Measuring Flood Resilience in Punjab, Pakistan Syed Ali Kamal & - PowerPoint PPT Presentation

Measuring Flood Resilience in Punjab, Pakistan Syed Ali Kamal & Dr. Uzma Hanif Department of Economics Forman Christian College ( A Chartered University ) Lahore

Outline • Introduction • Review of literature • Theoretical Framework • Methodology • Results • Policy Recommendations

Introduction • Pakistan is naturally vulnerable to flood hazard. • The combined financial loss incurred by the floods from 1950 to 2009 amounts to $20 billion, 8,887 people died. • The 2010 floods alone resulted in a combined financial loss of $10 billion along with 2,000 causalities. • Major flood events in the history of Pakistan are: 1950, 1956, 1957, 1973, 1976, 1978, 1988, 1992, 2010, 2011, 2012, 2013, 2014, and 2015. • After viewing the damages the question of Resilience arises

Objectives of the Research • To identify the flood resilience determinants in Punjab by taking the data of 13 districts  Application of a flood damages approaches for valuing the resilience in Punjab  Panel data collection, compilation, and analysis  Conduct economic analysis

Review of Literature

Climate Change and floods in Pakistan • Climate change has triggered the frequency and intensity of natural disasters (Pachauri and Reisinger, 2007). • For the past seven years, Pakistan has been among the top ten countries worst affected due to extreme weather events, (Global Climate Risk Index by Germanwatch) securing first place in 2010. • Within the Indus basin system flash floods are expected to increase in the uplands (300-3000m) whereas riverine and coastal floods are expected to increase in the lowlands (<300m) (Xu et al, 2009).

Impact of Natural Disasters on Economic Growth (Short Run) Studies Approach Time periods Sample Results Raddatz (2007) Panel- VAR 1965-1997 40 countries Negative framework Relationship Raddatz (2009) Panel study 1975-2006 112 countries Negative relationship Noy (2009) Panel study 1970-2003 109 countries Negative relationship Cavallo et al. Comparative 1968-2002 202 countries Negative (2009) study relationship Impact of Natural Disasters on Economic Growth (Long run) Skidmore and Cross- 1960-1990 89 countries Expansionary Toya (2002) sectional effect Noy and Nualsri Panel- VAR 1990-2005 44 countries Negative effect (2008) framework Raddatz (2009) Panel study 1975-2006 112 countries Negative effect Leiter et al Difference in 1980 -2008 Firm level Positive effect (2009) difference approach (DID)

Resilience • Resilience can be defined as the capacity of a system to absorb a disturbance or shock, and then re-organize or restore into a fully functional system. • It includes not only a system’s capacity to return to the same state that existed before the disturbance but also to improve that state through learning and adaptation (Adger et al., 2005; Folke, 2006).

Types of Resilience Ecological Social Resilience is composed of Economic Transformative Capacity Batica and Gourbesville, 2012 Adaptive Capacity Coping Capacity Keck and Sakdapolrak, 2013

Qualities of a Resilient system Redundancy Diversity Efficiency Godschalk, 2003 A Socially Resilient System can endure stress on Health Technical Education Social Resilience Social Welfare Political Employment Keck and Sakdapolrak, 2013 Adger, 2000

Causes of Deficient Flood Resilience in Pakistan Land use Changes Environmental Degradation Oxley, 2011 Construction of built environment Poor water use practices Mustafa and Wrathal, 2011 Hydrological priorities of policy makers

Resilience can be improved by Learning about the past mistakes Developing Flood management options Marrero and Creating effective linkages Tshakert, 2011 Mutual trust, integrity, and confidence Flood adaptation Institutional interplay Schelfaut et al., 2011 Communication of risk

Measurement of Resilience • The measurement of resilience is an emerging development concept. • The identification of the measurement standards of resilience is still a big challenge. • There is currently no agreement on any one particular way to measure resilience (Mitchell, A., 2013; Winderl, T., 2014). • Indices have been made to capture resilience at global, national, sub national level and even the household level.

Authors Scale of the study Study Area Estimation technique Chang and Earthquake resilience at Memphis, earthquake loss Shinozuka (2004) city level Tennessee USA estimation model Rose, A. ( 2004) Earthquake Resilience USA Computable General Equilibrium (CGE) Cutter et al. (2008) resilience assessment at USA Disaster Resilience of local and community Place (DROP) level Cimellaro et al Earthquake resilience California State Recovery Model (2010) framework for hospital of USA building Renschler et al, Disaster Resilience at PEOPLES Resilience (2010) Community level Framework Cutter et al (2010) Urban vs Rural Resilience Florida, USA Baseline Characteristics Approach Frazier et al. (2013) Flood and disaster Sarasota county Place specific, diff. Resilience Florida weighting , spatio - temporal approach Nguyen and James Flood Resilience Mekong River Subjective well-being (2013) Delta , China approach

Resilience Developing Unit of Focus Components Methodology Measurement Organization Analysis progress towards self-assessment Hyogo HFA using 31 outcome indicators, local by governments Framework for UNISDR indicators on three priority areas and government on scale from 1 to Action levels (outcomes, strategic goals level 5; mostly input- goals, priorities) related hazards, vulnerability, quantitative; Global Focus Maplecroft and vulnerabilities and hazard, country level weighted Model UN OCHA response capacity at humanitarian need composite index country-level susceptibility, Composite disaster risk value for exposure, coping World Risk Index UNU-EHS country level weighted index 173 countries capacities, with 28 indicators adaptation Socio Economic socio-economic Maplecroft Unknown country level Unknown Resilience Index resilience Western prototype simulation loss estimation community ResilUS Washington model of community module and not known level (USA) University resilience in U.S. recovery module

Flood Profile of Punjab Total Years 2010 2011 2012 2013 2014 2015 Damages Month of July September September August September July Flooding Riverine Riverine Riverine flood in Riverine Riverine flooding in Riverine flooding flood in Causes of Indus flood in flooding, Hill Chenab in Jhelum/Chenab Indus and Flood Chenab Sutlej and torrents and and Sutlej Nullahs Torrential and Hill torrents heavy rainfall Nullahs rains Jhelum Affected 11 12 3 9 16 8 59 Districts Affected 1810 335 110 1628 3484 558 7925 Villages Affected .120 o.445 6.2 million .026 Million .389 million 2.47 million 9.884 million Population million million Deaths 262 4 60 109 286 35 756 House 353,141 1,284 25,556 3,378 83,593 16,374 483,326 Damages Affected Area 5.23 .195 10.405 .270 million 1.96 million 2.41 million 0.34 million (acre) million million million Livestock Loss 3572 59 898 81 737 0 5347

Conceptual Framework

Resilience Approach • Resilience quantification is in its early stages of development and presently there exists no agreement on the most proficient method to measure resilience. (Béné, 2013; Mitchell, 2013) • Quantification of resilience to natural disasters can be conducted in a number of ways using different methods and various approaches. – Well being Approach – Vulnerability Approach – Capacity to cope, adapt and transform approach – Recovery Approach – Damages Approach

Damages Approach • Based on evaluating and measuring the effect of calamities. • The shocks, losses, or damages in themselves are considered to be a set of measures of resilience (Winderl, 2014). • EM-DAT, DesInventar , The PREVIEW Global Risk data Platform , are all examples of initiatives that measure the shocks, losses, or stress of the natural disasters. • In this study we use the damages caused by floods to measure the resilience. • We developed a damage function where dependent variable is the damages and independent variables are the various damage influencing variables.

Conceptual Framework of Model Flooding for a longer duration is • likely to cause more damages than a short lived flood Flood • Flood peak flow Impact (Jonkman et al., 2008; Merz et • Flood duration Parameters al., 2004; Merz et al, 2013). Flood peak flow has been • chosen as a relevant flood impact parameter in accordance Socio- Flood with the practices of FFD. • Population density economic Resilience • Literacy rate Greater population density variables • means greater house damage and a greater loss of life. Adult literacy rate as a proxy for • knowledge of flood hazard or Admin. • Expenditures on awareness (Messner and Meyer, variables embankments 2006; Merz, et al., 2013) Expenditure on embankments is • used as a proxy variable representing precautionary measures (Thieken et al., 2005)

The Damage Function

Methodology