Refined Modelling for Flood Extent Predictions Using Laser ... · Nagwa El-Ashmawy . Refined Modelling for Flood Extent Predictions Using Laser Scanning by ... 1 ˘ 8 9ˆ Predicting

Refined Modelling for Flood Extent Predictions

Using Laser Scanning

Nagwa El-Ashmawy ��

Refined Modelling for Flood Extent Predictions Using Laser Scanning

by

Nagwa Taha Hamdy El-Ashmawy Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-informatics. Degree Assessment Board

Chairman External examiner Supervisor Second supervisor

: : : :

Prof. Dr. A. Stein Dr. Ir. R.F. Hanssen Dr. K. Tempfli Mr. G. Huurneman

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

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

I

Acknowledgement I would like to express my sincere gratitude to all those who have contributed directly or indirectly in successfully completing my study.

My gratitude goes to my organization Survey Research Institute, National Water Research Center (SRI, NWRC) for giving me the opportunity and the necessary support of my academic carrier.

I extend my grateful to the Dutch Government for providing the financial assistance to support my study in ITC.

I would like to express my thankful to Ms. Ardis Bollweg of Rijkswaterstaat, the Survey Department of the Ministry of Transport, Public Works and Water Management (Delft/NL), who kindly made the laser altimetry data available for this research.

I would like also to express my sincere gratitude to Dr. K. Tempfli, my main supervisor, who gave me the correct guidance to conduct this research in a constructive and professional way. I fully say that without the valuable discussions and suggestions, this thesis would not have been achieved. I highly appreciate his guidance.

I also like to thank Mr. G. Huurneman my second supervisor for his support and valuable suggestions during the discussions.

I appreciate very much Dr.Ir. C. Mannaerts for his precious guidance in choosing the suitable models.

I extend my thanks to Dr. M.M. Radwan for his precious support starting from applying to the fellow-ship and during my staying here.

My great thanks to Mr. A. Brown the GFM Program Ex-director who helped me to transfer from the PM to M.Sc. programs. God bless his soul.

I appreciate very much my classmates for their friendly and helpful relationship.

I would like also to thank my friends; Francis and Tsolmon for helping me during my staying here and for the very fruitful discussions.

I would like to express my grateful thanking to all my family for their support and encouragement dur-ing my studying abroad, specially my cousin Salwa.

Last but the most important, I would like to thank uncle Mahmoud and aunt Mimi, who were more than parents to me here in the Netherlands, with them I have never felt homesick.

Finally I would like to dedicate this thesis to my loved mother, my sisters Amal and Azza, my brother, Gamal, and to my father’s soul, God Bless Him.

Nagwa El-Ashmawy

II

Abstract Flood is one of the serious, common, and costly natural disasters several countries are facing. Pres-ently the magnitude and frequency of floods increase. Building up floodplains expends their sizes. Using an accurate digital surface model is essential for accurate flood extent predictions. In the past, detailed information about built-up areas could not be included in terrain modelling. Recently the la-ser scanning technique is available in several countries, and highly accurate digital terrain or surface models start to be generated. Laser data without further specific processing would include buildings as part of the terrain model for flood extent predictions, but these as “solid blocks”. In the reality, the buildings are not solid blocks; they allow the flood to flow inside. Thus, modifying surface modelling to treat buildings as partially solid blocks is the aim of this research. This thesis throws the light on the effects of treating buildings as solid blocks. Analysing the sensitivity of the model to the accuracy of elevation values and to the grid size of the elevation model is another goal of this thesis. Simulation studies in an area along part of the Dutch river, river IJssel, are done in this research. These studies show that the flood extent is affected significantly by building existence, especially when the built-�� esides, solid blocks raise the flood water level twice as much as the partially solid blocks do. From the sensitivity analysis, it is clear that the flood extent predictions are influenced more by the point spacing than the random errors of the terrain elevation. Furthermore, the systematic errors in the elevation values are more significant than the random errors. Achieving more accurate results is possible by combining a high-resolution DSM for the building borders with a low-resolution DTM, as well as, a high-density laser dataset for the important features, like dykes, with a low-density dataset for the remained floodplain areas. The challenging question is how to translate these findings into a cost effective data acquisition strategy.

III

Table of Contents

�� Introduction ....................................................................................................................................� �� Introduction ............................................................................................................................� �� Problem Definition.................................................................................................................� �� Objectives...............................................................................................................................� �� Previous Work........................................................................................................................ �� Effects of Land-Use Changes in the Catchment Areas .................................................. �� Effects of Urbanization on Water Quality (Ecological Risk Assessment) ....................� �� Risk and Damage Assessments and Flood Control, Managements and Insurance ........� �� Effect of DTM Quality on Flood Extent Calculations...................................................�

�� Thesis Structure......................................................................................................................� �� Flood Hazard ..................................................................................................................................�

�� Flood Definition .....................................................................................................................� �� Types of Flood .......................................................................................................................� �� River Flood.....................................................................................................................� �� Coastal Flood..................................................................................................................� �� Urban Flood....................................................................................................................� �� Flash Floods ...................................................................................................................�

�� Floodplain ..............................................................................................................................� �� Flood Model ...........................................................................................................................� �� Data Collection...............................................................................................................� �� Flood Profile Estimation ................................................................................................ �� Flood Extent Calculations ............................................................................................� �� Requirements of Flood Model......................................................................................�

� Digital Terrain Models.................................................................................................................�� Introduction ..........................................................................................................................�� Definitions and Basics..........................................................................................................�� Digital Terrain Model and Digital Surface Model.......................................................�� Resolution.....................................................................................................................��

�� Importance of DTM for Hydrological Modelling ................................................................�� Technologies Used for DTM/DSM Generation...................................................................�� From Existing Maps .....................................................................................................�� Ground Survey .............................................................................................................�� Photogrammetry Techniques........................................................................................�� Active Remote Sensing Techniques.............................................................................��

�� Technologies Used for Riverbed Generation.......................................................................� �!� Effects of DTM Vertical Accuracy on the Flood Extent Calculations ................................� �� Effect of DTM Resolution on the Flood Extend Calculations.............................................�

�� Laser Scanning Technique ...........................................................................................................�� Introduction ..........................................................................................................................�� System Components .............................................................................................................�� On Board ......................................................................................................................�� On Ground....................................................................................................................��

�� System Type .........................................................................................................................��

IV

�� How it Works .......................................................................................................................�! �� Steps of Generating DTM from the Laser Scanning System...............................................�� !� Accuracy of the Laser Scanning System..............................................................................�� Advantages and Limitations.................................................................................................� �� Building Extraction ..............................................................................................................� �� Using of Laser Scanning Data for Floodplain Modelling....................................................�

�� Modelling Buildings for Available Flood Models .......................................................................�� Introduction ..........................................................................................................................�� HEC-GeoRAS Flood Model ........................................................................................�� ILWIS Software ...........................................................................................................��

�� Limitation of the Chosen Flood Models ..............................................................................�� Concept Behind Choosing the Flood Model ........................................................................� �� Implementation of Flood Model ..........................................................................................� �� Evaluation Method ...............................................................................................................� ��!� Cases to Be Studied..............................................................................................................�� !�� Different Water Levels.................................................................................................�� !�� Solid Buildings.............................................................................................................�� !�� Borders of the Buildings ..............................................................................................�� !�� Core of the Buildings ...................................................................................................�� !�� Borders of the Buildings with Openings ......................................................................�� !�!� Different Percentages of Buildings ..............................................................................�� !�� Collapsed Buildings .....................................................................................................�!

�� Sensitivity Analyses .............................................................................................................�! �� Vertical Accuracy.........................................................................................................�! �� Grid Resolution ............................................................................................................�!

!� Experimental Work ......................................................................................................................� !�� Study area.............................................................................................................................� !�� Dataset Description .............................................................................................................. !�� Data Preparation................................................................................................................... !�� For HEC-GeoRAS Flood Model ..................................................................................� !�� For ILWIS Flood Model...............................................................................................�

!�� Results of the Flood Model ..................................................................................................� !�� Different Water Levels without Buildings...................................................................� !�� Different Percentages of Solid Buildings with Different Water Levels ......................� !�� Different Treatments of Buildings as Partially Solid Blocks.......................................� !�� Collapsed Buildings ..................................................................................................... !�� Sensitivity Analyses .....................................................................................................�

!�� Discussing the Results..........................................................................................................�� !�� The Effects of Buildings on the Flood Extent Predictions...........................................�� !�� Sensitivity Analyses .....................................................................................................��

�� Conclusions and Recommendations.............................................................................................� �� Summary ..............................................................................................................................� �� Conclusions ..........................................................................................................................�� Recommendations ................................................................................................................�� Further Researches ...............................................................................................................��

V

�� References ....................................................................................................................................�� References ............................................................................................................................�� "#$�� %�&�&'..................................................................................................�

� Appendix A .................................................................................................................................... a (�� Script for Flood Extent Calculations, with Cases of: No Buildings, Solid Buildings, Border of Buildings, Core of Buildings, ........................................................................................................ a (�� Script for Flood Extent Calculations, in the Cases of Border of Buildings with Openings ..b (�� Script for Flood Extent Calculations, in the Cases of Collapsed Building............................ c

�� Appendix B ................................................................................................................................ e

VI

List of Tables

)�*� ��-��$�� +,�� -�� ...............................................................................................�! )�*� �!-��-��. ��/��0 �� $ vel Due to Random Errors.........................................................�� )�*� �!-��-��. ��/��0 �� $ 1 ��2� ��+,�� 3��....................................................� )�*� �!-�-��. ��/��0 �� $ 1 ��Due to DTM Resolutions ....................................................�� )�*� ��-��/�� (� ��2�� +� ��............................................................................... e )�*� ��-��0 �� 4�� /�� (� ��2�� rent Scenarios........................................... f )�*� ��-�/�� (� ��0 �� 4�� 2)��5�1 �4��3��3� 1��4�� ..g )�*� ��-��/�� (� a and Water Volume with DTMs Have Various Resolutions .........................h

VII

List of Figures

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8"#$�9...........................................................................................................................................� /�.�� 6��/��+�� 5�� 0�� +�� -��8"#$�9.....................� /�.�� 6��/��>�� 5��? ��(.�............................................................................� /�.�� 6��),��>��-��+ ��%53--RAS) .......................................................................... /�.�� 6��$�� +��.�+,�� .......................................................................................................�� /�.�� 6��-��+ �� #�1 ��+��/�� (� ��2� ��2�� /��.�� ....� /�.�� !6��>1 ��@�� ;��1�� .............................................................................................................� /�.�� !6��+��,�(� � ..........................................................................................................................� /�.�� !6�>��.��2�� ......................................................................................................................� /�.�� !6��2��*��ons of Different Percentages of Buildings ..........................................................� /�.�� !6��#�1 ��0 �� ......................................................................................................� /�.�� !6!�#�1 ��-��+ ��2�� /�ood Magnitudes ................................................... /�.�� !6��# ��53--GeoRAS for the River and Flooded Areas............................................... /�.�� !6��23��#�� ................................................................................................................� /�.�� !6 �#�1 ��#�� ................................................................................................................� /�.�� !6��0 �� ....................................................................................................................� /�.�� !6��+��;��........................................................................................................................� /�.�� !6��AB�B2��,�/�� ;�C ��(��.� ��/�� 4�� .....................................................� /�.�� !6��->#B3#��,�/�� ;�C ��(��.� ��/�� 4�� ...................................................! /�.�� !6��+5#AB7��,�/�� ;�C ��(��.� ��)�� 4�� ...................................................! /�.�� !6��+5#AB7��,�/�� ;�C ��(��.� ��)�� 4�� ...................................................! /�.�� !6�!�/�� (� ��2� ��2�� 0 �� $ 1 ��.................................................................� /�.�� !6��/�� (� ��2� ��2�� ; �� . ��-up Areas %!��0 �� $ 1 �'.....� /�.�� !6��/�� (� ��2�� )� �� .�.....................................................� /�.�� !6� �2 ��/�� (� ��(��ng to Different Treatment of Buildings ....................... /�.�� !6��2�� /�� (� ��2�� 5 �.��-�� .......................... Figure�!6��/�� (� ��"��.�2)��0��5�1 �2�� 3��3� 1��4�� ............� /�.�� !6��/�� (� ��"��.�2)��2�� # ��.................................................�� /�.�� !6��# ��-��*��.�2�� 2)��# ��...........................................................�� /�.�� !6��2�� 0 �� 4�� 2�� -up Areas (Buildings as Solid Blocks) .�� /�.�� !6��2�� 0 �� 4�� 2�� -up Areas (Borders of Buildings).........�� /�.�� !6�!�2�� 0 �� 4�� 2�� ilt-��(� ��%-�� .��

Solid) ............................................................................................................................................� /�.�� !6��2�� 0 �� 4�� 2�� -��(� ��%-�� .��

Solid) ............................................................................................................................................� /�.�� !6��2�� 0 �� 4�� 2�� -up Areas (Borders of Buildings with

Openings) .....................................................................................................................................�� /�.�� !6� �-��. ��/��0 �� $ 1 ��2�� -up Areas as Solid Blocks...............�� /�.�� !6�-��. ��/��0 �� $ 1 ��2�� )� �� .��-up

Areas.............................................................................................................................................��

VIII

/�.�� !6��3�� A�� .�� .��+��4�� /��0 �� $ 1 �......�� /�.�� !6��-��; �� . ��-up Area ................................................................................�! /�.�� !6�3�� .��-�� /��0 �� $ 1 �..................................................�� /�.�� !6��-��. ��/��4�� 2� ��#��3��2)��3� 1��4�� ..................�� /�.�� !6��3��/��0 �� $ 1 ��2� ��#��3��2)��3� 1��4�� ...............�� /�.�� !6!�-��. ��/��4�� 2� ��+,�� 3��2)��3� 1ation Values..............� /�.�� !6��3��/��0 �� $ 1 ��2� ��+,�� 3��2)��3� 1��4�� .........� /�.�� !6��-��. ��/�ood Volume Due to DTM Resolution..........................................................� /�.�� !6 �3��/��0 �� $ 1 ��2� ��2)��# ��......................................................��

REFINED MODELLING FOR FLOOD EXTENT PREDICTIONS USING LASER SCANNING

�

�� Introduction

�� Introduction

Flood is one of the most serious, common and costly natural disasters many countries are facing these days, each year hundreds of people lose their lives to floods. When the amount of water excesses the capac��,�� 1 ��8��9�� Predicting the flood extent accurately is necessary to reduce losses especially in lives. Expecting less area than the true will increase the risk, and expect-ing greater area will reduce the use of the land in any project as well as its value. There are many factors affecting the predictions of flooded areas, such as: flood magnitude, soil type, land cover, quality of the digital terrain model (DTM), build-ings and their structures, and the quality and capac-ity of the sewerage system. Built-up areas are part of the land cover that has an influence on the flooded area predictions for two main reasons; first because they consume space, and second because water runoff has different behaviour in the built-up areas compared to natural surfaces, the runoff in-creases two to six times in the built-up areas over what would occur on natural terrain, which is a result of converting fields and woodlands to roads and parking areas where there is no or low absorp-tion capability, and the roads become like channels or swift moving rivers and basements like death �� ,�� 8"#$��

"#$�9��

�� Problem Definition

In the past, the topographic maps were used to generate the DTM to be used in flood area calcula-tions. From literature the buildings were either not contributed or treated as an area that prevented from flood, which means that buildings were treated as solid blocks with height more than the flood water level. Treating buildings in such two ways does not represent the reality, because when flood occurs in built-up area, buildings will be flooded as well.

Figure �:� Flooding in Sheung Shui During Ty-

��

Figure �:� Effects of Urbanization on Flood-�� !��"


�

-�� .�*��.��2��

model is a relatively new issue. When the air-borne laser scanning technique became available, several countries start using it to generate surface model. Since the laser scanning technique can provide high accurate data about the ground sur-face, then by using this technique high precise surface models can be achieved. Besides, high accurate terrain models can be produced by filter-ing out the non-ground objects from the surface model. Moreover, this technique can provide high-density grid points, which give more detailed information about the ground surface and the ob-jects, even the small ones, over the ground. Using high accurate data as an input in the flood models will output a high accurate prediction of flood extent. Refined the surface model in such a way to repre-sent the reality for calculating the flooded area accurately is must. Which can be gained by modi-fying the surface model to treat buildings as par-tially solid blocks that could be filled with water when a flood occurs.

�� Objectives

Aiming at calculating predicted flooded area and/or flood water level accurately, the main ob-jective of this research is to check the effect of the main factors of surface/terrain modelling on the accuracy of flood extent calculations. The specific objectives are:

�� Modifying surface modeling in such a way to treat buildings in flooded built-up areas as partially solid blocks that can be flooded.

�� Analyzing the sensitivity of the refined surface modeling on flood extent pre-dictions.

� Analyzing the sensitivity of the model to the accuracy of the DTM elevation values.

�� Checking the effect of grid size of the terrain model and the buildings on flooded extents and level calculations.

�� Verifying the effect of combining high resolution DTM for the important features (dykes) and low resolution DTM for the floodplain area on the flood extent predic-tions.

Figure �:� Photography Complements of the Cali-fornia Governor's Office of Emergency Services.

��

Figure �:# Flood Streets and Homes in the Winter

�� $��%�&��$��

Figure �:% Flood Occurred ��'�%"�(��

Ago


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Achieving these objectives will give hints to both adequate data acquisition and data processing. By answering the following questions, the objectives of this research will be achieved

�� What is the most suitable flood model that can be used? �� What major factors should be taken into consideration apart from the terrain/ surface

model in order to predict the accurate extent of flooded areas? � To what extent consideration buildings as solid blocks, instead of partially solid

blocks, as inputs for the flood model would change the output? �� What is the critical percentage of built-up land after which it is essential to consider

buildings with DTM? �� What is the effect of uncertainty in the percentage of the building space that can be

filled with water when a flood occurs on the flood extent predictions? !� What indicators should be constructed in order to analyze the sensitivity of modified

model to the accuracy of DTM elevation values? �� How much the precision uncertainty of the height values affects the predicted flooded

area calculations? �� How does systematic errors in the DTM elevation values influence the flood extent

predictions? � To what extent the model sensitivity depends on the DTM elevation values vs. the grid

resolution? �� How can the low resolution DTM be modified in order to improve the predicted flood

extent accurately? �� What difference in estimates is given when comparing modified low resolution DTM

with high resolution one? �� To what extent the additional information used with the low-resolution DTM compen-

sate the change of accuracy when using low-resolution data?

�� Previous Work

Studying the effect of urbanizing the watersheds of rivers has been discussed before from different perspectives because of its importance, specially urbanizing catchment areas.

�� Effects of Land-Use Changes in the Catchment Areas

Changes in land cover have an influence on the natural rainfall-runoff regime of various river basins. Changing the catchment areas from fields and forests to streets and parking areas re-�� *��*��,�� 8"#$��"#$�9�� .�� n-�� 1 ��8��9��(�� creasing the amount of runoff water, the volume of the flow in the streams and rivers increases. When the volume of the water exceeds the ca��,�� 1 ��*��8��9��)��*��:��.��

in the catchment area itself and increases the magnitude and frequency of river floods. When floods happen more frequently, the streams in the catchment areas become deeper and wider and consequently transfer more water and sediments to the main river, which decreases the river depth and increases the river discharge, as a result larger magnitude of river flood occurs. Quantitatively modelling the impact of land-use changes has been attempted by a number of environmental researchers who are interested in the flood hazard, and this is beyond the scope of the present research.


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�� Effects of Urbanization on Water Quality (Ecological Risk Assessment)

When the catchment areas of a river are urbanized, the ecological risk increases, because the runoff water will be mixed with any source of pollution in its way to the water bodies, thus the water quality becomes lower and the water bodies usually will be polluted, like what happened in Grank Fork, N.D., �� )�C�� 8��9��/�� the ��.��D��* �� *,�� . � �� 8�9��(��u-sion, from the environmental point of view the effect of urbanizing the floodplains on the water qual-ity and purity are under research. Such researches, however, do not have answers to the questions raised above.

�� Risk and Damage Assessments and Flood Control, Managements and Insur-ance

Flood damage occurs where people are endangered or when the utilities’ functions or values are im-�� 8]. Urbanization in the floodplain make the flood more dangerous and some-�� 8�9�� ,�* �� *�� ,�

other area, so when the flood occurs in an urban area more lives are at risk and evacuations are more essential but more difficult as well. The rapid and uncontrolled urbanization make the flood more de-��1 �8�9��A�� *�� *��.��,�* �� a-ter, which cause some losses in the buildings and sometimes the buildings collapse because of the ef-fect of water on the building or the soil movement under the building. Furthermore, when the flood �� 1 �� ,��8"#$��"#$�9�� *��:��.�� around the wa-terways is considered as one of the main factors that could increase the flood risk and/or damage. Insurance companies are one of the most interested users in flood risk and damage assessment re-searches. According to the flood damage and risk the insurance amounts can be estimated. Flood control, flood management and flood protection programs have been discussed in a number of researches and projects, governmental organizations specially organizations that have the responsibil-ity of producing f��D��8"#$��"#$!9�� D�� 8��9��)� �

flood forecasting (magnitude and frequency) is an important task, on which alarm systems depend, to warn the people of the floods in areas within the flood extent in a maximum ��*� �� 8�9� In the countries, which have elevated areas, confided the river, only the low parts, of the cities and villages, adjacent to the river are seriously threatened. Thus preventing these parts can be done by lo-��@ ��8��9��A�� large areas, like the lower part of Nordhein-Westfalen, Germany, and the Netherlands, because they do aware of the problem of spreading the flood over large areas, these countries use the dykes which protect vast areas of cities, villages, arable and industrial areas. Thus �� D��* �� * ��,��8��9� For more details about how the Netherlands controls the risk of flood, this web site http://www.geerts.com/holland-modern.htm describes the delta project in the Netherlands.

�� Effect of DTM Quality on Flood Extent Calculations

DTM is an essential data out of which several surface details are derived, such as slope, terrain as-pects, and flow path, which are used in the hydrological models. Thus the qualities of these factors are directly influenced by the quality of the DTM. The quality of the DTM and its effect on determining �� 1 �� C� �� 1��D��A��7�,�� studied the effect of the DTM accuracy on the accuracy of the water depth calculations, and he found that the er-ror in the depth of water is influenced by the accuracy of the DTM. With considering the specifica-


�

tions of the US floodplain management authority and assuming the velocity of the flow does not change due to small errors in cross section area, Kaystha found that the error in the depth should be � �� 2)��* �� 8��9��)� � �� c-cording to another assumption, that a large number of equal intervals in the trapezoidal method were used in determining the cross section area. This large number of equal intervals means small spacing between the grid points or high resolution DTM. From this study we can conclude that the DTM accu-racy and resolution have a high effect on the calculated water depth, and that it is recommended to use 2)�� C� �� ,��.� ��

�� Thesis Structure

The thesis consists of seven main sections; first section defines the problem, objectives and the struc-ture of the thesis. Second section discusses the flood as a hazard several countries suffer from and gives a general idea about the flood models that can be used for calculating flood extent. The third section defines the DTM/DSM, explains the available methods of generating the DTM, its importance for Flood area calculations, its horizontal and vertical accuracy and the effect of these accuracies on the flood area and level calculations. In the fourth section, the laser scanning is reviewed as a tool for DTM, DSM generation, how it is implemented and its advantages for flood modelling. Chapter five explains the concept of refined surface modelling, the choice of the flood model, what are the cases that will be checked experimentally in the research work and why, and what is the evaluation method. In chapter six the study area, data sets, the different results that could be achieved by changing the inputs are described, moreover the results are analysed and discussed. The thesis ends with the con-clusions and recommendations for the future works.


�


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�� Flood Hazard

�� Flood Definition

A flood is an anomalous high water flowing, which goes over the embankment along either natural or �� 8�9��(��.�� National Flood Insurance Program (NFIP), the definition of flood is "a general and temporary condition of partial or complete inundation of normally dry land areas from overflow of inland or tidal waters or from the usual and rapid runoff of surface waters ��,�� E�8"#$�9��,�� noff than an area can store or the capacity of the stream within its normal channel.

�� Types of Flood

There are different types of flood according to what cause them or where they occur. The following sections describe the main types of floods.

�� River Flood

When a river basin is filled with too much water, more than the capacity of the river channel, it floods. Since the river flood usually occurs seasonally, it is considered as an expected event. Some-times when the seasonal rains are complemented with melting snows, an unexpected large amount of wat �� 1 ��*��F��D�,�� 1 ��8"#$��"#$�9��

�� Coastal Flood

Costal floods are the floods that occur in costal areas as a result of driving the ocean waters inland. The ocean water is driven toward the land as a consequence of a tropical storm, hurricanes, or an in-tense offshore low pressure. In addition to that, the costal flood may occur as a result of tidal sea waves, which hap� �� F��D ��1��1��,�8"#$��"#$�9�

�� Urban Flood

It is the flood that occurs in an urban area due to rainfall and changes in runoff behaviours as a result of land cover changes from natural fields, which have absorbing ability, to paved roads and parking areas that do not have permeability characteristics. The rainfall runoff in the urban areas is much more than it in the natural fields; it could reach six times what it has to be. As a result of that, roads become ��1��.��1 ��*�� ,�� 8"#$��"#$�9�

�� Flash Floods

When a large amount of water floods within short period, few minutes or hours, it is called flash flood. Normally these short events, Flash Floods, occur locally and suddenly without or with little ��.�8�9��/�� rainfall, a dam or levee failure, or a sud-den release of water held by an ice jam. Since flash floods can destroy buildings, move boulders, wrench trees, and bridges, and create new channels in their ways, they are considered as the most se-rious and fatal �� )� �� 1 �� ,�� eters height


or more. Furthermore, flash flood may also cause a very dangerous event, the mudslides, which is a �� 8"#$�9�� This research will concentrate on the river flood, as it will discuss the flood extent in the areas adja-cent to a river, the effect of buildings, and the role of dykes on controlling the flood extension.

�� Floodplain

Floodplains are the dry land areas next to a river, stream, ocean, lake, pond, or bay, and could be cov-ered by water when a flood of the adjacent water body will occur. These areas can be considered as areas which carry the excess flow of the stream capacity. These areas could shrink to the width of a narrow stream valley or expend to areas along streams in flat wide valleys, depending on the topogra-��,�� 8�9��/��D�� . ��*�� F��

datasets for floodplain analysis. Furthermore, for determining the flood discharge, data about flood flow is required or it can be calculated using rainfall data by applying a rainfall-runoff analysis. For estimating the water surface profile, one or two-dimensional hydrological models can be used. Once the water surface profile is known, the floodplain area can be determined by finding ground points, on both sides of the extension of the stream cross sections, have the same elevation as the water surface 8�9�� ,�� pends mainly on the accuracy of the topog-raphic data, using high accurate topographic data sets is highly recommended. Any encroachment on floodplain by artificial fill materials or building constructions, the carrying ca-pacity of the floodplain will be reduced, and as a result the floodplain will extent wider than the natu-�� 8�9��)�� ,��.�� *��.� C�� d-plain extent.

�� Flood Model

Flood model is a hydraulic modelling, which is used to estimate the flood extent. After collecting the data of the river area and the flood magnitude, the flood modelling procedure follows two main steps. The first step is determining the channel profile at different locations along the stream, and the second is calcu��.�� C� �� * �� 8��9�

�� Data Collection

For calculating the floodplain extent, flooded area and flood water level, some data are required among them: �� Flood discharge data along the river or rain fall data ��Land-cover data, including vegetation and soil types, for roughness coefficients calculations. �� Surveyed cross sections data. ��Channel roughness estimates at several points along the stream. If there are gauging stations nearby the area of interest and sufficient data are available, then the flood discharge can be calculated for different return periods using flood flow data provided from these gauging stations. The flood discharge of a certain return period is the minimum magnitude that has a fixed probability to occur only once �� )� ��

some�� - year periods. In this case a statistical method can be used to represent the profile of the flood at different return periods using average flood magnitudes, but this statistical method re-F�� . ��* ��.��.��.��.�� 8��

�9�


When gauging station data are not available, rainfall-runoff data have to be analysed to estimate the flood discharge using a hydrological model. There are different models that can be used for this pur-pose, they depends mainly on the rainfall intensity data, the area that is prone to the rain, land cover information, ground slope data and drainage density data in flood discharge calculation�8��9��(��d-��.��7�,��%� �'�� elates peak discharge to the rainfall intensity and drainage area by an empirical relationship. In addition to the flood discharge data, surveyed cross sections data are needed to give information about the elevation of the streambed and the ground data surrounding it at different locations along the river. Other topographic data is required as well to give information about the land cover types surrounding the river to estimate the roughness of the ground area adjacent to it, which is needed for �� C� �� /�� *�� .�� F�� 8�9�

�� Flood Profile Estimation

The first main step after collecting the data is simulating the flood discharge to determine the flood profile along the stream using a hydraulic simulation model. Some of the hydraulic models are one-dimensional and others are two-dimensional models. The one-dimensional models consider the varia-tion along the stream only while in the two-dimensional models the flow properties vary in both direc-��.��1 �� 8��9��>� �� * �� 53--RAS model, which is a product of the US Army Crops of Engineers, Hydraulic Engineering Centre. This HEC-RAS Model is a River Analysis System designed for determining water surface elevations throughout a river network and analysing bridges and culverts. This model uses the geometric and ��2�� 1 �� ss-section structures along the river network and �� 1 �� 1 ��8�9��/�.�� -��,��53--RAS flood �� 8�9�

Figure �:� Typical Output Cross Section (HEC-RAS)


��

The bed and the sides of the river streams are natural, thus the cross sections of the rivers do not have regular shapes. As the cross sectional area of the river is not similar everywhere along its stream, the actual ground elevation for the river bed and the surrounding areas, including the banks, are required to estimate the cross section area and consequently the water level, at any discharge value.

�� Flood Extent Calculations

According to a certain amount of flood discharge, the profile of the channel can be established. When the channel water level exceeds the banks level water overflows to the adjacent areas to the channel. By combining the information about the water level and the ground elevation data at different cross sections along the river, the flooded area can be calculated. Some GIS packages provide models for calculating the flooded area by intersecting the ground elevation data with a plane, which represents the water level. Some of these packages deal with the data in vector format, like HEC-GeoRAS, and others deal with raster format data, such as ILWIS.

�� Requirements of Flood Model

As conclusion, any flood model depends mainly on; the flood flow discharge, land cover types for the surrounding area of the channel and the ground elevation data, which are represented by the digital terrain models, DTM. The definition and uses of DTM are described in the next chapter.


��

�� Digital Terrain Models

�� Introduction

Digital Terrain Model (or more precisely digital terrain relief model), DTM, plays an important role in different applications. Some of these applications do not require high accurate DTM, especially those for planning purposes. In contrast, other fields need high accurate ones. In the following sec-tions the uses of the DTM in the hydrological application will be illustrated.

�� Definitions and Basics

�� Digital Terrain Model and Digital Surface Model

The concept of a Digital Terrain Model can be used for digitally representing terrain relief, using a large number of points, whose three coordinat ��G��?��H�� D��8�9��)� � �D��

enable computer processing to solve problems faster and better than dealing with the natural ground. In the representation of the surface the heights of objects above certain datum may be included. Thus the term Digital Surface Model, DSM, is used to describe the geometry of the ground surface includ-ing the objects above it, that because in some applications the height of the objects above the ground surfaces is reF�� 8!9�

�� Resolution

The digital terrain or surface model resolution can be defined as the distance between the grid points �� % F��1�� C ��: �� 2)�&2+�'�8!9�

�� Importance of DTM for Hydrological Modelling

Any floodplain model needs information about the elevation of the ground points inside the flood prone areas. Since the digital terrain/ surface models are the three dimensional presentation to the ter-rain, they become very important information to floodplain modelling. Thus the digital terrain and surface models can be considered as the backbone of the flood predictions. There are different uses for the Digital Terrain Models, the most important uses of DTM in the hydro-��.�� .�8��96

∗ Storage of elevation data for digital topographic maps in national databases. ∗ Displaying of land in three-dimension form. ∗ Planning the networks of irrigation systems (canals and drains) and the locations of dams, etc. ∗ Determination of cut-and-fill volumes. ∗ Calculating the volume of reservoirs in the hydrological projects. ∗ Determination of the parameters in hydrological models such as slope and aspect maps, and

slope profiles that can be used to predict the flood magnitudes and to assist geomorphologic studies like predicting erosion hazards and determination of the probability of landslide as well.


��

A�� 2)�� 2+��* �� *��.�� 2��,��d-els, which can be used instead of the DTM in flood extent predictions in urban areas, for the reason that the buildings occupy some areas, which affect the flood extent predictions. As well as the loca-tions of the buildings are required for some hydrological elements calculations such as velocities of the flood flow.

�� Technologies Used for DTM/DSM Generation

After studying the use of DTM/DSM, knowing how the DTM can be generated is needed. There are many possible ways for producing the DTM some of them are easy and fast and others are more com-plex and time consuming. The available techniques can be summarized as follows:

∗ Digitising/Scanning existing maps ∗ Ground survey ∗ Photogrammetry techniques ∗ Active Remote Sensing techniques, i.e. using either Radar or Laser sensors.

�� From Existing Maps

Contour lines and/or spot heights on existing topographic maps can be digitised or scanned then by using one of any various interpolation techniques of a DTM can be generated. This method is cheap and sometimes fast but the output accuracy is not so good and it depends on the accuracy, quality and the scale of the map itself as well as the professionalism of the operator and how accurate s/he is.

�� Ground Survey

The tachometer instruments, the traditional ones like the Distomate and the Theodolite, and the new ones as the total stations, can be used to measure the X, Y, and Z coordinates for grid points with equal distances in between. The new ground techniques as GPS can be used as well. There are differ-ent techniques of GPS that can be used, kinematic and semi-kinematic (stop-and-go) GPS. By observ-��.�� 2��dinates of points covering the whole area, and then interpolating these data, X, Y, and Z coordinates of a grid points with constant distances can be calculated. These methods give a high resolution DTM but they are time consuming for large areas.

�� Photogrammetry Techniques

From a stereo pair, either aerial photos or satellite images, and by applying the coliniarity equations %��*� '�� 2�� * �� -�� ,��

software packages, which have the capability for the automatic aerial triangulation, which signifi-cantly reduces the time and effort of orienting the images of large blocks.

�� Active Remote Sensing Techniques

There are different active sensors that can be used for DTM generation, like Radar images and Laser Scanning altimeter. In Radar images, the different phase information is used, this information is ob-tained from a slight offset of two emitted microwaves, based on two different passes or two different antennas mounted on the same platform. By measuring the two backscattered waves received at the antenna the range difference can be calculated and the elevation of the objects on the ground can be � � �� 1 �,�� D��8��9��$�� +��.�� que is a ranging system, the system on board sends pulse signals to the ground and receives the reflected sig-nals, by measuring the difference in time between sending and receiving signals and by knowing the


��

speed of the light, which is the same speed as the laser pulses, the distance between the sensor and the ground point can be determined. By knowing accurately the position and altitude of the laser system at the moment of sending pulses, the position of the points on the ground can be calculated precisely. More details about the description of the system are included in the next chapter.

�� Technologies Used for Riverbed Generation

For riverbeds DTM generation, other techniques more convenient to under water acquisition should be used. Among these techniques:

∗ The Ground survey techniques that using tachometer instruments or GPS. This method is suit-able for the shallow water and small areas.

∗ From a vessel, using the traditional method, the weight and rob, but this method is too slow and not suitable for deep �� 8"#$9

∗ From a vessel, using ultrasonic instruments, for the reason that the sound travel very fast in the �� 8"#$9��eflect on the bed. The same concept as laser system but with using sound waves instead of light is used. By measuring the difference in time between sending and receiv-ing the sound signals, the water depth can be calculated, and by having accurate position data at the moment of sending and receiving the signals, the coordinates of the river or sea bed can be calculated.

∗ Special kind of laser scanning system, which transmits both infrared and blue-green pulses can be used. The infrared pulses reflect on the water surface and the blue-green pulses penetrate the water surface and re�� * ��8��9��? ��,�� * �� when there is clear water, otherwise the laser pulses reflect on the particles in the water, which means that it is not suitable for the rivers, the main reason for that is the turbidity of the river water.

�� Effects of DTM Vertical Accuracy on the Flood Extent Calculations

The vertical accuracy of the DTM is very highly recommended in the floodplain extent calculations as it has a direct influence on the accuracy of the flood extent calculations, any error in the ground level will cause error in the flooded area calculations. In the flat areas like in the Netherlands, small differ-ences in elevation values may extend over several miles. When a flood occurs in such an area, the source of risk appears to be far from the dwellings or businesses constructions in areas located well away from the river but floodwaters that rise by an additional foot can inundate thousands of acres 8��9��Since the hydrological models depend on the elevation data in the calculations, the DTM with high accurate elevation values is must for flood risk mapping, and it can have significant ramifications if a certain degree of accuracy, which guarantee the accuracy of floodplain extent calculations, is left �� 8��9��)� � �� 2)��* �� .�� epth calculation ��,�� "+��. � ��,�8��9��Since airborne laser scanning technique now allows large areas of accurate data to be acquired, it mostly suited to ��.�8��9�

�� Effect of DTM Resolution on the Flood Extend Calculations

A�� 1 �� 8�!��9�� 2)��.��: �� u-lation of the rainfall-runoff analyses were studied, but each of these researches discuss a cer-tain model on a certain study area with several grid sizes from different sources. None of the results of these researches can be generalized, so it is important to do the sensitivity analysis


��

�� ,�� (��.��-�@��%��'�� F��,�� ,��e-lated to the quality (resolution) of the DTM, and that the high resolution DTM from laser scanning data will provide more accurate grid than using topographic maps. As well as the resolution of the DTM that can be provided from the U.S. Geological Survey ("+<+'��

not sufficient for pro��.�� 8 9�


��

�� Laser Scanning Technique

�� Introduction

Airborne laser scanning system is a system fastened on an aircraft, where the laser is used in measur-ing distances between the platform carrying the system and the surface on the ground. The Laser Scanning is one of the active remote sensing sensors where the system itself is the source of energy and does not depend on an external source like the optical systems.

�� System Components

This system is an integrated multi-� ��,�� $�� <;+��A�"��,�� 8�9��

Whatever the type of the system, it consists of several components some of them on board and the others on the ground. The main components of this system are [!96

�� On Board

��Laser range finder includes the laser, transmitter and receiver, signal detector, amplifier, time counter and the necessary electronic components

�� Scanner ��Global Positioning System (GPS) receivers with their antennas (usually two, one for navigation

and the other for positioning). �� Inertial Measurement Unit (IMU) ��Registration units (usually two, one for laser data and one for GPS/IMU data). �� Some systems compliment the laser system with digital camera or video camera for documenta-

tion.

�� On Ground

�� Planning mission software and post processing software (could be one software for both plan-ning and post processing)

��GPS reference station(s) (with GPS device(s)working simultaneously with the device on board for DGPS)

��Radio link (for real time navigation)

�� System Type

There are different systems working with the airborne laser scanning technique some of them are commercial and others are not. The types of the systems are mainly according to their use and the �� )�*� ��-��8!9��: �� racteristics of the different systems that use a pulsed laser.


��

Table #-� Laser System Characteristics

Characterize Min Value Max Value Typical Values

Scan Angle (°)

Pulse rate (kHz)

Scan rate (Hz)

Flying height (h) (m)

GPS frequency (Hz)

IMU frequency (Hz)

Beam divergence (mrad)

Swath width (m)

Across-track spacing (m)

Along-track spacing (m)

Angle precision (roll, pitch / yaw) (°)

Range accuracy (cm)

Height accuracy (cm)

Planimetric accuracy (m)

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You can visit http:www.airbornelasermapping.com web site, for more updated information about laser scanning technique and its uses.

�� How it Works

The laser instrument sends laser pulses to the ground and receives the reflected signals, the travelling time between sending the pulse and receiving the reflectance is measured very accurately in terms of �

-� sec, by which the distance between the vehicle and the surface can be measured (using the speed �� .��'�89��)� �<;+��,�� .�� I��ually there is another GPS receiver on a reference station on the ground, together with the receiver on the aircraft, constitute a DGPS system for achieving accurate positions. The IMU system measures the attitude of the aircraft that lead to knowing the pitch, roll and yaw angles of the laser scanner instrument at the moment of sending and receiving the signals. The combination of these three systems makes it possi-ble to know the accurate positions of the aircraft and the distance between the aircraft and the surface at each pulse, so using these information the coordinates of each point on the ground that is hit by la-� ��* ��* �� /�.�� -��8�9�� .��,stem and its main components. )��,�� .�� .��.�� ,��.�� ,�� 89�


��

Figure #:� Laser Scanning System

�� Steps of Generating DTM from the Laser Scanning System

The laser systems are almost similar in the steps of working. The following points describe the chronological sequence of the laser-scanning mission starting from the preparing for the flight till .��.�� 2)��8�9� �� Preparation for the mission by doing some measurements before and during the first period of

time to calibrate the instruments. �� Time synchronization between laser and GPS. � Data integration: interpolation of all these data sets to the same instances of time. �� -�� 0<+�� ,stem. �� Adjusting the strips to each other. !� Filtering the data to delete the incorrect data, blunders, or to generate DTM from the DSM data-

set. �� Resampling the data after the transformation to the target format. For delivering raw data, any laser system service providers follows the first four steps. The last steps are followed if the client has further requirements.

�� Accuracy of the Laser Scanning System

The accuracy of the laser scanning system can be divided mainly into ranging accuracy and position-��.��,��/�.�� -�� ,�� 8�9��)� ��.�

accuracy depends mainly on the accuracy of the GPS/IMU systems. The atmosphere has a great effect on the ranging results. The travelling laser pulses through the at-mosphere are affected by diffraction, absorption, scattering, and propagation delays. The flying height affects the height accuracy greatly, as the propagation delays increase tremendously with increasing �� ,��.�� .��8�9��)� �� .�� *�� .�� *,�� sture and dust in the atmosphere, and the diffraction causes a curvature in the laser beam. Moreover, using �� "+��

��D��,��.�� .��8�9��(�� * �� uracy of the GPS/IMU systems, scan an-


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.� �� .��,��1 �� ,�� ,�� .��1 �,�8�9��/��

�� 0<+�� ,�� ,�� r-�� 8�9��A�� .��,s-tem calibration, using the system without calibration may reduce the accuracy of the point coordi-�� 5��.�%� �'��: �� ,�� laser systems, and the ��C�� .�� 8��9�

The Airborne Laser Scanning System is developed for the acquisition of the elevation data in large �� .��,��-�� )� ��,�� ,�� laser meas-�� ,��

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%� �'�� 1��.��.�� 2)��.��y��.��,��.�� .��

�*�1 �� .�� 1 ��.� ��°, which�� ,�� A��

�� * ��1 �. �� .�� olution DTM with footprint radius of ��. � �� A�� .�� .��

second the laser scan��.��,�� * ��

�� 8��9��/�� 1��.��.� �� ,�� * �� C ��.��r-craft which can be reduce the flying height and as a result increasing the density of the laser points to * ��-�� F��

Mirror Laser

Punting inaccuracy of the scan-ning mirror

Internal or hardware factors: Pulse intensity Size of the detector aperture Sensitivity of the detector Beam divergence influencing the foot-print size

External factors: Atmospheric transmittance Flying height influencing the foot-print size Reflection characteristic of the terrain

… Determine…

SNR ratio�

Produces

Pulse length variation

Affects the accuracy of the planimetric position determination

Affect the accuracy of the pulse detection

Produces

Pointing Jitter

Figure #:� Error Sources of the Laser System


�

�� Advantages and Limitations

Each system has advantages and disadvantages; any new technique has to have some advantages over the other existing techniques. When the advantages of any system are much more than the limitations, this system will be considered as a good system, in contrast, if the disadvantages are many so the use of this system will be very limited. The main advantages of the laser scanning system are: ��Has a h�.��,��-�� 1��1�� Can map the surfaces with little/no texture. ��Can map the surface in the forest areas, since it can pass through the holes between the leaves

and branches, where the optical sensors cannot photo the ground. ��Measures the height of trees and vegetations, since some of the pulses can hit the tree canopies

and the others reflect on the ground. ��Can map any time, day or night, since it is an active sensor, so it can be used when the Photo-

grammetry fail because of the limitation of acquisition time. ��Can map the narrow linear features, like the streams, roads, etc. ��5��1 �,�� * �� -D city models. ��Offers near real time production, faster in processing than collecting the data in field or the Im-

age Matching, as long as there are no digital cameras. ��Does not need large number of control points, since the system is an integration of laser scanner,

<;+��AB+��(��.��5��%� �'��.� �<;+�.��* �� 8��]. On the other hand, there are some limitations ��Relatively expensive for large areas [��]. �� Some limitation in weather conditions, no-fog and no-��8�9� �� It is blind system, so it needs complementary instruments as digital camera or video camera, if

inte�� 89�

�� Building Extraction

Automatic building extraction is still under research, first it depended on the aerial photos, but when the laser scanning technique becomes available many researcher start to establish models for building extraction using laser scanning data. That because the laser scanning technique gives high accurate � 1��1�� %�-��'��* �� * �� *��.��-buildings objects. Some models are automatic and some are semi-automatic. Generally the procedure of build-ing extraction models is as fol��.�8�!96

�� Segmenting of the grid data, based on the maximum allowed height difference between any successive grid points to be in the same segment, and the inferior limit to the segment length.

�� Comparing segments and detecting areas where collinear couples of the segments are pre-sents, to form the seed of the planner based on the minimum similarity value that specified.

�� Appling a region-growing algorithm to aggregate other segments to the original seeds. Considering that linear element and planar patches belong to the same plan if they have the same orientation in the space.

�� Appling another similarity function for better recognition of the scene, and noise removing. Usually the building extraction models are dependent on both the laser data and a complementary data such as aerial photographs or GIS data for the buildings.


��

�� Using of Laser Scanning Data for Floodplain Modelling

As mentioned in chapter three, it is important to use a high accurate DTM in floodplain modelling. +�� ,�� 2)��. � �� *,�� .��* ��1 ��. �� 1��

values, then using the laser scanning in the DTM generations for the flood modelling is recommended. In the Netherlands, there is a project meant to counter the demand for detailed information about the elevation from water borders, provinces, and national government, the AHN project (Actual Height model of the Netherlands). This information is required for the management of coasts, dykes, polders and higher-level areas in the Netherlands. Models that simulate the discharge of the rivers, or that simulate what would happen if a dyke breaks somewhere require high accurate elevation information as these produced in the AHN project. These elevation models contain, in addition to information about the ground, also elevation data about built-up areas, dykes and roads, which can be used in sev-eral applica��8"#$�9�� C� �� In addition to the AHN project, there is another project call DTB-Nat, which contains high-density-elevation points for the wetlands around the great rivers in the Netherlands. As well as information about the exact location of dykes, quays, locks, banks and breakwaters are included. These kinds of information help in predicting flood extent accurately.


��

�� Modelling Buildings for Available Flood Models

�� Introduction

Checking the effect of buildings on the flood extent can be done through different alternatives; col-lecting real data from plain and built-up fields and compare the results is one of these alternatives. However, having this kind of data is not applicable, as data for the same area that is flood prone be-fore and after building up is not easy to be available. And comparing flood extent in natural area and in another area, which is built-up, is not logical, as other factors rather than the building existence, such as the land cover and the roughness coefficient, will affect the results. Mathematically formulat-ing to model the behaviour of flood in built-up areas may be another alternative. Yet, it is likely to be very difficult to formulate the relationship between the predicted flood water level and all its influenc-ing factors including the effect of surface modelling, nobody has attempted yet to do so. A third method through which the effects of the buildings in floodplains could be studied is simulating the surface model and different flood magnitudes in such a way to represent the behaviour of flood in built-up floodplain. The simulation method is chosen to check the effect of buildings existence in floodplain on flood extent. Thus, the available flood models are studied to choose the most appropri-ate one for realistic simulation. From the geo-informatics point of view there are two available flood models, the first model is HEC-GeoRAS (ArcView extension) with HEC-RAS program, and the other is the flood area calculation model in ILWIS software.

�� HEC-GeoRAS Flood Model

The HEC-GeoRAS ArcView extension has two roles in floodplain modelling; first it prepares the geometric data to the HEC-RAS program, which uses these geometric data as well as the hydrological data about the flood to estimate the profile of the channel during the flood event. In addition to data preparation, the HEC-GeoRAS determines and calculates the areas that are expected to be flooded according to the flood profiles estimated in HEC-RAS program. The requirements of the HEC-GeoRAS program are:

• TIN for the whole area including the channel bed. • Land-use map (to define the land cover and the roughness coefficient for the channel and the

surrounding area). • The rate of discharge or the rainfall amount (to be used in HEC-RAS program for the flow

data). �� Data Preparation:

)� �� .�� 2 Analyst ArcView extension to produce TIN and generate contour lines from it. These contour lines are used to determine the channel centreline, the bank lines and the flow lines with taking into consideration the flow direction. Since the bed and the sides of the river streams are natural, and there is no regular shape, for the cross sections, along the


��

rivers, so cross sections at several locations should be surveyed, which in turn are used in channel profile estimation. In addition to that, Manning’s coefficient can be determined for each part in the cross sections depending on the land cover type, which can be de� �� 8� 9�

�� Channel Profile Estimation: The HEC-RAS program allows the geometric data that are prepared in the HEC-GeoRAS program, as described in the previous step, to be edited to add any existing hydraulic structures to the cross sec-tions, such as culverts, bridges, and walls and also to modify any incorrect data. In the HEC-RAS pro-gram, the flow data with the geometric data are used to estimate the profile of the flood. The flow data that can be used are flood discharge or rainfall amount and the depth of the water or the type of flow. The HEC-RAS model allows estimat��.�� 1 �� ,�8�9�

�� Flood Area Calculations: After estimating the flood profile, the ArcView program is used with the HEC-GeoRAS extension to �� 1 �� )� �(��4� �� C� ��2�(��,��. � �� a-ter surface TINs for the different flood amounts using the flood profiles that were calculated before. By intersecting the water surface TIN with the ground data, the flooded area is defined.

�� ILWIS Software

The flood modelling in ILWIS calculates the flooded areas based on dam construction. In this soft-ware, the digital elevation model should be modified to include the dam in the proposed location as a part of the surface model. After including the dam, the point(s) from where the flood will start should be selected. Depending on the elevation of the neighbours of the starting point(s), which should be less than or equal to the design height of the dam, the area that could be flooded if the water level reaches the design height will be determined. By determining the area that could be flooded, the vol-�� * �� 8�9� With the same concept of dam construction, by assuming the river polygon as the dam and the flood water level in the channel is equal to the design height of the dam, and selecting start points along the riverbanks, the flooded area can be calculated. By using different river water levels, the flooded area due to different amounts of flood can be calculated.

�� Limitation of the Chosen Flood Models

Each one of the available flood models has some limitations. The HEC-GeoRAS model is considered as one of the easiest flood models that can be used. On the other hand, the HEC-RAS model estimates the river profile depending on the ground elevations with-out taking into account the location of the area that is considered to be flooded, if it is adjacent to the river or not. So areas along the river, which are low but protected by a high riverbank, will be consid-ered as flooded areas, as illus�� .�� )�� * �� 1 ��flow over the banks to flood these areas. The ILWIS flood model produces a connected water surface covering these areas that are lower than the water level starting from points along the riverbanks. In contrast, the river water level will be con-sidered as constant level along the river and that does not represent the slope of the river water level. Furthermore, it does not take the roughness coefficient into consideration and that may affect the re-sult.


��

Figure %:� Cross Section of the River Shows Flooded Areas Due to Different Flood Magnitudes

�� Concept Behind Choosing the Flood Model

From the previous section, it is clear that each model has limitations. Since the area adjacent to the river is often lower than the banks elevations and has the same land cover, then using ILWIS flood model is more appropriate than using HEC-GeoRAS model, specially because ILWIS takes into ac-count the elevations and the locations of the whole area, the channel, the start points and the surround-ing area. This model can be modified in further software development to include the hydrological as-pects in flooded area calculations.

�� Implementation of Flood Model

To achieve the objectives of this research, several river water levels have to be simulated with the chosen flood model to represent different flood amounts, and different percentages of blocks should be included in the surface model of the floodplain area to represent the buildings. Treating these blocks by different ways is needed to achieve the refined model, which can represent the flood behav-iour, in a built-up area, in more reasonable way. To study the effect of the main factors of surface modelling on the accuracy of flood extent predic-tions, random and systematic errors should be added to the original DTM elevation values to check the sensitivity of the model to the elevation accuracy. Additionally, resampling the DTM file to low-resolution ones is important for checking the effect of the grid resolution on the flood extent predic-tions.

�� Evaluation Method

After choosing the flood modelling, the evaluation method should be defined. The evaluation method will base on comparing different cases of building existence in a selected study area to the case of no buildings. Comparing different cases of flood extent has to be based on the flood magnitude, which is the amount of water upstream the study area. Since the flood area determination depends only on the ground and river water surface levels and since the actual flood magnitude is not known, then at each river water level, the volume of water, which can be contained in the flooded area in the case of no buildings, can be assumed as the flood magnitude. This flood magnitude can be used as a base for the comparison. According to a certain flood magnitude, the flood extent in the different cases could be compared.


��

In ILWIS the flood model deals with data in raster format. Thus to calculate the volume of the water, the flooded area and the depth of water at each pixel of flooded area should be calculated. The cross operation in ILWIS can be used to cross the DTM/DSM map with the flood map; this operation over-��,�� C �� 8�9��A�� *� ��

identifiers or values of the pixels of the two input maps are stored. The cross table also includes the combinations of input values, classes or IDs, the number of pixels that occur for each combination and �� *��8�9��A�� .�� 2)��e flood map, a com-bination of the ground level with the flooded area is done and the number of pixels at each ground level can be calcu�� )� �� .�� F��%�'

A = n . s %�' Where,

A Area at each ground level n Number of pixels s Pixel size

To calculate the depth of the water, each ground level that is tabulated in the cross table should be subtracted from the planned water level (river water level), which is the level condition that used in creating the flood maps�� F��%�' Di = Lp - Lg %�' Where,

Di Depth at ground level i Lp Planned water level. Lg Ground level.

From the areas that are calculated in the cross table and the depth of water at each ground level the volume of flood water can be calculat �� F��%'� V = � (Di . Ai) %' Where,

V Volume of Water Di Depth at ground level i Ai Size of flooded area at ground level i

Since this method depends only on the elevation of the ground and the river water level, at any river water level the flooded area will be different for different cases of building existence. And as a result the floodwater volume (the volume of water that can be contained in the flooded area) will be differ-ent. Although, comparing the flood extent according to the flood magnitude is more realistic, the flood extent predictions in any two cases can be compared based on the floodwater volume in the no build-ings case. Therefore, at each floodwater volume, the changes in the flood water level due to the build-ing existence can be calculated and compared. To calculate these changes, the difference in the floodwater volume between the cases of building existence and the no buildings case have to be dis-tributed over the whole flooded area, with assumption that the flooded area is constant, using equation %�'� ∆d = ∆V/A %�' where,

∆d Difference in flood water level ∆V Difference in floodwater volume A Flooded area


��

�� Cases to Be Studied

�� Different Water Levels

Different river water levels have to be used to represent different amount of flood. Since the bank ele-1�� C�� ,�� 1��1�� ,D ��!�� osen river �� 1 �� J �� !��* �� * ��

��!�� 1 l will not make effective difference in the result as the whole area will be flooded.

�� Solid Buildings

As a result of the new technologies in data acquisition like the laser scanning technique, it becomes faster and easier to acquire the ground surface at any particular area. In urban areas buildings are parts of the surface model. Extracting digital terrain model from the surface model needs time, effort and experience, so it is easier and faster to use the surface model instead of the terrain model in the flood modelling. In flood area calculations when the digital surface model is used, it means that buildings are treated as solid blocks. For that solid buildings have to be checked as a case of building existence to study the effect of using the digital surface model (DSM) on the flood extent expectations. In the following different possibilities are identified to model buildings.

�� Borders of the Buildings

Since the buildings are not absolutely solid blocks but there are empty spaces inside the buildings, we need to represent the solid parts of the buildings. The most important solid parts of the buildings to be considered are the walls, so we can use the borders of the buildings to represent the buildings in more realistic way.

�� Core of the Buildings

Since the buildings in the flooded areas are not solid blocks, and the water can fill the empty spaces inside the buildings, then considering the buildings as partially solid is required. Representing the solid parts of the buildings by shrinking their sizes can be studied as a method of treating buildings to act as partially solid buildings.

�� Borders of the Buildings with Openings

When the borders of the buildings are completely closed, they will not allow the floodwater to enter the buildings and the result will be nearly same as the case of the solid buildings. Since the exact loca-tions, the outlines, of the buildings are required in other hydraulic elements, e.g. flow velocity calcula-tions, then making some openings in the borders of the buildings is must to allow the water flows in-side the buildings. So filtering out the corners of the borders will represent the buildings as the bor-ders of them with openings.

�� Different Percentages of Buildings

In the case of having the terrain model without buildings and to find out how the buildings existence will influence the flood extent, different percentages of built-up areas should be checked to estimate the critical percentage of built-up areas after which the buildings should be included in the surface model. The chosen p �� . �� 6��


��

�� Collapsed Buildings

As it is logically that the buildings could collapse in flood event, it means that solid blocks will exist covering some areas without allowing the water to go through. The height of these solid blocks will be much less than the building height. So treating collapsed buildings as solid blocks can be considered as a case to be studied. There are many other factors should be taken into account in studying the effect of building existence in the floodplain on the flood extent predictions, such as the real characteristics of the buildings. Some of them are; the structure of the buildings, barring walls or skeleton structure, the foundation type including the basements if there is any, the roof type, the type of walls and its permeability, the location of the openings, the type of the solid parts inside the buildings and its density and permeabil-ity, the sewerage system, its quality and capacity, the surrounding area, the exact locations of the buildings referring to the terrain elevations, and many other factors could affect the results. But be-cause of the time limitation all these factors are postponed to be studied in more detailed further re-search.

�� Sensitivity Analyses

�� Vertical Accuracy

To check the sensitivity of the flood modelling to the accuracy of the elevation values of the surface model, different error values will be added to the original data.

�� Random Error: Different values of random error could be used to check the sensitivity of the model to the height ac-��,�� (��*� ��-�� .��,�� ,�� * ��

��!�� 1�� * ��±�� Systematic Error:

+ ��!�� r system; in addition to that, a thick layer of � �� .��,�� .��,�� %��'��(��.��,s-tematic error to the elevation values of the grid points will represent this type of error, which will af-fect the flood extent. To check the sensitivity of the model to systematic errors, different values will be added to the original data, we choose values same as the values of the random errors, which are described in the previous section, to can compare between the effect of the random and systematic errors on the flood extent predictions.

�� Grid Resolution

The resolution of the raster maps or the density of the grid points could dramatically change the result of flood extent predictions. To check the effect of the grid resolution on the model different grid sizes could be checked. The highest grid density that is produced from the raw data, according to the AHN ��@ ��C��.��: �� *�� C��C��.��: ��2��*��.��

spacing between the grid points will reduce the time needed for data processing. Thus the original ��* ��.��: �� .��: ��* �� D �� *� ��.��

��.�� .�� ,��* ��*�� *� ��c��.�� .��

��: ��)� ��C��.��: ��* �� ,��* �� ,�� ,�

changes in the ground elevations, especially at the dykes.


��

In addition to that, a comparison should be made between the elevation accuracy and the grid resolu-tion to find out which one of these factors has more influence on the model.


�


�

�� Experimental Work

�� Study area

The Netherlands is one of the flood prone countries, it is a delta for three of Europe’s main rivers. Both sea and river floods have been threatening the country since centuries. Moreover, large parts of the Netherlands are below the mean sea level, and most of the country is flat. Moreover, the Netherlands is one of the countries that use the Laser Scanning technique to acquire geo-information, such as DTM, DSM. Therefore, it was logically to choose the study area along a part of one of the rivers that run through the Netherlands. An area along the river IJssel, in Overijssel province, North to Deventer was chosen. This data is a part of AHN project, in Duursche Waarden, delivered by Rijkswaterstaat, we gratefully acknowledge that Ms. Ardis Boll-weg of Rijkswaterstaat was made the data available to us free of charge. The chosen area is around ��D��.�� 1 ��*,��D��1 ��.�� @��* ,�� ,D �� *��

river.

Figure ):� Overijssel Province

Figure ):� Study Area

Figure ):� Original Data


��

�� Dataset Description

The obtained data are laser-scanning data, i.e. X, Y, and Z coordinates, in the national Dutch levelling reference system, RD/NAP. The density of �� K��L�� !�� cap-�� .�� ,�� 2<;+��.��C��*�� D��)� �

raw data representing irregularly distributed points are used to produce grid DTMs with various grid ��.��C��C��C��)� ��b�� C��.��2)�� .� ��

density grid provided from the AHN project. These data are supposed to be filtered, thus a DTM data-set, representing the terrain without vegetations or buildings. And these data are supposed to have �� 1��C�� 1��1�� C��,��

�� Data Preparation

The original data are ASCII files with extension “.xyz”, so the first step is to convert it to text files so that they can be imported to ArcView program. Each text file is added as a table in ArcView and a theme is created according to the X, Y coordinates in this table. To check the effect of buildings on flood extent in such an area, several building scenarios are gener-ated, which represent different percentages of the total area covered by buildings. By observing the buildings in the Nether�� *��D��*��.�� .��.��.��

��: ��C��)� �*��D�� of a number of buildings and created in such a way to simulate the area that will be considered as built-up area, e.g. form a curved shape parallel to the riv-erbanks in the areas where the river bends. The different built-up areas are constructed such that for each scenario the buildings are spread over the whole area, starting with placing buildings in the lower areas and proceeding to the highest ones. This is likely to conflict with actual building planning, but this sequence is chosen to find out the ef-fect of building existence on the flood extent predictions. Each of these distributions of buildings is saved as a different theme to be used separately.�/�.�� !-��*��.�� . � r-ated in the study area. The provided data covers the ground areas only, however the water bodies have no data. Therefore the ground elevations surrounding the water bodies can be considered as the water level in these areas, for that, the water surface of these water bodies are given values equal to the elevation values of the adja-cent ground points to each water body.


��

c) ��-up Area d) ��-up Area

Figure ):# Distributions of Different Percentages of Buildings

a) ��-up Area b) ��-up Area


��

�� For HEC-GeoRAS Flood Model

Since the HEC-GeoRAS program requires TIN data for the whole area including the riverbed, and since such data are not available to us, we must simulate the bed elevations. To gener-ate the bed levels, it is assumed that at the centreline of the river �� .��,��

river borders. The centreline is drawn referring to the river width and then buffer zones for that centre line are created each �� .�� .��: � The polygons representing the buffer zones are converted to raster format and exported to an Excel file to give them nega-tive values representing the depth according to their distances �� 1 �� +��

added to the depths values, as the riverbed is not smooth. By subtracting these depths from the water surface, the riverbed is obtained. For the water bodies the outer boundaries are digi-tised and buffer zones are created towards the inside of the polygons. Each buffer zone is given a certain depth, the same way as what is done to the river but with smaller depth values. By converting the raster data to point data, a grid point set for the whole area including the beds of the ��1 �� *�� . � �� .�� !-�� (�� 1��.��2�� .��

flood model: In ArcView: ��Create TIN from the point data set. ��Generate contour lines from the TIN. ��Create land use map from the available topographic map. Using the Pre RAS menu: ��Creating stream centreline. ��Creating banks. ��Creating flow path. ��Creating X cut-lines (perpendicular on the flow path). ��Defining Manning’s coefficient, according to the land cover information. ��-� ��.�2�� .�� -� ��.�2�G��-lines using the created themes. ��Exporting GIS File to be used in HEC-RAS program. In the HEC-RAS program: �� Importing the Geometric Data, GIS file prepared in HEC-GeoRAS. ��/�� .�� C�� Checking the position of the bank points on the cross sections, and modified it if necessary. ��Checking Manning’s coefficient along the river and the surrounding area. ��/��6�� . �1�� I��

/sec. ��Running the analysis.

Figure ):% River and Water Bodies


��

In HEC-GeoRAS, Using the Post RAS menu: �� Importing the HEC-RAS files. ��Creating Water surface TIN for each water profile. ��Delineating floodplain, using the water surface TIN and the Ground Information. ��Calculating depth grid using the water surface TIN and the Ground TIN. ),��.�� .�� 53-�� .�� !-!��!-��A�� that, although there are some unconnected areas to the river, they are considered to be flooded just because they have low ground elevations.

Figure ):)��&��*��%��$$�� Flood Magnitudes

Figure ):! Results of HEC-GeoRAS for the River and Flooded Areas

Therefore, as mentioned before, the ILWIS program will be more adequate for our investigation.


��

�� For ILWIS Flood Model

In ILWIS software, almost all the cases require the same process to be done. These processes will use some common files. Thus, designing a script that includes all the processes in such a flexible way to allow us to change the uncommon values and files according to the requirements of each case. There-fore, we have to prepare the files for all the cases to speed up the processing. �� For Studying the Effects of Buildings In ILWIS, the following procedure has been designed to prepare the data: �� Importing the table of the points (containing X, Y, and Z coordinates). �� Creating attribute point map with coordinates X and Y and values equal to the Z coordinate. � Converting point map to raster map, representing the surface, DEM file, based on geo-reference

file with pixel size� F��%��.�� !-�'� �� Digitising the river area as polygon. �� Converting the river polygon to a raster map with the same geo-reference as the DEM raster file,

in a binary map without giving level values, as it will be given later depending on the simulated �� 1 ��%��.�� !- '�

!� Digitising the water bodies. �� -��1 ��.�� *��,M��,.�� %��.�� !-�'� �� Using the original point map to interpolate the missing areas, to give the water bodies a value

equal to the water surface level, which equal to the elevations of the boundary points of each area. � -� ��.��.�� *��,�� 1 ��.� F�� esent the

�� %��.�� !-��'� �� Converting point map to raster map with the same geo-reference as the DEM and the river file.

Figure ):+ DEM Raster Map

Figure ): River Raster Map


��

Figure ):�" Water Bodies

Figure ):�� Start Points

Till this step the prepared data can be used for flood area calculations in the case of no buildings. The following steps are for preparing the buildings datasets. �� Importing buildings themes, which are prepared in ArcView (each percentage of buildings in a

separate file). �� Creating attribute maps from the building maps with values equal to the buildings heights. �� Converting the buildings’ polygons to raster maps with the same geo-reference file as the DEM,

River and start points files. Since the buildings are near to each other, some of them combined in the raster conversion proc-ess.

�� /�� *��.�M�� AB�B2��*��,�� . � rate the boundaries of the *��.�M�*��D�� D��C�D �� .�� 1��

pixel is true and at least one of the horizontal and vertical neighbours is false (which means that ��C �� . '�8�9��/�.�� !-�� C �� .� �� 1��

Figure ):��,-.-�#�.��/�0��1�2��3��4��'�0��


��

(�� C ��: �� *�� M��C �� )� ,��1 ��

much larger than it would be in reality. �� Filtering out the buildings’ borders to make some openings to allow water to flow inside the

buildings and keep the outer shapes of the buildings as they are, which will allow the flow to be-have as if there were buildings. It will reduce the solid area of the buildings (the area that the bor-� ��. � �� *,�AB�B2�� !�� *��.��

�� *�� 1 � �� *��.�� '� The filter that we used in this step is a new filter created for the purpose mentioned earlier. It is a *��,�� C�D �nel and it assigns false value if the centre pixel is false or if at least one �� :�� 1 �� .�*��C �� /�.�� !-�� C ��which are assigned false values.

Figure ):�� CORNER Binary Filter, Pixels Assigned False Values

�!� Filtering out the building maps to reduce the size of the buildings, to create solid blocks at the core of the buildings representing the solid volume of the buildings. Two types of binary filters �� I��D��D��lters. +��D�� .�� 1�� ,�� C �� :��1 rtical � �.�*�� 8�9��

Figure ):�#��,- #�.��/�0��1�2��3��4��'�5��

+��D��.�� 1�� ,�� C �� .�*�� 8�9��

Fig�� !-��!-�� C �� .� �� 1��

Figure ):�%��,- +�.��/�0��1�2��3��4��'�5��


��

�� Producing scripts: all the cases will have as input the DEM file, River binary file, and the start points. All cases will follow the same steps for flood expectations and flood area and volume cal-culations. It is logically to create a script that will be run to achieve the flood calculation results. The only difference between cases are the files representing the buildings, as there are different files for various building scenarios and their filter out files (see appendix A).

�� For the case of collapsing buildings another script is created (appendix A).

�� For Sensitivity Analyses The case of terrain only is used for analysing the sensitivity of the model to the surface model main factors. ��Vertical Accuracy: (��.�� 1��1�� .��* �� 3C� ��

as the Excel ca��,��!��,��)� � �� (�� *�� .��

create a table that contains the point coordinates with some random errors. The values of the random errors are ±��)� ��,�� vation values in the DTM files di� ��,��)� ��,�� -�� Grid Resolution: All the common files, DEM, River and Start points are resampled with other geo-references, which have different pixel sizes. The geo-reference files��1 ��C ��: �� F��

�� Results of the Flood Model

Having completed the above outlined preparations, we can start run the scripts for all the cases. The script includes the part to calculate the volume of the water in the flooded area for each case.

�� Different Water Levels without Buildings

)� �� !��%��'�� 1 �� * �� !��

�� .�� !-�!��A�� nt river water levels. It is clear that increasing the river water level will in�� B�� 1�� 1 ��rger area is flooded.

�� Different Percentages of Solid Buildings with Different Water Levels

/�.�� !-�� 1 �� 1 �� f-fect of different percentages of solid built-up areas on the results. The figure shows that the buildings are higher than the water levels and that the areas that are covered by buildings are excluded from the flooded area.

�� Different Treatments of Buildings as Partially Solid Blocks

/�.�� !-��e� �� 1 �� 1 �� F��!��

building �� )� �� .�� !-� I�� .��

obvious that the borders of buildings behave almost the same effect as the solid blocks. That is be-cause the borders are closed and in almost all the borders do not allow the water to flow inside the buildings. In the case where there are some openings in the borders the area inside the buildings are assumed as flooded areas, and the outlines of the buildings are the same as the buildings. However the outlines of �� *��.�� *�� .�� !-� �� are represented by the cores of the buildings.


�

��

�� !��

Figure ):�) Flooded Areas Due to Different Water Levels

��

Figure ):�! Flooded Areas Due to Different Percentages of Built-��3��6)�" �7��8

a) Buildings’ Borders b) Core of Buildings

%��+��' c) Core of Buildings

%��+��' d) Refined Model

Figure ):�+ Flooded Areas with Different Treatment of Buildings


�

a) Buildings’ Bor-

ders b) Core of Buildings

%��+��' c) Core of Buildings

%��+��' d) Refined Model

Figure ):� Detail of Flooded Areas According to Different Treatment of Buildings

�� Collapsed Buildings

As shown in the previous cases, the areas covered by buildings are not included in the flooded area * �� 1�� *��.��.� �� 1 ��ater level. This is �� *��.�� 0 �� *��.��* ��

build��.�1�� (��.�,�� .�� *��.�� *,��

treated as solid blocks. Different heights are used to check the effect of the building heights on the flood extent. The results of the flooded area in the case of collapsing buildings are displayed in figure !-��

a) ��dings height b) !��dings height c) ��dings height d) ��dings height

Figure ):�" Different Flooded Areas with Different Heights of Collapsed Building

)�*� ��-��-��(ppendix B, show the results of the flooded areas and the water volumes that can be contained in the flooded areas, respectively, with the different scenarios of building existence and treatment.


��


��Vertical Accuracy /�.�� !-��s the results in part of the area, as shown, due to various errors in the elevation val-� �� ,�� 1 �� 1 �� F��)� ��. �� e-sults can be noticed by comparing the result of each case to the result of no errors case.

No Errors ��#��3�� #��3�� #��3��

��+,�� 3�� -��+,�� 3rror ��+,�� 3�� +,�� 3��

Figure ):�� Flooded Areas Using DTMs Which Have Different Errors in Elevation Values

)�*� ��-��(�� C�� 1�� * �

contained in the flooded areas with various errors in the DTM elevation values. ��Grid Resolution /�.�� !-�� 1�� .�� !-��under the same conditions of water level, but with different DTM resolutions. By comparing the re-�� 2)�� .�� as shown in


��

the fig�� * ��.��

��2)�� .inal case, specially the area behind the dykes.

��2)��# �� 2)��# �� 2)��# �� 2)��# ��

Figure ):�� Flooded Areas Using DTMs with Different Resolutions

From fig�� !-�� 2)�� C� �� c-�� .��1 �,��.�� .�� 2)��# ��)��*��.�� u-tion DTM and high resolution one at the dykes is expected to give more accurate results than the low � ��2)��/�.�� !-�� ution �� %��'�� *�� ,D �� r-eas referring to th �� .�� 2)��%��'��

��2)��# �� 2)��# ��

N��# ��

Dykes

��2)��# ��

Figure ):�� Result of Combining Different DTM Resolutions

)�*� ��-��(�� C�� 1�� * �

contained in the flooded areas, with different DTM resolutions.

�� Discussing the Results

�� The Effects of Buildings on the Flood Extent Predictions

��The Effect of Percentage of Built-up Area: Volumes of floodwater are used as a comparison between the different cases. For each case of build-ing treatment (no buildings, solid buildings, borders of buildings with and without openings, and the two cases of shrinking the size of buildings) a graph is drown using the volume of the water in the �� ,�� 1�� *� ��-��(�� n-dix B. The graphs in fi.�� !-��!-�� . �� 1�� * ��ntained in an area with elevations less than or equal to certain river water level, with the change in the percent-


��

age of built-up areas, for the cases where buildings are treated as: solid, borders of buildings, core ��*�� *��.�� .��espectively.

Different in Flood Water Volume with Different Percentages of Buildings(As Solid Blocks)

��

��

��

��

��

��

��

��

��

� ��

River Water Level

Floo

d W

ater

Vol

ume

Without Buildings ��

Figure ):�# Different in Water Volume with Different Built-up Areas (Buildings as Solid Blocks)

Different in Flood Water Volume with Different Percentages of Buildings(As Borders Only)

��

��

��

��

��

��

��

��

��

� ��

River Water Level

Floo

d W

ater

Vol

ume


Figure ):�% Different in Water Volume with Different Built-up Areas (Borders of Buildings)


��

Different in Flood Water Volume with Different Percentages of Buildings��

��

��

��

��

��

��

��

��

� ��

River Water Level

Floo

d W

ater

Vol

ume


Figure ):�) Different in Water Volume with Different Built-��3��6&��$�.��'��4��*��"9��'8

Different in Flood Water Volume with Different Percentages of Buildings��

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��

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��

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��

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� ��

River Water Level

Floo

d W

ater

Vol

ume


Figure ):�! Different in Water Volume with Different Built-��3��6&��$�.��'��4��*��9 ��'8

We can notice from the graphs that in the two cases of shrinking buildings, for the different scenarios of the built-up areas, the lines represent the changes in floodwater volume with various water level are closer to each other than they are in the solid blocks and the border of buildings cases. That is because in the case of solid blocks, the areas that are protected from the flood are exactly equal to the built-up areas and the case of the borders the results are almost the same as the solid blocks as explained ear-�� !��-�nversely, for the cases of shrinking buildings, the solid parts of the buildings �� ,�� *��.��1�� enario, so the areas that considered not being �� %�� *��.�1�� *��-up �� dplain area).


��

Different in Flood Water Volume with Different Percentages of Buildings(As Borders with Openings, Refined Model)

��

��

��

��

��

��

��

��

��

� ��

River Water Level

Floo

d W

ater

Vol

ume


Figure ):�+ Different in Water Volume with Different Built-up Areas (Borders of Buildings with Open-

ings)

/�� .��.�� !-�� ,�� 1 �� 1�� * �

contained in the flooded area, decreases with increasing the percentage of built-up areas; see the verti-cal line at any water level. As a result, at any water level, which can be contained in the flooded area, the level of water increases with increasing the percentage of built-up areas; see the horizontal line at any water volume. The graph shows the results of treating buildings as objects that are flooded. By calculating the difference in water volume, between the various building scenarios and the no buildings case, and distributing this volume over the whole flooded area, assuming constant area, the change in flood water level can be determined. In the case of the buildings as solid blocks, the changes in flood water level with the changes in percentages of built-up areas are displayed in the graph in fig�� !-� ��(�� .�� .� �� . ��*��-up area the higher the flood water level will be.

Raising in Flood Water Level due to Building Existence(As Solid Blocks)

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�� !"�# ��$��

Add

. Flo

od W

ater

Lev

el (m

)

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Figure ):� Changes in Flood Water Level with Different Built-up Areas as Solid Blocks

��Effect of Building Treatment Method For the same percentage of buildings the type of treatment buildings have different effects on the flooded area and the volume of floodwater calculations. Consequently they have different effect on


��

the flood water level. /�.�� !-�illustrates the effect of the buildings on the flood water level and the difference in the flood level according to the method with which the buildings are treated, in the �� *��-up area. It is clear that the borders of buildings have almost the same effect as the solid buildings. Moreover, the core of the buildings as they have a volume equal to approximately �� 1�� *��.��1 �� 1 ��Conversely, this case cannot be considered as the refined model because it does not represent the outer lines of the buildings. By calculating the percentage of the solid parts in the two cases of the cores of the buildings, with ��*��-up area scenario�� *��.��1�� ,��m-��.�� @�� *��.�1��

�� 1 ��A��.�� !-�� 1�� f the buildings, in �� *��-up area, in�� 1 ��

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The estimation of the changes in the flood water level th��1 �* �� .�� !-��ac��.�� *��-up area, this estimation will be different a different percentage of the built-


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up area is used. Obviously, the greater the percentage of the built-up area is, the more sensitive the prediction to a good estimation of solid volume of the buildings. ��Finding-out the Critical Percentage of Buildings in the Floodplain Obviously, the more buildings in a floodplain the more relevant it becomes to use refined surface modelling in order to make more accurate predictions. In answering the question at what percentage of built-up area should be start implement refined modelling, one may think of different criteria that are considered reasonable as prediction error. An interesting one is related to cars; because of the buoyancy, when flood occur in built-up areas, for each foot the water rises up the side of the car, the �� *�� .��*�� .��(��e-sult, two feet of water will c��,��,��8"#$�9��)��

critical percentage of built-up areas that may increase the flood water level one foot, or for more �� ,��)�� t�. �� .��.�� !-��* used. From this graph we can see that the built-�� *��.��

*��D�� 1 ��* �� *,�� 1 �� ncrease the �� 1 ��*,�� he refined surface model is used. That because the refined surface model treated the buildings in such a way that allow the water to enter the buildings and the area that considered to be solid is half the area of the solid blocks. 0 �� of buildings as the critical percentage, for a higher percentage of built-up areas, in a flat area like this one, the surface model should be refined.

Critical Percentage of Built-up Areas

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��Checking the Effect of Building Heights on the Flood Level: (�� !��.�� .�� *��.��

the lowest building has roof elevation higher than the highest water level, in the case of building col-lapsing, as illustrated in the dia.��.�� !-�� *��.�� .��1 ��.� �� flood water level, the higher the buildings are the higher the flood level will be. We can conclude that in the case of collapsing buildings, the lower the buildings are the greater the area that can be included in the flood extent predictions, which means the greater the volume of water this area can contain, and as a result the lower the flood water level will be.


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Effect of Buildings Collapse on Flood Water Level

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Figure ):�� Effect of Buildings Collapse on the Flood Water Level

From the previous circumstances we can see that buildings existence reduces the area that can be flooded and consequently the water volume that can be contained in the flooded areas, in other words we can say that it reduces the capacity of the floodplains. Calculating the additional flood water level caused by buildings existence based on constant flooded area is applicable for the small change in the floodwater volume. But for the large changes in the water volume the flooded area will be change as well as the flood water level. Therefore iterating the flood modelling using the calculated flood water level, as an input, to calculate the floodwater level and the flooded area, is required.


��Vertical Accuracy: Adding errors to the DTM data changes the results of flood extent calculations. The graphs in figures !-��!-�� . ��1��e, and the change of the flood water level due to the random errors, respectively.

Change in Volume of Water Due to Random Errors

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It is clear from the graphs that the random errors have no significant effect on the flood water level �� 31 �� 2)�� 1��1�� .�i-�� .�1 �� 1 ��)��.�� e river water level reaches �� .�� 1 �� 1 ��n�� *�� )�*� �!-�� 1�� 1 �� 2)�� 1��1��

and the RMSE for these values.

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Figure ):�% Error in Flood Water Level Due to Random Errors in DTM Elevation Values

Table )-� Change in Flood Water Level Due to Random Errors

Water Level �"� �:�� "� �:rror %"� �:rror � -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -�� -��

RMSE �� To demonstrate the influence of a simple systematic error, we have experimented with a datum shift ��–��/�.�� !-!��!-�� . ��1�� 1 ��

change in the elevation accuracy of the grid points of the digital terrain model. )� �.��.�� !-!�� 1��ume that can be contained in the �� 1 �� 1 �� .�� i-��1 �� .��1 �� .��1�� )� �� .��

means that the predicted capacity of the area, the volume of the water it can contain, is less than the correct one, and thus the flood water level will be higher, thus over estimating the flood level. Con-versely, the shift of the line to the left means over estimation of the flooded area capacity and as a re-sult under estimation of the flood water level.


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Change in Volume of Water Due to Systematic Errors

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Figure ):�) Change in Flood Volume Due to Systematic Errors in DTM Elevation Values

Error in Flood Level Due to Systematic Errors in DTM Elevation Values

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)�*� �!-�� 1�� 1 �� ,�� 2)�� 1ation values and the RMSE.


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Table )-� Change in Flood Water Level Due to Systematic Errors Water Level �"� �:�� "� �:rror %"� �:rror -�"� �:��

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RMSE �� By comparing the results of the systematic and the random errors, it is obvious that the flood extent prediction is much more influenced by the systematic errors than the random errors. The negative values in the flood level graphs mean under estimation of the flood level, which are more risky cases. Recently, there are several researches studying the systematic errors of the laser systems and their ef-f �� ,�� .�� 8��9��(��.��5��.�%� �'�� ,�� .�� ,�� * �� 8��9��

Some of these errors can be reduced by calibration �� 8��9� ��Grid Resolution: )� � �� .�� C� �� I��

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finer dataset of the dykes are used as well.

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Figure ):� Error in Flood Water Level Due to DTM Resolution

)� �� 1��.��.�� !-��!- �� 2)�� e-sults of flood extent predictions significantly, the low resolution DTM gives under estimated flood water levels, which make the area under more risk. However, the combination of low resolution DTM with high resolution one for the dykes, gives much better results than the low resolution DTM only. Besides, it can compete with the results of the high resolution DTM for the whole area, which may be much more costly. The values of the flood water level errors due to the change in the DTM resolutions and the RMSEs �� *�� *� �!-��A�� *� ��*��.��.�� 2)�� ,Des with the low resolution one increases the accuracy of the results.

Table )-� Change in Flood Water Level Due to DTM Resolutions

Water Level ��,�� ,�� ,�� ,��/

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RMSE �� By comparing the results of the DTM resolution and the error of the DTM elevation values, we can find that the results are more sensitive to the DTM resolution than the random errors in the elevation values, which means that for achieving more accurate flood extent predictions it is much more rec-ommended to use high resolution DTM.


��

In almost all cases that were studied, at river water level equal to the critical elevations of the study �� ,�� .��. �� 1�� 1 ��

Therefore, more detailed data at the position of the critical elevations are required. The location of the critical parts of the floodplain, which require more detailed data, were identified by doing such a simulation study that has been used in this research. The same concept of combining low resolution DTM with high resolution one for the important fea-�� .�� *�� .��

��.� �� *��.��*�� C ��: �� *�� (�� v-ered by the�*�� *��.�� *��.�� 1 �� tage of the built-�� 1�� *��.��

volume of the buildings. Using this combination produces more accurate results but it is time consum-ing, as it requires time around five times more than that the obtained data required, and it needs capac-��,�� ,�� .�� Therefore, as mentioned earlier about the critical percentage of the built-up area, the greater the built-up area in the floodplain, the higher the resolution for the borders of the buildings has to be. Accord-��.��.�� !-�� *��-�� .� �� rface modelling is required.


��

�� Conclusions and Recommendations

�� Summary

Flood is a natural disaster, which is considered as one of the most serious threatens many countries ��1 ��"�*��:��.�� C� ��.�� -�� when river flood occurs in built-up area, not only the streets and parks will be flooded but also the buildings as long as there are openings in the buildings, which will allow the water to enter the buildings. Ignoring the buildings, or treating the built-up areas as protected areas, is what the floodplain analys-ers used to do. However, when the laser scanning technique became available, several providers de-livered accurate surface models for floodplains including the buildings. Thus in the floodplain analy-sis, these surface models started to be used instead of the DTM. In this case the buildings are treated as solid blocks, which prevent the areas occupied by buildings from flood, and that will give inaccu-rate flood extent predictions. Therefore, refining the surface model to predict the flood extent in a floodplain area more accurately was the first main objective of this research. To achieve this objec-tive, different flood magnitudes were represented by different river water levels in a simulation study on an area along one of the Dutch rivers, river IJssel, North to Deventer city in the province Overi-jssel. The simulation study was done with different inputs for the different circumstances of building existences. During this study,

i) The most suitable flood model was chosen. ii) The effect of building existence in the floodplain on the flood extent predictions was checked. iii) The difference in the results between the different treatments of the buildings was studied. iv) The critical percentage of built-up area in the floodplain was found out. v) The sensitivity of the refined modelling to the percentage of the solid volume of the buildings

was analysed. vi) The effect of the height of the collapsed buildings on the predictions of the flood extent was

checked. The second main objective of this research was analysing the sensitivity of the flood model to the ac-curacy of the DTM elevation values and the DTM resolution. Aiming at achieving this objective, the simulation study was done for the different cases of the error values and the resolutions. In this study,

i) The effect of random errors in the DTM elevation values on the flood extent predictions was checked.

ii) The influence of datum shift, which represents a systematic error in the DTM elevation val-ues, on the flood extent predictions was studied.

iii) The effect of using lower DTM resolutions on the accuracy of the flood extent prediction was discussed.

iv) The advantage of combining low resolution DTM with high resolution one for the important features was pointed out.


��

�� Conclusions

We first summarize the conclusions reached, which are valid for this particular study area, and then indicate how these can be generalized. According to this study, in an area where the low lands surrounding the river are protected by high-elevated banks and there are dykes to protect the further areas, we conclude:

�� The ILWIS flood model is more realistic than the HEC-RAS modelling, because whatever the water level is, the HEC-RAS flood model considers the low lands which have ground eleva-tions less than the water level to be flooded in spite of the existence of river banks.

�� The more buildings in the study area the more the flood extent will be. In the case of solid blocks, changing the percentage of the built-�� *� �� .�

of the flood water level. � Considering buildings as solid blocks will give over estimated flood extent predictions, the

solid buildings raise the flood water level twice as much as the refined modelling does. �� .�� F�� area will increase the flood water level by

�� *��.�� *��D��)� �� 1 ��* �� 1 ��

built-�� *�� *��.��D �� If the built-up area is less than ten percent of the floodplain area the buildings do not require

special modelling; they can either be treated as solid blocks, as part of the surface model, or ��*�� )� �� A��

the built-�� .� �� then buildings should be treated by the refined model. !� In the case of built-up area equal to twenty percent of the whole floodplain area, the misesti-

�� *,�� 1�� *��.�� 1 ��*,��Ci-�� ,��.�� !-��)� �.� �� *��-up area the more important the accuracy of the estimated solid volumes because the higher the percentages of the built-up area the larger the volume of the solid parts referring to the whole area, and consequently the ��. �� 1��

�� The heights of the buildings do not influence the flood extent predictions because the lowest roof building elevation is higher than the highest water level. However, in the case of col-lapsed buildings, the higher the buildings are the more their influence on the flood extent. Therefore, it is important to maintain the higher buildings to guarantee their stability when a flood occurs.

�� The random errors in height values have less effect on the results than the studied systematic errors. As well as, the higher the systematic error in the DTM elevation values, the lower the accuracy of the flood extent predictions will be.

� As expected, the lower the DTM resolution the lower the accuracy of the flood extent predic-tions. However, combining the low-resolution surface model with high resolution one for the important features gives more accurate flood extent predictions, which can compete with the ��.�� 2)��.�� !-��!- ��a*� �!-��

Generally: �� We expect that very similar findings would be obtained in other areas with the same charac-

teristics. �� As expected, when a flood occurs in a built-up area, the existence of buildings in the flood-

plain increases the flooded area and water level. The higher the percentage of the built-up area the greater the flood extent will be.


��

� Considering buildings as solid blocks gives an over estimated flood extent. �� Treating buildings as partially solid by representing them by closed borders does not help to

estimate the flood extent correctly, because the closed borders prevent the water from entering the buildings, as long as the water level is lower than the roofs elevations. Also shrinking the buildings to represent the solid parts does not represent the shapes of the buildings, which are likely to be important for hydrological elements.

�� Refining the surface model in such a way, as created in this research, is more realistic than considering buildings as either solid blocks, or shrinking buildings.

!� Obviously the more refined the surface model the more accurate the flood extent predictions in function of surface geometry will be.

�� The greater the built-up area the more refined the surface model should be to represent the re-ality more accurately.

�� Recommendations

�� Checking the refined model in an area has different terrain type is required, because the flood extent predictions are possibly to be different in different terrain types rather than the flat ter-rain. The effect of the grid spacing most likely to influence the predictions negatively, and the variability of the terrain could produce natural prevented areas from flood.

�� In the real cases, the refined model needs more effort and time than using the surface model, the buildings should be extracted first, then the borders should be filtered, and then the open-ings should be added. So studying the relationship between the volume of buildings and the expected flood extent would be useful if a study will be done on real data, and establishing a model to predict the flood extent accurately using the surface model, which can be generated by laser scanning without having to refine it but by reducing the flood extent.

� Trade off studies between the cost of the high-resolution data and the required accuracy of the flood extent predictions are recommended.

�� Since the ILWIS flood model represents the river water surface as a constant level along the river instead of the real profile, therefore modifying the flood model to represent the real pro-file is required for more realistic modelling.

�� It is recommended to iterate the flood modelling using the calculated floodwater level as an input to determine the flooded area and the floodwater level correctly.

�� Further Researches

This research pinpointed further research questions, the most important ones are: �� To what extent the quality of the flood extent predictions depending on the cost of the data? �� What would be the different in the conclusions if this study is done on a real flood prone

built-up area? To what extent does this simulation study represent the reality? � How does the sensitivity of flood extent prediction to inaccuracies of the surface model relate

to the sensitivity of the other factors to be considered in flood prediction? �� What is the effect of including other characteristics of buildings, such as the structure type,

the location of openings, the quality of the sewerage system, on the flood extent predictions?


��


��

� References

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�

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�

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Netherlands.

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�� 0,1� !++�� 2��2��

�� http://www.srh.noaa.gov/lmrfc/education/safety.shtml �� http://www.insurance.ca.gov/EXECUTIVE/CatSeries/flooding/floodintro.htm � http://www.gvsu.edu/wri/education/manual/depth.htm �� 6&&��1 ��&��&��&. ��D �&�� C�� 6&&��F�� ,��&� ��&��*�&@��&��,�� !� http://www.rms.com/publications/UK_Flood.asp


��


A

� Appendix A

'�� Script for Flood Extent Calculations, with Cases of: No Buildings, Solid Buildings, Border of Buildings, Core of Buildings,

// No buildings B30P23�Q�� %��' B30P23�P��1Q�� %��B30P23��' flood=Ma�A� �;��%��%�B30P23�P��1�R��B��(G%��S''' ��P23�Q��%%�� %�'��%��Q�''�� P� ��T' fld_area.tbt=TableCross(flood.mpr,fld_DEM.mpr,IgnoreUndefs) calc fld_area.tbt ��*��P�� Q��- fld_dem tabcalc fld_area Vol=depth*area // // Buildings as solid blocks ��P23�Q�� %�!��!��%B30P23�N�!'' DEM_Build=MapIterProp(Build_DEM.mpr, ifundef(build_dem, build_dem, nbmax(build_dem#))) NEW_DEM_Build=ifundef(build_height, NEW_DEM, DEM_BUILD) B30P23�P��1P��Q�� %��B30P23�P��) /��P�Q��A� �;��%��%�B30P23�P��1P��R��B��(G%��S''' ��P23�P*Q��%%�� %�'��%��P�Q�''�B30P23�P��T' fld_area_b.tbt=TableCross(flood_B.mpr,fld_DEM_b.mpr,IgnoreUndefs) calc fld_area_b.tbt ��*��P�� P*�� P*Q��- fld_dem_b tabcalc fld_area_b Vol_b=depth_b*area // // Borders of Buildings *��P*��Q��%��Q�T��!' brd_Build_DEM=ifundef (brd_build, brd_build , (NEW_DEM+brd_Build)) brd_DEM_Build=MapIterProp(brd_Build_DEM.mpr, ifundef(brd_build_dem, brd_build_dem, nbmax(brd_build_dem#))) brd_new_DEM_Build=ifundef(brd_build, NEW_DEM, brd_DEM_BUILD) *��P� �P23�P��1P��Q�� %��*��P� �P� �P*��' *��P��P�Q��A� �;��%��%�*��P� �P� �P��1P*��R��B��(G%��S''' *��P��P23�P*Q��%%�� %�'��%*��P��P�Q�'),brd_new_dem_build,?) brd_fld_area_b.tbt=TableCross(brd_flood_B.mpr,brd_fld_DEM_b.mpr,IgnoreUndefs) calc brd_fld_area_b.tbt ��*��*��P��P�� P*�� P*��Q��- brd_fld_dem_b tabcalc brd_fld_area_b Vol_brd=depth_brd*area // &&�+��D��.� ��P*��Q��%��Q�T��!' ��P��P23�Q�� %��P*��P*��%B30P23�N��P��'' ��P23�P��Q��A� �;��%��P��P23�� %��P*��P� ��P*��P� ��*��C%��P*��P� �S''' ��P� �P23�P��Q�� %��P*��B30P23��P23�P�"A$2' ��P� �P23�P��1P��Q�� %��P� �P� �P*��' ��P��P�Q��A� �;��%��%��P� �P� �P��1P*��R��B��(G%��S''' ��P��P23�P*Q��%%�� %�'��%��P��P�Q�''��P� �P� �P*��T'


B

��P��P�� P*��*�Q)�*� -��%��P��P��P��P2EM_b.mpr,IgnoreUndefs) ��P��P�� P*��*� ��*��P��P�� P*�� P��Q��-��P��P� �P* ��*��P��P�� P*�4��P��Q� ��P��U�� // &&�+��D��.� ��P*��Q��%� Q�T��!' ��P��P23�Q�� %��P*��P*��%B30P23�N��P��ild)) ��P23�P��Q��A� �;��%��P��P23�� %��P*��P� ��P*��P� ��*��C%��P*��P� �S''' ��P� �P23�P��Q�� %��P*��B30P23��P23�P�"A$2' ��P� �P23�P��1P��Q�� %��P� �P� �P*��' ��P��P�Q��A� �;��%��%��P� �P� �P��1P*��R��B��(G%��S''' ��P��P23�P*Q��%%�� %�'��%��P��P�Q�''��P� �P� �P*��T' ��P��P�� P*��*�Q)�*� -��%��P��P��P��P23�P*��A.�� "�� ' ��P��P�� P*��*� ��*��P��P�� P*�� P��Q��-��P��P� �P* ��*��P��P�� P*�4��P��Q� ��P��U��

0� � �� - Water Surface file for the water bodies, (raster map) �- DTM file, (raster map) - River file, (raster map) �- Water level, (value) �- Start points, (raster map) !- Buildings, (raster map) �- Border of Buildings, (raster map) �- +��D�� *��.��%�� ' - +��.�� *��.��%�� '

'�� Script for Flood Extent Calculations, in the Cases of Border of Buildings with Openings

// Borders of Buildings with openings �� P*��P*��Q��%��Q�T��!' corner_brd_Build_DEM=ifundef (corner_brd_build, corner_brd_build , (NEW_DEM+corner_brd_build)) corner_brd_DEM_Build=MapIterProp(corner_brd_Build_DEM.mpr, ifundef(corner_brd_build_dem, cor-ner_brd_build_dem, nbmax(corner_brd_build_dem#))) corner_brd_new_DEM_Build=ifundef(corner_brd_build, NEW_DEM, corner_brd_DEM_BUILD) �� P*��P� �P23�P��1P��Q�� %�� P*��P� �P� �P*��' �� P*��P��P�Q��A� �;��%��%�� P*��P� �P� �P��1P*��R��B��(G%��S''' �� P*��P��P23�P*Q��%%�� %�'��%�� P*��P��P�Q�''�� P*��P� �P� �P*��T' corner_brd_fld_area_b.tbt=TableCross(corner_brd_flood_B.mpr,corner_brd_fld_DEM_b.mpr,IgnoreUndefs) calc corner_brd_fld_area_b.tbt tabcalc corner_brd_fld_are�P*�� P�� P*��Q��- corner_brd_fld_dem_b tabcalc corner_brd_fld_area_b Vol_corner_brd=depth_corner_brd*area

0� � �� - Water Surface file for the water bodies, (raster map) �- DTM file, (raster map) - River file, (raster map) �- Water level, (value)


C

�- Start points, (raster map) !- Buildings, (raster map) �- Border of Buildings with openings, (raster map)

'�� Script for Flood Extent Calculations, in the Cases of Collapsed Building

//No Buildings B30P23�Q�� %��' B30P23�P��1Q�� %��B30P23��' ��Q��A� �;��%��%�B30P23�P��1�R��B��(G%��S''' ��P23�Q��%%�� %�'��%��Q�''�� P� ��T' fld_area.tbt=TableCross(flood.mpr,fld_DEM.mpr,IgnoreUndefs) calc fld_area.tbt ��*��P�� Q��- fld_dem tabcalc fld_area Vol=depth*area // // Collapsed Buildings -�� Q�!U�� Build_DEM=ifundef (colapse, colapse , (NEW_DEM+colapse)) DEM_Build=MapIterProp(Build_DEM.mpr, ifundef(build_dem, build_dem, nbmax(build_dem#))) NEW_DEM_Build=ifundef(colapse, NEW_DEM, DEM_BUILD) NEW_DEM_riv_B��Q�� %��B30P23�P��' /��P�Q��A� �;��%��%�B30P23�P��1P��R��B��(G%��S''' ��P23�P�Q��%%�� %�'��%��P�Q�''�B30P23�P��T' fld_area_c.tbt=TableCross(flood_c.mpr,fld_DEM_c.mpr,IgnoreUndefs) calc fld_area_c.tbt tabcal��P�� P�� P�Q��- fld_dem_c tabcalc fld_area_c Vol_c=depth_c*area

0� � ��!�� - Water Surface file for the water bodies, (raster map) �- DTM file, (raster map) - River file, (raster map) �- Water level, (value) �- Start points, (raster map) !- Buildings, (raster map)


D


E

�� Appendix B Table �"-� Flooded Area with Different Scenarios

�� Water Level Solid Blocks Borders of Buildings Refined Model *�#� �� *�#� �� No Buildings

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F

Table �"-� Water Volume in the Flooded Areas with Different Scenarios

�� Water Level Solid Blocks Borders of Buildings Refined Model *�#� �� *�#� �� No Buildings

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�� Solid Blocks Borders of Buildings Refined Model *�#� �� Core ��

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G

Table �"-� Flooded Area and Water Volume with DTMs Have Various Errors in Elevation Values Flooded areas

Random Error Systematic Error Water Level No Error

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Flood water volumes contained in the flooded areas

Random Error Systematic Error Water Level No Error

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Table �"-# Flooded Area and Water Volume with DTMs Have Various Resolutions Flooded areas

Water Level � � ��

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Flood water volumes contained in the flooded areas Water Level � � ��

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