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Flood risk management

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  • 1. FLOOD RISK MANAGEMENT INCORPORATINGSTAKEHOLDER PARTICIPATION AND CLIMATIC VARIABILITY PhD Dissertation Defence Presentation Hemalie Kalpalatha Nandalal Supervised by p y Dr. U.R. Ratnayake Department of Civil Engineering University of Peradeniya Peradeniya d Sri Lanka 24 th August 2011
  • 2. IntroductionObjectiveStudy area and Data usedMethods usedR ltResultsConclusions
  • 3. Problems related t fl diP bl l t d to flooding h have greatly i tl increased dover recent decades because of p p population ggrowth development of extensive infrastructures in close proximity to rivers increased frequency of extreme rainfall eventsGovernments all around the world spend millions offunds to reduce flood risk by taking flood protectivemeasures; mainly in two different approaches Structural measures (levees, flood walls, channel improvements and storage reservoirs) Non-structural measures (flood plain zoning, flood proofing, land use conversion, warning and evacuation, relief and rehabilitation and flood insurance rehabilitation,
  • 4. There has been a shift in paradigms from technical-Th h b hift i di f t h i loriented flood protection measures towards non-structural measures to reduce flood damage gFlood risk management is not only assessment andmitigation of flood risk, but also a continuous andholistich li ti societal adaptation and mitigation i t l d t ti d iti tiThere is a growing demand for better approachesfor risk identification and assessment particularly atlocal levelMain scope of this research is to find non-structuralmeasures that can be taken to reduce flood riskincorporating climate changes and stakeholders’views
  • 5. Investigate and iI i d incorporate climatic li ivariability in the process for managing floodrisk i kEvaluation of flood risk using conventionalmethod and investigating the application offuzzy logic in risk assessmentInquire how to create a management processwith enhanced participation of stakeholders p pDevelopment of an information system fordecision makers
  • 6. Kalu-Ganga river b i i S i L kK l G i basin in Sri Lanka Population density varies from 100 to 1000 persons per sq. km in the basin area River basin is located in an area that receives very high rainfall where average annual rainfall varies from 2000mm to 5000mm
  • 7. Kalu-Ganga river b i i S i L kK l G i basin in Sri Lanka
  • 8. Kalu-Ganga river b i i S i L kK l G i basin in Sri Lanka Administrative divisions of the Locations of rainfall and Kalu-Ganga river basin discharge gauging stations
  • 9. Topographical data On-line accessible topographic data sets used in this studyData set Link Coverage g Horiz. Res. (m) ( )SRTM http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp International ~ 90USGS http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html International ~ 900NGDC http://www.ngdc.noaa.gov/mgg/topo/globe.html http://www ngdc noaa gov/mgg/topo/globe html International ~ 900 GIS data used in the studyTypeT Scale S l Date f Production S D t of P d ti SourceContour Map, Land use Map, Spot heights, 1:10,000 2002 Survey Department, Sri LankaAdministrative boundariesLiDAR Data 2005 Survey Department, Sri LankaCross section data of the Kalu-Ganga river at 2007 NBRO1000 m interval
  • 10. Hydro-meteorological data / Census dataType Source DescriptionDaily Rainfall data Meteorological Daily rainfall during 1986 to 2009 at 14 gauging Department, stations Sri Lanka Daily rainfall from 1901 to 2009 at rainfall gauging station no. 14Discharge data Irrigation Discharges at 3 gauging stations) from 1986 to Department, 1996 and years 2003 and 2009 Sri LankaCensus data from the Census and Statistic Department of Sri Lanka as of2001
  • 11. Satellite dataSatellite/Sensor Date Source RemarksALOS/PALSAR 3rd March 2008 JAXA/GIC Dry dayALOS/PALSAR 3rd June 2008 JAXA/GIC Two days after a major flood
  • 12. Field dataFi ld d Social survey Based on a sample size calculation (WHO, 2005) 200 households in each district were surveyed Flood depth records p At random points where flood depths could be found either from people or marked surfaces were recorded with corresponding GPS coordinates
  • 13. Estimation of climate variabilityE i i f li i biliFlood hazard, vulnerability and riskassessmentStakeholder participation in flood riskmanagementFormulation of decision support system
  • 14. RainfallR i f ll gauging stations were selected i t ti l t dLong term rainfall data were tested usingstandard statistical test and tested to identifyany t trends dDifferent approaches were tested to identifyany trend that exists in the data series topredict rainfall with 0.01 probability (rainfall ith 0 01 probabilitwith 100 year return period) Using the parameters of the Gumbel distribution
  • 15. Redistribution of rainfall among the availableR di ib i f i f ll h il blrainfall gauging stations
  • 16. Estimation of flood hazardE i i f fl d h d Application of Rainfall-runoff model Application of Inundation modelTwo approaches were used to assess floodrisk Crisp approach and fuzzy approach y pp
  • 17. Application of Rainfall-runoff modelA li i f R i f ll ff d l
  • 18. Application of inundation modelA li i fi d i d l
  • 19. Hazard assessment (fH d (for th h Depth ND ∑ A(i, j ) ⋅HI ( j ) j =1 i HFD (i ) = ND ∑ A(i, j ) Area j =1 Area under flood in land unit i HFA (i ) = ×100 Total area of land unit i
  • 20. Hazard assessment (fH d (for th h Standardization HF (i ) HF S (i ) = HFmax Hazard Factor HFD (i ) + HFAS (i ) S HF (i ) = 2
  • 21. population d l i density (f i (for th h Poluation VFD (i ) = Land area dependency ratio (for th number of persons under age 20 + number of persons aged 55 or overVFA (i ) = ×100 Total T t l population l ti
  • 22. SimilarSi il to the Hazard factors, both of these h H df b h f hwere standardized VF (i ) VF (i ) = S VFmaxVF ( ) was taken as the hazard factor of theland unit as given VFP (i ) + VFA (i ) S S VF (i ) = (i 2
  • 23. InI general, risk i l i k incorporates the concepts of h fhazard and vulnerability (for th RF (i ) = HF (i ) × VF (i )
  • 24. Basic architecture of fuzzy expert system
  • 25. The membership functionsPopulation density of, 36 persons per ha need not be assignedto either ‘low’ or ‘medium’ vulnerable category, but can be amember of both categories, having a certain degree ofmembership in each category (27% low as well as 68% mediumvulnerable). )
  • 26. Input fI functions were identified i id ifi d For hazard identification the average flood depth and fl d extent of each GND d to 100 d h d flood f h due year rainfall were taken and fuzzy membership functions were developed Vulnerability was represented by the population density and the dependency ratio, similar to crisp risk evaluation
  • 27. Fuzzy rule bF l base The f fuzzified variables are related to each other f with a knowledge‐based rule system The rules describing the system can be: Rule 1: If population density is low and flood depth is low, then the risk is low. Rule 2: If population density is low and flood depth is high, then the risk is medium.
  • 28. Fuzzy model developed to estimate floodrisk depending on the hazard andvulnerability levels
  • 29. Adaptation i the only response available f theAd i is h l il bl for hrisk that will occur over the next several decadesbefore mitigation measures can have an effectIncreasing the adaptability of affected people tofloods or any natural disaster is a main objectiveof allocating funds by governmentsI thi research a model was dIn this h d l developed t l d toallocate available funds according to preferencesof flood affected people to improve theiradaptability to floods
  • 30. Increasing the adaptability or adaptiveI i h d bili d icapacity of the affected people will lead toreduce the vulnerability to a fl d or any d h l bili floodnatural disasterThus the adaptability incorporated to therisk formula can be written as,Risk = Hazard x Vulnerability x (1- Adaptability) As indicated by United Nations publications.
  • 31. Stakeholders iS k h ld involved i fl d events i the l d in flood in hKalu‐Ganga river basin were analysed toidentify the most contributing or the mostid if h ib i himportant stakeholdersThey were queried to investigate theirpreferences for non‐structural floodalleviation measures to improve adaptabilityDepending on the views of affected people p g p pthe adaptability was formulated Adaptability = f (View1, View2, ……….) (View1 View2 )
  • 32. Fuzzy model was dF d l developed to assess l dadaptability depending on the views of thestakeholders k h ldMembership function was selected such thatif 50% of the community prefer boats there isno improvement in adaptability by spendingmore than 50% of the available funds toprovide boats for flood affected people
  • 33. Providing a website fP idi b i for people to access lflood risk information is an effective way ofinforming the public about the susceptibilityi f i h bli b h ibilito flooding that they may otherwise not beaware of fThe Adobe Dreamweaver software was used yto create flood information system
  • 34. Estimation of climate variabilityFlood hazard, vulnerability and riskassessmentStakeholder participation in flood riskmanagementFormulation of the decision support system pp y
  • 35. FittedFi d trends f d found f l d for long term d data series i(all with increasing trends) Linear y = 0.041x + 74.24 Exponential y = 217.2e-2E-0x Logarithmic y = 84.07ln(x) - 481.1 Power y = 2721.x-0.38Trend of parameters of Gumbel distributionwas found and that was used to determinethe rainfall at different return periods due toclimatic variation
  • 36. Parameters of GP f Gumbel di ib i b l distribution for time periods of 30 years f f i i d f from 1901For Ratnapura g g g p gauging 1901-1930 1931-1960 1961-1990 1991-2009station (1) (2) (3) (4)Average of the dataseries 150.64 163.66 152.03 158.16St dev. of the data 40.38 77.15 56.35 81.08441seriesScale parameter (α) 0.031 0 031 0.016 0 016 0.0227 0 0227 0.015 0 015Location parameter (m) 132.47 128.95 126.68 121.69
  • 37. Plot of the trend of parameters of Gumbel distribution
  • 38. Comparison of the expected and observed rainfallC f h d d b d f llPeriods of Predicted Gumbel parameters Expected 100 Maximum rainfallyears m Alpha year rainfall observed so far1901-1930 133.10 0.02900 291.7 269.21931-1960 128.12 0.02206 336.5 394.4 96 9901961-1990 125.21 5 0 0 80 0.01801 380 5 380.5 294.9 9 91991-2020 123.14 0.01513 427.0 392.5------2021-20502021 2050 121.54 121 54 0.01290 0 01290 477.9 477 92051-2080 120.23 0.01108 535.3
  • 39. Predicted Gumbel parameters Expected 100 year rainfallPeriod of years (Basin average) m Alpha Area ave /Arithmetic ave ave./Arithmetic ave.1901-1930 139.95 0.049626 220.1 232.61931-1960 134.97 0.042695 232.5 242.71961-1990 132.06 0.038640 245.8 251.11991-2020 129.99 0.035763 253.6 258.62021-2050 128.39 0.033532 259.8 265.62051-2081 127.08 0.031708 265.4 272.2
  • 40. Gauge 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Stations100yr100 293 320 325 356 331 447 293 479 302 271 330 292 352 40650yr 268 290 289 322 302 392 269 426 275 248 300 262 315 36320yr 235 249 240 278 263 318 236 355 239 217 261 222 266 30510yr 210 218 203 243 233 262 211 300 212 193 231 192 228 2602yr 143 137 105 153 154 113 146 157 139 132 153 111 129 142
  • 41. Comparison of the selected rainfall with rainfall at realC f h l d f ll h f ll lflood events
  • 42. HEC HMSApplication of HEC-HMSRainfall at 14 gauging stations and runoff at 3 gauging stationsfrom 1984 to 2009 were used to calibrate the hydrologic model
  • 43. HEC HMS Application of HEC-HMS Two sub-basin configurations developed with HEC-GeoHMS4 sub-basin model 10 sub-basin model
  • 44. HEC HMS Application of HEC-HMSTen storm events were used for calibration and verification ofboth models Event Time period 1989 May-June 22 days 1992 N November b 13 d days 1993 May 26 days 1993 October 17 days 1994 May 34 days 1996 June 14 days 2003 May 13 days 2003 July 14 days 2008 M J May-June 15 d days 2008 July 14 days
  • 45. HEC HMS Application of HEC-HMSHydrographs resulted from calibrated and verified HEC-HMS model forKalu-Ganga river Rainfall runoff at Putupaula for Rainfall runoff at Putupaula for 1994 rainfall event for 4 basin 1994 rainfall event for 10 basin model model
  • 46. HEC HMSCalibrated HEC-HMS model was used to derive discharges due to expected100 year rainfallRiver reach Flow data/(m3/s)Kalu Ganga 403.2Wey Ganga 465.90Maha Ela 123.10 123 10Hangamuwa 263.70NiriElle 155.70Yatipuwa Ela 106.40Kuru Ganga 594.50Galathure 147.00Elagawa 2605.50Mawakoya 245.50Kuda Ganga 1260.70
  • 47. HEC RAS Application of HEC-RAS Flood modelling was carried out in two sections separately due to the difficulty in handing large data p y y g g filesRiver reach - downstream of Ellagawa River reach -upstream of Ellagawa upstream
  • 48. FloodFl d extent and d d depth d i d f h derived from HEC RAS HEC-RASmodel For Kalutara district For Ratnapura district
  • 49. ModelM d l was verified using two approaches ifi d i h field survey satellite SAR images
  • 50. Flood depths d iFl d d h during the fl d on J h flood June 2008 werecollected from flood affected people and recorded withcoordinates taken from GPS receivers during a fieldsurvey
  • 51. Verification of the flood depth and flood extentV ifi i f h fl d d h d fl dby satellite SAR imagesThe number of pixels rated aswet b satellite i by lli image and the d hHEC-RAS model were calculatedas 55%
  • 52. Number of GNDs fall into each category of Risk:Crisp approachDistrict Very low Low Medium High Very HighKalutara 83 98 4 0 0Ratnapura 33 26 7 0 1Number of GNDs fall into each category of risk level:Fuzzy approachDistrict Very low Low Medium High Very HighKalutara 7 66 77 32 3Ratnapura 8 12 29 13 5
  • 53. Flood relief expenses for June 2008 flood and risklevels obtained by the crisp and fuzzy approaches forGNDs in Ratnapura District p GND Relief expense/ha Risk criteria (LKR) Crisp Fuzzy Ratnapura Rs.8,085.00 Very high risk Very high risk Godigamuwa Rs.5,108.00 Medium risk Very high risk Muwagama g Rs.4,511.00 , Low risk High risk g Pallegedara Rs.2,547.00 Medium risk High risk Angammana Rs.2,004.00 Very low risk Medium risk Pahala Pahala- Rs.1,260.00 Rs 1 260 00 Low risk Medium risk Hakamuva Mada Baddara Rs. 505.00 Very low risk Low risk Withangagama Rs. 43 00 Rs 43.00 Very low risk Very low risk
  • 54. A structured questionnaire survey was carried out d i i i d to gather views of flood affected people in 8 GNDs in the Ratnapura district and 12 GNDs in the Kalutara district covering 400 familiesSuggestions on possible solutions to reduce the flood risk were obtained from them
  • 55. Following suggestions were idF ll i i identified as the ifi d hmost preferred solutions Improve infrastructure facilities Installation of a better warning system Improve river flow system Release funds to improve individual dwellings Supply of boats for flood affected people Resettlement of the flood affected people
  • 56. Preference for non-structural fl d alleviationP f f l flood ll i imeasures of the residents 10% 10% 10% River flow Resettlement Boats 20% Dwelling 10% 40% Warning Infra structures
  • 57. Preferences of a flood affected communityP f f fl d ff d iwere taken as fuzzy variables in thedevelopment of the modeld l f h d lThe membership functions were developedusing the preferences of the flood affectedpeople
  • 58. Fuzzy model developed to estimate finaladaptability depending on the % fundallocation
  • 59. Adaptability for different fund allocation combinations Number b % of f d provided f each proposed d l f fund d d for h d developments Boats Infrastructure Warning Dwelling Re settlement River flow Adaptability 1 5 50 20 15 5 5 0.630 2 10 60 10 20 0 0 0.731 3 20 60 10 10 0 0 0.725 4 40 20 10 10 10 10 0.533 5 50 10 0 20 10 10 0.470 6 10 10 20 20 20 20 0.599 7 10 20 20 10 20 20 0.607 8 10 30 20 20 20 10 0.623 9 0 30 20 30 10 10 0.580 10 0 10 10 10 50 20 0.584 11 10 40 10 20 10 10 0.710 12 5 33 3 30 14 15 0.584 13 10 33 12 23 11 11 0.609 0 609 14 13 41 10 28 3 5 0.773
  • 60. Risk HRi k = Hazard x Vulnerability x (1 d d V l bili (1-adaptability) bili )
  • 61. Providing a website f people to access fl dP idi b i for l floodrisk information is an effective way ofinforming the public about the susceptibility toi f i h bli b h ibiliflooding that they may otherwise not be awareoff Website
  • 62. DATA the topographical data taken from websites, that is the SRTM DEM data are fairly acceptable data, the best representation of the topography is achieved by 1:10,000 contour maps available at y , p the Department of SurveySoftware used HEC software series developed by US Army Corps of Engineers of Hydrological Engineering Centre can be used effectively in the data rich Kalu-Ganga river basin for rainfall-runoff modelling as well as for flood modelling
  • 63. Investigation of climatic variationI i i f li i i i The analysis indicated that the Gumbel parameters of the extreme rainfall intensity over f h i f ll i i the Kalu-Ganga river basin have an increasing trend The proposed method could be used to determine extreme rainfalls expected to occur if same trend in the climate change exists The method used to redistribute return periods p among the rainfall gauging stations was very much applicable in similar situations
  • 64. Hydrological d hydraulic modellingH d l i l and h d li d lli The results confirmed the applicability of the hydraulic model HEC RAS in the prediction of h d li d l HEC-RAS i h di i f flood inundation in the Kalu-Ganga river basin fairly accurately The results of this study indicate that the event based semi distributed conceptual model HEC-HEC HMS as suitable in modelling rainfall runoff of the Kalu-Ganga river basin
  • 65. RiskRi k analysis l i Two approaches were used to estimate the risk The conventional crisp method based flood risk levels did not capture the risk as expected The fuzzy logic based approach has captured the levels of indicator parameters, h l l f i di hazard and d d vulnerability factors, effectively and resulted in a fair risk distribution The adaptability model proposed could be used for fund allocation to reduce flood risk The novel technique presented in this research is the application of fuzzy inference systems which can be recommended as a good method for the evaluation of risk f i k
  • 66. The dTh developed W b b l d Web-based d i i d decision supportsystem provides information regardingfloodsfl d to general public, d i i l bli decision makers kand scientific community to make betterdecisions i fl d risk reductiond i i in flood i k d i
  • 67. It i recommended th t l d use change also is d d that land h lincorporated in future flood predictionsIt i b tt if unsteady flow conditions are is better t d fl ditiapplied in the flood modelling to capture theduration of flooding flood wave velocity and flooding,rate of rise of water levelIt is better if infrastructure vulnerability forcritical facilities are also included such as,roads, railroads, hospitals, public buildings,police stations, water treatment or sewagepplants, airports, etc p
  • 68. Instead of kI d f keeping fl d related i f i flood l d information iin institutional environment it isrecommended to place them where anyone d d l h hcan access and use themApart from informative web page if aninteractive graphical user interface usingweb GIS system can be developed it will bemore useful for decision makers at each level
  • 69. Papers presented at local conferences1. Nandalal, H.K. and U. Ratnayake (2008), “Verification of a delineated stream network from a DEM: Application to Kalu River in Sri Lanka”, Proceedings, The fifth National Symposium on Geo-Informatics, Colombo, Sri Lanka, pp. 187.2.2 Nandalal, H.K. Nandalal H K and U R Ratnayake (2008) “Comparison of a Digital Elevation Model with the U.R. (2008), Comparison heights extracted from the contour map”, Proceedings, Peradeniya University Research Sessions, Vol 13,1, pp. 145-147.3. Nandalal, H.K. and U.R. Ratnayake (2009), “Editing a Digital Elevation Model to Achieve a correct Stream Network: An application to Kalu-Ganga river in Sri Lanka”, Proceedings, 4th Annual Conference on Towards the Sustainable Management of Earth Resources-A Multi-disciplinary Resources A Multi disciplinary Approach, University of Moratuwa, Sri Lanka, pp. 9-12.4. Nandalal, H.K. and U. R. Ratnayake (2009), “Effect of Different Rainfalls on Kalu-Ganga River Runoff”, Abstracts, First National Symposium on Natural Resources Management (NRM2009), Department of Natural Resources, Sabaragamuwa University of Sri Lanka, pp. 30.5. Nandalal, H.K. and U. R. Ratnayake (2009), “Effect of Grid Size on Delineating River Network”, Proceedings, The Sixth National Symposium on Geo-Informatics, Colombo, Sri Lanka, pp. 75- 80.6. Nandalal, H.K. and U. R. Ratnayake (2009), ”Modeling Kalu-Ganga River Basin for Predicting Runoff for Different Frequency Rainfalls , Proceeding, Peradeniya University Research Sessions, Rainfalls”, December 2009, pp. 486-488.7. Nandalal, H.K. and U. R. Ratnayake (2009), “Use of HEC-GeoHMS and HEC-HMS to perform grid- based hydrologic analysis of a watershed”, Proceedings, Annual Research Sessions, Sri Lanka Association for the Advancement of Science , December 2009, In CD.8.8 Nandalal, H.K. Nandalal H K and U Ratnayake (2010) “Prediction of Rainfall Incorporating Climatic U. (2010), Prediction Variability”, Proceeding, Peradeniya University Research Sessions, December 2010, pp. 546-548.
  • 70. Papers presented at I tP t d t International conferences ti l f1. Nandalal, H.K. (2008), “Global on-line GIS Data Availability for Hydrological Modeling in SriLanka”, Proceedings, Second International Symposium, University of Sabaragamuwa, Sri Lanka, pp. 95-1002. Nandalal, H.K. and U.R. Ratnayake (2008), “Comparison of a river network delineated from different digital elevation models available in public domain”, Proceedings, 29th Asian Conference on Remote Sensing, CD_ROM, Colombo, Sri Lanka.3. Nandalal, H.K. (2009), “Stakeholder Analysis in Flood Risk Management at Ratnapura”, Presentation made at International Conference on “Impacts of Natural hazards and Disasters on Social and Economic” held at Ahungalla, Sri Lanka.4. Nandalal, H.K. and U. R. Ratnayake (2009), “Flood Plain Residents’ Preferences for Non-Structural Flood Alleviation Measures in The Kalu-Ganga River, Ratnapura, Sri Lanka”, Proceedings, International Exchange Symposium, University of Ruhuna Sri Lanka, pp. 116-119.5. Nandalal, H.K. and U. Ratnayake (2010), “Setting up of indices to measure vulnerability of structures during a flood”, published at “International Conference on Sustainable Built Environments – The state of the art”, 13-14 December 2010, Kandy, Sri Lanka, pp. 379-386.
  • 71. Journal papersJ l1. Nandalal, H.K.1 Nandalal H K and U R Ratnayake (2010) U.R (2010), “Event Based Modelling of a Watershed using HEC-HMS”. Engineer (Journal of Institution of g Engineers, Sri Lanka), 43(2), 28-37.2. Nandalal, H. and Ratnayake, U. (2011), Flood risk analysis using fuzzy models. Journal of models Flood Risk Management, 4: 128–139. doi: 10.1111/j.1753-318X.2011.01097.x

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