Flood risk assessment for sustainable urban development : Case study of Marikina-Pasig-San Juan river basin, Manila

Transcription

1 International Conference in Urban and Regional Planning "Planning towards Sustainability and Resilience" March, 2018 Manila, Philippines Flood risk assessment for sustainable urban development : Case study of Marikina-Pasig-San Juan river basin, Manila Mohamed KEFI, PhD Dr. Binaya Kumar MISHRA, Dr. Yoshifumi MASAGO March 14 th, 2018

2 Water and Urban Initiative project (WUI) 2 Overall objectives Contribute to evidence-based policymaking for sustainable water environments by assessing their values in Asian cities Provide scientific tools to forecast the future state of urban water environments Support capacity development aiming at improving urban water environments Research components Future Projections 1. Flood inundation 2. Urban water quality 3. Flood-related infectious diseases Project period Economic evaluation 1. Flood damage 2. Value of improving water quality 3. Low carbon technologies in WWTPs Policy Recommendations 1. Flood control 2. Wastewater management August 2014 March 2018

3 3 Background : Global Natural Disaster (1) 2016 : 750 natural events and USD 175 Bn Overall Losses Events 32% 50% Hydrological disasters include flood and landsides. This group is the most important class of natural disaster (Munich RE, 2017)

4 4 Background : Global Natural Disaster (2) Events Overall Losses Asian regions are considered as the most exposed areas in the world to natural disasters (Munich RE, 2017)

5 Background : Flood 5 - Frequency and intensity of natural disasters such as flood events are increasing - Unplanned Land use and climate change are the main drivers to the rise of flood events - Increasing flood events can conduct to heavy damages with negative social and economic impact - Asian Regions such as the Philippines are considered as the most exposed areas in the word to flood hazard - Typhon Ondoy hit many regions (Luzon Island) in September Million persons were affected and more than 155,000 houses were damaged in totally or partially (NDCC, 2009) (Ondoy 2009, Marikina DRRM) (Ondoy 2009, Marikina DRRM) (Ondoy 2009, LLDA)

6 6 Objectives To explore feasible options for flood risk management towards improving urban water environment of Manila Establishment of flood models (hydrologic and hydraulic) for future flood assessment in Manila Scenarios development for climate and landuse change considerations and alternative countermeasures Evaluate the future tangible flood damages in 2030 using spatial analysis approach

7 Overview of the methodology 7 Flood risk assessment Components Hazard Exposure Vulnerability Tools Flood simulation (FLO-2D) Parameters Flood water depth based on Return Period Land Use map/replacememt cost Asset exposed/element at risk value Flood damage functions Susceptibility of the exposed assets at contact with water Flood Damage assessment

8 -Climate Change (RCPs and GCMs) -Topography (DEM) Data -Soil type -Land Use Change using Land Change Modeler (LCM) and LANDSAT products -Stromwater Infrastructure Hypothesis : 1- Assessment of flood damage at Meso-scale 2- Built-up class is applied as an aggregated land use categories 3- Property value is assumed to be the replacement cost of degraded assets Raster-based GIS approach FLO-2D Software Inundated areas/flood Depth Comp.1 Local property value data Calibration/Validation Overlay Land Use Land Cover (LULC) Land Use Classification/LCM LANDSAT Affected LULC Comp.2 Flood Damage rate for built-up (Depth damage function) Total Damage per grid = Damage rate X Affected area X Unit property value Flood Damage map at grid level Scenarios/Future Assessment Comp.3 Grid size : 100 m Flood simulation : 100 years return period

9 Study Area 9 Marikina-Pasig-San Juan River system Hydrologic modeling area 401 Km 2 Inundation modeling area 334 Km 2

10 Modeling Approach Hazard

11 Inundation modeling : Model set up 1- Hazard Ondoy flood event was used for calibration of the flood inundation model Peak discharge at St Nino for 2009 flood was about 3500 m 3 /s (considering upstream inundation). Simulated flood was 3413 m 3 /s Damage analysis Inundation model set up at FLO-2D platform

12 Land Use projection Projection of future land use based on past data using Land Change Modeler (LCM) Exposure Landsat 7 ETM 03/04/2002 Supervised Classification Land Use 2002 Land Use 2014 Landsat 8 OLI/TIRS 07/02/2014 Supervised Classification Land Change Modeler (LCM) Factors : Elevation/Slope Land Use 2030

13 Flood Damage function 3-Vulnerability 13 Flood damage Depth function is graphical relationships of the losses expected at a specified depth of flood water Physical damage : Flood depth function derived from field survey Data collection from Barangays

14 Scenarios Analysis 14 Current situation Scenario 1 : Business as Usual Scenario 2 :With Mitigation Climate and land use change Countermeasures Climate Change Countermeasures: Dam (75 MCM), greater flow capacity (600 to 1200 m3/s of Pasig river), infiltration measures and flood canal diversion (with full capacity 2400 m3/s instead of 1600 m3/s (Average Daily rainfall during Ondoy typhoon (2009) estimated to mm) Daily maximum rainfall for current and future climate ( ) Return period RCP 45 RCP85 (Year) Current MRI MIROC MRI MIROC

15 Result : Comparison of flood inundations 15 Current 1- Hazard Manila City Business-as-Usual 30% increase of Peak Discharge Montalban for current and future conditions Pasig City/Taytay With Mitigation

16 Comparison of flood inundations Hazard -47% +94% Comparison of current and future conditions pointed out an increase of 94% in Business as usual scenario and a reduction with 47% with the implementation of specific measures

17 Land use land cover (LULC) change

18 Result : LULCC analysis Exposure Urban will Increase by 10% Urban area will increase Exposure to floods will increase

19 Rizal Metro-Manila Result : LULCC by City 2- Exposure 19 Urbanization of some cities of Rizal in Future

20 20 Flood Damage function 3-Vulnerability Logistic model (2 parameters) Flood depth function for built-up Nonlinear regression* Damage rate(%) = 1 (1 + Exp( Depth) R² = (*XLSTAT)

21 Flood Damage Assessment 21 2 Current Situation (2015) Total Damage : 212% 1 3 Business-as-usual (2030) 81 Millions USD 1/ The damage is important in Manila and Pasig City 2/ Total damage in Rodriguez and San Mateo will be significant in the future 3/ Serious damages along Marikina and San Juan river Legend Flood Damage per grid No Damage < 10,000 10,000-25,000 25,000-50,000 > 50,000 Mitigation (2030)

22 Flood Damage vs Flood Hazard 22 Damage related to Depth Low risk Damage related to urbanization Damage along river Low protection Flood measures will contribute to reduce the impact of flood damage.

23 23 Conclusion The climate change projections revealed an increase of 25% and in 100-yrs daily maximum precipitation ; The effect of the projected climate change in 2030 can increase peak discharge by 30% at Montalban from 4000 m 3 /s to 5300 m 3 /s for 100-yrs return period ; The climate scenario reveals : 94% increase in inundation area for 100 years return period (> 1.5 Depth). Flood damage will increase by 212% ; The implementation of combined flood risk reduction will decrease flood damage by 35% comparing to current situation ;

24 Recommendations (1) 24 1-Marikina-Pasig-San Juan rivers runs through cities which will increase their susceptibility to flood such as Marikina city Revitalization of the river can be a solution to avoid risk to population and buildings 2-Manila, Pasig, Taytay and Cainta are prone to floods Hard and soft flood measures will contribute to reduce flood hazard 3-Flood damage will increase in Rodriguez and San Mateo. It is mainly due to urbanization which will increase the vulnerability of these cities Effective urban resilience strategies should be adopted 4- More attention to San Juan river

25 Recommendations (2) 25 High resolution simulations, more accurate land use, additional GCMs/RCPs are expected to increase the accuracy of the results of flood hazard ; Improvement of flood databases can conduct to reduce the degree of uncertainty ; Flood damage can be more accurate with the use of highresolution satellite images and the establishment of flood depth function for different land use type ;

26 Contribution to SDGs 26 Target 1.5: Build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climaterelated extreme events and other economic, social and environmental shocks and disaster Target 11.3: Enhance inclusive and sustainable urbanization Target 11.5: Significantly reduce the number of deaths and people affected and direct economic losses caused by water-related disasters Target 13.1: Strengthen resilience and adaptive capacity to climaterelated hazards and natural disasters Target 13.2: Integrate climate change measures into national policies, strategies and planning

27 Thank you!