Infrastructure Climate Resilience Assessment Data Starter Kit for Syria
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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.
本入门数据集套件收录了来自全球开放数据集的节选内容，主题涉及气候灾害（climate hazards）与基础设施系统（infrastructure systems）。 这些节选内容源自全球数据集，我们采用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（一般是为了分离离岸岛屿或非毗连区域），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。 人为引起的气候变化正在加剧气候与极端天气事件的发生频率与严重程度，这对社会、经济与基础设施造成了广泛的负面影响。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。数据常散布各处、难以查找，格式不便使用，或需要较高的专业技术门槛。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一个起点。 ### 灾害类型： - 海岸与河流洪水（Ward等，2020；Baugh等，2024） - 极端高温与干旱（Russell等，2023，源自Lange等，2020） - 热带气旋风速（Russell，2022，源自Bloemendaal等，2020与Bloemendaal等，2022） ### 暴露要素： - 人口（Schiavina等，2023） - 建成区（Pesaresi等，2023） - 道路（OpenStreetMap，2025） - 铁路（OpenStreetMap，2025） - 发电厂（Global Energy Observatory等，2018） - 输电线路（Arderne等，2020） ### 背景信息： - 海拔（European Union与ESA，2021） - 土地利用与土地覆盖（Copernicus Climate Change Service与Climate Data Store，2019） - 来自geoBoundaries的行政边界（Runfola等，2020） 将灾害与暴露要素数据集进行空间叠加（spatial intersection），是分析基础设施与人群面临的脆弱性与风险的第一步。 若想了解更多相关概念，可通过开放大学（Open University）获取一门关于基础设施与气候韧性的免费短期课程，课程概述可查阅更多细节。 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：旨在助力开展基础设施暴露、损失与风险计算 open-gira仓库包含了一套更完整的全球尺度开放数据基础设施风险与韧性分析工作流。 如需更完善的示例，部分此类数据集曾作为关键输入，用于一项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（2023）。 ## 参考文献 1. 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》(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)：全球河流洪水灾害地图。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、气候数据存储库(2019)：1992年至今基于卫星观测的土地覆盖分类格网地图。哥白尼气候变化服务中心（C3S）气候数据存储库（CDS）. DOI: 10.24381/cds.006f2c9a（2024年8月9日访问） 6. 哥白尼DEM - 全球数字高程模型(2021) DOI: 10.5270/ESA-c5d3d65（基于哥白尼WorldDEM™-90制作，© DLR e.V. 2010-2014与© Airbus Defence and Space GmbH 2014-2018，由欧盟与ESA根据COPENICUS协议提供；保留所有权利） 7. 全球能源观测站、Google、斯德哥尔摩KTH皇家理工学院、Enipedia、世界资源研究所(2018)：全球发电厂数据库。发表于Resource Watch与Google Earth Engine；resourcewatch.org/ 8. Hickford等(2023)：《低收入国家韧性战略交通网络决策支持系统——最终报告》. 在线可访问：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的极端气候影响事件暴露预测. 《地球的未来》, 8, e2020EF001616. DOI: 10.1029/2020EF001616 10. Natural Earth(2023)：行政0级地图单元，v5.1.1. [数据集]. 在线可访问：www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details 11. OpenStreetMap贡献者、Russell T.、Thomas F.、nismod/datapkg贡献者(2025)：基于OpenStreetMap的道路与铁路网络[数据集]. 可访问：global.infrastructureresilience.org 12. Pesaresi M., Politis P. (2023)：GHS-BUILT-S R2023A——基于Sentinel2合成影像与Landsat的多时相GHS建成地表格网(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. 《公共科学图书馆·综合》15(4): e0231866. DOI: 10.1371/journal.pone.0231866. 14. Russell, T., Nicholas, C., & Bernhofen, M. (2023). 源自Lange等2020的极端高温与干旱事件年度概率（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——多时相GHS人口格网(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) 《水渠道洪水方法学》. 技术报告. 华盛顿特区：世界资源研究所. 在线可访问：www.wri.org/publication/aqueduct-floods-methodology.
