Another way to enter into assessment of resilience to climate impacts and infrastructure planning is to think about the information you might need to adjust infrastructure projects for long-term risk reduction. I wonder about the scale and type of information communities find most useful regarding increased flooding, sea level rise, urban heat, and more. Do any communities currently grappling with preparation for climate impacts have feedback regarding information that would be most useful?
Please see the Overview Note for the Disaster Risk Finance Impact Analytics Project, http://hdl.handle.net/10986/24374, a project let by Daniel Clarke (Senior Financial Sector Specialist, F&M) and Ruth Hill (Senior Economist, Poverty and Equity). It sets forth a framework for such considerations and supporting evidence.
Thank you, Darcy. The Disaster Risk Finance Impact Analytics Project, Overview Note and subsequent articles raise critical questions, establishes a basis for disaster preparation, and the economic case for preemptive action. My concern is meeting the named need for good data and scenario planning.
Some cities most vulnerable to climate impacts do not have all of data required to do climate change impact scenarios and obtaining this data can be costly depending on desired resolution. In addition, the impacts vary from city to city, meaning the required data also varies. A city wanting to prepare for potential climate change impacts (including the associated uncertainty) would do well to consider and define the specific information needed/desired to adjust planned infrastructure projects. The consideration of the desired information can also yield greater incorporation of the progressive nature of climate adaptation or resilience (to both episodic disaster events and slow-moving shifts in seasonal patterns). While such work is similar to hazards planning, it differs in the dynamic nature of the impacts being assessed. Cities must prepare for changing recurrence and severity profiles of disaster events and slow progressive alteration of seasonal weather patterns. Infrastructure often has a long planned lifespan (80 to 100 years), this means evaluation of long-term climate projections and, in many cases, consideration of worst-case scenarios. As the Disaster Risk Finance Impact Analytics Project points out, lower risk location and design of infrastructure influences the patterns of surrounding development and makes critical services provided by infrastructure projects less likely to be adversely impacted by disaster events.
Disaster has its toll in different places, altitudes, and climates. One way that we have experienced it has been through rain pouring landslides which are generally located at slopes of highlands and valleys. Since it is difficult to warn people not use these areas for settlements, it becomes perilous and an unavoidable risk to prevent from settlements to grow. Generally, resentment from developing these areas is understood after tragedy has hit dwellers to move out at sakes of loss of life.
Programs to guide safe settings has been a priority previous to season dangers. Mapping these areas is one way to prevent from settlement growth in danger areas, even though season changes may be unpredictable and even unsuspected.
Thank you for the comment, Jorge Llanos Pedraja. You raise critical issues.
One important aspect I struggle with is the resolution needed to inform planning and future settlement pattern. Landslides are one of the most detrimental disasters in cities, the world over. The key to mapping is to figure out what the minimum data needed for a setting to identify areas to restrict settlement. Does this mean an extreme event runoff model to assess the amount of runoff likely to be present and an assessment of slope? You can do both of these things conservatively (high rainfall and moderate slope) to make sure all areas with any landslide potential are identified (in any season). You can also be much more detailed and include soils, geology, and land cover. Slope is also something that requires an evaluation of needed resolution. Many cities have high resolution topography data, but others do not. While 30m DEM are free and publicly available, higher resolution data such as 2m DEM can be difficult or expensive to obtain if not already available.
Do you have a sense for how cities are balancing data needs and the information needed to inform infrastructure placement and settlement patterns?
There was some confusion in the posting of discussions. That's why there are two that appear identical. In the interest of keeping all discussions in the same place, I am posting Joshua Gallo's question and comment here.
"Integrating resilience into urban planning seems like a no-brainer. Planning for cities makes only sense if it's long-term planning. And if the planning is not resilient, it's not long-term. Yet, as you said, the challenge for cities is that data to inform resilient-planning is not always available and, even when available, it is not necessarily factored in capital investment plans.
"Based on your experience, what are the basic datasets/analyses that can make a real difference for cities as they go about planning for their future? And can you help us understand how difficult it is to get this information (especially for cities that have not yet started that process)? Many of our community members are from small/medium-sized municipalities in developing countries, so it's essential we offer solutions that are accessible without major funding/capacity requirements."
Thank you, Joshua, for the comment and question. You have posed a critical question for resilience in developing countries, on what information do you base such planning? This can be difficult due to variation in a city's biophysical context. The things to which a city must be resilient will vary widely from place to place meaning the data needed will also vary. That said, there are some key items that are widely available and can be used to conduct a first cut at resilience planning. First, either from the IPCC or other entities, a projection of climate change impact. Many parts of the world have existing down-scaled climate projections. This helps determine the other data that may be necessary.
In addition, basic spatial data just as land use and land cover, road networks, topography, fault lines and soils all provide a foundation for vulnerability assessment. Other data that may or may not be spatial, but are no less important can include past and projected future population, average precipitation and rainfall data, seasonal temperature pattern, and historic natural hazards documentation. If there is a companion, comprehensive plan showing intended areas of future development, that too can be important for evaluation of future climate and natural hazard impacts. There are short cuts or 'work arounds' for some of these data, if they are not locally available. There are also freely available datasets that may not be as high a resolution as city may desire, but in many cases they still can provide information critical to future development patterns. This includes topography, historic rainfall, and land cover. The needed resolution of data will depend on a city's context. Not all cities are vulnerable to landslides, but those that are may want to have higher resolution topography data, rainfall/runoff projections, and soils.
Resilience should be viewed as an iterative process. If it is the first assessment, a broad analysis that identifies potential impacts and potential areas of concern can be incredibly helpful to inform development. If a particular impact appears to be important due to the speed of onset or scale of impact, higher resolution and additional resource allocation can be directed there. The final piece to keep in mind is that resilience is not a one-time effort. It requires ongoing monitoring and update. Many of the impacts being planned for are projected to occur decades in the future, meaning the surrounding urban context, climate change projections, and other factors may change, which alters the nature of the impact being planned for.