Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

The Global Wind Power Tracker (GWPT) is a worldwide dataset of utility-scale wind facilities. It includes wind farm phases with capacities of 10 megawatts (MW) or more. A wind project phase is generally defined as a group of one or more wind turbines that are installed under one permit, one power purchase agreement, and typically come online at the same time. The GWPT catalogs every wind farm phase at this capacity threshold of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each wind farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Wind Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual wind farm phases and includes information such as project owner, status, installation type, and location. A wiki page for each wind farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Wind Power Tracker data set draws on various public data sources, including:

• Government data on individual power wind farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking wind farm permits and applications;
• Reports by power companies (both state-owned and private);
• News and media reports;
• Local non-governmental organizations tracking wind farms or permits.

Global Energy Monitor researchers perform data validation by comparing our dataset against proprietary and public data such as Platts World Energy Power Plant database and the World Resource Institute’s Global Power Plant Database, as well as various company and government sources.

Wiki Pages

For each wind farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a wind farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

The Global Solar Power Tracker is a worldwide dataset of utility-scale solar PV facilities. It includes solar farm phases with capacities of 20 megawatts (MW) or more (10 MW or more in Arabic-speaking countries). A solar project phase is generally defined as a group of one or more solar units that are installed under one permit, one power purchase agreement, and typically come online at the same time. The Global Solar Power Tracker catalogs every solar farm phase at these capacity thresholds of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each solar farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Solar Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual solar farm phases and includes information such as project owner, status, and location. A wiki page for each solar farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Solar Power Tracker data set draws on various public data sources, including: - Government data on individual power solar farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking solar farm permits and applications; - Reports by power companies (both state-owned and private); - News and media reports; - Local non-governmental organizations tracking solar farms or permits.

Wiki Pages

For each solar farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a solar farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

1 - OVERVIEW

This dataset contains overhead images of wind turbines from three regions of the United States – the Eastern Midwest (EM), Northwest (NW), and Southwest (SW). The images come from the National Agricultural Imagery Program and were extracted using Google Earth Engine and wind turbine latitude-longitude coordinates from the U.S. Wind Turbine Database. Overall, there are 2003 NAIP collected images, of which 988 images contain wind turbines and the other 1015 are background images (not containing wind turbines) collected from regions nearby the wind turbines. Labels are provided for all images containing wind turbines. We welcome uses of this dataset for object detection or other research purposes.

2 - DATA DETAILS

Each image is 608 x 608 pixels, with a GSD of 1m. This means each image represents a frame of approximately 608 m x 608m. Because images were collected from overhead the exact wind turbine coordinates, images used to be nearly exactly centered on turbines. To avoid this issue, images were randomly shifted up to 75m in two directions.

We refer to images without turbines as "background images", and further split up the images with turbines into the training and testing set splits. We call the training images with turbines "real images" and the testing images "test images".

Distribution of gathered images by region and type:

Domain

Real

Test

Background

EM

267

100

244

NW

213

100

415

SW

208

100

356

Note that this dataset is part of a larger research project in Duke's 2021-2022 Bass Connections team, Creating Artificial Worlds with AI to Improve Energy Access Data. Our research proposes a technique to synthetically generate images with implanted energy infrastructure objects. We include the synthetic images we generated along with the NAIP collected images above. Generating synthetic images requires a training and testing domain, so for each pair of domains we include 173 synthetically generated images. For a fuller picture on our research, including additional image data from domain adaptation techniques we benchmark our method against, visit our github: https://github.com/energydatalab/closing-the-domain-gap. If you use this dataset, please cite the citation found in our Github README.

3 - NAVIGATING THE DATASET

Once the data is unzipped, you will see that the base level of the dataset contains an image and a labels folder, which have the exact same structure. Here is how the images directory is divided:

| - images

| | - SW

| | | - Background

| | | - Test

| | | - Real

| | - EM

| | | - Background

| | | - Test

| | | - Real

| | - NW

| | | - Background

| | | - Test

| | | - Real

| | - Synthetic

| | | - s_EM_t_NW

| | | - s_SW_t_NW

| | | - s_NW_t_NW

| | | - s_NW_t_EM

| | | - s_SW_t_EM

| | | - s_EM_t_SW

| | | - s_NW_t_SW

| | | - s_EM_t_EM

| | | - s_SW_t_SW

For example images/SW/Real has the 208 .jpg images from the Southwest that contain turbines. The synthetic subdirectory is structured such that for example images/Synthetic/s_EM_t_NW contains synthetic images using a source domain of Eastern Midwest and a target domain of Northwest, meaning the images were stylized to artificially look like Northwest images.

Note that we also provide a domain_overview.json file at the top level to help you navigate the directory. The domain_overview.json file navigates the directory with keys, so if you load the file as f, then f['images']['SW']['Background'] should list all the background photos from the SW. The keys in the domain json are ordered in the order we used the images for our experiments. So if our experiment used 100 SW background images, we used the images corresponding to the first 100 keys.

Naming conventions:

1 - Real and Test images:

{DOMAIN}_{UNIQUE ID}.jpg

For example 'EM_136.jpg' with corresponding label file 'EM_136.txt' refers to an image from the Eastern Midwest with unique ID 136.

2 - Background images:

Background images were collected in 3 waves with the purpose to create a set of images similar visually to real images, just without turbines:

The first wave came from NAIP images from the U.S. Wind Turbine Database coordinates where no wind turbine was present in the snapshot (NAIP images span a relatively large time, thus it is possible that wind turbines might be missing from the images). These images are labeled {DOMAIN}_{UNIQUE ID}.jpg, for example 'EM_1612_background.jpg'.

Using wind turbine coordinates, images were randomly collected either 4000m Southeast or Northwest. These images are labeled {DOMAIN}_{UNIQUE_ID}_{SHIFT DIRECTION (SE or NW)}.jpg. For example 'NW_12750_SE_background.jpg' refers to an image from the Northwest without turbines captured at a shift of 4000m Southeast from a wind turbine with unique ID 12750. Using wind turbine coordinates, images were randomly collected either 6000m Southeast or Northwest. These images are labeled {DOMAIN}_{UNIQUE_ID}_{SHIFT DIRECTION (SE or NW)}_6000.jpg, for example 'NW_12937_NW_6000_background.jpg'.

3 - Synthetic images

Each synthetic image takes in labeled wind turbine examples from the source domain, a background image from the target domain, and a mask. It uses the mask to place wind turbine examples and blends those examples onto the background image using GP-GAN. Thus, the naming conventions for synthetic images are:

{BACKGROUND IMAGE NAME FROM TARGET DOMAIN}_{MASK NUMBER}.jpg.

For example, images/Synthetic/s_NW_t_SW/SW_2246_m15.jpg corresponds to a synthetic image created using labeled wind turbine examples from the Northwest and stylized in the image of the Southwest using Southwest background image SW_2246 and mask 15.

For any remaining questions, please reach out to the author point of contact at caleb.kornfein@gmail.com.

This dataset presents the technical potential for offshore wind development at the country level, split into potential for fixed and floating foundations. The files are grouped per WB regions and delivered in PDF, .SHP, .KMZ (CRS:4326) and .EXCEL format. We recommend non-GIS users to explore the EXCEL files and PDFs that summarize the technical potential per country; furthermore, users can explore the KML files interactively in Google Earth/ Google Maps application. Please read the METADATA file for more information on the methodology used for the spatial analysis. This analysis was undertaken as part of the World Bank Group’s Offshore Wind Development Program which is led by ESMAP in partnership with IFC. The program is supporting the inclusion of offshore wind into the energy sector policies and strategies of WBG client countries and the delivering the technical work needed to build a pipeline of bankable projects. This work was originally created to support the program's report 'Going Global: Expanding Offshore Wind to Emerging Markets'. For more information on the report please see: https://esmap.org/offshore-wind The World Bank and ESMAP do not guarantee the accuracy of this data and accept no responsibility whatsoever for any consequences of their use. The maritime boundaries do not imply on the part of the World Bank any judgement on the legal status of any territory or the endorsement or acceptance of such boundaries.

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

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.

We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: 1) Google Earth Engine (GEE) and 2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). FANO brought substantial improvements in model accuracy and operational implementation.

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....

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.

NEMAC Climate Resilience/Risk Raw DataLandslide, Flood and Wildfire Risk, 7 layers in total.Assessment Report: https://drive.google.com/file/d/1X_Gr4eUCmkXPOzAcvyxCe-uZPkX84Byz/viewThe assessment report give field names, data source information and metadata.For Asheville's Climate Resource guide please visit: https://www.ashevillenc.gov/news/asheville-climate-change-guide-release-and-renewable-energy-initiative-draft-plan/Open Data - to download, use the Download Filtered Dataset option to download the individual layers.

This data set contains modal energy terms from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in https://doi.org/10.1016/j.ocemod.2020.101656 (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). Please cite this article when using these data.

This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).

The data set contains

1. The time-mean and depth-integrated M2 mode 1-5 energy terms: x (eastward) and y (northward) fluxes, KE, APE, conversion, flux divergence, and the intermodal energy conversion (topographic mode coupling) term. The terms are computed for a two-week time series starting on GMT 01-Sep-2016 01:00:00. For details see the paper.
2. Positive seafloor depth, and latitude and longitude coordinates

The M2 mode-1 SSH of the same simulation can be found here: https://doi.org/10.5281/zenodo.5514226

https://sites.google.com/site/maartenbuijsman/

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.

Ecological processes and biodiversity patterns are strongly affected by how animals move through the landscape. However, it remains challenging to predict animal movement and space use. Here we present our new R package enerscape to quantify and predict animal movement in real landscapes based on energy expenditure.

Enerscape integrates a general locomotory model for terrestrial animals with GIS tools in order to map energy costs of movement in a given environment, resulting in energy landscapes that reflect how energy expenditures may shape habitat use. Enerscape only requires topographic data (elevation) and the body mass of the studied animal. To illustrate the potential of enerscape, we analyze the energy landscape for the Marsican bear (Ursus arctos marsicanus) in a protected area in central Italy in order to identify least-cost paths and high-connectivity areas with low energy costs of travel.


Enerscape allowed us to identify travel routes for the bear that minimize energy costs of movement and regions that have high landscape connectivity based on movement efficiency, highlighting potential corridors. It also identifies areas where high energy costs may prevent movement and dispersal, potentially exacerbating human-wildlife conflicts in the park. A major strength of enerscape is that it requires only widely available topographic and body size data. As such, enerscape permits a first cost-effective way to estimate landscape use and movement corridors even when telemetry data is not readily available, such as for the example with the bear. 


Enerscape is built in a modular way and other movement modes and ecosystem types can be implemented when appropriate locomotory models are available. In summary, enerscape is a new general tool that quantifies, using minimal and widely available data, the energy costs of moving through a landscape. This can clarify how and why animals move in real landscapes and inform practical conservation and restoration decisions.

Methods This data repository contains only the shapefiles and javascript code that were not publicly available, but needed to reproduce the analysis of the linked article. All other publicly available data sources, which were not included in this data repository, were:

Digital elevation model (DEM) for Italy was obtained from TINITALY (http://tinitaly.pi.ingv.it/).
Sirente-Velino shapefile from Protected Planet (https://www.protectedplanet.net/en/search-areas?search_term=sirente-velino+regional+park&geo_type=site).
DEM and Tree cover density for Denmark was obtained from the Danish National database: https://download.kortforsyningen.dk/content/dhm-2007terr%C3%A6n-10-m-grid and https://download.kortforsyningen.dk/content/treecoverdensity-tcd.
NDVI was obtained from Sentinel-2 imagery accessed through Google Eearth Engine: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2.
L'Eroica shapefile was obtained from the official website of the event: https://eroica.cc/en/gaiole/permanent-route.
GPS records of horses and cattle are under embargo for one year. For more information contact emilio.berti@idiv.de.