Infrastructure Climate Resilience Assessment Data Starter Kit for El Salvador
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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.
本数据入门套件收录了来自全球开放数据集的、与气候灾害及基础设施系统相关的节选数据。 本套件节选数据源自全球数据集，这些数据集已基于Natural Earth（2023）的行政边界裁剪至国家尺度（若国家边界存在拆分，通常是为分离离岸岛屿或非连片区域，则裁剪至次国家级尺度），本套件无意就边界、领土或主权表达任何立场。 人为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、经济及基础设施造成广泛的不利影响。气候风险分析对于制定旨在降低风险的政策决策至关重要。然而，数据获取往往是一大障碍，在中低收入国家尤为如此。数据通常较为零散、难以查找，格式不便使用，或需要较强的专业技术能力才可处理。尽管如此，仍有部分全球开放数据集可提供气候灾害、社会、基础设施及经济相关的信息。本“数据入门套件”旨在助力相关工作的起步，为后续模型开发与情景分析提供基础起点。 一、灾害类型 - 沿海与河流洪水（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） 二、暴露要素 - 人口（Schiavina等人，2023） - 建成区（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面临的脆弱性及风险的首要步骤。 若欲了解更多相关概念，可通过开放大学（The Open University）获取关于基础设施与气候韧性的免费短期课程，该课程的概述页面可提供更多细节。 以下Python库可助力本工作流产出的数据包内数据的分析工作： - snkit：用于清理网络数据 - nismod-snail：专为开展基础设施暴露、损失与风险计算而设计 open-gira代码仓库包含一套更完整的工作流，用于开展全球尺度的开放数据基础设施风险与韧性分析。 如需更完善的应用示例，本套件部分数据集曾作为关键输入，用于一项针对东非交通网络当前及未来洪水风险的区域气候风险评估。该评估配套的在线可视化工具可访问https://east-africa.infrastructureresilience.org/，相关细节详见Hickford等人（2023）的研究。 参考文献 1. Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). 数据集来源：基于开放数据的全球电力系统预测制图[数据集]. 载于《自然-科学数据》（1.1.1，第7卷，第19号论文）. Zenodo. DOI: 10.5281/zenodo.3628142 2. 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 3. 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 4. Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) 全球发电厂数据库. 发布于Resource Watch与Google Earth Engine；resourcewatch.org/ 5. 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 6. Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., 等. (2020). 六大事件类别与三种空间尺度下极端气候影响事件暴露情况的预测. 《地球的未来》, 8, e2020EF001616. DOI: 10.1029/2020EF001616 7. 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 8. 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] 可访问global.infrastructureresilience.org 9. Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A——基于Sentinel2合成影像与Landsat数据的GHS建成地表网格，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 10. 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 11. 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 12. Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, 等. (2020) 阿奎杜克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.
