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Source-This post on Climate Risk and Disaster Management has been created based on the article “As in Kerala, local climate processes can worsen climate extremes” published in “The Hindu” on 5 August 2024.
UPSC Syllabus-GS Paper-3- Disaster and Disaster Management.
Context- The article highlights how global climate patterns and local factors combine to drive extreme weather. It stresses the need for better climate models, a deeper understanding of local influences, and a thorough approach to disaster management.
What are the limitations of Global Models to predict disasters?
1) Translating Broad Risk Warnings into Effective Actions- Early warnings usually start with weather forecasts for the affected areas. However, broad risk warnings are hard to turn into specific actions. For ex- declaring the Western Ghats at risk of landslides may prompt protective measures, but it won’t eliminate all the risks.
2) Local Factors on Climate Extremes – Climate extremes often begin far away but are worsened by local factors. For instance, warmer coastal sea temperatures can intensify rain in areas like Wayanad, 80 km from the coast. Global models often miss these small-scale effects, leading to underestimates of heavy rainfall.
Read More- Wayanad Landslide- Causes, impact and management of Landslides in India- Explained Pointwise
3) Inaccurate Weather Predictions -There is a lack of understanding of local factors that worsen extreme weather. Global models miss these local details, making predictions less accurate.
What should be the way forward?
1) Coastal Observations -Enhancing coastal observations and assimilating them into forecast models can improve predictions.
2) Local Measures to Reduce Risks -There is a need to adopt local measures, like legal protection for biodiversity, to reduce disaster risks.
3) Collaboration and Adequate Budgeting -Effective disaster management requires collaboration between national and local governments, shared responsibilities, and adequate budget provisions to address the complexities of global warming and local impacts.
4) Early Risk Predictions and Local Data- Predicting risks a few weeks ahead helps disaster teams prepare for high-risk areas and avoid surprises. Accurate local data and combining long-term forecasts with local assessments are essential for effective crisis management.
6) Downscaling Techniques-This technique uses a global model to create more detailed, local forecasts with a higher-resolution regional model, capturing weather details that the global model might miss.
7) Enhancing Disaster Resilience- There is a need for more local weather and climate data to improve predictions and strengthen disaster resilience. Creating a detailed data network to track local factors that worsen extreme events is important for effective long-term disaster management.
Question for practice
What are the limitations of Global Models to predict disasters? What should be the way forward?
