HYDRODYNAMIC MODELLING OF THE 2003 NUNA …...Hydrodynamic modelling of the 2003 Nuna river flood using terrain information obtained from remote sensing sources by Lesslie A Thesis

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HYDRODYNAMIC MODELLING OF THE 2003 NUNA RIVER FLOOD USING

TERRAIN INFORMATION OBTAINED FROM REMOTE SENSING SOURCES

esslie A

Hydrodynamic modelling of the 2003 Nuna river flood using terrain information obtained from remote sensing

sources

by

Lesslie A 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-information Science and Earth Observation, Specialisation: (Geo-Hazards) Thesis Assessment Board Supervisors Chairman: Prof. Dr. V.G. (Victor) Jetten (ITC) Dr V Hari Prasad (IIRS)

Dr. C.J. (Cees) van Westen (ITC) Drs. D. (Dinand) Alkema (ITC) External Examiner : Dr. S.K Jain (NIH) IIRS Member : Dr. S.P Agarwal (IIRS) Supervisor : Dr. V. Hari Prasad (IIRS)

iirsiirs

INDIAN INSTITUTE OF REMOTE SENSING, DEHRADUN, INDIA

& INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH

OBSERVATION ENSCHEDE, THE NETHERLANDS

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I certify that although I may have conferred with others in preparing for this assignment and drawn upon a range of sources cited in this work, the content of this thesis is my original work. Signed……………………

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

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Dedicated to

My Parents

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Abstract

In India, flood is the most devastating and frequently occurring natural hazardous event. In 1976 Government of India, has constituted Rashtriya Barh Ayog (RBA - National Flood Commission). It was reported that 400,000 km2 geographical area is prone to floods, constituting about 12% of the country’s geographical area. Severe floods occurred in 1978 affecting 175,000 km2 area. Hence it is important and necessary to take up flood studies and improve the techniques to understand flood dynamics so as to facilitate planning appropriate remedial measures. The floodplain mapping for riverine floods using geoinformation has many limitations. It is difficult to use optical remote sensing data, due to cloud cover and digital classification is difficult with microwave remote sensing data. To overcome these limitations, the simulation of flood event using MIKE FLOOD hydrodynamic 1D / 2D model is carried out in this study. From the available literature and field data, it became clear that the digital representation of the terrain with high level of accuracy is a primary input for hydrodynamic model. Hence different methods were investigated in this study to generate accurate surface model using RS imagery from CartoSat-1/TERRA-ASTER satellites. The study mainly focus on defining optimum resolution of Digital surface model (DSM) derived using CartoSat-1 / TERRA-ASTER datasets to run the Hydrodynamic (HD) model, describe methodology to downgrade the high resolution elevation information without loosing the exact elevation of critical flow-influencing objects like dikes, embankments, etc. and identify the most reliable data source to calibrate the flood model in Indian conditions. Kendrapara district in Orissa state, located close to Bay of Bengal coast experiences floods every year. During August – September 2003 one of the major flood events occurred in the district for Nuna - Barandia Rivers. The same event was taken up for simulation of HD model. In this region, in spite of flood protection works (dikes) severe floods occur, mainly due to excess flow of water in the river. Hence accurate surface model of the region becomes the basic requirement for this study. The Global Positioning System (GPS) survey was conducted in differential mode to generate a library of Ground Control Points (GCPs) to be used in surface model generation. Using CartoSat-1 satellite data DSMs were derived using automatic DSM extraction tools of Leica Photogrammetry Suite (LPS) at different resolutions (10m, 12.5m, 15m, 17.5, & 20m). DSMs of 15m, 30m and 45m resolution were generated from ASTER using Topographic tools of ENVI. For a better representation of features that influence flow of water, break line in stereo point measurement tool is used to generate DSM. The study also tried to address the problems of using derived DSM for hydrodynamic model, since WGS 84 datum (used for DSM generation) is ≈ 60m above mean sea level (MSL) in the study area. Using the elevation statistics, validation and examining the flow influencing feature representation in different resolutions of derived DSM, defining the optimum CartoSat-1/TERRA-ASTER surface model resolution to run HD model becomes a distinct possibility. By using array of points in GIS, the high-resolution elevation information was downgraded from 10m to 30m and 300m resolutions

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http://wrmin.nic.in/development/rb_ayog.htm

without losing the exact information of critical flow-influencing objects like dikes, embankments, etc. The Flood event was simulated using MIKE FLOOD for river and floodplain with the downgraded surface models of 30m and 300m resolutions. The calibration of HD model was carried out by comparison of the flood extent derived using the MIKE FLOOD model with Radarsat-1 satellite data and filed data. From this study it was concluded that the DSM derived from CartoSat-1 satellite data was much better surface model when compared to TERRA-ASTER surface model. The optimum resolution of CartoSat-1 surface model was found to be 10m derived using break lines in stereo point measurement tool and 15m using classical point measurement tool of LPS. The 30m and 300m downgraded resolution DSMs were compared to source 10m DSM for representation of Shape and elevation for flow influencing objects on surface models. The result showed that the 30m resolution DSM represents better shape and elevation compared to 300m resolution. The simulation of flood model on 300m resolution for the entire event was simulated for floodplain. In case of rivers, simulation was carried out using 26 cross sections at every 1.5km along the river system. The overtopping of river levees (MIKE 11) and flood extent on floodplain (MIKE 21) were validated with RadarSat-1 satellite data of 4th and 11th September, 2003 and found to be representing 63% and 50% inundation extent respectively. The variation of flood depth using the MIKE FLOOD model and ground data collected in 6 villages in the study area was analyzed. The comparison of flood depth on 30m and 300m-flood simulation with field data was carried out for Bachharai Village. The calibrated peak flood depth of the event was found to be 1.5m less than that of field data on 300m grid. For 30m resolution simulation, flood depth was found to be 1.5m more than that recorded in the field data. Keywords: Hydrodynamic modeling, DSM, CartoSat-1, GPS, MIKE FLOOD, MIKE 11, MIKE 21.

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Acknowledgements There are lots of people I would like to thank for a huge variety of reasons. I would like to express my deep debt of gratitude to my IIRS Supervisor Dr. V. Hari Prasad, I/C Water Resource Division, Indian Institute of Remote Sensing, Dehradun, for his motivation to do this work, I also have to appreciate for his caring nature which helped me to approach him for suggestion that made to complete the task at easy. I would like to thank my ITC Supervisor Drs. D. (Dinand) Alkema, who gave a free hand to implement my ideas, guide me to give shape to the thesis work. My sincere thanks to Dr. K. Radhakrisnan, Director, NRSA, for allowed me to take up this course at IIRS, former director NRSA Dr. R.R. Navalgund who has shown keen interest in me and suggested to join this programme at IIRS, I sincerely thank Dr. P.S. Roy Dy. Director-AA, NRSA for his support, encouragement and guidance to do this course. Dr. A. Perumal, GH NRSA for his support and Dr. M.V. Ravi Kumar for support and guidance during the course. Dr. V.K. Dadhwal, Dean IIRS who has guided me to join the MSc programme at IIRS. His sound technical comments helped me in building the concepts during the course period, Dr. George Joseph Director CSSTEAP, Dr. S.K. Saha and Dr. Yogesh kant who permitted me to work at research laboratory, extend their possible help and guidance for the thesis work. My Sincere thanks to Mr. Ashutosh Bhardwaj for help and guidance in GPS data collection during my absence and Mr. Praveen Thakur for his technical support during my course work. Both scientists shown a lot of interest in my thesis work, without which it would haven’t possible to complete in short course of time, Dr. S.P. Agarwal, who always encouraged me in my course study. My thanks are due to Mr. I.C. Das, course coordinator for his support during this course period. My thanks are due to Dr. SVC Kameswar Rao for guiding me to take-up this programme and moral support to my parents during my stay at IIRS and ITC. My thanks are due to Mr. K. Kalyanaraman. GM (AS&DM), NRSA and Mr. V. Raghu Venkataraman, Head, AS&DPD, NRSA for permit me to carryout task at AS&DM, NRSA. My thanks to Mr. P. Sreenivas and Mr. Narendran, scientists from NRSA, who helped me in complete task at NRSA, Mr. G.V. Hari Kishore and Mr. Vijay Kumar for their help on DSM generation, during my short stay at NRSA. My thanks are due to ITC for providing me fellowship to undergo part of the programme at ITC, The Netherlands. I would like express deepest gratitude to Dr. C.J. (Cees) van Westen, Dr. P.M. (Paul) van Dijk, Ms. Drs. N.C. (Nanette) Kingma, Dr. E.M. (Ernst) Schetselaar, Drs. R.P.G.A. (Robert) Voskuil; Prof. Dr. F.D. (Freek) van der Meer; Prof. Dr. V.G. (Victor) Jetten and many more who made my stay at ITC nice and fruitful. My special thanks to Dr. D.G. (David) Rossiter and Drs. M.C.J. (Michiel) Damen for support and suggestion during mid-term evaluation at IIRS. My thanks are due to Mr. Ajay Pradhan and Mr. Rashmi Ranjan Patra of DHI, India for their support in providing the software and valuable suggestion made easy to execute the model. It feels great to have such nice colleagues and friends, also greatly honoured to be among them, Mr. Prashanth Kawishwar, Mr. Shivraj Ghorpade, Mr. Virat Sukla, Mr. Rajeev RK Nair, Mr. Pankaj Sharma, Mr. Rajesh Bhakar, Miss. Divyani Kohli, Mrs. Sreyasi Maiti, Miss. Chandrama Dey, Miss. Surabhi Kuthari, Miss. Anandita Sengupta, Mr. Vinod Nautiyal and Mr. Bernard Majani for their support during my stay at IIRS. My thanks are due to supporting staff at IIRS, Mr. Vijay Pal Singh, Mr. B.K. Payal, Mr. Param Jeet Singh, Mr. Kailash and many more have supported me during my stay at IIRS.

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List of CONTENTS:

Abstract ................................................................................................................................................iv Acknowledgements............................................................................................................................. vi List of CONTENTS:.......................................................................................................................... vii List of TABLES .................................................................................................................................. ix List of FIGURES ..................................................................................................................................x 1. Introduction ..................................................................................................................................1

1.1. Research background ............................................................................................................1 1.1.1. Hydrodynamic model .......................................................................................................2 1.1.2. Elevation data ...................................................................................................................2 1.1.3. Satellite stereo pair ...........................................................................................................2 1.1.4. Global Positioning system (GPS): ....................................................................................3

1.2. Floods in India: .....................................................................................................................4 1.3. Flood events in the Mahanadi delta: .....................................................................................5 1.4. Problem Statement ................................................................................................................7 1.5. Hypothesis ............................................................................................................................7 1.6. Objectives .............................................................................................................................7 1.7. Research Questions...............................................................................................................8 1.8. Thesis outline ........................................................................................................................8

2. Literature Review.........................................................................................................................9 2.1. Concepts of Geohazards .......................................................................................................9 2.2. Floods....................................................................................................................................9 2.3. Floods in India ....................................................................................................................10 2.4. Geoinformation in flood studies .........................................................................................10

2.4.1. Role of Remote Sensing .................................................................................................11 2.5. Hydrodynamic modeling for Flood studies ........................................................................14

2.5.1. 1D Hydrodynamic modelling .........................................................................................15 2.5.2. 2D Hydrodynamic modelling .........................................................................................16 2.5.3. Integrated 1D / 2D Hydrodynamic modelling ................................................................16

3. Study Area ..................................................................................................................................18 3.1. Introduction to study area ...................................................................................................18 3.2. Mahanadi basin ...................................................................................................................19 3.3. Location of study area.........................................................................................................20 3.4. Salient features of the study area ........................................................................................20 3.5. Water Level and Location of Gauging station on Study area .............................................21

4. Methods and Material................................................................................................................22 4.1. Introduction to the research methodolgy ............................................................................22 4.2. DSM generation using stereo satellite imagery ..................................................................23

4.2.1. Field Visit related: ..........................................................................................................24 4.2.2. Digital Surface Model (DSM) generation: .....................................................................26 4.2.3. Datum transfer (Common datum)...................................................................................28

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4.2.4. Generation of River Cross sections ................................................................................31 4.2.5. Digital Elevation Model (DEM) generation for river course .........................................32 4.2.6. Downgrading the resolution of Digital surface model ...................................................33 4.2.7. Generation of Land use / Land cover map......................................................................33 4.2.8. Generation of Flood Inundation map..............................................................................33

4.3. Hydrodynamic Modeling ....................................................................................................33 4.3.1. MIKE 11.........................................................................................................................34 4.3.2. MIKE 21.........................................................................................................................35 4.3.3. MIKE FLOOD (Integrated MIKE 11 & MIKE 21) .......................................................36

5. Results and Discussion ...............................................................................................................37 5.1. Introduction.........................................................................................................................37 5.2. Geoinformation (DEM – reconstruction)............................................................................37

5.2.1. Field Visit .......................................................................................................................37 5.2.2. Datum transfer (Common datum)...................................................................................47 5.2.3. Generation of River cross sections .................................................................................51 5.2.4. Digital Elevation Model generation – River...................................................................54 5.2.5. Downgrading the resolution of Digital surface model ...................................................54 5.2.6. Generation of Manning’s – n using Land use / Land cover map....................................57 5.2.7. Generation of Flood Inundation map..............................................................................59

5.3. Hydrodynamic modelling ...................................................................................................59 5.3.1. MIKE 11.........................................................................................................................60 5.3.2. MIKE 21.........................................................................................................................77 5.3.3. MIKE FLOOD (Integrated MIKE 11 & MIKE 21) .......................................................79 5.3.4. Flood inundation results of MIKE FLOOD model.........................................................79 5.3.5. Calibration of Field (interview) data with Flood model results......................................80 5.3.6. Comparison of flood model with visually interpreted data and Satellite data................82

6. Conclusion and Recommandation ............................................................................................89 Reference.............................................................................................................................................91

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List of TABLES

Table 1-1: The details of the floods in 2003 in Orissa state...................................................................... 6 Table 1-2: Table showing the Damage to Government Property.................................................................... 6 Table 2-1: Summary of ASTER DEM generation and accuracy assessment for the study area .. 14 Table 3-1: Gauge Stations on the Mahanadi river ................................................................................... 20 Table 3-2: Details of the Gauging station on Nuna River....................................................................... 21 Table 5-1: Ambiguity resolved Ground Control Points................................................................................. 38 Table 5-2: Results of Post-processing .............................................................................................................. 39 Table 5-3: Accuracy of Post-processed points ................................................................................................ 40 Table 5-4: Elevation validation of TERRA – ASTER DSM using GCPs..................................................... 42 Table 5-5: Elevation validation of CartoSat-1 DSM using GCPs ................................................................. 43 Table 5-6: Statistics of the validated points .................................................................................................... 43 Table 5-7: Statistics of the elevation profiles of DSM’s ................................................................................. 45 Table 5-8: Values of converted heights............................................................................................................ 47 Table 5-9: Mean difference table for the GCPs used to generate Digital surface model ............................ 48 Table 5-10: Height from MSL vis-à-vis GPS ellipsoidal (WGS 84) .............................................................. 50 Table 5-11: Height difference between the levees.......................................................................................... 50 Table 5-12: Comparison of the different methods for datum transfer......................................................... 51 Table 5-13: Table showing allotted manning coefficient................................................................................ 58 Table 5-14: Discharge in the river system....................................................................................................... 69 Table 5-15: Water depth and flood depth in the simulated river system on 4th September, 2003 and 11th

September, 2003 ........................................................................................................................... 70 Table 5-16: Extreme water depth in the river for the event .......................................................................... 74 Table 5-17: Extreme discharge in the river for the event .............................................................................. 75 Table 5-18: Extreme flood depth in the river for the event ........................................................................... 76 Table 5-19: Overtopping of the floodwater at the cross sections over the levees......................................... 77 Table 5-20: Effect of floodwater depth with resolution ................................................................................. 88

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List of FIGURES

Figure 1-1: Diagram of the imaging geometry for ASTER along-track stereo ............................................. 3 Figure 1-2: Diagram of the imaging geometry for CartoSat-1 along-track stereo........................................ 3 Figure 2-1: Orientation of PAN cameras on Cartosat – 1 satellite ............................................................... 13 Figure 3-1: Location map of the Study area .............................................................................................. 18 Figure 3-2: Mahanadi river basin ................................................................................................................ 19 Figure 3-3: CartoSat-1 data of the study area .......................................................................................... 20 Figure 3-4: Location of the Gauging Stations on the Study area.......................................................... 21 Figure 4-1: Showing flowchart of the methodology ....................................................................................... 22 Figure 4-2: Map showing proposed Ground Control Point used during the Field Survey ........................ 24 Figure 4-3: Locations of the Ground Control Point on the cartoSat-1 Satellite data and some respective

photographs of the stationed Rover ............................................................................................. 25 Figure 4-4: Profile line divided into 50m Length............................................................................................ 28 Figure 4-5: Point layer along the Profile line -1.............................................................................................. 28 Figure 4-6: Datum transfer for the study area ............................................................................................... 29 Figure 4-7: Offset method to transfer the datum ........................................................................................... 30 Figure 4-8: Feature identification method to bring the databases to a common datum............................. 31 Figure 4-9: Elevation information for editing................................................................................................. 32 Figure 4-10: Spatial databases generated to derive river DEM for MIKE 21 model ................................. 33 Figure 5-1: Digital Surface Model generated using 15m resolution of TERRA - ASTER ......................... 41 Figure 5-2: Digital surface model of 10m, 12.5m, 15m, 17.5m & 20m resolution using Classical point

measurement tool........................................................................................................................... 41 Figure 5-3: Map showing the Digital Surface Model generated using LPS – terrain editor ...................... 42 Figure 5-4: Profile comparison of Digital Surface Models between CartoSat-1, TERRA – ASTER and

SRTM model .................................................................................................................................. 44 Figure 5-5: Correlation among the flow influencing structures in HRS data and different Surface models

........................................................................................................................................................... 46Figure 5-6: Polynomial equation for best curve fit......................................................................................... 49 Figure 5-7: Extracted elevation information from the Digital Surface Model ............................................ 52 Figure 5-8: The Attribute data generated to apply in Hydrodynamic model.............................................. 53 Figure 5-9: Cross section information in MIKE 11........................................................................................ 54 Figure 5-10: Digital elevation model generated using river cross-section.................................................... 54 Figure 5-11: Grid point method of downgrading the surface model resolution .......................................... 55 Figure 5-12: Map of Downgraded DSM in the form of bathymetry grid on 300m cell size of the MIKE 21

file format ....................................................................................................................................... 56 Figure 5-13: Map of Downgraded DSM in the form of bathymetry grid on 30m cell size of the MIKE 21

file format ....................................................................................................................................... 56 Figure 5-14: Effect of Spatial resolution of the downgraded DSM of 30m and 300m from sources DSM

10m.................................................................................................................................................. 57 Figure 5-15: Manning “n” value map for river generated as resistance map ............................................. 58 Figure 5-16: Manning “n” value map for flood plain generated as Resistance map .................................. 58 Figure 5-17: Map showing Flood inundation on the 4th September, 2003.................................................... 59 Figure 5-18: Map showing Flood inundation on the 11th September, 2003.................................................. 59 Figure 5-19: Locations of H-point (Cross section) and Q-point (H-Q relation can be obtained)............... 60

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Figure 5-20: Time series data for Pubansa Gauging station ......................................................................... 60 Figure 5-21: Time series data for Marshaghai Gauging station ................................................................... 61 Figure 5-22: Nuna River system for model simulation .................................................................................. 62 Figure 5-23: GUI showing the verities of each point to generate a spatial network database ................... 62 Figure 5-24: GUI showing branch definition in the river system ................................................................. 63 Figure 5-25: GUI showing the cross section details for building the database ............................................ 64 Figure 5-26: GUI showing the definition of boundary condition for the simulation................................... 64 Figure 5-27: GUI to define the initial water level & discharge (Global / Local) in “a” and Bed resistance in

Manning’s - n................................................................................................................................ 65 Figure 5-28: GUI showing the MIKE 11 set-up files for model simulation.................................................. 66 Figure 5-29: Longitudinal profile of MIKE 11 simulated result of Nuna river on 4th September, 2003 ... 66 Figure 5-30: Longitudinal profile of MIKE 11 simulated result of Barandia river on 4th September, 2003

.................................................................................................................................................................... 67 Figure 5-31: Longitudinal profile of MIKE 11 simulated result of Nuna river on 11th September, 2003. 67 Figure 5-32: Longitudinal profile of MIKE 11 simulated result of Barandia river on 11th September, 2003

....................................................................................................................................................... 68 Figure 5-33: Time series water level before bifurcation of Nuna river ........................................................ 71 Figure 5-34: Time series water level after bifurcation of Nuna river ........................................................... 71 Figure 5-35: Time series water level of Barandia river.................................................................................. 72 Figure 5-36: Time series water level after union of Baandia river into Nuna river .................................... 72 Figure 5-37: Time series discharge in Nuna river for the event.................................................................... 73 Figure 5-38: Time series discharge in Barandia river.................................................................................... 73 Figure 5-39: Bathymetry data generated using simple integration method into MIKE............................. 78 Figure 5-40: GUI showing definition of Lateral Links .................................................................................. 79 Figure 5-41: Flood simulation using MIKE FLOOD model (Figure showing flood inundation situation on

4th September, 2003 @ 12:00 noon) ............................................................................................ 80 Figure 5-42: Flood simulation using MIKE FLOOD model (Figure showing flood inundation situation on

11th September, 2003 @ 12:00 noon) .......................................................................................... 80 Figure 5-43: Overlay of settlement locations on the inundated grid in MIKE results file.......................... 81 Figure 5-44: Comparison of Results with datasets for the event in Bachharai Village............................... 83 Figure 5-45: Comparison of interpreted information to the model output for 4th September, 2003 ......... 83 Figure 5-46: Comparison of interpret information over the model output for 11th September, 2003....... 84 Figure 5-47: Model output overlaid on the RadarSat-1 satellite data for 4th September, 2003 @ 12:00 noon

....................................................................................................................................................... 84 Figure 5-48: Model output overlaid on the RadarSat-1 satellite data for 11th September, 2003 @ 12:00

noon ............................................................................................................................................... 84 Figure 5-49: Velocity along X- direction on 4th of September 2003 at 12:00 noon...................................... 85 Figure 5-50: Velocity along Y- direction on 4th of September 2003 at 12:00 noon ...................................... 85 Figure 5-51: Velocity along X- direction on 11th of September 2003 at 12:00 noon.................................... 86 Figure 5-52: Velocity along Y- direction on 11th of September 2003 at 12:00 noon .................................... 86 Figure 5-53: Effect of resolution on the Flood inundation in Hydrodynamic model .................................. 87 Figure 5-54: Flood depth for the event on 30m grid size ............................................................................... 88

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HYDRODYNAMIC MODELLING OF THE 2003 NUNA RIVER FLOOD USING TERRAIN INFORMATION OBTAINED FROM REMOTE SENSING SOURCES

1. Introduction

1.1. Research background

Geo-Hazards is a potential damaging phenomena or human activity that may cause the loss of life or injury, property damage, social and economic disruption or environmental degradation on the earth’s surface. The natural hazards are in form of flood, landslide, volcano, earthquake, tsunami, cyclone, drought, etc. In the Asia-Pacific, from 1990 to 2003 there was a economic loss of about US$ 380 billion due to cyclones/floods (57%) and earthquake (37%) (Guoxiang, 2005). During the decade 1993-2002, natural disasters resulted in 531,000 human deaths, 2.5 billion affected people, and US $ 654 billion property damage (IOE, 2004). Flooding is water where it is not wanted and occurs most commonly due to heavy rainfall when natural watercourses do not have the capacity to convey the excess water. Floods need not necessarily be caused by heavy rainfall alone. In coastal areas, inundation may occur because of a storm surge, tsunami, or a high tide coinciding with higher than normal river levels. Storm surges are most commonly caused by tropical cyclones. A sudden movement in the ocean floor triggers tsunamis, such as a landslide on the ocean floor or earthquake. Snow melt may also cause flooding in many countries. Dam failure, triggered for example by an earthquake or poor dam construction, will result in flooding of the area downstream of a dam. Flooding can occur even in dry weather conditions with the factors high intensity and long duration of rainfall over the upper part of the catchments / basin; catchments and weather conditions prior to the rainfall event; ground cover; the capacity of the watercourse or stream network to convey the runoff and tidal influence. Riverine flooding occurs in relatively low-lying areas adjacent to streams and rivers. In the flat inland regions, floods may spread hundreds of square kilometers and last for several weeks, with flood warnings sometimes issued weeks in advance. In the mountainous and coastal regions, flooding can occur rapidly and warning times are short, perhaps only a few hours. Flash floods can occur almost anywhere where there is a relatively short intense burst of rainfall such as during a thunderstorm. During these events the capacity of the drainage system has insufficient time to cope with the downpour. Although flash floods are generally localized, they pose a significant threat to the loss of human life, property, etc because of their unpredictability and the short duration of the event. The relatively broad and smooth valley floor is formed by an active river system and periodically covered with floodwater from that river during intervals of over-bank flows. Engineers consider the floodplain to be any part of the valley floor subject to occasional floods. Various channel improvements or impoundments may be used to restrict the natural process of over-bank flow. Geomorphologists consider the floodplain to be a surface that develops by the active erosion and depositional processes of a river. Floodplains are underlain by a variety of sediments, reflecting the fluvial history of the valley.

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Floodplains in many catchments have been extensively encroached upon, thereby increasing the vulnerability of a number of structures to flooding. In addition, the construction of new structures on the floodplain in one location can increased flood levels at another location by increasing in the amount of runoff in the floodplain. Because an area has not been flooded in the past does not necessarily exclude the possibility of the area being flooded in future. Such scenario studies can be carried out using hydrodynamic models in combination with geo-information.

1.1.1. Hydrodynamic model

In the last decade there has been an increase in flood modelling studies, i.e., to simulate the flood events and study its nature in the laboratory. The Hydrodynamic model in flood simulation is extensively used and these models provide a library of computational methods for steady and unsteady flow in branched and looped channel networks as well as flow simulation on flood plains. Today there is an increase in requirements for accuracy and level of details of flood modelling, which resulted in the introduction of two-dimensional models, which represent the spatial variations that are resolved using a two dimensional grid or mesh of flooding on the flood plain. The two-dimensional models do not always give an accurate and efficient result at narrow rivers, culverts, weirs, etc. So, to over come this problem a combination of one-dimensional and two-dimensional flood models is used. The dynamic coupling of one-dimensional and two-dimensional model is run simultaneously with exchange flow between the models being calculated at each time step.

1.1.2. Elevation data

The Elevation data of the study area is one of the primary data inputs that describe the variation in floodplain topography. The hydrodynamic model needs elevation data with very high vertical accuracy. The sources of elevation information will be the prime concern. The present available sources of acquiring elevation data are:

• Light detection and ranging (LiDAR) • Stereo pair (Satellite/Aerial) • Radio detection and ranging (RADAR) • Global Positioning System (GPS) • Total station (an optical instrument used in modern surveying)

1.1.3. Satellite stereo pair

The elevation data can be acquired from satellite stereo images. The acquisition of images is of two types. i) The across-track stereoscopy, which acquires data from two different orbits. In this system, single camera is used, the sensor captures the image of an area and the same area is captured from the next adjacent orbit with the camera tilted across the orbit. In the second system, i.e., in-track or along-track stereoscopy, the images are acquired from the same orbit using, fore and aft cameras fixed in positions. The latest Indian Remote Sensing (IRS) satellite CartoSat-1 and TERRA-ASTER stereo images are examples of this type. The image geometry of TERRA-ASTER and also IRS CARTOSAT – 1 are shown in Figure 1-1 and Figure 1-2 respectively.

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Figure 1-1: Diagram of the imaging geometry for ASTER along-track stereo

Source: (Hiranoa et al., 2002)

Figure 1-2: Diagram of the imaging geometry for CartoSat-1 along-track stereo

Source: Parameters obtained from (Krishnaswamy et al., 2004)

1.1.4. Global Positioning system (GPS):

The traditional methods of surveying and navigation resorted to field and astronomical observation for obtaining positional and directional information. The astronomical observation of celestial bodies is one of the standard methods of obtaining coordinates of a position, which depended on the weather

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condition, visibility and expertise of the observer. In early 1960’s attempt were made to use space based artificial satellites. The navigation system along with global positioning system (NAVSTAR GPS) is a satellite based radio navigation system that provides a three dimensional position and time information. The NAVSTAR consists of 21 satellites with 3 active spare satellites arranged in orbits to have at least four satellites visible above the horizon anywhere on the earth at the altitude of 20200 km from earth’s surface. GPS satellites transmit two types of code P-code and C/A code with frequencies of L1=1575.42 MHz and L2=1227.6 MHz. These satellites act as reference points from which the receivers on the ground reset their position. The navigation principle is based on the measurement of pseudo ranges between the user and the four satellites. Ground stations precisely monitor the orbit of every satellite and by measuring the travel time of the signals transmitted from the satellite. The four distances between receiver and satellites will yield accurate position, direction and speed. Though three ranging measurements are sufficient for knowing the position, the fourth observation is required to solve the clock synchronization error between satellite and receiver. The high frequencies L1 and L2 signal can easily penetrate ionosphere, dual frequency observations are important for large station separation and for eliminating most of the error parameters. GPS has been designed to provide navigational accuracy of ± 10m to ± 15m and sub-meter can be achieved by using differential mode (Mathur et al., 2002).

1.2. Floods in India:

In India, floods constitute the major natural hazard that is most devastating and frequently occurring. In 1976 Government of India, has constituted Rashtriya Barh Ayog (RBA - National Flood Commission). It was reported that 400,000 km2 geographical area was prone to flood. The maximum was in 1978 when 175,000 km2 was affected (Ministry of Water Resources, 2006). From this, it becomes necessary to work on reducing the loss due to floods and efforts are required to communicate about the flood to the people. The efforts are also in progress towards developing flood models so as to predict the occurrence of floods in the floodplain for taking up precautionary measures. Heavy rains that pour huge quantities of water into rivers and other waterways, making natural channels unable to carry all the water, usually cause floods. Water flowing over banks or breaching the banks of water bodies result in the surrounding land to be inundated or flooded. Other causes of floods include masses of snow melting, high tidal waves, dam break, etc. The rivers of India can be divided into four groups based on the meteorological, geological and topographical conditions. These are the Brahmaputra river system, the Ganga river system, the northwest river system and the central India & Deccan river system (Dhar et al., 2003). Himalayan rivers are snow fed and maintain a high to medium rate of water flow throughout the year. The heavy annual average rainfall levels in the Himalayan catchments areas further add to their rates of flow. During the monsoon months of June to September, the catchments areas are prone to flooding. The volume of the rain-fed peninsular rivers also increases. Coastal streams, especially in the west, are short and episodic. Rivers of the inland system, centered in western Rajasthan state, are few and frequently disappear in years of scant rainfall. The Mahanadi, rising in the state of Chhattisgarh, is an important river in the state of Orissa. In the upper drainage basin of the Mahanadi, which is centered on the Chhattisgarh Plain, periodic droughts contrast with the floods in the delta region leading to damage of crops. Hirakud Dam, constructed in the middle reaches of the Mahanadi, has helped in alleviating these adverse effects by creating a reservoir.

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http://wrmin.nic.in/development/rb_ayog.htm


1.3. Flood events in the Mahanadi delta:

In 2001 July, Orissa state had devastating floods with more water being released from the Hirakud reservoir on the Mahanadi, and heavy rains lashing the catchment areas of the river. Almost 40,000 cumecs of water was discharged from the Naraj gauge station near Cuttack, the figure exceeded the 45,000 cumecs mark in just 6 hours. This has worsened the situation in the coastal districts of Cuttack, Puri, Jagatsinghpur, Kendrapara and Jajpur. Over 5 million people have been affected and 9,000 villages were marooned (Das, 2001). In 2003 September, Orissa state was again affected by floods in the Mahanadi and other rivers, 15 districts have been affected, from 27th of August to 28th of September 2003 with continuous rains in the upper and lower catchments areas causing flooding of the Mahanadi river system. The situation was bad in the coastal districts of Cuttack, Puri, Jagatsinghpur, Kendrapara and Jajpur. The death toll in the floods has gone up to 13 by September 2nd, 2003. The flood waters have caused breaches on roads at more than 609 points, affecting communication. Although there had been a slight decrease in the volume of water passing at Naraj gauge station near Cuttack, the situation did not improve for the next two days. About 35,000 m3/sec of water was passing at Naraj barrage. Of the total 3,824 affected villages, 789 were marooned. With the prediction of more rainfall in the State and in Chhattisgarh, more than 75,000 people have been evacuated. The details about the floods in 2003 in the state of Orissa are given in Table 1-1. The details of damage to government building / tanks / irrigation projects in Kendrapara district were shown in the Table 1-2.

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Table 1-1: The details of the floods in 2003 in Orissa state

1 Total no. of Districts affected 23 2 Total no. of Blocks affected 131 3 Total no. of GPs affected 1588 4 Total no. of villages affected 6754 5 Villages Marooned 1429 6 Population affected 35,07,785 7 Casualties: Human lives lost 56 8 Livestock lost 2226 9 No. of houses damaged 1,22,641 10 Crop Area affected 4388 km2

11 No. of people evacuated 84,823 12 No. of Temporary shelters 299

Table 1-2: Table showing the Damage to Government Property

1 No. of GP’s 2 2 No. of Villages Affected 14 3 Population Affected 12439 4 Area affected in Km2 22.41 5 No. of School Building 14 6 Revenue Building 14 7 Block Building GP 2 8 Other Department Building 2 9 Value of Loss in INR 5 Million

Source: (Distrcit Collectorate, 2003) Note: The hierarchy of the administrative boundaries of India is as follows Country is divided into states, each state is divided into districts, each district is divided into thesils, each thesil is divided into blocks, each block is divided into gram panchyats (GP) and each gram panchyat is divided into villages. Satellite Remote sensing systems from their vantage position can unambiguously demonstrate the capability of providing vital information; They provide comprehensive and multi-temporal coverage of large area in real time and at frequent intervals (Bedient et al., 2003). This technology combined with real time ground information can be used to monitor, assess and predict the floods. Remote sensing or Earth Observation System (EOS) and GIS are among many tools available to disaster management professionals today making effective project planning very much possible and more accurate now than ever before. Although none of the existing satellites and their sensors has been designed solely for the purpose of observing natural hazards, the variety of spectral bands in visible (VIS), near infrared (NIR), infrared (IR), short wave infrared (SWIR), thermal infrared (TIR) and microwave region. The Synthetic Aperture Radar (SAR) provide adequate range of wave length (3.75 to 7.5cm) with frequency (5.3GHz) (Lillesand et al., 2000) and allow digital analysis of the data for

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this purpose. Repetitive or multi-temporal coverage is justified on the basis of the need to study various dynamic phenomena where changes can be identified over time (Nirupama et al., 2002). Various techniques and methodologies have been developed to capture flood extent and to map the flooded extent using various data source before, during and after disaster event. These outputs are used for response and mitigation works. Growing availability of multi-temporal satellite data has increased opportunities for monitoring large rivers from space. Various passive and active sensors are being used to identify inundation areas and delineate flood boundaries (Subramanya, 2002). In the present study, flood event occurred during August – September 2003 in Orissa is considered. Actual flood event started on 28-08-2003 and continued up to 30 days (26-09-2003). This study is an attempt to use Hydrodynamic model to derive flood inundation extent and its depth. The validations of the results were done with flood inundation extent derived using remote sensed datasets.

1.4.

1.5.

1.6.

Problem Statement

Riverine flood occurs in monsoon season due to heavy rainfall in the upper catchments in the study area. Since they occur very frequently and disaster event is most devastating, quick mitigation and response are very much required. In August 2003 there was heavy rainfall in the upper catchment of Mahanadi river system, which resulted an increase in water level on upstream side of Hirakund dam. Hence floodgates were opened which resulted in sudden rise in water level in the downstream that in turn flooded the Mahandi delta area. Potential floodplain mapping for Riverine flood using Geoinformation during Indian monsoon period have many limitations such as unsuitable climatic condition for optical remote sensing and classification becomes difficult because of complex ground and system variables for microwave remote sensing, etc. To understand the dynamics of the flow of floodwater in the river and floodplain for the event, it is necessary to model event using advanced hydraulic 1D / 2D model. The model requires different input data from different sources to simulate the flood event. It is required to understand the possibility of using geoinformation data to derive elevation models and calibrate the hydraulic model.

Hypothesis

• The terrain information obtained from remote sensing sources can be used as the input for Hydrodynamic modelling

• Simulation of Hydrodynamic model with limited available ground data. • Optimum resolution of Digital Surface Model to represent flow influencing objects like dikes,

embankments, etc. can be derived for the study area to run MIKE FLOOD.

Objectives

General objectives: Simulation of the 2003 Nuna River flood using Remote Sensing derived data to construct terrain models and model calibration

The Specific objectives identified are as follows • Generation of Digital elevation model for Hydrodynamic modelling using Satellite Stereo pair

(Cartosat-1 / ASTER)

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• Simulation of Hydro-dynamic modeling for the 2003 flood event - Part of Nuna river, Orissa, India (MIKE FLOOD)

1.7.

1.8.

Research Questions

• Can terrain information obtained from remote sensing sources be used as basis for 1D/2D flood modeling?

Sub Research Question: • How to downgrade high-resolution elevation data, without losing the exact elevation of critical

flow-influencing objects like dikes, embankments, etc.? • What is the optimum digital surface model resolution to run Mike Flood in the study area? • What is the most reliable data source to calibrate the flood model in Indian conditions?

(Satellite imagery or field interview data or the combination of the two)

Thesis outline

In Chapter 1 the rationale behind the research and basic information that underpins the background of the research with problem statement, research questions and objectives are covered. Also Chapter 1 presents a review of fundamentals of flooding and about digital elevation model generation and hydrodynamic modeling. Chapter 2 presents a review of the work done till date in digital elevation model generation using satellite stereo pair and hydrodynamic modeling. It outlines the research work carried out till date and the recent trends in generation of elevation data. Chapter 3 describes the study area along with general information about Orissa state, the study area and Nuna river system, a tributary of Mahanadi. Chapter 4 describes in detail the data generated and used in the present study as well as the methods followed to achieve the objectives of the research. Chapter 5 gives the results obtained by the application of the techniques described in Chapter 4 and also discussion on research findings in detail. The conclusions drawn from this research are presented in Chapter 6 along with the recommendations for future research direction and a bibliography of the references cited in this thesis.

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2. Literature Review

2.1.

2.2.

Concepts of Geohazards

A hazard is the probability of occurrence of a potentially damaging phenomenon within a specific period of time in a given area on the surface of the Earth. The general types of hazards are floods, earth quakes, landslides, droughts, wildfire, industrial hazards, technological hazards, etc. The devastating impacts of hazard events can prevent communities from achieving the most basic of human goals i.e., human survival. Hazards can be subdivided into natural, human-made and human-induced. Natural hazards are those that are caused by natural phenomena like floods, earthquakes, volcanic eruptions, landslides, etc. The human-made are caused by human activities like industrial accidents, armed conflicts, oil spills, nuclear accidents, etc. Human-induced hazards are accelerated and aggravated by human influence like crop disease, forest fire, acid rain, ozone depletion, etc (Westen et al., 2000). Natural hazards can be divided into two categories, i) Rapid onset events and ii) Slow onset events. In rapid onset events are floods, earthquakes, landslides, volcanic eruptions, tsunamis, sinkholes collapse, hurricanes, etc. Slow onset events are drought, subsidence, sea level change, soil erosion, desertification, expansive or swelling soils, salt intrusion, siltation, reduction in biodiversity, etc (Organization of American States, 2006). The hazards are defined as part of the ground investigation process, the principles of which are well established. Nevertheless uncertainty remains and communication between the professionals and the public is not always effective. Unfamiliar terminology and a lack of a forum for education and exchange of views create barriers. It is argued that the public must be continuously involved-not only as recipients, but also as contributors (Rosenbaum et al., 2003).

Floods

A flood is the overflow of a river or other body of water that causes threat or damage to the floodplain or any relatively high stream flow overtopping the natural or artificial banks in any reach of a stream. They are the most common and widespread of all natural disasters. Floods are one of the most common hazards in the world. Its effects can be local, impacting a neighborhood or community, or very large, affecting entire river basins and multiple countries and states. All floods are not alike. Some floods develop slowly, sometimes over a period of days. But flash floods can develop quickly, sometimes in just a few minutes and without any visible signs of rain. Flash floods often carry rocks, mud, and other debris and can sweep away most things in their path. Flooding can also occur when a dam or levee breaks, producing effects similar to flash floods. Flooding actually occurs from a range of causes and conditions like heavy rains or rapid snowmelt on upstream watersheds Coastal flooding is also very common. In many places, coastal land is very close to sea level, and therefore vulnerable. During hurricanes or other large storms, waves may be much higher than normal, and super-low atmospheric pressure often forces sea level to rise above normal in

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a “storm surge.” When violent surf and storm surge coincide with normal high tides, the results can be catastrophic. Less often thought of are the floods that can result from the failure of dams, impoundments, or other regulatory systems. Another cause of flooding in some areas is ice jams. In colder polar region, ice sheets form on the surface of a river during cold winter months of low flow. Warmer weather and higher flows cause the ice to break up into huge slabs that the current pushes downstream. When these slabs pile up against some obstacle, they form a dam that causes water to pool upstream and flooding results, when these obstacle breaks.

2.3.

2.4.

Floods in India

The heavy rainfall is the main cause of floods in Indian rivers during summer and monsoon months. Based on their occurrences India is divided into four zones, which are Brahmapura river basin, Ganga river basin, North-west rivers basin and Central Indian and Deccan rivers basin. The causes of incidences of heavy and very heavy rains, which are associated with any one or combination of more than one of the following synoptic systems, are tropical disturbances like monsoon depressions and cyclonic storms moving through the country from the neighboring seas of Bay of Bengal and Arabian Sea which travel in a northwesterly to westerly direction over the Indo-Gangetic plain and its neighborhood after crossing the coast and further move into the interior of the country. These disturbances recurve and move towards north or northeast and break over the foothills of the Himalayas. Low-pressure systems are less intense than monsoon depressions but they form quite frequently during monsoon months. In certain years the lows travel one after another in quick succession through north India, causing a continuous heavy spell of rainfall for a good number of days, sudden break of monsoon situations generally prevails during July and August months, mid-latitude westerly systems moving from west to east and mid-tropospheric cyclonic circulations over western region of the country. In addition to this, inadequate capacity within river banks to contain high flows, river bank erosion, silting of river beds, the other factors like land slides obstruct the flow of water in river / stream, cause changes in river course. (Dhar et al., 1998). The central Indian and Deccan river basins in which the present study is being carried out, have many rivers are such as Narmada, Tapi, Mahanadi, Godavari, Krishna and Cauvery rivers. These rivers flow in the states of Andhra Pradesh, Chhattisgarh, Karnataka, Tamil Nadu, Kerala, Orissa, Maharashtra, Gujarat and parts of Madhya Pradesh. In Orissa, Mahanadi, Brahmani and Baitarani share a common delta. Water from higher reaches intermingles in the delta region resulting in very high rise in water level in the rivers, so the rivers in these regions often overflow their banks or break through new channel causing damage (Mohapatra et al., 2003).

Geoinformation in flood studies

In the context of geoinformation, flood studies are carried out as an integrated approach of Remote Sensing (RS) and Geographic Information System (GIS) from which flood maps are generated. It has a great role in analyzing the risk due to floods. In geo-hazards management, a multi-dimensional activity with a spatial variable to it, GIS can be good tool for visualization. By means of spatial analysis, flood extent, velocity, flood depth, etc in the floodplain and river can be updated for different periods of flood event. It involves operation like overlay, neighborhood and connectivity analysis. The spatial extent of the flood can give the route for relief activities during a flood event.

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In the field of geoinformatics, flood related studies have received considerable attention during the last decade. Many agencies around the world are working on timely flood monitoring and impact assessment. In India, National Disaster Management Authority under ministry of Home Affairs, Government of India is working on major natural hazards. National Remote Sensing Agency (NRSA) under Department of Space and Government of India has been identified as the nodal agency to monitor various hazards. Potential uses of remote sensing technology in flood disaster management are flood inundation mapping, flood monitoring, rapid and scientific damage assessment, monitoring and mapping of changes in river course, identification of river bank erosion, identification of chronic flood prone areas (DSC - NRSA, 2004).

2.4.1. Role of Remote Sensing

In developing countries, remote-sensing applications in flood studies is an upcoming research area. Extreme flood event with high return periods and low density of gauging stations in the affected areas make it difficult to understand the floods spatially. In such situations, remote sensing technology provides a synoptic coverage over a large area for the event at the time of data acquisition, which is reliable and cost effective. The technology overcomes the limitation of the ground stations to acquire the data for hydrological events. The advancements in the technology in this aspect are measurements of rainfall (Foufoula-Georgio et al., 1995), soil moisture (Hoeben et al., 2000), water surface width, elevation and velocity with accuracies sufficient to provide discharge (Bjerklie et al., 2003). Remote sensing technology can also be used to derive digital elevation model using stereo pair or LIDAR data.

2.4.1.1.

2.4.1.2.

Optical Remote Sensing

The uses of optical remote sensing datasets for classification are relatively simple when compared to the microwave remote sensing datasets. The investigations of flood mitigation were predominantly confined to use remote sensing as a tool of flooded area delineation. Landsat MSS data (band 7 of wavelength of 800 to 1100 nm) was found suitable for distinguishing water or moist soil from dry surface. The NIR band of Landsat TM cannot be used in the urban area because of little energy reflection, hence resulting in dark pixel in the image (Smith, 1997). This has been solved by adding Landsat TM band 7 to the NIR band 4 to delineate the inundation area (Wang et al., 2002).

Microwave Remote Sensing

The advantage of using microwave remote sensing data in flood studies is its ability to penetrate cloud cover, to capture the progress of floods in bad weather condition, apart from its ability to sharply distinguish between land and water (Sanyal et al., 2004). Threshold is one of the most frequently used techniques to segregate flooded areas from non-flooded area. The threshold values are determined by a number of parameters depending on the study area and overall spectral signatures in the image. The separation of the flooded and non-flooded area in the urban scenario is difficult because the high back scatter of the buildings overlays the back scatter of flood water within the settlement (Sanyal et al., 2004, Brivio et al., 2002). The use of Radar altimeter would help in direct measurement of stage variation in large rivers. It is also possible, to estimate discharge of water in rivers from space, using ground measurements and satellite data through developing empirical relationships that relate water surface area to discharge. However, multiple frequencies and polarizations are required for optimal discrimination of various inundated vegetation cover types. Existing single-polarization, fixed-frequency SARs are not sufficient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-parameter SAR, a synergistic approach using single-frequency, fixed-polarization SAR and

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visible/infrared data will provide the best results over densely vegetated river floodplains (Smith, 1997). The feasibility of microwave imagery to detect flooded areas has been investigated in coastal Louisiana after Hurricane Lili, which occurred during October 2002. In this context, Radarsat-1 SAR data has been used and further investigated to develop a relationship between backscatter and water level changes. Strong positive correlations were observed between water level (obtained from ground stations) and SAR backscatter within coastal marsh areas limited to Atchafalaya Bay. Although variations complicated the radar signature at individual sites, multi-date differences in backscatter largely reflected the patterns of flooding in the study area. The analysis revealed that marsh flooding was best revealed by differencing the flood image from the mean of two reference images (Kiage et al., 2005).

2.4.1.3. Remote Sensing for DEM generation

DEMs play an important role in flood studies because topography, for a large part, defines the flow of water. There are two general ways in which RS contributes to the generation of digital terrain model: 1) stereo pair analysis and 2) LIDAR. Stereo-Pair: Derivation of Digital Elevation Models (DEMs) derived from satellite stereo pair had been an important field from last few decades. However, the generation of an accurate DEM without much loss of time had been challenging, especially using satellite stereo data. Some satellites are capable of acquiring the stereo data from across track and some have a capability of obtaining across and as well as along tack. However, in both the cases since the data is acquired in different orbits and with time difference, difficulties arise while transferring ground control points (GCPs) in the model as well as during automatic image matching for extraction of DEM (Kornus et al., 2005; Reinartz et al., 2005; Toutin, 2006). The above problems could be overcome by using IRS-P5 (Cartosat-1) stereo data where data is obtained from two panchromatic sensors on the platform with fore (+26o o) and aft (-5 ) tilt without time difference. Kumar (2006) highlighted the processing of stereo data acquired from Cartosat-1 data to derive DEM as well as an Ortho-image. When the DEMs were generated using only RPC (Rational polynomial coefficients), information for cartosat-1 stereo data, the errors in height were in the range 100 to 200m. When 8 GCPs were used, the errors ranged from 2 to 13m. The 4m contours were found to be close to ground height. In case of data obtained with time difference (IRS-1C stereo data), it was found that there were lots of conjugate points hanging and giving spicks impression on the DEM. From this study it was found that Digital Elevation Model generated from Cartosat-1 Stereo data could be improved with using more accurate and well-distributed GCPs for refining the coefficients. Millimetre accurate GCPs can be collected while using Geodetic Dual Frequency GPS in relative mode, which can improve accuracy of stereo model (Kumar, 2006). Fraser (2005) has studied use of rational functions in ground point determination from high-resolution satellite imagery through the model of terrain independent rational polynomial coefficients (RPCs). The concept of RPCs block adjustments with compensation for exterior orientation biases is discussed, as the means to enhance the original RPCs through a bias correction procedure. The potential of RPC block adjustment for getting sub-pixel ground position accuracy for the imagery are also been reported (Fraser et al., 2005).

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Table 2-1: Comparative evaluation of height from DEM generated using Cartosat-1 Stereo data vis-à-vis ground observations

Source: (Kumar, 2006)

Figure 2-1: Orientation of PAN cameras on Cartosat – 1 satellite

Source: (Interface, 2005) Hiranoa, (2002) carried out study, to generate DEM with ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer on-board NASA’s satellite Terra) stereo pair. The study investigated the possibilities of generation of DEMs for four test areas. The DEMs were generated using PCI Geomatica OrthoEngine and R-WEL packages with images of good quality and adequate ground control points. The automated stereo-correlation has become a standard method of generating DEMs from digital stereo images. Stereo-correlation is a computational and statistical procedure utilized to derive a DEM automatically from a stereo-pair of registered images (Ackermann, 1984; Ehlers et al., 1987). The procedure for stereo-correlation involves, the collection of GCPs and determination of parallax values on a per pixel DEM post-basis using automatic image matching techniques and post-processing to remove anomalies from the DEM. The DEM can be generated in two ways, 1) relative DEM where the elevations are not tied to the ground or map datum and 2) absolute DEM using GCPs with map coordinate system (Hiranoa et al., 2002).

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Table 2-1: Summary of ASTER DEM generation and accuracy assessment for the study area

Source: (Hiranoa et al., 2002)

Light Detection and Ranging (LiDAR): Light Detection and Ranging has become a well-recognized technology in the geoinformatics community since a decade. LiDAR has advantages in measuring surfaces in terms of accuracy, density, automation, and fast delivery time and has a large market in geo-data acquisition and object recognition technology. The instrument consists of several sensors like laser scanner, GPS, INS, all integrated to yield the coordinates of the ground points where the laser pulses fired from laser transmitter strike the ground. The direct product that can be derived is the DSM (Digital Surface Model), which depicts the topography of the earth's surface, including objects above the terrain. Further processing can be carried out to generate DTM (Digital Terrain Model) i.e., bare surface elevation and object models like buildings, which is very useful information in telecommunication, city planning, disaster management, and tourism. (Lohani et al., 2004).

2.5. Hydrodynamic modeling for Flood studies

The Hydrological modeling has various aspects of combination of computer application in hydraulics; the hydrodynamic modeling is application of mass and momentum equation on the basis of observation in laboratory and field to simulate the fluid flow or movement of fluids. These fluids movements can be simulated in one dimension, two dimensions or three dimensions. Floods are considered the most significant natural disaster affecting tropical world from the perspective of their frequency, financial cost and most importantly the impact on the population and the disruption to socio-economic activities. Since it is clearly evident that it is neither possible nor desirable to stop floods completely, the state of preparedness and mitigation should be improved with an operational flood early warning system so that the amount of damage caused due to it could be reduced. The development of flood early warning system by combining the remote sensing for quantitative precipitation forecasting (QPF) and GIS with hydrodynamic modeling for deriving flood inundation extent will be very useful for planning appropriate mitigation and response activities. Real-time remote sensing data is processed for rainfall estimates using cloud indexing and model based techniques. Digital hydrological and cadastral data are used to generate DEM and river geometry, hydrodynamic model for the operational hydraulic modeling of runoff and simulation of flooding scenarios. Expected flood inundation area map are developed. The operational coupling of remote sensing techniques with a 'hydrologically oriented' Geographical Information System is done with particular emphasis on the suitability of distributed hydrological modeling for the implementation of reliable and fully automated flood simulations and early warning.

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There are some commercial softwares like MIKE FLOOD developed at Danish Hydraulic Institute (DHI), Denmark and SOBEK developed at DELFT Hydraulics, The Netherlands.

2.5.1. 1D Hydrodynamic modelling

The one-dimensional model is based on the cross-sectional averaged Saint-Venant equations, describing the development of the water level and the discharge or the mean flow velocity. It is best simulation model to simulate the river / stream / channel conditions. These river / stream / channels are described in cross section along the chainage from upstream to downstream with boundary condition in the from of water level database in the observed time step in the field (Gauging station). The advantages of the one-dimensional model are its fast computation and relatively less field data requirement to build the model set-up. It is powerful in describing the structures for flood predictions. However, it does not describe the horizontal and vertical velocity components. It cannot simulate water bodies like lakes, pond, etc very effectively. Mike 11 is the modeling software package for simulation of Hydrodynamic modeling (HD), rainfall-runoff modeling (RR), sediment transport modeling (ST), Advection-Dispersion (AD), water quality modeling (ECO Lab), Ice river modeling, Flood forecast model (FF), etc. In MIKE 11 Hydrodynamic modeling, there are four components incorporated in the simulation file (*.sim11) such as simulation mode, input, simulation parameters and HD Results file (*.res11). The unsteady or quasi-steady flow condition can be defined in the simulation mode; it requires input files to build the HD model set-up (Network file, Cross-section file, Boundary file and HD parameters file). The MIKE 11 hydrodynamic model combines advance time series simulation and automated water level, discharge and rainfall-runoff process. The simulation period and initial conditions are to be defined and for results the path and location of the results file are to be defined. The Network file (*.nwk11) contains spatial and tabular database of the river / stream / channel system with defined projection parameters, in addition to these chainage at described cross-section location with width in projected coordinates and connection of branches are to be well defined. The Cross-section file (*.xns11) contains a series of cross sections defined with information such as river name, cross section ID, chainage in the network file, location of the ends (left/right) of the cross section in projected coordinates, datum of the cross section, chainage of the cross section with elevation, Resistance number and Markers (defining left / right levees / low point). The Boundary file (*.bnd11) contains Boundary description, type, ID, branch name, chainage in the network file and Time series file (*.dfs0) which describes the time series data (Water level / Discharge at observed time step in the field). The Hydrodynamic parameters file (*.HD11) contains Initial condition of water level and discharge at defined chainage in the network file and Bed Resistance (Global / Local value) (DHI, 2003 (a); DHI, 2003 (b)) Lawal Billa (2004) has carried out a study to investigate flood early warning system for Langat river basin through the combination of remote sensing and GIS hydrodynamic modeling using MIKE 11 software. The remote sensing is used to quantify the quantitative precipitation forecasting (QPF) using near real-time NOAA-AVHRR data. The data is processed for rainfall estimates using cloud indexing and model based techniques. The model is calibrated based on expected pre-flood rainfall data computed from the QPF and the historical time series hydrological data of rainfall. Rainfall runoff is computed based on the NAM distributed model of MIKE 11. By using the digital hydrological and cadastral data DEM and expected flood inundation area map were developed using hydrodynamic model of MIKE 11 and MIKE 11 GIS respectively (Billa et al., 2004).

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2.5.2. 2D Hydrodynamic modelling

MIKE 21 is the software commercially available for 2D hydrodynamic modeling. This is based on the depth averaged Saint-Venant equation, describing the elevation of the water level and two Cartesian velocity components along x and y – direction. The basic requirement to set-up the model in MIKE 21 is by setting up the Bathymetry and defining boundary conditions. The Bathymetry data file (*.dfs2) is the array of grids with elevation information in each grid. It is generated by defining the origin in the projected coordinates, the size of the study area in width and height. The bathymetry data in the form of xyz or Arc Info grid format can be imported into *.dfs2 format. The boundary file which is a called as profilet document (*.dfs1) has to be generated. This profilet document contains water level information for each cell, which is to be defined in boundary condition. The Manning’s number in the form of Resistances map (*.dfs2) format to be defined or a constant value for the entire study area can be applied in the Resistance portion of the 2D HD model set up. The results file can be obtained in the form of point, line or area series data file in hydrodynamic model simulation for required sub-area or sub-sets with defined time column. The output files contain information on Water level, P flux, Q flux, Surface elevation, U – velocity, V–velocity and Still water depths. The biggest disadvantage in two-dimensional hydrodynamic model is intensive computational time. The CPU time required by a 2D hydrodynamic simulation depends on the size of the model, number of time steps in the simulation, features specified in the simulation and general computational speed of the computer (DHI, 2005). McCowan described developments made in MIKE 21. The developments have been aimed to increase the robustness of flooding and drying routines, extending the capability of high Froude number flows. The author has suggested to eliminate any potential for mass balance errors through the use of very small water depths (<0.001m) in the initial flooding cycle, eliminate sticking problem with flows of very shallow grid points. With the improvements, it has been possible to carryout realistic simulations of flood wave propagation over an initally dry bed and high froude number flow conditions. The changes were made, to ensure the high accuracy and computationaal efficiency of the MIKE 21 model (McCowan et al., 2001).

2.5.3. Integrated 1D / 2D Hydrodynamic modelling

The MIKE FLOOD is a tool that integrates the one-dimensional and two-dimensional model into a coupled dynamic modeling system. The two separate models are to be linked well so as to interactively run the coupled model. There are four types of links in which the two models can be linked 1) Standard link, 2) Lateral link, 3) Structure link and Zero flow link (X and Y direction). The Standard links one or more MIKE 21 cells to MIKE 11 branch. The Lateral link allows a string of MIKE 21 cells to be laterally linked to a MIKE 11 reach. This link could be used for a section or for an entire branch of the river / channel; the flow through the lateral link is calculated by a structure equation (weir equation) or by QH table. This link is particularly useful for floodplain studies where the floodwater tops the river or channel levees. The structure link takes the flow terms from a structure in MIKE 11 and fits them into the momentum equation of MIKE 21 that does not affect the time step in MIKE 21. The links consist of three point MIKE 11 branch i.e. upstream cross section, structure and down stream cross section. The flow terms are applied to the face of MIKE 21 cells. The Zero flow link in x - direction passes across the right side of the cell and in across / over the top of the cell y – direction. It is developed to complement the lateral flow link and does cross the river to the other side (opposite side) without passing through MIKE 11; these are inserted to block MIKE 21 cells which are lying within the river width, to ensure the water body within the river is not included twice.

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McCowan has made efforts to improve the model and to describe flood events using MIKE FLOOD. The description of links between one-dimensional and two-dimensional models with flooding and drying properties does provide a feasible way to describe the complication in the flow during the flood event. The use of Froude number dependent upwinding scheme enables the model to propagate the wave over dry land (McCowan et al., 2001). Rungo (2003) has studied the possibilities for flood mitigation along the Gumti River in Bangladesh using MIKE Flood model. The study area is the Gumti River and floodplains along a 40 km reach. (Rungo et al., 2003).

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3. Study Area

3.1. Introduction to study area

Orissa state in India is located on the eastern part of India extending from 17○ ○ 49’ N to 22 34’ N and 81○ ○ 27’ E to 87 29’ E. The state is bounded by Bay of Bengal coast in the east with a coastline of about 450 km, Chhattisgarh state in the West, Andhra Pradesh state in the South, Jhardkhand state in the North and West Bengal state in North-east. The state has a geographical area of about 155,707-km2 covering 4.87% of India’s geographical area. The coastal plains are the gift of six major rivers, which bring silt from their catchments, have reclaimed this area from the depths of the Bay of Bengal. The rivers from North to South are the Subarnarekha, the Budha Balanga, the Baitarani, the Brahmani, the Mahanadi and the Rushikulya. The Chilka lake of Orissa is largest lagoon along the east coast of India, situated on the South-East part of Orissa state. It covers an geographical area of about 1165 km2 in the Monsoon to 906 km2 in the Winter/summer seasons of the year. The lagoon has 32 km long narrow outer channel connects the lagoon to the Bay of Bengal. The population of the Orissa state is 31,512,070 that are around 3.73% of India’s population. The study area is located in the Kendrapara district of Orissa state with geographical area of about 2,644 km2. The river Gobari flows through this district. The district has 9 blocks, 203 Gram panchayats and 1,389 inhabit villages and forest of 9.8% of the geographical area of the district. The population of Kendrapara district is 1,301,856 with a population density of 492 per square kilometre. The percentage of urban population to the total population is about 5.69% according to 2001 census. The location map showing the study area (Figure 3-1)

LOCATION MAP

Figure 3-1: Location map of the Study area

Source: (Maps of India, 2004)

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3.2. Mahanadi basin

The Mahanadi basin has a catchment area of about 141,134 km2 . The Mahanadi basin catchment area falls in the states with an area of 65,628 km2 in Orissa, 132 km2 in Jharkhand, 75,136 km2 in Chhtisgarh and 238 km2 in Maharashtra, further an area of 23,020 km2 of forest falls within the Mahanadi basin catchment. The length of 494 km falls in Orissa and 357 km in Chhatisgarh states. The Major tributaries of Mahanadi in Orissa are Ib, Jeera, Ong, Tel, Brutang, Manjore, Karandijore, Hariharjore, Surubalijore, etc as shown in the Figure 3-2. The Hirakud reservoir is in the Mahanadi basin that has a dead storage level of 179.832 m, Full reservoir level (FRL) of 192.024 m, Reservoir area at FRL is 743 km2, and the Maximum water level is 192.024 m. The Hirakud dam is Gravity earthen dam constructed for Irrigation purposes on Mahanadi River. It was constructed in the year 1957 near Sambalpur town, height of the dam above the lowest foundation is 60.96 m, length of 4.8 km, and Volume of the dam is 19,330 Million m3, Gross storage capacity of 81360 Million m3, Effective storage capacity of 5818 Million m3, and Design spillway capacity of 42,450 cumecs. The Dam is designed for Irrigation, Hydro-electricity, Flood control and Water supply.

STUDY AREA

Figure 3-2: Mahanadi river basin

Source: (Department of Water Resources, Govt. of Orissa, 2004)

The major gauging station on the Mahanadi River with warning level, Danger and highest gauge level recorded till 2004 as shown in the Table 3-1.

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Table 3-1: Gauge Stations on the Mahanadi river

23.58m on 31-Aug-8221.94m-Jobra (U/S)4

27.60m on 31-Aug-82 at 1700hrs26.41m25.41mNaraj (IB)3

74.98m on 30-Aug-82 at 1800hrs--Tikerpara2

107.46m on 28-Jul-92 at 2400hrs109.00m108.00mKhairmal1

Highest LevelDanger LevelWarning LevelGauge StationS. No

23.58m on 31-Aug-8221.94m-Jobra (U/S)4

27.60m on 31-Aug-82 at 1700hrs26.41m25.41mNaraj (IB)3

74.98m on 30-Aug-82 at 1800hrs--Tikerpara2

107.46m on 28-Jul-92 at 2400hrs109.00m108.00mKhairmal1

Highest LevelDanger LevelWarning LevelGauge StationS. No

Source: (Department of Water Resources, 2004)

3.3. Location of study area ○The study area is located in south-west part of Kendrapara district of Orissa state with 86 14’ 44.257”

to 86○ 28’ 30.03” East Longitude and 20○ 22’ 12.43” to 20○ 28’ 38.65” North Latitude. The study area with river network for the research is shown (Figure 3-3).

Nuna River

Barandia River

MAHANADI DELTA

Figure 3-3: CartoSat-1 data of the study area

Source: (NRSA Data Centre, 2006)

3.4. Salient features of the study area

The study area is situated on the South-west part of the Kendrapara district in Mahanadi delta region. Nuna River a tributary of Mahanadi flowing from west to east. Nuna River gets bifurcated near Danpur village and known as Barandia River (bifurcated river) further gets connected to the Nuna River at Kalaparha village. The length of the Barandia River is approximately 16.1 km. The left side floodplain (south) of Barandia and right side floodplain (north) of the Nuna River are protected by Dikes. There is a canal along the north side of the dike that is used for irrigation in summer and as escape channel during floods. The Island within the two rivers has an area of about 8.6 km2 with around 11 small villages. Dikes do not protect this island. These villages are situated in high-elevated area because of the dynamic water level in rivers during flood seasons. Villages in the study area are in small clusters populated in few hundreds and main occupation is agriculture where Rice and Pulses are grown. Almost every year during flood season the communication between the villages gets disconnected due to floodwater inundation. National highway NH 5A passes through the study area.

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3.5. Water Level and Location of Gauging station on Study area

The River system in the study area extent with its location of the gauging station is shown in the Table 3-2 and Figure 1-1.

Table 3-2: Details of the Gauging station on Nuna River

6.71Kendrapara Irr Div73L/7MarsaghaiNuna3

9.92Kendrapara Irr Div73L/7DanpurNuna2

10.74Kendrapara Irr Div73L/7PubansaNuna1

Danger Level in metersName of Division

ToposheetReference

Gauge Station

Name of RiverS.No

6.71Kendrapara Irr Div73L/7MarsaghaiNuna3

9.92Kendrapara Irr Div73L/7DanpurNuna2

10.74Kendrapara Irr Div73L/7PubansaNuna1

Danger Level in metersName of Division

ToposheetReference

Gauge Station

Name of RiverS.No

Source: (Irrigation Department, Govt. of Orrisa, 2003)

Figure 3-4: Location of the Gauging Stations on the Study area

Source: (Irrigation Department, Govt. of Orrisa, 2003; NRSA Data Centre, 2006)

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4. Methods and Material

4.1. Introduction to the research methodolgy

The methodology adopted in this research is based on the formulation of the research question, literature study and concept built during the course period. The inventory was made for the data requirements to carryout the hydrodynamic modelling. Another inventory was made based on the relevant data available for the study. Based on the literature study and available data it has been felt that proper digital representation of the terrain certainly limitation, to overcome this problem like spatial resolution of DSM, accuracy of DSM, etc methods were sought to generate accurate surface model using remote sensing imagery from CartoSat-1 stereo pair and additional ground control points that were to be collected during the field visit. For the above said reasons, the research is mainly divided into two components 1) Generation of a detailed and accurate surface model and 2) Hydrodynamic modelling to simulate floodwater moving over the surface topography as shown in the flow chart, Figure 4-1.

Figure 4-1: Showing flowchart of the methodology

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The first part of the research deals with the generation of detailed and accurate surface model. The CartoSat-1 stereo data was used to derive the DSM. Ground control points were used in generation of DSM for high-level of accuracy. So GPS field survey was conducted. Digital surface models were generated in different resolutions and validated. The profiles were generated to compare the surface profiles of CartoSat-1, ASTER and SRTM DSM. WGS 84 datum is approximately 60m above MSL; the datum transfer of surface model from WGS 84 to MSL was essential. Downgrading of DSM was carried out without losing flow-influencing features on the surface, since it is the input for Hydrodynamic model. The spatially varying Manning coefficient ‘n’ was derived with Land use / Land cover information from satellite data interpretation. The flood inundation maps for the event were interpreted using RadarSat-1 satellite data. The MIKE FLOOD software package is used for Hydrodynamic modelling. This part of the study deals with the generation of dataset for simulation MIKE 11 and MIKE 21 separately. These simulation set-up files are used to simulate MIKE FLOOD. In MIKE 11 set-up file, the required inputs were River network, times series data as boundary condition, river cross section, initial water level and Manning roughness as hydrodynamic parameters. In the case of MIKE 21 set-up file, the required inputs were Bathymetry (DSM for floodplain and simulated DEM for the river), Manning’s coefficient as surface roughness, initial surface elevation, limits of flooding and drying. In the case of MIKE FLOOD set-up file, the required inputs were lateral links (linkage between MIKE 11 and MIKE 21 simulation). Further model results were calibrated with satellite data and Field (interview) data. The following datasets were obtained and applied to carry out the research. Remote sensing data:

• CartoSat-1 (IRS-P5) stereo-data (2.5m spatial resolution) captured on 19th Feb. 2006. Purpose: Generation of Digital Surface Model. Source: National Remote Sensing Agency Data Centre (NDC)

• TERRA – ASTER (15m spatial resolution) data captured on 5th Dec 2004. Purpose: Generation of Digital Surface Model. Source: RSG, ITC

• IRS 1D PAN (5.8m spatial resolution) data captured on 19th Nov. 2003 Purpose: Generation of Land cover map. Source: National Remote Sensing Agency Data Centre (NDC)

• RadarSat-1 (50m spatial resolution - dB) data captured on 4th Sep 2003 and 11th Sep 2003. Purpose: Generation of Flood inundation map. Source: National Remote Sensing Agency Data Centre (NDC)

Ground data: • River Cross section data at Gauge station Purpose: Generation of Cross section set-up and

build Bathymetry of river. Source: Irrigation Department, Govt. of Orissa. • Gauge level time series data at Gauge station Purpose: Generation of Time series data and

further to use as boundary condition. Source: Irrigation Department, Govt. of Orissa.

4.2. DSM generation using stereo satellite imagery

The CartoSat-1 stereo data was used to derive the DSM. The ground control points are used in generation of DSM to obtain high level of accuracy. So GPS field survey was conducted in differential mode. Digital surface model were generated in different resolutions and validated. The profiles were generated to compare the surface profiles of CartoSat-1, ASTER and SRTM DSM.

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4.2.1. Field Visit related:

The field visit was carried out to conduct Global Positioning System (GPS) survey in differential mode to generate a library of Ground Control Point (GCP) and this dataset was used to improve the quality of the DSM generated from the CartoSat-1 data.

4.2.1.1. Pre-field survey planning

As a part of pre-field visit task, the proposed GCP locations were identified on the satellite data, The factors considered for the identification of points are clearly identifiable points on the satellite data (on Cartosat-1, IRS – 1D PAN and TERRA – ASTER) and topographical map. Care was taken to use these points as ground control points to generate Digital surface model (DSM) as well as geo-referencing these datasets. These points were placed in such a way that one set of points would be used for generation of DSM and other for validation of DSM. The maps were prepared as shown in Figure 4-2 and taken to the field.

Figure 4-2: Map showing proposed Ground Control Point used during the Field Survey

4.2.1.2. Processing - survey

The Global Positioning System survey conducted in differential mode using single frequency GPS instrument (Leica GPS 500, System). It consists of one pair of instruments, which has one base station and other Rover station. First the base station is set-up followed by Rover with unique point id. The unit was stationed for approximately 45 minutes to 1 hour at every proposed ground control point location. Care was taken such that, the distance between the base station and the Rover falls within 25 km. The systems were set-up to read the signal for every 10 seconds. There were 21 locations, where Rover was stationed. In the field, some proposed GCP locations were found unsuitable to station the instrument, so alternate points were identified, surveyed and plotted on the satellite data. The map showing the locations of ground control points is given in Figure 4-3.

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#

#

#

##

##

#

#

#

#

#

#

# #

#

#

#

#

# 6

9 8

5

4

3

2

20

13

11

23

2221

19

18

17

16

15

1412

10

MA P S H OW IN G GR OUN D CON TR OL POI NT LOC AT ION

GCP: 13

GCP:6

GCP:17

GCP:8

GCP:10

GCP:14

#10

Figure 4-3: Locations of the Ground Control Point on the cartoSat-1 Satellite data and some respective photographs of the stationed Rover

4.2.1.3. Post-processing of field data

The following steps were involved in the post-processing of the GPS data using SKI Pro software package.

• Defining the time zone to GMT • Defining projection parameters • Importing raw data to the native format of the software • Defining the Base point as control point and Rover as navigation point • Defining the vertical offset of the Base station (each day) and Rover station (each point /

location) • Running the process for all the base point in Single point processing mode. • Running the process to remove the ambiguity for all the Rover points with the following in

Configuration GPS-Processing Parameters: • Cut-off angle - 10○ • Active Satellites – ALL • Minimum time for common data – 200 seconds • Maximum Baseline length – 28 km

The ambiguity resolved points are measured points in the point class, unresolved points are navigated point and Base points are Control points.

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• Running the process to remove the ambiguity for each point that is not measured. By changing the parameters of “Configuration GPS Processing”. (Parameter modification only in Cut-off angle and selection of active satellite, i.e. for unmeasured points, decision has to be taken to deactivate unwanted satellites by understanding, dilution of precision (DOP) value which should be less than 8 for single frequency static measurements, Elevation (angle of elevation of each satellite in coverage from the stationed point) and Azimuth of each satellite from the stationed point(Leica-Geosystems, 2003 (a); Leica-Geosystems, 2003 (b); Schwieger et al., 2005)

4.2.2. Digital Surface Model (DSM) generation:

The digital surface model is generated using stereo pair of CartoSat-1 and TERRA – ASTER data. DSM was generated in different resolutions and validated for their vertical accuracy. DSM was also generated with CartoSat-1 stereo pair in Stereo method using Stereo workstation system.

4.2.2.1.

4.2.2.2.

DSM generation with TERRA - ASTER stereo data (with and without GCPs)

The 3N and 3B bands were used to generate the surface model with and without GCPs. Topographic tool of ENVI was used. The method adopted in generating surface model in topographic tool of ENVI software, is as follows.

• Open the Band 3N image in the left image frame and Band 3B in the right image frame • Define minimum and maximum elevation (default considered) • No GCPs (Relative DEM values only) • Generation of Tie points (automatic): No. of Tie points - 75, Search window size – 81 (9x9),

Moving window size - 81 (9x9), Region elevation – default • Define extent of the stereo image (left/right) – default • Define output file path/name to generate Epi-polar image (left / right) • Output projection parameter, map extent, false easting / northing and Cell size • No of Cells in the output • DEM extraction parameters: Minimum correlation – 0.7, back ground value - -999, edge trim

– 0, moving window size – 5 x 5, Terrain details – level 4, output data type – float and output file path/name. Note: Tools identify the Relational Polynomial Coefficients (RPC) file and sensor parameters. To generate an absolute DEM, GCPs are to be given with X, Y coordinates and elevation. Terrain Detail is a major controlling factor determining the processing time needed to extract a DEM and accuracy of the output DEM. The higher the level, the longer the processing time and more terrain details are represented.

DSM generation with Cartosat-1 stereo data (Classical Point Measurement tool– with GCPs)

The Digital surface model is generated using CartoSat-1 stereo data. The BandA (Aft image) and BandF (Fore image) were used to generate the surface model with GCPs. The orientation of the PAN cameras on Cartosat-1 is shown in Figure 2-1. Leica Photogrammetry Suite (LPS) in classical Point Measurement tool was used. To generate Automated DSM using CartoSat-1 RPC file, it is prerequisite to install CartoSat-1 RPC patch developed by Lieca geosystems. The following steps are involved:

• Create the New block file (*.blk) by defining model set-up (i.e.) Geometric model category – Rational Functions, Geometric model – Cartosat-1 RPC, define the reference coordinate system (Horizontal & Vertical) in Block property set-up. Reference coordinate system:

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Horizontal: Projection type – UTM, Spheroid/Datum – WGS 84, UTM Zone – 45 and North & Vertical: Spheroid/Datum – WGS 84 and units – meters.

• In “add frame”, define path of stereo pair in TIFF format supplied by NRSA data centre • In “frame editor”, define the path of RPC file • Compute the pyramid layers • Select the classical point measurement tool • Set the Aft image to the left and fore image to the right • Add the ground control points on both the images and define the UTM coordinate of X and Y

in X & Y reference and WGS 84 elevation information in Z reference, define the type as Full and usage as Control, repeat the process for all the GCPs, use 10 points as control points and remaining as check points. (Checkpoints are not used in the triangulation).

• Run the triangulation process • Automatic generation of Tie points – 150 points

Note: Care was taken to eliminate the Tie which fall on the shadow part in the image • Run triangulation – Total RMSE should not be more than 1 pixel when second order

polynomial was used • Generate DSM by defining the file path/name and cell size in the raster output.

Note: To generate a relative DSM without GCPs, the step “Add GCPs” should be skipped.

4.2.2.3.

4.2.2.4.

DSM generation with Cartosat-1 stereo data (Stereo point measurement tool)

The DSM generation with CartoSat-1 stereo data using Stereo point measurement tool in Stereo workstation was carried out. The process involved is the same as described in Section 4.2.2.2, except that GCPs are added in stereo mode and the break lines were digitised for flow influencing features like dikes, river course, terraces (where there is sudden change in elevation) in stereo mode. These break lines were used in generation of DSM.

Validation of Digital Surface Model

The Validation of Digital Surface Model was the crucial part of study because of the unavailability of good quality reference DSM. To validate the relative accuracy of DSM, surface profiles are to be matched with reference DSM. Surface model was also validated for vertical accuracy. The following procedure was adopted to validate vertical accuracy. The ground control points that were used as check points in generation of DSM and for validation. The steps involved are

• Point layer was generated using X and Y coordinates and UTM projection parameters were defined.

• “Extract value to point tool” is used to extract the elevation information from the generated DSM.

• Validation was carried out for the Extracted information with reference to the DGPS elevation data.

The procedure adopted to validate relative accuracy of DSM included matching the cross sectional profile of the study area. The following steps were carried out.

• Layer with profile lines in the study area are generated; these profile lines were divided into line length of 50m as shown in the Figure 4-4.

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Length of 50m

Profile Line: 1

Length of 50m

Profile Line: 1

Figure 4-4: Profile line divided into 50m Length

• This layer with profile lines are converted into point layer as shown in the Figure 4-5

Distances between points

50 m Profile: 1Distances between points

50 m Profile: 1

Figure 4-5: Point layer along the Profile line -1

• Value of elevation from the Surface models are extracted at these points and its attribute tables further exported to excel sheet and these profile lines are plotted in the form of charts, which are compared with the field survey profiles.

4.2.2.5. Removal of Artefacts:

The Artefacts are the topographic depressions or elevations in the digital surface model. The Artefacts in digital surface model are frequently a combination of artefacts and actual features. It is necessary to remove them, if the surface model is an input for simulation in hydrological modeling. Identification of the artefacts itself is difficult. In the present study, identification of these artefacts was done by image slicing operation followed by correlation with high-resolution satellite data, surface correlation with topographical map and visual identification in the stereo window (stereo mode). Editing was carried using LPS - terrain editor. The step-by-step procedure as follows:

• Open the stereo-pair in stereo window and set-up the device. • Set the terrain correlation properties • Load the images (the imagery are displayed, a separate left / right / stereo image) • Load the terrain dataset and set the terrain display setting to point • Stereo window is ready for the editing of the floating points.

4.2.3. Datum transfer (Common datum)

The spatial database generated should have a common datum to use in hydrodynamic modelling. The hydrodynamic boundary condition and gauge station cross section database obtained for the study area are in mean sea level (MSL) datum while the derived surface models are in WGS 84. From the study,

28


it was found that in the study area, the earth’s surface in the WGS 84 datum is ≈60m above the actual surface, when compared to mean sea level shown in the Figure 4-6. Hence it is necessary to bring all the databases to common datum. If there is a known point (pertaining to MSL) in the study area, by taking a GPS reading and obtaining vertical height at that point can be used as an offset to transfer the datum to MSL. But, there was no survey of India - benchmarks in the study area. Hence this option of datum transfer in above said mode is not possible. The following methods were attempted for datum transfer.

Figure 4-6: Datum transfer for the study area

4.2.3.1. Datum transfer using EGM 96 Geoid model

To convert GPS height to Mean Sea Level height, a program is used that is designed for the calculation of a geoid undulation at a point whose latitude and longitude were specified. The program was designed to use the potential coefficient model EGM96 and a set of spherical harmonic coefficients of a correction term. The correction term is composed of several different components, the primary one being the conversion of height anomaly to a geoid undulation. It is designed to be used with the constants of EGM96 and those of the WGS84 (G873) System (Rapp, 1997). The derived Mean Sea level heights are used to derive digital surface model. In the present study, transfer of datum using EGM96 geoid model has been carried out by three methods.

• Using EGM 96 height (MSL heights) in generation of DSM itself • Offset method, by averaging the EGM 96 heights and GPS heights of the study area and used

as offset (Figure 4-7). • Best-fit curve method, Plot the elevation information of EGM 96 heights in the X-axis and

GPS heights in the Y-axis to derive the best-fit curve, derive an equation to transform the DSM using Raster calculator of GIS software.

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Figure 4-7: Offset method to transfer the datum

4.2.3.2.

4.2.3.3.

Datum transfer using Topo-spot heights (average offset / best fit curve)

In this method, spot heights from topographical maps were extracted into a spatial database with X, Y, Z values. By using same points, Z values from the derived surface model (WGS 84 datum) were extracted. The standard deviations (SD) were derived for elevations of each point and difference of SD is calculated for each point. The elevation point that has less than 0.5 of difference of SD was considered. The datum transfer was carried out using Offset method and best-fit curve method as described in section 4.2.3.1.

Datum transfer using feature identification

An attempt was made to derive an offset to transfer the datum by feature identification method. Features like dikes at the known cross section of the river (at river gauge station) were used. Since the gauge station is an identifiable feature on the high-resolution satellite data, it is easy to locate the location of gauge station in the DSM. Hence the elevation information of surface in the WGS 84 and for the dike of the known river cross section was obtained and further used to derive the offset (Figure 4-8).

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RIVER CROSS SECTION @ DANPUR GAUGE STATION(at reduced distance of 42.27 km from Jagatpur gauge station)

0

4

8

12

16

-100 0 100 200 300 400 500 600 700Distance along the cross section of river in merters

Hei

ght i

n m

eter

s fr

om

Dat

um

Dikes

Identification of Gauge station:

* During the field survey* Latitude & Longitude obtained from Govt. Department

Figure 4-8: Feature identification method to bring the databases to a common datum

4.2.4. Generation of River Cross sections

For generation of river cross sections at location other than field survey points, the following procedure was adopted.

a) Location of cross section (c/s) should be approximately 1.5 km from each other and should be at the overtopping of floodwater as identified in RadarSat-1 satellite data of September 4th and

11th, 2003. b) A segment (line) spatial database was generated to represent the extent of the cross section

spatially from right side to left side of the dike (bund). The cross section segments were divided into parts of 20m starting from right side to left side of the dike as described in section 4.2.2.4 and Figure 4-4.

c) The segment layer was converted into point layer (tool used “feature to point” of Arc GIS software) as described in section 4.2.2.4 and Figure 4-5

d) Feature ID are preserved in a field and cross section ID are given to each point e) Digital surface model is used to extract the elevation information for each point (tool used

“Extract values to points” of Arc GIS software) f) The longitude and latitude in UTM are added to the attribute table in form of fields g) This database (attribute table) is exported to Excel worksheet h) Using X-coordinate and Y-coordinate values, the chainage for each point in each cross section

was derived for further use in MIKE 11 model.

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i) The points that are falling over the water surface in the river are edited for elevation correction using the CartoSat-1 PAN data as shown in the Figure 4-9. Considering the low points of field-surveyed cross-section, the low point of derived cross section are linearly interpolated depending up chainage of the cross section location.

Elevation Information of Points to be edited

Figure 4-9: Elevation information for editing

4.2.5. Digital Elevation Model (DEM) generation for river course

The generation of digital elevation model for the river course to be used in the MIKE 21 model is as follows.

a) Point layer-A, point located along the dikes, levees of the rivers, over the sandbars, etc as shown in the Figure 4-10.

b) Point layer-B, cross section of the river that has elevation information extracted from DSM (inclusive of edited elevation details over water surface) and field surveyed cross sections.

c) Point layer-C, centre line of the river, this layer is generated to give low point elevation information (interpolated database along the chainage as described in section 4.2.4-i) in DEM generated. Pictorially the same is shown in Figure 4-10.

d) Line layer, Centre line of the river, this layer is used as stream information to generate DEM Note: Care taken to digitise the line from upstream to downstream

e) Polygon layer, river boundary which is used as the DEM generation boundary The DEM is generated using Topo to Raster tool of Arc GIS software; the generated DEM is hydrologically corrected surface from point, line and polygon information. The point layers (point elevation defined in the tool) are the elevation layers, line layer (stream) that guides the tool to hydrologically correct the surface from upstream to downstream and polygon (boundary) is layer used to generate surface within the polygon.

32


Figure 4-10: Spatial databases generated to derive river DEM for MIKE 21 model

4.2.6. Downgrading the resolution of Digital surface model

The downgrading of DSM resolution was attempted by using GIS technique; the grid mesh in vector is generated (Arc Info Coverage polygon is represented by combination of point and line). The points were moved to interested location within the grid mesh. The values of the point are extracted from the fine resolution DSM. The values are transformed to the points of new grid mesh (unmoved points). These point locations (X, Y coordinates) and elevations are stored in text file, further converted into xyz file for use into MIKE model.

4.2.7. Generation of Land use / Land cover map

The Land use / Land cover map was generated to derive manning coefficient ‘M’ which is used as resistance map in MIKE 21Model. The IRS 1D PAN (5.8m spatial resolution) data captured on 19th Nov. 2003 is been used to visually interpret land use / land cover map. This polygon map is further converted to raster to generate for Manning roughness map based on land use / land cover.

4.2.8. Generation of Flood Inundation map

The Flood inundation maps were generated to validate the output from MIKE Flood model. The RadarSat-1 data captured on 4th and 11th September 2003, which were acquired during the flood period, are visually interpreted to generate flood inundation maps on 4th and 11th September, 2003.

4.3. Hydrodynamic Modeling

The MIKE FLOOD software package is used for Hydrodynamic modelling. The generation of dataset for MIKE 11 and MIKE 21 simulation were done separately, only to conform that there is no error in simulation set-up files. These set-up files are used to simulate MIKE FLOOD. In MIKE 11 set-up file, the required inputs are river network, times series data as boundary condition, river cross section, Initial water level and Manning roughness as hydrodynamic parameters. In MIKE 21 set-up file, the

33


required inputs are Bathymetry (DSM for floodplain and simulated DEM for the river), Manning’s coefficient as surface roughness, initial surface elevation, limits of flooding and drying. In the case of MIKE FLOOD set-up file, the required inputs are lateral links (linkage between MIKE 11 and MIKE 21 simulation). Further validations of simulated inundation results were done with satellite data and Field (interview) data.

4.3.1. MIKE 11

The MIKE 11 is a one-dimensional model, which simulates hydrodynamics within the river system. The input parameters required are network of the river system for simulation, cross sections at intervals and boundary conditions with time series data at gauge station, hydrodynamic parameters.

4.3.1.1.

4.3.1.2.

4.3.1.3.

4.3.1.4.

Generation of time series data at gauge station

The Gauge level hydrograph at the gauge station that was obtained from the Irrigation department, Govt. of Orissa had been used. The data obtained was in hard copy, database was built in excel sheet for three gauge stations for 2003 flood event starting from 30th August, 2003 to 12th September, 2003 (Figure 5-20 and Figure 5-21).

Generation of network database

In the River system network of MIKE 11 model, the river network is the spatial database information with geographic information in UTM projection. The river network information is the centreline of the river system that can be digitised in MIKE software or any GIS software, which further can be imported into MIKE environment. The digitised network should be from upstream to downstream. The connectivity between the branches should be well defined.

Generation of cross section database

The cross-section database is generated using Digital Surface Model (DSM). Further these cross-sections are edited for the elevations, which are falling on the water surface. The model requires spatial location of the extreme end of the cross section (left and right), the elevations of the cross section is defined along the chainage from left to right end or vice-versa of the cross section, cross section ID, branch name, branch chainage, resistance, defining markers in the cross section such as left / right levee bank, left / right low flow bank, left / right coordinate marker, lowest point, section type such as open, closed irregular, closed rectangular, closed circular. In the present study, the elevation and its chainage are derived using GIS and exported to dbf format. The database is copied and pasted in the appropriate location allotted in graphic user interface (GUI). The gauge station cross-sections that were obtained from the field were also incorporated.

Generation of boundary condition database

The boundary condition database are generated and its basic requirement is the time series database of flood discharge / gauge level which is defined as the boundary conditions with the description of boundary, type, name of branch and chainage in the river system, ID. It is required to mention the data type and file type of the time series data. In the present study, there are two boundary conditions defined in the river system one at the start of simulation and other at end of the simulation.

34


4.3.1.5.

4.3.1.6. Simulation

4.3.2.1.

4.3.2.2.

4.3.2.3.

Generation of hydrodynamic parameters

The hydrodynamic parameters like initial local and global water level and discharge in the system (local is defined with chainage) and bed resistance using Manning’s ‘n’ are set-up for the model.

set-up

The simulation set-up consists of computation of controlled parameters. The selections of the model, in the present study are about Hydrodynamic, Input set-up, simulation set-up, Results set-up and validation status such as run parameters and HD parameters.

4.3.2. MIKE 21

The MIKE 21 has two models, Flow model and Flow model FM, The flow model is a modelling system for 2D free-surface flows and Flow model FM is based on a flexible mesh approach. Since the present study is based on the integrated 1D / 2D model, Flow model was considered. The basic requirement of the model is bathymetry and resistance database.

Generation of bathymetry database

In this model, the river and the floodplain are considered as bathymetry. No boundary condition was given. The automatic boundary conditions were generated for floodplain during the simulation of integrated model. The digital surface model obtained from remote sensing stereo pair and simulated DEM for the river bathymetry is imported in the form of grid file *.dfs2. Generation of bathymetry for river in MIKE 21: The generation of river bathymetry is simulated in grid file as stated in section 4.2.5. The simulation of bathymetry for river is carried out only within the two dikes along the river and excluding the island portion between two braches of the river. Generation of bathymetry for floodplain in MIKE 21: The generation of bathymetry for the floodplain using the DSM obtained from remote sensing sources as discussed in section 4.2.2.3. It is clipped for only floodplain area and edited for the pixels that have trees and settlements to represent true elevation. Generation of bathymetry by grid point in Arc GIS environment: The Grid points were generated for the pixel size adopted in the model. The “extract value to point” tool is used to extract elevation information to the points from simulated river and floodplain DSM. These points were used to generate a 2D grid in MIKE model.

Generation of resistance database

The resistance database was generated based on the guide for selecting Manning's roughness coefficient for natural channels and flood plains (Arcement et al., 1984). The resistance map is based on the Manning’s ‘n’ value of roughness. The Arc GIS grid map is generated for Manning’s ‘n’ value and converted to Manning’s ‘M’ value, exported to grid ASCII file further imported to MIKE in 2D grid file *.dfs2.

Setting up of simulation

The simulation set-up for two-dimensional MIKE 21 has basic components like Module selection, Bathymetry, Simulation period and Flood and Dry.. The initial surface elevation is to be set; Eddy Viscosity of constant value of 0.5 is set. Result file was set-up. It has additional components like Boundary condition, Source & Sink and Mass Budget which are not used for 1D / 2D integrated modelling

35


4.3.3. MIKE FLOOD (Integrated MIKE 11 & MIKE 21)

MIKE FLOOD is a tool that integrates the one-dimensional model MIKE 11 and the two-dimensional model MIKE 21 into a single, dynamically coupled modelling system. It enables the best features of both MIKE 11 and MIKE 21 to be utilised.

4.3.3.1.

4.3.3.2.

Setting up input simulation database

The MIKE FLOOD has three basic components definition, options for Standard / Structure Links and options for lateral Links. The option of standard / Structure link are not used since they are not considered in the study In definition: Set the path for the simulation file of MIKE 11 and MIKE 21, define the lateral links for all the branches for simulation, define the MIKE 21 coordinates for lateral links with chainage and number of MIKE 21 cells. In Lateral link option: Right and Left link as to define based on the direction of flow.

Generation of lateral links

The Lateral Links are to be generated along the river. It is sequential line of 2D cell in the river used to transfer flow of water from 1D model to 2D model. It should be defined for Left as well as for the Right banks / Levees (in case of overtopping of river levees). A 2D grid is generated in MIKE 21 and the cells are given a value depending upon the levees. The database is exported to ASCII file and opened in excel and reformat according to requirement for the model (i.e. sequential line of 2D cell). The cell coordinates in the form of rows and columns are incorporated in the model definition.

36


5. Results and Discussion

5.1.

5.2.

5.2.1.1. GPS

5.2.1.2.

Introduction

The results are grouped into two components, namely 1) DEM-reconstruction and 2) Hydrodynamic modelling. In the DEM-reconstruction, component field survey activities, results of post-processing of GPS data with quality of GPS survey, results on surface model generation; validation and optimum resolution of surface model that can be used in Hydrodynamic model, datum transfer and GIS methods used to transfer the datum, bathymetry of the river, downgrading the spatial resolution of surface model without losing flow influencing features, generation of Manning’s coefficient database and visual interpretation of flood inundation maps for 4th September, 2003 and 11th September, 2003 have been discussed. Under hydrodynamic modelling component, the database generation for MIKE 11, MIKE 21 and MIKE FLOOD models which includes time series data, river system network, river cross section, bathymetry, resistance layer, lateral links and simulation files of 1D, 2D and integrated models and validation with inundation maps of 4th September and 11th September 2003 were discussed.

Geoinformation (DEM – reconstruction)

5.2.1. Field Visit

survey

The maps generated for the field visit were used to identify the Ground control points on the field. The global positioning system (GPS) survey in differential mode was carried out on the identified points and also noted on the image. In the field, in some proposed points, which were unreachable due to bad road conditions, alternate points were selected and the survey was carried out.

Post-processing of field data

The surveyed data were post-processed using SKI-Pro software package. The configuration of GPS processing parameters were used to resolve the ambiguity (chapter 4, section: 4.2.1.3) (Schwieger et al., 2005). The results are presented in Table 5-1.

37


Table 5-1: Ambiguity resolved Ground Control Points

28 km3 min15None4,8,11,17,24,27,280h 15m 00sbase20pnt2321

28 km3 min25288,11,13,17,19,27,280h 25m 00sbase20pnt2220

28 km3 min15233,8,11,13,19,23,27,280h 31m 30sbase20pnt2119

28 km3 min15None1,3,7,13,16,19,20,23,250h 30m 00sbase20pnt2018

28 km3 min15None1,11,14,16,20,22,25,301h 04m 30sbase20pnt1917


28 km3 min15273,8,11,13,17,19,23,27,280h 54m 30sbase19pnt1715

28 km3 min15231,3,7,13,16,19,20,23,25,271h 03m 00sbase19pnt1614







--------1,3,7,14,16,20,23,250h 14m 30sbase2_17pnt97

28 km3 min15None1,7,11,14,16,20,22,250h 44m 00sbase2_17pnt86




28 km3 min2513,25 not manual1,3,7,13,16,19,20,23,250h 20m 30sbase15pnt32


Maximum baseline length

Minimum time for common

data

Cut-off angle in degrees

Excluded Satellite for ProcessingSatellite Viewed (satellite ID)Time of

OperationBase PointPoint-IdS.No


28 km3 min25288,11,13,17,19,27,280h 25m 00sbase20pnt2220





28 km3 min15273,8,11,13,17,19,23,27,280h 54m 30sbase19pnt1715

28 km3 min15231,3,7,13,16,19,20,23,25,271h 03m 00sbase19pnt1614







--------1,3,7,14,16,20,23,250h 14m 30sbase2_17pnt97





28 km3 min2513,25 not manual1,3,7,13,16,19,20,23,250h 20m 30sbase15pnt32


Maximum baseline length

Minimum time for common

data

Cut-off angle in degrees

Excluded Satellite for ProcessingSatellite Viewed (satellite ID)Time of

OperationBase PointPoint-IdS.No

Ambiguity of point id pnt9 could not be resolved; hence, single point processing was carried out. For the pnt3, the cut-off angle was changed to resolve the ambiguity, for pnt4, pnt15, pnt16, pnt17, pnt21 and pnt22, the ambiguity was resolved individually by deactivating satellites and changing the cut-off angle (to understand the active satellites for rover points taken on the same day, understanding angle of elevation, azimuth and DOP). The results obtained after post-processing of the GPS survey data is shown in Table 5-2. The accuracy obtained for each point is shown in the Table 5-3. The surface of study area being flat and nearer to the seacoast, the average elevation of the earth surface on the study area from MSL is 12.62 m and from WGS 84 is 50.04m. Due to the above said reason the elevation values in the WGS 84 are in negative. The difference between two datum’s is 62.66m.

38


Table 5-2: Results of Post-processing

-51.83686° 25' 25.39076" E20° 29' 53.45898" N8/20/2006 17:56Measuredpnt2321

-53.402886° 24' 18.83666" E20° 26' 24.92092" N8/20/2006 16:38Measuredpnt2220

-52.400486° 19' 17.53927" E20° 26' 20.67123" N8/20/2006 14:52Measuredpnt2119

-52.444986° 17' 41.05846" E20° 31' 45.31695" N8/20/2006 12:32Measuredpnt2018

-51.484686° 16' 37.85672" E20° 35' 26.73840" N8/20/2006 9:40Measuredpnt1917

-54.110386° 26' 29.98084" E20° 21' 40.57313" N8/19/2006 17:11Measuredpnt1816

-51.145586° 24' 40.05162" E20° 22' 45.43703" N8/19/2006 15:32Measuredpnt1715

-49.644686° 17' 38.40652" E20° 21' 52.25464" N8/19/2006 12:47Measuredpnt1614

-45.422586° 14' 09.20175" E20° 23' 42.88959" N8/19/2006 10:52Measuredpnt1513

-44.763786° 15' 12.31960" E20° 27' 58.33228" N8/18/2006 17:26Measuredpnt1412

-46.936286° 18' 40.82925" E20° 27' 41.38699" N8/18/2006 15:54Measuredpnt1311

-49.006686° 21' 59.18318" E20° 27' 25.42075" N8/18/2006 13:40Measuredpnt1210

-52.153586° 27' 01.09512" E20° 25' 51.96985" N8/18/2006 11:25Measuredpnt119

-46.745786° 25' 30.85092" E20° 26' 56.06070" N8/18/2006 9:48Measuredpnt108

-49.376586° 19' 26.26636" E20° 36' 38.08378" N8/17/2006 11:41Navigatedpnt97

-51.290286° 23' 56.40651" E20° 36' 35.89862" N8/17/2006 10:21Measuredpnt86

-53.460186° 20' 30.12241" E20° 32' 27.66244" N8/17/2006 8:11Measuredpnt65

-55.584286° 29' 23.63848" E20° 27' 45.06765" N8/16/2006 18:05Measuredpnt54

-52.267386° 29' 40.14067" E20° 36' 35.37953" N8/16/2006 13:59Measuredpnt43

-54.981386° 27' 38.26956" E20° 35' 39.52379" N8/16/2006 12:53Measuredpnt32

-55.497686° 28' 33.51460" E20° 33' 42.36241" N8/16/2006 10:15Measuredpnt21

ElevationLongitudeLatitudeDate TimePoint TypePoint IDS. No.

-51.83686° 25' 25.39076" E20° 29' 53.45898" N8/20/2006 17:56Measuredpnt2321

-53.402886° 24' 18.83666" E20° 26' 24.92092" N8/20/2006 16:38Measuredpnt2220

-52.400486° 19' 17.53927" E20° 26' 20.67123" N8/20/2006 14:52Measuredpnt2119

-52.444986° 17' 41.05846" E20° 31' 45.31695" N8/20/2006 12:32Measuredpnt2018

-51.484686° 16' 37.85672" E20° 35' 26.73840" N8/20/2006 9:40Measuredpnt1917

-54.110386° 26' 29.98084" E20° 21' 40.57313" N8/19/2006 17:11Measuredpnt1816

-51.145586° 24' 40.05162" E20° 22' 45.43703" N8/19/2006 15:32Measuredpnt1715

-49.644686° 17' 38.40652" E20° 21' 52.25464" N8/19/2006 12:47Measuredpnt1614

-45.422586° 14' 09.20175" E20° 23' 42.88959" N8/19/2006 10:52Measuredpnt1513

-44.763786° 15' 12.31960" E20° 27' 58.33228" N8/18/2006 17:26Measuredpnt1412

-46.936286° 18' 40.82925" E20° 27' 41.38699" N8/18/2006 15:54Measuredpnt1311

-49.006686° 21' 59.18318" E20° 27' 25.42075" N8/18/2006 13:40Measuredpnt1210

-52.153586° 27' 01.09512" E20° 25' 51.96985" N8/18/2006 11:25Measuredpnt119

-46.745786° 25' 30.85092" E20° 26' 56.06070" N8/18/2006 9:48Measuredpnt108

-49.376586° 19' 26.26636" E20° 36' 38.08378" N8/17/2006 11:41Navigatedpnt97

-51.290286° 23' 56.40651" E20° 36' 35.89862" N8/17/2006 10:21Measuredpnt86

-53.460186° 20' 30.12241" E20° 32' 27.66244" N8/17/2006 8:11Measuredpnt65

-55.584286° 29' 23.63848" E20° 27' 45.06765" N8/16/2006 18:05Measuredpnt54

-52.267386° 29' 40.14067" E20° 36' 35.37953" N8/16/2006 13:59Measuredpnt43

-54.981386° 27' 38.26956" E20° 35' 39.52379" N8/16/2006 12:53Measuredpnt32

-55.497686° 28' 33.51460" E20° 33' 42.36241" N8/16/2006 10:15Measuredpnt21

ElevationLongitudeLatitudeDate TimePoint TypePoint IDS. No.

Note: The elevation information in the above table are in WGS 84 datum, hence they are in negative. The WGS 84 datum in the study area is ≈ 60m above the mean sea level (Figure 4-6) and hence in GPS survey, WGS 84 is used. Hence the elevation information from the GPS survey has negative values. These surveyed points (Latitude, Longitude and elevation) are used as control points in the DSM generated to obtain the absolute location information for the DSM. The Hydrodynamic model requires the DSM as one of the inputs that is in WGS 84 datum and the ground data are in mean sea level. So all the database should have a common datum and it is necessary for DSM to transfer the datum to MSL as discussed in the section 4.2.3.

39


Table 5-3: Accuracy of Post-processed points

0.00170.0009Pnt23

0.00460.0018Pnt22

0.0040.0028Pnt21

0.00350.0014Pnt20

0.00230.0012Pnt19

0.00270.0015Pnt18

0.00220.0009Pnt17

0.00670.0024Pnt16

0.00530.0033Pnt15

0.00150.0008Pnt14

0.00280.0012Pnt13

0.00170.0011Pnt12

0.00210.0014Pnt11

0.00180.0009Pnt10

0.0030.0016Pnt8

0.00170.0008Pnt6

0.00140.0007Pnt5

0.00250.0019Pnt4

0.00770.0021Pnt3

0.00240.0013Pnt2

Standard Deviation Height

in m

Position Quality in mPoint-ID

0.00170.0009Pnt23

0.00460.0018Pnt22

0.0040.0028Pnt21

0.00350.0014Pnt20

0.00230.0012Pnt19

0.00270.0015Pnt18

0.00220.0009Pnt17

0.00670.0024Pnt16

0.00530.0033Pnt15

0.00150.0008Pnt14

0.00280.0012Pnt13

0.00170.0011Pnt12

0.00210.0014Pnt11

0.00180.0009Pnt10

0.0030.0016Pnt8

0.00170.0008Pnt6

0.00140.0007Pnt5

0.00250.0019Pnt4

0.00770.0021Pnt3

0.00240.0013Pnt2

Standard Deviation Height

in m

Position Quality in mPoint-ID

5.2.1.3. DSM generation with TERRA - ASTER stereo data (with and without GCPs):

The Digital surface generated without GCPs was used to understand the terrain as a pre-field study. The DSMs with GCPs on 15m, 30m & 45m resolution were generated using topographic tool of ENVI software package. The minimum and maximum elevation extracted from RPC file were -38.07m and 722.7m respectively. After generation of tie points, system generated points using correlation between stereo imagery. The tie points generated were used to define the epi-polar geometry and create epi-polar images, which are used to extract the DSM. If Y-parallax error (The Maximum Y-Parallax value is the quickest way to check how good the tie point placement is in your images) of any of the tie point exceeds 10 pixels, error message is prompted, which can be edited by means of shifting the points or deleting the tie points. As a part of resample technique in the process to generate epi-polar image, bilinear re-sampling technique was used. The triangulation technique was used to interpolate the parallax and geo-coding DEM. These surface models are generated with and without using Ground

40


Control Points (GCPs). The generated Surface model using TERRA-ASTER stereo pair with GCPs on 15m cell size is shown in the Figure 5-1 and validation of this model is discussed in section 5.2.1.6.

Figure 5-1: Digital Surface Model generated using 15m resolution of TERRA - ASTER

5.2.1.4. DSM generation with Cartosat-1 stereo data (Classical Point Measurement tool – with GCPs)

The Surface models were generated using Cartosat-1 stereo pair using GCPs. The Surface models were generated for 10m, 12.5m, 15m, 17.5m and 20m resolutions with Leica Photogrammetry Suite (ERDAS, 2005) using Classical point measurement tool. After selecting the GCPs on the image, triangulation is carried out on the GCPs only and report is generated to check the errors in selected GCPs on the image. Again the same process is repeated after generating Tie points. The generated surface models using CartoSat-1 stereo-pair with RPCs (rational polynomial coefficients, containing necessary information about the sensor model) and GCPs on 10m, 12.5m, 15m, 17.5m & 20m resolution are shown in the Figure 5-2 and its validation part was discussed in section 5.2.1.6.

12.5m cell s12.5m cell s

15m cell size DEM15m cell size DEM

20m cell size DEM20m cell size DEM

10 m cell size DEM10 m cell size DEM

0.4412Total

0.616Image "Y"

0.62Image "X"

1.66Ground "Z"

1.43Ground "Y"

1.44Ground "X"

Root Mean Square Error (in Pixels)

2Order of Polynomial

0.4412Total

0.616Image "Y"

0.62Image "X"

1.66Ground "Z"

1.43Ground "Y"

1.44Ground "X"

Root Mean Square Error (in Pixels)

2Order of Polynomial

CARTOSAT-1 DSM12.5m cell size DEM

17.5m cell size DEM

5.2.1.5.

Figure 5-2: Digital surface model of 10m, 12.5m, 15m, 17.5m & 20m resolution using Classical point measurement tool

DSM generation with Cartosat-1 stereo data (Stereo Point Measurement tool)

The Surface models were also generated using Cartosat-1 stereo pair at 10m resolution with Leica Photogrammetry Suite (LPS) using Stereo point measurement tool. The Stereo workstation was used to digitise break lines to bring out the flow influencing features like dikes, levees, etc and reduce artefacts. The LPS – terrain editor is used to edit DSM to remove artefacts. The generated surface

41


model is shown in the Figure 5-3 and validation part is discussed in section 5.2.1.6. In the Figure 5-3, Dikes along the river are very clear and the water cover area is also linearly interpolated using point information along on both banks of the river.

Figure 5-3: Map showing the Digital Surface Model generated using LPS – terrain editor

5.2.1.6. Validation of Digital Surface Model:

The Digital Surface Models for ASTER were validated with the GCPs collected from the field. The generated DSM’s using automated DSM extraction tool with GCPs was validated. Table 5-4, shows points pnt3 and pnt15 have high elevation difference. On investigation it was found that there was mis-correlation between the pixels in the stereo pair. The range in elevation difference of 15m, 30m & 45m DSMs are 25.61m, 26.2m & 28.2m respectively. (Kumar, 2006)

Table 5-4: Elevation validation of TERRA – ASTER DSM using GCPs

3.376.613.316.662.417.569.97pnt23

-6.6116.02-7.8117.21-1.0710.489.41pnt21

-4.5915.25-4.5915.25-7.3818.0510.66pnt17

-20.2236.61-19.2935.68-22.8439.2316.39pnt15

7.986.96.917.962.7712.1114.87pnt13

-2.2611.92-2.0811.74-0.019.679.66pnt11

0.727.6326.35-4.3612.718.35pnt6

-14.3621.19-15.3522.18-13.5420.376.83pnt3

ELEVATIONDIFFERENCE

(a-d)

ELEVATION ON 45 m CELL SIZE DSM (d)

ELEVATIONDIFFERENCE

(a-c)

ELEVATION ON 30 m CELL SIZE DSM (b)

ELEVATIONDIFFERENCE

(a-b)


ELEVATION OF GCP (a)GCP ID

3.376.613.316.662.417.569.97pnt23

-6.6116.02-7.8117.21-1.0710.489.41pnt21

-4.5915.25-4.5915.25-7.3818.0510.66pnt17

-20.2236.61-19.2935.68-22.8439.2316.39pnt15

7.986.96.917.962.7712.1114.87pnt13

-2.2611.92-2.0811.74-0.019.679.66pnt11

0.727.6326.35-4.3612.718.35pnt6

-14.3621.19-15.3522.18-13.5420.376.83pnt3

ELEVATIONDIFFERENCE

(a-d)

ELEVATION ON 45 m CELL SIZE DSM (d)

ELEVATIONDIFFERENCE

(a-c)


ELEVATIONDIFFERENCE

(a-b)


ELEVATION OF GCP (a)GCP ID

In case of CartoSat-1 DSM, artefacts were detected. These occurred due to mis-match between the pixels in the image and they are not falling on the GPS surveyed points. The range in the elevation difference of 10m, 12.5m, 15m, 17.5m & 20m DSM are 6.861m, 4.527m, 5.548m, 4.054m & 4.652m respectively. Since range in elevation difference and DSM resolution does not show any significant

42


pattern, it cannot be considered as factor for deriving optimum resolution. The automated generated DSM contained artefacts, so it was decided to generate semi-automated DSM i.e., generation of DSM using break lines (Stereo point measurement Tool) as discussed in section 4.2.2.3 and the validation of CartoSat-1 DSM was shown in Table 5-5.

Table 5-5: Elevation validation of CartoSat-1 DSM using GCPs

Note : Elevation of Ground Control Point (GCP) collected using Global Positioning System (GPS) in differential mode

0.5849.3900.5449.430-0.29610.270-1.05611.0300.4949.4809.974pnt238

-1.59011.000-2.76012.170-2.10011.510-2.56011.970-1.14610.5559.410pnt217

2.0958.5701.2659.4002.7257.9400.58510.0801.0229.64210.665pnt176

-2.40318.790-1.93318.320-2.82319.210-2.61319.000-4.65721.04416.388pnt155

1.52413.3501.29413.5801.94412.9301.91412.9602.20412.67014.874pnt134

1.7977.8601.0478.6101.9377.7200.9178.7401.1468.5119.657pnt113

-2.15010.500-1.5509.900-2.57010.920-2.48010.830-0.3278.6778.350pnt062

2.2494.5801.0395.7901.5795.2501.5895.2401.8774.9526.829pnt031

ELEVATION DIFFERENCES

(A-F)

ELEVATION ON 20m CELL SIZE DSM (F)


(A-E)

ELEVATION ON 17.5m CELL

SIZE DSM (E)


(A-D)

ELEVATION ON 15m CELL SIZE DSM (D)


(A-C)


SIZE DSM (C)


(A-B)

ELEVATION ON 10m CELL SIZE DSM (B)

ELEVATION OF GCP (A)

GCP IDS.NO

Note : Elevation of Ground Control Point (GCP) collected using Global Positioning System (GPS) in differential mode

0.5849.3900.5449.430-0.29610.270-1.05611.0300.4949.4809.974pnt238

-1.59011.000-2.76012.170-2.10011.510-2.56011.970-1.14610.5559.410pnt217

2.0958.5701.2659.4002.7257.9400.58510.0801.0229.64210.665pnt176

-2.40318.790-1.93318.320-2.82319.210-2.61319.000-4.65721.04416.388pnt155

1.52413.3501.29413.5801.94412.9301.91412.9602.20412.67014.874pnt134

1.7977.8601.0478.6101.9377.7200.9178.7401.1468.5119.657pnt113

-2.15010.500-1.5509.900-2.57010.920-2.48010.830-0.3278.6778.350pnt062

2.2494.5801.0395.7901.5795.2501.5895.2401.8774.9526.829pnt031


(A-F)

ELEVATION ON 20m CELL SIZE DSM (F)


(A-E)


SIZE DSM (E)


(A-D)

ELEVATION ON 15m CELL SIZE DSM (D)


(A-C)


SIZE DSM (C)


(A-B)

ELEVATION ON 10m CELL SIZE DSM (B)

ELEVATION OF GCP (A)

GCP IDS.NO

Table 5-6: Statistics of the validated points

14.21012.53013.96013.76016.09230.00029.33031.6709.560Range

0.4010.3480.3930.3490.4410.6580.6440.6280.301Coefficient of Variation

4.2103.7914.2173.9194.71210.0409.91110.2133.241Std Deviation

10.50510.90010.71911.23110.69115.26615.37916.27310.768Mean

18.79018.32019.21019.00021.04436.61035.68039.23016.390Maximum

4.5805.7905.2505.2404.9526.6106.3507.5606.830Minimum

20m cell size DSM

17.5m cell size DSM

15m cell size DSM

12.5m cell size DSM

10m cell size DSM

45m cell size DSM

30m cell size DSM

15m cell size DSM

CartoSat-1 DSMASTER DSMGPS

ElevationStatistics

14.21012.53013.96013.76016.09230.00029.33031.6709.560Range

0.4010.3480.3930.3490.4410.6580.6440.6280.301Coefficient of Variation

4.2103.7914.2173.9194.71210.0409.91110.2133.241Std Deviation

10.50510.90010.71911.23110.69115.26615.37916.27310.768Mean

18.79018.32019.21019.00021.04436.61035.68039.23016.390Maximum

4.5805.7905.2505.2404.9526.6106.3507.5606.830Minimum

20m cell size DSM

17.5m cell size DSM

15m cell size DSM

12.5m cell size DSM

10m cell size DSM

45m cell size DSM

30m cell size DSM

15m cell size DSM

CartoSat-1 DSMASTER DSMGPS

ElevationStatistics

Table 5-6 gives the statistics for the validated points of the GPS elevation and that was compared with statistics of points of derived DSM from two different satellite data. The mean of the GPS elevation is 10.76m, while CartoSat-1 DSM ranged from 10.5m to 11.23m on different resolutions and that of ASTER DSM ranged from 15.2m to 16.2m. In the case of standard deviation, among GPS elevations points, it was 3.24m that for CartoSat-1 ranged from 3.7m to 4.7m and for ASTER, it ranged from 9.9m to 10.2m. The percentage of coefficient of variation is 30% for GPS points, 62% to 66% for points on ASTER and 34% to 44% for the points on CartoSat-1 DSM on different resolutions. The surface models were validated using profile comparison method for CartoSat-1 DSM (10m resolution derived from Stereo point measurement tool), ASTER DSM (15m) and SRTM DSM (90m). The result show that the surface profile of CartoSat-1 DSM is almost the same as the profile derived from the SRTM DSM. The SRTM profile cannot be considered as the reference profile due to the reason that its spatial resolution is 90m and accurate GCPs were not used. The three derived surface profiles show that CartoSat-1 surface model is most suited model for hydrodynamic studies, rather than TERRA-ASTER surface model. Since, it does not have undulating surface profile, as the terrain profile of the study area is flat. By investigating the profile, it is clear that the surface profile of CartoSat-1 toward the coast is lower than that of SRTM.

43


3

2

1

7

4

5

6

Profile: 1

Profile: 2

Profile: 3

Profile: 4

Profile: 5

Profile: 6Profile: 7

Profile numbers

Profile Comparison ofDigital Surface Models

3

2

1

7

4

5

6

Profile: 1

Profile: 2

Profile: 3

Profile: 4

Profile: 5

Profile: 6Profile: 7

Profile numbers

Profile Comparison ofDigital Surface Models

Figure 5-4: Profile comparison of Digital Surface Models between CartoSat-1, TERRA – ASTER and SRTM model

44


Table 5-7: Statistics of the elevation profiles of DSM’s

28.8110.187.00RangeRangeRange

0.430.880.29CVCoefficient of VariationCV

5.341.801.54SDStandard DeviationSD

12.542.045.27meanMeanmean

28.926.639.00maxMaximummax

0.11-3.552.00MinMinimumMin

Profile 7

49.4815.6516.00Range45.9211.0010.00Range

0.520.630.33CV0.620.260.20CV

5.092.632.00SD7.452.281.69SD

9.704.166.13mean11.948.658.56mean

31.4312.7711.00max30.8114.2313.00max

-18.05-2.88-5.00Min-15.113.233.00Min

Profile 6Profile 3

38.7912.8111.00Range38.8913.5612.00Range

0.610.420.26CV0.430.220.17CV

5.052.191.75SD6.592.401.64SD

8.215.166.80mean15.4910.989.77mean

24.7911.4113.00max34.1518.1013.00max

-14.00-1.412.00Min-4.744.541.00Min

Profile 5Profile 2

46.6814.8912.00Range30.2110.3012.00Range

0.770.530.34CV0.280.250.19CV

8.163.392.51SD4.852.782.12SD

10.606.387.39mean17.2811.1211.30mean

34.2414.0613.00max32.2217.0716.00max

-12.44-0.831.00Min2.006.774.00Min

Profile 4Profile 1

ASTERCARTOSATSRTMStatistics


28.8110.187.00RangeRangeRange

0.430.880.29CVCoefficient of VariationCV

5.341.801.54SDStandard DeviationSD

12.542.045.27meanMeanmean

28.926.639.00maxMaximummax

0.11-3.552.00MinMinimumMin

Profile 7

49.4815.6516.00Range45.9211.0010.00Range

0.520.630.33CV0.620.260.20CV

5.092.632.00SD7.452.281.69SD

9.704.166.13mean11.948.658.56mean

31.4312.7711.00max30.8114.2313.00max

-18.05-2.88-5.00Min-15.113.233.00Min

Profile 6Profile 3

38.7912.8111.00Range38.8913.5612.00Range

0.610.420.26CV0.430.220.17CV

5.052.191.75SD6.592.401.64SD

8.215.166.80mean15.4910.989.77mean

24.7911.4113.00max34.1518.1013.00max

-14.00-1.412.00Min-4.744.541.00Min

Profile 5Profile 2

46.6814.8912.00Range30.2110.3012.00Range

0.770.530.34CV0.280.250.19CV

8.163.392.51SD4.852.782.12SD

10.606.387.39mean17.2811.1211.30mean

34.2414.0613.00max32.2217.0716.00max

-12.44-0.831.00Min2.006.774.00Min

Profile 4Profile 1



Attempts were made to understand elevation pattern of surface models obtained from different sources and statistics of seven elevation profiles that were derived covering the study area. The mean values of profile1 to profile7, for SRTM range from 11.3m to 5.3m, for CartoSat-1 from 11.12m to 2.04m and for ASTER it is from 17.3m to 12.54m. The percentage of coefficient of variation for CartoSat-1 profile has less variation when compared to ASTER, when profiles 6 and 7 are not considered.

5.2.1.7. Optimum resolution of DSM for Hydrodynamic model

The hydrodynamic model requires accurate representation of the flow influencing structures in the surface model. An attempt was made to understand optimum resolution of DSM that can be used for hydrodynamic modelling. The surface models tested are CartoSat-1 DSM on 10m cell size using

45


stereo point measurement tools (Carto-10-SPMT), CartoSat-1 DSM on 12.5m, 15m, 17.5m & 20m cell sizes using classic point measurement tool (Carto-12.5, Carto-15, Carto-17.5 & Carto-20). The structure representation by means of correlation between the point locations of the structure on the High Resolution Satellite (HRS) data and its corresponding locations on the surface model were compared. From the Figure 5-5, it is clear that Carto-10-SPMT represents the best feature representation among the surface models considered and if the DSM’s of classical point measurement tool (CPMT) are considered then Carto-15.0 is the best DSM. Considering the statistics of surface models with GCP elevation, the profiles of the surface model and the method / tools used to derive surface model further narrow down the range of optimum resolution that can be considered for Hydrodynamic model.

DIKE DIKE

Figure 5-5: Correlation among the flow influencing structures in HRS data and different Surface models

5.2.1.8. Removal of artefacts

From the investigation of CartoSat-1 digital surface model, the identified artefacts were in an array of approximately 20 x 20 pixels. Hence, to remove the artefacts using filter operation could not be possible. By doing so, it could affect the pixels with true elevation such as flow influencing feature (dikes, embankment, etc.). The artefacts could be identified in two ways 1) slicing operation and comparing with the topographical map and viewing in anaglyph and 2) visiting each artefact manually using anaglyph. The causes of artefacts in the surface model are due to mis-correlation between the images in the stereo pair, interpolation and inadequate horizontal and vertical resolution. In the present case, they could be due the mis-correlation between the datasets.

46


5.2.2. Datum transfer (Common datum)

The possibility of different techniques were attempted and the results are discussed based on chapter 4, section 4.2.3 as follows

5.2.2.1. Datum transfer using EGM 96 Geoid model

The converted GPS height values converted to mean sea level datum using the program for the calculation of a geoid undulation at the points are shown in Table 5-8.

Table 5-8: Values of converted heights

63.30512.165-51.14pnt17

62.92815.998-46.93pnt13

62.62610.366-52.26pnt4

63.1189.718-53.40pnt22

63.01910.619-52.40pnt21

62.65310.213-52.44pnt20

63.18311.033-52.15pnt11

62.6899.229-53.46pnt6

62.6497.669-54.98pnt3

62.37910.899-51.48pnt19

62.92812.998-49.93pnt13

63.2213.58-49.64pnt16

63.3769.266-54.11pnt18

63.02817.608-45.42pnt15

62.81118.051-44.76pnt14

63.0214.02-49.00pnt12

63.10916.369-46.74Pnt10

62.51311.223-51.29pnt8

63.1147.534-55.58pnt5

62.787.29-55.49pnt2

Geoid height

EGM 96 height

WGS 84heightPoint ID

63.30512.165-51.14pnt17

62.92815.998-46.93pnt13

62.62610.366-52.26pnt4

63.1189.718-53.40pnt22

63.01910.619-52.40pnt21

62.65310.213-52.44pnt20

63.18311.033-52.15pnt11

62.6899.229-53.46pnt6

62.6497.669-54.98pnt3

62.37910.899-51.48pnt19

62.92812.998-49.93pnt13

63.2213.58-49.64pnt16

63.3769.266-54.11pnt18

63.02817.608-45.42pnt15

62.81118.051-44.76pnt14

63.0214.02-49.00pnt12

63.10916.369-46.74Pnt10

62.51311.223-51.29pnt8

63.1147.534-55.58pnt5

62.787.29-55.49pnt2

Geoid height

EGM 96 height

WGS 84heightPoint ID

Input parameters:

Latitude = 20.5617673361111° N = 20° 33' 42.36" N Longitude = 86.4759762777778° E = 86° 28' 33.51" E GPS ellipsoidal height = -55.49 (meters)

Output: Geoid height = -62.780 (meters) Orthometric height (height above mean sea level) = 7.29 (meters)

47


(Note: orthometric Height = GPS ellipsoidal height - geoid height)

Using height values computed in EGM 96 (Orthometric height), surface model was generated. It was found that the study area was elevated at east; hence the low values of the terrain are on the west part of the area and the river is flowing from east to west instead of west to east. The probable reason for this condition is explained as follows. The mean difference derived from the GCPs used for generating DSM in EGM 96 was compared to that derived in WGS 84 (mean of EGM96: 12.62 and WGS84: -50.04) are shown in Table 5-9.

Table 5-9: Mean difference table for the GCPs used to generate Digital surface model

-0.281-1.441-1.722pnt19

-2.7313.1080.377pnt13

0.560.3990.959pnt16

0.711-4.066-3.355pnt18

0.3664.6214.987pnt15

0.155.285.43pnt14

0.3621.0371.399pnt12

0.453.2983.748pnt10

-0.152-1.246-1.398pnt8

0.453-5.54-5.087pnt5

0.123-5.454-5.331pnt2

Difference (a-b)

Standard Deviation of WGS 84 – (b)

Standard Deviation of EGM 96 – (a)Point ID

-0.281-1.441-1.722pnt19

-2.7313.1080.377pnt13

0.560.3990.959pnt16

0.711-4.066-3.355pnt18

0.3664.6214.987pnt15

0.155.285.43pnt14

0.3621.0371.399pnt12

0.453.2983.748pnt10

-0.152-1.246-1.398pnt8

0.453-5.54-5.087pnt5

0.123-5.454-5.331pnt2

Difference (a-b)

Standard Deviation of WGS 84 – (b)

Standard Deviation of EGM 96 – (a)Point ID

From the above table, the range obtained for the column “Difference (a-b)” is -3.44m, where the range should be near to zero. Due to the huge difference in values of pnt18 located in the eastern part of study area and pnt13 (western part) difference in standard deviation between EGM 96 and WGS 84 were 0.711m and -2.73m respectively. This elevation difference in the GCPs represented the reverse flow of the river system in DSM for the study area. Hence this study showed that direct use of EGM 96 values to generate DSM is not feasible. Attempt was also made towards using best-fit curve for DGPS elevation data and EGM 96 derived height values. The polynomial equation derived was shown in Figure 5-6. This equation was used in Raster calculator tool of Arc GIS to derive MSL – DSM.

48


y = 0.0007x2 + 1.0814x + 65.162R2 = 0.993

0

2

4

6

8

10

12

14

16

18

20

-60.00-50.00-40.00-30.00-20.00-10.000.00

GPS - WGS 84 Heights in m

EGM

96

Hei

ghts

in m

Figure 5-6: Polynomial equation for best curve fit

5.2.2.2.

5.2.2.3.

Datum transfer using Topo-spot heights (Average / best fit curve)

In this method, 65 spot heights were extracted from topographical map. Elevation information was also derived using location values of above spots from DSM (WGS 84). These were plotted in excel sheet, the geoid height was calculated and statistics for the geoid height was generated from WGS 84 datum. The parameteric values derived as follows. Minimum height -65.75 m, Maximum height -49.92 m, Average elevation -60.13 m, Standard deviation 2.83, Coefficient of variation -4.7% and Range 15.83m. For each spot height the Standard deviation was calculated and difference of Standard deviations (Topographical spot heights and DSM heights) was computed. The points with values less than 0.5m were considered for calculations to derive the average offset, to be further used for transfer of datum (60.185 m). Another method, attempted was to use best fit curve for the short listed points from the above said process, then after observing the range, it was found that the selected values had less range (-48.43m to -55.75m) when compared to those derived from DGPS (-44.76m to -55.58m) because of this, further processing was not attempted.

Datum transfer using feature identification

In this method, the field obtained cross sections at the gauge stations were identified linked to those on satellite data to obtain the height values of mean sea level and WGS 84 height values. The summed-up value was used as offset for the WGS 84 – DSM. The height values obtained from field locations of river cross-section at the gauge station and same values derived from DSM are given in Table 5-10 and Figure 4-8.

49


Table 5-10: Height from MSL vis-à-vis GPS ellipsoidal (WGS 84)

66.517.9258.59Left Levee

65.0928.4856.612Right Levee

Marshaghai gauge station

61.7313.9847.752Left Levee

58.7811.5547.408Right Levee

Danpur gauge station

60.6214.1746.45Left Levee

58.1813.345.45Right Levee

Pubansa gauge station

Height MSL to GPS ellipsoidalMSL heightGPS ellipsoidal

HeightGauge station

location

66.517.9258.59Left Levee

65.0928.4856.612Right Levee

Marshaghai gauge station

61.7313.9847.752Left Levee

58.7811.5547.408Right Levee

Danpur gauge station

60.6214.1746.45Left Levee

58.1813.345.45Right Levee

Pubansa gauge station

Height MSL to GPS ellipsoidalMSL heightGPS ellipsoidal

HeightGauge station

location

Note: Units are in meters

By further investigation the height difference between left levee and right levee for obtained cross-section and on DSM was shown in the Table 5-11. Since there was a height difference between the levees, average height in the mean sea level datum as well as in WGS 84 was obtained and used for further processing. On summing the average height of MSL and WGS 84, a value of 61.81 m was obtained and this value used as offset to bring the model surface closer to mean sea level (MSL).

Table 5-11: Height difference between the levees

0.561.97Marshaghai gauge station

2.430.34Danpur gauge station

0.871Pubansa gauge station

MSL heightGPS ellipsoidal heightGauge station location

0.561.97Marshaghai gauge station

2.430.34Danpur gauge station

0.871Pubansa gauge station

MSL heightGPS ellipsoidal heightGauge station location

Note: Units are in meter

5.2.2.4. Comparison of the datum transfer methods

DSM’s were generated using the above-discussed techniques and compared to identify the best suitable method to transfer the datum for the study area. The comparisons of the dike heights at the gauging station (Field Information) were carried out on the derived DSM’s (EGM 96 – WGS 84 best fit Curve, Spot Height – DSM Offset @ 60.185m and Feature Identification offset @ 61.81m) to derive best suitable method to adopt for datum transfer. From the Mean and Standard Deviation (SD) values (Table 5-12), it is clear that feature identification method described in this section is best method to transfer the datum with limited information and best applicable for Hydrodynamic model in the present study.

50


Table 5-12: Comparison of the different methods for datum transfer

5.695.695.732.77SD

11.439.8012.4711.57Mean

Statistics

3.221.594.207.92Left

5.193.576.188.48RightMarshaghai

14.0512.4315.1113.98Left

14.4012.7715.4611.55RightDanpur

15.3613.7316.4414.17Left

16.3614.7317.4513.30RightPubansh

Feature Identification

Offset @ 61.81m

Spot Height -DSM Offset @

60.185m

EGM 96 - WGS 84 best fit

Curve

Field InformationGauge Station

5.695.695.732.77SD

11.439.8012.4711.57Mean

Statistics

3.221.594.207.92Left

5.193.576.188.48RightMarshaghai

14.0512.4315.1113.98Left

14.4012.7715.4611.55RightDanpur

15.3613.7316.4414.17Left

16.3614.7317.4513.30RightPubansh

Feature Identification

Offset @ 61.81m

Spot Height -DSM Offset @

60.185m

EGM 96 - WGS 84 best fit

Curve

Field InformationGauge Station

Note: Units are in meter

5.2.3. Generation of River cross sections

The cross sections were generated as discussed in Chapter 4, section 4.2.4. The extracted elevation information from the DSM is depicted in Figure 5-7.

51


Figure 5-7: Extracted elevation information from the Digital Surface Model

The related attribute information of the spatial database generated to apply in Hydrodynamic model is shown in Figure 5-8.

52


S.No. of the pointCross section ID

Extracted Elevation information from DSM

X – Coordinate of the Point

Y – Coordinate of the PointS.No. of the pointCross section ID

Extracted Elevation information from DSM

X – Coordinate of the Point

Y – Coordinate of the Point

Figure 5-8: The Attribute data generated to apply in Hydrodynamic model

Note: The X and Y coordinates of the point are in UTM meter .Chainage of the points in the cross section is derived from right to left levees The values of points on the water cover location of each cross section are edited as shown in the Figure 4-9, then applied in the MIKE 11 model as per software requirement (Figure 5-9).

53


Figure 5-9: Cross section information in MIKE 11

5.2.4. Digital Elevation Model generation – River

The Digital Elevation model was generated as discussed in chapter 4, section 4.2.5 using Topo to Raster tool of Arc GIS; the Figure 5-10 shows digital elevation model generated using river cross section.

Figure 5-10: Digital elevation model generated using river cross-section

5.2.5. Downgrading the resolution of Digital surface model

The downgrading of 10m resolution DSM (downgraded to 30m and 300m cell size) was carried out with GIS operations. The polygon layers were generated using Fishnet tool of Arc Info software. The polygons in the layer (layer-1) are in the form of grid mesh, which represents the bathymetry cells of MIKE 21 model. In Arc Info coverage, the combinations of line and point are represented as polygons. So, one point in each grid polygon is represented in this layer (at the centre of the polygon). The new field was created and the attribute information of feature ID is copied to preserve the unique feature ID. A duplicate copy of the layer-1 is created (layer-2). Using the High-resolution satellite data in the

54


background, the points were moved to the required location (over the flow influencing objects) within the polygon for preserving the elevation information of that location in the downgraded surface model. This exercise was carried out only in the grid polygon where flow-influencing features are located. With the extract value to point tool, elevation values from the DSM were extracted to the point. The elevation data in the layer-1 was transformed to the layer-2 using unique feature ID as shown in the Figure 5-11. In the layer-2, “Addxy” tool was used to add field of the coordinate (X – coordinate and Y – coordinate) locations of the points in the point attribute table (PAT). The field of X – coordinate, Y – coordinate and Elevation values in the PAT are exported to a text file and text file is renamed into xyz file. This xyz file database is used to generate Bathymetry grid in MIKE 21 model. The above said procedure was carried out for both the layers (30m and 300m). Hence the semi-automatic downgrading of the DSM was possible.

Layer-1

Layer-2

Move point over flowinfluencing objects

Extract elevation value to pointTr

ansf

er e

xtra

cted

ele

vatio

n va

lue

to L

ayer

-2

Layer-1

Layer-2

Move point over flowinfluencing objects

Extract elevation value to pointTr

ansf

er e

xtra

cted

ele

vatio

n va

lue

to L

ayer

-2

Figure 5-11: Grid point method of downgrading the surface model resolution

55


Figure 5-12: Map of Downgraded DSM in the form of bathymetry grid on 300m cell size of the MIKE 21 file format

Figure 5-13: Map of Downgraded DSM in the form of bathymetry grid on 30m cell size of the MIKE 21 file format

The effects of spatial resolution on the digital surface model using grid point method of downgrading the spatial resolution from 10m DSM are shown in the Figure 5-14. The spatial resolution pattern of 30m and 300m of the DSM are shown in the Figure 5-13 & Figure 5-12 respectively for the study area.

56


0.00

5.00

10.00

15.00

20.00

25.00

0 200 400 600 800 1000 1200 1400

Distance in m

Elev

atio

n in

m

10m - DSM30m - DSM300m - DSM

Representation of Flow influencing feature

Figure 5-14: Effect of Spatial resolution of the downgraded DSM of 30m and 300m from sources DSM 10m

5.2.6. Generation of Manning’s – n using Land use / Land cover map

The Manning ‘n’ roughness map was derived based on the interpretation carried out for the river and floodplain from the Land use / Land cover map that is generated through visually interpretation of IRS 1D PAN data. From the interpretation of the present river reach, four land use / land cover classes have been defined. They are water covered sand, Sandbar, Dry land with Vegetation and Land with thick vegetation and for the flood plain, two classes namely 1) Very small settlement with Vegetation and 2) Agriculture land. The Manning’s n values have been assigned as per guidelines for selecting Manning's Roughness Coefficient for Natural Channels and Flood Plains. Table 5-13 shows the allotted values of Manning’s coefficient (Arcement et al., 1984) for different land cover classes.

57


Table 5-13: Table showing allotted manning coefficient

0.045Settlement with Vegetation

0.028Land with vegetation

0.026Sandbar

0.022Wet sand

River

0.045Settlement (with vegetation)

0.03Agriculture land

Floodplain

Manning’s Coefficient (n)Type of Land cover

0.045Settlement with Vegetation

0.028Land with vegetation

0.026Sandbar

0.022Wet sand

River

0.045Settlement (with vegetation)

0.03Agriculture land

Floodplain

Manning’s Coefficient (n)Type of Land cover

The following are some of the Land use / Land cover features for assigning Manning’s – n value

Figure 5-15: Manning “n” value map for river generated as resistance map

Figure 5-16: Manning “n” value map for flood plain generated as Resistance map

58


5.2.7. Generation of Flood Inundation map

The following flood inundation map was generated by visual interpretation using RadarSat-1 data (Figure 5-17) for 4th September 2003 and 11th September 2003 (Figure 5-18). A general observation was that for smooth open water bodies without vegetation radar backscattering was low. The Radar data cannot be used in the case of highly vegetated and settlement area because there is a high backscattering due to corner reflections. Hence interpretation of inundation extent could be difficult to determine (Smith, 1997).

Figure 5-17: Map showing Flood inundation on the 4th September, 2003

Figure 5-18: Map showing Flood inundation on the 11th September, 2003

The inundated area on 4th September 2003 is approximately 26.5 km2 and on 11th September 2003 is approximately 25.84 km2

5.3. Hydrodynamic modelling

The Hydrodynamic modelling results are discussed in this chapter based on the methodology described in the chapter 4. The MIKE FLOOD model is used to simulate the dynamics of water during the flood event and further it was validated with inundation maps interpreted using the 4th and 11th of September, 2003 RadarSat-1 satellite data.

59


5.3.1. MIKE 11

The MIKE 11 simulations are based within the river. The floodwater at the cross section is obtained for the simulation time step for the flood event. Discharges at the Q-point (mid point of two adjacent cross sections) and H-Q relationship are derived as shown in Figure 5-19 after simulation.

Q – point(Red)

System generated pointat the mid of two c/s

H – point(Blue)

Cross section (c/s)

Q – point(Red)

System generated pointat the mid of two c/s

H – point(Blue)

Cross section (c/s)

Figure 5-19: Locations of H-point (Cross section) and Q-point (H-Q relation can be obtained)

5.3.1.1. Upstream / Downstream boundary conditions

The database has been generated for the upstream and downstream boundaries of the study area based on recorded gauge levels at the gauge stations Pubansa (upstream) and Marshaghai (downstream) for the flood event. From 12:00 AM on 30th of August 2003 to 11:00 PM of 12th September 2003.

Figure 5-20: Time series data for Pubansa Gauging station

60


Figure 5-21: Time series data for Marshaghai Gauging station

5.3.1.2. Generation of network database

The spatial network of the river system is described in this part of the database such as defining the each vertex spatially (Latitude & Longitude) and chainage is derived from start point to the end point (Figure 5-22). The river system has 245 points with two branches as defined (Figure 5-23). In this study the centre line of the river system is defined using the IRS 1D satellite, PAN data captured on 19th November 2003. The network started from the Pubansa gauging station and ends at Marshaghai gauging station. The bifurcation of the Nuna River into two rivers (Nuna and Barandia rivers) is at 5043m from the Pubansa gauging station and rejoins to Nuna River at 18,487m of the main Nuna River as shown in Figure 5-24. The Barandia River has a length of 16,356m.

61


Figure 5-22: Nuna River system for model simulation

Figure 5-23: GUI showing the verities of each point to generate a spatial network database

62


Figure 5-24: GUI showing branch definition in the river system

5.3.1.3. Generation of Cross section database

The cross section was generated as discussed in the chapter 4, section 4.3.1.3. There are 15 cross sections (inclusive of 3 cross sections obtained from Irrigation Department, Govt. of Orissa) derived in Nuna river section and 13 cross sections derived in Barandia river section. The parameters required to define the cross sections are spatial location of the extreme end of the cross section (left and right), the elevations of the cross section is to be defined along the chainage from left to right end or vice-versa of the cross section, cross section ID, Branch/River Name, Branch chainage, Resistance, defining markers in the cross section such as left / right levee bank, left / right low flow bank, left / right coordinate marker, lowest point, section type such as open, closed irregular, closed rectangular, closed circular as shown in the Figure 5-25.

63


Figure 5-25: GUI showing the cross section details for building the database

5.3.1.4. Generation of boundary database

The Boundary condition database is generated as discussed in the chapter 4, section 4.3.1.4. The first condition for the simulation is Pubansa gauge station at 0m chainage where the simulation starts. The water levels are defined at these points and the second condition is Marshaghai gauge station at 25,073m from starting point where the simulation ends as shown in the Figure 5-26. These conditions are given to control the flow of water in the simulated river system at inflow (0m chainage) as well as outflow (25,073m chainage).

Figure 5-26: GUI showing the definition of boundary condition for the simulation

64


5.3.1.5. Initial water level parameters

The Hydrodynamic parameters are defined as discussed in the chapter 4, section 4.3.1.5. The initial water level and discharge are to be defined at the simulation start and end points in both the branches and the global initial water level and discharge can also be given as shown in Figure 5-27. The riverbed resistance is defined by using Manning’s n resistance formula. Since the bed condition in the Nuna river system is sandy hence 0.030 as global resistance is given (Figure 5-27).

Figure 5-27: GUI to define the initial water level & discharge (Global / Local) in “a” and Bed resistance in Manning’s - n

5.3.1.6. Simulation set-up

The Simulation of the Mike 11 model is described section 4.3.1.6. The simulation time step is to be set keeping in view of MIKE 21 model. In present situation 15 minute time step is defined for simulation and input files such as Network set-up file, Cross section set-up file, Boundary condition set-up file, Hydrodynamic parameter set-up file and Time series set-up file defined in boundary condition are defined. The Results of the simulation are viewed in MIKE view tool of the software package.

65


Figure 5-28: GUI showing the MIKE 11 set-up files for model simulation

The following are results obtained from the MIKE 11 simulation. On the 4th of September, 2003 there was overtopping of flood water over the dikes in the longitudinal section of the river for Nuna and Baradia rivers and its was compared to RadarSat-1 satellite data for above said dates as shown in the Figure 5-29, Figure 5-30 Figure 5-31 and Figure 5-32. The table showing the discharge in the river system (Table 5-14) and The table showing the water depth and flood depth in the simulated river system on 4th September, 2003 and 11th September, 2003 (Table 5-15).

Map showing locations of overtopping of Nuna river Dikes on 4th Sep. 2003 @ 12:00 hours

Starting pointof Simulation

Ending pointof Simulation

Left levee

Right levee




Left levee

Right levee

Figure 5-29: Longitudinal profile of MIKE 11 simulated result of Nuna river on 4th September, 2003

66




Barandia River extent

Map showing locations of overtopping of Barandia river Dikes on 4th Sep. 2003 @ 12:00 hours

Left levee

Right levee

Figure 5-30: Longitudinal profile of MIKE 11 simulated result of Barandia river on 4th September, 2003

Left levee

Right levee




Left levee

Right levee




Figure 5-31: Longitudinal profile of MIKE 11 simulated result of Nuna river on 11th September, 2003.

67





Left levee

Right levee





Left levee

Right levee


Figure 5-32: Longitudinal profile of MIKE 11 simulated result of Barandia river on 11th September, 2003 thThe water depth and flood water depth (water level over the dike) in the river was derived for 4 of

September 2003 and 11th September 2003 at the defined cross sections of simulated river system (Figure 5-28). The flood depth is the water depth over the top of the dike (margin to the dike crest). The Negative values represent water below the top of the dike and positive values represent overtopping.

th th September 2003 and 11The discharge was also derived for 4 September 2003 at the mid of the every two cross sections with chainage. The time series graphs were derived for water levels in river stretches before bifurcation (Figure 5-33), after bifurcation of Nuna River (Figure 5-34), Barandia River (Figure 5-35), after union of Barandia river into Nuna river (Figure 5-35 & Figure 5-36). The time series graphs were derived for discharge in the river stretches of Nuna River and Barandia River (Figure 5-37 & Figure 5-38). The extreme water depth, extreme discharge and extreme flood depth were also derived (Table 5-16, Table 5-17, Table 5-18 & Table 5-19).

68


Table 5-14: Discharge in the river system

5973.926353.633BARANDIA @ 15464.65

5972.6136366.625BARANDIA @ 14098.94

6058.1346594.317BARANDIA @ 12769.95

6125.5016667.722BARANDIA @ 11080.33

6122.1996671.829BARANDIA @ 9803.09

6119.2866675.4BARANDIA @ 8366.46

6115.9826679.25BARANDIA @ 6973.42

6114.736680.657BARANDIA @ 6024.43

6112.7236682.856BARANDIA @ 4573.28

6110.4576685.272BARANDIA @ 3096.81

6104.3656709.862BARANDIA @ 1958.27

6099.4726714.945BARANDIA @ 697.41

12748.22513392.649NUNA @ 24023.72

12374.44113188.523NUNA @ 21480.87

12283.37313148.876NUNA @ 19237.89

6293.8546804.576NUNA @ 17739.45

6279.0126811.505NUNA @ 15994.04

6281.9876844.35NUNA @ 14000.76

6270.4966859.59NUNA @ 12000.48

6266.2976864.806NUNA @ 9755.22

6263.5496867.771NUNA @ 8023.90

6262.0446869.375NUNA @ 7026.20

6259.6936871.826NUNA @ 5780.91

12350.09813596.035NUNA @ 4052.33

12346.11113600.108NUNA @ 2312.25

12341.51113604.788NUNA @ 781.60

11/9/2003 12:004/9/2003 12:00RIVER NAME @ CHAINAGE

DISCHARGE IN THE RIVER SYSTEM

5973.926353.633BARANDIA @ 15464.65

5972.6136366.625BARANDIA @ 14098.94

6058.1346594.317BARANDIA @ 12769.95

6125.5016667.722BARANDIA @ 11080.33

6122.1996671.829BARANDIA @ 9803.09

6119.2866675.4BARANDIA @ 8366.46

6115.9826679.25BARANDIA @ 6973.42

6114.736680.657BARANDIA @ 6024.43

6112.7236682.856BARANDIA @ 4573.28

6110.4576685.272BARANDIA @ 3096.81

6104.3656709.862BARANDIA @ 1958.27

6099.4726714.945BARANDIA @ 697.41

12748.22513392.649NUNA @ 24023.72

12374.44113188.523NUNA @ 21480.87

12283.37313148.876NUNA @ 19237.89

6293.8546804.576NUNA @ 17739.45

6279.0126811.505NUNA @ 15994.04

6281.9876844.35NUNA @ 14000.76

6270.4966859.59NUNA @ 12000.48

6266.2976864.806NUNA @ 9755.22

6263.5496867.771NUNA @ 8023.90

6262.0446869.375NUNA @ 7026.20

6259.6936871.826NUNA @ 5780.91

12350.09813596.035NUNA @ 4052.33

12346.11113600.108NUNA @ 2312.25

12341.51113604.788NUNA @ 781.60

11/9/2003 12:004/9/2003 12:00RIVER NAME @ CHAINAGE

DISCHARGE IN THE RIVER SYSTEM

69


Table 5-15: Water depth and flood depth in the simulated river system on 4th September, 2003 and 11th September, 2003

0.8788.4340.8298.385BARANDIA @ 13625.14

0.5038.5350.4798.511BARANDIA @ 11914.75

1.1369.031.3489.242BARANDIA @ 2521.73

0.199.560.0499.419NUNA @ 22973.90

0.4448.9040.3328.792NUNA @ 19987.83

0.4998.1770.4338.111NUNA @ 16990.94

0.757.7660.737.746NUNA @ 13004.39

0.3377.9460.3978.006NUNA @ 10996.57

-0.6489.002-0.7288.922BARANDIA @ 16356.56

-0.4228.84-0.4868.775BARANDIA @ 14572.73

-0.3538.262-0.3178.298BARANDIA @ 10245.92

-0.388.081-0.3328.13BARANDIA @ 9360.26

-0.0918.135-0.0018.225BARANDIA @ 7372.67

-0.7488.207-0.6468.309BARANDIA @ 6574.18

-0.668.401-0.538.531BARANDIA @ 5474.68

-1.3938.854-1.2019.046BARANDIA @ 3671.89

-1.6198.887-1.4029.104BARANDIA @ 1394.81

-1.4088.232-1.1878.453BARANDIA @ 0.00

-0.4379.553-0.6059.385NUNA @ 25073.55

-0.6489.002-0.7288.922NUNA @ 18487.96

-0.6489.002-0.7288.922NUNA @ 18487.96

-0.9677.802-1.0187.751NUNA @ 14997.14

-2.128.608-1.9658.763NUNA @ 8513.88

-1.2798.601-1.0988.782NUNA @ 7533.92

-1.6618.709-1.4548.916NUNA @ 6518.47

-1.4088.232-1.1878.453NUNA @ 5043.35

-1.4088.232-1.1878.453NUNA @ 5043.35

-3.4678.013-3.2188.262NUNA @ 3061.30

-3.6128.723-3.3269.009NUNA @ 1563.21

-2.1148.586-1.8098.891NUNA @ 0.00

FLOOD DEPTHWATER DEPTHFLOOD DEPTHWATER DEPTHRIVER NAME @ CHAINAGE

ON 11TH SEPTEMBER, 2003 @ 12:00 NOON

ON 4TH SEPTEMBER, 2003 @ 12:00 NOONDATE / TIME

-0.6489.002-0.7288.922BARANDIA @ 16356.56

-0.4228.84-0.4868.775BARANDIA @ 14572.73

-0.3538.262-0.3178.298BARANDIA @ 10245.92

-0.388.081-0.3328.13BARANDIA @ 9360.26

-0.0918.135-0.0018.225BARANDIA @ 7372.67

-0.7488.207-0.6468.309BARANDIA @ 6574.18

-0.668.401-0.538.531BARANDIA @ 5474.68

-1.3938.854-1.2019.046BARANDIA @ 3671.89

-1.6198.887-1.4029.104BARANDIA @ 1394.81

-1.4088.232-1.1878.453BARANDIA @ 0.00

-0.4379.553-0.6059.385NUNA @ 25073.55

-0.6489.002-0.7288.922NUNA @ 18487.96

-0.6489.002-0.7288.922NUNA @ 18487.96

-0.9677.802-1.0187.751NUNA @ 14997.14

-2.128.608-1.9658.763NUNA @ 8513.88

-1.2798.601-1.0988.782NUNA @ 7533.92

-1.6618.709-1.4548.916NUNA @ 6518.47

-1.4088.232-1.1878.453NUNA @ 5043.35

-1.4088.232-1.1878.453NUNA @ 5043.35

-3.4678.013-3.2188.262NUNA @ 3061.30

-3.6128.723-3.3269.009NUNA @ 1563.21

-2.1148.586-1.8098.891NUNA @ 0.00

FLOOD DEPTHWATER DEPTHFLOOD DEPTHWATER DEPTHRIVER NAME @ CHAINAGE

ON 11TH SEPTEMBER, 2003 @ 12:00 NOON

ON 4TH SEPTEMBER, 2003 @ 12:00 NOONDATE / TIME

0.8788.4340.8298.385BARANDIA @ 13625.14

0.5038.5350.4798.511BARANDIA @ 11914.75

1.1369.031.3489.242BARANDIA @ 2521.73

0.199.560.0499.419NUNA @ 22973.90

0.4448.9040.3328.792NUNA @ 19987.83

0.4998.1770.4338.111NUNA @ 16990.94

0.757.7660.737.746NUNA @ 13004.39

0.3377.9460.3978.006NUNA @ 10996.57

Note: The red colour chainage indicate that overtopping of floodwater occurred on the above specified date and time. The Flood depth is the water level above the crest of the dike represented in positive value in the flood depth column of above table. Negative values represent no flood (below the crest of dike).

70


Time series water level before bifurcation of River

Main Nuna River

Figure 5-33: Time series water level before bifurcation of Nuna river

Time series water level after bifurcation of Nuna River

Main Nuna River

Figure 5-34: Time series water level after bifurcation of Nuna river

71


Time series water level Barandia River

Barandia River

Figure 5-35: Time series water level of Barandia river

Time series water level after union of Rivers

Main Nuna River

Figure 5-36: Time series water level after union of Baandia river into Nuna river

72


Time series Discharge in Nuna river for flood event

Before BifurcationBefore Bifurcation

After BifurcationAfter Bifurcation

After union

Figure 5-37: Time series discharge in Nuna river for the event

Time series Discharge in Barandia river

Figure 5-38: Time series discharge in Barandia river

73


Table 5-16: Extreme water depth in the river for the event

30-8-2003 14:08:30 9.53712/9/2003 23:007.997BARANDIA @ 16356.56

30-8-2003 14:11:00 9.35712/9/2003 23:007.848BARANDIA @ 14572.73

30-8-2003 14:12:30 8.94712/9/2003 23:007.431BARANDIA @ 13625.14

30-8-2003 14:14:30 9.05612/9/2003 23:007.538BARANDIA @ 11914.75

30-8-2003 14:17:29 8.912/9/2003 23:007.248BARANDIA @ 10245.92

30-8-2003 14:18:59 8.73312/9/2003 23:007.072BARANDIA @ 9360.26

30-8-2003 14:22:30 8.86712/9/2003 23:007.137BARANDIA @ 7372.67

30-8-2003 14:22:59 8.94912/9/2003 23:007.224BARANDIA @ 6574.18

30-8-2003 16:09:29 9.23112/9/2003 23:007.404BARANDIA @ 5474.68

30-8-2003 16:09:00 9.86612/9/2003 23:007.834BARANDIA @ 3671.89

30-8-2003 16:07:59 10.10712/9/2003 23:007.975BARANDIA @ 2521.73

30-8-2003 16:07:00 9.99112/9/2003 23:007.823BARANDIA @ 1394.81

30-8-2003 16:05:30 9.35112/9/2003 23:007.166BARANDIA @ 0.00

30-8-2003 14:00:00 10.08612/9/2003 23:008.593NUNA @ 25073.55

30-8-2003 14:03:00 10.07612/9/2003 23:008.591NUNA @ 22973.90

30-8-2003 14:05:59 9.42212/9/2003 23:007.912NUNA @ 19987.83

30-8-2003 14:08:30 9.53712/9/2003 23:007.997NUNA @ 18487.96

30-8-2003 14:08:30 9.53712/9/2003 23:007.997NUNA @ 18487.96

30-8-2003 14:11:00 8.72812/9/2003 23:007.165NUNA @ 16990.94

30-8-2003 14:13:29 8.37412/9/2003 23:006.784NUNA @ 14997.14

30-8-2003 14:16:00 8.412/9/2003 23:006.729NUNA @ 13004.39

30-8-2003 14:20:00 8.71912/9/2003 23:006.936NUNA @ 10996.57

30-8-2003 16:07:59 9.53312/9/2003 23:007.601NUNA @ 8513.88

30-8-2003 16:07:30 9.59312/9/2003 23:007.585NUNA @ 7533.92

30-8-2003 16:06:29 9.78112/9/2003 23:007.665NUNA @ 6518.47

30-8-2003 16:05:30 9.35112/9/2003 23:007.166NUNA @ 5043.35

30-8-2003 16:05:30 9.35112/9/2003 23:007.166NUNA @ 5043.35

30-8-2003 16:03:00 9.23312/9/2003 23:006.925NUNA @ 3061.30

30-8-2003 16:01:30 10.10712/9/2003 23:007.562NUNA @ 1563.21

30-8-2003 16:00:00 10.04912/9/2003 23:007.397NUNA @ 0.00

Date / TimeMaximum depthDate / TimeMinimum depthRiver Name @ Chainage

Extreme water depth in the river for the event

30-8-2003 14:08:30 9.53712/9/2003 23:007.997BARANDIA @ 16356.56

30-8-2003 14:11:00 9.35712/9/2003 23:007.848BARANDIA @ 14572.73

30-8-2003 14:12:30 8.94712/9/2003 23:007.431BARANDIA @ 13625.14

30-8-2003 14:14:30 9.05612/9/2003 23:007.538BARANDIA @ 11914.75

30-8-2003 14:17:29 8.912/9/2003 23:007.248BARANDIA @ 10245.92

30-8-2003 14:18:59 8.73312/9/2003 23:007.072BARANDIA @ 9360.26

30-8-2003 14:22:30 8.86712/9/2003 23:007.137BARANDIA @ 7372.67

30-8-2003 14:22:59 8.94912/9/2003 23:007.224BARANDIA @ 6574.18

30-8-2003 16:09:29 9.23112/9/2003 23:007.404BARANDIA @ 5474.68

30-8-2003 16:09:00 9.86612/9/2003 23:007.834BARANDIA @ 3671.89

30-8-2003 16:07:59 10.10712/9/2003 23:007.975BARANDIA @ 2521.73

30-8-2003 16:07:00 9.99112/9/2003 23:007.823BARANDIA @ 1394.81

30-8-2003 16:05:30 9.35112/9/2003 23:007.166BARANDIA @ 0.00

30-8-2003 14:00:00 10.08612/9/2003 23:008.593NUNA @ 25073.55

30-8-2003 14:03:00 10.07612/9/2003 23:008.591NUNA @ 22973.90

30-8-2003 14:05:59 9.42212/9/2003 23:007.912NUNA @ 19987.83

30-8-2003 14:08:30 9.53712/9/2003 23:007.997NUNA @ 18487.96

30-8-2003 14:08:30 9.53712/9/2003 23:007.997NUNA @ 18487.96

30-8-2003 14:11:00 8.72812/9/2003 23:007.165NUNA @ 16990.94

30-8-2003 14:13:29 8.37412/9/2003 23:006.784NUNA @ 14997.14

30-8-2003 14:16:00 8.412/9/2003 23:006.729NUNA @ 13004.39

30-8-2003 14:20:00 8.71912/9/2003 23:006.936NUNA @ 10996.57

30-8-2003 16:07:59 9.53312/9/2003 23:007.601NUNA @ 8513.88

30-8-2003 16:07:30 9.59312/9/2003 23:007.585NUNA @ 7533.92

30-8-2003 16:06:29 9.78112/9/2003 23:007.665NUNA @ 6518.47

30-8-2003 16:05:30 9.35112/9/2003 23:007.166NUNA @ 5043.35

30-8-2003 16:05:30 9.35112/9/2003 23:007.166NUNA @ 5043.35

30-8-2003 16:03:00 9.23312/9/2003 23:006.925NUNA @ 3061.30

30-8-2003 16:01:30 10.10712/9/2003 23:007.562NUNA @ 1563.21

30-8-2003 16:00:00 10.04912/9/2003 23:007.397NUNA @ 0.00

Date / TimeMaximum depthDate / TimeMinimum depthRiver Name @ Chainage

Extreme water depth in the river for the event

Note: Units of depth are in meters

74


Table 5-17: Extreme discharge in the river for the event

30-8-2003 07:39:59 7237.57612/9/2003 23:004519.844BARANDIA @ 15464.65

30-8-2003 07:41:29 7407.6112/9/2003 23:004511.427BARANDIA @ 14098.94

30-8-2003 09:13:29 8150.94812/9/2003 23:004448.447BARANDIA @ 12769.95

30-8-2003 09:17:29 8429.56112/9/2003 23:004445.229BARANDIA @ 11080.33

30-8-2003 09:16:59 8448.16312/9/2003 23:004438.179BARANDIA @ 9803.09

30-8-2003 16:16:00 8669.50112/9/2003 23:004431.984BARANDIA @ 8366.46

30-8-2003 16:13:00 8840.86112/9/2003 23:004425.526BARANDIA @ 6973.42

30-8-2003 16:12:30 8841.79112/9/2003 23:004422.808BARANDIA @ 6024.43

30-8-2003 16:11:00 8849.07512/9/2003 23:004419.171BARANDIA @ 4573.28

30-8-2003 16:08:30 9069.08112/9/2003 23:004415.674BARANDIA @ 3096.81

30-8-2003 16:07:00 9540.57312/9/2003 23:004409.5BARANDIA @ 1958.27

30-8-2003 16:05:30 9542.03712/9/2003 23:004399.727BARANDIA @ 697.41

1/9/2003 18:0115694.62912/9/2003 23:009053.309NUNA @ 24023.72

30-8-2003 20:03:30 15617.79312/9/2003 23:008971.938NUNA @ 21480.87

30-8-2003 20:05:30 15721.72412/9/2003 23:008948.872NUNA @ 19237.89

30-8-2003 18:09:29 8633.62412/9/2003 23:004414.693NUNA @ 17739.45

30-8-2003 18:11:00 8794.52912/9/2003 23:004397.744NUNA @ 15994.04

30-8-2003 18:13:59 8969.71312/9/2003 23:004388.851NUNA @ 14000.76

30-8-2003 09:16:30 9024.19112/9/2003 23:004381.918NUNA @ 12000.48

30-8-2003 16:11:00 9153.51212/9/2003 23:004374.08NUNA @ 9755.22

30-8-2003 16:08:30 9152.00712/9/2003 23:004368.63NUNA @ 8023.90

30-8-2003 16:07:30 9151.56912/9/2003 23:004365.751NUNA @ 7026.20

30-8-2003 16:05:59 9151.51412/9/2003 23:004361.229NUNA @ 5780.91

30-8-2003 16:03:59 18696.14512/9/2003 23:008742.563NUNA @ 4052.33

30-8-2003 16:01:59 18695.48812/9/2003 23:008734.819NUNA @ 2312.25

30-8-2003 16:00:29 18696.45312/9/2003 23:008725.931NUNA @ 781.60

Date / TimeMaximum dischargeDate / TimeMinimum dischargeRiver Name @ Chainage

Extreme discharge in the river for the Event

30-8-2003 07:39:59 7237.57612/9/2003 23:004519.844BARANDIA @ 15464.65

30-8-2003 07:41:29 7407.6112/9/2003 23:004511.427BARANDIA @ 14098.94

30-8-2003 09:13:29 8150.94812/9/2003 23:004448.447BARANDIA @ 12769.95

30-8-2003 09:17:29 8429.56112/9/2003 23:004445.229BARANDIA @ 11080.33

30-8-2003 09:16:59 8448.16312/9/2003 23:004438.179BARANDIA @ 9803.09

30-8-2003 16:16:00 8669.50112/9/2003 23:004431.984BARANDIA @ 8366.46

30-8-2003 16:13:00 8840.86112/9/2003 23:004425.526BARANDIA @ 6973.42

30-8-2003 16:12:30 8841.79112/9/2003 23:004422.808BARANDIA @ 6024.43

30-8-2003 16:11:00 8849.07512/9/2003 23:004419.171BARANDIA @ 4573.28

30-8-2003 16:08:30 9069.08112/9/2003 23:004415.674BARANDIA @ 3096.81

30-8-2003 16:07:00 9540.57312/9/2003 23:004409.5BARANDIA @ 1958.27

30-8-2003 16:05:30 9542.03712/9/2003 23:004399.727BARANDIA @ 697.41

1/9/2003 18:0115694.62912/9/2003 23:009053.309NUNA @ 24023.72

30-8-2003 20:03:30 15617.79312/9/2003 23:008971.938NUNA @ 21480.87

30-8-2003 20:05:30 15721.72412/9/2003 23:008948.872NUNA @ 19237.89

30-8-2003 18:09:29 8633.62412/9/2003 23:004414.693NUNA @ 17739.45

30-8-2003 18:11:00 8794.52912/9/2003 23:004397.744NUNA @ 15994.04

30-8-2003 18:13:59 8969.71312/9/2003 23:004388.851NUNA @ 14000.76

30-8-2003 09:16:30 9024.19112/9/2003 23:004381.918NUNA @ 12000.48

30-8-2003 16:11:00 9153.51212/9/2003 23:004374.08NUNA @ 9755.22

30-8-2003 16:08:30 9152.00712/9/2003 23:004368.63NUNA @ 8023.90

30-8-2003 16:07:30 9151.56912/9/2003 23:004365.751NUNA @ 7026.20

30-8-2003 16:05:59 9151.51412/9/2003 23:004361.229NUNA @ 5780.91

30-8-2003 16:03:59 18696.14512/9/2003 23:008742.563NUNA @ 4052.33

30-8-2003 16:01:59 18695.48812/9/2003 23:008734.819NUNA @ 2312.25

30-8-2003 16:00:29 18696.45312/9/2003 23:008725.931NUNA @ 781.60

Date / TimeMaximum dischargeDate / TimeMinimum dischargeRiver Name @ Chainage

Extreme discharge in the river for the Event

Note: Discharge units are m3/sec.

75


Table 5-18: Extreme flood depth in the river for the event

30-8-2003 14:08:30 -0.11312/9/2003 23:00-1.653BARANDIA @ 16356.56

30-8-2003 14:11:00 0.09512/9/2003 23:00-1.414BARANDIA @ 14572.73

30-8-2003 14:12:30 1.39112/9/2003 23:00-0.125BARANDIA @ 13625.14

30-8-2003 14:14:30 1.02412/9/2003 23:00-0.494BARANDIA @ 11914.75

30-8-2003 14:17:29 0.28512/9/2003 23:00-1.367BARANDIA @ 10245.92

30-8-2003 14:18:59 0.27112/9/2003 23:00-1.39BARANDIA @ 9360.26

30-8-2003 14:22:30 0.64112/9/2003 23:00-1.089BARANDIA @ 7372.67

30-8-2003 14:22:59 -0.00612/9/2003 23:00-1.731BARANDIA @ 6574.18

30-8-2003 16:09:29 0.1712/9/2003 23:00-1.657BARANDIA @ 5474.68

30-8-2003 16:09:00 -0.38112/9/2003 23:00-2.413BARANDIA @ 3671.89

30-8-2003 16:07:59 2.21312/9/2003 23:000.082BARANDIA @ 2521.73

30-8-2003 16:07:00 -0.51512/9/2003 23:00-2.683BARANDIA @ 1394.81

30-8-2003 16:05:30 -0.28912/9/2003 23:00-2.474BARANDIA @ 0.00

30-8-2003 14:00:00 0.09612/9/2003 23:00-1.397NUNA @ 25073.55

30-8-2003 14:03:00 0.70612/9/2003 23:00-0.779NUNA @ 22973.90

30-8-2003 14:05:59 0.96212/9/2003 23:00-0.548NUNA @ 19987.83

30-8-2003 14:08:30 -0.11312/9/2003 23:00-1.653NUNA @ 18487.96

30-8-2003 14:08:30 -0.11312/9/2003 23:00-1.653NUNA @ 18487.96

30-8-2003 14:11:00 1.0512/9/2003 23:00-0.513NUNA @ 16990.94

30-8-2003 14:13:29 -0.39512/9/2003 23:00-1.985NUNA @ 14997.14

30-8-2003 14:16:00 1.38412/9/2003 23:00-0.287NUNA @ 13004.39

30-8-2003 14:20:00 1.1112/9/2003 23:00-0.673NUNA @ 10996.57

30-8-2003 16:07:59 -1.19512/9/2003 23:00-3.127NUNA @ 8513.88

30-8-2003 16:07:30 -0.28712/9/2003 23:00-2.295NUNA @ 7533.92

30-8-2003 16:06:29 -0.58912/9/2003 23:00-2.705NUNA @ 6518.47

30-8-2003 16:05:30 -0.28912/9/2003 23:00-2.474NUNA @ 5043.35

30-8-2003 16:05:30 -0.28912/9/2003 23:00-2.474NUNA @ 5043.35

30-8-2003 16:03:00 -2.24712/9/2003 23:00-4.555NUNA @ 3061.30

30-8-2003 16:01:30 -2.22812/9/2003 23:00-4.773NUNA @ 1563.21

30-8-2003 16:00:00 -0.65112/9/2003 23:00-3.303NUNA @ 0.00

Date / TimeMaximum flood depthDate / TimeMinimum flood depthRiver Name @ Chainage

Extreme flood depth in river for the Event

30-8-2003 14:08:30 -0.11312/9/2003 23:00-1.653BARANDIA @ 16356.56

30-8-2003 14:11:00 0.09512/9/2003 23:00-1.414BARANDIA @ 14572.73

30-8-2003 14:12:30 1.39112/9/2003 23:00-0.125BARANDIA @ 13625.14

30-8-2003 14:14:30 1.02412/9/2003 23:00-0.494BARANDIA @ 11914.75

30-8-2003 14:17:29 0.28512/9/2003 23:00-1.367BARANDIA @ 10245.92

30-8-2003 14:18:59 0.27112/9/2003 23:00-1.39BARANDIA @ 9360.26

30-8-2003 14:22:30 0.64112/9/2003 23:00-1.089BARANDIA @ 7372.67

30-8-2003 14:22:59 -0.00612/9/2003 23:00-1.731BARANDIA @ 6574.18

30-8-2003 16:09:29 0.1712/9/2003 23:00-1.657BARANDIA @ 5474.68

30-8-2003 16:09:00 -0.38112/9/2003 23:00-2.413BARANDIA @ 3671.89

30-8-2003 16:07:59 2.21312/9/2003 23:000.082BARANDIA @ 2521.73

30-8-2003 16:07:00 -0.51512/9/2003 23:00-2.683BARANDIA @ 1394.81

30-8-2003 16:05:30 -0.28912/9/2003 23:00-2.474BARANDIA @ 0.00

30-8-2003 14:00:00 0.09612/9/2003 23:00-1.397NUNA @ 25073.55

30-8-2003 14:03:00 0.70612/9/2003 23:00-0.779NUNA @ 22973.90

30-8-2003 14:05:59 0.96212/9/2003 23:00-0.548NUNA @ 19987.83

30-8-2003 14:08:30 -0.11312/9/2003 23:00-1.653NUNA @ 18487.96

30-8-2003 14:08:30 -0.11312/9/2003 23:00-1.653NUNA @ 18487.96

30-8-2003 14:11:00 1.0512/9/2003 23:00-0.513NUNA @ 16990.94

30-8-2003 14:13:29 -0.39512/9/2003 23:00-1.985NUNA @ 14997.14

30-8-2003 14:16:00 1.38412/9/2003 23:00-0.287NUNA @ 13004.39

30-8-2003 14:20:00 1.1112/9/2003 23:00-0.673NUNA @ 10996.57

30-8-2003 16:07:59 -1.19512/9/2003 23:00-3.127NUNA @ 8513.88

30-8-2003 16:07:30 -0.28712/9/2003 23:00-2.295NUNA @ 7533.92

30-8-2003 16:06:29 -0.58912/9/2003 23:00-2.705NUNA @ 6518.47

30-8-2003 16:05:30 -0.28912/9/2003 23:00-2.474NUNA @ 5043.35

30-8-2003 16:05:30 -0.28912/9/2003 23:00-2.474NUNA @ 5043.35

30-8-2003 16:03:00 -2.24712/9/2003 23:00-4.555NUNA @ 3061.30

30-8-2003 16:01:30 -2.22812/9/2003 23:00-4.773NUNA @ 1563.21

30-8-2003 16:00:00 -0.65112/9/2003 23:00-3.303NUNA @ 0.00

Date / TimeMaximum flood depthDate / TimeMinimum flood depthRiver Name @ Chainage

Extreme flood depth in river for the Event

As per derived results of MIKE 11 model it was found that there was an extreme floodwater depth that occurred in the River Barandia at the chainage of 2,522m with 2.2m on 30th August 2003 at 16:08 hours over the Dike crest and in Nuna River at 13,004m with 1.38m on 30th August 2003 at 14:16 hours.

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Table 5-19: Overtopping of the floodwater at the cross sections over the levees

9/2/2003 12:459/1/2003 9:00

8/30/2003 17:158/30/2003 11:45

8/30/2003 0:008/30/2003 11:45BARANDIA @ 14572.73

9/12/2003 18:308/30/2003 0:00BARANDIA @ 13625.14

9/12/2003 6:008/30/2003 0:00BARANDIA @ 11914.75

9/2/2003 18:158/30/2003 5:30BARANDIA @ 10245.92

9/2/2003 16:308/30/2033 5:15BARANDIA @ 9360.26

9/11/2003 6:309/9/2003 1:00

9/6/2003 2:459/4/2003 12:30

9/3/2003 8:308/30/2003 0:00BARANDIA @ 7372.67

8/31/2003 2:458/30/2003 7:00BARANDIA @ 5474.68

9/12/2003 23:008/30/2003 0:00BARANDIA @ 2521.73

9/2/2003 6:099/2/2003 0:57

9/1/2003 17:099/1/2003 13:41

8/30/03 15:398/30/03 11:40NUNA @ 25073.55

9/11/03 19:459/8/03 11:07

9/12/03 4:008/30/03 3:36NUNA @ 22973.9

9/12/03 4:178/30/03 0:00NUNA @ 19987.83

9/12/03 5:138/30/03 0:00NUNA @ 16990.94

9/12/03 13:308/30/03 0:00NUNA @ 13004.39

9/12/03 0:038/30/03 0:00NUNA @ 10996.57

ENDSTARTRIVER NAME @ CHAINAGE

FLOODING (OVER TOPPING LEVEE)

9/2/2003 12:459/1/2003 9:00

8/30/2003 17:158/30/2003 11:45

8/30/2003 0:008/30/2003 11:45BARANDIA @ 14572.73

9/12/2003 18:308/30/2003 0:00BARANDIA @ 13625.14

9/12/2003 6:008/30/2003 0:00BARANDIA @ 11914.75

9/2/2003 18:158/30/2003 5:30BARANDIA @ 10245.92

9/2/2003 16:308/30/2033 5:15BARANDIA @ 9360.26

9/11/2003 6:309/9/2003 1:00

9/6/2003 2:459/4/2003 12:30

9/3/2003 8:308/30/2003 0:00BARANDIA @ 7372.67

8/31/2003 2:458/30/2003 7:00BARANDIA @ 5474.68

9/12/2003 23:008/30/2003 0:00BARANDIA @ 2521.73

9/2/2003 6:099/2/2003 0:57

9/1/2003 17:099/1/2003 13:41

8/30/03 15:398/30/03 11:40NUNA @ 25073.55

9/11/03 19:459/8/03 11:07

9/12/03 4:008/30/03 3:36NUNA @ 22973.9

9/12/03 4:178/30/03 0:00NUNA @ 19987.83

9/12/03 5:138/30/03 0:00NUNA @ 16990.94

9/12/03 13:308/30/03 0:00NUNA @ 13004.39

9/12/03 0:038/30/03 0:00NUNA @ 10996.57

ENDSTARTRIVER NAME @ CHAINAGE

FLOODING (OVER TOPPING LEVEE)

5.3.2. MIKE 21

The MIKE 21 model set-up is based on the input requirement of MIKE FLOOD. The main requirement for MIKE 21 is setting up of bathymetry and resistance for the floodplain. The results of set-up of the database are discussed in detail in further sections of this chapter.

5.3.2.1. Generation Bathymetry database

The bathymetry database was generated using the DSM that was derived from the stereo data of the CartoSat-1 satellite and the simulated river bathymetry. These datasets were integrated and converted into *.xyz format and further to MIKE 2D grid.

77


Generation of bathymetry was generated as discussed in the section 4.3.2.1 for river in MIKE 21. The simulated surface is converted into xyz file format and from xyz format to MIKE 21 grid format. The same was done for the floodplain area and then integrated in the MIKE model. Generation of bathymetry for Integrated River & Floodplain in Arc GIS environment: The grid datasets of the river and floodplain surface were exported to ERDAS ASCII file format using AOI (area of interest) layers for River and floodplain separately. These datasets were opened in excel and converted to xyz point layer. The point layers generated were integrated to form a single layer in grid format and further converted into MIKE 2D grid. Generation of bathymetry by gridded points in Arc GIS environment: The bathymetry database was generated as discussed in the chapter 4 sections 4.2.6. Better result obtained from this approach, which has control over editing the surface for the floodplain. These grid point layers are used to generate MIKE 2D grid as discussed in the section 4.2.6

Figure 5-39: Bathymetry data generated using simple integration method into MIKE

5.3.2.2. Setting up of Simulation and Results

As per requirement of MIKE FLOOD model, the simulation set-up file for MIKE 21 was generated; the model requirement was fulfilled by generating bathymetry shown in Figure 5-39, the Resistance map for River shown in the Figure 5-15 and for floodplain Figure 5-16. For defining the Flood and Dry parameters minimum water depth allowed at a point before it is taken out of calculation for drying depth are given, and also the water depth at which the point will be re-entered into the calculation for flooding depth. The initial surface elevation is given as –3m, since simulations was to be started as dry condition. The flood extent with water depth and velocity vector on X (U) and Y (V) directions were obtained at defined time step interval in the MIKE 21 output results grid file.

78


5.3.3. MIKE FLOOD (Integrated MIKE 11 & MIKE 21)

MIKE FLOOD is a tool that integrates the one-dimensional model MIKE 11 and the two-dimensional model MIKE 21 into a single, dynamically coupled modelling system. The MIKE FLOOD results are the results obtained in MIKE 21. The definition of MIKE 11 and MIKE 21 were set-up, the lateral links were defined as discussed in chapter 4, section 4.3.3.2 and its result is shown in the Figure 5-40 and these lateral links are to be defined for which side of the river it belongs.

Lateral Link(4,35)

Lateral Link(Right levee)

Lateral Link(Left levee) Centre line of

River system

Figure 5-40: GUI showing definition of Lateral Links

5.3.4. Flood inundation results of MIKE FLOOD model

The flood inundation results of the simulated MIKE FLOOD model was generated at a time step of one-hour interval in two-dimensional grids. MIKE 11 results which were earlier discussed in the section 4.2.8 have a slight modification in the simulation of time step, since it is set up to 30 seconds with output generation of 15 minutes. For flood simulation in MIKE 21 model it was again set up to 30 seconds and the output is derived for every one hour as shown in the Figure 5-41 and Figure 5-42.

79


Figure 5-41: Flood simulation using MIKE FLOOD model (Figure showing flood inundation situation on 4th September, 2003 @ 12:00 noon)

Figure 5-42: Flood simulation using MIKE FLOOD model (Figure showing flood inundation situation on 11th September, 2003 @ 12:00 noon)

The flood inundation results of the simulated MIKE FLOOD model was generated at a time step of an hour in two-dimensional grids. The simulation was carried out in 30m grid size and was found to be satisfactory.

5.3.5. Calibration of Field (interview) data with Flood model results

The present study area for 2003 flood event, the model results were calibrated with field collected interview data (Maiti, 2007). The investigation conducted to compare the results with respect to the flood water level obtained from the MIKE FLOOD model vis-à-vis flood level data collected from field interviews in village settlements. The villages in which field interviews were conducted are

80


Indalo, Jalapoka, Aitpur, Pentha, Raghunathpur and Bachharai. The procedure adopted for the calibration of the model output consisted of overlaying the settlement location (point layer with flood depth database in the attribute table) on the time series grid water depth database in MIKE results view tool. The hourly flood conditions were noted for pixels under the settlement points (change in flood water level) as shown in the Figure 5-43.

Figure 5-43: Overlay of settlement locations on the inundated grid in MIKE results file.

The same exercise was repeated for agricultural locations and daily flood conditions were noted in these areas. The following are results observed (village-wise) for the simulation period between 30th August 2003 and 12th September 2003: Indalo village: Before the 30th August 2003 the floodwater has entered the village. Settlement no: 25 was inundated for the entire flood period with 2 to 3m water level. Settlement no: 22, 23 and 24 were inundated from 30th August 2003 at 6:00 hours to 21st hours of 2nd September 2003 with depth of 0 to 1m. Settlement nos: 17 to 21 were inundated from 30th August 2003, 6:00 hours to end of the simulation period with a flood level of 0 to 1m. As per Field interview data, flood level in the settlements was varying from 0.7 to 1.2m Jalapoka village: As per field interview data, there was 0 to 0.7m of inundation, but as per model there was no inundation. Aitpur: As per field interview data, there was 0 to 1.2m of inundation, but as per model there was no inundation. Raghunathpur village: The settlements are clustered in two groups. First cluster with settlement no 44 to 49 started getting inundated on 30th August 2003 at 9:00 hours and the second cluster (settlement no: 50 to 52) was not inundated for the flood event. The settlement no 45 to 46 have experienced dynamic flood level, which varied with time. On 30th August 2003, 9:00 hours inundation of the cluster started with 0-1m. On 1st September, 2003 it rose to 1-2m, on 3rd September, 2003 reduced to 0-1m, on 10th September it again rose to 1-2m and on the same day at 22: 00 hours it came down to 0-1m and remained till the end of the simulation. For the settlement no: 44,47,48 and 49, inundation started on 30th August at 9: 00 hours but there was no inundation for the period from 3rd to 9th

81


September, 2003, but rose by 0-1m till 11th September, 2003 at 8:00 hours. As per field interview data the flood level was about 1-2m during the flood. Pentha village: The settlement no 26 to 29, inundation started from 31st August 2003 till the end of the simulation with 0-1 m flood depth. In the Settlement no: 30 to 32, the inundation started on 30th August, 2003 at 9:00 hours with 0-1m, increased to 1-2m on 31st August at 6:00 and further increased to 2-3m on same day at 18:00 hours and remained same till the end of the simulation. As per field interview data the flood level was about 0.7 to 1.4m. Bachharai: The settlement no 41& 42 were inundated from 30th August 2003 at 18:00 hours with 0-1m, increased to 1-2m on 31st August at 10:00 hours, further increased to 2-3m on 6th September 2003, then reduced to 1-2m on 9th September at 10:00 and increased again 2-3m on 11th September 2003 at 21:00 hours and remained same till the end of the simulation. For the settlement nos: 34,35,36,37 and 43 inundation started on 30th August 2003 at 18:00 hours with 0-1m, then increased to 2-3m on 31st August 2003 at 7:00 hours then after stayed at the same level till the end of the simulation. In settlement no: 38, inundation started on 31st August 2003 at 6:00 hours with 0-1m then increased to 1-2m on 31st August 2003 at 17:00 hours, then remained same till the end of the simulation. As per field interview data settlement nos 41 and 42 have experienced 1.2m, 38 with 1.2m, 34,35,36,37 and 43 with 0.9 to 1.7m and 33, 39 and40 with 0.7 to 1.7m. The settlement nos: 33, 39 & 40 were not inundated as per model because these settlements were closer to Chitrapala river, which was not included in the simulation study. For Agriculture fields, In Indalo village inundation was from start of simulation till end with 0-2m water depth. As per field interview data it was 2.2m. In Jalapoka village, there was inundation for one day i.e. for 30th August with 0-1m. As per field interview data it is 2.7m flood depth. In Aitpur village there was inundation partially with 0-1m. As per field interview data it is 3.7m. Raghunathpur village, was inundated upto 0-2m which increased to 1-3m and remained throughout the event. However as per field interview data it is 2.7m. Pentha Village experienced flood of 0-2m starting from 30th August 2003 and increased to 1-3m till 1st September 2003, further increased upto 2-3m on 10th September 2003 and reduced to 1-3m on 11th September 2003. As per field interview data, it is 2.7m. In Bachharai village flooding started on 30th August 2003, increased to 6 to 7m on 2nd September 2003, reduced to 4-5m on 3rd September, 2003 and further reduced to 2-4m on 11th September, 2003 and remained same till the end of the simulation. As per field interview data, it is 3.7 – 4.7m flood level.

5.3.6. Comparison of flood model with visually interpreted data and Satellite data

The results obtained from the model, visual interpretation and satellite data were compared. On overlaying the interpreted information with the model output (Figure 5-45 and Figure 5-46), it was found that over flooding seems to have occurred in the model simulation. When it was overlaid on the RadarSat-1 satellite data (Figure 5-47 and Figure 5-48), it was found that model represented a true picture of flooding situation. There were some patches of water bodies that were near to Cithrapala river on the interpreted image and they were mis-interpreted as Nuna or Barandia River not having influence on those water bodies and over the settlement, leading to no inundation. Hence it was not considered for interpretation. The model result was overlaid on the interpreted data. It was found that the model result represented over inundation in general. When these were matched with RadarSat-1 satellite data, the interpretation was proved wrong. In RadarSat-1 satellite data, there was no inundation over the Settlement / Vegetation areas. As per field interview data, there was inundation in Bachharai village. But as per model results, the village was inundated on 4th September 2003 (Figure

82


5-44). Hence it is proved that Radar data does not show inundation extent over settlement / Vegetation areas. This might be due to high backscattering of signals from the features (Settlement / Vegetation) (Smith, 1997).

CartoSat-1 PAN

RadarSat – 1Peak Flood event

MIKE FLOODWater depth resultPeak Flood event

Inun

datio

n de

pth

Figure 5-44: Comparison of Results with datasets for the event in Bachharai Village.

Figure 5-45: Comparison of interpreted information to the model output for 4th September, 2003

83


Figure 5-46: Comparison of interpret information over the model output for 11th September, 2003

Figure 5-47: Model output overlaid on the RadarSat-1 satellite data for 4th September, 2003 @ 12:00 noon

Figure 5-48: Model output overlaid on the RadarSat-1 satellite data for 11th September, 2003 @ 12:00 noon

Statistics derived for the island between the two rivers, on 4th of September 2003 revealed that the inundation extent derived by model is 2.1 km2, whereas the visual interpretation derived area was 3.3

84


km2 th. On 11 September 2003 the inundation area derived by model is 1.8 km2 and that of visual interpretation is 3.6 km2. The variation in the results could be due to the effect of spatial resolution in which the model is simulated. Better results can be obtained when simulation in finer resolution is carried out. The velocity maps were produced along X – direction and Y – direction on 4th September and 11th September 2003 as shown in the Figure 5-49, Figure 5-50, Figure 5-51 & Figure 5-52.

Figure 5-49: Velocity along X- direction on 4th of September 2003 at 12:00 noon

Figure 5-50: Velocity along Y- direction on 4th of September 2003 at 12:00 noon

85


Figure 5-51: Velocity along X- direction on 11th of September 2003 at 12:00 noon

Figure 5-52: Velocity along Y- direction on 11th of September 2003 at 12:00 noon

86


0 m

CartoSat-1 PAN

MIKE FLOODResult

on 300m Grid

MIKE FLOODResult

on 30m Grid

Inundation depth

13.5 m

Figure 5-53: Effect of resolution on the Flood inundation in Hydrodynamic model

87


Table 5-20: Effect of floodwater depth with resolution

1.702.3843

2.22.392.5142

2.22.391.2141

2.22.39040

2.22.392.239

2.203.538

1.701.337

1.202.4936

1.202.9135

1.704.2434

1.703.3633

Interview dataon 300m Grid model result

on 30m Grid Model resultFeature ID

1.702.3843

2.22.392.5142

2.22.391.2141

2.22.39040

2.22.392.239

2.203.538

1.701.337

1.202.4936

1.202.9135

1.704.2434

1.703.3633

Interview dataon 300m Grid model result

on 30m Grid Model resultFeature ID

Note: units are in meters From the Table 5-20 and Figure 5-53, the effect on resolution in the flood inundation depth derived from Hydrodynamic model in Bachharai Village can be seen. The MIKE FLOOD model was simulated on 30m and 300m. The depth of inundation was calibrated with the field interview collected for the 2003 Orissa flood event. On analyzing depth, feature ID 33 to 38 represented inundation of 3m approximately on 30m grid and no flooding on 300m grid. 1.5m over inundation was observed on 30m grid in comparison with field interview data and 1.5m under inundation was computed with 300m grid. Feature ID 41 to 43 represented better correlation of flood results. In this study an average flood depth of ±1.5m was predicted, using hydrodynamic modelling.

Figure 5-54: Flood depth for the event on 30m grid size

Note: The simulation of 30m results is based on 5 seconds time step and result stored for every one hour for peak flood event

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6. Conclusion and Recommandation

This study presents, the role of geoinformation and hydrodynamic modelling in flood studies. The advantage of applying geoinformation techniques is demonstrated by being able to obtain the terrain geometry of the study area from RS data, extraction of flood inundation extent to validate the model results and using satellite data to generate land cover map, a source map to derive the Manning’s coefficient. The terrain geometry was derived using CartoSat-1 and TERRA – ASTER stereo pair data by means of Digital Surface model. Based on the research question and objectives, the following conclusions were derived. The CartoSat-1 DSM was derived using automatic DSM extraction tools of LPS in different resolutions (10m, 12.5m, 15m, 17.5, & 20m) and ASTER DSM in (15m, 30m and 45m) using Topographic tools of ENVI. The GPS survey was conducted in differential mode to generate GCP library. The GCPs were used to generate DSM. The analysis shows that the 30m resolution DSM with 9.91m standard deviation with respect to GCPs was found better among DSMs generated using ASTER data. For CartoSat-1, 15m and 17.5m DSM represented SD of 4.2m & 3.8m and mean of 10.7m and 10.7m respectively. As per representation of shape of flow influencing features, 15m DSM was found to represent near-true shape as in satellite data. The DSM derived with break lines in the stereo point measurement tool was found to represent much better shape. The datum of derived DSM (WGS 84) of the study area was found to be ≈60m above the mean sea level. By using GIS technique three methods of datum transfer were evaluated (EGM 96, Spot heights and feature identification on High-resolution satellite data). From the analysis, feature identification on High-resolution satellite data was found to give better results among the other methods studied. The downgrading of spatial resolution of DSM was attempted for faster computation in 2D hydrodynamic model. The grid points were used to downgrade the resolution of DSM. From the 10m source DSM, 30m and 300m DSMs were derived. The derived DSMs showed good representation of flow influencing features at both resolutions. The two factors considered to decide the optimum resolution of digital surface model to run in MIKE FLOOD model are 1) vertical accuracy and 2) representation of the features on different resolutions of the DSM. As per the statistics of vertical accuracy of DSM 15m and 17.5m resolution DSM represented good results. Based on representation of the flow influencing features, 15m resolution DSM showed good result among the DSMs derived using classical point measurement tool. The 10m resolution DSM derived using Stereo point measurement tool represented the study area much better. Hence the optimum DSM resolution to be used to run MIKE FLOOD would be 15m resolution DSM in case of using classical point measurement tool. The integrated 1D / 2D hydrodynamic model was simulated using MIKE FLOOD software for the flood event occurred in Nuna and Barandia rivers of Kendrapara district in Orissa during August-

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September, 2003. The simulation was carried out at 300m and 30m resolutions of DSM. Validation of results with Satellite data (RadarSat-1) and field interview data was carried out. The analysis showed that the inundation extent derived from 300m resolution simulated model was found to be 63% of the inundation extent observed with RadarSat-1 data on 4th September 2003. The match reduced to 50% on 11th September, 2003. Flood depth on floodplain (Bachharai village) was found to ≈1.5m more on 30m resolution DSM and ≈1.5m less in 300m DSM compared to Field interview data. The analysis showed that the validation of hydrodynamic model results with combined Satellite (RadarSat-1) and field interview data was found to be a better method. Satellite data gave the inundation extent only in open areas while the field interview data gave depth of inundation in the settlement areas. Limitations of the study:

• The weather condition in the study area was considered as dry for simulation • Only gauge level data was used for river simulation • Surveyed river bathymetry data for the river simulation could not be used • No validation for the simulated cross section of the river bathymetry • The flow influencing features within the river like piers, embankment, etc. could not be

considered. Future prospects of the study: Further studies can be conducted using the database generated for the simulation.

• Model simulation in the floodplain in different resolutions can be taken up • Different grid conversion techniques into HD model and its effect on the floodplain can be

taken up • Simulation of the river and floodplain behaviour for different flood return periods • Scenario studies by incorporating flow influencing features within the river like embankment,

weirs, etc. which could be used as flood control structures.

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