Infrastructure Climate Resilience Assessment Data Starter Kit for Yemen
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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; Baugh et al, 2024) 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, 2025) railways (OpenStreetMap, 2025) power plants (Global Energy Observatory et al, 2018) power transmission lines (Arderne et al, 2020) Contextual information: elevation (European Union and ESA, 2021) land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019) administrative boundaries from geoBoundaries (Runfola 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 Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif 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 Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on 09-AUG-2024) Copernicus DEM - Global Digital Elevation Model (2021) DOI: 10.5270/ESA-c5d3d65 (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) 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 (2025) 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 Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866. 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提取数据。 这些提取数据源自全球数据集，我们采用自然地球（Natural Earth，2023）的边界数据，将其裁剪至国家尺度；若国家边界存在分割情况（通常是为了分离离岸岛屿或非连片区域），则裁剪至次国家尺度。本数据集无意就边界、领土或主权表达任何立场。 人为活动驱动的气候变化正在加剧极端气候与天气事件的发生频率与严重程度，给社会、经济与基础设施带来广泛的不利影响。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突出。数据常分散零散、难以查找，格式不便使用，或是需要极高的专业技术门槛。尽管如此，仍有诸多全球开放数据集能够提供有关气候灾害、社会、基础设施与经济的部分信息。本‘数据入门套件’旨在启动相关研究流程，为后续模型开发与情景分析提供起点。 一、灾害： - 沿海与河流洪水（Ward等人，2020年；Baugh等人，2024年） - 极端高温与干旱（Russell等人，2023年，源自Lange等人，2020年） - 热带气旋风速（Russell，2022年，源自Bloemendaal等人，2020年与Bloemendaal等人，2022年） 二、暴露度： - 人口（Schiavina等人，2023年） - 建成区（Pesaresi等人，2023年） - 道路（开放街道地图（OpenStreetMap），2025年） - 铁路（开放街道地图（OpenStreetMap），2025年） - 发电厂（Global Energy Observatory等人，2018年） - 输电线路（Arderne等人，2020年） 三、背景信息： - 海拔（欧盟与欧洲空间局（ESA），2021年） - 土地利用与土地覆盖（哥白尼气候变化服务中心（Copernicus Climate Change Service）与气候数据商店（Climate Data Store），2019年） - 行政边界（源自geoBoundaries，Runfola等人，2020年） 将灾害与暴露度数据集进行空间交集分析，是评估基础设施与人群脆弱性及风险的第一步。 若想了解相关概念，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，课程详情可参见该课程概述页面。 以下Python库可作为分析本工作流生成数据包中数据的起点： - snkit：用于清理网络数据 - nismod-snail：专为开展基础设施暴露度、损毁程度与风险计算而设计 open-gira仓库包含了用于全球尺度开放数据基础设施风险与韧性分析的更完整工作流。 如需更成熟的示例，部分本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/，详细内容参见Hickford等人（2023）的研究。 参考文献： 1. Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). 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》旗下《科学数据》（Nature Scientific Data）(1.1.1, 第7卷, 第19号文章). Zenodo. DOI: 10.5281/zenodo.3628142 2. Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): 全球河流洪水灾害图。欧盟委员会联合研究中心（JRC）[数据集] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif 3. 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 4. 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 5. 哥白尼气候变化服务中心, 气候数据商店 (2019): 基于卫星观测的1992年至今土地覆盖分类网格化地图。哥白尼气候变化服务中心（C3S）气候数据商店（CDS）. DOI: 10.24381/cds.006f2c9a（2024年8月9日访问） 6. 哥白尼数字高程模型（Copernicus DEM）- 全球数字高程模型 (2021) DOI: 10.5270/ESA-c5d3d65（基于欧盟与欧洲空间局在哥白尼框架下提供的Copernicus WorldDEM™-90制作，© 德国航空航天中心（DLR e.V.）2010-2014年、© 空客防务与航天有限公司2014-2018年，保留所有权利） 7. Global Energy Observatory, Google, 斯德哥尔摩皇家理工学院（KTH）, Enipedia, 世界资源研究所. (2018) 全球发电厂数据库. 发表于Resource Watch与谷歌地球引擎; resourcewatch.org/ 8. Hickford等人 (2023) 低收入国家弹性战略交通网络决策支持系统——最终报告. 在线访问: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries 9. 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的暴露度预测. 《地球的未来》, 8, e2020EF001616. DOI: 10.1029/2020EF001616 10. 自然地球（Natural Earth，2023）Admin 0地图单元，v5.1.1. [数据集] 在线访问: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details 11. 开放街道地图（OpenStreetMap）贡献者, Russell T., Thomas F., nismod/datapkg贡献者 (2025) 源自开放街道地图的道路与铁路网络. [数据集] 在线访问: global.infrastructureresilience.org 12. Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS建成地表网格，源自Sentinel2合成影像与Landsat，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 13. Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, 等. (2020) geoBoundaries：全球政治行政边界数据库. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866. 14. 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年（版本2）[数据集]. Zenodo. DOI: 10.5281/zenodo.8147088 15. 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 16. 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. 在线访问: www.wri.org/publication/aqueduct-floods-methodology.
