496,243) 365,255 (130,988) NO2 13,740 12,164 13,324 (1,576) 1,160 (416) NO3 15,114 13,380 14,656 (1,734) 1,276 (458) Mg 6,183 5,474 5,996 (709) 522 (187) Fe 6,870 6,082 6,662 (788) 580 (208) Ca 8,244 7,298 7,994 (946) 696 (250) NH3 3,092 2,737 2,998 (355) 261 (94) PO4 106,829 94,575 103,594 (12,253) 9,019 (3,234)

Pollutant Load(Kg) Pollutant Load ChangePollutant

1986 1999 2002 1999-1986 2002-1999 2002-1986TDS 8,661,582 8,293,682 8,377,690 (367,900) 84,009 (283,891) NO2 27,508 26,340 26,606 (1,168) 267 (902) NO3 30,259 28,974 29,267 (1,285) 293 (992) Mg 12,379 11,853 11,973 (526) 120 (406) Fe 13,754 13,170 13,303 (584) 133 (451) Ca 16,505 15,804 15,964 (701) 160 (541) NH3 6,189 5,926 5,986 (263) 60 (203) PO4 213,875 204,790 206,865 (9,084) 2,074 (7,010)

PollutantPollutant Load(Kg) Pollutant Load Change

Table 28: Pollutant Load change in the Grass Land of Angereb watershed

From the spatial distribution of the pollutant loads in the year 2002 (Fig 10) it is observed that the

dominant pollutant load is on the Western part of the watershed since the intensively cultivated

land (Fig 7) in this year is located foremost in the Western part of the watershed. In contrary to

this the intensively cultivated land is concentrated in the central part of the watershed in the year

1999 and the highest pollutant load is also mainly on the central part of the watershed in this year

(Fig 9). In the year 1986 the intensively cultivated land is almost uniformly distributed in the

watershed (Fig 5) and the same trend of distribution existed for the highest pollutant load in this

year (Fig 8). In general, the pollutant load contribution increased or decreased along with its land

use size and type.

5.3 Prediction of Annual Pollutant Load of Angereb Watershed Trends in the forest coverage of Angereb watershed show that there was an impressive increment

in 2002 as compared to the 1999. In this case the plantation (Artificial) forest by individual farmers

is expected to have a great contribution. But there was a decline in the forest coverage in year

1999 as compared to the 1986 coverage. Hence it seems that the trend in the forest coverage

fluctuated from year to year but it has shown a growth trend after 1999. Hence it is possible to

predict the annual pollutant load from this increasing trend of forest coverage.

To predict the impact of land management on the amount of Pollutant load; Best management

practice (BMP) has been considered in this study. BMPs serve to reduce pollutant loads and PLOAD

has an option to calculate loads based on the remedial effects of the various BMP types. The

equations used to calculate pollutant loads influenced by BMPs and their results in two scenarios

are described in section 4.4 of this study. To show the impact of the application of BMP, the 2002

annual pollutant load is compared with annual pollutant load result with a 15% BMP efficiency

1986 1999 2002 1999-1986 2002-1999 2002-1986TDS 571,813 1,387,969 949,663 816,156 (438,306) 377,850 NO2 1,816 4,408 3,016 2,592 (1,392) 1,200 NO3 1,998 4,849 3,318 2,851 (1,531) 1,320 Mg 817 1,984 1,357 1,166 (626) 540 Fe 908 2,204 1,508 1,296 (696) 600 Ca 1,090 2,645 1,810 1,555 (835) 720 NH3 409 992 679 583 (313) 270 PO4 14,119 34,272 23,449 20,153 (10,823) 9,330

PollutantPollutant Load(Kg) Pollutant Load Change

(Table 29) and with a 45% BMP Efficiency (Table 30). As it has been shown on tables 29 & 30 the

annual pollutant load of Angereb watershed decreased after applying the BMP. In the first

scenario; it is assumed that the current land use management practice (Tree planting) continues in

the future and the pollutant load decreased by 2,103 metric ton annually. But it is also possible to

decline the annual pollutant load by 6,579 Metric ton annually by applying selected BMP’s with an

efficiency value of 45%.

Table 29: Pollutant Load with and without BMP Scenario 1

Table 30: Pollutant Load with and without BMP Scenario 2

With out BMP With BMP Variation1 TDS 13,522,748 11,494,336 2,028,412 2 NO2 42,946 36,504 6,442 3 NO3 47,241 40,155 7,086 4 Mg 19,326 16,427 2,899 5 Fe 21,473 18,252 3,221 6 Ca 25,768 21,903 3,865 7 NH3 9,663 8,213 1,449 8 PO4 333,908 283,822 50,086

S/N Pollutant Annual Pollutant Load (Kg/Year)

With out BMP With BMP Variation1 TDS 13,522,748 7,178,665 6,344,083 2 NO2 42,946 22,798 20,148 3 NO3 47,241 25,078 22,163 4 Mg 19,326 10,259 9,067 5 Fe 21,473 11,399 10,074 6 Ca 25,768 13,679 12,089 7 NH3 9,663 5,130 4,533 8 PO4 333,908 177,258 156,650

S/N Pollutant Annual Pollutant Load (Kg/Year)

6 Conclusion and Recommendations

In general the major activities carried out in this study can be summarized as image classification,

biophysical modeling and Analysis in the GIS environment. The biophysical modeling relates

quantitatively the results of the image classification to the measured biophysical features and

phenomena specifically to the pollutant loads of Angereb watershed. Finally in the GIS

environment analysis different land use/ cover maps are produced and weighted sum analysis is

accomplished to identify the spatial distribution of pollutants.

The forest land use/cover of Angereb watershed is subdivided in to forest, shrub and scrub. And its

coverage is 34, 31 and 33% in the year 1986, 1999 and 2002 respectively. The forest land declined

by 11.5% and 3%; in the years 1986 – 1999 and 1986 – 2002 respectively. In the years between

1999 and 2002, the forest land increased by 9.6%.

The cultivated land use/cover of Angereb watershed is subdivided in to intensive, moderate and

sparse. And its coverage is 61, 58 and 59% for the years 1986, 1999 and 2002 respectively. The

cultivated land declined by 2.54 and 3.3% in the years 1986 – 1999 and 1986 – 2002. But the

cultivated land increased by 1%in the years between 1999 and 2002.

The grass land coverage is 5, 11 and 8% of the total watershed in the year 1986, 1999 and 2002

respectively. The grazing land increased by 143 and 66% in the year 1986 – 1999 and 1986 – 2002

respectively. But it declined in the years between 1999 and 2002 by 32%.

The change in land use of Angereb watershed shows some extraordinary trends because of the

following reasons.

1. In contrary to the natural forest, the trend of the plantation forest in Angereb watershed

shows increasing due to the high price of fuel and construction wood resulted from the

geographical proximity of the watershed area to Gondar town and other reasons;

2. Prolonged agricultural use without appropriate land husbandry resulted in poor

productivity of crop land which in turn forced farmers to change it to other land uses.

Along with the change in the cultivated land use cover the pollutant load change result showed

that there is an increment in the year between 1999 and 2002, and a decline in the year between

1986 and 1999 in this land use. Thus the change in the cultivated land resulted in the change of the

pollutant loads. In the same manner the pollutant load change in the forest land is directly

proportional to the change in the forest cover. The pollutant load showed a trend of increment in

the year between 1999 and 2002 and declined in the year 1986 – 1999 and 1986 -2002. In contrary

to the forest land; the case of pollutant load change in the grass land declined in the year between

1999 and 2002, but it showed an increment in the year 1986 – 1999 and 1986 – 2002.

When the spatial distribution of pollutants and its relation with the land use management is

considered the following trend is observed. The highest pollutant load is spatially distributed in the

Western, central and in all parts almost uniformly in the years 2002, 1999 and 1986 respectively.

The same trend of distribution of the intensively cultivated land is seen in the three years. Hence

the spatial distribution of pollutants is dependent on the land use management.

In addition to the assessment of current and past land use management and its relation with the

annual pollutant load, this study predicts the annual pollutant load of Angereb watershed

considering two scenarios. The first scenario assumes that the tree planting activity which is being

carried out in the watershed has 15% efficiency. The result shows that a total of 2103 metric tons

of pollutant can be minimized annually using these assumptions. In the second scenario modern

BMP are considered and their efficiency is assumed to be 45%. In this case the pollutant load

minimized by 6,579 metric ton annually.

In general this study is directed towards the assessment of the biophysical condition of the

watershed to resolve the pollution and land degradation problem of the Angereb watershed to

benefit both the town dwellers of Gondar and the rural inhabitants of the watershed. Moreover

this study gives attention for problem solving from the source than curing symptoms. This is to

mean that the water quality problem of Angereb watershed emanated from the problem of the

poor rural inhabitants. Unless the land degradation problem of the watershed is resolved and the

livelihood of the rural inhabitants is improved; it is not possible to tackle the water pollution

problem of the town of Gondar. Furthermore it is cost effective to control the pollutants from run

off before entering to the dam and this is possible by understanding the trends of the land use and

knowledge of the pollutants of watershed.

In this study the PLOAD model of the BASIN software is used for:

Estimating pollutant loads from non-point sources,

Estimating changes in pollutant load due to changes in land use

Estimates changes in pollutant load after incorporation of BMPs, and

Generates map outputs of pollutant loads.

From this study we can conclude the following about the PLOAD model and the BASINS software:

BASIN and PLOAD can play in screening environmental impacts

The experience obtained from Angereb watershed can be used for assessing

environmental impacts on lakes and it could also be used for environmental impact

assessment of other Dams and reservoirs.

This technique can be applied in permission of new construction near environmentally

sensitive areas like Lakes and wetlands

In general this study reveals that the pollutant load entering to the dam is vigorous that it needs a

due attention to decline the annual pollutant load of the watershed. This is possible by practicing

different BMP techniques. In the selection of BMP the trends of land use management of the

watershed has been given attention. As it has been presented in the discussion part of this study

tree planting activities are getting acceptance in the Angereb watershed. Hence this study

recommends activities related to the biological measures of soil and water conservations and the

following BMP’s are recommended.

1. Riparian Forest Buffers: are linear wooded areas along rivers, stream and shorelines.

Forest buffers help filter nutrients, sediments and other pollutants from runoff as well as

remove nutrients from groundwater.

2. Riparian Grass Buffers: are linear strips of grass or other non-woody vegetation

maintained between the edge of fields and streams, rivers or tidal waters that help filter

nutrients, sediment and other pollutant from runoff.

3. Land Retirement: Agricultural land retirement takes marginal and highly erosive cropland

out of production by planting permanent vegetative cover such as shrubs, grasses, and/or

trees.

4. Tree Planting (Row Crop): includes any tree planting on agricultural lands, except those

used to establish riparian forest buffers, targeting lands that are highly erodible or

identified as critical resource areas. This BMP results in a land use conversion from row

crop to forest.

5. Cereal Cover Crops: reduce erosion and the leaching of nutrients to groundwater by

maintaining a vegetative cover on cropland and holding nutrients within the root zone.

This practice involves the planting and growing of cereal crops (non-harvested) with

minimal disturbance of the surface soil. The crop is seeded directly into vegetative cover or

crop residue with little disturbance of the surface soil. These crops capture or “trap”

nitrogen in their tissues as they grow.

Annexes

Annex 1: Chemical Test Results of Row water at Angereb Treatment plant (Source: Gondar city water supply and sewerage office)

Name of Result Time Date Name of Result Time Datechemical mg/l chemical mg/l

Magnisum 20 9:40 7/7/2000 Sulpnide 0.03 12:05 9/6/2000Hard ness 6.9 9:40 7/7/2000 15.45 9:00 10/6/2000Sulphate 5 8:40 8/7/2000 Silica 0.003 8:40 10/6/2000Sulphide 0.07 8:40 8/7/2000 Ammenia 0.33 8:40 10/6/2000Ammonia 0.8 10:25 10/7/2000 Alummnium 0 4:10 12/6/2000Amminum 0.02 10:25 10/7/2000 Nitrite 0.08 4:10 12/6/2000Nitrite 0.08 7:35 12/7/2000 0.095 4:10 12/6/2000

160 5:00 14/7/2000 Nitrite 134 3:05 14/06/2000copper 0.05 5:00 14/07/2000 T.Alkalinity 129 7:00 2/6/2000Hydrogen per oxyede 0 11:05 15/07/2000 Copper FREE 0.91 6:00 3/6/2000Iron 0.2 11:05 15/07/2000 PH(Alcalinity 0.04 6:00 3/6/2000Ammonia 0.04 10:40 17/07/2000 Copper 0.1 6:00 3/6/2000Silica 3.2 11:00 19/07/2000 Hydrogen per oxyede15 9:00 5/6/2000Calcium hardness 81 12:00 21/07/2000 Iron 15 8:00 7/6/2000Magnesium hardness 21 12:00 21/07/2000 Silica 68 8:00 7/6/2000Hardness 102 12:00 21/07/2000 Magesium 95 8:30 21/5/2000

0.09 9:30 22/7/2000 Hrdeness 0 1:15 23/6/2000Sulphate 6l 7:50 24/07/2000 Hrdeness 0 4:55 25/6/2000Sulphide 0.09 7:50 24/07/2000 Nitirite Nitrogen 0.06 4:55 25/6/2000Nitrate 0.337 11:35 26/06/2000 Sulphate 169 8:00 26/5/2000Nitrate 0.007 11:30 30/06/2000 Sulphide 0.34 7:30 28/5/2000Aluminium 160 4:20 1/7/2000 Nitrate 0.03 7:30 28/5/2000copper 0.46 4:20 1/7/2000 Alminium 0 6:00 30/05/200Hydrogen per oxyede 0 4:40 3/7/2000 Ammonia 0.089 9:30 12/5/2000Iron 0.31 4:40 3/7/2000 Nitrite Nitrogen 121 4:40 14/5/2000Silica 17.1 10:30 5/7/2000 Nitrate 0.6 4:40 14/5/2000Calcium 49 9:40 7/7/2000 PH(Alcalinity) 0.11 5:00 16/5/2000copper Total 0.6 3:05 14/06/2000 Copper 0.13 3:40 17/5/2000Shulphate 0.08 5:00 16/06/2000 Hydrogen per oxyede7.11 6:10 19/5/2000Shulphide 0.13 5:00 16/06/2000 Aluminum 79 8:30 21/5/2000Amonia (Ammonia 0.02 5:10 17/06/2000 Silica 16 8:30 21/5/2000Silica 13.21 8:50 19/06/2000 Calcium 65 9:00 7/5/2000Nitrite 0.008 10:40 23/06/2000 Magesium 22 9:00 7/5/2000Sulphate 5 10:30 24/06/2000 Calicium 87 9:00 7/5/2000Sulpide 0.01 10:30 24/06/2000 Magesium 0 6:10 9/5/2000Sulpnate 9 12:05 9/6/2000

Name of Result Time Date Name of Result Time Datechemical mg/l chemical mg/l

Hard ness 0.01 6:10 9/5/2000 Hard ness 0.002 11:20 30/3/2000Sulphate 0.06 10:10 11/5/2000 Nitrate 0.91 12:10 2/3/2000Sulphide 0 10:10 11/5/2000 Sulpha as So4 0.05 10:00 4/4/2000Alumunium 0.009 9:30 12/5/2000 Sulphide as S 1.5 10:00 4/4/2000Amonia 0.01 7:00 28/4/2000 Nitrate 19.9 10:50 5/4/2000Nitrite 0.15 7:00 28/4/2000 26.5 10:50 5/4/2000Ammoniad 0.003 7:40 30/4/2000 Ammonia 0.02 10:10 9/4/2000Alminium 287 11:00 2/4/2000 Almmunim 0.007 6:10 13/3/2000Nitrite 0.28 10:00 4/5/2000 Nitrate 0.01 8:15 14/3/2000Allcalaine 0.34 10:00 4/5/2000 Copper 103 8:00 16/3/2000Copper 19 11:00 5/5/2000 Alcaline 0.7 8:00 16/3/2000Iron 81 6:15 21/4/2000 Copper Free 0.09 2:00 18/3/2000Silica 20 6:15 21/4/2000 copper Total 0.15 2:00 18/3/2000

101 6:15 21/4/2000 Silica 39 9:00 20/3/2000Calcium hardness 0.009 8:15 23/4/2000 Sulphid 60 10:30 21/3/2000Magesium 0l 11:30 25/4/2000 Sulphate 84 11:30 7/3/2000Total Hardness0.06l 11:30 25/4/2000 Nitrate 55 11:30 7/3/2000Nitrite 0.107 8:30 27/4/2000 Ammonia 139 11:30 7/3/2000Sulpide 0.2 5:00 11/4/2000 PH(Alcalinity 11 9:00 9/3/2000Nitrate 0.11 5:00 11/4/2000 Cooper 0.04 9:00 9/3/2000Aluminium 0.48 9:30 16/4/2000 Iron Mras Fe 0 3:00 11/3/2000Silica 0.38 9:30 16/4/2000 Hydrogen per oxyede0.19 3:00 11/3/2000Nitate 0.01 9:30 16/4/2000 Silica 0.069 6:10 13/3/2000

0.47 11:00 20/4/2000 Calcium hardness 0.19 1:10 29/2/2000Nitrite 0.002 11:20 30/3/2000 Calcium hardness 0.002 4:40 30/2/2000Hydrogen per oxyede0.91 12:10 2/3/2000 0.026 4:40 30/2/2000Iron 0.05 10:00 4/4/2000 Magesium Hardness 0.806 10:20 30/2/2000NH4 Ammonia 1.1 10:00 4/4/2000 Sulphate 0 1:35 4/3/2000Alminium 19.9 10:50 5/4/2000 Sulphide 0.07 1:35 4/3/2000

26.5 10:50 5/4/2000 Ammonia 25 5:00 6/3/2000Nitrate 0.02 10:10 9/4/2000 Almunium 67.2 1:30 20/2/2000Copper 71 10:30 21/3/2000 Nitrate 77 1:30 20/2/2000Alcaline 131 10:30 21/3/2000 Almunium 144.25 1:30 20/2/2000

101 10:30 21/3/2000 Nitrite 0.005 2:00 22/2/2000copper Total 0 10:40 25/3/2000 Nitrate 0.05 10:50 23/2/2000Silica 0.04 10:40 25/3/2000 Allcalaine 5 10:50 23/2/2000Sulphide 0.11 5:00 27/3/2000 Nitrite 0.115 11:20 25/2/2000Sulphate 0.02 6:30 28/3/2000 Iron as Free 0.02 2:00 27/2/2000Magesium Hardness 0.37 6:30 28/3/2000 0.04 2:00 27/2/2000

Name of Result Date Name of Result Time Datechemical mg/l chemical mg/l

Silica 165 2:00 23/2/2000 Magesium 0.23 5:30 5/13/1999pati magnesium hardness2.87 2:00 23/2/2000 Hardness total 0 5:30 28/12/99Calcium hardness 0 1:50 15/2/2000 Nitrite Nitrogen as N0.01 7:15 28/12/99Toal hardness 1.16 1:50 15/2/2000 Sulpate 0 11:30 30/12/99Nitrite Nitrogen 0 11:00 16/2/2000 Copper 49 3:30 2/13/1999

0.04 11:00 16/2/2000 Calcium hardness 13.65 10:45 5/13/1999Sulphide 19.05 9:00 18/2/2000 Calcium hardness 26 8:40 19/12/99Sulphate 68 2:00 4/2/2000 Magesium 24 8:40 21/12/99Nitrate 88 2:00 6/2/2000 Hardness 50 8:40 21/12/99Ammonia Nitrogen as N150 2:00 6/2/2000 64 8:40 21/12/99Nitrite 5 2:00 6/2/2000 0.005 7:15 21/12/99Total Alkalinity as 0.01l 10:20 8/2/2000 0.002 7:15 21/12/99Cooper as Cu 0.4 10:20 9/2/2000 Aluminium 0.009 7:15 21/12/99Iron 0.109 9:00 9/2/2000 Aluminium 81 8:30 23/12/99Hydirogen per oxide 0.397 10:15 11/2/2000 Ammonia 0.02 8:30 25/12/99

0.519 10:15 25/1/2000 Nitrite 0.946 10:40 25/12/99Ammonia 0 10:15 27/1/2000 Amonia Nitrogen 0.707 8:30 26/12/99Silica 0.006 10:15 27/1/2000 Calcium hardness 0.005 8:30 12/12/1999Silica 110 1:50 28/1/2000 Silica 1.44 4:30 12/12/1999Calcium hardness 0.23 1:50 1/2/2000 28 4:30 14/12/99Magesium Hardness 0.09 7:45 1/2/2000 Aluminium 0.04 4:45 14/12/99Total Hardness 0.23 7:45 2/2/2000 0.32 4:45 16/12/99Sulphate 0.17 8:10 2/2/2000 Silica 0 8:00 16/12/99Ammonia 0.06 8:10 15/5/2000 Calcium 10.1 8:30 18/12/99Almunium 29.3 11:00 15/5/2000 Magesium 35 7:45 5/12/1999Nitrate 43 7:00 12/1/2000 Hardness total 7 7:45 7/12/1999Nitrate 9 7:00 20/1/2000 Nitrite 42 7:45 7/12/1999Ammonia 52 7:00 20/1/2000 Sulphate 0.04 7:45 7/12/1999Almunium 0 1:20 20/1/2000 Sulphide 53 7:45 7/12/1999Nitrite Nitrogen5l 3:10 22/1/2000 Nitrate 0.15 2:00 7/12/1999RH 2.03 5:30 24/1/2000 Nitrate 0.02 2:00 11/12/1999Cooper Free 70 5:30 1/1/2000 copper Total 73 1:30 23/11/99Hydirogen per oxide 0.03 11:00 1/1/2000 Alkaphat (PH) 0.04 2:20 25/11/99Iron 0.11 11:00 3/1/2000 Hydrogen peroxid 44 8:30 30/11/99Iron(MR) 55 4:05 3/1/2000 Iron 1.53 8:30 2/12/1999Hydirogen per oxide 31 5:50 4/1/2000 Amonia Nitrogen 0.09 8:10 2/12/1999Silica 5 5:50 6/1/2000 Silica 1.01 8:10 4/12/1999Calcium hardness 36 5:50 6/1/2000 Magesium 0.08 2:00 9/11/1999

0.19 10:10 6/1/2000 Hardness 0.13l 2:00 11/11/1999

Time

Name of Result Date Name of Result Time Datechemical mg/l chemical mg/l

Sulphate 0.005 8:30 13/11/99 Calcium hardness 0.17 >> 17/9/1999Almunium 0.2 2:00 13/11/99 Hardness 28 >> 17/9/1999Ammonia 0.32 3:50 18/11/99 Sulphate 67 >> 18/9/1999Nitrite 0.006 8:00 21/11/99 Sulphaid 81 >> 20/9/1999Sulphate 0.02 5:10 27/10/99 Nitrate 0 >> 20/9/1999Sulphide 101 6:00 30/10/99 11l >> 20/9/1999Nitrite 0.69 6:00 2/11/1999 nitrite 0 >> 22/9/1999PH (alkalinity) 0.57 8:15 2/11/1999 PH(total Alkalinty) 1 5.4 24/9/2000Copper 0.04 10:45 4/11/1999 Allimunium 0.05 5.4 8/9/1999Hydero per oxide 19.0l 10:45 6/11/1999 Nitrate as N 0 9.2 8/9/1999Iron 53 5:10 6/11/1999 Nitrite Nitrogen 0.3 9.2 10/9/1999Sulphate 0.03 8:00 7/11/1999 Phosphate 0.23 4.1 10/9/1999Aluminium 0.09 7:55 22/10/99 0.009 4.1 11/9/1999Ammonia 0.09 7:55 23/10/99 Copper 1.45 8.45 11/9/1999Calcium hardness 0.72 5:00 Hydrogen peroxide 1.15 8.45 11/9/1999Sulphide 0.46 4:40 Iron MR 135 8.1 15/9/1999Amonia 0.19 7:00 15/10/99 Silica 0.22 8.1 15/9/1999Aluminium 0.19 7:00 16/10/99 hydrogene per oxide 1.16 2.2 1/9/1999Alkalinity 11 6:10 16/10/99 iron 0.08 2.2 1/9/1999Sulphate 64 8:20 18/10/99 Silica 20 6.5 3/9/1999Sulphide 15 8:20 20/10/99 calicon hardness 82 6.2 3/9/1999Hydrogen peroxide 79 8:20 20/10/99 Hardness (total) 51l 6.2 4/9/1999Magnsium 29 8:00 20/10/99 sulphate 49 6.2 6/9/1999Calcium 19 6:25 22/10/99Total Hardness 62 6:25 6/10/1999Nitrate 15l 2:30 6/10/1999Copper 0.04 2:30 8/10/1999Iron 0.119 2:30 8/10/1999Amonia 0.001 8/10/1999Nitrate 135 4:40 8/10/1999Copper total 0.08 8:00 15/10/99Hydrogen peroxide 0.102l 5:50 25/9/99Iron 0.121 7:30 25/9/99Silica 105 2:50 27/9/99Calcium hardness 0.46 2:50 1/10/1999Magnisum 0.192 10:00 1/10/1999Hardness total 0.59 10:00 2/10/1999Sulphate 9.5 8:10 2/10/1999Magnisum 0.12 >> 4/10/1999

Time

Annex 2: Monthly and Annual Rainfall Data of Gondar Area

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec1952 4.5 5.0 17.7 40.0 88.7 132.6 226.3 357.4 50.4 37.2 3.2 7.6 1195.31953 0.0 52.7 1.5 53.4 109.0 162.3 310.1 481.8 126.4 26.7 3.7 24.3 1351.91954 0.0 19.3 1.9 22.4 15.8 242.3 421.0 392.6 261.6 3.6 9.5 10.0 1400.01955 5.0 10.7 24.2 68.7 44.5 223.0 370.1 331.3 146.8 0.0 90.8 23.3 1338.41956 0.0 0.0 11.4 102.3 15.7 149.0 342.8 280.9 109.6 87.8 10.2 0.0 1109.71957 4.7 3.1 60.4 1.0 105.8 224.5 295.2 294.0 50.5 38.1 58.1 6.5 1141.91958 1.2 17.5 1.8 64.4 105.2 123.0 335.2 331.9 86.3 112.2 10.3 71.2 1260.21959 4.0 0.4 15.1 91.8 98.1 208.2 332.8 344.7 198.6 59.6 14.2 3.0 1370.51960 4.5 5.0 17.7 37.8 88.9 91.4 318.6 270.7 106.6 4.3 19.1 23.8 988.31964 4.5 0.0 10.0 15.0 176.4 247.0 548.7 560.4 153.0 83.2 13.0 11.2 1822.41965 14.0 0.0 23.4 19.3 17.8 94.3 351.1 301.6 43.6 122.3 27.3 13.5 1028.21966 6.5 5.0 12.8 10.8 118.3 103.1 218.4 237.2 96.6 103.0 35.6 0.0 947.31967 0.0 8.5 89.3 30.3 57.2 161.6 319.5 229.7 50.4 53.4 18.8 0.0 1018.71968 0.0 0.0 0.0 4.7 17.5 169.6 314.4 195.1 133.3 35.0 24.9 33.3 927.81969 4.3 12.5 88.8 72.4 42.0 90.2 324.8 268.5 70.8 40.0 3.8 0.0 1018.11970 0.5 0.0 0.0 6.1 36.6 116.7 285.5 148.8 142.0 97.5 0.0 4.4 838.11971 0.0 0.0 22.0 42.4 138.0 93.6 261.0 208.9 103.4 87.1 31.2 0.0 987.61972 18.0 1.5 0.9 39.2 48.0 212.7 255.5 258.4 163.0 40.4 84.6 0.0 1122.21973 0.0 0.0 11.9 63.5 160.0 103.7 400.7 377.5 147.1 99.8 43.7 0.0 1407.91974 17.0 6.8 0.5 0.5 219.7 175.6 429.6 304.0 230.8 39.3 2.2 11.2 1437.21975 26.9 12.3 1.0 18.8 61.3 308.9 468.0 295.7 118.7 5.6 43.8 38.6 1399.61976 0.0 4.6 30.7 106.7 160.1 255.8 385.5 307.0 132.7 27.2 39.4 5.0 1454.71977 0.0 0.0 18.6 2.2 143.0 142.1 267.0 351.8 86.1 72.6 36.6 36.3 1156.31978 3.9 0.0 23.6 43.6 57.9 118.9 243.8 274.0 198.8 26.8 26.6 10.7 1028.61979 3.8 2.6 0.0 21.3 108.7 122.5 194.5 368.1 74.1 93.6 0.0 0.0 989.21980 0.0 5.2 29.8 130.2 47.9 184.7 352.0 298.5 128.6 89.1 57.3 0.0 1323.31981 4.0 0.0 11.4 60.9 96.2 74.7 265.6 212.1 86.2 26.4 5.5 0.5 843.51982 14.6 0.0 20.3 21.8 41.9 50.6 214.2 218.2 70.3 36.4 23.5 0.0 711.81983 0.0 0.0 0.0 5.3 99.7 148.7 271.6 194.5 92.1 69.0 19.3 0.0 900.21984 0.0 0.0 4.6 7.0 92.9 214.3 264.1 238.3 151.5 13.2 25.1 21.7 1032.71985 0.0 0.0 56.4 62.2 89.8 80.3 287.7 337.2 92.8 61.5 4.4 16.0 1088.31986 0.0 0.0 6.9 29.6 10.5 159.0 283.5 269.4 85.7 79.3 2.2 3.2 929.31987 12.8 0.0 2.1 36.5 210.6 207.5 232.6 195.2 125.1 90.6 17.4 3.7 1134.11988 0.0 32.6 0.0 12.2 62.2 190.5 306.6 304.1 92.1 83.3 7.7 0.7 1092.01989 0.0 1.4 38.7 32.4 59.7 208.4 269.1 279.7 108.1 34.5 7.0 11.7 1050.71990 13.2 0.0 6.5 29.7 13.0 59.4 359.1 255.2 129.1 1.4 1.2 0.0 867.81992 0.0 0.0 2.7 51.7 80.7 86.8 249.5 218.2 117.6 79.6 11.9 23.6 922.31993 0.0 3.5 30.8 78.5 104.2 166.6 305.4 201.9 136.6 86.7 16.8 0.5 1131.51994 0.0 1.0 0.0 7.8 84.5 156.0 282.4 265.9 120.0 38.0 20.0 2.8 978.41995 0.0 0.0 34.5 23.9 99.3 105.9 283.0 307.1 91.8 11.9 0.9 19.8 978.11996 0.0 4.4 22.2 83.6 183.8 194.7 249.3 260.0 74.8 67.7 23.2 0.4 1164.11997 0.0 1.8 28.2 42.8 124.2 184.8 239.7 230.4 33.1 200.3 40.2 13.7 1139.21998 0.0 0.0 10.0 3.7 88.5 234.6 383.0 487.9 125.7 126.4 126.4 4.8 1591.01999 35.5 0.0 1.1 42.0 116.3 146.4 440.9 412.6 211.1 334.3 11.3 52.6 1804.12000 0.0 1.4 3.9 23.2 60.7 364.3 451.4 368.6 186.6 268.7 1.9 11.6 1742.3

Mean 4.5 4.9 17.7 39.2 88.9 162.0 315.8 296.2 118.7 71.0 23.9 11.6 1159.2ETO 134.4 134.6 151.9 156.0 136.4 108.6 80.6 86.6 105.0 117.8 111.0 105.51/2ETO 67.2 67.3 76.0 78.0 68.2 54.3 40.3 43.3 52.5 58.9 55.5 52.8P>1/2ETO 0.0 0.0 2.0 6.0 27.0 44.0 45.0 45.0 40.0 24.0 5.0 1.0P>1/2ETO(%) 0.0 0.0 4.4 13.3 60.0 97.8 100.0 100.0 88.9 53.3 11.1 2.2

Monthsyear Annual R.F

Annex 3: Summary of Hydrometric discharge data

SUMMARY OF HYDROMETRIC DISCHARGE DATASTATION:- Angereb River Nr. Gonder (Old Station)BASIN:- Blue NileDRAINAGE AREA, Km^2:- 76 37d03'e

* I. MONTHLY RUNOFF IN MILLION M^3

YEAR * JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Annual Total Runoff

Monthly Mean

1983 I 0.236 0.088 0.019 0.002 0.065 0.196 0.279 5.800 4.480 0.511 0.156 0.089 11.921 0.9931984 I 0.043 0.761 0.032 0.044 0.082 2.100 2.430 4.490 3.050 1.470 0.647 0.394 15.543 1.2951985 I 0.113 0.048 0.087 0.200 0.509 8.077 24.083 6.580 3.330 1.070 0.285 0.049 44.430 3.7031986 I 0.023 0.005 0.003 0.003 0.000 3.360 12.830 55.120 11.770 2.680 0.1.5 0.036 85.830 7.1531987 I 0.012 0.001 0.000 0.000 0.859 3.310 2.740 20.290 13.760 0.987 0.256 0.066 42.281 3.5231988 I 0.015 0.119 0.008 0.007 0.008 0.206 8.966 15.825 5.117 1.293 0.334 0.154 32.052 2.6711989 I 0.073 0.027 0.023 0.019 0.032 10.420 20.800 11.390 3.440 1.100 0.572 0.359 48.255 4.0211990 I 0.235 0.137 0.084 0.064 0.126 0.371 7.75 5.52 2.871 0.166 0.105 0.089 17.518 1.4601991 I 0.098 0.041 0.045 0.044 0.370 0.044 0.865 54.490 0.862 0.224 0.054 0.014 57.151 4.7631992 I 0.004 0.002 0.001 0.003 0.006 8.077 8.197 3.833 10.528 3.264 1.679 0.688 36.282 3.0231993 I 0.405 0.227 0.200 0.203 1.133 0.783 5.912 5.034 3.633 2.036 2.856 1.391 23.813 1.9841994 I 1.107 0.761 0.516 0.486 0.919 8.077 2.631 9.156 8.560 1.141 0.862 0.676 34.892 2.9081995 I 0.449 0.296 0.262 0.331 5.327 0.614 16.206 33.360 3.961 0.646 0.361 0.229 62.042 5.1701996 I 0.180 0.078 0.073 0.114 0.102 8.077 68.913 272.496 12.747 0.154 5.246 2.84 371.020 30.9181997 I 1.555 0.664 0.669 0.512 3.337 1.466 95.306 28.259 24.43 36.066 0.394 0.116 192.774 16.0651998 I 2.889 1.114 0.648 0.4 - - - - - - - - 5.051 0.4211999 I - - - - - - - - - 15.276 5.373 2.781 23.430 1.9532000 I 1.842 2.704 1.304 1.909 1.833 7.019 76.622 130.999 51.596 30.304 17.868 11.506 335.506 27.9592001 I 8.957 5.677 4.852 4.819 4.682 30.66 88.991 170.134 20.456 6.663 16.982 3.679 366.552 30.5462002 I 2.19 1.271 1.147 0.51 0.828 2.557 36.544 145.553 39.248 10.966 5.963 3.863 250.640 20.8872003 I 1.747 1.203 0.339 0.054 0.009 106.509 49.752 234.316 51.599 6.769 2.837 1.589 456.723 38.0602004 I 1.066 - - - - - - - - - - - 2005 ITotal 23.239 15.224 10.312 9.724 20.227 201.923 529.817 1212.645 275.438 122.786 62.830 30.608 2513.707 209.476Average 1.107 0.725 0.491 0.463 0.963 9.615 25.229 57.745 13.116 5.847 2.992 1.458 119.700 9.975

Co-Ordinate:- 11d22'n

Annex 4: Catchment Pollution Calculator: EMC to EXPORT converter

STEP HOW TO

1 Enter the event mean concentration for your EMC 146.83 mg/Lpollutant in the blue box (in mg/L)

2 Enter the annual rainfall for the catchment or sub- Annual rainfall 1159 mm/yrcatchment in mm

3 Enter the runoff co-efficient for the drainage area Runoff co-efficient 0.74 (unitless)(this may be for a roof, road, sub-catchment or a whole catchment). This co-efficient can becalculated in a coarse way using the equation:

Annual rainfall (mm) x drainage area (m2)Measured runoff (m3) x 1000

4 Enter the catchment area in hectares Catchment area 9869.0 ha

5 View the result Export Rate 1259.3 kg/ha/yr

ADDING THE DETAIL AND MAKING THE CALCULATIONS

Annex 5: KGHAYR EXPORT Calculator

Catchment Area 9869 ha% ha

Forest 7% 1059Grazing 11% 754Intensively_Cultivated 34% 3708Moderately Cultivated 13% 1130Scrub 10% 1014Shrub 14% 1258Sparsely_Cultivated 11% 946

Total 100% 9869Forest Grazing Intensively_Cultivated Moderately_Cultivated Scrub Shrub Sparsely_Cultivated

TDS 1259.5 1259.5 1448.4 1448.4 1259.5 1259.5 1448.4Nitrite 4.0 4.0 4.6 4.6 4.0 4.0 4.6Nitrate 4.4 4.4 5.1 5.1 4.4 4.4 5.1Magnesium 1.8 1.8 2.1 2.1 1.8 1.8 2.1Iron 2.0 2.0 2.3 2.3 2.0 2.0 2.3Calcium 2.4 2.4 2.8 2.8 2.4 2.4 2.8Ammonia 0.9 0.9 1.0 1.0 0.9 0.9 1.0Phosphate 31.1 31.1 35.8 35.8 31.1 31.1 35.8

TDS Nitrite Nitrate Magnesium Iron Calcium Ammonia PhosphateForest 1333810.5 4236.0 4659.6 1906.2 2118.0 2541.6 953.1 32934.9Grazing 949663.0 3016.0 3317.6 1357.2 1508.0 1809.6 678.6 23449.4Intensively_Cultivated 5370759.9 17056.8 18762.5 7675.6 8528.4 10234.1 3837.8 132616.6Moderately Cultivated 1636720.3 5198.0 5717.8 2339.1 2599.0 3118.8 1169.6 40414.5Scrub 1277133.0 4056.0 4461.6 1825.2 2028.0 2433.6 912.6 31535.4Shrub 1584451.0 5032.0 5535.2 2264.4 2516.0 3019.2 1132.2 39123.8Sparsely_Cultivated 1370210.1 4351.6 4786.8 1958.2 2175.8 2611.0 979.1 33833.7

Total 13,522,748 42,946 47,241 19,326 21,473 25,768 9,663 333,908

kg/ha/yr

ADDING THE DETAIL AND MAKING THE CALCULATIONS

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