What does the data show?

The Drought Severity Index is not threshold based. Instead, it is calculated with 12-month rainfall deficits provided as a percentage of the mean annual climatological total rainfall (1981–2000) for that location. It measures the severity of a drought, not the frequency.

12-month accumulations have been selected as this is likely to indicate hydrological drought. Hydrological drought occurs due to water scarcity over a much longer duration (longer than 12 months). It heavily depletes water resources on a large scale as opposed to meteorological or agricultural drought, which generally occur on shorter timescales of 3-12 months. However this categorisation is not fixed, because rainfall deficits accumulated over 12-months could lead to different types of drought and drought impacts, depending on the level of vulnerability to reduced rainfall in a region.

The DSI 12 month accumulations are calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period.

What are the possible societal impacts?

The DSI 12-month accumulations measure the drought severity. Higher values indicate more severe drought. The DSI is based on 12-month rainfall deficits. The impacts of the differing length of rainfall deficits vary regionally due to variation in vulnerability. Depending on the level of vulnerability to reduced rainfall, rainfall deficits accumulated over 12 months could lead to meteorological, agricultural and hydrological drought.

What is a global warming level?

The DSI 12-month accumulations are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the DSI 12-month accumulations, an average is taken across the 21 year period.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

This data contains a field for each global warming level and two baselines. They are named ‘DSI12’ (Drought Severity Index for 12 month accumulations), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'DSI12 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'DSI12 2.5 median' is 'DSI12_25_median'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘DSI12 2.0°C median’ values.

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, DSI 12 month accumulations were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Useful links

This dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report. Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal

About this AppThe Drought Aware app provides information about areas in the U.S. affected by drought across different time intervals and over multiple drought intensities. The app shows summaries about the affected population and the potential impacts to crops, agricultural labor, rivers, and reservoirs.Use this AppDisplay drought maps for different weeks by clicking on the time-series chart (top bar) or by scrolling through time using the sector chart (top-left). Hover on each drought intensity level in the sector chart to highlight the areas on the map and display the area percentage. Click on the map to display a panel with summary information for the selected area. The panel includes three categories (1) population, (2) water, and (3) agriculture. App CategoriesThe Drought Aware app summarizes information in three categories:Population: displays the estimated people and households affected by drought at each intensity level, describes some of the vulnerable populations, and lists the related drought risk indexes. The data is available at County and State levels. Water: depicts the major local rivers, the average inter-annual river flow, and the relevant local reservoirs. The data is available at the Subregion Hydrologic Units (HUC4)Agriculture: shows the potential economic impact by major crop, the affected labor, and the agricultural exposure to droughts. The data is available at County and State levels. Drought Definitions Abnormally Dry (D0) Going into drought there is short-term dryness slowing planting, growth of crops or pastures. Coming out of drought there are some lingering water deficits; pastures or crops not fully recovered. Moderate Drought (D1) Some damage to crops and pastures. Streams, reservoirs, or wells low, some water shortages developing or imminent. Voluntary water-use restrictions requested. Severe Drought (D2) Crop or pasture losses likely. Water shortages are common. Water restrictions imposed. Extreme Drought (D3) Major crop/pasture losses. Widespread water shortages or restrictions. Exceptional Drought (D4) Exceptional and widespread crop/pasture losses. Shortages of water in reservoirs, streams, and wells create water emergencies.Data SourcesThe data layers used in this app can be found in ArcGIS Living Atlas of the World:U.S. Drought Monitor American Community Survey (ACS)USDA Census of AgricultureFEMA National Risk IndexNational Water Model (NWM)National Hydrography Dataset (NHD)National Inventory of Dams (NID)National Boundary Dataset (WBD)UpdateThe data behind the app is updated every week once a U.S. Drought Monitor map is released. The update process is automated using a live feed routine. This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.RevisionsOct 16, 2024: Official release of the Drought Aware app.

Drought is a natural hazard with far-reaching impacts that range from economic losses to loss of agriculture and livelihood. Drought can cause or exacerbate water, food, and national security hazards. The maps, tools, and resources on this page address drought conditions around the world.

The crop condition mapping product monitors cropland pixels affected by drought using Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Red Edge (NDRE), and Green Normalized Difference Vegetation Index (GNDVI), generated from Sentinel-2 images. A binary classification is performed to map drought-affected and unaffected croplands. A random forest model is trained using VI time series data from both drought and non-drought years for each Agro-Ecological Zone (AEZ). The outputs display the spatial characteristics of drought impacts on croplands at a national scale. The dataset includes seasonal crop condition maps for 2016-2022 at a 20m spatial resolution, classifying pixels as 0: non-croplands, 1: unaffected pixels, and 2: drought-affected pixels. Two maps per year are provided for the long rains (season 1) and short rains (season 2). The output is validated through comparison with other datasets such as the Global Drought Observatory, FAO Agriculture Stress Index System (ASIS), East Africa Drought Watch, and reports from the National Drought Management Authority (NDMA). Additionally, user validation has been conducted through engagement with relevant stakeholders, ensuring the outputs align with ground realities and user needs. Each map is accompanied with quality maps based on number of available clear sky observations and classification probability.

Global hydrological model used to carry out simulations of daily river discharge and runoff. Model forced using daily meteorological fields of precipitation, temperature, and radiation for four different time periods, namely: (a) 1960-1999, which represents the baseline climate; (b) 2010-2049 (representing 2030); (c) 2030-2069 (representing 2050); and (d) 2060-2099 (representing 2080). The meteorological data for the baseline climate are taken from the WATCH Forcing data (WFD) (Weedon et al., 2011). The future meteorological data are provided by the ISI-MIP project, and consist of bias-corrected data (Hempel et al., 2013) for an ensemble of five Global Climate Models (GCMs) from the ISIMIP project (Taylor et al., 2012). The GCMs used are GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M. For this study, we used climate projections based on 2 representative concentration pathways (RCPs), namely RCP2.6 and RCP8.5. The resolution of the input meteorological datasets for the current and future climate conditions is 0.5° x 0.5°.

The resulting drought hazard maps express probabilities of occurrence of the intensity of drought conditions. The intensity is measured as the number of months of long-term mean discharge which would be needed to overcome the maximum accumulated deficit volume under a certain return period. The deficit volume is calculated as the monthly flow deficit below the 20-percentile climatological flow. The different return period maps were generated by performing extreme value analysis on the yearly extreme values of the number of months of long-term mean discharge needed to overcome a discharge deficit. Please note that the used climate forcing in future climate still contains bias due to inter and intra annual variability in rainfall. This is not resolved in the bias correction scheme. Therefore, the drought hazard maps prepared with the GCM data still contain bias. This bias should be corrected by a comparison between 1960-1999 GCM runs of risk estimates and 1960-1999 EU-WATCH runs of risk estimates.

Further reading in: Veldkamp, T.I.E., Wada, Y., de Moel, H., Kummu, M., Eisner, S., Aerts, J.C.J.H., Ward, P.J. (in press). Changing mechanism of global water scarcity events: impacts of socioeconomic changes and inter-annual hydro-climatic variability. Global Environmental Change. DOI: 10.1016/j.gloenvcha.2015.02.011

Drought occurs when a region has an imbalance between water supply and water demand over an extended period of time. Droughts can have significant environmental, economic, and social consequences. Between 1980 and the present time, the cost of drought exceeded 100 billion dollars, making drought monitoring a key factor in planning, preparedness, and mitigation efforts at all levels of government.Data Source: U.S. Drought Monitor, National Drought Mitigation Center, GISData DownloadUpdate Frequency:  Weekly, typically on Friday around 10:00AM UTC. Using the Aggregated Live Feed MethodologyFor Full Historical data: See USA Drought Intensity 2000 - Present Online Item!For Standard Symbology Style: See USA Drought Intensity - Current Conditions - Standard Color Scheme Online Item!Dataset Summary:This feature service provides access to current drought intensity categories for the entire USA. These data have been produced weekly since January 4, 2000 by the U.S. Drought Monitor, see the Full Historical data for the full time series. Drought intensity is classified according to the deviation of precipitation, stream flow, and soil moisture content from historically established norms, in addition to subjective observations and reported impacts from more than 350 partners across the country. New map data is released every Thursday to reflect the conditions of the previous week.Layer Summary:'US_Drought_Current': Polygon areas for most recent weekThis Layer contains a series of drought classification summaries that fall into two groups: Categorical Percent Area and Cumulative Percent Area.Categorical Percent Area statistic is the percent of the area in a certain drought category and excludes areas that are better or worse. For example, the D0 category is labeled as such and only shows the percent of the area experiencing abnormally dry conditions.Cumulative Percent Area statistics combine drought categories for a comprehensive percent of area in drought. For example, the D0-D4 category shows the percent of the area that is classified as D0 or worse.Drought Classification Categories are as follows:ClassDescriptionPossible ImpactsD0Abnormally DryGoing into drought: short-term dryness slows growth of crops/pastures. Coming out of drought: some lingering water deficits; drops/pastures not fully recovered.D1Moderate DroughtSome damage to crops/pastures; streams, reservoirs, or wells are low with some water shortages developing or imminent; voluntary water-use restrictions requested.D2Severe DroughtCrop/pasture losses are likely; water shortages are common and water retrictions are imposed.D3Extreme DroughtMajor crop/pasture losses; widespread water shortages or restrictions.D4Exceptional DroughtExceptional and widespread crop/pasture losses; shortages of water in reservoirs, streams, and wells creating water emergencies.The U.S. Drought Monitor is produced in partnership between the National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. It is the drought map that the USDA and IRS use to define which farms have been affected by drought conditions, defining who is eligible for federal relief funds.

Forests cover approximately 30% of Earth's land surface, absorb more carbon than all other terrestrial ecosystems, and provide trillions of dollars’ worth of ecosystem services (Food and Agriculture Organization of the United Nations, 2005). However, climate change-induced droughts pose a significant threat to these vital ecosystems. As climate change intensifies, it is critical for our planning and management that we understand how and where trees will be the most threatened. Previous research has examined the effects of these droughts on forests at a global scale, but these large-scale analyses are not particularly helpful for land managers who often focus on specific regions and only a limited number of species. Our project addresses this gap by assessing species-specific sensitivity to increasingly severe and frequent droughts, considering the variations within their ranges. This localized information is crucial for land managers to develop targeted conservation strategies. By analyzing species-specific data, we demonstrate that the impacts of drier conditions are not uniform across or within species. Our findings suggest that effective management strategies must adopt a multifaceted and area-specific approach. To make our findings easily usable, we developed an interactive dashboard for land managers and the public. Here, users can find species-specific sensitivity maps that highlight the areas of greatest concern within manageable spaces, providing a valuable tool for informed decision-making. Our project contributes to the understanding of the potential future drought impacts on forests and emphasizes the need for targeted conservation efforts to mitigate the consequences of climate change on these essential ecosystems. Methods No data was actively collected to complete this project. Processed data is not available as it is being used in ongoing research, please contact the client Dr. Joan Dudney for more information (dudney@bren.ucsb.edu). Raw data was accessed from the following, publicly available sources:

Tree ring data - International Tree Ring Data Bank

Date of Access: 2020-07-05

Version: ITRDB v.7.22

Climate data - Terra Climate

Date of Access: 2024-04-02

Version: Annual data (1958-present)

Data pre-processing and the developed workflow were conducted using the R programming language.

About this AppThe Drought Aware app provides information about areas in the U.S. affected by drought across different time intervals and over multiple drought intensities. The app shows summaries about the affected population and the potential impacts to crops, agricultural labor, rivers, and reservoirs.Use this AppDisplay drought maps for different weeks by clicking on the time-series chart (top bar) or by scrolling through time using the sector chart (top-left). Hover on each drought intensity level in the sector chart to highlight the areas on the map and display the area percentage. Click on the map to display a panel with summary information for the selected area. The panel includes three categories (1) population, (2) water, and (3) agriculture. App CategoriesThe Drought Aware app summarizes information in three categories:Population: displays the estimated people and households affected by drought at each intensity level, describes some of the vulnerable populations, and lists the related drought risk indexes. The data is available at County and State levels. Water: depicts the major local rivers, the average inter-annual river flow, and the relevant local reservoirs. The data is available at the Subregion Hydrologic Units (HUC4)Agriculture: shows the potential economic impact by major crop, the affected labor, and the agricultural exposure to droughts. The data is available at County and State levels. Drought Definitions Abnormally Dry (D0) Going into drought there is short-term dryness slowing planting, growth of crops or pastures. Coming out of drought there are some lingering water deficits; pastures or crops not fully recovered. Moderate Drought (D1) Some damage to crops and pastures. Streams, reservoirs, or wells low, some water shortages developing or imminent. Voluntary water-use restrictions requested. Severe Drought (D2) Crop or pasture losses likely. Water shortages are common. Water restrictions imposed. Extreme Drought (D3) Major crop/pasture losses. Widespread water shortages or restrictions. Exceptional Drought (D4) Exceptional and widespread crop/pasture losses. Shortages of water in reservoirs, streams, and wells create water emergencies.Data SourcesThe data layers used in this app can be found in ArcGIS Living Atlas of the World:U.S. Drought Monitor American Community Survey (ACS)USDA Census of AgricultureFEMA National Risk IndexNational Water Model (NWM)National Hydrography Dataset (NHD)National Inventory of Dams (NID)National Boundary Dataset (WBD)UpdateThe data behind the app is updated every week once a U.S. Drought Monitor map is released. The update process is automated using a live feed routine. This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.RevisionsOct 16, 2024: Official release of the Drought Aware app.

This map presents the intrinsic vulnerability of groundwater systems and the sensitivity or resistance of those systems to natural disasters. The concept of groundwater vulnerability takes into account that the geological characteristics of the aquifers and the physical environment provide different degrees of protection against natural or human activities related impacts. Aquifers in karst formations or in fluvial unconsolidated deposits of large rivers are highly vulnerable to floods and droughts, and coastal aquifers are particularly prone to tsunamis, while the groundwater resources in deep-seated aquifers are naturally less vulnerable and more resilient to external influences due to their protection from the earth surface by geological layers with low permeability. Some of these aquifers, if accurately managed, could supply drinking water in the post-disaster emergency phase, replacing damaged water supply systems.

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

These data were compiled using a new multivariate matching algorithm that transfers simulated soil moisture conditions (Bradford et al. 2020) from an original 10-km resolution to a 30-arcsec spatial resolution. Also, these data are a supplement to a previously published journal article (Bradford et al., 2020) and USGS data release (Bradford and Schlaepfer, 2020). The objectives of our study were to (1) characterize geographic patterns in ecological drought under historical climate, (2) quantify the direction and magnitude of projected responses in ecological drought under climate change, (3) identify areas and drought metrics with projected changes that are robust across climate models for a representative set of climate scenarios. These data represent geographic patterns in simulated ecological drought metrics based on SOILWAT2 simulations under climate conditions representing historical (current) time period (1980-2010) and two future projected time periods (2020-2050, d40yrs) and (2070-2100, d90yrs) for two representative concentration pathways (RCP4.5, RCP8.5) as medians across simulation runs based on output from each of the available downscaled global circulation models that participated in CMIP5 (RCP4.5, 37 GCMs; RCP8.5, 35 GCMs; Maurer et al. 2007). Additional information about the setup of SOILWAT2 simulation experiments can be found in Bradford et al. 2020. These data were created in 2020 and 2021 for the area of the sagebrush region in the western North America. These data were created by a collaborative research project between the U.S. Geological Survey and Yale University. These data can be used with the high-resolution matching algorithm (Renne et al., 202X), within the scope of Bradford et al. 2020, and as defined by the study. These data may also be used to evaluate the potential impact of changing climate conditions on robust ecological drought metrics within the scope defined by the study.

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present at four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Depth: This layer has four depth levels. By default they are summed, but you can view each using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter. It is also possible to toggle between depth layers using raster functions, accessed through the Image Display tab.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

A global monthly data set of Palmer Drought Severity Index (PDSI) from 1870 to 2005 has been produced using historical observed surface air temperature and precipitation data for global land areas, except Antarctica and Greenland, on a 2.5 x 2.5 degree grid. Calibration (or reference) period is 1950-1979.

The PDSI was created by Palmer (1965) with the intent to measure the cumulative departure (relative to local mean conditions) in atmospheric moisture supply and demand at the surface. It incorporates antecedent precipitation, moisture supply, and moisture demand into a hydrological accounting system.

To calculate the global monthly PDSI, Climate Research Unit (CRU) surface air temperature data (Jones and Moberg, 2003 and updates) were regridded to a 2.5 x 2.5 degree grid. National Centers for Environmental Prediction (NCEP) precipitation data for 1948–2003 from about 5,000 to 16,500 rain gauges during 1948–97 and about 3,500 gauges thereafter (Chen et al., 2002) were gridded using the optimal interpolation scheme. For the pre-1948 period, precipitation data from Dai et al. (1997) were used. The monthly anomalies of Dai et al. (1997) were adjusted to have zero mean values for 1950–79 and then added to the 1950–79 mean of NCEP data to obtain the total precipitation used for the PDSI calculation. For field water-holding capacity (awc), a soil texture–based water-holding-capacity map from Webb et al. (1993) was used. The time series are thought to be reliable over most land areas. The relatively low resolution used, however, does not resolve small scale variations such as those over mountains.

The global monthly PDSI data are provided by UCAR/NCAR as compressed binary, text, and netCDF files with an associated README file.

Oak Ridge National Laboratory (ORNL) also provides the global monthly PDSI data set, but in GeoTiff format. ORNL converted the UCAR/NCAR data from netCDF format to GeoTIFF format. The processed GeoTIFF data were fed into ORNL DAAC Web Map Service v1.1.1 (WMS), Web Coverage Service v1.0.0 (WCS), and Spatial Data Access Tool (SDAT) to provide data visualization and distribution capabilities. Sources:

Chen, M., P. Xie, J. E. Janowiak, and P. A. Arkin. 2002. Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3, 249–266.

Dai, A., K. E. Trenberth, and T. Qian. 2004. A global data set of Palmer Drought Severity Index for 1870-2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeorology, 5, 1117-1130.

Dai, A., I. Y. Fung, and A. D. Del Genio. 1997. Surface observed global land precipitation variations during 1900–88. J. Climate, 10, 2943–2962.

Jones, P. D., and A. Moberg. 2003. Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. J. Climate, 16, 206–223.

Palmer, W. C. 1965. Meteorological drought. Research Paper 45, U.S. Dept. of Commerce, 58 pp.

Webb, R. S., C. E. Rosenzweig, and E. R. Levine. 1993. Specifying land surface characteristics in general circulation models: Soil profile data set and derived water-holding capacities. Global Biogeochem. Cycles, 7, 97–108.

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.

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, 2023)

railways (OpenStreetMap, 2023)

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 (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



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.

We found: a) growth variability was mainly affected by warm-induced drought and increased at lower latitudes. Climate warming in winter could decrease growth variability, but this effect is by far not enough to offset the threat of hotter drought; b) there existed a trade-off between fast- and slow-growing (drought tolerance) strategies for global conifer species, and abiotic and stand factors affected growth variability via functional traits. Contrary to common conjecture, species with higher drought tolerance revealed higher growth variability due to their occupation of more xeric sites, and may also because higher investment in drought tolerance leads to less investment remaining for growth; c) older trees revealed higher growth variability due to their more conservative growth strategy, while at large scales taller trees showed lower growth variability due to occupying more productive sites; and d) moderate N deposition could reduce growth variability by leading conifers to adopt a ..., Raw tree-ring widths data were extracted from the International Tree-Ring Data Bank (ITRDB) V.7.23 (https://www1.ncdc.noaa.gov/pub/data/paleo/treering/) in October 2021. The monthly temperature, precipitation, and potential evapotranspiration during 1970-2010 were extracted for each tree-ring site based on its latitude and longitude, from the Climatic Research Unit (CRU) database (https://crudata.uea.ac.uk/cru/data/hrg/). N deposition data were obtained from the N Deposition Database (Ackerman et al. 2019; https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018GB005990), which used the GEOS-Chem Chemical Transport Model to estimate total (wet and dry) inorganic N deposition globally at a spatial resolution of 2°×2.5°. For stand factors, the stand age of each chronology was calculated as the mean age for all tree rings used to build the chronology. This global canopy height map was estimated with new satellite LiDAR (light detecting and ranging) data, with a high accuracy and a ver..., , ## GENERAL INFORMATION

1. Title of Dataset:

Hotter drought and trade-off between fast and slow growth strategies as major drivers of tree-ring growth variability of global conifers

https://doi.org/10.5061/dryad.31zcrjdt

2. Author information:

First author: Xuemei Wang, Beijing Forestry University, China; wxmbju@163.com;

Corresponding author: Xiangping Wang, Beijing Forestry University, China; wangxiangpingbjfu@edu.cn;

3. Summary of the dataset:

We used global conifer (mainly from North America, Asia, and Europe) tree-ring records (123 species from 1,780 sites) from 1970–2010 to calculate growth variability and assess how climate factors and stand factors affect growth variability (coefficient of variation)

Description of the data and file structure

DATA-SPECIFIC INFORMATION FOR: database.xls

1. Number of variables: 25

2. Number of cases/rows: 2023

3. Variable...

This Map shows natural climatological drought disasters occurrence from 1900 to 2015. The data source is from the Centre for Research on the Epidemiology of Disasters, EM-DAT database.

EM-DAT is a global database on natural and technological disasters that contains essential core data on the occurrence and effects climatological disasters in the world from 1900 to present. EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université catholique de Louvain located in Brussels, Belgium. The database is compiled from various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies. The main objective of the database is to serve the purposes of humanitarian action at national and international levels in order to rationalise decision making for disaster preparedness, as well as providing an objective base for vulnerability assessment and priority setting. In EM-DAT data are considered at the country level for two reasons: first, it is at this level that they are usually reported; and second, it allows the aggregation and disaggregation of data. In order to facilitate the comparison over time, the event start date has been used as the disaster reference date.

Affected people are the number of people requiring immediate assistance during a period of emergency; this may include displaced or evacuated people. Total affected are the sum of injured, homeless and affected. Total Deaths are the number of people who lost their life because the event happened (it includes also the missing people based on official figures). Homeless are the number of people whose house is destroyed or heavily damaged and therefore need shelter after an event.

The final Composite Drought Index is a contribution of ranked drought indicators. Ranking the indicators based on percentiles helps characterizing the drought since its categories are associated with historical occurrence/likelihood (percentile ranking). Methodology The inputs that have a native grid of 0.05 deg (Rainfall, NDVI and LST) have been regridded to 0.25 deg and ranked into percentiles. All inputs are monthly except rainfall. We decided to take into account 2 months to reflect the effect of vegetation and soil lag response to a rainfall deficit or excess. The final CDI is a combination of the four inputs after attribution of individual weights to each component. The weights are attributed based on land use/land cover map provided by ESA and UCLouvain, GlobCover-L4. The data is published on MAWRED (Monitoring Agriculture and Water Resources during Droughts) and was generated by ICBA (International Centre for Biosaline Agriculture)

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.