2017年-世界发展银行全球_Santa_Catarina___Disaster_Risk_Profiling_for_Improved_Natural_Hazards_Resilience_Planning_60页_7mb

Santa Catarina Disaster Risk Profiling Summary

Core Content

This report presents a comprehensive disaster risk management (DRM) analysis for the state of Santa Catarina, Brazil, with the goal of improving natural hazards resilience planning. It includes historical data on natural disasters, financial response capacity, and the development of a state-level Catastrophe (CAT) model. The study was conducted in collaboration with the World Bank and the Santa Catarina state government, and it is the first of its kind in Brazil.

Main Objectives

• Identify flood asset exposure risks in Santa Catarina.
• Empower the state government to integrate DRM practices into daily operations and decision-making.
• Develop a CAT model to provide metrics for understanding disaster exposure and financial impacts.

Key Findings

Natural Hazards in Santa Catarina

• Santa Catarina is exposed to a variety of natural hazards, including floods, droughts, flash floods, hail, mass movements, windstorms, tornadoes, and coastal erosion.
• Floods are the most common natural hazard, with significant economic and social impacts.
• The state was affected by Hurricane Catarina, the only recorded hurricane in Brazil.

Historical Damage and Losses

• From 1995 to 2014, natural disasters caused R$17.6 billion in damage and losses, which is approximately 0.4% of the state's GDP per year.
• In 2008, a major flood event caused R$9.8 billion in losses, with over 1.5 million people affected and 135 deaths.
• Over 95% of municipalities reported flood-related losses at least once in the 20-year period.
• Floods tend to occur more frequently in the western and southern regions, and have a higher incidence in the eastern portion of the state.

Financial Response Capacity

• Between 2009 and 2015, disaster response funds amounted to approximately R$400 million.
• The initial allocation was low (R$189 million), and the majority of funds were mobilized through extraordinary credits.
• The state's commitment capacity was identified as a key bottleneck in disaster response funding.
• Despite increased allocations, the disbursement rate as a percentage of final allocation declined from 2012 to 2015, indicating potential inefficiencies or lack of flexible instruments.

Funding Gaps

• Even under an optimistic scenario where only 30% of damage and losses are state liabilities, significant funding gaps were observed.
• The 2008 floods resulted in over R$1 billion in damages, yet the state's disbursement in 2009 was only R$120 million, highlighting the need for improved financial preparedness.

Methodology and Tools

CAT Model Development

• The report outlines a six-step methodology for developing a CAT model:
  1. Analysis of disaster data to identify loss patterns.
  2. Collection and analysis of geographic data to generate geo-spatial layers.
  3. Creation of residential and nonresidential exposure databases using the 2010 National Census.
  4. Development of a flood model using historical hydro-meteorological data and hydraulic modeling techniques.
  5. Derivation of a vulnerability model based on exposure and flood models.
  6. Generation of a CAT model to produce metrics such as AEP, OEP, EP, and AAL.

Flood Hazard Modeling

• Flood hazard maps were created for different return periods (e.g., 1 in 1000 years, 1 in 20 years).
• These maps were used to assess the potential economic and social impacts of flooding across the state and its municipalities.
• The methodology included hydrological modeling using the Revitalized Flood Hydrograph approach and hydraulic modeling using 2D models.

Exposure and Vulnerability Models

• The exposure model estimates the constructed value of residential and nonresidential buildings.
• The vulnerability model assesses the physical damage and economic impact of flood events based on construction patterns and materials.
• Residential buildings were classified by materials and construction standards, with an average ground floor area of 74.0 m² and an average construction cost of R$62,217.41 per house, or R$840.783 per m².

Policy and Decision-Making Implications

• The study highlights the need for improved DRM strategies and financial preparedness.
• The flood maps and models can support DRM planning by identifying high-risk areas and guiding investment decisions.
• The methodology is replicable and based on national and commonly accessible data, making it applicable to other Brazilian states and municipalities.

Conclusion

• The report provides a foundational knowledge base for DRM in Santa Catarina.
• It emphasizes the importance of forward-looking DRM strategies and the integration of risk assessment into public and private planning.
• The results can be used to enhance resilience across various sectors, including infrastructure, production, and urban development.