Café Du Monde, New Orleans

River and Coastal Flood

Still-water depth (1% chance)

Riverine flood depths are based on the JRC global river flood model, which combines ERA5-forced GloFAS hydrology with the two-dimensional LISFLOOD-FP hydraulic model, run on the MERIT-Hydro digital elevation model, to estimate inundation depth for the 100-year return period at approximately 90 m resolution. Coastal flooding is screened using a connectivity-based inundation model driven by the 100-year COAST-RP extreme water level, which combines ERA5-based extratropical storm surge, synthetic tropical-cyclone surge, and tides over the approximately 30 m DeltaDTM coastal terrain model. Mean dynamic topography is used to account for differences between the vertical reference frame of the coastal extreme water levels and the coastal terrain elevations, so that water levels and land elevations can be compared consistently. Areas are identified as potentially inundated where low-lying terrain is hydraulically connected to the ocean and the adjusted extreme water level exceeds the local ground elevation. The coastal screening includes a friction-based attenuation component to account for the reduction of inland water levels with distance and land-surface resistance, so the method is not a simple bathtub fill. This approach represents a static coastal inundation screen rather than a full dynamic hydrodynamic simulation. Wave setup, wave runup, overtopping, erosion, and local drainage interactions are not included. Both flood layers represent undefended hazard conditions; levees, seawalls, surge barriers, and other local flood-protection infrastructure are not explicitly modeled.

Surface Water Flooding

1-hr rainfall (2% annual chance)

Pluvial flooding is screened using the frequency and magnitude of extreme short-duration rainfall, which serves as a proxy for stormwater flooding potential because drainage systems, runoff generation, and local ponding cannot be simulated consistently at global scale. Hourly rainfall extremes are estimated using a rainfall-specific extreme-value framework, the Simplified Metastatistical Extreme Value (SMEV) method, applied to quality-controlled GHCN-Hourly gauges and supplemented by convection-resolving simulations at approximately 2.5 to 4 km resolution. These point- and model-derived estimates are then regionalized to a 30 arc-second (~1 km) global grid using machine-learning methods trained on geographic, topographic, and climatological predictors associated with short-duration rainfall extremes. This step transfers information from gauges and convection-resolving simulations to locations without reliable hourly rainfall observations while preserving broad spatial gradients related to climate regime, terrain, and storm environment. The resulting layer represents the rainfall forcing associated with pluvial flood potential, not modeled inundation. Actual flooding depends on local imperviousness, terrain, soil and land-cover conditions, drainage capacity, maintenance, and other site-specific factors.

Low-Lying Coastal

Elevation above high tide

The Coastal Exposure Index identifies low-lying coastal land that falls below selected extreme high-water levels and is hydraulically connected to the ocean. It is intended as a screen for chronic tidal exposure and sea-level-rise susceptibility, not as a storm-surge, wave, or event-based flood simulation. Water levels are referenced to the mean higher-high-water tidal datum, using NOAA VDatum in the United States and FES2022 tides elsewhere. Outside the United States, mean dynamic topography is used to align the modeled tidal water levels with the land-elevation reference frame before comparing water levels with terrain. Elevation data come from lidar-based DEMs in the United States and the approximately 30 m global DeltaDTM coastal terrain model outside the United States. A connectivity check removes low-lying terrain that is isolated from the coast, helping distinguish potentially exposed coastal areas from inland depressions or disconnected basins. The index does not model groundwater emergence, drainage interactions, land subsidence, shoreline change, or flood defenses.

FEMA Flood Zones

FEMA flood designation

FEMA flood zones are based on FEMA’s National Flood Hazard Layer (NFHL), the official regulatory dataset used for U.S. floodplain management and flood insurance. The card reports the mapped flood designation at the selected location, such as Zone AE, including Special Flood Hazard Areas associated with the 1% annual-chance flood and areas of 0.2% annual-chance flood hazard. Coverage is limited to the United States and depends on digitized NFHL availability. FEMA maps change over time, and some areas may be unmapped, incomplete, or updated after the data snapshot used here.

Extreme Heat

Annual max temp (10% annual chance)

Extreme heat is represented by the annual maximum dry-bulb air temperature at a specified annual-chance level. Quality-controlled NOAA GHCN station records are fit with a Generalized Extreme Value (GEV) distribution in a nonstationary Bayesian framework, allowing distribution parameters to vary with global mean temperature. The resulting station-level estimates are regionalized to a 30 arc-second (~1 km) global grid using machine-learning methods trained on high-resolution climatological predictors derived from reanalysis, terrain-aware downscaling, and global land-surface climatologies, together with terrain and climate-zone variables. This layer represents extreme outdoor air-temperature hazard, not indoor heat exposure or heat stress, which also depend on humidity, radiation, wind, building characteristics, acclimatization, and exposure duration.

Human Heat Stress

Max wet-bulb (10% annual chance)

Human heat stress is represented by the annual maximum wet-bulb temperature at the 10% annual-chance level. Wet-bulb temperature combines air temperature and humidity into a single thermodynamic measure related to the body’s ability to cool through evaporation. It is computed from quality-controlled station air-temperature and humidity records. Extremes are fit with a Generalized Extreme Value (GEV) distribution in a non-stationary Bayesian framework, allowing distribution parameters to vary with global mean temperature, and are then regionalized to a 30 arc-second (~1 km) global grid using the same machine-learning framework applied to extreme heat and cold. This layer represents large-scale outdoor heat-humidity hazard; it does not account for direct solar radiation, wind speed, clothing, activity level, indoor conditions, acclimatization, or exposure duration.

Wildfire

Annual burn probability

This model produces global annual burn-probability estimates at 10 arc-second (~300 m) resolution from a tree-based gradient-boosting regressor trained on USFS FSim burn-probability values over CONUS, Alaska, and Hawaii. Predictors span fire weather (Canadian FWI System indicators), fuels and land cover (LULC, aboveground biomass), terrain (elevation, slope, TPI), and human/ignition factors (population, roads, lightning), and the U.S.-learned relationships are extrapolated worldwide. A post-processing step then “oozes” wildland burn probability into adjacent wildland–urban interface pixels to represent structure-fire exposure.

Drought

Composite drought score

The composite drought score is a simple, globally comparable screening metric designed to identify basins where severe, multi-year drought could result in low remaining precipitation availability. ERA5 precipitation is aggregated over major river basins, and rolling 48-month basin-total precipitation accumulations are analyzed with extreme-value statistics to estimate the precipitation deficit associated with a 25-year drought. This deficit is subtracted from normal 48-month precipitation to estimate the remaining precipitation available to the basin under severe drought conditions. The resulting remaining-precipitation values are then ranked across basins worldwide, with higher drought scores assigned to basins with lower remaining precipitation. This structure prevents very wet regions from being classified as high drought risk solely because they can experience large absolute precipitation deficits.

The score is intentionally simplified and emphasizes absolute precipitation scarcity under severe drought conditions rather than departure from local norms. It does not represent full water availability, which depends on runoff generation, groundwater, snowpack, reservoirs, interbasin transfers, irrigation, water rights, infrastructure, demand, and water-management operations. These factors are not observed or modeled consistently at global scale, and available global water-stress datasets can depend heavily on uncertain assumptions about withdrawals, storage, routing, and management. Water availability is highly system-specific, so this layer should be interpreted as a first-pass drought-scarcity screen rather than a basin water-balance or water-security model.

Human Cold Stress

Annual min temp (10% annual chance)

Cold stress is represented by the annual minimum dry-bulb air temperature at the 10% annual-chance level, with lower temperatures indicating greater hazard. Quality-controlled station minimum-temperature records are fit with a Generalized Extreme Value (GEV) distribution in a nonstationary Bayesian framework, allowing distribution parameters to vary with global mean temperature. The resulting station-level estimates are regionalized to a 30 arc-second (~1 km) global grid using the same machine-learning framework applied to the heat metrics, with gridded predictors representing local climate, elevation, terrain, geographic location, and climate-zone context. This layer represents outdoor cold-temperature hazard; actual impacts depend on asset design, exposure duration, wind, snow/ice conditions, building characteristics, and operational tolerances.

Cooling Demand

Cooling °C-days / yr

Cooling demand is represented by annual Cooling Degree Days (CDD), the cumulative amount by which daily mean temperature exceeds the 18.3 °C (65 °F) balance point. CDD is computed from a 30 arc-second (~1 km) daily temperature climatology derived from reanalysis using terrain-aware statistical downscaling for 2004–2023. The metric approximates temperature-driven cooling energy demand, but does not account for humidity, solar gains, wind, building characteristics, occupancy, equipment loads, or cooling-system efficiency.

Heating Demand

Heating °C-days / yr

Heating demand is represented by annual Heating Degree Days (HDD), the cumulative amount by which daily mean temperature falls below the 18.3 °C (65 °F) balance point. HDD is computed from a 30 arc-second (~1 km) daily temperature climatology derived from reanalysis using terrain-aware statistical downscaling for 2004–2023. The metric approximates temperature-driven heating energy demand, but does not account for wind, solar gains, building characteristics, occupancy, internal heat gains, heating-system efficiency, fuel type, or behavioral responses.

Extreme Wind

3-s gust (1% annual chance)

Extreme wind hazard is represented by terrain-aware 3-second peak-gust return levels at 30 arc-second (~1 km) resolution. The global wind model combines separate tropical-cyclone and non-tropical-cyclone wind estimates, then applies terrain and surface-roughness adjustments to better represent local exposure.

The non-tropical-cyclone component is derived from ERA5 pressure-level winds, with tropical-cyclone days removed using historical track data. Wind profiles are vertically interpolated to represent near-surface conditions, annual maxima are extracted, and extreme-value statistics are used to estimate return levels. These estimates are refined to 30 arc-second (~1 km) resolution using elevation and surface-roughness exposure adjustments.

The tropical-cyclone component is based on CHAZ, the Columbia HAZard model, a statistical–dynamical tropical-cyclone model that generates large synthetic tropical-cyclone catalogs from large-scale environmental conditions. CHAZ represents tropical-cyclone activity through separate genesis, track, and intensity components, allowing coastal wind return levels to be estimated from many physically plausible storms rather than the limited observed record alone. In this workflow, CHAZ tropical-cyclone wind return levels are converted from 1-minute sustained winds to 3-second gusts using the WMO offshore conversion factor and then combined with the non-tropical-cyclone wind branch after surface-roughness and terrain-speedup adjustments.

Tornado

EF2+ within 40 km / yr

Tornado hazard is represented by a U.S. climatology of significant tornado occurrence based on NOAA Storm Prediction Center records. The metric is the mean number of days per decade with an EF2 or stronger tornado reported within 25 miles (40 km) of the location over 1986–2015. The 25-mile search radius accounts for spatial uncertainty in tornado tracks and provides a smoother local climatology, while the EF2+ threshold focuses on events most likely to cause substantial damage. This layer represents historical tornado occurrence, not a forecast or event-based tornado simulation. Coverage is limited to the United States.

Derecho

Derecho events / yr

Derecho hazard is represented by a U.S. climatology of long-lived, widespread straight-line windstorms produced by organized thunderstorm complexes. The layer is based on a 4 km observational climatology for 2004–2021 that uses machine-learning bow-echo detection and storm tracking to identify derecho events. Event wind-damage footprints are summed, annualized, and smoothed to produce a derecho-frequency surface expressed as events per year. This layer captures organized convective wind hazard that is not represented in the Extreme Wind layer. Coverage is limited to the United States east of the Rocky Mountains.

Large Hail

Hail ≥ 5 cm probability / yr

Large-hail hazard is represented by a global climatology of very large hail occurrence, defined as hailstones at least 5 cm (2 in) in diameter. The layer is based on the AR-CHaMo statistical hail model applied to ERA5 reanalysis for 1950–2023. AR-CHaMo estimates hail occurrence as the product of thunderstorm probability and the conditional probability of very large hail given a storm, using convective-environment predictors such as instability, vertical wind shear, and moisture. The model is calibrated against hail reports from Europe, the United States, and Australia. Because ERA5 does not explicitly simulate hailstones, occurrence is inferred from atmospheric environments favorable for very large hail rather than observed or modeled hail impacts at a specific site.

Lightning

Annual thunder hours

Lightning activity is represented by annual thunder hours, defined as hours with at least two lightning strokes detected within 15 km. The metric is derived from a satellite-calibrated global lightning dataset beginning in 2013 and aggregated to annual totals on an approximately 5 km grid. Thunder hours provide a proxy for thunderstorm activity and electrical-storm exposure, but they do not directly measure storm severity, hail, tornadoes, damaging winds, or rainfall intensity. Detection efficiency and reporting quality can vary by region, so the layer should be interpreted as a broad climatological screen rather than a site-specific lightning risk estimate.

Land Subsidence

Land subsidence rate

Land subsidence represents the estimated near-present-day rate of vertical ground-surface lowering. The layer is derived from a global deep-learning model trained on more than 46,000 quality-controlled observation points and 23 predictors related to groundwater abstraction and recharge, climate, geology, soil conditions, and topography. Groundwater pumping is an important driver in many high-subsidence regions, but local rates can also reflect sediment compaction, natural consolidation, hydrogeology, and land-use history. Estimates are provided at 30 arc-second (~1 km) resolution and should be interpreted as a broad screening layer, since subsidence can vary sharply over short distances due to local pumping, aquifer properties, construction history, and other factors not fully captured in global datasets. The source dataset does not provide estimates for urban areas, so subsidence may be unavailable or underrepresented in some locations where exposure is otherwise important.

Earthquake

PGA (0.2% annual chance)

Seismic hazard is represented by peak ground acceleration (PGA) with a 10% probability of exceedance in 50 years, equivalent to an approximately 475-year return period or about a 0.2% annual chance. Values represent firm-rock conditions and are provided on an approximately 1 km global raster. The layer combines the best available regional probabilistic seismic-hazard models, including USGS, ESHM20, GEM, and other regional sources, with GSHAP used as background coverage where higher-resolution regional models are unavailable. Where models overlap, the maximum PGA value is used. The layer does not include local soil amplification, liquefaction, earthquake-triggered landslides, tsunami hazard, or other secondary effects. Boundaries or discontinuities may appear where independently developed regional models meet.

Landslide

Susceptibility index

Landslide hazard is represented by a relative susceptibility index that reflects terrain conditions and rainfall-triggering potential. Outside the United States, the layer uses a global rainfall-driven landslide model that combines ERA5 daily rainfall with five susceptibility classes derived from factors such as slope, lithology, vegetation, and soil moisture to estimate landslide-initiation potential at approximately 90 m resolution. Within the United States, the layer uses USGS slope–relief susceptibility models trained on more than 600,000 documented landslides and 10 m elevation data. The outputs represent relative susceptibility and screening-level landslide potential, not event-specific probability, runout extent, depth, velocity, or site-scale slope stability.