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Flood Risk Magement Incorperating Stakeholder Participation and Climatic Variability

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PhD Presentation

  1. 1. 8/22/2011 FLOOD RISK MANAGEMENT INCORPORATING STAKEHOLDER PARTICIPATION AND Introduction CLIMATIC VARIABILITY Objective PhD Dissertation Defence Presentation Study area and Data used Hemalie Kalpalatha Nandalal Methods used Supervised by Dr. U.R. Ratnayake Results Department of Civil Engineering University of Peradeniya Conclusions Peradeniya Sri Lanka 24th August 2011Problems related to flooding have greatly increased There has been a shift in paradigms from technical-over recent decades because of oriented flood protection measures towards non- population growth structural measures to reduce flood damage development of extensive infrastructures in close Flood risk management is not only assessment and proximity to rivers mitigation of flood risk, but also a continuous and increased frequency of extreme rainfall events holistic h li ti societal adaptation and mitigation i t l d t ti d iti tiGovernments all around the world spend millions of There is a growing demand for better approachesfunds to reduce flood risk by taking flood protective for risk identification and assessment particularly atmeasures; mainly in two different approaches local level Structural measures (levees, flood walls, channel improvements and storage reservoirs) Main scope of this research is to find non-structural Non-structural measures (flood plain zoning, flood measures that can be taken to reduce flood risk proofing, land use conversion, warning and evacuation, incorporating climate changes and stakeholders’ relief and rehabilitation, and flood insurance viewsInvestigate and incorporate climatic Kalu-Ganga river basin in Sri Lankavariability in the process for managing flood Population density varies fromrisk 100 to 1000 persons per sq. km in the basin areaEvaluation of flood risk using conventionalmethod and investigating the application offuzzy logic in risk assessmentInquire how to create a management process River basin is located in an area that receives very highwith enhanced participation of stakeholders rainfall where average annual rainfall varies from 2000mmDevelopment of an information system for to 5000mmdecision makers 1
  2. 2. 8/22/2011 Kalu-Ganga river basin in Sri Lanka Kalu-Ganga river basin in Sri Lanka Administrative divisions of the Locations of rainfall and Kalu-Ganga river basin discharge gauging stations Topographical data Hydro‐meteorological data / Census data On‐line accessible topographic data sets used in this study Type Source Description Data set Link Coverage Horiz. Res. (m) SRTM http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp International ~ 90 Daily Rainfall data Meteorological Daily rainfall during 1986 to 2009 at 14 gauging Department, stations USGS http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html International ~ 900 Sri Lanka NGDC http://www.ngdc.noaa.gov/mgg/topo/globe.html International ~ 900 Daily rainfall from 1901 to 2009 at rainfall gauging station no. 14 GIS data used in the studyType Scale Date of Production Source Discharge data Irrigation Discharges at 3 gauging stations) from 1986 toContour Map, Land use Map, Spot heights, 1:10,000 2002 Survey Department, Sri Lanka Department, 1996 and years 2003 and 2009Administrative boundaries Sri LankaLiDAR Data 2005 Survey Department, Sri Lanka Census data from the Census and Statistic Department of Sri Lanka as of 2001Cross section data of the Kalu‐Ganga river at 2007 NBRO100 m interval Satellite data Field data Social survey Based on a sample size calculation (WHO, 2005) 200 households in each district were surveyed Satellite/Sensor Date Source Remarks ALOS/PALSAR 3rd March 2008 JAXA/GIC Dry day Flood depth records ALOS/PALSAR 3rd June 2008 JAXA/GIC Two days after a major flood At random points where flood depths could be found from either from people or marked surfaces were recorded with GPS coordinates 2
  3. 3. 8/22/2011Estimation of climate variability Rainfall gauging stations were selected Long term rainfall data were tested usingFlood hazard, vulnerability and risk standard testsassessment Different approaches were tested to identify anyStakeholder participation in flood risk trend that exists in the data series to predictmanagement rainfall with 0.01 probability (rainfall with 100 year return period) Using standard trends available in Microsoft ExelFormulation of decision support system Using the parameters of the Gumbel distribution Redistribution of rainfall among the available rainfall gauging stationsEstimation of flood hazard Application of Rainfall-runoff model Application of Rainfall-runoff model Application of Inundation modelTwo approaches were used to assess floodrisk Crisp approach and fuzzy approach 3
  4. 4. 8/22/2011Application of inundation model Hazard assessment (for th GN division Depth ND ∑ A(i, j ) ⋅HI i ( j ) j =1 HFD (i) = ND ∑ A(i, j ) Area j =1 Area under flood in land unit i HFA (i ) = × 100 Total area of land unit iHazard assessment (for th GN division population density (for th GN division Standardization Poluation HF (i ) VFD (i) = HF S (i ) = Land area HFmax Hazard Factor dependency ratio (for th GN division HF (i ) + HF (i ) S S HF (i ) = D A number of persons under age 20 + number of persons aged 55 or over 2 VFA (i ) = ×100 Total populationSimilar to the Hazard factors, both of these In general, risk incorporates the concepts ofwere standardized hazard and vulnerability (for th GN division VF (i ) VF S (i ) = VFmaxVF ( ) was taken as the hazard factor of theland unit as given RF (i ) = HF (i ) × VF (i ) VFP (i) + VFA (i) S S VF (i ) = 2 4
  5. 5. 8/22/2011 The membership functions Population density of, 36 persons per ha need not be assigned to either ‘low’ or ‘medium’ vulnerable category, but can be a member of both categories, having a certain degree of membership in each category (27% low as well as 68% medium Basic architecture of fuzzy expert system vulnerable).Input functions were identified Fuzzy rule base For hazard identification the average flood depth and flood extent of each GND due to 100 The fuzzified variables are related to each other year rainfall were taken and fuzzy membership with a knowledge‐based rule system functions were developed The rules describing the system can be: Vulnerability was represented by the population density and the dependency ratio, similar to Rule 1: If population density is low and flood depth crisp risk evaluation is low, then the risk is low. Rule 2: If population density is low and flood depth is high, then the risk is medium. Adaptation is the only response available for the risk that will occur over the next several decades before mitigation measures can have an effect Increasing the adaptability of affected people to floods or any natural disaster is a main objective of allocating funds by governments In this research a model was developed to allocate available funds according to preferences of flood affected people to improve their adaptability to floods 5
  6. 6. 8/22/2011Increasing the adaptability or adaptive Stakeholders involved in flood events in thecapacity of the affected people will lead to Kalu‐Ganga river basin were analysed toreduce the vulnerability to a flood or any identify the most contributing or the mostnatural disaster important stakeholdersThus the adaptability incorporated to the They were queried to investigate theirrisk formula can be written as, preferences for non‐structural flood Risk = Hazard x Vulnerability x (1- Adaptability) alleviation measures to improve adaptability As indicated by United Nations publications. Depending on the views of affected people the adaptability was formulated Adaptability = f (View1, View2, ……….)Fuzzy model was developed to assess Providing a website for people to accessadaptability depending on the views of the flood risk information is an effective way ofstakeholders informing the public about the susceptibilityMembership function was selected such that to flooding that they may otherwise not beif 50% of the community prefer development aware offof infrastructure there is no improvement inadaptability by spending more than 50% of The Adobe Dreamweaver software was usedthe available funds to create flood information system Fitted trends found for long term data seriesEstimation of climate variability (all with increasing trends) Linear y = 0.041x + 74.24Flood hazard, vulnerability and risk Exponential y = 217.2e-2E-0xassessment Logarithmic y = 84.07ln(x) - 481.1 Power y = 2721.x-0.38Stakeholder participation in flood risk Trend of parameters of Gumbel distributionmanagement was found and that was used to determine the rainfall at different return periods due toFormulation of the decision support system climatic variation 6
  7. 7. 8/22/2011Parameters of Gumbel distribution for time periods of 30 years from 1901 Plot of the trend of parameters of Gumbel distributionFor Ratnapura 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 series 40.38 77.15 56.35 81.08441Scale parameter (α) 0.031 0.016 0.0227 0.015Location parameter (m) 132.47 128.95 126.68 121.69 Comparison of the expected and observed rainfall Predicted Gumbel parameters Expected 100 year rainfall  Period of years (Basin average) m Alpha Area ave./Arithmetic ave.Periods of  Predicted Gumbel parameters Expected 100  Maximum rainfall years m Alpha year rainfall observed so far 1901‐1930 139.95 0.049626 220.1 232.61901 19301901‐1930 133.10 0.02900 291.7 269.21931‐1960 128.12 0.02206 336.5 394.4 1931‐1960 134.97 0.042695 232.5 242.71961‐1990 125.21 0.01801 380.5 294.9 1961‐1990 132.06 0.038640 245.8 251.11991‐2020 123.14 0.01513 427.0 392.5‐‐‐‐‐‐ 1991‐2020 129.99 0.035763 253.6 258.62021‐2050 121.54 0.01290 477.9 2021‐2050 128.39 0.033532 259.8 265.62051‐2080 120.23 0.01108 535.3 2051‐2081 127.08 0.031708 265.4 272.2 Gauge  Comparison of the selected rainfall with rainfall at real 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Stations flood events 100yr 293 320 325 356 331 447 293 479 302 271 330 292 352 406 50yr y 268 290 289 322 302 392 269 426 275 248 300 262 315 363 20yr 235 249 240 278 263 318 236 355 239 217 261 222 266 305 10yr 210 218 203 243 233 262 211 300 212 193 231 192 228 260 2yr 143 137 105 153 154 113 146 157 139 132 153 111 129 142 7
  8. 8. 8/22/2011 Application of HEC-HMS Application of HEC-HMS Rainfall at 14 gauging stations and runoff at 3 gauging stations Two sub-basin configurations developed with HEC-GeoHMS from 1984 to 2009 were used to calibrate the hydrologic model 4 sub-basin model 10 sub-basin model Application of HEC-HMS Application of HEC-HMS Ten storm events were used for calibration and verification of Hydrographs resulted from calibrated and verified HEC-HMS model for both models Kalu-Ganga river Event Time period 1989 May‐June 22 days November 1992 N b days 13 d 1993 May 26 days 1993 October 17 days 1994 May 34 days 1996 June 14 days 2003 May 13 days Rainfall runoff at Putupaula for Rainfall runoff at Putupaula for 2003 July 14 days 1994 rainfall event for 4 basin 1994 rainfall event for 10 basin 2008 May‐June 15 days model model 2008 July 14 daysCalibrated HEC-HMS model was used to derive discharges due to expected Application of HEC-RAS100 year rainfall Flood modelling was carried out in two sectionsRiver reach Flow data/(m3/s) separately due to the difficulty in handing large data filesKalu 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 245.50 River reach - downstream of Ellagawa River reach -upstream of EllagawaKuda Ganga 1260.70 8
  9. 9. 8/22/2011Flood extent and depth derived from HEC-RAS Model was verified using two approachesmodel field survey satellite SAR images For Kalutara district For Ratnapura districtFlood depths during the flood on June 2008 werecollected from flood affected people and recordedwith coordinates taken from GPS receivers during afield surveyVerification of the flood depth and floodextent by satellite SAR imagesThe number of pixels rated aswet by satellite image and the b lli i d hHEC-RAS model were calculatedis 55% 9
  10. 10. 8/22/2011 Number of GNDs fall into each category of Risk: Crisp approach District Very low Low Medium High Very High Kalutara 83 98 4 0 0 Ratnapura 33 26 7 0 1 Number of GNDs fall into each category of risk level: Fuzzy approach District Very low Low Medium High Very High Kalutara 7 66 77 32 3 Ratnapura 8 12 29 13 5Flood relief expenses for June 2008 flood and risk A structured questionnaire survey was carried outlevels obtained by the crisp and fuzzy approaches for to gather views of flood affected people in 8GNDs in Ratnapura District GNDs in the Ratnapura district and 12 GNDs in GND Relief expense/ha  Risk criteria the Kalutara district covering 400 families (LKR) Crisp Fuzzy Ratnapura Rs.8,085.00 Very high risk Very high risk Godigamuwa Rs.5,108.00 Medium risk Very high risk Suggestions on possible solutions to reduce the Muwagama Rs.4,511.00 Low risk High risk flood risk were obtained from them Pallegedara Rs.2,547.00 Medium risk High risk Angammana Rs.2,004.00 Very low risk Medium risk Pahala‐ Rs.1,260.00 Low risk Medium risk Hakamuva Mada Baddara Rs.  505.00 Very low risk Low risk Withangagama Rs.    43.00 Very low risk Very low risk 10
  11. 11. 8/22/2011 Following suggestions were identified as the Preference for non-structural flood alleviation most preferred solutions measures of the residents Improve infrastructure facilities 10% 10%  10% River flow Installation of a better warning system Resettlement Boats Improve river flow system Release funds to improve individual dwellings 20% Dwelling Supply of boats for flood affected people Resettlement of the flood affected people 10%  40%  Warning Infra structures Preferences of a flood affected community Fuzzy model developed to estimate final adaptability depending on the % fund were taken as fuzzy variables in the allocation development of the model The membership functions were developed using the preferences of the flood affected peopleAdaptability for different fund allocation combinations Risk = Hazard x Vulnerability x (1-adaptability) Number % of fund provided for each proposed 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 14 13 41 10 28 3 5 0.773 11
  12. 12. 8/22/2011Providing a website for people to access flood DATArisk information is an effective way of the topographical data taken from websites,informing the public about the susceptibility to that is the SRTM DEM data, are fairly acceptable the best representation of the topography isflooding that they may otherwise not be aware achieved by 1:10,000 contour maps available at y , poff the Department of Survey Website Software 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 Investigation of climatic variation Hydrological and hydraulic modelling The analysis indicated that the Gumbel The results confirmed the applicability of the parameters of the extreme rainfall intensity over hydraulic model HEC-RAS in the prediction of the Kalu-Ganga river basin have an increasing flood inundation in the Kalu-Ganga river basin trend fairly accurately The proposed method could be used to The results of this study indicate that the event determine extreme rainfalls expected to occur if based semi distributed conceptual model HEC- same trend in the climate change exists HMS as suitable in modelling rainfall runoff of The method used to redistribute return periods the Kalu-Ganga river basin among the rainfall gauging stations was very much applicable in similar situations Risk analysis The developed Web-based decision support Two approaches were used to estimate the risk system provides information regarding The conventional crisp method based flood risk levels did not capture the risk as expected floods to general public, decision makers The fuzzy logic based approach has captured the and scientific community to make better levels of indicator parameters, h l l f i di hazard and d d decisions i fl d risk reduction d i i in flood i k d i 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 12
  13. 13. 8/22/2011 It is recommended that land use change also Instead of keeping flood related information incorporated in future flood predictions in institutional environment it is It is better if unsteady flow conditions are recommended to place them where anyone applied in the flood modelling to capture the can access and use them duration of flooding, flood wave velocity and flooding Apart from informative web page if an rate of rise of water level interactive graphical user interface using It is better if infrastructure vulnerability for web GIS system can be developed it will be critical facilities are also included such as, roads, railroads, hospitals, public buildings, more useful for decision makers at each level police stations, water treatment or sewage plants, airports, etcPapers presented at local conferences Papers presented at International 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 1. Nandalal, H.K. (2008), “Global on-line GIS Data Availability for Hydrological Geo-Informatics, Colombo, Sri Lanka, pp. 187.2. Nandalal, H.K. and U.R. Ratnayake (2008), “Comparison of a Digital Elevation Model with the Modeling in SriLanka”, Proceedings, Second International Symposium, heights extracted from the contour map”, Proceedings, Peradeniya University Research Sessions, University of Sabaragamuwa, Sri Lanka, pp. 95-100 Vol 13,1, pp. 145-147. 2. Nandalal, H.K. and U.R. Ratnayake (2008), “Comparison of a river network3. Nandalal, H.K. and U.R. Ratnayake (2009), “Editing a Digital Elevation Model to Achieve a correct delineated from different digital elevation models available in public domain”, Stream Network: An application to Kalu-Ganga river in Sri Lanka”, Proceedings, 4th Annual Proceedings, 29th Asian Conference on Remote Sensing, CD_ROM, Colombo, Sri Conference on Towards the Sustainable Management of Earth Resources-A Multi-disciplinary Resources A Multi disciplinary Lanka. 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 3. Nandalal, H.K. (2009), “Stakeholder Analysis in Flood Risk Management at Runoff”, Abstracts, First National Symposium on Natural Resources Management (NRM2009), Ratnapura”, Presentation made at International Conference on “Impacts of Department of Natural Resources, Sabaragamuwa University of Sri Lanka, pp. 30. Natural hazards and Disasters on Social and Economic” held at Ahungalla, Sri5. Nandalal, H.K. and U. R. Ratnayake (2009), “Effect of Grid Size on Delineating River Network”, Lanka. Proceedings, The Sixth National Symposium on Geo-Informatics, Colombo, Sri Lanka, pp. 75- 4. Nandalal, H.K. and U. R. Ratnayake (2009), “Flood Plain Residents’ Preferences 80. for Non-Structural Flood Alleviation Measures in The Kalu-Ganga River,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, Ratnapura, Sri Lanka”, Proceedings, International Exchange Symposium, December 2009, pp. 486-488. University of Ruhuna Sri Lanka, pp. 116-119.7. Nandalal, H.K. and U. R. Ratnayake (2009), “Use of HEC-GeoHMS and HEC-HMS to perform grid- 5. Nandalal, H.K. and U. Ratnayake (2010), “Setting up of indices to measure based hydrologic analysis of a watershed”, Proceedings, Annual Research Sessions, Sri Lanka vulnerability of structures during a flood”, published at “International Association for the Advancement of Science , December 2009, In CD. Conference on Sustainable Built Environments – The state of the art”, 13-148. Nandalal, H.K. and U. Ratnayake (2010), “Prediction of Rainfall Incorporating Climatic December 2010, Kandy, Sri Lanka, pp. 379-386. Variability”, Proceeding, Peradeniya University Research Sessions, December 2010, pp. 546-548. Journal papers 1. Nandalal, H.K. and U.R Ratnayake (2010), “Event Based Modelling of a Watershed using HEC-HMS”. Engineer (Journal of Institution of Engineers, Sri Lanka), 43(2), 28-37. 2. Nandalal, H. and Ratnayake, U. (2011), Flood risk analysis using fuzzy models. Journal of Flood Risk Management, 4: 128–139. doi: 10.1111/j.1753-318X.2011.01097.x 13

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