Global Climate Adaptation Layer
Last updated
Last updated
Currently, many attempts at constructing global scale climate resilience layers struggle with resolution and are often weighted heavily towards country or city level attributes due to the lack of localized data. Apart from high-level societal resilience indicators such as income, education, and vulnerable populations, localized data on the built environment, focusing on adaptation infrastructures such as urban greenery, drainage capacity, and fire response stations are equally crucial at explaining climate resilience.
Our Global Climate Adaptation Dataset seeks to create multiple layers of globally consistent, localized climate adaptation at up to 30 meters in resolution which can effectively bring climate resilience analysis down to the asset or neighborhood level. Combined with societal resilience indicators, these localized adaptation datasets can offer a more true-to-the-ground portrayal of the actual climate resilience of any location in the world.
The Global Climate Adaptation Layer aims to be a globally consistent, high-resolution climate adaptation dataset engineered from a diverse set of geospatial data sources that provide information on the built environment with up to 30 meters resolution for each type of climate hazard.
The overall methodology can be broken down into four steps as show in the flowchart below. The processes are iterative as any step in the process could help inform decision making in other steps.
For each climate hazard, a throughout study on the best practices in hazard mitigation and adaptation is conducted. Then, we define the thematic areas that can be potentially quantified to explain the degree of adaptation.
For example, water treatment facilities—including filtration systems, wastewater treatment plants, and water storage solutions—play a critical role in managing dry spells. During prolonged periods of drought, a location’s proximity to these facilities and its access to their services become key indicators of drought resilience. Thus, we define the feature ‘Availability and Capacity of Water Works’ to indicate whether a location falls within the service area of water treatment facilities.
After defining the features to be engineered, we then find the suitable data source to construct the proposed feature. The selected data sources need to meet at minimum one of the three criteria below:
Peer-reviewed - the data is published in academic journals that underwent a peer-review process. E.g. Local-climate zone data from World Urban Database and Access Portal Tools (WUDAPT)
Well-documented - the data is published with clear explanation of methodologies and/or code repositories for third parties to reproduce the results. E.g. OpenStreetMap.
Well-established - the data is published by reputable organizations and are widely used by the industry. E.g. 10m Global Land Cover by Impact Observatory.
In the case of the feature defined above, OpenStreetMap provides a global map of geospatial features containing water works, wastewater treatment plants, water access point, and other related vector features. Additionally, data from the Global Dam Watch Database provides the locations, capacity, and outlines of freshwater reservoirs, which complements the data from OpenStreetMap.
In most situations, simply taking raw data from the source is insufficient in explaining the defined adaptation themes. The raw data need to be cleaned, processed, and engineered into the desired thematic features for better explainability and compatibility with other data layers. While each data source and adaptation theme require unique processing pipeline, the general workflow is as follows:
Clean and fix source data anomalies at native resolution
Enhance the resolution or complete missing areas of the source data if necessary
Convert raw data into structured data with spatial indexes
Apply spatial and statistical operations to obtain the targeted thematic feature
Rescale the thematic feature to a scale of 0 - 100, with 100 indicating the highest resilience in each theme. This can be achieved using min-max normalization or percentile ranking depending on the distribution of the thematic feature.
Using the drought resilience example, vector data from OpenStreetMap are first cleaned and buffered to determine the radius of coverage, and then aggregated into a unified spatial index. Similarly, data from reservoir datasets are processed by buffering reservoir polygons with a corresponding radius. By overlaying these layers, we can identify the number of services or adaptation features available at each location. A location that meets all criteria within the Water Treatment category would fall within the service radius of water infrastructure, benefit from high water capacity, and have access to multiple water access points, and thus score 100, indicating the highest resilience in terms of ‘Availability and Capacity of Water Works’.
Finally, all adaptation features for a single hazard are aggregated to one distribution which is then used to adjust for the intensity of the hazard. As such, a location that scores 100 in all constituent themes enjoys the highest possible offset for the relevant hazard. This process is applied to each of the six climate hazards to obtain the Resilience-adjusted Risk using methods described in our Climate Risk and Resilience Framework.
