DOI: 10.5281/zenodo.10410903
title: Infrastructure Climate Resilience Assessment Data Starter Kit for Benin

description: 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.
description: Funding


    This research received funding from the FCDO Climate Compatible Growth Programme.


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