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Integrating Geo-information for the Management of River Basins in the

Ethiopian part of the Nile Basin

Wubeshet Demeke Tefera February 2003

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Integrating Geo-information for the Management of River Basins in the Ethiopian part of the Nile Basin

by

Wubeshet Demeke Tefera Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Natural Resources Man-agement. Degree Assessment Board Chairman: Professor Dr. IR. Willem H. van den Toorn External Examiner: Name Examiners Internal Examiner: Mr. Van Lieshout Supervisors: Mr. Walter de Vries and Dr. Dick van der Zee

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

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Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Acknowledgement

I would like to express my gratitude to my employer the Ministry of Water Resources for permitting me to continue this study. I would like to thank the Environmental Support Project (ESP): a joint project financed through a bi-lateral cooperation agreement, by the government of Ethiopia and the government of the Netherlands, for granting me the financial support for this study. I owe my especial thanks to my supervisors Mr. Walter de Vries and Dr. Dick Van der Zee, without whom this study would never have become real. I have learnt a lot from the rich experience Mr. Wal-ter de Vries, particularly in the area of spatial data modelling for which I never had past experience. I also greatly value the advice of Van der Zee who gave the courage to continue on this topic since the initiation of this work. I acknowledge the dedicated guidance and careful coaching of both supervi-sors, for their constructive comments and critical review of this work all the way through. I also want to thank to all those ITC staffs that have contributed through providing me technical ad-vice and study materials. To mention a few: Dr, Willem van de Toorn and Dr, de Man for the impor-tant hints at the very start of this study. Especial thanks also goes to the staffs of the computer and GIS service of the MoWR, for all the moral and material support you have provided me to make this study a success. Many thanks to Ato Teshome Estifanos and Ato Gizachew from the Ministry of Agriculture, for shar-ing your experience in land use planning, during my field visit to Ethiopia. Many thanks to Ato Gulilat Berhane, Ato Tsegaye Debebe and Ato Abera Oli for all the back-up sup-port you have provided. Thanks also to my course mates for the friendliness and moral support during this stressful period. I extend my thanks to my father, brothers and sisters for all the loving and encouraging support. Fi-nally, I am very grateful to extend my thanks to my wife Rahwa, and my sons: Kidus, Biruk and Bisrat whom have tolerated my absence for so long.

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Abstract Since the early 1990’s, an integrated development master plan study has become one of the strategies of the Government of Ethiopia, for the development of water resources in the country. However, in the context of regional, national and trans-national planning, the lack of integrity of the different data-bases hindered the maximum exploitation of the databases. In addition, the lack of coordination of effort affected data sharing among the various users. For this integrating the river basin databases us-ing proper spatial data modelling technique has become very important. This study has developed a conceptual model using the object-oriented modelling approach. This ap-proach provides a natural method for describing real world spatial entities avoids data fragmentation and enables useful capabilities for managing databases. The object-oriented modelling allows better integration, data consistency, minimizes reformatting, convenient data merging and saves a lot of time. It is a feasible approach to allow data sharing among the different organizations and users. Spa-tial data modelling necessitates the clear understanding of the real world objects, the representation of these objects in a GIS environment, topological relationships and the underlying field of application that interests users. In this study, water resources, water resources application and land use and land cover are only con-sidered in the modelling. In effect, spatial data modelling is recognized as a tool to solving the prevail-ing problems of the various users through providing required information for planning and decision-making. However, the need for standardization of geo-information processing is very crucial to avoid the present heterogeneous data processing. This model can be successfully transformed to logical and physical model through applying a Stan-dard Data Transfer System (STDS), which is a proven approach in dealing with situations where in-teractive software systems that are based on different data models are desired to work together to sup-port user defined tasks. SDTS provides not only flexibility, but also allows focusing on the particular tasks of interest that users want to perform. However, the transformation of this conceptual model to logical and physical model alone will not be enough. It should be coupled with addressing other data sharing problems such as inter organizational and intra-organizational management and coordination issues.

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Table of Contents

1. Introduction ..................................................................................................................1 1.1. Background ............................................................................................................................1 1.2. Problem Statement .................................................................................................................3 1.3. The Study Area.......................................................................................................................5 1.4. Research Problem...................................................................................................................7 1.5. Conceptual Framework ..........................................................................................................8 1.6. Research Objective.................................................................................................................8

1.6.1. Main Objective...............................................................................................................8 1.6.2. Specific Objectives.........................................................................................................9

1.7. Research Questions ................................................................................................................9 1.8. Research Methods ..................................................................................................................9 1.9. Conclusion............................................................................................................................11

2. Spatial Object Modeling.............................................................................................12 2.1. Introduction ..........................................................................................................................12 2.2. Spatial Object Modeling ......................................................................................................12 2.3. Spatial Objects .................................................................................................................14

2.3.1. Thematic Object Description .......................................................................................14 2.3.2. Geometric Object Description......................................................................................15

2.4. Spatial Object Classification and Aggregation ....................................................................16 2.4.1. Object Classification ....................................................................................................16 2.4.2. Object Aggregation ......................................................................................................17 2.4.3. Object Association .......................................................................................................18 2.4.4. Spatial Database Generalization ..................................................................................18 2.4.5. Topologic Relationship ................................................................................................20

2.5. Data Standard, Data Sharing and Abstraction......................................................................20 2.5.1. Data Standard ...............................................................................................................20 2.5.2. Data Sharing.................................................................................................................21 2.5.3. Data Abstraction...........................................................................................................21

2.6. Interoperability.....................................................................................................................21 2.7. Conclusion............................................................................................................................23

3. Data Description and Requirement Analysis..............................................................24 3.1. Introduction ..........................................................................................................................24 3.2. Field Data Collection ...........................................................................................................24

3.2.1. Integrated Development Master Plan ...........................................................................28 3.2.2. User Requirement Analysis..........................................................................................29

3.3. Data Acquisition and Data Requirement..............................................................................32 3.3.1. Data Processing............................................................................................................32 3.3.2. Spatial Data Requirement.............................................................................................33

3.4. The Importance of GIS in Planning .....................................................................................33 3.5. Conclusion............................................................................................................................34

4. Results and Analysis...................................................................................................35

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4.1 Introdusction ..............................................................................................................................35 4.2. Hierarchical Process.............................................................................................................35

4.2.1. Hierarchical Levels ......................................................................................................35 4.2.2. Hierarchical Activities .................................................................................................36

4.3. Spatial Data ..........................................................................................................................38 4.3.1. Spatial Objects and Data Types ...................................................................................38 4.3.2. Data Needs and the object of modelling ......................................................................38 4.3.3. Thematic and Geometric Partition ...............................................................................39

4.4. Data Model and Documentation ..........................................................................................44 4.5. Model Criteria and Functional Requirements ......................................................................44 4.6. Modelling the River Basin Databases ..................................................................................44 4.7. Land Use and Land Cover....................................................................................................46

4.7.1 Land Use and Land Cover Mapping Units...................................................................46 4.7.2 Land use and Land cover in the River Basins..............................................................47

4.8. Conclusion............................................................................................................................47 5. The Conceptual Model ...............................................................................................48

5.1. Introduction ..........................................................................................................................48 5.2. Model Design .......................................................................................................................48

5.2.1. Generalization Strategies .............................................................................................48 5.2.2. Object Class and the Universe .....................................................................................48

5.3. River Basin Water Resources Model ...................................................................................49 5.4. Water Resources Application Model ...................................................................................49 5.5. Land Use and Land Cover Model ........................................................................................50 5.6. Conclusion............................................................................................................................58

6. Model Testing & Evaluation ......................................................................................60 6.1. Introduction ..........................................................................................................................60 6.2. Testing the Model ................................................................................................................60 6.3. Opportunities and Constraints..............................................................................................66 6.4. Conclusion............................................................................................................................67

7. Conclusion and Recomendation ....................................................................................68 7.1. Introduction ..........................................................................................................................68 7.2. Conclusion: ..........................................................................................................................68 7.3. Recommendation:.................................................................................................................69 Reference: ........................................................................................................................................71

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List of Tables Table 1 Thematic datasets......................................................................................................................6 Table 2 International Consultants in Master Plan Study ......................................................................6 Table 3 River Basin typical map list at 1:250,000 Scale .......................................................................7 Table 4 River basin projection data checklist......................................................................................25 Table 5 Thematic data checklist- Tekeze river basin...........................................................................26 Table 6 Thematic data checklist Abbay river basin.............................................................................26 Table 7 Analysis of the status of CGIS Service & RBDSD ..................................................................32 Table 8 Activities at different hierarchical levels ................................................................................37 Table 9 Land use and land cover mapping units in the river basins ...................................................42 Table 10 Generalization and aggregation ...........................................................................................53 Table 11 cumulative percentages of land cover and land use units ....................................................55 Table 12 Typical questions and solutions ............................................................................................58 Table 13 Model evaluation & testing...................................................................................................62

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List of Figures Figure 1 Location map of the study area...............................................................................................5 Figure 2 Conceptual frame schematic flow diagram.............................................................................8 Figure 3 Research design schematic flow diagram.............................................................................10 Figure 4 A framework for the design of an information system (Laurini and Thompson, 1992)........13 Figure 5 Formal data structure for area objects, (Kraak and Molenaar 1997) .................................15 Figure 6 Formal data structure for area objects, (Kraak and Molenaar 1997) .................................16 Figure 7 Spatial object relations, (Molenaar, 1998) ...........................................................................17 Figure 8 Typical object association.....................................................................................................18 Figure 9 Possible standard data transfer format ................................................................................22 Figure 10 Hierarchical levels and linkages between organizations in Ethiopia ................................36 Figure 11 Hierarchical linkages..........................................................................................................37 Figure 12 Example of two river basins................................................................................................39 Figure 13 Example of combining two river basins ..............................................................................40 Figure 14 River basin data model........................................................................................................45 Figure 16 Water resources application model ....................................................................................50 Figure 17 Land use and land cover model...........................................................................................51 Figure 18 Aggregation structure .........................................................................................................52 Figure 19 Class generalization Figure 20 Object aggregation .....................................................52 Figure 21 An example of the generalization and aggregation process in land cover.........................54 Figure 22 Conceptual Model ...............................................................................................................56

List of Appendix Appendix I: River basin data checklist - format Appendix II: Thematic data checklist - format Appendix III: Thematic data checklist Tekeze river basin Appendix IV: Thematic data checklist Abbay river basin Appendix V: Land use and land cover thematic data aggregation levels

INTEGRATING GEO-INFORMATION FOR THE MANAGEMENT OF WATER RESOURCES IN THE ETHIOPIAN PART OF THE NILE BASIN

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1. Introduction

1.1. Background

The Nile Basin is one of the largest river basins located in the eastern part of Africa. It crosses 10 ri-parian states, namely: Burundi, Democratic Republic of Congo, Egypt, Eritrea, Ethiopia, Kenya, Rwanda, the Sudan, Tanzania, and Uganda. It covers an area of more than 3,349,000 km2. Given this trans-boundary nature of this natural feature, and the dependency of all these states on the shared wa-ter resources there is a need for joint and/or integrated planning and management. One of such efforts is the Nile Basin Initiative Program, which was launched in February 1999. ‘The Immediate objective of this program is to attain a regional cooperation framework acceptable to all basin countries in order to promote basin-wide cooperation in integrated water resources planning and management (UNDP, 2002). For the government of Ethiopia, the Ethiopian part of the Nile Basin is believed to be an immediate need to advance planning and cooperation initiatives taking place in the Nile Basin in general and in the Eastern Nile Basin in particular. Eastern Nile Basin comprises three countries Egypt, Ethiopia and the Sudan. Towards the use of it’s rivers, the Government of Ethiopia since the early 1990’s vested keen interest to support the undertaking of master plan studies for the twelve major river basins of the country. In Ethiopia alone, there are 12 major river basins, out of which Mereb, Tekeze, Abbay and Baro-Akobo located in the western part of the country, form the Ethiopian part of the Nile Basin. This part of the basin is about 364,250 km2. The government mobilized a huge amount of money and capi-tal to allow a study on these four areas under the framework of “Integrated River Basin Develop-ment Master Plan”. This integrated river basin development master plan has two principal objectives (BCEOM, 1999)

• The preparation of the river basin development Master Plan that will guide the development of the resources of the basin particularly with respect to the occurrence, distribution, quality and quantity of the water resources for the coming 30 – 50 years.

• To prepare water allocation and utilization plans under alternative development scenarios and

to generate data, information and knowledge that will contribute to the future water allocation negotiation with the downstream countries.

In view of fulfilling these objectives, different International Consultants were contracted to undertake the studies, normally within a period of three years. Among other tasks, the various studies have in-ventoried, compiled and analysed the resources of the river basins using geo-information technology. Geographic information systems (GIS) enable to collect, compile, analyse and store geographical data. Over the years, through the development of hardware and software technology, the use of GIS has been realized in many application areas such as: change detection, crop monitoring, conflict manage-


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ment, environmental monitoring, natural resources management, water resources development etc. It has facilitated the activities of planners, policy and decision makers through the provision of ade-quate, viable and timely information. The choice of using GIS to support the data collection for the Integrated River Basin Development Master Plan also seemed in line with similar efforts and publica-tions. GIS based and GIS oriented studies have been undertaken to support the management of river basins such as the Rhine, Mekong, Tana etc, The main utilization of this GIS tool in these cases was for the planning, management and monitoring of resources. Because of the differences in approaches by the international consultants for each of the basins, inde-pendent databases were created. Although each of these databases serve the intended purpose of the master plan objectives, there exists a lack of integration between the individual data sets, despite the various calls and the demands of various users (Governmental, NGOs, the private sector etc,). More-over, Ministry of Water Resources (MoWR) has a mandate to build a national database for the water resources of the country. For these, combining the already completed master plan datasets will be of paramount importance. However, combining datasets needs proper integration of the underlying data models. Data integration is the process by which different sets of data within a GIS are made compatible with each other (Flowerdew R., 1991). Spatial data integration should include horizontal integration (merging adja-cent data sets), vertical data integration (operations involving the overlay of maps), and temporal data integration (John Jensen et al., 2002). With regard to sharing of information, Ian Masser (1997) pointed out ‘Most importantly data integration is a political and organisational as well as a technical matter. This can be seen particularly when data integration is looked at from the standpoint of infor-mation sharing’. Sharing of geographic information is important because the more it is shared, the more it is used, and the greater it becomes society’s ability to evaluate and address the wide range of pressing problems to which such information may be applied (Onsrud & G.R. Rushton, 1995). For an optimal use of any information system, it has to be well organized and need to have standardized da-tabase. The need for standardization becomes more urgent not only to optimise the use of resources but also to allow regional and national governments to draw from local systems detailed or aggregated data for decision making and planning purposes (Amer, 1993). In addition to integration, there is the issue of aggregation. The monitoring and management of the aggregation process requires the identification of which type of process, which type of data and at which scale should it be processed. The aggregation process normally implies a bottom-up approach. That is to say, from the details to a more general class or in other words from a higher scale to a small scale. As Molenaar described it, many processes at the earths surface can only be monitored and man-aged if they are understood in their geographical context. Part of this context is defined by the scale range at which these processes work (Molenaar, 1998). Data aggregation in a spatial context involves a number of processes such as: merge operation, object aggregation, geometry driven generalization, functional generalization and structural generalization. The result of the aggregation process will be a generalized product. Generalization can be defined as the process of the content of a representation of geographic information when the scale of the map is changed (Molenaar, 1998).


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In the generalization hierarchies, one has to consider the geometric and thematic characteristics of geographic objects. According to Molenaar, in an edited book (Groot & McLaughlin, 2000) objects were divided into three types (point, line and area), based on geometric characteristics and thematic classes or simply classes according to thematic characteristics. The importance of knowing these dis-tinctions is that objects that belong to the same class share the same descriptive structure, whereas objects belonging to different classes in general carry different information. Geometric driven generalization is the strategy where the geometric resolution is the deriving factor for the aggregation process (Molenaar, 1998). This applies for raster type data by reducing the resolu-tion of the smaller scale pixels to a higher. On the other hand for the vector type, class driven gener-alization is a strategy where regions are identified, consisting of mutually adjacent objects belonging to the same class. These objects will then be aggregated to form larger spatial units with uniform characteristics. The generalization is driven in this case by the thematic information of the spatial data. In the discussion of generalization hierarchy, aggregation hierarchy and object associations, Molenaar (1998) emphasized to choosing the classification structure before building the database. He further mentioned the choice to be made within the users’ context and considering the following char-acteristics:

1 The disciplines of the users, 2 The type of use that is to be made of the data, 3 The point in time in which the terrain description is made

1.2. Problem Statement

Nile Basin regional, National (MoWR), Regional administrations and other data users such as gov-ernment, NGOs and the private sector form the hierarchical levels of organizations, that will have dif-ferent levels of data requirement at different scales. To satisfy these multi-scale data requirements the aggregation of the completed master plan datasets in the Ethiopian part of the Nile Basin becomes important. In view of this, this study will design a prototype conceptual database aggregation model in order to allow various data users at various capacities, make efficient use of the existing databases of the completed master plan studies. During the master plan studies of the river basins, independent GIS set-ups have been created. The studies have adopted independent software, data collection, processing, and analysis and classification methods, which resulted in utilisation problems. For many years, agencies at the various governmental levels have been collecting data about land, but for the most part, they have worked independently and without coordination. Too often, this has meant duplication of effort, or it has been found that data collected for a specific purpose were of little or no value for a similar purpose only a short time Latter (Anderson, Hardy, Roach, & Witmer, 2001). Similarly, Roger pointed out “not only all GIS software platforms use unique and proprietary internal data formats, but also the data collected by thousands of users, for thousands of different purposes are often collected and/or stored in yet another in-house formats for reasons that may be peculiar to individual project or intended use” (Burrough & Masser, 1998). Following the independent establishments, no efforts were made to standardize or harmonize river basin’s GIS set-up as well as the resulting databases. As a result, giving planning support in a


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regional national and/or trans-national context in the Ethiopian part of the Nile Basin has not been so effective. For example, in the Eastern Nile Basin through the Shared Vision Program, opportunities have been created to launch developmental projects. The basin-wide Shared Vision Program (SVP) currently includes seven projects, which build upon each other to form a coordinated program. Four of these are thematic in nature, addressing issues related to environmental management, power trade, efficient wa-ter use for agriculture, and water resources planning and management (The World Bank Group). Due to the independent databases of the river basin studies, planning is approached from individual project databases, which implied difficulty. There is no question that the master plan’s datasets satisfy the intended purpose of the individual master plan objectives. However, this is not just enough when a coordinated planning at regional, national and/or trans-national level is desired or different users raise different requests. Many existing maps and digital databases don’t meet multi-user requirements. One of the main causes though under estimated, is the type of classification or legend used to describe ba-sic information such as land cover. Classification and legends are generally not comparable with one another and very often are single project oriented or take a narrow sectoral approach, (FAO: Afri-cover Project, 1994). Similarly, support to other institutions such as the Regional Administration that depend on data across basin boundaries, has remained to be a critical problem. Of course sharing of available data will save time and money. However, for the reasons mentioned above these datasets are not readily available to meet various demands. The fact that data exists does not mean that it is readily available to potential users (Masser, 1997). AAmmoonngg tthhee cchhaalllleennggeess ooff uussiinngg tthhee mmaasstteerr ppllaann ddaattaa iinn iittss pprreesseenntt ffoorrmm aarree::

•• AAggggrreeggaattiioonn ooff tthhee ddaattaasseettss ttoo bbuuiilldd aa nnaattiioonnaall ddaattaabbaassee,,

•• SSeerrvvee ssoommee pprroojjeeccttss iinniittiiaatteedd tthhrroouugghh tthhee rreeggiioonnaall ccooooppeerraattiioonn pprrooggrraamm iinn tthhee ppaarrtt ooff tthhee NNiillee BBaassiinn,, aanndd

• Serving other data users, The master plan thematic databases are generated using different methodologies, software, hardware tools, also applying different coding, and classification schemes. Under this condition, the heterogene-ity of the spatial data becomes obvious. As Bishr and Radwan pointed out the heterogeneity problem occurs when different communities wanting to share their data with each other have to contend with different views on the real world features, different modelling schemes, and different tools to repre-sent, store, process and manage geo-spatial datasets (Bishr, Radwan, Driza, & Pandya, 1997). They described these heterogeneity issues as syntactic, schematic and semantic heterogeneity. According to Bishr (1997), these terms are defined as: Syntactic heterogeneity refers to the differences in software and hardware platforms, database management systems, and the representation of geo-spatial objects. Schematic heterogeneity refers to the differences in database models or schemas. Semantic heteroge-neity refers to the way the same real world entity may have several meanings in different databases. From this, it is possible to deduce that the databases of the river basins, which were generated by dif-ferent projects, using different software and data structure, are subject to the above-mentioned hetero-geneities. This study is aimed at exploring the present situation of the datasets and creates opportuni-ties for data aggregation, through the designing of the conceptual model


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1.3. The Study Area

The following map represents the location of Ethiopian Nile Basin.

Figure 1 Location map of the study area

For each of the river basins, thematic data have been generated in the major fields of natural re-sources, water resources, agriculture, infrastructure, socio-economy and environment.. IInn lliinnee wwiitthh tthheessee mmaajjoorr ffiieellddss,, ssttuuddyy tteeaammss hhaavvee bbeeeenn ffoorrmmuullaatteedd ttoo ccoommppiillee ddaattaa//iinnffoorrmmaattiioonn aanndd pprroodduuccee rreessppeecc--ttiivvee pprroojjeecctt rreeppoorrttss.. TTaabbllee 11 pprreesseennttss mmaajjoorr ffiieellddss aanndd ssoommee ooff tthhee ppoossssiibbllee tthheemmaattiicc ddaattaasseettss ggeenneerr--aatteedd bbyy tthhee tteeaammss iinn aa mmaasstteerr ppllaann ssttuuddyy..

Source: Master Plan Studies

Location map of the Ethiopian Part of the Nile Basin

Baro-Akobo

Abbay

Tekeze

Mereb


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Table 1 Thematic datasets

Major fields Thematic datasets

Natural resources Land use/land cover, land suitability, forestry and wildlife, geology,

geo-morphology, soils, mineral resources.

Water resources Climate, hydrology, hydrogeology, dams and hydropower, irrigation

potential, surface and groundwater, river basin modelling

Agriculture Agronomy, livestock, fishery, bee keeping, Agro-ecology

Socio-economy Population density, demography, Income distribution, settlement

pattern, migration and immigration, organization and institution

Environment Water quality, pollution, erosion and soil conservation, ecology,

environmental impact assessment

Physical planning Transport and infrastructure, energy, water supply and sanitation,

agro-industry.

The indicated grouping of fields may show slight differences from project to project. Table 2 presents the list of Consultants involved in the master plan studies.

Table 2 International Consultants in Master Plan Study

River basin Consultant Year com-pleted

Mereb NEDECO-Netherlands Engineering Consulting 2001

Tekeze NEDECO-Netherlands Engineering Consulting 1999

Abbay BECOM-French engineering consultants- in associa-

tion with BRGM and ISL Consulting

1999

Baro-Akobo TAMS and ULG 1998

TThhee MMaasstteerr ppllaann ssttuuddiieess hhaavvee pprroodduucceedd tthheemmaattiicc mmaappss aatt 11::225500,,000000 ssccaallee ffoorr tthhee wwhhoollee bbaassiinn aanndd 11::5500,,000000 ssccaallee ffoorr sseelleecctteedd aarreeaass wwhheerree ffeeaassiibbiilliittyy oorr pprree--ffeeaassiibbiilliittyy ssttuuddiieess wweerree ccoonndduucctteedd.. FFoollllooww--iinngg tthhee ccoommpplleettiioonn ooff tthhee mmaasstteerr ppllaann ssttuuddiieess,, aallll rreeppoorrttss aanndd tthhee iinnffoorrmmaattiioonn ggeenneerraatteedd aarree hhaannddeedd ttoo tthhee MMooWWRR.. RRiivveerr BBaassiinn DDeevveellooppmmeenntt SSttuuddyy DDeeppaarrttmmeenntt ((RRBBDDSSDD)) iiss rreessppoonnssiibbllee ffoorr tthhee oovveerraallll ffooll--llooww--uupp ooff tthhee pprroojjeeccttss..


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Table 3 River Basin typical map list at 1:250,000 Scale

Description Data type Status Agro ecological Polygon Derived from soil, land

use/land cover Population Density Polygon Derived from statistical and

administrative data Hydro-geological Point, line, polygon Derived Hydrology Line, polygon Not derived Geology Polygon, line Not derived Soils Polygon Not derived Geomorphology Polygon Derived Land use/land cover Polygon Not derived Base map Polygon, line, point Mixed Road network Line Not derived Rivers Line Not derived Location of towns Point Not derived Development zones Polygon Derived Suitability maps (irrigation, live-stock, mechanized farming, rain fed agriculture

Polygon Derived

Climate Line Derived Borehole location Line Not derived Ground water Line Derived Rainfall distribution Line Derived Administrative map Polygon, point Not derived

1.4. Research Problem

In situations where multi-national and multi-sectoral organizations are involved, Kwaduk J. and Sprokkereef E, in connection with the river Rhine pointed out, the names of many databases are often known, but it takes quite a while to determine what data are in the database and which procedures should be followed to get them out (Burrough & Masser, 1998). It was further mentioned that it will be an enormous advantage if the data providers could prepare a bulletin in which they make clear what kind of data they own including the units, classifications, geo-references, temporal and spatial resolution, method used for collection, area covered and as well as in what format the data can be ob-tained. From personal knowledge, the only river basin study that prepared a data description document is the Abbay basin project. For the rest of the basins one has to explore the databases as well as the master plan reports to be able to obtain information about the data. Exploring the above list of available data sets, it has become clear that data integration strategies and methodologies have not kept pace with the separate data collections. It remains difficult to analyse even two spatial data sets acquired at different times, for different purposes, using different datum, positional accuracy (x, y, z), classification schemes, and levels of in-situ sampling or enumeration


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precision. Due to the different mapping procedures by different projects and for different purposes, currently all natural realities can only be portrayed differently and in isolation. If one has to make a plan across basin boundaries, accessing these datasets in a common platform becomes necessary. One of such platforms is a spatial data aggregation model. To this effect, this study will design a prototype data aggregation model that will enable the aggregation of the river basin databases to enhance plan-ning and decision-making process at all levels.

1.5. Conceptual Framework

IInn ggeenneerraall,, ccoommppuutteerr ssyysstteemmss pprroovviiddee ffoouurr ttyyppeess ooff iinntteerrrreellaatteedd sseerrvviicceess;; tthhee ddaattaa ssttoorraaggee sseerrvviiccee pprroo--vviiddeess uusseerrss wwiitthh eeffffiicciieenntt ssttoorraaggee mmeeddiiaa.. TThhee ddaattaa aacccceessss sseerrvviicceess pprroovviiddee ffuunnccttiioonnss ffoorr rreettrriieevviinngg ddaattaa ffrroomm tthhee ssttoorraaggee mmeeddiiaa.. TThhee aapppplliiccaattiioonn sseerrvviicceess pprroovviiddee uusseerrss wwiitthh ccaappaabbiilliittiieess ttoo eexxeeccuuttee ssppeecciiffiicc ttaasskkss aanndd ffiinnaallllyy tthhee pprreesseennttaattiioonn sseerrvviicceess pprroovviiddee ddiissppllaayy ffaacciilliittiieess aanndd uusseerr iinntteerrffaacceess ttoo eenndd--uusseerrss BBaasshhiirr aanndd RRaaddwwaann iinn ((GGrroooott && MMccLLaauugghhlliinn,, 22000000)).. IInn ffiigguurree 22,, aa ccoonncceeppttuuaall ffrraammeewwoorrkk hhaass bbeeeenn pprreesseenntteedd.. IInn tthhee ccoonncceeppttuuaall ffrraammee,, tthheerree iiss iinntteerraaccttiioonn bbeettwweeeenn eexxiissttiinngg ddaattaa,, uusseerrss aanndd ddaattaa pprroocceessss--iinngg.. DDeeppeennddiinngg oonn tthhee ssppeecciiffiicc nneeeeddss ooff uusseerrss,, eexxiissttiinngg ddaattaa ccaann bbee uusseedd ddiirreeccttllyy wwiitthhoouutt pprroocceessssiinngg.. UUssuuaallllyy tthheessee ddaattaa aarree nnoott rreeaaddiillyy aavvaaiillaabbllee aaccccoorrddiinngg ttoo eenndd--uusseerrss nneeeeddss.. TThhee ffaacctt tthhaatt ddaattaa eexxiissttss ddooeess nnoott mmeeaann iitt iiss rreeaaddiillyy aavvaaiillaabbllee ttoo ppootteennttiiaall uusseerrss ((MMaasssseerr,, 11999977)).. IItt nneeeeddss pprroocceessssiinngg.. PPrroocceesssseedd ddaattaa ggiivveess aaddddiittiioonnaall iinnffoorrmmaattiioonn aanndd bbeetttteerr iinnssiigghhtt,, ooff ccoouurrssee wwiitthh aaddddiittiioonnaall oorr ddiiffffeerriinngg ssppeecciiffiiccaa--ttiioonnss ffrroomm tthhee ddaattaa tthhaatt iiss uusseedd ttoo ggeenneerraattee iitt..

Figure 2 Conceptual frame schematic flow diagram

Knowing the context of interaction between data, users and data processing will allow making a co-herent data aggregation and map generalization systems that will suit potential users and decision-makers at various capacities.

1.6. Research Objective

1.6.1. Main Objective

TThhee oobbjjeeccttiivvee ooff tthhiiss ssttuuddyy iiss ttoo ddeevveelloopp aa pprroottoottyyppee ccoonncceeppttuuaall ddaattaa aaggggrreeggaattiioonn mmooddeell,, wwhhiicchh ccaann ssuuppppoorrtt ppllaannnniinngg aanndd ddeecciissiioonn--mmaakkiinngg pprroocceessss aatt aallll lleevveellss iinn tthhee EEtthhiiooppiiaann ppaarrtt ooff tthhee NNiillee BBaassiinn

Data Process-

Existing Data Users


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1.6.2. Specific Objectives

1. To decide for each type of process, which information should be handled at each

scale,

2. To verify which spatial data aggregation techniques could be relevant for this case,

3. To design database aggregation model,

1.7. Research Questions

1. What are the current and future data usage and needs?

2. How to specify the criterion for a common database?

3. How to design and test spatial data aggregation model?

4. What are the opportunities to aggregate the datasets?

5. What are the constraints to aggregate the datasets?

1.8. Research Methods

Resulting from the integrated approach of the studies, the master plan datasets cover a wide range of sector disciplines. Consequently, these datasets could be used for very diverse application fields. To come up with a meaningful aggregation structure each of the river basins thematic data structures, the classification methods, coding systems and how similar datasets are represented in the adjacent river basin’s dataset need to be identified. Furthermore, the users needs in terms of scale, data type, process and functional relationships needs to be defined. In order to meet the objectives of this study the fol-lowing methodologies will be used: For objective 1:

• From literature review, study spatial object variants (thematic and geometric) in connec-

tion with map aggregation, • From literature review, study multi-scale objects, their relationships and the aggregation

procedure, • Using field data information, understand the data types, processes and the purpose of spa-

tial maps at various hierarchical (national, regional or local) levels,

For objective 2: • From literature review, understand the aggregation techniques applicable for each data

type (point, line, or area based), • Define applicable aggregation technique for the data types that are identified by the users

as in for objective 1, For objective 3:


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• Based on literature review on object oriented modelling, design a prototype conceptual model for the datasets selected,

• Test the model against existing models developed for similar case, • Project these techniques for possible aggregation of other data components of the master

plan studies, For objective 4:

• Based on the experiences of the field assessment and findings of the study, identify the constraints of data aggregation in the Ethiopian part of the Nile Basin and recommend remedial measures, if possible do a SWOT analysis,

Both at national, regional or local level there are no guidelines or standards applicable for Geo-spatial information. For example, which scale to use at what level? Referring personal knowledge, river basin thematic datasets are available at a 1:250,000 scale for the whole basin and 1: 50,000 scale for some selected areas. As to projection and geo-referencing, the master plan studies are expected to have used the same. However, the status of projection, geo-referencing as well as the scale will be verified dur-ing field data collection. The master plan studies have generated lots of thematic datasets ref. Table 3. This study addresses only a few datasets. Therefore, selection of limited data is inevitable. The users perspective is a key for the selection of the dataset. Figure 3 shows the intervention of the conceptual model to improve the status of the master plan dataset.

Figure 3 Research design schematic flow diagram

Abbay Tekeze Mereb Baro-Akobo

Datasets . Land . Soils . Geology . Hydrology . Agriculture . Socio-economy

. etc,

Conceptual Model

Users

. Object aggregation

. Geometry driven

. Functional generaliza-. Structural generaliza-


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To come up with a meaningful conceptual model each of the river basins datasets thematic and geo-metric units, their structures, classification methods, coding systems, and their relationship in the ad-jacent river basin’s dataset need to be carefully studied. Furthermore, the users needs in terms of scale, process and functionality needs to be defined.

1.9. Conclusion

In this chapter, the research problem has been discussed; the main objectives, the specific objectives and, the research questions have been defined and the methodology for addressing these questions are described. The next chapter presents the basic theories of spatial data modelling that will allow an-swer the research questions.


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2. Spatial Object Modeling

2.1. Introduction

Given the objective of designing a conceptual model, this chapter provides the basic theories of spa-tial data modelling, related to the theses topic. In view of this, the sections have been organized as follows: section 2.2 presents spatial object modelling. Section 2.3 presents spatial object description; section 2.4 deals with spatial object classification and aggregation, section 2.5 deals with data stan-dard, data sharing and abstraction, section 2.6 presents on interoperability, and, finally section 2.7 gives the conclusion of the chapter.

2.2. Spatial Object Modeling

Spatial object modelling is the process of abstraction of information from the real world in to a model. This model is not an exact duplication of the real world, some things are approximated, others are simplified and some things are ignored (Tshangho, 1999). This aspect has been briefly discussed by many researchers such as (Laurini & Thompson;, 1992; Molenaar, 1998; Molenaar M., 1998). The representation of the real world in a GIS environment or map is the abstraction of the object of inter-est. These objects of interest, which in some literature are referred as processes, are the application fields such as water resources, environment, geology, land use, soil, socio-economy, etc that govern the mode of abstraction of information. Each of these processes dictates the abstraction of the real-world information according to users interests. For example, the mapping of land use and land cover, which uses land unit information, is an area of interest for land use planners, whereas geologic infor-mation, which uses rock type information as a unit of abstraction, is an area of interest for geologists. Thus, the process of database design that is based on application fields proceeds from the real world to the computer model, within a certain context. The set of features in the real world, which are of interest to a particular context, forms its universe of discourse (Molenaar, 1998). Therefore, in designing an information system, knowledge about the users perception of the real world plays an important role. That is to say, knowledge about how data is abstracted from the real world and how users make use of this data to satisfy a certain process or application, is the focal point of the conception of a model. In this regard, Laurini and Thompson (1992) recognized four levels of data modelling. These are external, conceptual, logical and internal or physical models. According to them, the very beginning of a database design process is the external modelling (figure 4) in which users define their own subset of the real world, that is, what is relevant for their particular application field. In this regard, water resources, soils, environmental, land use and land cover and hydrological datasets of the master plan projects, are examples of external models. The second level of modelling is the conceptual model, which deals with partly the common ground among users, in part the universe of discourse, in part the universe of modelled entities. Using tools and formalisms,


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the phenomena of interest are identified, their pertinent characteristics described, and the way in which objects relate to each other is mapped out as well as possible. This level corresponds with the syntheses of all external records. The third level of modelling is the logical model, which translates the conceptual model into something more practical. It is perhaps simply thought of as putting nu-merical values into tables of data, but avoiding the details of storage of data on physical media. At this level, the phenomena are organized into a database as tables of data records and connections to other tables. The last level is the internal modelling. It deals with the organizations of data on hardware storage media (Laurini & Thompson;, 1992). This study, using the individual master plan external models, deals with the designing of the concep-tual model. Information system design follows standard methodologies, such as ANSI-APARC (American National Standards Institute, Standards Planning and Requirements Committee), which emphasizes the conceptual and logical levels especially by means of the entity- relationship approach. Conceptual data modelling provides high-level concepts of understanding of real world in its abstract form.

Figure 4 A framework for the design of an information system (Laurini and Thompson, 1992)

In dealing with the conceptual modelling, it should be noted that data abstraction from the real world is not without problems. This idea is substantiated by (Molenaar, 1998) in his discussion, that the earths surface as a spatio-temporal-continuum is in conflict with the abstraction and representation of natural phenomena in a mapping/GIS environment. Because the present GIS systems force the GIS community to represent a geographically continuous space in the form of discrete spatial elements:

Real World

External Model 1

Internal Model

Logical Model

Conceptual Model

External Model 3

External Model 2

Users View


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Point, line, polygon and/or raster cells. Spatial data modelling necessitates the clear understanding of the objects of interest, their representation in a GIS environment and understanding topological rela-tionships. The following sections will present a discussion on these topics.

2.3. Spatial Objects

A spatial object is an information system that is abstracted from the real world and describes a phe-nomena or process for an application. Different researchers/authors in this field use spatial object dif-ferently, such as spatial entities. According to Laurini and Thompson (1992), entity is defined as a phenomenon that could not be subdivided into like units. Entity and object are used interchangeably to mean a real-world abstraction of an existing feature. The term spatial refers to the existence of something in space. All geographic objects can be grouped into three generic spatial object classes, namely: point, line and polygon (Kraak & Molenaar, 1997). The mathematical formulation of spatial objects employs the 2D-geometry of objects, which will be represented by means of three discrete geometric elements, i.e. nodes, edges, and faces (figure 5). The geometric primitives used to construct these discrete geometric elements are the ones, which construct terrain elementary spatial objects or simply spatial objects (Molenaar, 1998). They are also the bases for topologic relationships. Spatial objects are described by the geometric and non-geometric (thematic) terrain description. In the object-structured approach, the link between the thematic and geometric data is through an object identifier. The formulation of spatial data models will be abstracted from the actual implementation of opera-tional geo-information systems such as the river basin databases. As pointed out by Molenaar (1998), thematic information is abstracted in two approaches field and object. In the former case, the information will be linked to geometric data, i.e. to the position of the object, which will be represented by x, and y coordinates. In an object-structured approach, the link will be indirect and is to the object identifier (ID). The former type is applicable to a raster data for-mat while the latter is applicable for a vector data. This study has adopted the object-structured ap-proach, which is applicable for vector data structure. Most contemporary geographical information systems do not directly represent spatial objects; rather the geometry of the objects is decomposed into simple geometric primitives (points, lines, and polygons) with associated attributes (Onsurd & Rushton, 1995). For example in a GIS environment borehole, river and lake are represented by a point, line and polygon respectively, each with attribute values contained in the data. These attribute values are contained in the thematic component, and will be discussed in the next section.

2.3.1. Thematic Object Description

The object data stored in a database contains information about the state of the objects and the state of the process. This means that the role of an object in a process will determine what should be the rele-vant state information and thus what the thematic description should be (Molenaar, 1998). Objects with similar roles will have similar descriptions, so that categories of classes of objects can be defined in the context of the type of processes for which the databases should give state information. The thematic description of objects can often be organized in hierarchies of classes. If one descends from the top down through such a hierarchy, then the least detailed description of the object will be found in the most general classes at the top of the hierarchy (Molenaar, 1998). Thematic object description maintains logical relationships of objects in the representation of hierarchical units for the applica-tions of interest. This is because aggregation/generalisation hierarchies are formed from classification


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structures. Thematic information of objects is contained in the attribute fields of objects attached to a geographic position. Semantics should be understood as the link between a database representation and a real world situation. The thematic description will be structured by means of attribute lists of classes within class hierarchies (Molenaar, 1998). For most applications the thematic aspect of a terrain description are of prime importance. This means that the data querying and processing will be organized and formulated primarily from thematic perspective. The structuring and formulation of the geometric aspect of the data will be secondary, i.e. this formulation will depend on the thematic prob-lem formulation (Molenaar, 1998).

2.3.2. Geometric Object Description

The geometric representation of real world object in a GIS environment is through the construction of elementary entities, i.e. nodes, edges and faces. Once these entities are constructed, the thematic data is attached to the objects, through the object identifier (figure 7). The underlying mathematics for the geometric description of spatial objects in a vector map is provided by graph theory (Bishr, Molenaar, & Madwan, 1996). The Formal Data Structure (FDS) identifies six types of topologic relationships, namely: point to point, point to line, point to area, line to line, and line to area and area to area. Each object type has its own peculiar data storage structure in a GIS system. For example, a well, spring, school or hospital that is represented as a point object is identified by the attribute attached to its posi-tion ((x, y) coordinates). On the other hand, a river, which has a line object representation, requires the position information of all the node points that construct the edges, which will form its shape, i.e. a line object has both position and length information. Likewise, lake, swamp, etc, of the study area, which are constructed from nodes, edges and faces are of area representations. Their geometric con-struct information is composed of position and shape information as well as the perimeter of the line and size of the area. One has to note that the representation of objects in 2D planar graph is scale de-pendent. For example, a point representation in one instance of scale can become an area representa-tion in another scale (Laurini & Thompson;, 1992). (Figure 5 shows the construct of the formal data structure of elementary geometric primitives: node, edge and face).

Figure 5 Formal data structure for area objects, (Kraak and Molenaar 1997)

Area Object

Node

Edge

Face

Thematic Object

XY

Object level

Geometric level

Part of

Left

Right

Be-

En


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Super class

Class

Object

A1 A2………An

Ak, Ak+1,Ak+2,…..Am

a1a2….anakak+1….am

In modelling datasets such as land cover and land use, the area units are more important. It is in agreement with Laurini and Thompson’s (1992) suggestion that area units are important in socio-economic studies, analysis of terrain conditions, land use and natural resources inventory and re-cording of real states. The mathematical concepts necessary for the formulation of spatial data models have been briefly explained in Molenaar, (1998). It presents the concepts of using binary relations to model a crisp or fuzzy real-world phenomenon. Natural objects such as water bodies, soil or land cover doesn’t have a crisp boundary, but a fuzzy one. However, in mapping of these phenomena they are represented as if they have crisp boundaries, thus have a discrete geometric representation. Mole-naar (1998) emphasized to make a basic assumption for the formulation of syntax (mathematical rep-resentation of objects) for spatial data models to be seen as collections of discrete datasets in which several elementary relationships between these datasets are stored explicitly. The notion of these properties of objects is fundamental for the construction of model formalism. The representation of geographical entities by point, line, area and volume descriptions, refers to the zero, one, two and three dimensions of geometric figures with respective properties of no possible measurement, length, length and width; length, width and depth. In the real world, everything is three-dimensional. The idea of zero, one, and two dimensions is man made. Since real world object modelling deals with 2D ob-jects only, the dimensions zero, one and two will only be applicable.

2.4. Spatial Object Classification and Aggregation

2.4.1. Object Classification

The universe is the set of all objects occurring in a map or data model. Objects in this universe can be distinguished because they have different characteristics. For most applications of GIS, these differ-ences will be thematic. Classification in a scientific sense is the assignment of individual occurrences of some phenomenon to categories defined on the bases of selected attributes or functions. Those categories will be more or less homogenous with regard to the defined phenomenon (Laurini & Thompson;, 1992). In most applications, the terrain objects will be grouped in several distinct classes and a list of attributes will be connected to each class. Attribute lists will be different for different classes. Terrain objects inherit the attribute structure from their class, i.e. each object has a list con-taining a value for each class attribute, thus for members of its class. When two or more classes have attributes in common, then a super-class can be defined with a list containing these common attributes (Molenaar, 1998). This is illustrated in figure 6.

K > 1 a is a value of A

Figure 6 Formal data structure for area objects, (Kraak and Molenaar 1997)


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Classification and aggregation hierarchies play an important role in linking the definition of spatial objects at several scale levels. These hierarchies play an essential role in the formulation of processes to derive a spatial database with small-scale representation of an area from a database with a large-scale representation.

2.4.2. Object Aggregation

Object aggregation is a process that combines elementary objects to build composite objects and will be based on rules referring to the thematic aspects of objects that are to be aggregated and rules refer-ring to their geometric and topologic aspects. These aggregation processes can be based on the Formal Data Structure (FDS) for single valued vector maps. FDS describes the syntax of geometric and the-matic aspects of spatial objects (Bishr et al., 1996). As shown in figure 7, in a formal data structure, an object that belongs to a class has an identifier, for a unique identification in a database as well as thematic and geometric descriptors.

Figure 7 Spatial object relations, (Molenaar, 1998)

The FDS combines aspects of object-oriented and topologic data models. Point, line and area objects are represented with their geometric and thematic aspects. Their geometric representation contains information about topologic object relationships, whereas their thematic description is structured in object classes that may form generalization hierarchies. Aggregation hierarchies are quite different from classification hierarchies. An aggregation hierarchy shows how composite objects can be built from elementary objects and how these composite objects can be put together to build objects that are more complex and so on. The upward relationships of an aggregation hierarchy are called “PART-OF” links. For example, Tertiary River is Part-of a secondary river and Secondary River is Part-of a Primary River. For composite objects, the Part-of links might be based on two types of rules involving the thematic and geometric aspects of the elementary objects. Consequently, the generic definition of a type of an aggregation should consist of the following rules (Molenaar, 1998):

Geometric Distribution

Object Identifier

Thematic Distri-bution

Class


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Water supply management

Water source Ownership

Supply A

Private Municipality

Supply C Supply B

• Rules specifying the class of the elementary objects building an aggregated object of this type,

• Rules specifying the geometric and topologic relationships among these objects,

If elementary objects are combined to form a compound object, their attribute values are often aggre-gated as well. An aggregation hierarchy has therefore a bottom-up character; in the sense that the ele-mentary objects from the lowest level are combined, to compose increasingly complex objects as one ascends in the hierarchy (Molenaar, 1998). The compound objects inherit the attribute values from the objects by which they are composed.

2.4.3. Object Association

Object association is a type of relation between objects whereby objects that belong to a different classification hierarchy are related to another class of objects in the neighbourhoods. Objects related in this way have a member-of relation. For example, as in figure 8, drinking water supply points A, B and C form a class of objects. Water supply point A belongs to the municipality and water supply point B and C are privately owned. According to the definition, water supply point B and C are a member-of the class privately owned where as water supply point A is a member-of the class munici-pality. Thus, have an association relation. In object association, an object can be linked to more than one class; hence, the relation usually is many to many (m: n). Objects linked by an association relation are not mutually exclusive, hence not well defined.

Figure 8 Typical object association

2.4.4. Spatial Database Generalization

In a cartographic context, generalization can be defined as the process of abstracting the representa-tion of geographic information when the scale of the map is changed. The process creates a derived dataset with more desirable and usually less complex properties than those of the original datasets from the users perspectives. In reality it is a complex database modelling process involving abstrac-tion of thematic as well as geometric data of objects (Molenaar, 1996). Generalizing or aggregating


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geometric aspects is much more complex than the thematic aspect. The four basic operations that will be used in generalization process are:

• The selection of objects to be represented at the reduced scale, this selection will be based on the attribute data of the objects.

• The elimination from the database of objects that should not be represented, • The aggregation of area objects that should not be represented individually, • The reclassification of the generalized objects,

Information about the spatial structure of the mapped area will be required for these four operations. Several strategies for database generalization can be formulated. Among those are geometric driven, class driven, functional generalization, structural generalization, etc. These different aggregation strategies have their own peculiar uses. Each of these is discussed in the following sub section.

Geometry Driven Generalization:

This approach is used to adjust the size of mapped objects according to the scale of representation. In a raster data format, this is achieved by the reduction of the resolution of the pixels. Changing the resolutions of the raster cells and changing the corresponding attribute values of cells is involved to construct new objects at different levels. It can imply the generalisation of the thematic as the bigger size pixel could be a representation of heterogeneous thematic cells as the scale is reduced. Class Driven Generalization: In a class driven generalization and aggregation, as pointed out by Bishir and Radwan (1997) the ag-gregation is executed based on the thematic information of the spatial objects, while the generaliza-tion is based on relationships between class intensions(Bishr;). (Bishr;)Class driven generalization is a strategy where regions are identified, consisting of mutually adjacent objects belonging to the same class. These objects will then be aggregated to form larger spatial units with uniform thematic charac-teristics. When a collection of objects forms a geometric partition before aggregation, then the new collection, after aggregation, will also form a geometric partition. Adjacent objects, which have the same type of attributes, are generalized at higher levels. For example, in chapter 5 figure 17 the classes bamboo and plantation forest are aggregated to form large objects of forest. Functional Generalisation: Object aggregation using this strategy involves the identification of objects linked by functional rela-tion rather than classification. In this type, objects can have many to many relations. For example, Borehole 1, borehole 2 and borehole 3 are water supply points that form a class of objects. Borehole 1 and borehole 2 at the same time are urban water supply points borehole 3 is a rural water supply point. This relationship between the objects is functional or purpose oriented. This type of aggregation is applicable to the river basin datasets, and requires the identification of the objects as well as the func-tions the objects serve. Structural Generalization: Structural generalisation is applicable in modelling of datasets with a networked structure such as in river and road network. For example, tertiary rivers can be aggregated to secondary rivers to which


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they are connected, for example to estimate flows. Similarly, secondary rivers can be aggregated to primary rivers to estimate flows at the primary level.

2.4.5. Topologic Relationship

The topologic relation of geo-objects is an important part of their definition. These topological object relationships in combination with object classification hierarchies appear to be fundamental in the definition of the aggregation rules for spatial objects (Molenaar, 1998). The methods used to model natural phenomenon are structured or object-oriented. Object-oriented modelling has been applied in this study. Linking geo-objects that form hierarchies at different abstraction level should involve their topologic relationships (Bishr et al., 1996) . The domain of the topologic relationship is dependent on the objects to be mapped, i.e. areas, lines and points. Two relationships are topologically similar if they share the same boundary-boundary, interior-interior and exterior-exterior specifications, but have opposite boundary-interior and interior-boundary and/or boundary-exterior and exterior-boundary, and/or interior-exterior and exterior-interior specifications. The field of application of datasets usually defines the aggregation rules of the thematic aspect. For example, an attribute value of a point object can be aggregated by various mathematical expressions such as addition, subtraction, multiplication, division, or using other statistical computation tech-niques etc. The result will be a new object with corresponding new relationships of higher order. This being the case for the thematic aspect, geometric aspects are constructed based on the definitions of thematic relationships. Left-right information of the edges of an area object is the means to construct geometric aggregation expressions. In a line object, both the geometric and thematic aggregation can be performed from the topologic information. For example in a structural generalization, tertiary riv-ers and secondary rivers are aggregated to form primary rivers using topologic information.

2.5. Data Standard, Data Sharing and Abstraction

2.5.1. Data Standard

If data are not standardized, uniquely structured and catalogued (with their metadata), there will be the situation of "having an ocean of data, but only drops of information" (Bechtold, 1997). It is important to guarantee that all GIS data are in a well-structured, transparent and documented system. Among the problems observed in the river basin databases is the absence of standards. It is attributed by project based GIS set-up. The absence of standards for data format, classification scheme, and coding system coupled with the use of heterogeneous software contributed to the inconveniences in the sharing of the river basin databases. Establishing standards will encourage the use of the data as well as serve as a quality control. Moreover, to insure that data is not misused; the assumptions and limitations affect-ing the collection of the data must be fully documented (Tshangho, 1999). In the river basin studies, in addition to the lack of standards the absence of such documentation is apparent.


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2.5.2. Data Sharing

The main advantages of GIS technology are regarded as the utility to integrate data sets from a wide variety of sources and, through the medium of the computer, to make this information more widely accessible as well as available in a variety of new forms (Campbell & Masser, 1995). With the steady rise in the use of GIS in recent years, there is a general agreement that the success of organizations in both the public and private sectors can be greatly enhanced by the open exchange of geographical in-formation across organization boundaries. However, between and among organizations there has been a general inability and unwillingness to share data and information across organization boundaries (Obermeyer & Pinto;, 1994). The waste caused by duplication of effort which is due largely to lack of information exchange among local, state, and federal governments and the private sector remains a significant impediment to the more effective and efficient use of GIS throughout society (Obermeyer & Pinto;, 1994). Fragmentation of data becomes apparent because of the combination of the subdivi-sion of tasks and the development of increasing specialized bureaucracy or control. The river basin databases are characterized by such type of data fragmentation. It hampered the possi-bility of data sharing within and outside of the organization. Organizations contemplating sharing of spatial data need to carefully define spatial objects, attributes and data quality associated with these objects (Campbell & Masser, 1995).

2.5.3. Data Abstraction

Data are stored in information systems so that they will be accessible for query operations to explore relationships between the datasets. To support such operations some elementary relationships will be stored explicitly. Abstraction translates phenomena, instances of databases by focusing only on rele-vant aspects of these phenomena (Yaolin, 2002). The complete set of answers to queries that can be generated by information systems will be called the “query space of the system” (Molenaar 1998). The potentiality of the data model to generate query spaces depends on the specified data type and the relationships that will be explicitly formulated between these data types. The concepts of interopera-bility in an open GIS environment will play an important role transforming the conceptual model to logical and physical models. The next section will present an over view discussion on interoperability.

2.6. Interoperability

In modelling spatial objects now days, there are specialised commercial software such as NEXPERT. In the river basins, it is observed that ArcInfo and ArcView are the main software available for use. Designing the logical or physical model using the above-mentioned software is cumbersome, not flexible or too difficult and time taking. Particularly, modelling the geometric aspect of objects is dif-ficult. In the case of river basin databases, where different software is operating, interoperable GIS could play an important role. As pointed out by Egenhofer and Goodchild (1996), in principle inter-operability offers one possible way of making GIS more useful and accessible to scientific research, by making the process of interaction with GIS easier, and obviating the need for complex techniques to overcome incompatibilities between software systems and datasets. Furthermore, noted that, inter-operability attempts to make software systems that are based on different models work together. Its operation in an open environment gives flexibility to users. Moreover, much of the technical platforms would be handled invisibly by the system, since the information necessary to complete such opera-


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tions successfully should be available directly, without user intervention (Egenhofer & Goodchild;, 1996).

Figure 9 Possible standard data transfer format

In figure 9, S1, S2, S3 and S4 refer to the Standard Data Transfer System (SDTS) for the respective river basins. The SDTS can be a single program or set of individual programs that will interact with the user interface. It is meant to perform operations such as data query, view, analysis, projection and software format conversion, etc. It operates in such a way that, a user sends its request through the user interface, and then this request will be translated into an action or set of actions that are neces-sary to perform the operations. For example, if one wants to get all bush land units from land cover and land use dataset, this request will be interpreted according to the representation of the unit in each river basin. This includes: rec-ognizing the operating software, identification of the file containing the object, the projection system and attributes that represents the requested object and performs the requested operation through the response process. The SDTS should take care of responding to such kind of queries. User interactive operations, such as view, query, overlay and production are among the basic functions of the STDS.

Abbay Basin

Mereb Basin

Baro-Akobo Basin

Tekeze Basin

User Interface

S1

S3 S4

S2

Request Response


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However, data exchange using SDTS, only provides a partial solution because, among other short-comings, it exclusively focuses on data, without provisions for transferring processes.

Key words: Spatial data modelling, object-oriented, spatial objects, aggregation, generali-zation, association, topological relationship, data standard and data sharing, interop-erability

2.7. Conclusion

Spatial modelling in an object-oriented modelling approach provides a natural method for describing real-world spatial entities, avoids data fragmentation, and enables useful capabilities for managing databases. To make a meaningful model design one has to thoroughly understand the interactive proc-esses involved in the databases. In this chapter, the topics covered give sufficient explanation that will address specific objective 2 of the research, which emphasises on spatial data aggregation techniques. Based on these basic theories the situation of the data and the users environment need to be thor-oughly studied. The next chapter will dwell on field data collection and user requirement description. .


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3. Data Description and Requirement Analysis

3.1. Introduction

Chapter 2 looked at the basic theory of spatial data modelling techniques. Based on this understand-ing, data checklists were designed to collect data from the study area. This chapter describes the field data and the users environment. In view of this, the sections have been organised as follows: section 3.2 presents field data collection and the background of data collection in an integrated river basin development master plan project. It also presents user requirements with a focus on the organisations involved. Section 3.3 presents data acquisition and requirement; section 3.4 discusses the importance of GIS in planning and the last section 3.5, presents the conclusion of the chapter.

3.2. Field Data Collection

Based on the research questions set out in section 1.6, data checklists have been prepared (appendix I & II) for field data collection (field data collection was conducted from Sept. 6 – Oct. 5, 2002). The formats used are the followings:

• River basin data checklist: a format used to collect geo-referencing and projection in-formation and the type of software used in the river basins,

• Thematic data checklist: a format used to collect information on the type of themes, data source, data type, scale, classification scheme, data coverage, data use, and up-dating,

• Discussion: to seek information about the need for aggregation with experts and deci-sion makers in the MoWR,

Field data collection was focused to get pertinent information on the datasets of the master plan stud-ies and the organizations closely working with the GIS of the MoWR. Background information on these organizations has been discussed in section 3.3. Data checklists (bullet 1 and bullet 2) have been utilized to collect data mainly from the Computer and GIS Service and the River Basin Development Studies Department. The discussion (last bullet) was held with RBDSD, Tranboundary Department and Easter Nile Basin Initiative Office. The findings of the field data collection have been summa-rized in the following section.


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Table 4 River basin projection data checklist

River Basin Projection Ellipsoid Zone Software Mereb Universal Transverse

Mercator (UTM)

Clarke 1880 37 ArcInfo 3.42b

ILWIS 2.0

AutoCAD 12

Tekeze Universal Transverse

Mercator (UTM)


ILWIS 2.0

AutoCAD 12

Abbay Universal Transverse

Mercator (UTM)

Clarke 1880 36 and

37

ArcInfo 3.42b

ERDAS Imagine 8.2

AutoCAD 14

Baro-Akobo Universal Transverse

Mercator (UTM)


ArcView, AutoCAD

12

As can be observed from table 4:

• The river basins used heterogeneous projection, i.e. they used Universal Transverse Mer-cator (UTM) projection and ellipsoid Clarke 1880, with a difference in Zone. Mereb and Tekeze basins used Zone 37, Abbay basin used Zone 36 and Zone 37, and Baro-Akobo basin used Zone 36. The country is covered by three Zones, i.e. Zone 36, 37 and 38. When executing geo-information at national scale, Zone 37 is the one used because the larger portion of the country is covered by this zone. Therefore, those datasets processed in other ways than zone 37 needs to be converted, to fit other datasets in adjacent areas, prior to any data integration.

• Heterogeneous software has been utilised to generate the river basin datasets, i.e. ArcInfo

with ILWIS in Mereb and Tekeze basin study, ArcInfo with ERDAS in the Abbay basin, ArcInfo and ArcView in the Baro-Akobo basin. In addition, all basin studies have used AutoCAD for digitising and engineering drawing activities. Even though, heterogeneous software are used by the river basin studies, the final products or digital data reaching end users are in ArcInfo and ArcView format. Therefore, this study gave due emphasis to the vector data format.


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Table 5 Thematic data checklist- Tekeze river basin Units Coverage Data use Frequency of use Updating Who is updating Data descrip-

tion Data source Scale Classification method

Thematic Geometric

Land use/cover Landsat TM dated December 5,

1989 and March 14, 1988 bands 5,

4, 3 and Aerial photographs

1994/1964 EMA

250,000 Follows the Woody Biomass

Inventory & Strategic Planning

Project (WBISPP, 1993/1994)

Ref. attribute

and map legend

Polygon Full basin Resource inventory and

planning

High No updating so far River Basin Development

Studies Department is in

charge.

Soil and Terrain Ditto as above and 1: 250,000

scale 1979 EMA topographic

Maps, 1:100,000 scale 1979

Russian topographic maps

250,000 and

50,000

reconnais-

sance &

feasibility.

Revised FAO-UNESCO soil

map of the world (1990) and

the SOTER format (ISRIC,

1995),

(FAO 1988)

Ref. attribute

and map legend

Polygon Full basin at

reconnaissance

scale and 3

selected sites at

feasibility

Resource inventory and

planning

High No updating so far River Basin Development

Studies Department is in

charge.

Geology &

Mineralogy

Landsat TM (same as above) 250,000 Landsat TM interpretation Ref. attribute

and map legend

Polygon, line

and point

Full basin Resource inventory and

planning

Low No updating RBDSD, but data has to be

obtained from sector

organizations

Hydrology EMA 250,000 topographic maps,

Department of Hydrology of the

MoWR

250,000 River order Ref. attribute

and map legend

Polygon, line

and point

Fill basin Water resources

inventory, planning

flow estimation

High No updating of master

plan data

RBDSD, but data has to be


organizations

River network EMA 1979 topographic maps 250,000 River order Ref. attribute &

map legend

Polygon and

line

Fill basin River basin modeling High No updating so far EMA topographic map

update

Table 6 Thematic data checklist Abbay river basin Units Coverage Data use Frequency of use Updating Who is updating Data descrip-

tion Data source Scale Classification method

Thematic Geometric

Land cover Landsat imagery (1986 -1990 and

from November to January, and

photo interpretation

250000 Adopted FAO and

WBISPP Method,

Ref. Map legend,

attributes and Data

description doc.

Polygon Full basin Master planning High So far, no updat-

ing of master plan

data

River Basin Development

Studies Department is in charge.

Land use Generated from land cover 250000 Generated Ref. Map legend Polygon Full basin Planning High No updating RBDSD is in charge.

Soil Landsat imagery from 1986 to 1990

and from November to January, and


250000 at

reconnaissance

& 50,000

feasibility

Described using FAO-

UNESCO guidelines

(1990) and classified FAO-

UNESCO-ISRIC (1988)

Ref. Map legend,

attribute, & Data

description docu-

ment

Polygon and

point

Full basin at

reconnaissance and

7 selected sites at

semi-detailed

Master planning High No updating RBDSD

Geology Ditto as above and Screen digitized

and generated

250000 Landsat TM interpretation Ref. Map legend,

attribute,

Polygon, and

line

Full basin Planning Low No updating RBDSD, but data has to be

obtained from sector organiza-

tions

Hydrology Generated and screen digitized 250000 River order Ref. Map legend Polygon, line

and point

Full basin Planning High No updating RBDSD, but data has to be

obtained from sector organiza-

tions


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As can be observed from table 5 and table 6, the followings could be summarised: 1. Generally, the river basin studies have generated a broader coverage of thematic data

in the different disciplines such as: natural resources, water resources, socio-economy, etc,

2. The river basins have utilized previous studies, satellite images, aerial photographs, existing maps, statistical data, and meteorological data as a source of information for thematic data generation. Knowledge about the history of the datasets is important to decide whether or not to rely on a particular dataset. For example, satellite images used to generate thematic datasets are of different times between the basins and within a basin as well,

3. Classification schemes: there are no standards in the country with regard to geo-information processing. Classification scheme is just one. In the context of land use and land cover and soils, more or less all the river basins seem to have adopted the same classification schemes or with some modification to those. The once used are: Land Use Planning and Regulatory Department (LUPRD), Woody Bio-mass Inven-tory and Strategic Planning Project (WBISPP), FAO-ISRIC, FAO 1990. Moreover, there is a difference in coding and categorizing mapping units.

4. Scale: all most all of the thematic datasets at reconnaissance level are at a scale 1: 250,000. In some thematic data types (particularly soil) and for selected sites, datasets are at a scale of 1:50,000. Other presentation maps range from 1:1,000,000 up to 1:2,000,000.

5. Coverage: there is full coverage information at 1: 250,000 scale for most of the themes and 1:50,000 scale for selected sites as in 4.

6. Data use: the data generated in the master plan studies have the specific task of meet-ing the master plan objectives. In this regard, the main user of these datasets is the RBDSD. Other data users emerged for the reason that existing data will save them time and other resources. These other users are secondary and most of their geo-information demand is focused for a framework data such as on: land use and land cover, soil, hydrology, road infrastructure and river network, in hardcopy as well as digital format.

7. Updating: so far, no updating of master plan datasets is performed. The RBDSD is re-sponsible for updating the datasets. Whereas, updating primary datasets such as cen-sus/agricultural statistics and topographic data, is the responsibility of the Central Sta-tistics Office (CSA) and the Ethiopian Mapping Authority (EMA) respectively.

From the field data observation, looking back into the source of data, satellite image interpretation is the ultimate source of information in generating natural resources based information such as soils, land use and land cover and geology. On the other hand, the most frequently requested data by users lie within these datasets. However, in view of the observed heterogeneities, such as in projection and classification scheme, it raises a number of questions, such as the followings:

1. Are the satellite images used in the four river basins comparable to each other for use in data aggregation?

2. Are the ground truth verification points representative in the context of the four basins? 3. Are the interpretation assumptions the same across the four basins?


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4. Are the interpretation techniques or expert judgments used to generate thematic datasets across the basins, comparable?

These above indicated and other possible questions depict, joining or combining the river basin data-sets requires an in-depth study of the history of theses datasets. The other important issue is that, sat-ellite image interpretation involves expert judgments; hence, actions such as these are difficult to make comparison. Answering each of these questions is fundamental to rely on the outcome of the model. Therefore, it is felt that these aspects deserve a further study. Furthermore, there is no one sin-gle approach applicable in aggregating/generalising all the datasets. Rather each thematic dataset re-quires a procedure of its own. In view of this, understanding the status of the master plan data will be important. The next sub-section presents data collection and processing in the master plan studies, to give an insight in to the type of data involved in the aggregation process.

3.2.1. Integrated Development Master Plan

The river basin provides a natural geographical boundary within which the socio-ecological interac-tions and interrelationships can be studied, analysed and appropriate developments planned and im-plemented based on the full understanding of the natural resources and their sustainable exploitation. The approach of the master plan study takes the river basin as a spatial planning unit in which natural resources occurrences, distribution, quality, quantity and interaction can be studied and their use for human welfare under a balanced condition be established (Ethiopian Valley Development Studies Au-thority, 1994). The river basin is a logical unit for planning natural resources management programs because it explicitly forces to recognize that sustained natural resources based development depends on the interaction of the activities that take place throughout the river basin. As pointed out in the in-troduction section in chapter 1, during an integrated development master plan study a huge amount of primary and secondary data is collected for analysis and master plan formulation. The studies are in-tended to meet the social, economical and environmental needs of the communities of the river basin in particular and of the country at large. Consequently, in line with the undertaking of the river basin studies, multi-disciplinary information is collected, where the MoWR is the primary data source for meteorological and hydrological data. Other data are collected from other government and non-government organizations. In addition to the above, satellite images are an important data source used in the interpretation and analysis of the resources of a river basin. The images are used for to prepare base maps, land use land cover maps, soils, geology and other thematic maps (chapter 2 table 5). In case of identified data gaps, field surveys; analytical, interpolation and/or expert knowledge were used to fill the data gaps. Of course, this aspect of data gap filling is dependent upon the type of the dataset and expert decision. One can say that thematic datasets generated by the river basin studies are subject to the specific situation of the river basin and the methods and techniques applied in each of these studies. In conclu-sion, the river basin datasets are the outcome of data processing, from multiple sources, such as: satel-lite images, field survey investigation, and expert knowledge. An integrated development master plan study in the Ethiopian case focuses on inventorying the natu-ral resources of a river basin in order to prepare a guiding plan for future developments specifically in the area of water resources. These efforts have created side advantages for many application areas; in


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a way, they are the rich source of information for many users. The next section, will address a discus-sion on the users.

3.2.2. User Requirement Analysis

A user in the context of the river basin databases is anyone who is affected by or who may affect the design, implementation, development, and the use of spatial data from the existing master plan data-sets. In a GIS environment, knowing the role, characteristics and relationships of the users is funda-mental in defining user needs. It is only then that data processing activities such as map joining, ag-gregating, integrating, overlaying, edge matching, simplification and generalization etc., fit users re-quirements. For the MoWR, the involvement into the GIS technology should support the dissemina-tion of its information to the users according to their needs. It is only then that cultural and techno-logical environments fit each other to providing focused services matching with the needs of the or-ganization. As pointed out by many researchers, the value and social utility of geographic information comes from its use. Likewise, sharing of its data/information is fundamental in sustaining cooperation with other organizations to promote development activities in the river basins. Computer and GIS Service (CGIS Service), the River Basin Development Studies Department (RBDSD), the Trans-boundary Department and the Eastern Nile Basin Initiative Office are the main involved in the GIS activity of the MoWR. Actively participating are also the regional administrations falling (fully or partly) within the river basins. This refers to Tigray, Amhara, Oromia, Benishangul, Gambela and Southern Nations Nationalities and Peoples regional administrations. There are also ex-ternal users from government, NGO’s and the private sector who pop in as data needs arise. Some of the most relevant users are discussed below.

Computer and GIS Service

Knowledge about GIS in the MoWR is realized in the early 1990’s. The CGIS Service is a centralized geo-information unit established in 1995. The service provides support to all the departments, services and projects of the MoWR and other outside users. Before it’s establishment, GIS activity was han-dled on a project base. Even after, especially during the earlier years, because the organization has to handle a number of projects at the same time, the trend of project based GIS continued for sometime. At project level, no long-term plans are incorporated nor a follow-up plan is associated, to sustain es-tablished GIS systems. Hence, it is characterized by short-term and narrow scope project objectives. The impact of project approach was realized as a utilization problem and duplication of effort. It be-came apparent that additional efforts are required to harmonize and standardize the datasets. Of par-ticular interest for this study is the situation of the spatial data. As many argue, standardization is not only particular to the situation of the organization. This is experienced elsewhere in other countries. For example, as pointed out by Eric de Man, lack of standardization and coordination is a major (cul-tural) condition that hampers the adoption and effective use of GIS in Italy (De Man & Van den Toorn, 2002). In connection with the master plan studies, CGIS Service receives GIS related databases compiled by the master plan projects. Consequently, all the geo-spatial datasets are residing in this Service. During the field data collection, it is observed that most of the activity of the CGIS service was reproduction


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of the outputs of the master plans and provision of extracted information to the users. However, this activity is dealt with difficulty, due to the differences in the format of the databases. In an instance of new, additional or modified information, support services are provided without affecting the original datasets. Apart from responding to the incoming data requests, there are no motivations or self-initiatives to do activities like data aggregation/integration. One of the major limitations could be the absence of technical capacity. However, as a service provider fulfilling the requests of its users is the ultimate concern of the CGIS Service. Many researchers believe that, once a given GIS set-up fails producing desired services, it will start loosing support of its users, then the top management as well as the administration of the organiza-tion. The implementation of GIS programs in organizations where it is not considered a critical prior-ity may result in a lack of support at an administrative level (Salling, Clapp, & Jane Lapointe, 1994). River Basin Development Studies Department

The RBDSD is the responsible organ that supervises the execution of the river basin master plan stud-ies. It is also responsible for the follow-up and updating of the master plan studies. With regard to the geo-information activity of the river basins, it stands as a service receiving body. The department is in charge of advising the MoWR on technical matters concerning the river basins. This includes the preparation of Terms of Reference (ToR) for new projects, preparation of appraisal report for poten-tial projects etc. The execution of these tasks forces the department to become the ultimate user of the master plan databases. At the time of the field visit, a team of experts from the department has been assigned to find ways of joining or combining the river basin datasets. The intention is to make an over all national master plan through the amalgamation of the individual master plan studies, includ-ing data and models (water resources, population support capacity, energy balance, etc.). In connec-tion with the activity of the department, two outstanding issues have been pointed out during the field visit. These are:

• The MoWR has a national master plan formulation policy, through the amalgamation of the completed master plan studies. The strategy here is to start with the part of the Nile Basin, where the master plan studies were completed, and sequentially, proceed with other river basins on completion of the master plan studies on those parts,

• Disaggregate the river basin master plan into sub-basin plans in order to allow master

plan implementation or encourage investment in the regional administrations through a watershed approach.

In view of joining or combining master plan datasets, the team of experts have prepared a review sum-mary document of the mater plan studies. The aspect of combining the databases and models has not progressed according to expectations. However, the department still requires the integration of the databases to fulfil its tasks. Trans-boundary Department

The Trans-boundary Department is the department through which most of the Nile Basin trans-national issues are handled. In this department, there is no GIS operation per se. However, there is a


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Focal Point Institution (FPI) GIS setting attached to it, with staff from the CGIS Service. Regarding the need for aggregation at trans-national level, it has mentioned that there is no agreed scale or state of geo-information activity at this capacity. Each of the countries is treating this aspect in its own way and interest. Geo-information activity and related data sharing at trans-national scale are more of a concern of the future in which the countries involved have to decide and agree. However, at present particularly on the hydro-meteorological databases, a number of trainings have been conducted to the representatives of the countries with a focus on database standardization and use of uniform format. It could be an indication that there are prospects for the future. Eastern Nile Basin Initiative Office

The Eastern Nile Basin Initiative Office has been established in the premises of the MoWR. It is headed by three representatives from the three member countries: Ethiopia, the Sudan and Egypt. The discussion in this office refers to the Ethiopian representative. Like the Trans-boundary Department, this office has also expressed similar situation. At present, the office is more engaged in office or-ganization and definition of tasks. Aspects such as dealing with aggregation/integration are expressed as the possible future exercises that might be considered after the setting of tasks, the organization of office and personnel. Regional Administrations

Ethiopia has a Federal Government system, which comprises nine administrative regions and two ad-ministrative councils. The Regions have an autonomous mandate to develop resources residing in the regions. Federal organizations like the MoWR are mandated to support the regional administrations technically and through capacity building. Providing data and information from the master plan stud-ies is amongst such support. In each of the regions, there are sector bureaus each tasked with activities such as the Water, Mines, and Energy Resources Development bureau is responsible for the develop-ment of water, mines and energy resources. Whenever these bureaus need any technical assistance from the federal organizations, they can communicate the responsible sector organization through the regional administration. In addition to the Water Mines and Energy Resources Development bureau, other regional administra-tive bureaus such as the Region’s Economic Development and Cooperation bureau, Agriculture bu-reau etc, are using master plan data for sectoral planning in an area of interest. Data needs from the regions are mostly for the coverage of the administrative region. For instance, in the part of the re-search area, the Tigray region is found in Mereb and Tekeze basins, The Amhara region is found in the Tekeze and Abbay Basin, The Oromia region is found in the Abbay, and Baro-Akobo Basin. Con-sequently, any data request by regional administrations necessitates the organization of the present data. When it comes to the development of water resources, the regions are the ultimate implementers of the master plan projects. In implementing those plans or initiate other development projects, they de-pend on the findings of the master plan studies. At present, not all the regions are equally participat-ing. Some are more actively involved than others are. The Amhara, Tigray and Oromia regional ad-ministrations are the most frequent users. This is because GIS activities in these regions are started much earlier than the other regions.


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Other Government, NGO’s and the Private Sector

This group comprises quite a very diverse group of users such as: higher learning institutions, gov-ernment organizations, NGOs, and the private sector. Whenever they have particular needs from the master plan datasets, they approach the organization. As it encompasses from government to a private firm, the treatments could vary significantly depending on the type of organization. That is to say, for example, when it comes to data provision higher learning institutions are more encouraged than a pri-vate firm is. Generally, the demands of this group can’t be predicted in terms of scale, coverage or type of data. In most cases, they get services for what is readily available and permissible. The rele-vance of spatial data aggregation to this group is that in the first place their requests are not limited by the individual basin boundaries. The patches of areas they request for can fall within or across the river basins. Secondly, some of them are interested in detailed information while others are depending on generalized once.

3.3. Data Acquisition and Data Requirement

3.3.1. Data Processing

From the discussion in section 3.2.2, the CGIS Service and the RBDSD are key executing bodies of the geo-information activity. However, in the fulfilment of tasks, both for internal and external use, there are some observed problems that are worth discussing. Table 7 summarizes these problems. For the assessment of geo-information activity in the MoWR; GIS facility, networked system, GIS knowl-edge/experience and professional background have been used as comparison points.

Table 7 Analysis of the status of CGIS Service & RBDSD

Description CGIS Service RBDSD

Geo-information facility: (Hardware, software)

Available Not available, but have computers and software for other use/application.

Networked system Local Area Network (LAN), lim-ited to 5-10 users

Not available

GIS Knowl-edge/experience

Available, with limited analytical capacities and experience

Not available, (may be some indi-viduals)

Professional Knowl-edge/experience

Limited to GIS only. Most staffs are sub-professionals in cartogra-phy, drafting and surveying and have on the job training on GIS.

Sector professionals in areas like: Agriculture, land use/cover, soil, livestock, geology, sociology etc, with limited or no knowledge about GIS

As can be observed from table 7, the GIS-technical capacities are confined in the CGIS Service whereas other professional knowledge necessary for the follow-up and updating of the river basin studies relies in the expertise of professionals of the RBDSD. This applies for the thematic datasets as well. On the other hand, the local area network, which is located in the CGIS Service, will not allow decentralized geo-information processing. For a successful GIS activity in the organization, this ob-


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served gap has to be bridged through creating cooperation environment between the CGIS Service and the RBDSD to execute the tasks jointly.

3.3.2. Spatial Data Requirement and Scale

The need for spatial data aggregation/integration emanates from the desire to have an over all over-view of the distribution of spatial objects at basin, region, national or trans-national level for various planning purposes. Here the problem though is that high-level planners and decision-makers apart from expressing what they need, they do not specify technical aspects such as details on scale. More often, scale becomes a technical decision made by experts rather than a management decision. Mole-naar (1998) described, information abstraction in these sub processes (such as cartographic generali-zation) is mainly determined by expert knowledge and can be usually expressed as logical rules. These rules are susceptible to being translated as database management procedures in a GIS environ-ment. On the other hand, many argue that understanding the user’s real requirements, rather than their requirements as perceived by the information system developer, as critical to the development of a successful information system. Conceptual modelling is one possible example of such a situation. In the instance of modelling the river basin databases, though users could not specify details such as scale, it can be established taking into account the essences of their needs. In order to enhance data or information sharing, the present state of the river basin data needs to be changed/processed according to users needs. In this regard, spatial data modelling will play a central role in creating such an environment. Aggregation serves to extract and visualize across basin datasets in a harmonized and hierarchical data structure that will suit user-defined needs. The aggregation process keeps the original information unchanged and allows the abstraction of generalized informa-tion, to support decision-making at all levels. Out of the various datasets of the river basins listed in table 5 and 6, Land use and land cover, soils, road and river network are the most frequently requested datasets by the external users. However, for the MoWR, modelling of all the datasets is important.

3.4. The Importance of GIS in Planning

Today, planners of all types and in all functional areas such as water resources, natural resources, city planning, transportation, social services, etc, use GIS in their daily work. The reasons for this are many. First, GIS is an excellent tool for planners, enabling them to integrate a variety of data from multiple sources and to perform spatial analyses that previously might have taken much more time. The close fit between the tools that planners need and the capabilities of GIS provide the strongest reason for the widespread use of GIS by planners (Al-Kodmany et al., 2001). GIS can play at least two important development planning and analysis such as in master planning. The first one is to use GIS in processing geographically related data and represent spatial relationships among geographical observation or entities. The second is use of GIS to perform spatial analysis by utilizing a variety of unique characteristics of GIS such as topology. However, unless otherwise these aspects of spatial relationships and topology are supported by appropriate modelling techniques it is difficult to assume that the existence of databases alone will suffice the users needs. The main shortcoming of the master plan databases is the lack of models such as spatial data modelling that will enhance planning, deci-sion-making and data sharing.


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3.5. Conclusion

In this chapter, the status of the data as well as the situation of the users environment has been de-scribed. The description has enabled analysing the current and future data usage and needs. It has been observed that various datasets are subject to varying heterogeneities and the users environments are facing some constraints, particularly, on the capacity issue, on the absence of standards and defini-tion of the scale of operation etc. This leads to further analysis. The next chapter will deal with the analysis of these aspects.


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4. Results and Analysis

4.1 Introdusction

Following the field data and the users environment description, the analysis of the findings becomes important. This chapter will provide a further analysis that will serve as a base for the design of the conceptual model. The chapter has been organized as follows: section 4.2 discusses the hierarchical processes; section 4.3 discusses on the spatial data types. Section 4.4 presents on the data model and documentation, section 4.5 presents model criteria, section 4.6 provides a discussion on modelling the river basin databases and section 4.7 presents Land use and land cover and section 4.8 represents the conclusion of the chapter.

4.2. Hierarchical Process

4.2.1. Hierarchical Levels

As discussed in chapter 3, users’ requirements in the Ethiopian part of the Nile Basin range from op-erational level to high-level decision-making. These decisions depend on detailed as well as general-ized information. In a multi-user environment, data requirement is very diverse in terms of type, cov-erage, quality and scope as well as scale. In section 3.2.2, it is discussed that the regions are the im-plementers of the master plan projects. The administrative structure of the regions consists of Re-gional states, Zones, Wereda (district), peasant associations in the rural areas, and Kebele associations in the towns and cities (figure 10). In the rural areas, the peasant association forms the smallest unit of administration. For the urban areas such as cities and towns, the details of the data are not good enough for modelling. Therefore, this study has focused on rural areas. In the regional states, the technical execution of important projects such as geo-data processing are handled at the regional level. Consequently, on those matters the lower administrative levels (Zonal and Wereda) do not communicate with the MoWR directly, but only through the regional state. On the other hand, field survey and data collection during the master plan studies involves all hierarchical levels of the regions. For example, the socio-economic study of the master plan projects takes care of the concerns of the peasant associations through Rapid Rural Appraisal (RRA) and Participatory Ru-ral Appraisal. Peasant associations are the basic units for which conclusions about Wereda, Zone and the Regions are drawn. The linkage between the MoWR and the regions is shown in figure 10.


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Council of Ministers

Regional State MoWR

Burundi D.R. Congo Egypt Ethiopia Eritrea Kenya Rwanda The Sudan Tanzania Uganda

Nile Basin

Lateral communication: Hierarchical communication:

State

Peasant associations

Wereda / district

Zone

Level 1 Level 2 Level 3

Figure 10 Hierarchical levels and linkages between organizations in Ethiopia

With regard to the aggregation hierarchy, because of the lower levels are not directly communicating with the MoWR; it is therefore possible to consider, the representation of the lower levels by the re-gional state itself. Consequently, the Nile Basin regional (trans-national), the MoWR and the region form three hierarchical levels.

4.2.2. Hierarchical Activities

Hierarchical activities (table 8) are the basis in the determination of hierarchical processes and related data requirements. In the study area, there is an overlap of activities; therefore, the demarcation of hierarchical levels in some instances is not distinct. For example, the responsibility of the MoWR to handle issues related with the Nile Basin at trans-national level places it to the highest hierarchical level (level 1). Whereas activities such as, creating a national database for the water resources of the country places it at the national level (level 2) and its involvement in the study and implementation of feasibility projects, places it at the lower hierarchical level (level 3). The other important element in the aggregation process is the scale of operation. From the discussion in section 3.2.2, the scale of operation at trans-national level is not yet defined or agreed up on. It is difficult to tell whether the scale will be the same or different from that of the national scale. However, given the fact that the area extent of the entire Nile Basin is about 10 times bigger than the Ethiopian part, it could be as-sumed that it will be a much more generalized scale than the national one.


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Table 8 Activities at different hierarchical levels

Levels Organisation Activity Nile Ba-sin re-gional (Level 1)

MoWR, as a gov-ernment body re-sponsible to deal with trans-boundary rivers issue

• Negotiation, • Policy formulation and implementation, • Develop strategies, scenarios and options • Regional development programs (SVP) • Capacity building,

National (Level 2)

MoWR, as a na-tional organ to de-velop the water resources of the country

• Inventorying the water resources of the country, • Creating a national database, • Undertake master plan and feasibility studies, • Strategy development for the water resources of the

country, • Technical cooperation such as information exchange

and capacity building Regional (Level 3)

Regional admini-strations in coop-eration with the MoWR.

• Implement master plan projects, • Implement strategies at regional level • Formulate strategies and options • Identify, prepare and implement other regional pro-

jects. In the aggregation/generalisation process, the most generalised information is a requirement for level 1 and the least generalised or the most detailed information is desirable for level 3. The need for Level 2 is in between. The activities indicated are linked or inter-dependent and built on one another. For example, national water development strategies are part of the framework of the trans-national strate-gies. In the mean time, water development strategies established by the MoWR are binding to the re-gions. The regions in their part can also develop regional implemental strategies. These strategies have impact on the data. Object aggregation, generalization and association, which build on a bottom-up process, are convenient to aggregate and use as a basis for planning and decision-making at hierar-chical levels. Policy decision at higher levels influences lower operational levels. In turn, the response of the lower levels affects the implementation of policy decisions.

Figure 11 Hierarchical linkages

Trans-national

National

Regional

Policy and strategy

Policy and strategy adoption & policy and strategy formulation

Policy and strategy adoption & policy and strategy formulation


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4.3. Spatial Data

4.3.1. Spatial Objects and Data Types

The construct of a conceptual model is formulated from objects, classes and, super classes and topo-logical relationships among these classes. Each object in a model represents the existence of some-thing of interest to the users, planners or policy decision-makers. For high level planners and decision makers, generalized information is very important for action. On the other hand, communities or or-ganizations close to the operational level require the most detailed information. Therefore, relating the spatial data aggregation levels with the hierarchical levels of the users is important. In addition, mod-elling spatial objects requires knowledge about the geometric and thematic representation of objects, which has been discussed in section 2.3 and knowledge about the construct of topology in both raster and vector data structure. The river basin vector datasets cover all ranges of spatial object types: points, lines and polygons. Some of these datasets are derived from satellite image interpretation such as land use and land cover and some are derived by interpolation techniques (table 5 in chapter 3). Datasets reaching users are all in ArcInfo or ArcView format hence are vector data types. In most GIS, thematic and topological in-formation is stored in attribute tables linked to the positional information. The values recorded in an attribute table depend on the type of theme as well as the applications involved. For example, a bore-hole attribute table contains information on: position, depth of well, yield, number of people served, year of construction etc. In a multi-value dataset such as the borehole data, the most important ques-tion to ask is, which attribute values to aggregate. This itself depends on the purpose or application of the data.

4.3.2. Data Needs and the Objects of Modelling

From the previous discussions, it is noted that aggregation provides users harmonized information across the river basins. Data requirement for a certain application will determine the spatial object and the type of aggregation. For instance, information on major rivers could be regarded as sufficient for higher level planning whereas details including secondary and tertiary rivers could be required to run simulation models for water balance computation. Similarly, crop type information by an agriculturist could be seen from the point of yield estimation, but for the machinery service company, this same information could be useful, to decide when and what type of machinery to use during harvesting. Hence, the users need defines the objects of modelling as well as the strategy to be followed in aggre-gating these objects. In the study area, the major data needs could be summarised as the followings:

1. Data need for a specific location, 2. Data need across two or more river basins, 3. Data need at detailed scale levels, 4. Generalized information be it for a specific site or larger coverage, 5. Aggregated information within and across the basins 6. Data need from multiple themes.


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7. Overlay possibilities for analysis and display Items listed in 2, 4 and 5 are relevant to be addressed in this study, those listed in 1, 3, 6, and 7 can be fulfilled either directly from the existing databases or from the results of modelling items 2, 4 and 5. In section 3.2, it is discussed that the different river basins have used heterogeneous classification, attribute coding, projection etc. Each one of these requires different operations. For example, projec-tion conversion is required to make datasets compatible with one another in geographical position. On the other hand, recoding is one possible way of resolving attribute heterogeneity. Nevertheless, het-erogeneity in classification is not an easy thing to resolve, particularly for a vector data derived from a satellite image. Identifying similar objects from the river basins can some how provides harmonised data. Thus seems to have solved some of the problems associated with it. Such discrepancy related to different classification could be observed by mismatched or discontinuous spatial features particularly across river basin boarders. Designing the model can give harmonized information across the river basins; the users can make use of than the present state of the datasets.

4.3.3. Thematic and Geometric Partition

The universe is the set of all objects occurring in a map. Objects in this universe can be distinguished because they have different characteristics. For most applications of GIS, these differences will be thematic. For the individual river basins, the master plan studies utilized their own universe that bounds the thematic datasets of the river basin. When considering aggregation across the river basins, similar objects need to be identified and cate-gorized to form the aggregation hierarchy. For example,

River basin 1 River basin 2

Figure 12 Example of two river basins

River basin 1 has thematic attributes A, B, C, and D and river basin 2 has thematic attributes 1, 2, 3, 4 and 5. River basin 1 has alphabetical attributes and river basin 2 has numeric codes. Identifying simi-lar objects becomes necessary. In this example, it is assumed that attribute class “B” of river basin 1 is the same as attribute class “1” of river basin 2. Similarly, “C” and “D” of river basin 1 are the same as “2” and “3” of river basin 2 respectively. Then A, B, C, D, 4, and 5 or A, 1, 2, 3, 4, and 5 constitute all the thematic object classes, which define the universe of discourse for the modelling of the two river basins. This being the case for the thematic, the geometric partition is formed by the 2D space covered by the objects. This is shown on the next example.

A B

C

A

B

D B C

3 5

2

4

2

3

4

1 3


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Before combining After combining

River basin 1 River basin 2

Figure 13 Example of combining two river basins

River basin 1 and 2 are oriented in a vertical position to resemble the four river basin’s situation. Once thematic similarities are identified as in figure 12, the boundary lines between similar objects are eliminated. This is between B and 1, C and 2, and D and 3 of the two river basins. Here one thing should be noted, the vertical lines at the crossing of the two basins do not exactly match. This is be-cause the river basin datasets derived from heterogeneous classification are likely to be subject to such kind of mismatch. In real world, natural features maintain continuity. Similar procedures need to be followed for thematic as well as geometric datasets of the four river basins. Depending on the type of theme, similar objects may or may not exist in the adjacent basins. However, given the fact that the purpose of data use defines the rules of aggregation, the river basin databases share the same hierarchical structure. To form this aggregation structure one has to look in to each basin’s thematic dataset to be able to identify similarities and differences between the objects. The total list of objects uniquely identified from the four basins will define the universe of discourse for the specified thematic dataset. Knowing how the elementary objects are represented as well as understanding the relationships of these objects will be an essential step for the aggregation of river basin databases. Consequently, the land use and land cover classes shown in table 9 will be aggregated using the procedures shown in figure 12 and 13. In the digital format, the attributes of objects in the Tekeze and Mereb river basins is by a numeric code, where as in The Abbay and Baro-Akobo river basins, it is in alphanumerical code. Extended description of these codes is derived from reports and map legends. In conclusion, the indi-vidual basin thematic objects need to be combined to form a common aggregation structure that forms the universe of discourse for the thematic dataset. The classes of objects in a universe will depend on the type of theme to be modelled. This definition of the universe is an important step for the aggrega-tion.

A B

C

A

B

D B C

2

3 5

2 4

3

3

4

1

A B

C

A

B

B

C

3 5

2 4 3

4

D


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Table 9 Land use and land cover mapping units in the river basins Mereb river basin Tekeze River Basin Abbay River Basin Baro-Akobo River Basin Code

Land Unit Code

Land Unit Code Land Unit Code Land unit

1 2 3 4 5 6 7 8

Bare soil (20%) with open shrub land and open grass-land Bare soil (20%) with open grassland and open shrub land Bare soil (20%) with open grassland or shrubby grass-land land Open shrub land or bush land Open woodland or bushed open woodland Open woodland with dense woodland Bare soil/sparsely vegetated (40%) with open shrub land or open bush land Bare soil (30%) with open shrub land

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Sparsely vegetated grassland, sparsely vegetated or bare Open grassland or shrubby grassland with bare soil Open shrub land or open bush land with bare soil/sparsely vegetated Open grassland or shrubby grassland Open grassland or shrubby grassland with open or dense shrub land Open grassland or shrubby grassland with open woodland or wooded grass-land Open grassland or shrubby grassland with (bushed) open woodland and open bush land Wooded grassland Wooded grassland with (bushed) open woodland Open woodland or bushed open woodland Open woodland with dense woodland (Bushed) open woodland with wooded grassland and/or open grass-land Bushed open woodland with wooded bushed dense woodland with (open) bush land Dense woodland or bushed dense woodland Open shrub land or open bush land Open shrub land with open woodland or wooded grassland and open grass-land Open bush land with bushed open woodland

C1.1 C1.2 C1.3 C1.4 C1.5 C1.6 C1.7 C5 C6 SF C2.1 C2.2 C2.3 C2.4 C2.5 C2.6 C2.7 C2.8 C2.9 C2.10 C2.11 C2.12 F2.1 F3.1 F3.2 F3.3 P1.1 WD.1 WD.2 WD.3 WD.4

Dominantly cultivated to significant other grass Dominantly cultivated with eucalyptus plantations Dominantly cultivated with open grass land Dominantly cultivated with shrubby grassland Dominantly cultivated with open shrub/bush Dominantly cultivated with woodland Dominantly cultivated with Afro-alpine Irrigated cultivation Perennial cultivation State farm Moderately cultivated with Afro-alpine Moderately cultivated with plantation and grass Moderately cultivated with shrubby grassland and bush Moderately cultivated with grassland and open grass-land Moderately cultivated with open shrub and grassland Moderately cultivated with open shrub and grassland Moderately cultivated with grassland Moderately cultivated with seasonal swamp and grass-land Moderately cultivated with open shrub and rock out-crops Moderately cultivated with shrubby grassland and rock Moderately cultivated with high disturbed forest Moderately cultivated with open woodland Disturbed forest with cultivation Very disturbed forest with cultivation Very disturbed forest with other Very disturbed forest with cultivation bush and wood Eucalyptus plantation with cultivation and grass Dense woodland with cultivation and grassland Dense woodland with cultivation and bamboo Dense woodland with cultivation and grassland Dense woodland with shrub or wooded grassland

BO C1 C2/FB3 C2/GO C6 C6/FB3 C6/FP1 FB2 FB3 FB3/C6 FB3/GO FO/WO FR2 SF WD WD/WO WO WO/GO WO/WD

Open bush land 20-50% cover Rain fed cultivation >60% cover Moderate cultivation/ very disturbed forest Moderate cultivation/ open grassland Perennial cultivation Perennial cultivation/very disturbed forest Perennial cultivation-major plantation Disturbed forest Very disturbed forest Very disturbed forest/perennial culti-vation Very disturbed forest/open grassland Bamboo/open woodland Undisturbed riparian forest State farm Dense woodland >50% cover, Dense woodland or open woodland Open woodland <50% covers, Open woodland/ open grassland Open woodland or dense woodland


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19

woodland Open bush land with (bushed) open woodland and wooded grassland Dense shrub land or dense bush land

WD.5 WD.6 WO.1 WO.2 WO.3 WR BA.1 B.1 B.2 S1 S2 G1.1 G1.2 G2.1 G2.2 G2.3 H1 H2 A R U

Dense woodland with grassland and bamboo Dense woodland with bamboo and grass Open woodland with cultivation Open woodland with bamboo and grassland Open woodland with grass and bush WR: Reverine woodland Bamboo with woodland and grassland Open bush land with cultivation and grassland Open bush land with woodland Open shrub with cultivation Open shrub with bush land and grassland Open grassland with cultivation Open grassland with woodland Shrubby grassland with cultivation Shrubby or wooded grass with woodland Shrubby or wooded grass with other Open water Perennial or seasonal swamp Afro-alpine with cultivation Rock outcrops Urban


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A model that will abstract similar objects across the river basins has to deal with attribute heterogene-ity like the one shown in table 9. Recoding of attributes could also be considered to maintain uniform-ity across the basins. It can simplify the aggregation process through creating easy understanding of spatial features. However, it is not a pre-requisite. The latter approach is more convenient because it allows remote processing of the data.

4.4. Data Model and Documentation

In chapter 2, it is discussed how real world objects are represented in computer models, i.e. through the creation of databases. The representation of real world objects by a computer model has some limitations such as representing a continuous natural phenomenon as a discrete object. In addition, not all information is sufficiently documented, when processing or storing data. As a result, users lack adequate information. For various reasons, users usually take products, leaving much of the informa-tion behind, which is contained in the process of data generation. The situation in the river basin data-bases is not any different from such general experience. For example, databases are symbolized by codes for which detailed information is kept in reports. In addition, map legends only portray limited information, leaving the details in documents or reports. Users relying on attribute values or legends alone will likely interpret it differently as how they perceive it. To have a consistent interpretation much closer to the reality, one has to consult the representation of real world features in computer models, as well as other documentations applicable to it. Designing the conceptual model needs to address this aspect. Laurini and Thompson (1992) described, the categorizing of spatial data require-ments based on spatial data process categories is a necessary step for the technical requirements analysis, which will follow the functional requirements analysis. Functional requirements of the model are presented as model criteria in the following section.

4.5. Model Criteria and Functional Requirements

Before proceeding into the actual design, the following criteria have been identified based on the dis-cussions in section 4.2 and section 4.3.

• Uniformity in geo-referencing and coordinate system (allow conversion), • Uniformity and consistency in thematic data abstraction and analysis within and across the

basins, • Uniform definitions in attribute coding and legends, which will allow automatic generaliza-

tion and aggregation, • Uniform classification schemes, • Pluriform output possibilities according to users demands, • Allow scale reduction or generalization, • Software format conversion,

4.6. Modelling the River Basin Databases

In the river basin studies, data is abstracted from the real world by the major themes: water resources, natural resources, socio-economy, environment, agriculture, and physical planning (figure 14). In the master plan study process, there is data sharing among these groups and the different disciplines within each group. Disciplines in a group share data and information more closely than across the


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Physical Planning Agriculture

Water Resources

Environment Socio-Economic

Natural Resources

Abbay Basin

River Basin Master Plan

Land use & land cover Soils Geology Wildlife

Dams and hydropower Irrigation development Hydrology Hydrogeology

Real world

Agronomy Livestock Fishery Bee keeping

Water Quality Pollution Ecology Erosion & Conservation

Population & Demography Organisation & Institution Migration & Immigration Tourism

Transport & Infrastructure Water supply & sanitation Energy

groups. From this, it is possible to expect that a class of object in a theme could be involved in a num-ber of processes or applications. For example, runoff estimation in a watershed could involve information from different data models such as: climate, soil, land use and land cover, topography as well as hydrology. Similarly, for water supply and sanitation analysis, information such as from water resources, topographic, demographic and administrative data could be required. Hence, spatial data modelling in an integrated development master plan context is a very complex process, which requires identifying all data linkages between application sectors and the identification of the process in which it is used.

Figure 14 River basin data model

The modelling of all the datasets as a necessity for the MoWR has been discussed in section 3.3.2. However, considering the vast datasets of the master plan studies, it is not practicable to cover all,


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especially since attribute maps are not always single valued vector maps. Usually they appear to have multiple values, which adds to the complexity of modelling. Therefore, in this study no attempt will be made to model all datasets. Instead, certain areas/disciplines will be selected to demonstrate the application of spatial data modelling. For those selected datasets, identifying interrelationships of ob-jects, linkages and the understanding of corresponding applications is a precursor for the designing of the conceptual model. In view of this, the design of the conceptual model will focus on some of the very frequently demanded datasets. Particular emphasis will be given to the modelling of land use and land cover.

4.7. Land Use and Land Cover

Land use is defined as “Man’s activities on land, which are directly related to the land”. It has also been defined as “the expressions of man’s management of the eco-systems to meet some of his needs (Van Wingaarden, 1991). On the other hand, land cover is defined as “ the vegetational and artificial constructions covering the land surface. It thus includes: cultural (buildings, artifacts, fields), vegeta-tional (grass, shrub, trees) and other (water, burned objects and areas, soil, lithology) features on the earths surface (Van Wingaarden, 1991). He described that the general procedures of mapping land cover and land use are well established, but the techniques of mapping have been strongly influenced in recent years by the new tools of remote sensing and Geographic Information Systems (GIS). Remote sensing has become essential for improv-ing the quality and efficiency of mapping programs through delineation of units, more efficient design of field sampling schemes, assessment of the homogeneity of mapping units, etc. (Van Wingaarden, 1991). In the study area, the land cover and land use data processing followed similar principles. The data is generated using satellite images, aerial photo interpretation and field sampling for ground truth (table 5 in chapter 3). However, the use of these tools and techniques alone will not warrant homoge-neity of units specifically when the operation is done for different areas and at different times. The situation is apparent in the river basin datasets. Land use and land cover can be mapped separately or as part of a larger land resources surveys. In the Latter, the map contains not only information on land cover and land use but also on lithology, land-forms, soils and occasionally climate (Van Gils, Huzing, & Kannegieter, 1991). The situation in the river basins corresponds to the former case where land use and land cover are mapped together. How-ever, it differs from one basin to the other. The Abbay river basin study generated land cover and land use as separate maps. In the Tekeze, Mereb and Baro-Akobo projects, both categories were combined into one map.

4.7.1 Land Use and Land Cover Mapping Units

The land use and land cover mapping units of the study area appeared to be complex, may be because of the reconnaissance scale mapping. However, some details could also be omitted for some other rea-son. Even if the land use and cover units are distinguished on aerial photographs and satellite images, they cannot always be mapped separately for cartographic reasons (Van Gils et al., 1991). Mapping units therefore, often represent complexes of land cover and land use classes. The situation in the river basin agrees with this concept. Background information for each of the river basins is as follows.


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4.7.2 Land use and Land cover in the River Basins

Land use and land cover in the Tekeze Basin: In the Tekeze basin study Landsat TM dated December 5, 1989 and March 14, 1988 bands 5, 4, 3 and aerial photographs 1994/1964 EMA have been used. The classification of land cover follows the Woody Biomass Inventory and Strategic Planning Project (WBISPP), (NEDECO, 1998). Some modi-fications are also made to reflect the specific conditions of the Tekeze river basin. The land use classi-fication has been derived from SOTER (soil and terrain) land use classification (ISRIC 1995). Land use and land cover in the Mereb Basin: The Mereb basin is studied as a continuation of the Tekeze river basin master plan project. As a re-sult, the land use and land cover classification employed for the Mereb basin is similar to that of Te-keze basin Land use and land cover in the Abbay Basin: In the Abbay river basin study landsat imagery of 1986 to 1990, and November to February, relative to the starting date of the study October 1994 has been used. The land cover classification was pre-pared, essentially from the Land Use Planning and Regulatory Department (LUPRD) and WBISPP legends (BCEOM, 1998), and modification is made based on field experiences. Land use and land cover in the Baro-Akobo Basin: In the Baro-Akobo basin, no satellite image interpretation is made by the project; rather the project obtained data from the WBISPP, and is modified for the project purpose. The land use and land cover classification of the WBISPP is attached in appendix. All of the river basin studies have indicated to adopt the LUPRD and the WBISPP classifications with some modifications to these to accommodate each basin’s situation. Identifying differences such as the effect of field experience are too difficult as these are very subjective. In order to deal with the modelling identifying similarities and differences of the land use and land cover units in each river basin has become important. The land use and land cover datasets, as pointed out in section 3.3 and table 9, are derived from varying processes such as heterogeneous classification. Experts in the field of land cover and land use agree that there is no one ideal classification and it is unlikely that one could ever be developed. There are different perspectives in the classification process and the process itself tends to be subjective even when an objective numerical approach is used (Anderson et al., 2001). The four basin’s datasets are not any different from this general understanding.

4.8. Conclusion

In this chapter, the status of the master plan data and the environment of the users have been analysed. It is asserted that the design of the conceptual model will improve information abstraction and infor-mation flow between the different hierarchical levels. However, it is concluded that given the fact that the master plan datasets are vast, focus needs to be given to selected datasets. The next chapter will design the conceptual model for the selected datasets. It appears that aggregation in a single basin is simpler when compared to aggregating multiple basins. In the latter case, it has to deal with the identi-fication of similar objects across the basins, which in turn could be subject to dealing with differences in classification schemes, coding as well as differences in documentation.


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5. The Conceptual Model

5.1. Introduction

In chapter 4, the hierarchical levels have been defined and the corresponding users requirement and the data needs are analysed. Based on this information the conceptual model will be designed. In view of this, chapter 5 have been organized as follows: section 5.2 presents model design, section 5.3 pre-sents river basin hydrology model, section, 5.4 presents water resources application model. Section 5.5 presents land use and land cover model, and the last section 5.6 draws conclusions of the chapter.

5.2. Model Design

Conceptual model design involves the application of mathematical and relational expressions on geo-metric and thematic elements of objects. The conceptual model should provide a framework that will enable the abstraction of spatial objects, which are of interest to the users at all levels. Because of time limitation, the water resources, the water resources application and land use and land cover, which are just part of the entire river basin databases will be incorporated in the conceptual model.

5.2.1. Generalization Strategies

In spatial object modelling, as noted in chapter 2, section 2.3, there are four strategies for generaliza-tion and aggregation. These are: 1. Geometry driven generalization; 2. Class driven generalization and aggregation; 3. Function driven aggregation and 4. Structural generalization, each of these strategies is applicable to model the river basin databases. However, for the above-mentioned datasets that will be incorporated in the conceptual model, class driven generalisation and aggregation and function driven aggregation will be considered.

5.2.2. Object Class and the Universe

In chapter 4, section 4.3.3, it has been noted that, by definition, all the thematic and geometric ele-ments of spatial data belong to a certain class in the universe. A class of object is the sub-set of all the objects from the universe. Once the universe is defined, the following procedures can be applied to aggregate objects:

• Identify elementary objects from each of the basins that are similar in representing the object of interest. This step will help to avoid problems that might arise from the different coding applied in the basins,

• Select these objects for aggregation, • Eliminate objects that should not be represented at the scale of aggregation, • Aggregate the selected objects to form units at hierarchical levels, and • Reclassify the remaining orders in the hierarchy.


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5.3. River Basin Water Resources Model

A river basin hydrological model is presented below. In this model, river basin water resource is a function of the super-classes surface water and ground-water. Similarly, watershed is again formed by these super classes surface water and groundwater. Watershed is part of a river basin. In a river basin, there are a number of watersheds. The classes: lake, reservoir, swamp and river have ISA relation to the super class surface water. It means that, for example, lake is-a surface water. Similarly, the classes spring, borehole and hand dug well have ISA relation to the super class groundwater. The super-class inherits the characteristics of the classes and the classes inherit the characteristics of the objects. The river basin water resources model presented in figure 15 follows class driven generalisation and aggregation. It allows the abstraction of informa-tion at river basin level, watershed level, and surface water and groundwater levels. Using these rela-tionships lower level objects can be abstracted from the model.

5.4. Water Resources Application Model

In the river basin water resources model, the objects are related by a class relationship. Apart from this, these objects are related by the type of application to which they are used, such as in the water resources application model shown in figure 16.

River Basin Water Resources

Surface water Groundwater

Watershed

Hand dug well Swamp Reservoir Lake Borehole Spring

Secondary

Tertiary

Primary Shallow Deep

River

Seasonal Permanent

Figure 15 River basin water resources model


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Water resources application

Water supply & sanitation Hydro-power Irrigation

Lake Hand dug well Borehole Spring River Reservoir

Secondary

Tertiary

Saline Non-saline Primary Shallow Deep

Navigation

Swamp

Figure 16 Water resources application model

In this model, water resources application is a function of the super classes water supply and sanita-tion, irrigation, hydropower and navigation. The super classes inherit the characteristics of the classes. Swamp, spring, borehole, hand dug well, reservoir, river and lake form the class of objects. They have a member-of relation to the super-classes they are forming. Therefore, follow the function driven ag-gregation. Shallow and deep borehole, primary, secondary and tertiary rivers, and non-saline and sa-line lake form the objects. In functional aggregation, the relations of classes are many to many (m: n). The river basin water resources model and the water resources application model are constructs from a mixture of point, line and polygon spatial objects. For example, classes borehole, hand dug well and spring are point representations. River is a line representation. Swamp, reservoir and lake are polygon representations. In aggregating these objects, according to the model, each of the objects should fol-low corresponding modelling formalisms applicable to a point, line or polygon object. Of course, de-pending on the size of object and scale of operation, the representations of objects can also change from one to the other.

5.5. Land Use and Land Cover Model

Land cover and land use is one of the most frequently used thematic datasets in the study area. In modelling this dataset, the classification structure plays a central role in defining the objects, classes, and super classes, in order to form generalisation and aggregation hierarchies. The source data pre-sented in table 9 have been used to model the datasets of the four basins.


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F2.1 F3.3 F3.1 F3.2

Eucalyp-tus

WD.1 WD.2 WD.3 WO.1 WO.2 WO.3

V.D forest With cultivation

Dense Woodland

Open Woodland D. forest

with

Woodland Forest

Forest & Woodland

Planta-tion

Shrub Grass

Bush

Ur-ban

Wetland/ Water

Cultiva-tion

Riverine De

nse

Open Open

Bushed

Wooded

Wa-ter

Seasonal swamp

Perennial swamp

Rain fed

Irriga-tion

Perennial

State farm

Acacia

Land cover and land use

Dense

Open

Bushed, shrubed grassland Wooded grassland L1

L2

L3

Bam-boo

Barren Land

Bare Rock

Dominant Cultivation

Moderate Cultivation

Ri-paian

Forest

Afro-Alpine

Mixed

Figure 17 Land use and land cover model


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F2.1, F3.1, F3.2, F3.3 P1.1

WD.1, WD.2, WD.3, WD.4, WD.5, WD.6

WO.1, WO.2, WO..3

Mixed Natural forest

Forest

Forest & Woodland

Plantation forest

Woodland

Dense Open

F P WD WO

Mixed Natural forest

Forest

Forest & Woodland

Plantation forest

Woodland

Dense Open

F2.1 F3.3 F3.1 F3.2 P1.1 WD.1

WD.2

WD.3

WO.1

WO.2

WO.3

V.D forest with Cultivation

Dense woodland Open woodland D forest with Cultivation

Natural Forest

Forest

Forest & Woodland

Plantation Forest

Woodland

Mixed

Eucalyptus

In this section, using part of the land use and land cover model presented in figure 17; figure 18, 19 and 20 have been generated to demonstrate the generalisation and aggregation process using the methods described in section 2.4.4. Figure 18 shows the class hierarchical structure. Figure 19 shows the class generalisation process and figure 20 shows object aggregation.

Figure 18 Aggregation structure

Figure 19 Class generalization Figure 20 Object aggregation


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In figure 18, each of the objects is identified by its thematic aspect. In figure 19, those identified ob-jects are categorised to form the classes in the hierarchy. In figure 20, the objects belonging to the same class are aggregated to form super classes and new codes are assigned to the objects formed by the aggregation.

Table 10 Generalization and aggregation

Land cover unit description (Object) (Class) New code (Super class)

(Super class)

Disturbed forest with cultivation F2.1 Disturbed forest

1

Very disturbed forest with cultiva-tion

F3.1

Very disturbed forest with other F3.2 Very disturbed forest with cultiva-tion bush and wood

F3.3

Very dis-turbed forest

2

Eucalyptus plantation with cultiva-tion and grass

P1.1 Plantation forest

3

Forest

Dense woodland with cultivation and grassland,

WD.1

Dense woodland with cultivation and bamboo

WD.2

Dense woodland with cultivation and grassland

WD.3

Dense woodland with shrub or wooded grassland

WD.4

Dense woodland with grassland and bamboo

WD.5

Dense wood-land

4

Open woodland with cultivation WO.1 Open woodland with bamboo and grassland

WO.2

Open woodland with grass and bush WO.3

Open wood-land

5

Woodland


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Original data showing land use land cover units (objects at a class level). F2.1: Disturbed forest F3.1, F3.2, F3.3: Very disturbed forest P1.1: Plantation forest WD.1- WD.5: Dense woodland WO.1 – WO.3: Open woodland

Land units belonging to the same sub-class are identified and selected. For example, land unit classes F3.1, F3.2 and F3.3 are identified and shaded with the same colour. This step is similar to the step taken in figure 18

Thematic class generalization performed. At this stage, the lines in between the same class objects are not eliminated. Object classes be-longing to the same class are assigned new codes or attribute values. For example, classes WD.1, WD.2, WD.3, WD.4, and WD.5 are designated with a new value = 4. This step is similar to the class generalisation step in figure 19

Once thematic generalization is performed, geometric generalization follows. At this stage, the lines in between the aggregated classes are eliminated. This is similar to the object aggregation shown in figure 20.

Figure 21 An example of the generalization and aggregation process in land cover

5

5

4

4

4

2

2

2

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4

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2

21 5

4

4

1

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14

3

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3

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4

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5

5

25

2

1

2

14

3

2

3

3

3

3

3

3

3

F3 .1

F3 .2

F3 .2

W O.1W D.5

W D. 3

W D.3

W O.1

F3 .1

F2 .1

W D.1

W D.2

F3.1

F3.2

F3 .3F2 .1

F2 .1

W O.2

W D. 1

W D. 3

W O.2

W D.1

F3 .2

W O.1

W O.3

P1.1

W O.1

F2 .1

P1.1

W D.3

WD.1

P1.1

P1.1

F3.1

P1.1

P1.1

P1.1

W D.4

P1.1

F3 .3

P1.1

F3 .1

F3 .2

F3 .2

W O.1W D.5

W D.3

W D.3

W O.1

F3 .1

F2 .1

W D. 1

W D.2

F3 .1

F3 .2

F3 .3F2 .1

F2 .1

W O.2

W D.1

W D.3

W O.2

W D.1

F3 .2

W O.1

W O.3

P1.1

W O.1

F2 .1

P1.1

W D.3

W D. 1

P1.1

P1.1

F3 .1

P1.1

P1.1

P1.1

W D.4

P1.1

F3 .3

P1.1


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Table 11 Cumulative percentages of land cover and land use units

aggre-gation Levels

Tekeze (Unit code)

Cum.%

Mereb (Unit code)

Cum.%

Abbay (Unit code)

Cum.%

Baro-Akobo (Unit code)

Cum. %

L1 1, 2, 3, 6, 7, 8, 9, 10,11,

47.37 1, 2, 3, 5, 7, 8

75.00 C1.1, C1.2, C1.3, C1.4, C1.5, C1.6, C1.7, C5, C6, SF, C2.1, C2.2, C2.3, C2.4, C2.5, C2.6, C2.7, C2.8, C2.9, C2.10, C2.11, C2.12, F2.1, F3.1, F3.2, F3.3, P1.1, WD.1, WD.2, WD.3, WD.4, WD.5, WD.6, W0.1, WO.2, WO.3, WR, BA.1, G1.1, G1.2, G2.1, G2.2, G2.3, H1, H2, R, U,

90.38

BO, C1, C2/FB3, C2/GO, C6, C6/FB3, C6/FP1, FB2, FB3, FB3/C6, FB3/GO, SF, WD, WD/WO, WO, WO/GD, WO/W FO/WO, FR2D,

100

L2 4, 5, 12, 13,14, 15, 16, 17, 18, 19

100 4, 6 100 A, B.1, B.2, S1, S2 100 100

L3 100 100 100

Total units

19 100 8 52 19 100

In figure 17, the model for the land use and land cover is designed in such a way it accommodates all the mapping units from the four basins. L1, L2, and L3 refer to the levels of generalisation/aggregation from the simple to the most complex respectively. According to the land cover and land use model, objects falling below L1 are tertiary land cover and land use classification units. Similarly, those fal-ling under L2 are the secondary classification units and those under L3 are the primary classification units. The aggregation hierarchy follows the backward root of the classification. The land use and land cover units of the study area appear to be too complex may be because of the reconnaissance scale (1:250,000) mapping. Due to this complexity, making the following assumptions have become necessary:

• Units described with a connection “or”, for example such as “open shrub land or open bush land”, then the unit is categorized in level 2 (L2), which means in either of the classes, open shrub or open bush.

• Units described with a connection “with” for example “open shrub land with open bush land”, then, the unit is categorized in the next higher level (L3) where shrub land and bush land are aggregated in forest and woodland.

The cumulative percentages in table 11 are calculated based on the units contained at the various ag-gregation levels. It should be noted that parts of a given unit could be excluded due to their size for the reason that aggregation involves scale change.


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Figure 22 Conceptual Model

River Basin Hydrology

Surface water Groundwater

Watershed

Hand dug well

Swamp Reservoir Lake Borehole Spring

Secondary

Tertiary

Primary Shallow Deep

River

Seasonal Permanent

Water resources application

Water supply & sanitation Hydro-power Irrigation

Lake Hand dug well Borehole Spring River Reservoir

Water transport

Swamp

F2.1 F3.3 F3.1 F3.2

Euca-

WD.1 WD.2 WD.3 WO.1 WO.2 WO.3

V.D forest Dense Open

D. forest

Woodland Forest

Forest &

Planta-tion

Shrub GrasBush

Ur-Wetland/

Cultiva-

Riv-De

Open Open

Bush

Woo

Wa-

Seasonal

Perennial

Rain Irriga- Perennial

State

Acacia

Land cover and land use

De

Op

Bushed, shrubed Grassland

Wooded grassland Bam- Barren

Bare Rock

Domi- Moder-

Ri-Forest

Afro-Alpine

Mixed

Aggregation link: Link between model objects:

INTEGRATING GEO-INFORMATION FOR WATER RESOURCES MANAGEMENT

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The conceptual model, encompasses, river basin water resources, water recourses application and land cover and land use models. The link between these models is established by the objects that appear to have a cer-tain functional or class relation. For example, as shown in figure 22, the object “irrigation” under the class cultivation is connected to object “irrigation”, in water resources application model. It means that, if one wants to abstract data for the land cover/use unit “irrigation”, a query question such as “show me the land cover/use object “irrigation” which uses reservoir as a source?” can deliver response. Similarly, the land cover/land use class wetland, which encompassed the water bodies, perennial and seasonal swamps has a link to both models. Therefore, query questions can be constructed from these data linkages. Similar connec-tions need to be established depending on purpose and relationships of the objects. The models developed for river basin water resources, water resources application and land use and land cover are intended to answering various users questions like those that are indicated in table 12. Only exam-ples of possible questions are presented here. In reality, the type of questions that could be asked are much more broader.

Table 12 Typical questions and solutions

Levels Typical questions asked Typical solution Trans-national level

• What is the annual flow of the primary riv-ers?

• What is the watershed management in the four basins?

• What are the potential cites for irrigation and hydropower development?

• Structural generali-sation

• Aggregation • Aggregation

National level

• Which land cover units are woodland? • Which areas are potential for cultivation? • Which land use and land cover unit is

plantation forest? • Which tributaries contribute to the flow of

the major rivers?

• Class generalisation • Class generalisation

& aggregation • Class generalisation

& aggregation • Structural generali-

sation & aggregation Regional level

• Which areas are potential for rain-fed & ir-rigated agriculture?

• Which rivers cross the region? • Which watersheds are located in the re-

gion?

• Class generalisation and aggregation

• Aggregation and overlay

• Association and overlay

Answering all potential questions demands the modelling of all thematic datasets, which can only be achieved by eventual development of other models.

5.6. Conclusion

In this chapter, the conceptual model has been designed. It has addressed part of the research question 3, which deals with designing and testing the model. Modelling land cover and land use is relatively simple as compared to water resources and water resources application models. Because, in the former case the spatial


59

objects to model are all polygons whereas, in the latter case it is a mix of point, line and polygon objects. Writing a program for multiple spatial objects is much more complex than writing for homogenous object type: point, line or polygon. To make sure that whether the conceptual model gives results in compliance with users requirements, designing the model by itself will not warrant the reliability of the output for fur-ther use. It needs to be tested and evaluated. The next chapter will present a discussion on testing and evaluation of the model.


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6. Model Testing & Evaluation

6.1. Introduction

In chapter 5, the conceptual model has been designed for selected datasets in the area of water resources, water resources application and land use and land cover. It needs to be tested against the fulfilment of the design objectives. In view of this, this chapter has been presented as follows: section 6.2 presents testing the model for query possibilities, section 6.3 presents testing the model schema, 6.4 presents the opportunities and constraints, and the last section 6.4 presents the conclusion of the chapter.

6.2. Testing the Model Query Possibilities

The conceptual model designed in this study is a data processing system, which is based on the input-out approach. The model receives the inputs; in this case, objects from the thematic datasets, and transforms them to classes and super classes (outputs). At any instance, the outputs are the objects, classes or super classes, which are the users needs. In this process, three elements are involved: the input, the transfer proc-ess and the output. The transfer process is dependent on the hierarchical linkages that are built on object relationships and user needs (Chapter 5, figure, 22). Whenever new user needs arise, other than the ones accounted in the model, new linkages and relationships can be constructed and included in the current model. In this regard, the transfer process is flexible and is dependent on the linkages between the objects. As a result, the testing is inclined to the evaluation of the outputs rather than the linkages themselves. In order to test the model, identifying some of the general users needs as possible scenarios becomes impor-tant (table 13). To ensure whether the model satisfies these users needs; data abstraction, constituency and completeness are considered as possible criterion for testing. Quantitative evaluation using these criterions is possible in a logical and/or physical model. The model designed in this study is a conceptual one. There-fore, evaluation could only be described in qualitative terms rather than quantitative. Possible scenarios and criterion are discussed below:

6.2.1 Possible Scenarios

1. Dataset from a river basin (full coverage): this refers to a condition when data abstraction/query from a single river basin: Abbay, Tekeze, Mereb or Baro-Akobo is involved,

2. Select dataset for a specific location or area in a basin: this refers to a situation where data ab-straction is from a particular site within a single basin for example for a watershed, in a river basin,

3. Select dataset across the adjacent river basins: this refers to the situation where data abstraction from more than one river basin is involved,

4. Generalized or aggregated information from a river basin: this refers to a condition where data from a single river basin in a generalized or aggregated form is involved,


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5. Generalized or aggregated information across the adjacent river basins: this refers to the situa-tion where data abstraction from more than one river basin in a generalized or aggregated form is involved:

6. Overlay information for analysis and display: this involves datasets from multiple themes. These themes are a product of generalisation/aggregation,

6.2.2 Testing criteria

• Data abstraction: refers to the abstraction/query of objects, classes or super classes or a combi-nation of these from the thematic datasets. It could be from a single basin or across the four ba-sins,

• Completeness: this refers to the abstraction of all the objects, all the classes or all the super classes requested at any level of aggregation/generalisation. Completeness need not be misun-derstood to mean the full coverage of a river basin,

• Consistency: refers to the level of uniformity of the output from generalisation or aggregation process,

Scenario items 1, 2, and 3 refer to data abstraction at object level without involving generalisation or aggre-gation. Scenario items 4 and 5 refer to generalisation and aggregation, where data abstraction is at object, class or super class level. Scenario item 6 is a little bit beyond modelling. It is an instance where, the over-lay of objects, classes or super classes for further analysis is desired. Below are some examples of the tou-ples (records) from the attribute tables:

Table 13 Sample data from the Tekeze basin

AREA PERIMETER LANDCOV_ LANDCOV_ID CULTIV2 NATVEG NATVEGNEW 431658200.00 261166.60 2 1 1 20 11 730691200.00 495074.50 3 2 3 19 6

3046310.00 13422.53 4 3 4 11 9 2643325.00 7373.93 19 18 3 4 10 5694187.00 13349.94 20 19 2 13 13

40727880.00 51835.29 77 76 2 1 15 No. of polygons 1490 Mapping units 19

Table 14 Sample data from the Mereb basin

AREA PERIMETER LCMER_ LCMER_ID CULTIV NATVEG 9660088.00 22468.03 28 27 3 4

115363300.00 103102.80 29 28 3 16 139990200.00 101559.00 30 29 3 23 42318620.00 35727.28 31 30 2 15

562697200.00 209214.50 32 31 3 21

No. of polygons 140 Mapping units 8


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Table 15 Sample data from the Baro-Akobo basin

AREA PERIMETER FS_LC_ FS_LC_ID DADB FS 2.481721 23.840690 2 1 WO 1 0.005729 0.354242 3 2 C1 3 0.001131 0.261178 8 7 FB3 7 0.020978 1.356670 11 10 WD 2

No. of polygons 66 Mapping units 19 Note: The columns indicated by the arrows are the attribute values for each of the polygons that represent the mapping units (objects) of land cover. All the touples have thematic values. These values are the once used in the modelling of the objects. No null values are observed in the attribute values. However, null values could be calculated during generalisation or aggregation, when the scale of the map is changed. One of the generalisation process described in chapter 2 section 2.4.4 is the omission of objects that are too small to be displayed at the specified scale. In the testing four levels of scores: ‘YES’, ‘NO’, ‘?’, and ‘*’ have been utilised. These are described as fol-lows:

Yes: Indicates criteria satisfied, No: Indicates criteria not satisfied, *: Indicates criteria not applicable, ?: Indicates the result is questionable,

Table 13 shows the evaluation results.

Table 16 Model evaluation & testing

Testing Criteria Completeness

Scenarios Data ab-

straction

Consistency

Object Class Super class 1. Dataset from a river basin (full coverage)

Yes Yes Yes * *

2. Select dataset for a specific location or area in a basin

Yes Yes Yes * *

3. Select dataset across the adjacent river basins

Yes ? Yes * *

4. Generalized or aggre-gated information from a river basin

Yes ? No Yes Yes

5. Generalized or aggre-gated information across the adjacent river basins

Yes ? No Yes Yes


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6. Overlay information for analysis and display

Yes * * * *

As shown in table 13, the results are positive for data abstraction, in all the scenarios. For scenario 1 and 2, there is consistency of the outputs, because the objects involved in the query process are from a single basin. For scenario 1, 2 and 3, the evaluation for completeness at class and super class level is not applicable, be-cause there is no generalisation or aggregation involved in these scenarios. Datasets from a single basin are considered as consistent and complete because there are no differences in classification or any other geo-information processing. For scenario 3, 4 and 5, where data abstraction is from more than one river basin., consistency is question-able, because objects abstracted from multiple basins as they are from heterogeneous classification, could not be consistent. Completeness is evaluated at object (L1), class (L2) or super class (L3) levels. According to the land use and land cover model (chapter 5 table 11), at the object level, 47 %, 75 %, 90.3% and 100% of the objects of the Tekeze, Mereb, Abbay and Baro-Akobo basins respectively are contained in the aggregation. At the class and super class levels, all are contained. It means at higher levels the complete coverage of the requested classes and super classes could be portrayed. Therefore, for completeness, the score is negative at object level, meaning not all the units are contained. From the earlier discussions in chapter 3, it is discussed that in the river basins, datasets such as land cover and land use are derived from satellite images. These images for the different basins are processed as inde-pendent sets and at different project study times. Separately processed satellite image is subject to producing discontinuous features at the cross boarders of the different sets even if the same classification is used. This aspect has been reflected in the adjacent river basins (figure 23). In real world, natural objects such as land use and land cover and soils appear as continuous features. While doing generalisation and aggregation, where the units of modelling are the objects, classes and super classes, the observed discontinuity of the objects will have a chance to be reflected on the results. However, such differences in discontinuity are minimized at the more generalised classes.


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(Bushed) open woodland with wooded grassland and/or open grassland

Bushed open woodland with bushed dense woodland with open (bush land)

Open shrub land or open bush land with 10- 40 % cultivation


Open shrub land or open bush land

Cultivated land > 70%

Bare soil (30%) with open shrub land

River basin boundary

(Bushed) open woodland with wooded grassland and/or open grassland

Bushed open woodland with bushed dense woodland with open (bush land)



Open shrub land or open bush land

Cultivated land > 70%

Bare soil (30%) with open shrub land

River basin boundary

Mereb Basin

Tekeze Basin

Figure 23 Sample data from Mereb and Tekeze Basin boarder

Legend


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As to the overlay of information for analysis and display, the result is positive for data abstraction, and is marked (*), for others. This is because; first, the model is not designed to substitute any GIS software opera-tion. Secondly, users needs could be very diverse; therefore programming for a very diverse and unpredict-able user demand could be very difficult. Therefore, overlay should be performed independently in GIS software. In this regard, the model is intended to serve abstracting data of interest for overlay analysis.

6.3 Testing the Model Schema

The followings are key considerations in the model schema.

• Flexibility: the model designed in this study is intended to adapt the principles of interoperability (chapter 2, figure 9), where it allows the abstraction of required information from the four basins. Models operating based on interoperability principles are flexible to use (Egenhofer & Goodchild;, 1996).

• Possibility of upgrade: the model designed say, the land cover and land use model considers the

present datasets of the river basins. It means all the objects of land use and land cover belong to a certain class in the model. To accommodate other classes of data, in case of upgrading, it is a matter of fitting the new class of data in the model where it corresponds, either to the present model or through modifying it. This being the case for thematic aspects, geometric changes such as changes in the boundary of polygons, through either addition or modification of existing objects, the model takes care automatically. This is because aggregation/generalisation is mainly defined by the the-matic aspect (chapter 3, section 3.2.1) and the geometric aspect follows these definitions.

• Robustness: Data abstraction from the river basin databases in its present form is not convenient,

especially when data abstraction from more than one river basin is involved. Data abstraction from multiple basins means data from heterogeneous attribute coding, and different software platforms. The conceptual model provides a medium that will allow data abstraction from such heterogeneous environments. Using the model will make the abstraction of objects from across basins possible, even without changing the present state of the data.

• Data quality: the quality of the output is pretty much dependent on the quality of the present data

itself. Therefore, the conceptual model will not improve the actual data quality. On the other hand, it can be argued that as far as it allows abstracting only the required data, it could be considered as an improvement to detecting quality, and therefore, can serve as quality control.

• Time limit: the model is not time bound. It is not also intended to handle time series data. For other

datasets (non-time series), outdated datasets can be replaced or modified with an updated one. In an instance of new or modified data that is not accounted in the current model, modifying the model is possible. The model in this case is not static with respect to time.

• Software/hardware: All most all the datasets of the river basins are in ArcInfo format (chapter 3

table 1). ArcInfo operates in an open GIS system, which can be programmed to handle the model functions. However, programming in this software could be laborious and time taking. There are


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also readily available commercial software programs such as NEXPERT. In this case procuring be-comes necessary. Hardware is not a limitation in the MoWR.

Table 17 Testing the model schema

Description Flexibility Possibility of upgrade

Robustness: Data quality Time limit Software/ hardware

Model Yes Yes Yes Not improved No No This implies that the model is flexible, upgradeable, and has no time limit and software/hardware limita-tion. The only thing it cannot improve is the quality of the existing data.

6.4. Opportunities and Constraints

To initiate the implementation of spatial data modelling in the MoWR, the conceptual model needs to be transformed into logical and physical models (chapter 2, section 2.6). This transition will have its own re-quirement that needs to be addressed by the implementing body. In this section, a checklist of opportunities and constraints has been presented, most of which are derived from the discussions in chapter 3, section 3.2.

6.4.1. Opportunities

• The availability of the river basin databases, • The users demand at regional, national, trans-national, and other government and the NGOs, • Availability of operational GIS system in the organisation, • The availability of networking environment, though limited to Local Area Network (LAN), • The containment of all the databases in one organisation allows easy updating, and follow-up, • Presently, there is an ongoing project (Environmental Support Project), which partly deals with

meta-database development, its’ platforms can serve as a medium of data sharing, • There is also a project intended for Wide Area Networking (WAN) between the MoWR and the Re-

gions. It can facilitate data collection and data sharing,

6.4.2. Constraints

• Absence of skilled manpower in the area of spatial data modelling and computer programming, • Lack of coordination between the CGIS service and RBDSD (chapter 3 section 3.3.1), • The absence of national standards such as for coding, classification and data sharing, • The continuation of project oriented GIS set-up, • Use of diversified software in a way that it complicates required modelling operation, • Lack of literature (scientific materials) for acquiring knowledge on the subject of spatial data model-

ling, The intention of providing the opportunities and constraints is not to provide an exhaustive listing, but an indicative one. In order to transform, the conceptual model into logical and physical model (chapter 2 figure 4), action that will exploit the opportunities and overcoming the constraints is an essential step. In the light of this, it is suggested that the followings could alleviate the situation:

• Improve the capability of the CGIS staff through capacity building,


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• Establish a coordination mechanism between the CGIS service and the RBDSD, to execute the GIS tasks jointly, including updating,

• Establish national standards for coding, classification, data sharing and other geo-information activi-ties,

• Strengthen the GIS of the organisation to focus on institutional needs, • Acquire scientific materials and lay communication mechanism for knowledge sharing to learn

experiences from other countries,

Apart from the ones indicated above, the implementation of computer model systems in a multi-user and multi-organisation environment has to deal with inter-organisation and intra-organisational management and coordination issues.

6.4. Conclusion

In this chapter, the conceptual model has been tested against the criteria. The test results for some of the scenarios are questionable. This is mainly because of the condition of the present data. However, the advan-tages of having the conceptual model outweighs, because; it contributes to providing harmonised data, which is an improvement to the data that is already in use. The opportunities and constraints presented are intended, to guide the necessary steps in implementing the conceptual model. Technical considerations such as spatial data modelling alone will not solve the prevailing problems of using the river basin databases. It needs to be coupled with appropriate management and institutional backing. The next chapter will present the conclusions and recommendations of the study.


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7. Conclusion and Recomendation

7.1. Summary

In the light of addressing the objectives of the research, this study has been described into five major chap-ters: chapter 2, 3, 4, 5 and 6. Chapter 2 has gained the objectives of the research through understanding the basic theories of spatial data modelling techniques in general and as applicable to the study area in the Ethiopian part of the Nile Basin in particular. Chapter 3 has presented the description of the data and a dis-cussion on the users environment. This chapter has addressed research question 1, which is set out to ex-plore the present and future data usage and needs. The discussion in this chapter enabled the identification of user requirements, which led to further analysis in chapter 4. In chapter 4, user requirements and hierarchical levels of organizations have been analysed. In this chapter, spatial data needs, thematic and geometric partition and model criteria are defined, which are used as a pre-cursor for the design of the conceptual model. Chapter 4 has fulfilled the intended purpose of the research, through answering research question 2, which deals with specifying the criterion for a common database. In chapter 5, the conceptual model has been designed using spatial data modelling techniques, which has been discussed in chapter 2. As the datasets of the river basins are vast, river basin water resources, water resources application and land cover and land use datasets are only covered to demonstrate the application of spatial data modelling in the study area. Out of these models, more emphasis is given to land cover and land use. Chapter 5 has fulfilled the research objective through answering part of the research question 3, which is the design and testing of the conceptual model. In chapter 6, to understand the reliability and feasibility of outputs of the conceptual model, the model is tested against criterion. In addition, indicative checklist of opportunities and constraints has been described. Opportunities and constraints are more to do with inter-organisational management and coordination issues. Addressing these aspects in this study has not become possible. However, further reading is suggested from the research of Getenet Besha, (2003), which has covered related issues in the same country. Chapter 6 has addressed the objectives of the research, through answering the testing aspect, which is part of the research question 3. Research questions 4 and 5, which deal with opportunities and constraints are also covered in this chapter. In general, the main chapters 2, 3, 4, 5 and 6 have addressed the specific objectives of the research, through solving the research problem.

7.2 Conclusion:

The main objective of this study was to design a conceptual data model, which can support the planning and decision-making process, for the management of the river basins in the Ethiopian part of the Nile Basin.


69

In this study, a conceptual model has been successfully developed to structure the spatial datasets based on the object-oriented concepts. Due to the vastness of the river basin datasets, the design in this study has not covered all the datasets. Datasets in water resources, water resources application and land use and land cover have been covered in the modelling. Land use and land cover is given more emphasis. From the modelling of these datasets, spatial data modelling in an object-oriented approach has been recog-nized as an important tool in supporting the query, abstraction, and analysis of the river basin databases. In order to attain the full advantage, eventual development of other data models becomes necessary. In the study area, getting the right information for planning and decision-making has been a constraint in the use of the river basin databases. The designing of the conceptual model has helped in solving these con-straints. Out of the entire river basin datasets, water resources, water resources application and land use and land cover have been considered in the model design. The design, using object-oriented modelling approach has been recognised as an important tool in supporting the query, abstraction, and analysis of river basin databases. In addition, it contributes to improving the status of data sharing, through the delivery of harmo-nised datasets for various planning purposes. Water resources planning and development activity requires a common plat form to access the river basin databases. This study has demonstrated the possibility through spatial data modelling and use of Spatial Data Transfer System in and interoperable GIS environment. However to realize it the conceptual model needs to be transformed to logical and physical model. The original dataset, particularly land use and land cover, is very complex. Mapping units of complex origin where some times the decisions are subjective, lack the distinction of the units. Further generalization of these data might lead to a much more complex dataset. Modelling an already complex data might lead to the omission of some units. Hence, aggregating such kinds of datasets needs proper justification with respect to the application it is intended to use. The absence of standards has been recognised as the main constraint, which has contributed to most of the problems, related to data format, classification scheme, and coding system. These aspects contributed to the difficulties of utilising the river basin databases

7.3. Recommendation:

1. Establishing standards for coding system, data format, as well as developing national strategies for

data collection and processing is fundamental. 2. Due to time limitation, the opportunities and constraints are not analysed in-depth. Further investi-

gation is needed to promote the implementation of spatial data modelling in the MoWR.

3. In chapter 3, section 3.2, the issue of comparability of the source data in the different river basins has been pointed out. With regard to the input data, “one of the problems is that it is often over-looked to assess whether the data used are indeed comparable”(Van der Zee, 2001). A further study


70

and comparison of the source dataset particularly on the once derived from satellite image interpre-tation, is important. The outcome of a model can only be trusted when the underlying input data is known to be from the right quality.

4. To insure compatibility of the adjacent river basin databases verifying the datasets particularly the

land use and land cover data through field investigation is necessary.

5. In line with utilising data from multiple sources, similar studies in Ghana, emphasized the undertak-ing of field verification as necessary to justify datasets from different sets are compatible to each other (Martesson, Eklundh, Gyamfi, & Tetteh, 1999). The idea is strongly supported to

6. As the datasets used in this model are older relative to the time of completion of the master plan studies (3-5 years), updating the datasets becomes necessary in order to verify the present data qual-ity as well as to account for the changes taking place over time. It will improve the reliability of the data, and thereby the model output.

7. In order to attain the full advantage of spatial data modelling, eventual development of other data

models is necessary. Similar procedures like the one used for land cover and land use could be ap-plied for soils and other datasets. Therefore, taking appropriate action for developing other data models is worthwhile.

8. Finally, realising the benefits of spatial data modelling for improving the utilisation of the river ba-

sin database, addressing the opportunities and constraints indicated in section 6.4, becomes impor-tant in order to transform the conceptual model into a meaningful logical and physical model.


71

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Al-Kodmany, Brooks, Budic, Hunter, Salling, Shiffer, Wiggins, & Obermeyer. (2001). GIS for Planning

Prospectus. Retrieved Nov. 18, 2002, from the World Wide Web: http://www.uic.edu/cuppa/ucgis/docs/gis_for_planning.htm

Amer. (1993). Modern Trends in Establishing National Information Systems in a Coordinated and Integrated Manner: Presented at Lands Department of Guaundung province China. 2-47.

Anderson, Hardy, Roach, & Witmer. (2001). A Land Use and Land Cover Classification System for use with Remote Sensor data. Retrieved July 4, 2002, from the World Wide Web: http://landcover.usgs.gov/pdf/anderson.pdf

BCEOM. (1998). Abbay river basin master plan project: Phase 2 land cover/land use. BCEOM. (1999). Abbay River Basin Integrated Development Master Plan, Main Report (Volume 1). Addis

Ababa: Ministry of Water Resources. Bechtold. (1997). IMPORTANCE OF STANDARDS: Land Use Mapping in Indonesia. Retrieved Nov. 14,

2002, from the World Wide Web: http://www.geocities.com/Athens/Olympus/6030/gpstand1.html Bishr, Molenaar, & Madwan. (1996). Semantics of Parallel Object Hierarchies in a Multi-Scale Environ-

mental Decision Support System for Watershed Management. Reprinted from: Cartographica,. Bishr, Radwan, Driza, & Pandya. (1997). Guidelines for the Development of a Geospatial Clearing House in

a Heterogeneous Environment. Reprinted from Geographical Information '97, 1, 277-287. Bishr;. (1997). Semantic Aspects of Interoperable GIS. Unpublished PhD thesis, Wageningen Agricultural

University (WAU), Wageningen. Burrough, & Masser (Eds.). (1998). European Geographic Information Infrastructures: Opportunities and

Pitfalls (Vol. 5). London: Taylor & Francis. Campbell, & Masser. (1995). GIS and Organizations : How effective are GIS in practice? London: Taylor

& Francis. De Man, & Van den Toorn. (2002). Culture and the adoption and use of GIS within organizations. Interna-

tional Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Nether-lands. International Journal of Applied Earth Observation and Geoinformatics, 13.

Egenhofer, & Goodchild;. (1996). Interoperating Geographic Information Systems Request for Approval in Detail. Retrieved November 11,, 2002, from the World Wide Web:

http://bbq.ncgia.ucsb.edu/conf/interop97/i20prop/i20prop.html Ethiopian Valley Development Studies Authority. (1994). Terms of Reference and Invitation Documents for

Consultancy Services: Tekeze River Basin Integrated Development Master Plan Project. Addis Ababa.

FAO: Afri-cover Project. (1994). Land Cover Classification System (LCCS). Retrieved June 17, 2002, from the World Wide Web: www.lccs-info.org

Flowerdew R. (1991). Spatial Data Integration. Geographical Information Systems, 1, 375-387. Groot, & McLaughlin. (2000). Geo-spatial Data Infrastructure: Concepts, Cases and Good Practice. Spatial

Information Systems and Geo-statistics Series, 286. John Jensen, Alan Saalfeld, Fred Broome, Dave Cowen, Kevin Price, Doug Ramsey, & Lewis Lapine.

(2002). Spatial Data Acquisition and Integration. Retrieved June 14, from the World Wide Web: http://www.cla.sc.edu/geog/ucgis/

Kraak, & Molenaar (Eds.). (1997). Proceedings of the 7th International Symposium on Spatial Data Han-dling: Advances in GIS Research University of Edinburgh. London: Taylor & Francis.

Laurini, & Thompson;. (1992). Fundamentals of Spatial Information Systems. London. Martesson, U., Eklundh, L., Gyamfi, J., & Tetteh, E. (1999). Ghana: Country at a glance (G-CAG): Data

description and instructions for the use of the G-CAG: Final report: Environmental Protection Agency.

Masser. (1997). Data Integration Research: Overview and Future Prospects. European Science Foundation. Retrieved June 14, 2002, from the World Wide Web: http://www.shef.ac.uk/uni/academic/D-H/gis/key2.html

Molenaar. (1996). The Role of Topologic and Hierarchical Spatial Object Models in Database Generaliza-tion, Reprinted from: Methods for the Generalization of Geo-database. 13-36.


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Molenaar. (1998). An Introduction to the Theory of Spatial Object Modelling for GIS: Taylor & Francis. Molenaar M. (1998). An Introduction to the Theory of Spatial Object Modelling for GIS: Taylor & Francis. NEDECO. (1998). Tekeze river basin integrated development master plan project Volume XV Natural re-

sources. Obermeyer, & Pinto;. (1994). Managing Geographic Information Systems. New York: The Guilford Press. Onsrud, H. J., & G.R. Rushton. (1995). Sharing Geographic Information. 510. Onsurd, & Rushton (Eds.). (1995). Sharing Geographic Information: New Brunswick: The State University

of New Jersey, Center for Urban. Salling, M. J., Clapp, L., & Jane Lapointe (Eds.). (1994). Integrating information & Technology: IT Makes

Sense: 1994 Annual Conference Proceeding, Volume I. The World Bank Group.Nile Basin Initiative. Retrieved June 26, 2002, from the World Wide Web:

http://www.worldbank.org/afr/nilebasin/countries.htm Tshangho. (1999). Metadata for geo - spatial data sharing : a comparative analysis. Annals of Regional Sci-

ence, 171-181. UNDP. (2002). Nile River Basin Cooperative Framework Project. Retrieved June 28,, 2002, from the World

Wide Web: http://www.nilebasin.org/cooperative_framework.htm Van der Zee. (2001). In Kunz; (Ed.), GIS and remote sensing in studies of landscape structure and function-

ing, Torun : Uniwersytet Mikolaja (pp. 303). Van Gils, H., Huzing, H., & Kannegieter, A. (1991). The evolution of the ITC system of rural land use and

land cover classfification (LUCC). ITC Journal 1991, 163-161. Van Wingaarden, W. (1991). The Green cover of the earth: a dynamic resources in a changing environment.

ITC Journal 1991, 113-121. Yaolin, L. (2002). Categorical Database Generalization in GIS. Unpublished PhD, Wageningen University,

Wageningen.


73

Appendix: Appendix I: River Basin data checklist No. River Basin Reference

system Projection Ellipsoid Zone Software

1 Mereb

2 Tekeze

3 Abbay

4 Baro-Akobo


74

Appendix II: Thematic data checklist Units Coverage Data

use Frequency of use

Updating Who is updating

No. Data description Data source

Data type

Scale Classification method

Thematic Geometric 1 Land use/cover

2 Soils

3 Geology

4 Hydrology

5 Agriculture

6 Agro-ecology

7 Hydro-geology

8 Geomorphology

9 Road network

10 River network

11 Development

Zones

12 Suitability

13 Groundwater

14 Climate

15 Administrative

16 Socio-economic

17 Others etc,

INT

EG

RA

TIN

G G

EO

-INFO

RM

AT

ION

FOR

WA

TE

R R

ES

OU

RC

ES

MA

NA

GE

ME

NT

76

A

ppendix III: Them

atic data checklist Tekeze R

iver Basin

Units

Coverage

Data use

Frequency of use U

pdating W

ho is updating D

ata descrip-tion

Data source

Scale C

lassification method

Them

atic G

eometric

Land use/cover Landsat T

M dated D

ecember 5,

1989 and March 14, 1988 bands 5,

4, 3 and Aerial photographs

1994/1964 EM

A

250,000 Follow

s the Woody B

iomass

Inventory & Strategic Planning

Project (WB

ISPP, 1993/1994)

Ref. attribute

and map legend

Polygon Full basin


planning

High

No updating so far

River B

asin Developm

ent

Studies Departm

ent is in

charge.

Soil and Terrain

Ditto as above and 1: 250,000

scale 1979 EM

A topographic

Maps, 1:100,000 scale 1979

Russian topographic m

aps

250,000 and

50,000

reconnais-

sance &

feasibility.

Revised FA

O-U

NE

SCO

soil

map of the w

orld (1990) and

the SOT

ER

format (ISR

IC,

1995),

(FAO

1988)

Ref. attribute

and map legend

Polygon Full basin at

reconnaissance

scale and 3

selected sites at

feasibility


planning

High

No updating so far

River B

asin Developm

ent

Studies Departm

ent is in

charge.

Geology &

Mineralogy

Landsat TM

(same as above)

250,000 Landsat T

M interpretation

Ref. attribute

and map legend

Polygon, line

and point

Full basin R

esource inventory and

planning

Low

No updating

RB

DSD

, but data has to be


organizations

Hydrology

EM

A 250,000 topographic m

aps,

Departm

ent of Hydrology of the

MoW

R

250,000 R

iver order R

ef. attribute

and map legend

Polygon, line

and point

Fill basin W

ater resources

inventory, planning

flow estim

ation

High

No updating of m

aster

plan data

RB

DSD



organizations

Agriculture

Agro-clim

atological map (E

MA

&

MoA

) & A

gronomic database

250,000 M

ajor cropping system

Ref. attribute

and map legend

Polygon Fill basin

Resources inventory

and planning

Low

No updating so far

River B

asin Developm

ent

Studies Departm

ent is in

charge

Agro-ecology

Hydro-geology

Landsat TM

Interpretation includ-

ing GPS survey of w

ell points

250,000

Ref. attribute

and map legend

Polygon, line

and point

Fill basin W

ater resources

planning

Low

No updating

River B

asin Developm

ent

Studies Departm

ent is in

charge

Road infra-

structure

EM

A 1979 topo m

aps & G

PS

measurem

ent for newly con-

structed roads

250,000 A

ll weather road and dry

weather road

Ref. attribute

and map legend

line Fill basin

Planning H

igh N

o updating so far D

ata to be obtained from

sector organization

River netw

ork E

MA

1979 topographic maps

250,000 R

iver order R

ef. attribute &

map legend

Polygon and

line

Fill basin R

iver basin modeling

High

No updating so far

EM

A topographic m

ap

update

Developm

ent

Zones

Basically C

SA data and agro-

climate

250.000 Zonal base

Polygon/line

Full basin M

aster planning Low

N

o updating so far R

iver basin Dept.

Suitability SO

TE

R &

Agro-clim

atological

map of T

ekeze project

250,000 Irrigation, Livestock, rain-fed

and Mechanized

Ref. attribute

&m

ap legend

Polygon and

line

Fill basin R

iver basin modeling

Medium

N

o updating so far

Groundw

ater B

orehole information- M

OW

R and

hydro-geological field survey

250,000

Ref. attribute

and map legend

Point and line Full basin

Water resources

planning

Low

No updating so far

Information from

well

drilling agencies

Clim

atology

(Mean annual

National M

eteorological Service

Agency

250,000

Ref. attribute

and map legend

line and Point Full basin

Planning Low

N

o updating so far R

BD

SD, but data has to be


INT

EG

RA

TIN

G G

EO

-INFO

RM

AT

ION

FOR

WA

TE

R R

ES

OU

RC

ES

MA

NA

GE

ME

NT

77

temperature,

Precipitation,

Evapo-

transpiration)

organizations

Adm

inistrative

Units

CSA

1994 census data and

Wereda m

aps,

50,000

Wereda m

aps

Adm

inistrative division R

ef. attribute

and map legend

Polygon,

point and line

Full basin Planning

Medium

N

o updating so far G

ovt./ regions.

Socio-economic

(infrastructure:

clinic, school,

market, m

ills, )

EM

A m

aps 250,000

R

ef. attribute

and map legend

Point Full basin

Socio-economic

analysis

Low

No updating so far

RB

DSD



organizations

Topogra-

phy/contours

1:100,000 scale Russian and 1:

250,000 EM

A topographic m

aps

250,000 C

ontour interval R

ef. attribute

and map legend

Line Full basin

Planning M

edium

No updating so far

EM

A

Appendix IV

: Them

atic data checklist Abbay R

iver Basin

Units

Coverage

Data use

Frequency of use U

pdating W

ho is updating D

ata descrip-tion

Data source

Scale C

lassification method

Them

atic G

eometric

Land cover Landsat im

agery (1986 -1990 and

from N

ovember to January, and


250000 A

dopted FAO

and WB

ISPP

Method,

Ref. M

ap

legend, attrib-

utes and Data

description doc.

Polygon Full basin

Master planning

High

So far, no updating of

master plan data

River B

asin Developm

ent

Studies Departm

ent is in

charge.

Land use G

enerated from land cover

250000 G

enerated R

ef. Map

legend

Polygon Full basin

Planning H

igh N

o updating R

BD

SD is in charge.

Soil Landsat im

agery from 1986 to 1990

and from N

ovember to January, and


250000 at

reconnais-

sance &

50,000

feasibility

Described using FA

O-

UN

ESC

O guidelines (1990)

and classified FAO

-UN

ESC

O-

ISRIC

(1988)

Ref. M

ap

legend, attrib-

ute, & D

ata

description

document

Polygon and

point

Full basin at

reconnaissance

and 7 selected

sites at semi-

detailed

Master planning

High

No updating

RB

DSD

Geology

Ditto as above and Screen digitized

and generated

250000 Landsat T

M interpretation

Ref. M

ap

legend, attrib-

ute,

Polygon, and

line

Full basin Planning

Low

No updating

RB

DSD



organizations

Hydrology

Generated and screen digitized

250000 R

iver order R

ef. Map

legend

Polygon, line

and point

Full basin Planning

High

No updating

RB

DSD

, but data has to

be obtained from sector

organizations

Agro-ecology

Generated/adopted from

Mengistu

and de pauw data

250,000 M

engistu / de pauw

Ref m

ap legend Polygon

Full basin Planning

Low

No updating

Hydro-

geology

Derived from

Geology m

ap 250,000

Borehole and spring

Line/polygon

Full basin Planning

Low

No updating

River B

asin Developm

ent

Studies Dept. is in charge

Road netw

ork E

MA

topographic maps 50000 and

250000 M

ain road and track road

Line Full basin

Planning H

igh N

o updating R

BD


INT

EG

RA

TIN

G G

EO

-INFO

RM

AT

ION

FOR

WA

TE

R R

ES

OU

RC

ES

MA

NA

GE

ME

NT

78

250000 obtained from

sector

organizations

River net-

work &

lakes

EM

A 250,000 topographic m

aps 250000

River order

Ref. M

ap

legend

Line and

polygon

Full basin Planning

High

No updating

RB

DSD



organizations

Mater plan

map

Generated

250,000

Ref. M

ap

legend

Polygon Full basin

Planning H

igh N

o updating R

BD

SD

Suitability D

erived from soil, land use land

cover and agro-climate

250000 Irrigation, rainfed, m

echaniza-

tion, cultivation

Ref. M

ap

legend

Polygon Full basin

Modeling

Medium

N

o updating R

BD

SD

Rainfall

NM

SA

250,000 Isohyets

Ref. M

ap

legend

Point and line Full basin

Planning Low

N

o updating R

BD


obtained from N

MSA

Adm

inistra-

tive Pa, Zone

& W

ereda

CSA

1994 census data and Wereda

maps,

50,000 A

dministrative division

Ref. M

ap

legend

Line Full basin

Planning Low

N

o updating R

BD


obtained from C

SA

Adm

inistra-

tive towns &

urban areas

1:50,000 & 1:250,000 E

MA

maps

250,000 Population size

Ref. M

ap

legend

Point and

polygon

Full basin Planning

Medium

N

o updating R

BD


obtained from C

SA

Contours

EM

A topographic m

aps 1:250000 &

1:50,000/ landsat photo interpreta-

tion

250000 R

ef. Contour interval

Ref. M

ap

legend

Polygon 1:250,000 full

basin and

1:50,000 partial

Planning M

edium

No updating

RB

DSD


obtained from E

MA

Mineral

Draw

n 1:250,000 EM

A

250,000 M

ineral type R

ef. Map

legend

Point Potential areas

/Mineral belts

Planning Low

N

o updating R

BD

SD


79

Appendix V: Land use and land cover hierarchical class structure Abbay Basin

Aggregation levels Land unit code

Description Level 1 Level 2 Level 3

C1.1 C1.2 C1.3 C1.4 C1.5 C1.6 C1.7

Dominantly cultivated

C2.1 C2.2 C2.3 C2.4 C2.5 C2.6 C2.7 C2.8 C2.9 C2.10 C2.11 C2.12

Moderately cultivated

Rain fed

C5 Irrigated cultivation Irrigated C6 Perennial cultivation Perennial SF State Farm State Farm

Cultivated

Cultivated

F2.1 Disturbed forest with cultivation F3.1 F3.2 F3.3

Very disturbed forest with cultivation Forest (broad leaf)

P1.1 Eucalyptus (Plantation forest) Plantation BA.1 Bamboo Bamboo WR Revreine woodland Revierine

Forest

WD.1 WD.2 WD.3 WD.4 WD.5 WD.6

Dense woodland

WO.1 WO.2 WO.3

Open woodland

Acacia

Mixed

Woodland

A Afro alpine Afro alpine Afro alpine B1 Open bush land with cultivation &

grassland B2 Open bush land with woodland

Bush land

S1 Open shrub land with cultivation S2 Open shrub land with bush land and

grassland

Shrub land

G1.1 G1.2

Open grassland

G2.1 G2.2 G2.3

Shrubby grassland

Bushed, shrubed grassland, wooded grassland

Grassland

Forest and woodland

Bare R Rock outcrops

Barren land Barren land Barren land

H1 Open water Water body Water body Water body


80

H2 Perennial or seasonal swamp U Urban Urban area Urban area Urban area

Tekeze Basin



14 Dense woodland 10 11 12 13

Open woodland

Mixed

Woodland

17 18

Open bush land

Bush land

3 15 16

Open shrub land

19 Dense shrub land

Shrub land

1 2 4 5 6 7

Open grassland

8 9

Wooded grassland

Bushed, shrubed grassland, wooded grassland

Grassland

Forest and wood-land

Mereb Basin


Description Level 1 Level 2 Level 3 1 2 3 6

Shrub land

4 5

Open grassland

7 8

Open woodland

Baro-Akobo Basin



C1 Dominantly cultivated

C2/FB3 C2/GO

Moderately cultivated

Rain fed

C6 C6/FB3 C6/FP1

Perennial cultivation

Perennial

SF State Farm State farm

Cultivated

Cultivated


81

FB2 Disturbed forest with cultivation Forest

FB3 FB3/C6 FB3/GO

Very disturbed forest with cultivation

FR2 Revreine woodland Reverine

FO/WO Bamboo Bamboo

Forest

WD WD/WO

Dense woodland

WO WO/GO WO/WD

Open woodland

Mixed

Woodland

Forest and wood-land


82

Appendix VI: WBISPP Land cover classification

Land cover classification Primary Secondary Tertiary Code

Rain fed Dominant > 70 % cultivated C1 Moderate 30-70 % cultivated C2 Sparse 10-30% cultivated C3 Scattered < 10% cultivated C4 Irrigated C5 Perennial C6 Coffee C6.1 Tea C6.2 Other C9 Fallow F

Cultivated

State Farm SF F Coniferous Undisturbed dense FC.1 Disturbed FC.2 Very disturbed FC.3 Broadleaf Undisturbed dense FB.1 Disturbed FB.2 Very disturbed FB.3 Mixed Undisturbed dense FM.1 Disturbed FM.2 Very disturbed FM.3 Riparian Undisturbed dense FR.1 Disturbed FR.2 Very disturbed FR.3 Plantation FP Major plantation FP.1 Community forest FP.2

Forest

Bamboo FO A Erica A1

Afro-Alpine

Grassland/ moorland A2 Mixed Dense > 50/ tree cover WD Open 20-50 % tree cover WO Combretum terminalia Dense WDc Open WOc Acacia Dense WDa Open WOa Riparian Dense WRD

Woodland

Open WRO Dense > 50% cover BD Bush land Open 20-50% cover BO Dense > 50% cover BWD Bushy woodland Open 20-50% cover BWO Dense > 50% cover SHD Shrub land Open 20-50% cover SHO Open GO Bushed/shrubed grassland, 10-20% cover GSH

Grassland

Wooded grassland 10-20% cover GW Open water H.1 Wetland Perennial swamp/marsh H.2


83

Seasonal swamp/marsh H.3 Seasonal swamp/ marsh with re-cession cultivation

H.4

Bare land Exposed rock T.1 Salt-flats T.2 Exposed sand/soil T.3 Urban U