Td corrigé PhD Whole Thesis - University of South Wales Research Explorer pdf

PhD Whole Thesis - University of South Wales Research Explorer

13 Jan 2010 ... (Systeme Probatoire d'Observation de Terre) ...... F3, R3 = TM4 Float ...... 10, p.p. 1993-2011. ...... In G. W. Kite, A Pietroniro and T. D. Pultz (Ed.), Application of Remote Sensing in Hydrology, Proceedings Symposium No.




part of the document



UNIVERSITY OF GLAMORGAN

Faculty of Advanced Technology
















HYDROLOGICAL ASSESSMENT

AND MODELLING OF THE RIVER FANI CATCHMENT, ALBANIA



by

Aphrodite Nicandrou

A thesis submitted for the

Degree of Doctor of Philosophy



January 2010
Declaration

This thesis is the original independent work of the author, except where acknowledgement is given, and has not previously been submitted for a degree in this or any other university.


Aphrodite Nicandrou
January 2010

………..……………
Abstract
Aid In Action Porthcawl (a registered South Wales Charity Organisation) has been carrying out charity work in the town of Rubik in the Mirdita Region of North Albania for many years. Rubik lies within the Catchment of the River Fani which is remote, ungauged and characterised by frequent flooding, erosion and deforestation. Over the years these processes have had a huge environmental and socioeconomic impact on the residents of Rubik. Aid In Action was concerned about this situation and wished to provide a sustainable solution. Following discussions with staff at the University of Glamorgan, it was agreed that a sustainable solution was the development of an integrated hydrological decision support system for the whole River Fani Catchment.

Hydrological models can be a valuable tool, providing a common platform for experts, decision-makers and stakeholders for the sustainable management of catchments, especially when used within the framework of a Geographic Information System (GIS). Such models and systems require quantitative data of good quality over appropriate spatial and temporal scales. For remote mountainous ungauged river catchments in developing countries the development of a catchment model and management system is often complicated due to limited availability of such data. Very often, any available data are difficult to obtain; they could, for example, be scattered among local authorities and are generally in the national language of the country concerned, thus adding the challenge of having records translated into the study language.

Over the last few decades, advances in hydrological data capture (e.g. using remote sensing) and data management systems (e.g. GIS) have provided opportunities for overcoming some of the challenges of modelling ungauged catchments. However, the data captured is often from different sensors and sources and at different scales. This research project sought out to creatively use multi-source and multi-scale data to develop a GISbased hydrological model of the River Fani Catchment in the North of Albania to provide, a long term solution for the sustainable management of the Fani Catchment, thus improving the quality of life for the residents of Rubik and the rest of the Catchment. Data from various remote sensing sensors (e.g. Landsat, MODIS, ASTER) and other sources such as published maps, limited gauged flow and rainfall records, local library archives, digital datasets (e.g. CORINE and radar rainfall) and interviews with residents were used to develop the integrated GIS-based hydrological (using WMS hydrological modelling environment) and hydraulic (HEC-RAS) model of the Fani Catchment. The model was then used to not only map significant environmental change in the Catchment (e.g. deforestation using various vegetation indices), but also to assess flooding impact and to analyse various “What-if” scenarios of conservation strategies (e.g. deforestation, afforestation and provision of runoff attenuation systems).

The results suggest that the changes in vegetation cover (apart from farming practices) are not considerably extensive in the Catchment between 1984 and 2000. It was observed that afforestation as a flooding mitigation measure did not play a decisive role in runoff reduction compared with attenuation measures. This study has demonstrated the effectiveness of remote sensing and GIS in generating quantitative information on land classification, change detection, soil erosion and general catchment management for remote and ungauged catchments in developing countries. This has been particularly so, owing to recent developments in sensor technologies and increasing available datasets from data providers and the global scientific community at little or no cost.
Acknowledgements
This research project was mostly funded by Aid In Action Porthcawl who are gratefully acknowledged. Dot Walters is recognised for her continuous support and in trying to make contacts in Albania in order to acquire some of the required data, and Patricia Curran for contacting Jane Malvisi in Albania to transfer some project requests to be pursued by Jane. Special thanks also to Jane Malvisi for her constant help and hospitality I had during my stay in Rubik town for data acquisition. Rev. Father Peter Cashin for his spirituality, Mark for his kindness and moral support and generally all the members of Aid In Action Porthcawl as well as the residents of Porthcawl for the interest they showed to this project.

I would like to express my appreciation for the guidance, advice and help I received throughout this project from my director of studies, Dr Linus A. Mofor and supervisors Prof. R. Delpak and Dr Rod Robinson.

During my first data acquisition trip to Italy, Prof. Paolo Ciavola (Ferrara University, Italy) was very helpful in providing me with contacts in Albania and guidance and also useful information and documentation and I wish to express my thank you to him. Also thanks to Uberto Tessari and Mantovanni (Ferrara University, Italy) for their assistance and Fabio Corbani and Mr. Macaluso for the time they devoted in helping with data issues at the Consorzio Venezia Nuova.

Many thanks go to all the people and friends in Albania for helping me in my search for data. My friend Eris Leca (training priest) was particularly helpful with translating documents and being my personal mediator (translator) in almost all the meetings I had in Albania. His valuable help is greatly appreciated. Also thanks to Maria Bardhi (Local teacher) for translating some documents for me. I would also like to thank the director of the Geographic Institute of Rubik, Nicola Colomy, for the data he provided me. Also, I am thankful to Artan Topjana, Artan Osmani, and Bardhyl çano (known as Luli, administrator of the Alba Sviluppo SH.P.K) as well as the former mayor of Rubik Mr Bardhoe Prenga for the time they have devoted to me in visiting different institutes in Tirana in search for data. Their support and guidance is greatly appreciated. Also Martin Legisi (Geology Engineer from Rubik working in a private company in Lesja) was helpful with data acquisition and field trips to affected/problematic areas (from erosion, landslides) in Lesja. Also Father Giovanni Kemal was very kind in organising a field trip along River Fani to the mountain springs – thanks for his kindness. Thanks to Polikron Vaso (Director of the Geologic Institute in Tirana) for the maps and assistance he provided. Thanks also to Merilynne B. Davis (Project Deputy Chief of the Local Government Assistance and Decentralization in Albania) for her hospitality during my stay in Tirana. Also I would like to thank Prof. Skender Sala (Academy of Science: Geographic Studies Centre, Tirana), Prof. Perikli Qiriazi (University of Tirana: Department of Geography), and Nico Pano (Chief of Marine Hydrology Department: Hydrological Institute Tirana). During my field trip for data acquisition in Albania, I have received a lot of help and kindness from a number of people too many to mention by names. I thank them all.

Thanks must also go to those who have provided me with help and advice throughout this project, namely Prof. Paul Bates (Bristol University), for the long meeting we had in discussing some of the research issues and in making available his assistance any time needed. I am very grateful to Mr Iacovos Iacovides (Hydrologist/Water Resources Specialist at I.A.CO Environmental and Water Resources Consultants Ltd in Cyprus) for his help in contacting his ex colleagues from the MED-HYCOS project and releasing historic discharge data of Albania.

I wish to express my gratitude to Valerie Hood and Mary Fouassier from EURISY for giving me the opportunity to participate in the winter school/meetings for Remote sensing users held in Strasbourg and in Paris by funding all expenses and get networked with other research students/expertise world wide.

I am also grateful to the technical staff who have willingly given their vital assistance throughout this research. Thank you to Toby Bradshaw and Andrew Davies for their IT support.

I am extremely grateful to my family and my friends for their encouragement, love and support.

Most importantly, I acknowledge the enormous contribution made by my loving and caring husband, Costas Nicolaou.











To my daughter Christiana












Abbreviations

AGNPSAgricultural Non-Point Source (pollution model)AIAAid in Action Porthcawl (charity organisation)AIRSAtmospheric Infrared SounderALIAdvanced Land ImagerAMAXAnnual MaximumAMLARC Macro LanguageAMSUAdvanced Microwave Sounding Unit ANSWERSAreal Nonpoint Source Watershed Environment Response SimulationAOIArea of InterestARSGISIPApplied Remote Sensing and GIS Integration for Model Parameterisationa.s.l.Above sea levelASTERAdvanced Spaceborne Thermal Emission and Reflection radiometerBMPsBest Management PracticesBRDFBi-directional Reflectance Distribution Function (model)CADDComputer Aided Design and DraughtingCERESClouds and the Earth's Radiant Energy SystemCORINEEuropean Land Cover Digital MapsCRVNBRCurve NumberDEMDigital Elevation Model DHIDanish Hydraulic InstituteDNDigital NumberDOSDark Object Subtraction DRASTICDepth of water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic ConductivityDSSDecision Support SystemEEAEuropean Environmental Agency EOEarth ObservationEOSEarth Observing Systems (NASA)EROSEarth Resources and Observation SystemERTASEarth Resources Data Analysis SystemERTSEarth Resources Technology SatelliteESRIEnvironmental System Research InstituteETMEnhanced Thematic MapperEVDExtreme Value DistributionEVIEnhanced Vegetation IndexfaparFraction of Absorbed Photosynthetically Active Radiation FYROMFormer Yugoslav Republic of MacedoniaGCPGround Control PointsGISGeographic Information SystemsGloVisGlobal Visualisation ViewerGOESGeostationary Orbiting Environmental SatelliteGPSGlobal Positioning Systems GRACEGravity Recovery and Climate Experiment GRASSGeographic Resources Analysis Support SystemGUFIMGIS-based Urban Flood Inundation Model HECHydrologic Engineering CentreHEC-RASHydrologic Engineering Centre – River Analysis SystemH & HHydrology and HydraulicsHSPFHydrological Simulation Program – FortranIRInfraredLACAtmospheric CorrectorLAILeaf Area IndexLCLand CoverLEISALinear Etalon Imaging Spectrometer ArrayLIDARLight Detection and RangingLP DAACLand Processes Distributed Active Archive CenterLULCLand Use/Land CoverMISRMulti-angle Imaging SpectroradiometerMODISModerate Resolution Imaging SpectroradiometerMOPITTMeasurements of Pollution in the TroposphereMSSMultispectral Scanner SystemNASANational Aeronautics and Space AdministrationNASA/NEWSNASA Energy and Water Cycle Studyn.d.no dateNDVINormalised Difference Vegetation IndexNOAANational Oceanic and Atmospheric AdministrationPCPrincipal ComponentsPCAPrincipal Components AnalysisRIBAMODRiver Basin Modelling, Management and Flood MitigationRMSRisk Management SolutionsRMSERoot-Mean-Square ErrorRSRemote SensingRVIRatio Vegetation Index SSARRStreamflow Synthesis and Reservoir Regulation ModelSAVISoil-Adjusted Vegetation Index SCSSoil Conservation ServiceSDSSSpatial Decision Support SystemsSHESysteme Hydrologique Europeen (European Hydrological System)SLURPSemi-distributed Land Use-based Runoff ProcessesSRTMShuttle Radar Topography MissionSWRRBSimulator for Water Resources in Rural BasinsSWATSoil Water Assessment ToolSWIRShort Wave InfraredTDVITransformed Difference Vegetation Index THMBTerrestrial Hydrology Model with BiogeochemistryTINTriangulated Irregular NetworkTIRThermal InfraredTMThematic MapperTMITRMM Microwave ImagerTRMMTropical Rainfall Measuring Mission UNUUnited Nations UniversityUSACEU. S. Army Corps of EngineersUHUnit HydrographUTMUniversal Transverse MercatorVI’ sVegetation IndicesVNIRVisible and Near-InfraredWEPPWater Erosion Prediction ProjectWGSWorld Geodetic SystemWMSWatershed Modelling SystemWSEWater Surface Elevation




TABLE OF CONTENTS
Abstract II
Acknowledgements III
Abbreviations VII
Table of Contents XI
List of Figures XV
List of Tables XX
List of Plates XXIII

 TOC \o "4-4" \h \z \t "Heading 1,1,Heading 2,2,Heading 3,3"  HYPERLINK \l "_Toc266166484" Chapter 1 Background and Research Motivation  PAGEREF _Toc266166484 \h 1
 HYPERLINK \l "_Toc266166485" 1.1 Introduction  PAGEREF _Toc266166485 \h 1
 HYPERLINK \l "_Toc266166486" 1.2 The River Fani Catchment Case Study - Overview of the Challenges  PAGEREF _Toc266166486 \h 4
 HYPERLINK \l "_Toc266166487" 1.3 Research Aims and Objectives  PAGEREF _Toc266166487 \h 5
 HYPERLINK \l "_Toc266166488" 1.4 Structure of the Thesis  PAGEREF _Toc266166488 \h 7
 HYPERLINK \l "_Toc266166489" Chapter 2 Literature Review of Hydrological Modelling of Mountainous Temperate Catchments  PAGEREF _Toc266166489 \h 9
 HYPERLINK \l "_Toc266166490" 2.1 Introduction  PAGEREF _Toc266166490 \h 9
 HYPERLINK \l "_Toc266166491" 2.2 Rainfall-Runoff Relationships  PAGEREF _Toc266166491 \h 10
 HYPERLINK \l "_Toc266166492" 2.3 Hydrological Models  PAGEREF _Toc266166492 \h 12
 HYPERLINK \l "_Toc266166493" 2.3.1 Classification of Hydrological Models  PAGEREF _Toc266166493 \h 13
 HYPERLINK \l "_Toc266166494" 2.3.1.1 Lumped Models  PAGEREF _Toc266166494 \h 14
 HYPERLINK \l "_Toc266166495" 2.3.1.2 Distributed Models  PAGEREF _Toc266166495 \h 15
 HYPERLINK \l "_Toc266166496" 2.3.1.3 Selection of Model Type  PAGEREF _Toc266166496 \h 17
 HYPERLINK \l "_Toc266166497" 2.3.2 Data Sources  PAGEREF _Toc266166497 \h 27
 HYPERLINK \l "_Toc266166498" 2.3.3 Modelling Tools  PAGEREF _Toc266166498 \h 29
 HYPERLINK \l "_Toc266166499" 2.4 Hydrological Modelling Applications  PAGEREF _Toc266166499 \h 30
 HYPERLINK \l "_Toc266166500" 2.4.1 GIS and Hydrological Modelling  PAGEREF _Toc266166500 \h 30
 HYPERLINK \l "_Toc266166501" 2.4.2 Remote Sensing and Hydrological Modelling  PAGEREF _Toc266166501 \h 36
 HYPERLINK \l "_Toc266166502" 2.4.3 GIS and Remote Sensing Technologies Combined for Hydrological Modelling  PAGEREF _Toc266166502 \h 43
 HYPERLINK \l "_Toc266166503" 2.5 Conclusions  PAGEREF _Toc266166503 \h 46
 HYPERLINK \l "_Toc266166504" Chapter 3 Study Area and Data Acquisition  PAGEREF _Toc266166504 \h 48
 HYPERLINK \l "_Toc266166505" 3.1 Introduction  PAGEREF _Toc266166505 \h 48
 HYPERLINK \l "_Toc266166506" 3.2 The Study Area - River Fani Catchment  PAGEREF _Toc266166506 \h 49
 HYPERLINK \l "_Toc266166507" 3.3 Physical Characteristics and Data Archives of the River Fani Catchment  PAGEREF _Toc266166507 \h 52
 HYPERLINK \l "_Toc266166508" 3.3.1 Topography  PAGEREF _Toc266166508 \h 52
 HYPERLINK \l "_Toc266166509" 3.3.2 Climate  PAGEREF _Toc266166509 \h 53
 HYPERLINK \l "_Toc266166510" 3.3.3 Hydrology  PAGEREF _Toc266166510 \h 53
 HYPERLINK \l "_Toc266166511" 3.3.4 Geology  PAGEREF _Toc266166511 \h 55
 HYPERLINK \l "_Toc266166512" 3.3.5 Land Cover and Land Use  PAGEREF _Toc266166512 \h 58
 HYPERLINK \l "_Toc266166513" 3.4 Environmental Issues Associated with the River Fani Catchment  PAGEREF _Toc266166513 \h 60
 HYPERLINK \l "_Toc266166514" 3.4.1 Catchment Degradation  PAGEREF _Toc266166514 \h 60
 HYPERLINK \l "_Toc266166515" 3.4.2 Erosion and Landslips  PAGEREF _Toc266166515 \h 62
 HYPERLINK \l "_Toc266166516" 3.4.3 Flooding  PAGEREF _Toc266166516 \h 65
 HYPERLINK \l "_Toc266166517" 3.5 The Data Challenge  PAGEREF _Toc266166517 \h 67
 HYPERLINK \l "_Toc266166518" 3.6 Overcoming the Data Challenge  PAGEREF _Toc266166518 \h 68
 HYPERLINK \l "_Toc266166519" 3.7 Data Management  PAGEREF _Toc266166519 \h 69
 HYPERLINK \l "_Toc266166520" Chapter 4 Remote Sensing Assessment and Analysis of the River Fani Catchment  PAGEREF _Toc266166520 \h 70
 HYPERLINK \l "_Toc266166521" 4.1 Introduction  PAGEREF _Toc266166521 \h 70
 HYPERLINK \l "_Toc266166522" 4.2 Remote Sensing Data for the River Fani Catchment  PAGEREF _Toc266166522 \h 73
 HYPERLINK \l "_Toc266166523" 4.2.1 Landsat TM and ETM+  PAGEREF _Toc266166523 \h 73
 HYPERLINK \l "_Toc266166524" 4.2.2 MODIS  PAGEREF _Toc266166524 \h 75
 HYPERLINK \l "_Toc266166525" 4.2.3 ASTER  PAGEREF _Toc266166525 \h 76
 HYPERLINK \l "_Toc266166526" 4.3 Software and Data Processing Tools  PAGEREF _Toc266166526 \h 77
 HYPERLINK \l "_Toc266166527" 4.4 Pre-processing of Remote Sensing Data  PAGEREF _Toc266166527 \h 78
 HYPERLINK \l "_Toc266166528" 4.4.1 Pre-processing of Landsat TM and ETM+ Data  PAGEREF _Toc266166528 \h 78
 HYPERLINK \l "_Toc266166529" 4.4.2 Pre-processing of Terra ASTER and MODIS Data  PAGEREF _Toc266166529 \h 85
 HYPERLINK \l "_Toc266166530" 4.5 Derived Parameters - Vegetation Indices (VI’s)  PAGEREF _Toc266166530 \h 86
 HYPERLINK \l "_Toc266166531" 4.5.1 Normalised Difference Vegetation Index (NDVI)  PAGEREF _Toc266166531 \h 86
 HYPERLINK \l "_Toc266166532" 4.5.2 Enhanced Vegetation Index (EVI)  PAGEREF _Toc266166532 \h 92
 HYPERLINK \l "_Toc266166533" 4.5.3 Leaf Area Index (LAI)  PAGEREF _Toc266166533 \h 100
 HYPERLINK \l "_Toc266166534" 4.6 Land Cover Classification  PAGEREF _Toc266166534 \h 103
 HYPERLINK \l "_Toc266166535" 4.6.1 Classification Number Optimisation  PAGEREF _Toc266166535 \h 103
 HYPERLINK \l "_Toc266166536" 4.6.2 CORINE Land Cover Classification Methodology  PAGEREF _Toc266166536 \h 105
 HYPERLINK \l "_Toc266166537" 4.6.3 Derived Land Cover Maps  PAGEREF _Toc266166537 \h 112
 HYPERLINK \l "_Toc266166538" 4.6.4 Ground-truth  PAGEREF _Toc266166538 \h 118
 HYPERLINK \l "_Toc266166539" 4.7 Change Detection  PAGEREF _Toc266166539 \h 118
 HYPERLINK \l "_Toc266166540" 4.7.1 Post Classification Change Detection  PAGEREF _Toc266166540 \h 118
 HYPERLINK \l "_Toc266166541" 4.7.2 Forest Maps  PAGEREF _Toc266166541 \h 124
 HYPERLINK \l "_Toc266166542" 4.7.3 Forest Change Maps  PAGEREF _Toc266166542 \h 126
 HYPERLINK \l "_Toc266166543" 4.7.4 Using NDVI Maps for Change Detection  PAGEREF _Toc266166543 \h 130
 HYPERLINK \l "_Toc266166544" 4.8 Soil Erosion Potential  PAGEREF _Toc266166544 \h 135
 HYPERLINK \l "_Toc266166545" 4.8.1 Bare Soil Index (BSI)  PAGEREF _Toc266166545 \h 136
 HYPERLINK \l "_Toc266166546" 4.8.1.1 Density Slicing  PAGEREF _Toc266166546 \h 141
 HYPERLINK \l "_Toc266166547" 4.9 Discussion  PAGEREF _Toc266166547 \h 144
 HYPERLINK \l "_Toc266166548" 4.10 Conclusions  PAGEREF _Toc266166548 \h 147
 HYPERLINK \l "_Toc266166549" Chapter 5 Hydraulic and Hydrological Modelling Using GIS – Results and Analysis  PAGEREF _Toc266166549 \h 153
 HYPERLINK \l "_Toc266166550" 5.1 Introduction  PAGEREF _Toc266166550 \h 153
 HYPERLINK \l "_Toc266166551" 5.2 Modelling Technique  PAGEREF _Toc266166551 \h 155
 HYPERLINK \l "_Toc266166552" 5.2.1 The Conceptual Model  PAGEREF _Toc266166552 \h 155
 HYPERLINK \l "_Toc266166553" 5.2.2 Initial Modelling Concepts  PAGEREF _Toc266166553 \h 158
 HYPERLINK \l "_Toc266166554" 5.3 The Hydrological Model - WMS  PAGEREF _Toc266166554 \h 162
 HYPERLINK \l "_Toc266166555" 5.3.1 Deriving Model Input Parameters using GIS  PAGEREF _Toc266166555 \h 164
 HYPERLINK \l "_Toc266166556" 5.3.1.1 Deriving Catchment Topography – DEM Layer  PAGEREF _Toc266166556 \h 164
 HYPERLINK \l "_Toc266166557" 5.3.1.2 Extracting the DEM of River Fani Catchment from ASTER Data – ASTER DEM Layer  PAGEREF _Toc266166557 \h 166
 HYPERLINK \l "_Toc266166558" 5.3.1.3 Drainage Catchment Properties – Delineation of Catchment Boundary, Outlet and Stream Network Layers  PAGEREF _Toc266166558 \h 169
 HYPERLINK \l "_Toc266166559" 5.3.1.4 Delineating Land Cover Layer  PAGEREF _Toc266166559 \h 174
 HYPERLINK \l "_Toc266166560" 5.3.1.5 Delineating Soil Layer  PAGEREF _Toc266166560 \h 175
 HYPERLINK \l "_Toc266166561" 5.3.1.6 Determining Base Flow (BF) Parameters  PAGEREF _Toc266166561 \h 180
 HYPERLINK \l "_Toc266166562" 5.3.1.7 Frequency Analysis of Precipitation Data  PAGEREF _Toc266166562 \h 181
 HYPERLINK \l "_Toc266166563" 5.3.1.8 Loss Method Calculations  PAGEREF _Toc266166563 \h 202
 HYPERLINK \l "_Toc266166564" 5.3.1.9 Unit Hydrograph (UH) and Lag Time Determination  PAGEREF _Toc266166564 \h 204
 HYPERLINK \l "_Toc266166565" 5.3.1.10 Flow Routing Calculations  PAGEREF _Toc266166565 \h 206
 HYPERLINK \l "_Toc266166566" 5.3.2 Model Calibration and Validation  PAGEREF _Toc266166566 \h 207
 HYPERLINK \l "_Toc266166567" 5.3.3 Hydrological Modelling - Results, Analysis and Discussion  PAGEREF _Toc266166567 \h 211
 HYPERLINK \l "_Toc266166568" 5.3.3.1 Surface Runoff Modelling using Designed AMAX Precipitation  PAGEREF _Toc266166568 \h 211
 HYPERLINK \l "_Toc266166569" 5.3.3.2 Analysis and Discussion  PAGEREF _Toc266166569 \h 215
 HYPERLINK \l "_Toc266166570" 5.3.3.3 Design Storms and Comparison of Results  PAGEREF _Toc266166570 \h 216
 HYPERLINK \l "_Toc266166571" 5.4 The Hydraulic Model - HEC-RAS  PAGEREF _Toc266166571 \h 225
 HYPERLINK \l "_Toc266166572" 5.4.1 Hydraulic Modelling Simulations  PAGEREF _Toc266166572 \h 226
 HYPERLINK \l "_Toc266166573" 5.4.2 Analysis and Discussion  PAGEREF _Toc266166573 \h 235
 HYPERLINK \l "_Toc266166574" 5.5 Conclusions  PAGEREF _Toc266166574 \h 236
 HYPERLINK \l "_Toc266166575" Chapter 6 Catchment Conservation and Management – ‘What-if’ Analyses  PAGEREF _Toc266166575 \h 239
 HYPERLINK \l "_Toc266166576" 6.1 Introduction  PAGEREF _Toc266166576 \h 239
 HYPERLINK \l "_Toc266166577" 6.2 ‘What-if’ Analyses using the Hydrologic Model (WMS)  PAGEREF _Toc266166577 \h 240
 HYPERLINK \l "_Toc266166578" 6.2.1 Scenario 1: A ‘Do nothing’ Scenario  PAGEREF _Toc266166578 \h 240
 HYPERLINK \l "_Toc266166579" 6.2.2 Scenario 2: Land Use/Cover Change Scenarios  PAGEREF _Toc266166579 \h 241
 HYPERLINK \l "_Toc266166580" 6.2.2.1 Scenario 2a: Deforestation Scenarios  PAGEREF _Toc266166580 \h 242
 HYPERLINK \l "_Toc266166581" 6.2.2.2 Scenario 2b: Afforestation Scenarios  PAGEREF _Toc266166581 \h 252
 HYPERLINK \l "_Toc266166582" 6.2.3 Scenario 3: Attenuation  PAGEREF _Toc266166582 \h 263
 HYPERLINK \l "_Toc266166583" 6.2.3.1 Reservoir  PAGEREF _Toc266166583 \h 263
 HYPERLINK \l "_Toc266166584" 6.3 ‘What-if’ Analysis using the Hydraulic Model (HEC-RAS)  PAGEREF _Toc266166584 \h 274
 HYPERLINK \l "_Toc266166585" 6.3.1 HEC-RAS Sensitivity Analysis  PAGEREF _Toc266166585 \h 274
 HYPERLINK \l "_Toc266166586" 6.3.2 Scenario 1: River Channel Engineering  PAGEREF _Toc266166586 \h 279
 HYPERLINK \l "_Toc266166587" 6.3.3 Scenario 2: Reservoir  PAGEREF _Toc266166587 \h 280
 HYPERLINK \l "_Toc266166588" 6.4 Catchment Conservation and Management Proposals  PAGEREF _Toc266166588 \h 282
 HYPERLINK \l "_Toc266166589" 6.4.1 Selection of a Potential Reservoir Site  PAGEREF _Toc266166589 \h 283
 HYPERLINK \l "_Toc266166590" 6.4.2 Hydroelectric Power Station  PAGEREF _Toc266166590 \h 283
 HYPERLINK \l "_Toc266166591" 6.4.3 Leisure Facilities  PAGEREF _Toc266166591 \h 284
 HYPERLINK \l "_Toc266166592" 6.4.4 Bird and Animal Sanctuary  PAGEREF _Toc266166592 \h 284
 HYPERLINK \l "_Toc266166593" 6.5 Discussion and Conclusions  PAGEREF _Toc266166593 \h 285
 HYPERLINK \l "_Toc266166594" Chapter 7 Conclusion and Recommendations  PAGEREF _Toc266166594 \h 288
 HYPERLINK \l "_Toc266166595" 7.1 Introduction  PAGEREF _Toc266166595 \h 288
 HYPERLINK \l "_Toc266166596" 7.2 General Achievements  PAGEREF _Toc266166596 \h 289
 HYPERLINK \l "_Toc266166597" 7.3 Specific Conclusions  PAGEREF _Toc266166597 \h 290
 HYPERLINK \l "_Toc266166598" 7.3.1 Land Cover and Soil Erosion Potential Maps of the River Fani Catchment Using Remote Sensing  PAGEREF _Toc266166598 \h 290
 HYPERLINK \l "_Toc266166599" 7.3.2 Land Cover Change Detection Maps of the River Fani Catchment Using Remote Sensing  PAGEREF _Toc266166599 \h 292
 HYPERLINK \l "_Toc266166600" 7.3.3 Hydrologic Modelling (WMS) and ‘What-if’ Results  PAGEREF _Toc266166600 \h 295
 HYPERLINK \l "_Toc266166601" 7.3.4 Hydraulic Modelling (HEC-RAS) and ‘What-if’ Results  PAGEREF _Toc266166601 \h 297
 HYPERLINK \l "_Toc266166602" 7.3.5 Sustainable Management  PAGEREF _Toc266166602 \h 298
 HYPERLINK \l "_Toc266166603" 7.4 Limitations of the Study  PAGEREF _Toc266166603 \h 299
 HYPERLINK \l "_Toc266166604" 7.5 Recommendations for Future Work  PAGEREF _Toc266166604 \h 299
 HYPERLINK \l "_Toc266166605" 7.6 Final Concluding Remarks  PAGEREF _Toc266166605 \h 300

LIST OF FIGURES

 TOC \h \z \c "Figure"  HYPERLINK \l "_Toc266166606" Figure 2.1: Classification of hydrologic models based on the way they represent variations with space and time and uncertainty of hydrologic systems (Source: Chow, Maidment and Mays (1988)).  PAGEREF _Toc266166606 \h 14
 HYPERLINK \l "_Toc266166607" Figure 3.1: Location of the River Fani Catchment, in Mirdita area, North Albania.  PAGEREF _Toc266166607 \h 49
 HYPERLINK \l "_Toc266166608" Figure 3.2: Hydrographic map of Mati River [2] showing the Catchment area (Fani and Mati Catchment), the main subsidiary Rivers and the hypsometry.  PAGEREF _Toc266166608 \h 51
 HYPERLINK \l "_Toc266166609" Figure 3.3: Tectonic zonation in Albania. SP: Shkodra – Peja transversal dividing the Dinarides from the Hellenides. 1, 2, 3, 4: Windows of the Kruja Zone (Source: Meco and Aliaj (2000)).  PAGEREF _Toc266166609 \h 56
 HYPERLINK \l "_Toc266166610" Figure 3.4: Geology map of Rubik Municipality area[] showing that the area mainly conforms of volcanic sedimentary and ultrabasic rocks.  PAGEREF _Toc266166610 \h 57
 HYPERLINK \l "_Toc266166611" Figure 3.5: Changes in direction, morphology and volume of water of the River Fani, near the Rubik, in the 1980s, 1990s and 2001.  PAGEREF _Toc266166611 \h 62
 HYPERLINK \l "_Toc266166612" Figure 4.1: Scheme for deriving NDVI maps from Landsat images for change detection of vegetation cover.  PAGEREF _Toc266166612 \h 87
 HYPERLINK \l "_Toc266166613" Figure 4.2: NDVI histogram of 10th of September 1984 TM image data for the River Fani Catchment. The histogram has a mean value of 0.276 of vegetation cover.  PAGEREF _Toc266166613 \h 88
 HYPERLINK \l "_Toc266166614" Figure 4.3: NDVI histogram of 30th of September 1991 TM image data. The histogram has a mean value of 0.118 of vegetation cover.  PAGEREF _Toc266166614 \h 89
 HYPERLINK \l "_Toc266166615" Figure 4.4: NDVI histogram of 13th of August 2000 ETM+ image data. The histogram has a mean value of 0.378 of vegetation cover.  PAGEREF _Toc266166615 \h 90
 HYPERLINK \l "_Toc266166616" Figure 4.5: Variation of the 16-day mean EVI of the River Fani Catchment spanning from 2000 to 2004.  PAGEREF _Toc266166616 \h 100
 HYPERLINK \l "_Toc266166617" Figure 4.6: Leaf Area Index trendlines for the major land cover types in the River Fani Catchment for the years 1984, 1991 and 2000. The values were derived from Landsat TM and ETM+ satellite images of the respective years.  PAGEREF _Toc266166617 \h 102
 HYPERLINK \l "_Toc266166618" Figure 4.7: Sensitivity of number of land cover classes to percentage change in River Fani Catchment (to find optimum number of classes).  PAGEREF _Toc266166618 \h 104
 HYPERLINK \l "_Toc266166619" Figure 4.8: Two variables band 1 and band 2 with effectively one dimensionality (some scatter).  PAGEREF _Toc266166619 \h 112
 HYPERLINK \l "_Toc266166620" Figure 4.9: Land cover percentages for the River Fani Catchment in 1984.  PAGEREF _Toc266166620 \h 116
 HYPERLINK \l "_Toc266166621" Figure 4.10: Land cover percentages for the River Fani Catchment in 1991.  PAGEREF _Toc266166621 \h 117
 HYPERLINK \l "_Toc266166622" Figure 4.11: Land cover percentages for the River Fani Catchment in 2000.  PAGEREF _Toc266166622 \h 117
 HYPERLINK \l "_Toc266166623" Figure 4.12: Comparison of change detection of the land use classes in the River Fani Catchment between 1984, 1991 and 2000 based on a 10-class system.  PAGEREF _Toc266166623 \h 120
 HYPERLINK \l "_Toc266166624" Figure 4.13: Comparison of change detection of the land cover classes in the River Fani Catchment between 1984, 1991 and 2000 based on a 5-class system.  PAGEREF _Toc266166624 \h 122
 HYPERLINK \l "_Toc266166625" Figure 4.14: Percentage covered by the land cover classes in River Fani Catchment for 1984.  PAGEREF _Toc266166625 \h 123
 HYPERLINK \l "_Toc266166626" Figure 4.15: Percentage covered by the land cover classes in River Fani Catchment for 1991.  PAGEREF _Toc266166626 \h 123
 HYPERLINK \l "_Toc266166627" Figure 4.16: Percentage covered by the land cover classes in River Fani Catchment for 2000.  PAGEREF _Toc266166627 \h 124
 HYPERLINK \l "_Toc266166628" Figure 4.17: Hierarchical tree structure, showing the NDVI layer of a Landsat Image distinguished into vegetation and non-vegetation. Vegetation is differentiated into forest and non-forest and the non-vegetation into water and non-water.  PAGEREF _Toc266166628 \h 130
 HYPERLINK \l "_Toc266166629" Figure 4.18: Forest and no forest discrimination model (and calculation of area that covers in the Catchment) from vegetation images in the River Fani Catchment.  PAGEREF _Toc266166629 \h 131
 HYPERLINK \l "_Toc266166630" Figure 4.19: Schematic of the BSI model created in ERDAS Imagine model, based on eq.4.1.  PAGEREF _Toc266166630 \h 137
 HYPERLINK \l "_Toc266166631" Figure 4.20: Schematic of the BSI model created in ERDAS Imagine model, based on eq. 4.2.  PAGEREF _Toc266166631 \h 138
 HYPERLINK \l "_Toc266166632" Figure 4.21: Schematic of the BSI model created in ERDAS Imagine model, based on eq.4.3.  PAGEREF _Toc266166632 \h 140
 HYPERLINK \l "_Toc266166633" Figure 4.22: Schematic of the density slicing model created in ERDAS Imagine model that classifies image according to threshold values into water, grass and bare soil.  PAGEREF _Toc266166633 \h 141
 HYPERLINK \l "_Toc266166634" Figure 5.1: Model representation of urban and forest hillslope elements draining to a channel reach, (Source: Wigmosta and Burges (1997) (Fig.1)).  PAGEREF _Toc266166634 \h 156
 HYPERLINK \l "_Toc266166635" Figure 5.2: Flowchart of the proposed hydrologic model (Adapted from De Roo et al. (1998)).  PAGEREF _Toc266166635 \h 157
 HYPERLINK \l "_Toc266166636" Figure 5.3: Hydrologic schema proposed for the River Fani Catchment. Rectangles represent data input and ovals show activities using the data.  PAGEREF _Toc266166636 \h 161
 HYPERLINK \l "_Toc266166637" Figure 5.4: GIS data management for the hydrological model.  PAGEREF _Toc266166637 \h 163
 HYPERLINK \l "_Toc266166638" Figure 5.5: Schematic of topographic maps of 1:25 000 scale acquired for the River Fani Catchment. Each map covers an area of approximately 95 km2.  PAGEREF _Toc266166638 \h 165
 HYPERLINK \l "_Toc266166639" Figure 5.6: A Schematic of the hydrologic modelling processes.  PAGEREF _Toc266166639 \h 167
 HYPERLINK \l "_Toc266166640" Figure 5.7: Streamflip arc macro language (aml) used for correcting the flow direction of the stream network of the River Fani Catchment.  PAGEREF _Toc266166640 \h 173
 HYPERLINK \l "_Toc266166641" Figure 5.8: Representation of the stream burning technique.  PAGEREF _Toc266166641 \h 173
 HYPERLINK \l "_Toc266166642" Figure 5.9: Annual maximum precipitation for the Rubik sub-catchment, Albania, plotted using Blom’s formula on a probability scale for the lognormal distribution.  PAGEREF _Toc266166642 \h 187
 HYPERLINK \l "_Toc266166643" Figure 5.10: Annual maximum precipitation for the Rubik sub-catchment, Albania, plotted on a probability scale for the Log Pearson Type III distribution.  PAGEREF _Toc266166643 \h 188
 HYPERLINK \l "_Toc266166644" Figure 5.11: Annual maximum precipitation for the Fani I Madh sub-catchment, Albania, plotted using Blom’s formula on a probability scale for the lognormal distribution.  PAGEREF _Toc266166644 \h 191
 HYPERLINK \l "_Toc266166645" Figure 5.12: Annual maximum precipitation for the Fani I Madh sub-catchment, Albania, plotted on a probability scale for the Log Pearson Type III distribution.  PAGEREF _Toc266166645 \h 192
 HYPERLINK \l "_Toc266166646" Figure 5.13: Annual maximum precipitation for the Rubik sub-catchment, Albania, plotted using Blom’s formula on a probability scale for the lognormal distribution.  PAGEREF _Toc266166646 \h 195
 HYPERLINK \l "_Toc266166647" Figure 5.14: Annual maximum precipitation for the Fani I Vogel sub-catchment, Albania, plotted on a probability scale for the Log Pearson Type III distribution.  PAGEREF _Toc266166647 \h 196
 HYPERLINK \l "_Toc266166648" Figure 5.15: Land use mapping in WMS. Each land use polygon is given an ID under which its properties are listed (e.g. SCS group and CN).  PAGEREF _Toc266166648 \h 205
 HYPERLINK \l "_Toc266166649" Figure 5.16: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 2 years.  PAGEREF _Toc266166649 \h 212
 HYPERLINK \l "_Toc266166650" Figure 5.17: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 5 years.  PAGEREF _Toc266166650 \h 212
 HYPERLINK \l "_Toc266166651" Figure 5.18: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 10 years.  PAGEREF _Toc266166651 \h 213
 HYPERLINK \l "_Toc266166652" Figure 5.19: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 25 years.  PAGEREF _Toc266166652 \h 213
 HYPERLINK \l "_Toc266166653" Figure 5.20: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 50 years.  PAGEREF _Toc266166653 \h 214
 HYPERLINK \l "_Toc266166654" Figure 5.21: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 100 years.  PAGEREF _Toc266166654 \h 214
 HYPERLINK \l "_Toc266166655" Figure 5.22: Hydrograph of River Fani Catchment at the outlet point for a designed precipitation event having a return period 200 years.  PAGEREF _Toc266166655 \h 215
 HYPERLINK \l "_Toc266166656" Figure 5.23: HEC-RAS Hydraulic model. The geometric data are shown with the cross sections, the junction and reaches names.  PAGEREF _Toc266166656 \h 226
 HYPERLINK \l "_Toc266166657" Figure 5.24: Cross section at Rubik town showing the water level of the river after a storm event of a 2 year return period.  PAGEREF _Toc266166657 \h 231
 HYPERLINK \l "_Toc266166658" Figure 5.25: Cross section at Rubik town showing the water level of the river after a storm event of a 5 year return period.  PAGEREF _Toc266166658 \h 231
 HYPERLINK \l "_Toc266166659" Figure 5.26: Cross section at Rubik town showing the water level of the river after a storm event of a 10 year return period.  PAGEREF _Toc266166659 \h 232
 HYPERLINK \l "_Toc266166660" Figure 5.27: Cross section at Rubik town showing the water level of the river after a storm event of a 25 year return period.  PAGEREF _Toc266166660 \h 232
 HYPERLINK \l "_Toc266166661" Figure 5.28: Cross section at Rubik town showing the water level of the river after a storm event of a 50 year return period.  PAGEREF _Toc266166661 \h 233
 HYPERLINK \l "_Toc266166662" Figure 5.29: Cross section at Rubik town showing the water level of the river after a storm event of a 100 year return period.  PAGEREF _Toc266166662 \h 233
 HYPERLINK \l "_Toc266166663" Figure 5.30: Cross section at Rubik town showing the water level of the river after a storm event of a 200 year return period.  PAGEREF _Toc266166663 \h 234
 HYPERLINK \l "_Toc266166664" Figure 6.1: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 2 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166664 \h 245
 HYPERLINK \l "_Toc266166665" Figure 6.2: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 5 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166665 \h 246
 HYPERLINK \l "_Toc266166666" Figure 6.3: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 10 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166666 \h 246
 HYPERLINK \l "_Toc266166667" Figure 6.4: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 25 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166667 \h 247
 HYPERLINK \l "_Toc266166668" Figure 6.5: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 50 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166668 \h 247
 HYPERLINK \l "_Toc266166669" Figure 6.6: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 100 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166669 \h 248
 HYPERLINK \l "_Toc266166670" Figure 6.7: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 200 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166670 \h 248
 HYPERLINK \l "_Toc266166671" Figure 6.8: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 2 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166671 \h 249
 HYPERLINK \l "_Toc266166672" Figure 6.9: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 5 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166672 \h 249
 HYPERLINK \l "_Toc266166673" Figure 6.10: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 10 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166673 \h 250
 HYPERLINK \l "_Toc266166674" Figure 6.11: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 25 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166674 \h 250
 HYPERLINK \l "_Toc266166675" Figure 6.12: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 50 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166675 \h 251
 HYPERLINK \l "_Toc266166676" Figure 6.13: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 100 years return period. Deforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166676 \h 251
 HYPERLINK \l "_Toc266166677" Figure 6.14: Hydrograph of the River Fani Catchment, at Rubik town, for the storm event of designed precipitation of 200 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the broad leaved forest have been replaced with sparsely vegetated areas i.e. 18% deforestation (blue line), (c) the coniferous and broad leaved forests have been replaced with sparsely vegetated areas (green line) i.e. 31% deforestation.  PAGEREF _Toc266166677 \h 252
 HYPERLINK \l "_Toc266166678" Figure 6.15: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 2 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166678 \h 255
 HYPERLINK \l "_Toc266166679" Figure 6.16: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 5 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166679 \h 256
 HYPERLINK \l "_Toc266166680" Figure 6.17: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 10 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166680 \h 256
 HYPERLINK \l "_Toc266166681" Figure 6.18: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 25 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166681 \h 257
 HYPERLINK \l "_Toc266166682" Figure 6.19: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 50 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166682 \h 257
 HYPERLINK \l "_Toc266166683" Figure 6.20: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 100 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166683 \h 258
 HYPERLINK \l "_Toc266166684" Figure 6.21: Hydrograph of the River Fani Catchment at the outlet for the storm event of designed precipitation of 200 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166684 \h 258
 HYPERLINK \l "_Toc266166685" Figure 6.22: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 2 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166685 \h 259
 HYPERLINK \l "_Toc266166686" Figure 6.23: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 5 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166686 \h 259
 HYPERLINK \l "_Toc266166687" Figure 6.24: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 10 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166687 \h 260
 HYPERLINK \l "_Toc266166688" Figure 6.25: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 25 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166688 \h 260
 HYPERLINK \l "_Toc266166689" Figure 6.26: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 50 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166689 \h 261
 HYPERLINK \l "_Toc266166690" Figure 6.27: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 100 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166690 \h 261
 HYPERLINK \l "_Toc266166691" Figure 6.28: Hydrograph of the River Fani Catchment at Rubik town for the storm event of designed precipitation of 200 years return period. Afforestation scenario: (a) do nothing scenario (red line), (b) the sparsely vegetated areas have been replaced with coniferous and broad leaved forest (blue line) (6% afforestation), (c) the natural grassland areas have been replaced with coniferous and broad leaved forests (green line) (28% afforestation).  PAGEREF _Toc266166691 \h 262
 HYPERLINK \l "_Toc266166692" Figure 6.29: Elevation of water versus discharge in the proposed reservoir, controlled to result to a 2 year return period storm outflow.  PAGEREF _Toc266166692 \h 267
 HYPERLINK \l "_Toc266166693" Figure 6.30: Accumulated volume of water versus time in the proposed reservoir, controlled to give an outflow corresponding to a storm event having a 2 year return period.  PAGEREF _Toc266166693 \h 268
 HYPERLINK \l "_Toc266166694" Figure 6.31: Water elevation versus time for the proposed reservoir, controlled to result to a 2 year return period storm outflow.  PAGEREF _Toc266166694 \h 268
 HYPERLINK \l "_Toc266166695" Figure 6.32: Hydrograph of the outflow water versus time for the proposed reservoir controlled to result to a 2 year return period storm.  PAGEREF _Toc266166695 \h 269
 HYPERLINK \l "_Toc266166696" Figure 6.33: Hydrograph of River Fani Catchment showing the controlled flow to result to a 2 year return period storm outflow (in green colour) at Rubik town once a reservoir is located upstream. The hydrograph in blue colour is the flow of a 100 year storm simulation.  PAGEREF _Toc266166696 \h 269
 HYPERLINK \l "_Toc266166697" Figure 6.34: Elevation versus discharge for the proposed reservoir, controlled to result to a 10 year return period storm outflow.  PAGEREF _Toc266166697 \h 270
 HYPERLINK \l "_Toc266166698" Figure 6.35: Accumulated volume of water versus time in the proposed reservoir, controlled to result to a storm outflow having a 10 year return period.  PAGEREF _Toc266166698 \h 271
 HYPERLINK \l "_Toc266166699" Figure 6.36: Hydrograph of the proposed reservoir showing the controlled outflow versus time (at Rubik town) to result to a controlled 10 year return period storm outflow.  PAGEREF _Toc266166699 \h 271
 HYPERLINK \l "_Toc266166700" Figure 6.37: Water elevation versus time for the proposed reservoir controlled to result to a 10 year return period storm outflow.  PAGEREF _Toc266166700 \h 272
 HYPERLINK \l "_Toc266166701" Figure 6.38: Hydrograph of River Fani Catchment showing the controlled flow to result to 10 a year return period storm (in green colour) at Rubik town once a reservoir is located upstream. The hydrograph in blue colour is the output simulation of a 100 year storm.  PAGEREF _Toc266166701 \h 272
 HYPERLINK \l "_Toc266166702" Figure 6.39: Sensitivity of Water Surface Elevation (WSE) of the application of 10, 20 and 30 percent increase bias to Manning’s number for different scenarios of varying frequency in the River Fani Catchment at the reach adjacent to Rubik town.  PAGEREF _Toc266166702 \h 278
 HYPERLINK \l "_Toc266166703" Figure 6.40: Sensitivity of Water Surface Elevation (WSE) of the application of 10, 20 and 30 percent increase bias to peak flows for different scenarios of varying frequency in the River Fani Catchment at the reach adjacent to Rubik town.  PAGEREF _Toc266166703 \h 278
 HYPERLINK \l "_Toc266166704" Figure 6.41: Storage Capacity Curve for the River Fani Catchment showing the amount of water that can be stored in a reservoir and the required elevation.  PAGEREF _Toc266166704 \h 280

LIST OF TABLES

 TOC \h \z \c "Table"  HYPERLINK \l "_Toc266166705" Table 2.1: Review of different hydrological models, describing their input requirements, strengths and limitations.  PAGEREF _Toc266166705 \h 18
 HYPERLINK \l "_Toc266166706" Table 3.1: Quantity of the eroded soil by the discharge of the Albanian Rivers (source: Qiriazi and Sala (1999)).  PAGEREF _Toc266166706 \h 61
 HYPERLINK \l "_Toc266166707" Table 4.1: Examples of remote-sensing instruments, their region of the electromagnetic spectrum (ER region) wavelengths covered and some applications. IR denotes infrared wavelengths (Table adapted from Jensen (2005)).  PAGEREF _Toc266166707 \h 71
 HYPERLINK \l "_Toc266166708" Table 4.2: Landsat Thematic Mapper (TM) spectral bands (source: USGS Science for a Changing World (2006)).  PAGEREF _Toc266166708 \h 74
 HYPERLINK \l "_Toc266166709" Table 4.3: Landsat Enhanced Thematic Mapper (ETM+) spectral bands (Source: USGS, EROS, (n.d., b) [online].  PAGEREF _Toc266166709 \h 75
 HYPERLINK \l "_Toc266166710" Table 4.4: Satellite remotely sensed data used in this research.  PAGEREF _Toc266166710 \h 75
 HYPERLINK \l "_Toc266166711" Table 4.5: MODIS (13Q1) Vegetation Indices band description (Sources: USGS, LPDAAC, (n.d., e) [online] and Huete et. al. (1999)).  PAGEREF _Toc266166711 \h 76
 HYPERLINK \l "_Toc266166712" Table 4.6: Characteristics of the three ASTER Sensor Systems (Source: Abrams and Hook (2002)).  PAGEREF _Toc266166712 \h 77
 HYPERLINK \l "_Toc266166713" Table 4.7: NDVI average values recorded from the histograms in Figures 4.2, 4.3 and 4.4.  PAGEREF _Toc266166713 \h 91
 HYPERLINK \l "_Toc266166714" Table 4.8: Classification of NDVI values to land cover (Source: Weier and Herring, (1999))  PAGEREF _Toc266166714 \h 91
 HYPERLINK \l "_Toc266166715" Table 4.9: 16-day mean EVI values for the River Fani Catchment from 2000 to 2004.  PAGEREF _Toc266166715 \h 99
 HYPERLINK \l "_Toc266166716" Table 4.10: Relationship between NDVI and LAI for different land covers.  PAGEREF _Toc266166716 \h 101
 HYPERLINK \l "_Toc266166717" Table 4.11: LAI results for the major land cover types in the River Fani Catchment for the years 1984, 1991 and 2000. The values were derived from Landsat TM and ETM+ satellite images of the respective years.  PAGEREF _Toc266166717 \h 102
 HYPERLINK \l "_Toc266166718" Table 4.12: LAI change detection between 1984-1991, 1991-2000 and 1984-2000. Minus and plus signs indicate, respectively, reductions and increases in vegetation biomass.  PAGEREF _Toc266166718 \h 102
 HYPERLINK \l "_Toc266166719" Table 4.13: CORINE hierarchical Land use Nomenclature (CORINE (1994))  PAGEREF _Toc266166719 \h 106
 HYPERLINK \l "_Toc266166720" Table 4.14: Land cover classes of the 1984, 1991 Landsat TM and 2000 Landsat ETM+ image of the River Fani Catchment and their associated area covered in square kilometres and in percentage values.  PAGEREF _Toc266166720 \h 116
 HYPERLINK \l "_Toc266166721" Table 4.15: Change detection (in percentage and in square kilometres) between 1984-1991, 1991-2000 and 1984-2000 of the ten land use classes of the River Fani Catchment. Negative and positive values indicate change reduction and increase, respectively.  PAGEREF _Toc266166721 \h 119
 HYPERLINK \l "_Toc266166722" Table 4.16: Level 2 and level 3 classification system for River Fani Catchment. Level 2 consists of 12 classes whereas Level 3 classes are aggregated down to 5 sets.  PAGEREF _Toc266166722 \h 121
 HYPERLINK \l "_Toc266166723" Table 4.17: Area covered by the five main land cover classes for the 1984, 1991 Landsat TM and 2000 Landsat ETM+ images of the River Fani Catchment.  PAGEREF _Toc266166723 \h 122
 HYPERLINK \l "_Toc266166724" Table 4.18: Percent change detection of the main five land cover classes in the River Fani Catchment from 1984 to 2000. Negative values indicate reduction and positive correspond to an increase.  PAGEREF _Toc266166724 \h 124
 HYPERLINK \l "_Toc266166725" Table 4.19: Forest area and change for Albania during 1990 and 2000 (Source: World Resources Institute, (n.d.) [online]).  PAGEREF _Toc266166725 \h 134
 HYPERLINK \l "_Toc266166726" Table 4.20: Forest cover statistics for Albania between 1990 and 2000 (Source: Butler (2000)).  PAGEREF _Toc266166726 \h 134
 HYPERLINK \l "_Toc266166727" Table 4.21: Land cover for Albania during 1950, 1990 and 1999 (Source: National Environmental Agency: Overview of the Steps Undertaken in the Context of the Convention to Combat Desertification, 2001 rfrd in: United Nation, (n.d.) [online]).  PAGEREF _Toc266166727 \h 135
 HYPERLINK \l "_Toc266166728" Table 4.22: Land area covered by forests for the whole of Albania, (United Nations University (UNU), (n.d.) [online]).  PAGEREF _Toc266166728 \h 135
 HYPERLINK \l "_Toc266166729" Table 4.23: Land use activities in Albania, (referred in: Republic of Albania, (n.d.) [online]).  PAGEREF _Toc266166729 \h 135
 HYPERLINK \l "_Toc266166730" Table 5.1: SCS Soil classification groups and their characteristics (USDA-SCS (1998)).  PAGEREF _Toc266166730 \h 176
 HYPERLINK \l "_Toc266166731" Table 5.2: SCS hydrologic soil group characteristics. The minimum infiltration rate (cm/hr) for each group is listed with its infiltration potential and runoff characteristics (USDA-SCS (1998)).  PAGEREF _Toc266166731 \h 177
 HYPERLINK \l "_Toc266166732" Table 5.3: SCS Curve number value (USDA-SCS (1998)).  PAGEREF _Toc266166732 \h 179
 HYPERLINK \l "_Toc266166733" Table 5.4: Annual maximum 24-hour precipitation in millimetres of the River Fani Catchment in Rubik sub-catchment, Albania 1948-1980.  PAGEREF _Toc266166733 \h 185
 HYPERLINK \l "_Toc266166734" Table 5.5: Designed annual maximum precipitation (in mm) in the Rubik sub-catchment for different return periods using Log-normal and Log-Pearson Type III distributions.  PAGEREF _Toc266166734 \h 185
 HYPERLINK \l "_Toc266166735" Table 5.6: Probability plotting using the normal distribution and Blom’s formula for the annual maximum precipitation of the Rubik sub-catchment, Albania.  PAGEREF _Toc266166735 \h 186
 HYPERLINK \l "_Toc266166736" Table 5.7: Probability plotting using the LPIII distribution for the annual maximum precipitation of the Rubik sub-catchment, Albania.  PAGEREF _Toc266166736 \h 187
 HYPERLINK \l "_Toc266166737" Table 5.8: Annual maximum 24-hour precipitation in millimetres of the Fani I Madh sub-catchment in Rubik, Albania 1957-1980.  PAGEREF _Toc266166737 \h 189
 HYPERLINK \l "_Toc266166738" Table 5.9: Designed annual maximum precipitation (in mm) in the Fani I Madh sub-catchment for different return periods using Log-normal and Log-Pearson Type III distributions.  PAGEREF _Toc266166738 \h 189
 HYPERLINK \l "_Toc266166739" Table 5.10: Probability plotting using the normal distribution and Blom’s formula for the annual maximum precipitation of the Fani I Madh sub-catchment, Albania.  PAGEREF _Toc266166739 \h 190
 HYPERLINK \l "_Toc266166740" Table 5.11: Probability plotting using the LPIII distribution for the annual maximum precipitation of the Fani I Madh sub-catchment, Albania.  PAGEREF _Toc266166740 \h 191
 HYPERLINK \l "_Toc266166741" Table 5.12: Annual maximum 24-hour precipitation in millimetres of the Fani I Vogel sub-catchment, Albania 1951-1980.  PAGEREF _Toc266166741 \h 193
 HYPERLINK \l "_Toc266166742" Table 5.13: Designed annual maximum precipitation (in mm) in the Fani I Vogel sub-catchment for different return periods using Log-normal and Log-Pearson Type III distributions.  PAGEREF _Toc266166742 \h 193
 HYPERLINK \l "_Toc266166743" Table 5.14: Probability plotting using the normal distribution and Blom’s formula for the annual maximum precipitation of the Fani I Vogel sub-catchment, Albania.  PAGEREF _Toc266166743 \h 194
 HYPERLINK \l "_Toc266166744" Table 5.15: Probability plotting using the LPIII distribution for the annual maximum precipitation of the Fani I Vogel sub-catchment, Albania.  PAGEREF _Toc266166744 \h 195
 HYPERLINK \l "_Toc266166745" Table 5.16: Percentage Impervious for each land use type (Dunne and Leopold (1978) and Klein (1979)).  PAGEREF _Toc266166745 \h 205
 HYPERLINK \l "_Toc266166746" Table 5.17: Precipitation values (in mm) of the River Fani sub-catchments used in the WMS hydrological model to simulate design storm events.  PAGEREF _Toc266166746 \h 211
 HYPERLINK \l "_Toc266166747" Table 5.18: Discharge values (in m3/s) of the River Fani sub-catchments derived from the WMS hydrological model.  PAGEREF _Toc266166747 \h 211
 HYPERLINK \l "_Toc266166748" Table 5.19: Peak discharges for design return period of 100 and 50 years for some Albanian rivers.  PAGEREF _Toc266166748 \h 216
 HYPERLINK \l "_Toc266166749" Table 5.20: Peak discharges for design return period of 100 and 50 years for some Albanian rivers, using Pearson III probability distribution (Selenica (2006)).  PAGEREF _Toc266166749 \h 217
 HYPERLINK \l "_Toc266166750" Table 5.21: Peak discharges for design return period of 100 and 50 years for some Albanian rivers, using Pearson III probability distribution (Selenica (2004)).  PAGEREF _Toc266166750 \h 217
 HYPERLINK \l "_Toc266166751" Table 5.22: The flood peaks of 1962-63 and 1970-71 in Albanian catchments (Kolaneci (2000)).  PAGEREF _Toc266166751 \h 218
 HYPERLINK \l "_Toc266166752" Table 5.23: Roughness values used in the HEC-RAS Hydraulic model.  PAGEREF _Toc266166752 \h 228
 HYPERLINK \l "_Toc266166753" Table 5.24: Flows of River Fani Catchment reaches derived from the WMS hydrologic model for different design precipitation events. Values were used in the HEC-RAS Hydraulic model.  PAGEREF _Toc266166753 \h 229
 HYPERLINK \l "_Toc266166754" Table 6.1: Discharge values (in m3/s) of the River Fani sub-catchments derived from the WMS hydrological model (restatement of Table 5.18).  PAGEREF _Toc266166754 \h 241
 HYPERLINK \l "_Toc266166755" Table 6.2: Volume of water (x106 m3) of the River Fani sub-catchments derived from the WMS hydrological model.  PAGEREF _Toc266166755 \h 241
 HYPERLINK \l "_Toc266166756" Table 6.3: Comparison of peak discharge of the hydrographs resulted from 18% and 31% deforestation in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166756 \h 244
 HYPERLINK \l "_Toc266166757" Table 6.4: Quantified change of the peak discharge (in m3/s and %) of the hydrographs produced from an 18% and 31% deforestation change scenarios in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166757 \h 244
 HYPERLINK \l "_Toc266166758" Table 6.5: Comparison of the volume of water of the hydrographs resulted from 18% and 31% deforestation in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166758 \h 244
 HYPERLINK \l "_Toc266166759" Table 6.6: Quantified change of the volume of water (in m3 and %) of the hydrographs produced from an 18% and 31% deforestation change scenarios in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166759 \h 245
 HYPERLINK \l "_Toc266166760" Table 6.7: Comparison of the peak discharge of the hydrographs resulted from 6% and 28% afforestation in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166760 \h 253
 HYPERLINK \l "_Toc266166761" Table 6.8: Quantified change (in m3/s and %) of the peak discharge for an 6% and 28% afforestation change scenarios in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166761 \h 254
 HYPERLINK \l "_Toc266166762" Table 6.9: Comparison of volume of water of the hydrographs resulted from 6% and 28% afforestation in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166762 \h 254
 HYPERLINK \l "_Toc266166763" Table 6.10: Quantified change (in m3 and %) of the volume of water for an 6% and 28% afforestation change scenarios in the River Fani Catchment outlet as opposed to the forest state during the year 2000 for storms with 2, 5, 10, 25, 50 100 and 200 year return periods.  PAGEREF _Toc266166763 \h 254
 HYPERLINK \l "_Toc266166764" Table 6.11: Elevation, discharge and volume data for the proposed reservoir, controlled to generate outflow of a 2 year return period storm.  PAGEREF _Toc266166764 \h 267
 HYPERLINK \l "_Toc266166765" Table 6.12: Elevation, discharge and volume data for the proposed reservoir, controlled to generate outflow of a 10 year return period storm.  PAGEREF _Toc266166765 \h 270
 HYPERLINK \l "_Toc266166766" Table 6.13: Sensitivity analysis on Manning’s number (n). Results are based on 10, 20 and 30 percent increase on the actual Manning’s number of the River Fani Catchment.  PAGEREF _Toc266166766 \h 274
 HYPERLINK \l "_Toc266166767" Table 6.14: WSE results of sensitivity analysis at a cross section on River Fani adjacent to Rubik town, using the Manning’s number (n) values as presented in the table above. The base of the riverbed at the cross section is 81.2m a.s.l.  PAGEREF _Toc266166767 \h 275
 HYPERLINK \l "_Toc266166768" Table 6.15: The effect (quantified change) of an increase bias applied on roughness values on water surface elevation of the River Fani Catchment at Rubik.  PAGEREF _Toc266166768 \h 275
 HYPERLINK \l "_Toc266166769" Table 6.16: WSE results of sensitivity analysis on a cross section of the River Fani adjacent to Rubik town, by introducing an increase bias of 10%, 20% and 30% in the peak discharge. The base of the riverbed at the cross section is 81.2m a.s.l.  PAGEREF _Toc266166769 \h 275
 HYPERLINK \l "_Toc266166770" Table 6.17: The effect (quantified change) of an increase bias applied on peak flows on water surface elevation of the River Fani Catchment at Rubik.  PAGEREF _Toc266166770 \h 275
 HYPERLINK \l "_Toc266166771" Table 6.18: The effect on surface water elevation of changing riverbed material (roughness value) on a reach downstream Rubik town on the River Fani Catchment.  PAGEREF _Toc266166771 \h 279

LIST OF PLATES

 TOC \h \z \c "Plate"  HYPERLINK \l "_Toc266166835" Plate 3.1: Rubik town and River Fani Catchment, from AIA Porthcawl (exact date unavailable - acquired during 1980s).  PAGEREF _Toc266166835 \h 62
 HYPERLINK \l "_Toc266166836" Plate 3.2: Rubik town and River Fani Catchment, from AIA Porthcawl (exact date unavailable - acquired during 1990s).  PAGEREF _Toc266166836 \h 62
 HYPERLINK \l "_Toc266166837" Plate 3.3: Rubik town and River Fani Catchment, from AIA Porthcawl (exact date unavailable - acquired during 1990s).  PAGEREF _Toc266166837 \h 62
 HYPERLINK \l "_Toc266166838" Plate 3.4: Picture of Rubik town and River Fani Catchment, taken during a site assessment visit in November 2001.  PAGEREF _Toc266166838 \h 62
 HYPERLINK \l "_Toc266166839" Plate 3.5: Soil erosion and bank instabilities along the banks of the River Fani (a) upstream Rubik town. The arrows show landslips and the erosive force of the River flow, (b) landslips, upstream of the River Fani at Rubik town, (c) landslips, downstream of the River Fani at Rubik town (Source: AIA Porthcawl).  PAGEREF _Toc266166839 \h 64
 HYPERLINK \l "_Toc266166840" Plate 3.6: Soil erosion and landslide problems in Lesja (situated near Rubik town) in North Albania. Mitigation measures underway (concrete piles) to stabilise the landslide (Source: AIA Porthcawl).  PAGEREF _Toc266166840 \h 64
 HYPERLINK \l "_Toc266166841" Plate 3.7: Flooding in Kruje in Albania, 30th September 2002 (Source: Co-operative Program on Water & Climate (CPWC), UNESCO-IHE (2002)).  PAGEREF _Toc266166841 \h 67
 HYPERLINK \l "_Toc266166842" Plate 4.1: Landat TM imagery acquired on the 10th of September in 1984. The red boundary represents the River Fani Catchment.  PAGEREF _Toc266166842 \h 80
 HYPERLINK \l "_Toc266166843" Plate 4.2: Landat TM imagery acquired on the 30th of September in 1991. The red boundary represents the River Fani Catchment.  PAGEREF _Toc266166843 \h 80
 HYPERLINK \l "_Toc266166844" Plate 4.3: Landat ETM+ imagery acquired on the 13th of August in 2000. The red boundary represents the River Fani Catchment.  PAGEREF _Toc266166844 \h 80
 HYPERLINK \l "_Toc266166845" Plate 4.4: ASTER DEM USGS 30m ground resolution (image acquired on the 25th of October 2000). Low relief is shown dark colour whereas bright colour indicates high relief.  PAGEREF _Toc266166845 \h 85
 HYPERLINK \l "_Toc266166846" Plate 4.5: NDVI derived from the 10th of September 1984 TM imagery for the River Fani Catchment. Dark areas indicate no vegetation (bare soil) while lighter (white) colour areas indicate vegetation cover and forests.  PAGEREF _Toc266166846 \h 88
 HYPERLINK \l "_Toc266166847" Plate 4.6: NDVI derived from the 30th of September 1991 TM imagery for the River Fani Catchment. Dark areas indicate no vegetation (bare soil) while lighter (white) colour areas indicate vegetation cover and forests.  PAGEREF _Toc266166847 \h 89
 HYPERLINK \l "_Toc266166848" Plate 4.7: NDVI derived from the 13th of August 2000 ETM+ imagery for the River Fani Catchment. Dark areas indicate no vegetation (bare soil) while lighter (white) colour areas indicate vegetation cover and forests.  PAGEREF _Toc266166848 \h 90
 HYPERLINK \l "_Toc266166849" Plate 4.8: MODIS seasonal EVI for 2000, showing vegetation strength in the River Fani Catchment. Black areas indicate no vegetation cover (water) and white areas indicate densely vegetated terrain. The in-between degrees of vegetation strength are illustrated with the different grey shades.  PAGEREF _Toc266166849 \h 93
 HYPERLINK \l "_Toc266166850" Plate 4.9: MODIS seasonal EVI for 2001, showing vegetation strength in the River Fani Catchment. Black areas indicate no vegetation cover (water) and white areas represent densely vegetated terrain. The in-between degrees of vegetation strength are illustrated with the different grey shades.  PAGEREF _Toc266166850 \h 94
 HYPERLINK \l "_Toc266166851" Plate 4.10: MODIS seasonal EVI for 2002, showing vegetation strength in the River Fani Catchment. Black areas indicate no vegetation cover (water) and white areas show densely vegetated terrain. The in-between degrees of vegetation strength are illustrated with the different grey shades.  PAGEREF _Toc266166851 \h 95
 HYPERLINK \l "_Toc266166852" Plate 4.11: MODIS seasonal EVI for 2001, showing vegetation strength in the River Fani Catchment. Black areas indicate no vegetation cover (water) and white areas show densely vegetated terrain. The in-between degrees of vegetation strength are illustrated with the different grey shades.  PAGEREF _Toc266166852 \h 96
 HYPERLINK \l "_Toc266166853" Plate 4.12: CORINE land cover first level classification (i.e. 5 classes) of North Albania.  PAGEREF _Toc266166853 \h 107
 HYPERLINK \l "_Toc266166854" Plate 4.13: CORINE land cover second level classification of North Albania. This level consists of 15 classes, which are listed in Table 4.13 as well on the left side of the plate on the legend.  PAGEREF _Toc266166854 \h 107
 HYPERLINK \l "_Toc266166855" Plate 4.14: CORINE land cover third level classification of North Albania. This level consists of 44 classes, which are listed in Table 4.13 as well on the legend.  PAGEREF _Toc266166855 \h 108
 HYPERLINK \l "_Toc266166856" Plate 4.15: CORINE land cover third level classification (15 classes) for the River Fani Catchment.  PAGEREF _Toc266166856 \h 109
 HYPERLINK \l "_Toc266166857" Plate 4.16: Land cover classes of the River Fani Catchment, North Albania (using Landsat 5TM image of 10th September 1984).  PAGEREF _Toc266166857 \h 113
 HYPERLINK \l "_Toc266166858" Plate 4.17: Land cover classes of the River Fani Catchment, North Albania (using Landsat 5TM image of 30th September 1991).  PAGEREF _Toc266166858 \h 114
 HYPERLINK \l "_Toc266166859" Plate 4.18: Land cover classes of the River Fani Catchment, North Albania (using Landsat 7 ETM+ image of 13th August 2000).  PAGEREF _Toc266166859 \h 115
 HYPERLINK \l "_Toc266166860" Plate 4.19: Forest cover map of the River Fani Catchment in 1984. Black colour indicates forest and white colour indicates other land cover classes.  PAGEREF _Toc266166860 \h 125
 HYPERLINK \l "_Toc266166861" Plate 4.20: Forest cover map of the River Fani Catchment in 1991. Black colour indicates forest and white colour indicates other land cover classes.  PAGEREF _Toc266166861 \h 125
 HYPERLINK \l "_Toc266166862" Plate 4.21: Forest cover map of the River Fani Catchment in 2000. Black colour indicates forest and white colour indicates other land cover classes.  PAGEREF _Toc266166862 \h 126
 HYPERLINK \l "_Toc266166863" Plate 4.22: Forest change detection between 1984 TM and 1991 TM image in the River Fani Catchment. Green colour indicates forest increase and red colour indicates forest decrease.  PAGEREF _Toc266166863 \h 128
 HYPERLINK \l "_Toc266166864" Plate 4.23: Forest change detection between 1991 TM and 2000 ETM+ image in the River Fani Catchment. Green colour indicates forest increase and red colour indicates forest decrease.  PAGEREF _Toc266166864 \h 128
 HYPERLINK \l "_Toc266166865" Plate 4.24: Forest land cover changes to open spaces and scrub (indicated with black colour) between 1984 and 2000 in River Fani Catchment.  PAGEREF _Toc266166865 \h 129
 HYPERLINK \l "_Toc266166866" Plate 4.25: Forest land cover changes to agriculture between 1984 and 2000 (indicated in black colour) in River Fani Catchment.  PAGEREF _Toc266166866 \h 129
 HYPERLINK \l "_Toc266166867" Plate 4.26: Forest cover of 1984 TM image using NDVI layer and a threshold of 182. Black colour indicates forest and white colour indicates other land cover classes.  PAGEREF _Toc266166867 \h 132
 HYPERLINK \l "_Toc266166868" Plate 4.27: The Landsat ETM+ imagery acquired on the 13th August 2000 was used to calculate the Bare Soil index of North Albania.  PAGEREF _Toc266166868 \h 142
 HYPERLINK \l "_Toc266166869" Plate 4.28: Soils affected by drought, erosion and other parameters (Source: United Nation, (n.d.) [online]).  PAGEREF _Toc266166869 \h 143
 HYPERLINK \l "_Toc266166870" Plate 5.1: Digital Terrain model of the River Fani Catchment extracted from the ASTER DEM combined with the DEM derived from topographic maps. Dark colour indicates terrain with low elevation such as the river network whereas high terrain areas such as mountains are shown in bright grey shades.  PAGEREF _Toc266166870 \h 168
 HYPERLINK \l "_Toc266166871" Plate 5.2: Painted relief of the River Fani Catchment showing the topography. The dark green colour indicates areas with low elevation whereas brown colour shows areas in high terrain.  PAGEREF _Toc266166871 \h 168
 HYPERLINK \l "_Toc266166872" Plate 5.3: Aspect DEM of the River Fani Catchment for visualisation of the topography.  PAGEREF _Toc266166872 \h 169
 HYPERLINK \l "_Toc266166873" Plate 5.4: Depressionless DEM of the River Fani Catchment.  PAGEREF _Toc266166873 \h 170
 HYPERLINK \l "_Toc266166874" Plate 5.5: Flow direction GIS layer of the River Fani Catchment.  PAGEREF _Toc266166874 \h 171
 HYPERLINK \l "_Toc266166875" Plate 5.6: Catchment Grid GIS layer of the River Fani Catchment.  PAGEREF _Toc266166875 \h 171
 HYPERLINK \l "_Toc266166876" Plate 5.7: River network-GIS layer of the River Fani Catchment.  PAGEREF _Toc266166876 \h 174
 HYPERLINK \l "_Toc266166877" Plate 5.8: Soil type of the River Fani Catchment. The colour-coded attributes are described in the key.  PAGEREF _Toc266166877 \h 176
 HYPERLINK \l "_Toc266166878" Plate 5.9: SCS Soil classification group of the River Fani Catchment. The soiltype groups (A, B, C and D) are illustrated. Their characteristics are described in Table 6.1.  PAGEREF _Toc266166878 \h 177
 HYPERLINK \l "_Toc266166879" Plate 5.10: Rainfall distribution in mm/hr recorded every 3-hours in North Albania (NASA, TRM, (n.d., c) [online]).  PAGEREF _Toc266166879 \h 201
 HYPERLINK \l "_Toc266166880" Plate 5.11: Right: Accumulated rainfall in mm, recorded on the 22nd September 2002 in North Albania. Left: River Fani Catchment boundary (correlation purposes). Inundation caused on the recorded day affected mainly River Fani Catchment.  PAGEREF _Toc266166880 \h 201
 HYPERLINK \l "_Toc266166881" Plate 5.12: Geologic map of Albania showing (from North to South) River Fani , Shoshaj and Ndroq Catchment boundaries in black outline  PAGEREF _Toc266166881 \h 209
 HYPERLINK \l "_Toc266166882" Plate 5.13: River Fani, Shoshaj and Ndroq Catchment boundary and their river network in Albania. The locations and names of the hydrologic stations are also recorded.  PAGEREF _Toc266166882 \h 209
 HYPERLINK \l "_Toc266166883" Plate 5.14: Ndroq Catchment boundary and the river network in Albania. Area (A), Basin Slope (BS) and Length (L) are recorded as shown above. The Catchment was derived from a DEM of the area of interest.  PAGEREF _Toc266166883 \h 210
 HYPERLINK \l "_Toc266166884" Plate 5.15: Shoshaj Catchment boundary and the river network in Albania. Area (A), Basin Slope (BS) and Length (L) are recorded as shown above. The Catchment was derived from a DEM of the area of interest.  PAGEREF _Toc266166884 \h 210
 HYPERLINK \l "_Toc266166885" Plate 5.16: Hydrographic map of Albania (Selenica (2006)).  PAGEREF _Toc266166885 \h 221
 HYPERLINK \l "_Toc266166886" Plate 5.17 Map of 24h precipitation for Albania for a 100-year return period. Average annual rainfall is 1485mm. The highest 24hr precipitation oscillates from 100 to 420mm (Selenica (2006)).  PAGEREF _Toc266166886 \h 222
 HYPERLINK \l "_Toc266166887" Plate 5.18: Flood potential map of Albania for specific peak discharges (Selenica (2006)).  PAGEREF _Toc266166887 \h 223
 HYPERLINK \l "_Toc266166888" Plate 5.19: Flood potential map of Albania for a 100-year return period (Selenica (2006)).  PAGEREF _Toc266166888 \h 224
 HYPERLINK \l "_Toc266166889" Plate 5.20: Flood risk map of Albania for a 100-year return period (Selenica (2006)).  PAGEREF _Toc266166889 \h 225
 HYPERLINK \l "_Toc266166890" Plate 5.21: HEC-RAS Hydraulic model. The elevation dataset in the form of TIN for the AOI are illustrated.  PAGEREF _Toc266166890 \h 230
 HYPERLINK \l "_Toc266166891" Plate 5.22: HEC-RAS Hydraulic model. The cross-sections along River Fani for the AOI are illustrated.  PAGEREF _Toc266166891 \h 230
 HYPERLINK \l "_Toc266166892" Plate 5.23: Rubik town along River Fani Catchment. Red perimeter shows the area surveyed by Coleman (1999).  PAGEREF _Toc266166892 \h 234
 HYPERLINK \l "_Toc266166893" Plate 5.24: Flooding of Rubik’s football pitch.  PAGEREF _Toc266166893 \h 235
 HYPERLINK \l "_Toc266166894" Plate 6.1: River Fani Catchment showing the location of the hypothetical reservoir used in the simulations.  PAGEREF _Toc266166894 \h 264
 HYPERLINK \l "_Toc266166895" Plate 6.2: Plan of Rubik town and surrounding area, showing the elevation contours and the location of the hypothetical reservoir used in the simulations.  PAGEREF _Toc266166895 \h 281
 HYPERLINK \l "_Toc266166896" Plate 6.3: Plan of Rubik town and surrounding area, showing the elevation contours and the flooded area delineated from a construction of 40m height reservoir upstream, designed to store 18x106m3 of water.  PAGEREF _Toc266166896 \h 281
 HYPERLINK \l "_Toc266166897" Plate 6.4: Plan of Rubik town and surrounding area, showing the elevation contours and the flooded area delineated from a construction of 60m height reservoir upstream, designed to store 36x106m3 of water.  PAGEREF _Toc266166897 \h 282

 Background and Research Motivation



Introduction
Catchment areas and their proper management have become a major issue of concern for resource managers world-wide. This growing level of concern was prompted by an increase in the number of major catchment areas being deteriorated by flooding and soil erosion (Ganoulis (2003), Gelsomino et. al. (2006), Chang Huang et. al. (2007), Malik and Matyja (2008) and Pignatelli et. al. (2009)), These and other catchment problems (e.g. water pollution) are not only isolated in solving water management issues but also include engineering, biophysical, economic, social, environmental, political and institutional issues. Resolving these problems requires integration of interdisciplinary organisations for acquiring all necessary resources (e.g. information, hardware, software, expertise, funding). In developing countries, integrated catchment management is further restricted by lack of quality and quantity of appropriate data as well as insufficient capacity in human resources. Such issues create challenges and so it is even more important to develop tools that could be used to define and apply the policies and strategies at least at the regional scale in order to effectively deal with catchment management problems. The main tool in this regard is a catchment hydrological model developed within the framework of a Decision Support System (DSS), i.e. a Geographic Information System (GIS) enabled model environment. GIS can store, analyse and manipulate spatial data. In this research project GIS was also used to develop management strategies and more importantly as a DSS for mitigation measures to be taken for flood prevention and control in the area under investigation i.e. River Fani Catchment in North Albania.

Traditionally catchment modelling is used for better understanding of the hydrology of the area under study and consequently for solving catchment problems. It is a complex process and involves representation of the water cycle processes at a scale and/or level appropriate to the catchment problem being addressed. Such methodology was adopted in this research project, by developing a rainfall-runoff model to predict the spatial and temporal response of the River Fani Catchment. Runoff prediction by hydrograph development is a very important part in rainfall-runoff modelling. This is because the hydrographs produced are often required as the basis for an engineering design. The amount of runoff produced by a catchment is dependant upon numerous factors (e.g. the way in which hydrological parameters such as rainfall intensity, Land Cover (LC) and soil type are distributed). Therefore a significantly large number of spatially and temporally distributed data (e.g. terrain models) are required to derive the hydrological parameters required in the modelling procedures. Additional modelling data required include: catchment area, boundary and shape, stream network, topography, land use/cover (LULC), soil type, drainage density and drainage pattern. The quality of these data and the robustness of the underlying assumptions used indicate how useful a model can be. In view of the data challenges encountered in this research project (see Chapter 3, Section 3.4), the use of satellite Remote Sensing (RS) became particularly important as a data source.

Satellite remote sensing technology has come a long way since the launch of the world’s first satellite, Sputnik, in 1957. In 1972, Landsat MSS, a high spatial resolution satellite was launched and the use of its data that became widely available, enabled significant advances in the field of earth monitoring. In 1984, spatial, geometrical, and radiometric resolutions were significantly enhanced with the launch of Landsat-4 and its TM sensor. Since then, more satellites were launched with improved sensors for example Landsat 5 that carried the MSS and TM sensors (Appendix A, Plate A1) and Landsat 7 launched in 1999 that carries the ETM+ sensor (Appendix A, Plate A2). Therefore, the Landsat satellites series prime emphasis is on remote sensing of land resources.

Since the launch of Landsat and generally high spatial resolution satellites for monitoring vegetation, the application of remote sensing in hydrology as well as the technique of land use mapping (which forms the backbone of this and many other hydrological projects), has increasingly gained recognition. Consequently, research evolved in the applications of remote sensing for land management and in hydrology, towards developing methods to use satellites of various spatial resolutions in monitoring the environment. For land management, data on reflective, thermal and dielectric properties of the Earth’s surface can be provided using a variety of sensors (Engman and Gurney (1991)). On the other hand, in the case of hydrology, remote sensing techniques measure hydrological variables indirectly and hence, the electromagnetic variables (measured by remote sensing techniques) have to be related to the hydrological variables empirically or with transfer functions. Additionally, in the case of hydrological models which are not structured to receive and analyse remotely sensed data directly, advanced computer hardware and software have to be used for imagery storage, analysis and interpretation (e.g. ERDAS Imagine, ENVI, IDRISI and PCI Geomatica). In the current research project, ERDAS Imagine software was opted for and used to analyse satellite imageries.



The River Fani Catchment Case Study - Overview of the Challenges
This research project was initiated and funded by Aid-in-Action (AIA) Porthcawl in collaboration with the University of Glamorgan. AIA Porthcawl is a South Wales charity organisation set up in 1989 which became a registered charity in 1991 with the motivation and ‘goal’: “We aim to challenge inequality, poverty and injustice, through sharing knowledge and skills to empower the disempowered. We will work with people of all faiths, ethnicity, gender orientation, background and ability.” Over the years AIA Porthcawl has helped many countries with difficulties namely, HYPERLINK "D:\\pages\\albania\\main.html"Albania, HYPERLINK "D:\\pages\\Belarus\\main.html"Belarus, HYPERLINK "D:\\pages\\india\\main.html"India, HYPERLINK "D:\\pages\\peru\\main.html"Peru, and HYPERLINK "D:\\pages\\zambia\\main.html"Zambia. In Albania, AIA’s work is focused on the town of Rubik, situated on River Fani, in North Albania.

Rubik town and generally Mirdita region in the northern part of Albania endure environmental problems such as pollution and deforestation (Samimi et al. (1997) and Qiriazi and Sala (2000)). These problems were mainly due to the lack of environmental protection strategies and resulted in social, health, environmental, and financial consequences for the populations of the area. According to Samimi et al. (1997), the following took place; (a) the uncontrolled mine activities in the surrounding mountains caused pollution, (b) illegal logging resulted in deforestation and (c) hunting activities damaged fauna. Consequently, the discharge of the River Fani increased (the average annual discharge is 103 m3/s, which is considered high for the catchment), causing major frequent flooding problems to Rubik town. In addition, these problems have lead to river bank instabilities, landslides and erosion on a regular basis, deterioration of agricultural land and changes in the morphology of the river reaches towards the town. Thus, these let to impacting heavily on the quality of life of the locals, endangering their lives, threatening property as well as affecting the environmental sustainability of the Catchment. As a result, the residents of Rubik town abandon their houses and relocate to safer areas on a regular basis.
In aid of this situation, AIA Porthcawl attempted to provide localised solutions, such as bank stabilisation with different methods but these were unsuccessful in abating the problems. AIA Porthcawl then approached the University of Glamorgan and after extended discussions it was agreed that a sustainable solution to the localised problems in Rubik was to carry out a catchment wide study of the area in order to better understanding its hydrological behaviour. This should help in the provision of localised solutions.

The solutions/measures that can be considered and implemented in catchment management can be distinguished in (a) structural and (b) non-structural (e.g. flood forecasting and warning systems, and flood risk maps). For both measures, a better understanding of the causes of floods, erosion and instability in the region, needs to be established, which requires the use of hydrological models at the scale of the river basin. Developing such a model could contribute not only in the provision of localised solutions that are required but also could significantly aid to the environmental management of the whole catchment.
Research Aims and Objectives
The general aims of the thesis were: (a) to develop and apply a physically based hydrological model of the River Fani Catchment (in the northern region of Albania) with an emphasis on the use of GIS and RS and, (b) to prepare a flood risk assessment study for the River Fani Catchment. The work in this thesis aimed also to merge both new and old techniques and demonstrate how technical advances may aid the hydrologist in the modelling of variable events such as rainfall and runoff.

The research investigation was focused on the application of both RS and GIS technologies to overcome data limitations of the mountainous remote Catchment and the application of different hydrological and hydraulic modelling environments and tools (e.g. WMS, HEC-RAS) for modelling different rainfall-runoff scenarios. Even though much work has been done on the application of GIS and RS in hydrological modelling, there are still many unresolved aspects that have to be addressed and therefore, there is a need for further work. For example, better understanding of spatial hydrology is required, especially in remote, mountainous and ungauged catchments with limited available conventional data.

The specific objectives of the thesis were:
Monitor and map various hydrologically significant changes (e.g. deforestation) in the River Fani Catchment, with the processing and analysis of remote sensing imagery (Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER)).
Develop a hydrologic model using multi-source and multi-scale data (land cover CORINE data, satellite images, field archived data and maps) in a GIS-integrated hydrological modelling package (WMS, HEC-RAS).
Quantify channel flow for various specified precipitation events and for various scenarios of change or developments in the Catchment area.
Develop a decision support management system with the use of GIS and RS of a remote mountainous catchment that makes recommendations on various conservation measures based on a ‘What-if’ analysis and simulation results of the hydrologic model, to be carried out later in this work.

The output of this research was anticipated to contribute towards the following:
Improved quality of life for the residents of the Rubik area of Albania through better catchment management measures that are proposed.
Extension of results and applications to other parts of Albania (since hydrological problems in the Mirdita region are common to other parts of the country).
Application of multi-source/multi-scale data (particularly from remote sensing) to enhance the understanding of catchment runoff dynamics in a mountainous area with limited archived field data.
Mapping of hydrologically significant change in the River Fani Catchment to show its effect on hydrological processes and their interactions within the area.
Structure of the Thesis
Following this introductory chapter, Chapter 2 is review of literature, looking at hydrological modelling; their different processes and relationships as well as the classification systems into which are aggregated. The limitations, advantages and disadvantages of several hydrological models are also examined and are on these that the selection criteria for an appropriate hydrological model for this research project are based. Once an appropriate model is selected, the review is concentrated on the data scarcity/limitations of remote catchments and goes into investigating advances in technology such as the GIS and RS in tackling them.

In Chapter 3, the River Fani Catchment topography, climate, hydrology, geology and land use are described. Catchment degradation, erosion, landslips and flooding are also covered in this Chapter giving an overview of the situation and the problems encountered in the area. Acquiring such data for this research project embraced challenges. In overcoming the challenges the use of GIS and RS is introduced and described in this Chapter.

Chapter 4 starts with a description of the remote sensing data acquired for the Catchment from different sensors (such as the Landsat TM, ETM+, MODIS and ASTER), followed by the pre-processing techniques used to correct and enhance the images. The analysis of the images using image processing systems and GIS aided the derivation of parameters, land cover and change detection maps to be used in the hydrological model. This Chapter also examines the changes that occurred in the Catchment from 1984 to 2000 and the soil erosion potential of the area.

Chapter 5 – This Chapter describes the development of the hydrologic model from the data derived in Chapter 4. It starts with the modelling technique used, followed by the use of the WMS tools to construct the hydrologic model of the Catchment, using the derived GIS layers (e.g. land cover, soil, Digital Elevation Model (DEM)). The calibration and validation of the model is presented based on hydrologically similar catchments and different rainfall-runoff scenarios are examined. In order to investigate the effects of these scenarios on the Rubik town (situated on the banks of River Fani Catchment) a hydraulic model was constructed using HEC-RAS. The results and analysis of the hydraulic model as well as the floodplain delineation are described in this Chapter.

One of the objectives of this research project is to make recommendations on various conservation measures based on a ‘What-if’ analysis and simulation results of the hydrologic model. Chapter 6 presents the different ‘What-if’ scenarios for both the hydrologic and the hydraulic models, analyses their results and makes catchment conservation and management proposals.

Finally, in Chapter 7 the conclusions of the research are summarised and recommendations made for areas of further research. To preserve the continuity of the main chapters some detailed information has been placed in the appendices at the end of this thesis.

 Literature Review of Hydrological Modelling of Mountainous Temperate Catchments



Introduction
“Hydrology is the science that treats the waters of the Earth, their occurrence, circulation and distribution, their chemical and physical properties, and their reaction with the environment, including their relation to living things. The domain of hydrology embraces the full life history of water on the Earth” (Maidmen, (1993a)). Hydrology is therefore a very broad subject area. The research project herein, deals with one segment of the subject, namely: rainfall/runoff modelling with particular emphasis on mountainous catchments.

The rainfall-runoff relationships and consequently all the processes involved in the hydrological cycle are described in the following section (section 2.2) with emphasis on the runoff process and the methods used to determine this. Such processes are simulated using hydrological models and these are classified into different groups as described in section 2.3. The applications of various hydrological models using advanced technologies such as GIS and RS are described in section 2.4, focusing on the advantages gained from such a combination especially in data-poor catchments. The Chapter ends with conclusions on the integrated modelling approach which could lead to DSS for catchment management.
Rainfall-Runoff Relationships
Rainfall and runoff have a complex relationship as indicated by the hydrological or water cycle. The hydrological cycle does not begin with any one particular activity but it can be characterised as a continuous cycle of water leaving the earth’s surface and eventually returning in the form of precipitation. It describes the storage and movement of water between biosphere, atmosphere, lithosphere, and the hydrosphere.

Therefore, precipitation falling on the ground can arrive at a stream channel by one of several paths. A portion of the water that does not evaporate or percolate, will flow over the soil surface as surface runoff, whilst the remainder will infiltrate through the soil and flow beneath the surface to a stream. Overland flow generally occurs over a small length with stream channels being reached quickly, and therefore, if surface flow occurs in sufficient quantities it becomes very important in the formation of hydrograph peaks. However, large amounts of surface runoff can only occur when the rate of rainfall exceeds the infiltration capacity of the soil. Subsurface flow, in comparison, moves slower than surface runoff, yet the amount of flow can be quite large, especially for storms of moderate intensity, and may be responsible for rises in stream flow for this type of event.

The processes involved in transforming rainfall to runoff have troubled hydrologists for many years, when attempting to obtain or predict runoff. Consequently, relationships between the rainfall over a catchment area and the resulting flow in a river have been developed. These depend on the timescale being considered. The shorter the time (e.g. hours) the more complex the rainfall versus streamflow relations are, but as time intervals increase (e.g. months) the connection becomes simpler, approaching a straight line correlation when the time interval approaches one year. Another factor affecting the rainfall-runoff relationship is the size of the catchment. Small homogeneous catchments give simple rainfall-runoff relationships but the case is different with large catchments (of national or international scale). In the intermediate scale, of both area and time, rainfall-runoff relationships are complicated due to physical factors (such as geology) and hydrological factors (such as evaporation, infiltration and groundwater flow).

The derivation of runoff from rainfall can be determined with the following methods: (a) Rational, (b) Time-Area, (c) Hydrograph Analysis, (d) Unit Hydrograph, (e) Instantaneous Unit Hydrograph and (f) Rainfall-runoff relationships. These methods are explained in various hydrology textbooks (see, for example, in Chapter 13 of Shaw (1994)). The method of runoff prediction used in this research project has made allowances for the amount of rainfall that would infiltrate into the soil, when calculating the amount of runoff for a rainfall event. The project was then concerned with routing this runoff through the catchment via overland flow paths and river channels in order to produce the hydrograph of runoff that corresponds to a particular rainfall event.

The intensity and extent of surface runoff in streams before and after a storm can be mapped using remote sensing techniques. Such techniques though cannot measure runoff directly but can be used to determine information (in the form/type of map) that can be used as input to hydrological models. These include: (a) catchment geometry and drainage network, (b) LULC classes that are used to define runoff coefficients, (c) soil moisture and other soil characteristics (d) topographic data. Topography is usually represented with a DEM and is considered an important element in hydrological models of mountainous catchments because it is used to extract parameters such as slope, aspect and surface roughness that influence significantly surface runoff and runoff velocities.

Last but not least, the rainfall-runoff model, like any model is understood as a simplified representation of the natural system it attempts to describe. In this case the natural system is the River Fani Catchment. However, a distinction is made between three different meanings of the general term model, namely the conceptual model, the model code and the model that here is defined as a site-specific model. Generally, the conceptual model (described in more detail in Chapter 5) is derived by considering all or some of the hydrological cycle processes (such as runoff, infiltration, groundwater movement), which in tern can be used for the development of the site-specific hydrological model (using the model code). In general, hydrological modelling can be divided into four components: (a) surface water hydrology, (b) surface water quality, (c) groundwater flow and (d) groundwater transport. These components are subsequently classified into different model categories.
Hydrological Models
Hydrological modelling has become an indispensable tool for studying flood-related processes and water management in catchments. Computer based hydrological models have been developed and applied at an ever increasing rate during the past four decades. The key reasons for that are twofold: (a) improved models and methodologies are continuously emerging from the research community, and (b) the demand for improved tools increases with the increasing pressure on water resources. Examples of hydrological models are: TOPMODEL (Beven and Kirkby (1979), Beven et. al. (1995), Beven and Freer (2001)), MIKE SHE (originally derived from the SHE model by Abbot et al. (1986a&b)), (Graham and Butts (2005)), LISFLOOD (De Roo (1998)), IHACRES (Post et. al. (1998)) and IHACRES Classic Plus (Croke et. al (2006)), for simulating surface hydrologic processes using spatially varying data (see for example, Littlewood et. al. (2003), Descroix et. al. (2007) and Moretti and Montanari (2007) amongst others).

The hydrological models may vary in terms of how processes are represented, in time and in space scale that are used and in what methods of solution to equations are used. With these principles in mind, several authors have developed different classification systems where they grouped the hydrological models to facilitate the modelling approach.
Classification of Hydrological Models
Considering that there is a plethora of hydrological models, it is expected to find a multitude of classifications into which various authors have grouped their respective model, (see, for instance, Maidment (1993b), Singh (1995) and Refsgaard and Knudsen. (1996)). Nevertheless, all hydrologic models can be classified according to the three space dimensions, time and randomness. This is due to the fact that the hydrologic phenomena vary in all three space dimensions and with time and are also random or uncertain (since they are driven by rainfall and because many of the properties of the flow domain are unknown, especially for subsurface flow). Maidment (1993b) developed such a classification system as used in this research project. The different types of models are separated according to the type of their processes, i.e. whether they are lumped or distributed (see  REF _Ref171263243 \h Figure 2.1). The system (hydrological model) can be classified into two main categories, deterministic (no randomness) and the stochastic (randomness). These categories are then further broken down into further classifications. The deterministic model is classified as lumped (processes are assumed spatially uniform) or distributed (processes vary in space). Each type is further classified as steady or unsteady depending on variations with time. This type of classification is very useful in understanding the different hydrological models and their processes as well as selecting an appropriate one for a specific application.

 SHAPE \* MERGEFORMAT 

Figure  STYLEREF 1 \s 2. SEQ Figure \* ARABIC \s 1 1: Classification of hydrologic models based on the way they represent variations with space and time and uncertainty of hydrologic systems (Source: Chow, Maidment and Mays (1988)).

Lumped Models
A lumped model is generally described as the model which is expressed by ordinary differential equations taking no account of spatial variability of processes, input, boundary conditions and the system’s (catchment’s) geometric characteristics. In most lumped models, some processes are described by differential equations based on simplified hydraulics laws. Other processes are expressed by empirical algebraic equations (see, for example, DeVantier and Feldman (1993)). The U. S. Army Corps of Engineers (USACE) hydrologic model HEC-1 (U. S. Army Corps of Engineers (USACE) (1998), Feldman, (1995)) is an example of a model classified as lumped, but can effectively operate as a distributed model through small subbasin and/or kinematic wave routing options. Other examples of lumped models are: RORB (Laurenson and Mein (1995a and, 1995b)), SSARR (U. S. Army Engineer (1972), Spears (1995)), tank model (Sugawara (1995)), HBV (Bergström (1995); Lindström et al. (1997)), INCA (Whitehead et al., (1998)), SWAT (Arnold et al. (1998), Arnold and Fohrer (2005)) and many more.
Distributed Models
Distributed models (e.g. Refsgaard et. al. (1996) and Reed et. al (2004)) take an explicit account of spatial variability of processes, input, boundary conditions, and/or system (catchment) characteristics (Shrestha et. al. (2006)). In practice, a lack of data – field or experimental (laboratory) – prevents such a general formulation of distributed models. In a majority of cases the system (catchment) characteristics, many of the processes, the input, and even some of the boundary conditions are all lumped, but some of the processes that are directly linked to the output are distributed - for example, the rainfall-runoff process. These models are not fully distributed; rather they are quasi-distributed at best.

Catchment models, in distributed hydrologic models, require physiographic information such as configuration of the channel network, location of drainage divides, channel length and slope, and sub-catchment geometric properties. Traditionally these parameters are obtained from maps or field surveys. However, over the last few decades, these parameters are derived directly from digital representations of the topography (i.e. DEMs). Bevin et al. (1991) used a raster DEM to extract hillslope flow paths with the aid of a flow path index. The index is based on the upflow area (contributing area) and local slope. According to the author, this approach has inherent assumptions of quasi-steady conditions and ground water tables roughly mirroring the topography. Jones et al. (1990) used a Triangulated Irregular Network (TIN) system and steepest descent and ascent to delineate drainage boundaries, and determine flow paths and stream networks. The TIN is particularly suited to steepest descent/ascent because of the uniform slope along each triangle facet. Le Coz et al. (2009), describes a technique used to aggregate the Shuttle Radar Topography Mission (SRTM) DEM from 3( (90 m) to 5( (10 km) in order to simulate the water balance of the Lake Chad basin (2.5 Mkm2). They used six algorithms (mean, median, mode, nearest neighbour, maximum and minimum) and each of these methods is assessed with respect to selected hydro-geomorphological properties that influence Terrestrial Hydrology Model with Biogeochemistry (THMB) simulations, namely the drainage network, the bottom topography of the basin and the floodplain extent. Although the proposed aggregated two-step procedure was specific for the case study where local variations in elevations are critical to the flow propagation due to the basin flatness, it can be assessed on other flat basins provided the filter window size is adapted by means of a variographic analysis.

Consequently several models have been developed which can utilise spatially distributed input maps on topography (such as the DEM). This was notably facilitated with the recent advent of powerful computers and the technological advances in spatial data collection processes (such as remote sensing, satellite telemetry systems and aerial photography). Examples of such models are ANSWERS (Areal Nonpoint Source Watershed Environment Response Simulation) (Zagolski and Gaillard (1999)), TOPMODEL (Beven et. al. (1995) and LISFLOOD (De Roo et. al. (2001)). ANSWERS uses a DEM to derive the slope, aspect and channel variables in evaluating various strategies for controlling surface runoff and sediment transport from intensively cropped areas whereas TOPMODEL, can predict the production of surface runoff based on the close relationship between topographic form and subsurface flow. In LISFLOOD, the DEM facilitates the hydrological modelling in a number of ways. For example, the actual stream network is 'burned' into the DEM for better flow direction calculations as well as for calculating parameters such as the slope gradient. Moreover, the inundation extent could be simulated by extrapolating predicted water levels onto a DEM (see section 2.3.1.3 for more details on LISFLOOD model).

Other examples of distributed models are: MIKE11/SHE (SHE: Abbott, et al. (1986a, 1986b), MIKE11/SHE: Refsgaard and Storm (1995), Danish Hydraulic Institute (DHI) (2000), Graham and Butts (2005)), (Kite (1998)) HYDROTEL (Fortin et. al. (2001a and 2001b)), WATFLOOD (Kouwen and Mousavi (2002)) and HMSMOD (Panday and Huyakorn (2004)).
Selection of Model Type
The criteria for choosing the ‘right’ hydrologic model are numerous but are always project-dependent, since every project has its own specific requirements and needs. Among the various project-depended selection criteria, there are four common, fundamental ones. These are: (a) what is required of the model to do i.e. what are the required outputs (e.g. peak flow, hydrograph), (b) the capability of the software to model the required hydrological processes – to estimate the desired outputs adequately (e.g. reservoir operation, snowmelt), (c) the type of data available or collectable and (d) the price/budget of the project.

To facilitate the selection process of the hydrological model,  REF _Ref237954282 \h Table 2.1, has been prepared for the purposes of this research project. It presents a summarised review of different hydrological models, describing their input requirements, strengths and limitations. These are explained in more detail in the following paragraphs and the reasons why a certain one was opted for use are analysed.




Table  STYLEREF 1 \s 2. SEQ Table \* ARABIC \s 1 1: Review of different hydrological models, describing their input requirements, strengths and limitations.Model Type,
AuthorApplication
Input RequirementsStrengthLimitationsComments1. LISFLOOD

De Roo (1998).Physically-based rainfall-runoff model for large European river basins.Spatially distributed input maps on topography (e.g. DEM), the river channel network, land cover (CORINE land use classes), and soils (soil depth and texture class). The driving meteorological variables that are required are rainfall, potential evaporation (for bare soil, closed canopy and open water reference surfaces), and daily mean air temperature. Manning’s n (for channel flow simulation).Written using PCRaster GIS environment, having a very flexible model structure which is easily adaptable. Allows remotely sensed data input, output can be validated using remotely sensed data (e.g. SAR images).The interception, evapotranspiration, snowmelt and groundwater modules are under development. The model is to be used to simulate floods in large European river basins. Reliable, good quality spatially distributed input data (e.g. land use) are required as can easily alter simulation results.Tested and validated in the Meuse and Oder European catchments.2. TOPMODEL
(Unix version)

TOPSIMPL
(windows version)

Beven et. al. (1995).Distributed rainfall-runoff model.Precipitation and evapotranspiration time series and topographic information are required. A minimum of four effective catchment parameters need to be estimated to characterise the discharge dynamics of the catchment. The parameters are fitted from the discharge predictions. Neither horizontal nor vertical soil parameters need to be supplied. However, to estimate water table or soil moisture content from the saturation deficit requires soil information.Structural simplicity. Results can be visualized in a spatial context. It requires few watershed parameters, low level of expertise and gives distributed outputs. Suited to implementation within a GIS. It has been studied extensively.
The model code is available for modification. Model assumptions such as: the exponential saturated zone store, quasi parallel water table and topographic control on water table depth may be met for catchments with relatively shallow homogeneous soils, which are quite wet and exhibiting the variable source area runoff mechanism. Another assumption is that the upslope contributing area should be constant for any point. This is valid usually in moist climatic regions but not in arid catchments. So the “effective contributing area” of the catchment may be variable. Model assumes that the effective area of the catchment is defined solely by the watershed. It only simulates watershed hydrology, although studies have been conducted to modify it to simulate water quality dynamics. Model results are sensitive to grid size and grid size *B*Uphÿj¤)h%rtU h%rt'j')h%rth%rt>*B*Uphÿh%rthâihh%rt0Jh%rtCJaJmHnHtH jhâihh%rt0JU hÉ!jh%rtUjª(h%rtU*ª>«>¬>È>É>Ê>Ë>Ð>Ñ>ö>÷>ø>?????????7?8?9?:?A?íßÖß¼í߭ߢ“¢‚“w“í­ja]aIja'j,h%rth%rt>*B*Uphÿh%rthâihh%rt0Jjhâihh%rt0JUhÉ!mHnHu j˜+h%rtUmHnHujh%rtUmHnHuh%rtmHnHuh%rtCJaJmHnHtH u2j+h%rth%rt>*B*UmHnHphÿuh%rtmHnHuhâihh%rt0JmHnHu$jhâihh%rt0JUmHnHuA?B?O?P?Q?k?l?m?o?p?q?r?s?t??‘?’?“?š?›?­?®?¯?É?Ê?Ë?Í?Î?Ï?Ð?Ñ?Ò?î?ï?ð?ñ?ø?ù?@@@,@-@.@0@1@2@3@òéäÚäÏÚÊÚ½ò½é¹é¥½éòéäÚäšÚÊÚ½ò½é¹é†½éòéäÚä{ÚÊÚ½òj†.h%rtU'j .h%rth%rt>*B*UphÿjŒ-h%rtU'j-h%rth%rt>*B*Uphÿh%rtjhâihh%rt0JU hÉ!j’,h%rtUjh%rtU h%rthâihh%rt0Jh%rtCJaJmHnHtH /3@4@5@Q@R@S@T@Y@Z@f@g@h@‚@ƒ@„@†@‡@ˆ@‰@Š@‹@§@¨@©@ª@¯@°@¿@À@Á@Û@íßÖß¼í߭ߢ“¢‚“w“í­íßÖß]í߭ߢ“¢2jý/h%rth%rt>*B*UmHnHphÿuhÉ!mHnHu j€/h%rtUmHnHujh%rtUmHnHuh%rtmHnHuh%rtCJaJmHnHtH u2j/h%rth%rt>*B*UmHnHphÿuh%rtmHnHuhâihh%rt0JmHnHu$jhâihh%rt0JUmHnHu3@‰@â@MAµA(B¹B C{CÏC*B*Uphÿh%rthâihh%rt0Jjhâihh%rt0JUh%rt5aJmHnHtH u$jhâihh%rt0JUmHnHuhÉ!mHnHujh%rtUmHnHu jV6h%rtUmHnHuîCïCðCóCôCDDD5D6D7D9D:D;DDZD[D\D]D`DaD§D¨D©DÃDÄDÅDÇDÈDÉDÊDËDÌDèDëÞÕÇÕ¸­¸¨¸ÞÇÞդՐÞÕÇÕ¸…¸¨¸ÞÇrd[h%rtmHnHuhâihh%rt0JmHnHu$jhâihh%rt0JUmHnHujD9h%rtU'jÇ8h%rth%rt>*B*Uphÿh%rt hÉ!jJ8h%rtUjh%rtU h%rth%rtCJaJmHnHtH hâihh%rt0Jjhâihh%rt0JU'jÍ7h%rth%rt>*B*Uphÿ#èDéDêDëDðDñDûDüDýDEEEEEEEE EE?EDEEELEMENEhEiEjEñ×Äñµñª›ªŠ››ÄµÄñvñ\Äñµñª›ªK› j8;h%rtUmHnHu2j»:h%rth%rt>*B*UmHnHphÿuh%rtmHnHuhÉ!mHnHu j>:h%rtUmHnHujh%rtUmHnHuh%rtmHnHuh%rtCJaJmHnHtH u$jhâihh%rt0JUmHnHu2jÁ9h%rth%rt>*B*UmHnHphÿuhâihh%rt0JmHnHujElEmEnEoEpEqEEŽEEE•E–EŸE E¡E»E¼E½E¿EÀEÁEÂEÃEÄEàEáEâEãEèEéEðEñEòE FõæÓÄÓ¶­¶“ӶĶˆæˆwæõæÓÄÓ¶­¶]ӶĶˆæˆ2j¯*B*UmHnHphÿu j2*B*UmHnHphÿuh%rtmHnHuhâihh%rt0JmHnHuh%rtCJaJmHnHtH u$jhâihh%rt0JUmHnHujh%rtUmHnHuhÉ!mHnHu" F
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