The most relevant datasets (updated, high resolution, scientific quality) representing extreme events and long-term hazards that were considered for inclusion in the CCDR and other risk-related activities across the Bank have been listed below for each hazard, explaining their pros and cons and providing suggestions for improvement.
Geophysical
Hydro-meteorological
Environmental factors
Earthquake
River flood
Air pollution
Tsunami
Landslide
Volcanic activity
Coastal flood
Tropical cyclones
Drought
Extreme heat
Wildfires
Some hazards are modelled using a probabilistic approach, providing a set of scenarios linked to hazard frequency for the period of reference. For the current data availability, this is the case for floods, storm surges, cyclones, heatwaves, and wildfires.
Others, such as landslides, use a deterministic approach, providing an individual map of hazard intensity or susceptibility.
GEOPHYSICAL HAZARDS
Earthquake
Tsunami
Volcanic activity
HYDRO-METEOROLOGICAL HAZARDS
River floods
Flood hazard is commonly described in terms of flood frequency (multiple scenarios) and severity, which is measured in terms of water extent and related depth modelled over Digital Elevation Model (DEM). Inland flood events can be split into 2 categories:
Fluvial (or river) floods occur when intense precipitation or snow melt collects in a catchment, causing river(s) to exceed capacity, triggering the overflow, or breaching of barriers and causing the submersion of land, especially along the floodplains.
Pluvial (or surface water) floods are a consequence of heavy rainfall, but unrelated to the presence of water bodies. Fast accumulation of rainfall is due to reduced soil absorbing capacity or due to the saturation of the drainage infrastructures; meaning that the same event intensity can trigger very different risk outcomes depending on those parameters. For this reason, static hazard maps based on rainfall and DEM alone should be used with extreme caution.
The only open flood dataset addressing future hazard scenarios
Despite missing projections, Fathom modelling has consistently proven to be the preferred option due to its higher quality (better resolution, updated data and a more advanced modelling approach). There are, however, important details and limitations to consider for the correct use and interpretation of the model. The undefended model (FU) is typically the preferred product to use in assessments, since the defended model (FD) does not account for physical presence of defense measures, rather proxies the defense standard by using GDP as proxy (FLOPROS database).
WRI hazard maps are the preferred choice only in cases when 1) data needs to be open/public; 2) explicit climate scenarios are required, however the scientific quality and granularity of this dataset is far from the one offered by Fathom – and far from optimal, in general (low resolution, old baseline, simplified modelling).
It is important to note that pluvial (flash) flood events are extremely hard to model properly on the base of global static hazard maps alone. This is especially true for densely-populated urban areas, where the hazardous water cumulation is often the results of undersized or undermaintained discharge infrastructures. Because of this, while Fathom does offer pluvial hazard maps, their application for pluvial risk assessment is questionable as it cannot account for these key drivers.
A complementary perspective on flood risk is offered by the Global Surface Water layer produced by JRC using remote sensing data (Landsat 5, 7, 8) over the period1984-2020. It provides information on all the locations ever detected as max water level, water occurrence, occurrence change, recurrence, seasonality, and seasonality change. However, this layer does not seem to properly account for extreme flood events, I.e. recorded flood events for the period 1984-2020 most often exceed the extent of this layer. Hence it can be used to identify permanent and semi-permanent water bodies, but not to identify the baseline flood extent from past events.
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Global Surface Water Layer
Coastal floods (storm surge)
Coastal floods occur when the level in a water body (sea, estuary) rises to engulf otherwise dry land. This happens mainly due to storm surges, triggered by tropical cyclones and/or strong winds pushing surface water inland. Like for inland floods, hazard intensity is measured using the water extent and associated depth.
Includes effect of local subsidence (2 datasets) and flood attenuation. Modelled future scenarios.
Essentially an evolution of the WRI
The current availability of global dataset is poor, with WRI products (recently updated by Deltares) representing the best option in terms of resolution and time coverage (baseline + scenarios), and water routing, including inundation attenuation to generate more realistic flood extent. The latest version has a much better resolution of 90 m based on MeritDEM or NASADEM, overcoming WRI limitations for local-scale assessment. Note that the Fathom is working to include coastal floods and climate scenarios in the next version (3) of the dataset (coming sometime in 2023/24), which will likely become the best option for risk assessment in the next future.
Additional datasets that have been previously used in WB coastal flood analytics are:
Name
Coastal flood hazard maps
Coastal risk screening
Developer
Muis et al. (2016, 2020)
Climate Central
Hazard process
Coastal flood
Mean sea level
Resolution
1 km
Analysis type
Probabilistic
Frequency type
Return Period (10 RPs)
One layer per period
Time reference
Baseline (1979–2014)
Baseline; Projections
Intensity metric
Water depth [m]
Water extent
License
Open data
Licensed
Notes
The update of Muis 2020 has been considered; however, the available data does include easily applicable land inundation, only extreme sea levels.
Does use simple bathtub distribution without flood attenuation – does not simulate extreme sea events.
Both these models seem to be affrom a simplified bathtub modelling approach, projecting unrealistic flood extent already under baseline climate conditions.
As shown in figure below, considering the minimum baseline values (least impact criteria), the flood extent drawn by the Climate Central layer is similar to the baseline RP100 from Muis, in the middle - both generously overestimating water spreading inland even under less extreme scenarios [the locaiton of comparison is chosen as both the Netherlands and N Italy are low-lying areas, which are typically the most difficult to model].
In comparison, the WRI is far from perfection (it is also a bathtub model), but it seems to apply a more realistic max flood extent, which ultimately makes it more realistic for application.
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Quick comparison of coastal flood layers over Northern Europe under baseline conditions, RP 100 years.
Sea level rise
Landslide
Landslides (mass movements) are affected by geological features (rock type and structure) and geomorphological setting (slope gradient). Landslides can be split into two categories depending on their trigger:
Dry mass movements (rockfalls, debris flows) are driven by gravity and can be triggered by seismic events, but they can also be a consequence of soil erosion and environmental degradation.
Wet mass movements can be triggered by heavy precipitation and flooding and are strongly affected by geological features (e.g. soil type and structure) and geomorphological settings (e.g., slope gradient). They do not typically include avalanches.
Name
Global landslide hazard layer
Global landslide susceptibility layer
Developer
ARUP
NASA
Hazard process
Dry (seismic) mass movement Wet (rainfall) mass movement
Wet (rainfall) mass movement
Resolution
1 km
1 km
Analysis type
Deterministic
Deterministic
Frequency type
none
none
Time reference
Baseline (rainfall trigger) (1980-2018)
Intensity metric
Hazard index [-]
Susceptibility index [-]
License
Open
Notes
Based on NASA landslide susceptibility layer. Median and Mean layers provided.
Although not a hazard layer, it can be accounted for in addition to the ARUP layer.
Landslide hazard description can rely on either the NASA Landslide Hazard Susceptibility map (LHASA) or the derived ARUP layer funded by GFDRR in 2019. This dataset considers empirical events from the COOLR database and model both the earthquake and rainfall triggers over the existing LHASA map. The metric of choice is frequency of occurrence of a significant landslide per km2, which is however provided as synthetic index (not directly translatable as time occurrence probability).
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Example from the ARUP landslide hazard layer (rainfall trigger, median): Pakistan. The continuos index is displayed into 3 discrete classes (Low, Medium, High).
Tropical cyclones
Tropical cyclones
Tropical cyclones (including hurricanes, typhoons) are events that can trigger different hazard processes at once such as strong winds, intense rainfall, extreme waves, and storm surges. In this category, we consider only the wind component of cyclone hazard, while other components (floods, storm surge) are typically considered separately.
Name
GAR15-IBTrACS
IBTrACSv4
STORMv3
Developer
NOAA
NOAA
IVM
Hazard process
Strong winds
Strong winds
Strong winds
Resolution
30 km
10 km
10 km
Analysis type
Probabilistic
Historical
Historical, Probabilistic
Frequency type
Return Period (5 RPs)
Return periods (10 10,000 years)
Time reference
Baseline (1989-2007)
Baseline (1980-2022)
Baseline (1984-2022)
Intensity metric
Wind gust speed [5-sec m/s]
Many variables
Many variables
License
Open data
Open data
Open data
A newer version (IBTrACSv4) has been released in 2018 and could be leveraged to generate an updated wind-hazard layer, with better resolution and possibly the inclusion of orography effect. There are several attributes tied to each event; the map shows the USA_WIND variable (Maximum sustained wind speed in knots: 0 - 300 kts) as general intensity measure.
The STORM database has recently released their new version (STORMv3), which includes synthetic global maps of 1) maximum wind speeds for a fixed set of return periods; and 2) return periods for a fixed set of maximum wind speeds, at 10 km resolution over all ocean basins. In addition, it contains the same set for events occurring within 100 km from a selection of 18 coastal cities and another for events occurring within 100 km from the capital city of an island.
The most relevant datasets (updated, high resolution, scientific quality) representing extreme events and long-term hazards that were considered for inclusion in the CCDR and other risk-related activities across the Bank have been listed below for each hazard, explaining their pros and cons and providing suggestions for improvement.
Some hazards are modelled using a probabilistic approach, providing a set of scenarios linked to hazard frequency for the period of reference. For the current data availability, this is the case for floods, storm surges, cyclones, heatwaves, and wildfires. Others, such as landslides, use a deterministic approach, providing an individual map of hazard intensity or susceptibility.
GEOPHYSICAL HAZARDS
Earthquake
Tsunami
Volcanic activity
HYDRO-METEOROLOGICAL HAZARDS
River floods
Flood hazard is commonly described in terms of flood frequency (multiple scenarios) and severity, which is measured in terms of water extent and related depth modelled over Digital Elevation Model (DEM). Inland flood events can be split into 2 categories:
Despite missing projections, Fathom modelling has consistently proven to be the preferred option due to its higher quality (better resolution, updated data and a more advanced modelling approach). There are, however, important details and limitations to consider for the correct use and interpretation of the model. The undefended model (FU) is typically the preferred product to use in assessments, since the defended model (FD) does not account for physical presence of defense measures, rather proxies the defense standard by using GDP as proxy (FLOPROS database).
WRI hazard maps are the preferred choice only in cases when 1) data needs to be open/public; 2) explicit climate scenarios are required, however the scientific quality and granularity of this dataset is far from the one offered by Fathom – and far from optimal, in general (low resolution, old baseline, simplified modelling).
It is important to note that pluvial (flash) flood events are extremely hard to model properly on the base of global static hazard maps alone. This is especially true for densely-populated urban areas, where the hazardous water cumulation is often the results of undersized or undermaintained discharge infrastructures. Because of this, while Fathom does offer pluvial hazard maps, their application for pluvial risk assessment is questionable as it cannot account for these key drivers.
A complementary perspective on flood risk is offered by the Global Surface Water layer produced by JRC using remote sensing data (Landsat 5, 7, 8) over the period1984-2020. It provides information on all the locations ever detected as max water level, water occurrence, occurrence change, recurrence, seasonality, and seasonality change. However, this layer does not seem to properly account for extreme flood events, I.e. recorded flood events for the period 1984-2020 most often exceed the extent of this layer. Hence it can be used to identify permanent and semi-permanent water bodies, but not to identify the baseline flood extent from past events.
Coastal floods (storm surge)
Coastal floods occur when the level in a water body (sea, estuary) rises to engulf otherwise dry land. This happens mainly due to storm surges, triggered by tropical cyclones and/or strong winds pushing surface water inland. Like for inland floods, hazard intensity is measured using the water extent and associated depth.
The current availability of global dataset is poor, with WRI products (recently updated by Deltares) representing the best option in terms of resolution and time coverage (baseline + scenarios), and water routing, including inundation attenuation to generate more realistic flood extent. The latest version has a much better resolution of 90 m based on MeritDEM or NASADEM, overcoming WRI limitations for local-scale assessment. Note that the Fathom is working to include coastal floods and climate scenarios in the next version (3) of the dataset (coming sometime in 2023/24), which will likely become the best option for risk assessment in the next future.
Additional datasets that have been previously used in WB coastal flood analytics are:
Both these models seem to be affrom a simplified bathtub modelling approach, projecting unrealistic flood extent already under baseline climate conditions.
As shown in figure below, considering the minimum baseline values (least impact criteria), the flood extent drawn by the Climate Central layer is similar to the baseline RP100 from Muis, in the middle - both generously overestimating water spreading inland even under less extreme scenarios [the locaiton of comparison is chosen as both the Netherlands and N Italy are low-lying areas, which are typically the most difficult to model]. In comparison, the WRI is far from perfection (it is also a bathtub model), but it seems to apply a more realistic max flood extent, which ultimately makes it more realistic for application.
Sea level rise
Landslide
Landslides (mass movements) are affected by geological features (rock type and structure) and geomorphological setting (slope gradient). Landslides can be split into two categories depending on their trigger:
Landslide hazard description can rely on either the NASA Landslide Hazard Susceptibility map (LHASA) or the derived ARUP layer funded by GFDRR in 2019. This dataset considers empirical events from the COOLR database and model both the earthquake and rainfall triggers over the existing LHASA map. The metric of choice is frequency of occurrence of a significant landslide per km2, which is however provided as synthetic index (not directly translatable as time occurrence probability).
Tropical cyclones
Tropical cyclones
Tropical cyclones (including hurricanes, typhoons) are events that can trigger different hazard processes at once such as strong winds, intense rainfall, extreme waves, and storm surges. In this category, we consider only the wind component of cyclone hazard, while other components (floods, storm surge) are typically considered separately.
A newer version (IBTrACSv4) has been released in 2018 and could be leveraged to generate an updated wind-hazard layer, with better resolution and possibly the inclusion of orography effect. There are several attributes tied to each event; the map shows the USA_WIND variable (Maximum sustained wind speed in knots: 0 - 300 kts) as general intensity measure. The STORM database has recently released their new version (STORMv3), which includes synthetic global maps of 1) maximum wind speeds for a fixed set of return periods; and 2) return periods for a fixed set of maximum wind speeds, at 10 km resolution over all ocean basins. In addition, it contains the same set for events occurring within 100 km from a selection of 18 coastal cities and another for events occurring within 100 km from the capital city of an island.
More recently (2022), simulated tracks for climate change scenarios have been developed as described in Bloemendaal, et al., 2022. Both synthetic tracks and wind speed maps are available.
Drought & Water scarcity
Heat stress
Wildfires
ENVIRONMENTAL FACTORS
Air pollution