Infrastructure Climate Resilience Assessment Data Starter Kit for Saint Pierre and Miquelon
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This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. Hazards: coastal and river flooding (Ward et al, 2020) extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020) tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022) Exposure: population (Schiavina et al, 2023) built-up area (Pesaresi et al, 2023) roads (OpenStreetMap, 2023) railways (OpenStreetMap, 2023) power plants (Global Energy Observatory et al, 2018) power transmission lines (Arderne et al, 2020) The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details. These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: snkit helps clean network data nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023). References Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142 Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3 Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3 Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/ Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616 Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088 Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.
本数据集入门套件收录了源自全球开放数据集的相关片段，涉及气候灾害与基础设施系统。 这些片段源自全球数据集，已依据国家尺度进行裁剪（若国家边界存在分割，通常为分离离岛或非连续区域时则按次国家尺度），裁剪所用边界来自Natural Earth (2023) 数据，本数据集无意就边界、领土或主权表达任何立场。 人为引发的气候变化正加剧气候与天气极端事件的发生频率与严重程度，对社会、经济与基础设施造成广泛负面影响。气候风险分析可为降低风险的政策决策提供依据，而数据获取往往是相关工作的瓶颈，在中低收入国家尤为突出。相关数据常散布各处、难以查找，格式不便使用，或需要深厚的专业技术知识。尽管如此，目前已有部分全球开放数据集可提供气候灾害、社会、基础设施与经济相关的信息。本“数据集入门套件”旨在启动相关研究进程，为后续模型开发与情景分析提供基础起点。 灾害类型： - 海岸与河流洪水（Ward等人，2020） - 极端高温与干旱（Russell等人，2023，源自Lange等人，2020） - 热带气旋风速（Russell，2022，源自Bloemendaal等人，2020与Bloemendaal等人，2022） 暴露要素： - 人口（Schiavina等人，2023） - 建成区面积（Pesaresi等人，2023） - 道路（OpenStreetMap，2023） - 铁路（OpenStreetMap，2023） - 发电厂（Global Energy Observatory等人，2018） - 输电线路（Arderne等人，2020） 将灾害数据集与暴露要素数据集进行空间叠加，是分析基础设施与人群的脆弱性及风险的首要步骤。 若需了解相关概念，可通过开放大学（Open University）选修一门关于基础设施与气候韧性的免费短期课程，课程概述文档可提供更多细节。 以下Python库可作为本工作流生成的数据包中数据分析的起点： - snkit：用于清理网络数据 - nismod-snail：专为开展基础设施暴露、损毁与风险计算而设计 open-gira仓库包含了适用于全球尺度开放数据基础设施风险与韧性分析的完整工作流。 一个更完善的应用示例中，部分上述数据集曾作为关键输入，用于东非交通网络当前与未来洪水风险的区域气候风险评估，相关在线可视化工具可访问https://east-africa.infrastructureresilience.org，该研究详情可参见Hickford等人（2023）。 参考文献 - Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). 数据来自：基于开放数据的全球电力系统预测制图[数据集]. 发表于《自然·科学数据》(Vol.7, 第19篇文章, 1.1.1版本). Zenodo. DOI: 10.5281/zenodo.3628142 - Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM热带气旋风速重现期. 4TU.ResearchData. [数据集]. DOI: 10.4121/12705164.v3 - Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; 等. (2022): STORM气候变化下热带气旋风速重现期. 4TU.ResearchData. [数据集]. DOI: 10.4121/14510817.v3 - Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) 全球发电厂数据库. 发表于Resource Watch与Google Earth Engine；resourcewatch.org/ - Hickford等人 (2023) 低收入国家韧性战略交通网络决策支持系统——最终报告. 可在线访问：https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries - Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., 等. (2020). 预测六种极端气候事件类别与三种空间尺度下的暴露风险. 《地球的未来》, 8, e2020EF001616. DOI: 10.1029/2020EF001616 - Natural Earth (2023) 行政0级地图单元，v5.1.1. [数据集]. 可在线访问：www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details - OpenStreetMap贡献者, Russell T., Thomas F., nismod/datapkg贡献者 (2023) 源自OpenStreetMap的道路与铁路网络. [数据集]. 可在线访问：global.infrastructureresilience.org - Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A——GHS建成地表网格，基于Sentinel2合成影像与Landsat数据生成，多时态（1975-2030）. 欧盟委员会联合研究中心(JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA - Russell, T., Nicholas, C., & Bernhofen, M. (2023). 极端高温与干旱事件年度概率，源自Lange等人2020（版本2）[数据集]. Zenodo. DOI: 10.5281/zenodo.8147088 - Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A——GHS多时态人口网格（1975-2030）. 欧盟委员会联合研究中心(JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE - Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, 等. (2020) Aqueduct洪水方法学. 技术报告. 华盛顿特区：世界资源研究所. 可在线访问：www.wri.org/publication/aqueduct-floods-methodology.
