Hydro-Meteorological hazards

Hydro-Meteorological hazards#

Process or phenomenon of atmospheric, hydrological or oceanographic nature that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage (UNISDR).

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.

Name	Fathom flood hazard maps	Aqueduct flood hazard maps
Developer	Fathom	WRI
Hazard process	Fluvial flood, Pluvial flood	Fluvial flood
Resolution	90 m	900 m
Analysis type	Probabilistic	Probabilistic
Frequency type	Return Period (11 scenarios)	Return Period (10 scenarios)
Time reference	Baseline (1989-2018)	Baseline (1960-1999); Projections – CMIP5 (2030-2050-2080)
Intensity metric	Water depth [m]	Water depth [m]
License	Commercial	Open data
Other	Includes defended/undefended option
Notes	Standard for WB analysis	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 presence of physical defence measures, rather uses GDP as a proxy (FLOPROS database) to set defence standards.

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.

Name	Aqueduct flood hazard maps	Global Flood map
Developer	WRI-Deltares	Deltares
Hazard process	Coastal flood	Coastal flood, SLR
Resolution	1 km	90 m, 1 km, 5 km
Analysis type	Probabilistic
Frequency type	Return Period (10 scenarios)	Return Period (6 scenarios)
Time reference	Baseline (1960–1999); Projections – CMIP5 (2030-2050-2080)	Baseline (2018); Projections – SLR (2050)
Intensity metric	Water depth [m]	Water depth [m]
License	Open data	Access requested
Notes	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 scenarios)	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 tend to project unrealistic flood extent already under baseline climate conditions, which might be due an oversimplified bathtub modelling approach, or due to the relatively low resolution and vertical accuracy of the Digital Terrain Model (DTM).

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.

../_images/CF_data.jpg — Fig. 12 Quick comparison of coastal flood layers over Northern Europe under baseline conditions, RP 100 years.#

Sea level rise#

Most of the listed models include flood hazard simulations that account for the effect of Sea Level Rise under climate change projections: RWI uses CMIP5 climate data, while Deltares and ClimateCentral dataset is based on CMIP6. In addition to increasing water volumes, sea level projections account for land movements (sinking or rising land) caused by tectonic activity, large-scale underground extraction, or glacial isostatic adjustment.

In additon to coastal flood projections, the NASA Sea Level Projection Tool allows users to visualize and download the sea level projection data from the IPCC 6th Assessment Report (AR6). The tool shows both global and regional sea level projections from 2020 to 2150, along with how these projections differ depending on future scenario or warming level. Data can be downloaded in multiple formats.

Landslides#

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 movement (rockfalls, debris flows) is 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 movement 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 (LHASA)
Developer	ARUP	NASA
Hazard process	Dry (seismic) mass movement Wet (rainfall) mass movement	none
Resolution	1 km	1 km
Analysis type	Deterministic	Deterministic
Frequency type	none	none
Time reference	Baseline (rainfall trigger) (1980-2018)
Intensity metric	Hazard frequency [-]	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).

../_images/LS_data.jpg — Fig. 13 Example from the ARUP landslide hazard layer (rainfall trigger, median): Pakistan. The continuous index is displayed into 3 discrete classes (Low, Medium, High) according to thresholds set by ARUP (Low: <0.01; Moderate: 0.01-0.1; High: >0.1).#

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	Empirical	Empirical, Probabilistic
Frequency type	Return Period (5 scenarios)		Return periods (10 to 10,000 years)
Time reference	Baseline (1989-2007)	Baseline (1980-2022)	Baseline (1984-2022); Projections (2015-2050; SSP5/8.5)
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 in figure below 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 STORM dataset comes with projections as described in Bloemendaal, et al., 2022: those are generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. Both synthetic tracks and wind speed maps are available. These data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.

../_images/SW_data.jpg — Fig. 14 Top: GAR 2015 cyclone max wind speed; Mid: IBTrACS v4 cyclone tracks; Bottom: STORMv3 synthetic cyclone tracks into max wind speed, RP 100 years.#

Drought & Water scarcity#

The Agricultural Stress Index (ASI) produced by FAO depicts the frequency of severe drought affecting crop areas by means of remote-sensed Vegetation Health Index (VHI). FAO provides annual drought frequency data split as the main crop season (S1) and secondary crop season (S2). For each season there are two indicators, according to two exposure intensity thresholds:

30 percent (1/3) of cropland area being affected by the drought event
50 percent (1/2) of cropland area being affected by the drought event

../_images/fao_asi.jpg — Fig. 15 FAO ASI global dataset showing historical drought frequency for >30% cropland area affected along the period 1984-2021.#

The historical frequency of severe droughts (as defined by ASI) is based on the entire the times series (1984-2022, 39 years). For the risk screening in CCDR analytics, seasonal data has been agggregated into one layer measuring the frequency as percentage of years over the timeseries. Specifically:

S1_30p: Percentage of years when drought affected at least 1/3 of cropland area during the main growing season
S1_50p: Percentage of years when drought affected at least half of cropland area during the main growing season
S2_30p: Percentage of years when drought affected at least 1/3 of cropland area during the secondary growing season
S2_50p: Percentage of years when drought affected at least half of cropland area during the secondary growing season

Heat stress#

Heat discomfort increases when hot temperatures are associated with high humidity [Coffel et al 2018]. Heat stress can cause long-term impairment and reduce labour productivity and incomes [Goodman et al 2018]. Extreme heat events lead to heat stress and can increase morbidity and mortality as well as losses of work productivity [Kjellstrom et al 2009, Singh et al 2015]. Not everyone reacts to the heat stresses in the same way, as individual responses are conditional on their medical condition, level of fitness, body weight, age, and economic situation [National Institute for Occupational Safety and Health 2016].

Various definitions regarding magnitude and duration thresholds and heat metrics exist. There are several heat indices involving both temperature and relative humidity, here are listed the most common ones.

Name	Global extreme temperatures (WBGT)	Universal Thermal Climate Index (UTCI)	Heat-humidity index
Developer	VITO	Copernicus	CORDEX
Hazard process	Extreme heat stress	Heat stress on human health	Extreme heat and humidity
Resolution	10 km	30 km	25 km
Analysis type	Probabilistic	Index	Probabilistic
Frequency type	Return Period (3 scenarios)	None
Time reference	Baseline (1980-2009)	Baseline (1979-2020)	Baseline (1970-2000); Projections (2040-2070, 2070-2100)
Intensity metric	Wet Bulb Globe Temperature [°C]	UTCI (°C)	Heat Index, Humidex
License	Open data	Open data	Open data
Notes	Accounts for air temperature, humidity, wind speed, radiation, fatigue-heating. Includes intensity-impact classification.	Accounts for air temperature, humidity. Includes intensity-impact classification.

Wet-Bulb Globe Temperature (WBGT °C): the WBGT combines temperature and humidity, both critical components in determining heat stress. A probabilistic dataset (3 return periods: 5, 20 and 100 years) based on 1980-2009 data has been developed for the GFDRR by VITO and has since been used to measure heat stress in risk screenings and assessment. This layer is generally sufficient for country-level hazard screening, but it has several limitations for any hazard and risk assessment. Although downscaled to consider gepmorphology and orography, the low grid resolution and relatively simple modelling makes it unfit to capture the heat island effect occurring in cities (Urban Heat Island) – nor the cooling effect of water bodies. That makes it unfit for any urban-scale assessment and generally sub-optimal for any sub-national assessment.

../_images/hzd_hs.jpg — Fig. 16 Comparing recent high-resolution temperature maps showing the heat island effect in Las Vegas, and the presence of the lake; the global WBGT map based on global models resolution cannot capture these details.#

Universal Thermal Climate Index (UTCI °C): the UTCI is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wetness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. It is calculated based on near-surface air temperature, solar radiation, vapor pressure, and wind speed. For this specific dataset provided, the influence of solar radiation and wind speed is not considered and the UTCI is calculated from near-surface air temperature and vapor pressure solely, thus representing indoor or under-shade conditions.

Wildfires#

Content under development.