How Localized Climate Projections Can Help Reduce Disaster Risk

By Manishka De Mel

Increasing resilience to disasters is becoming the focus of many governments, international development agencies and non-profits. In addition to utilizing other vital resilience-building steps (vulnerability assessments, past experiences, local knowledge, etc.), a comprehensive analysis of current and future projections of climate impacts on a specific region, using the best available scientific information to aid planners and stakeholders, is fundamental.

Scientists develop future climate projections using various climate models, data, and scenarios. These are generally presented as a range or average for global or regional geographical areas.  While this provides an overviewof what is to come, some climate scientists argue that localized projections are far more useful for planning purposes.

The January 18th, 2007 storm saw hurricane force winds lash much of the UK, killing 13 people. Blackpool promenade was closed as waves crashed over the sea wall flooding the road.

The January 18th, 2007 storm saw hurricane force winds lash much of the UK, killing 13 people. Blackpool promenade was closed as waves crashed over the sea wall flooding the road.

Often average global and regional conditions are not representative of local conditions, especially as planning and investments occur at the local level. Dr. Radley Horton at the Center for Climate System Research at Columbia University carries out hyper-local (highly localized) projections for the New York City Panel on Climate Change. According to Horton, the rate of sea-level rise in the city over the past century is approximately 50 percent higher than the global average. Thus using average global projections would not be useful for planning purposes in New York City, where authorities are investing billions of dollars to make the city more resilient. Hyper-local projections can be done for fine scale areas, as small as sub-city scales. Localized information that can be produced includes projections of sea level rise, coastal flooding, average precipitation and temperature, and the frequency of extreme events such as heat waves and cold days (where minimum temperature fall below a given threshold).

According to Horton, “Urban decision-makers consistently request climate information and projections at a local scale. Technological advances and increased data availability are making it increasingly possible to meet this request. This makes it even more critical that both the strengths–and the limitations–of local projections be emphasized to decision-makers and knowledge providers.”

Why aren’t hyper-local projections used more commonly?

Accessibility, uncertainty, cost and data availability are some of the major barriers preventing the use of hyperlocal projections for planning purposes. Also, these hyper-local projections are only carried out by a handful of credible academic institutions globally, and since this type of climate information can be considered cuttingedge, it is likely that knowledge of such techniques has not yet reached those who require it. In the future it is likely that demand for such information will exceed supply, with the risk of projections being carried out without scientific credibility or rigor.

Questions associated with the uncertainty of localized projections could be another reason for hesitation. As in the case of all climate models and projections, there is a degree of uncertainty. When projections are generated at local scales, uncertainties are greater due to both large unpredictable natural variability at local scales, and the potential for changes in local processes, such as urban land surfaces and coastal breezes which are not captured by global climate models. Despite these larger uncertainties, hyper-local projections can be superior to regional projections because they are anchored in the unique baseline climate at the local scale.

The cost of carrying out hyper-local projections can be another barrier to wide use. However, as long as statistical analysis is used, as opposed to computer simulations based on complex regional climate models, the cost may not be prohibitive. Historical data availability can be another constraint, but generally three decades of high quality daily historical climate data (and tidal data if applicable) provide a sufficient foundation for hyper-local projections.

Understanding the future, before investing in it … 

Extreme climate events can have considerable impacts on infrastructure and development projects – these could range from irrigation projects in drought-prone areas, construction along coastal areas or power stations in flood-prone areas. In many countries, the public money that is invested in such projects is in the form of loans that will be paid back with interest over decades. Many of these are currently implemented with little or no consideration of current and future climate risk, let alone cutting-edge localized projections. Investment in such projects, without adequate consideration of climate impacts is likely to be wasted with little realization of intended benefits.

“Disaster risk reduction practitioners are working to improve integration of climate change into their work. Localized projections can certainly support a better understanding of risk.”  – Anita Van Breda, Director of Disaster Response and Risk Reduction, WWF-US

The use of localized projections will enable viable projects to go ahead whilst understanding risks and incorporating resilience mechanisms during the early construction phases, preventing the higher cost of late integration once projects are completed. Although the focus of this post has been to see the usefulness of hyper-local projections for disaster risk reduction, these projections have a multitude of uses. They can be useful for species and ecosystem conservation (eg: to understand climate impacts to species’ habitats), identifying risks to investments, and infrastructure projects.

Currently greenhouse gas emissions continue to rise, increasing the need to build resilience against extreme events and disasters. Even if emissions decline, there will still be a need for climate risk information. Thus using localized climate projections can help reduce risks to human life, natural systems, disruption to everyday life and the functioning of economies.

Acknowledgement

The author would like to thank Dr. Radley Horton and Daniel Bader of the Center for Climate Systems Research, Columbia University and Anita Van Breda, Director of Disaster Response and Risk Reduction, WWF-US for their input.