Tropical cyclones
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Humanitarian impact of tropical cyclones

Tropical cyclones occur in regular seasons in all tropical basins, affecting Island States and coastal areas.

Tropical cyclones cause destruction in three major ways: extreme wind destroying structures, storm surge causing coastal flooding and, the major hazard for weaker storms, extreme rainfall causing flash-floods and landslides. Preparedness and mitigation measures help reduce the impact of cyclones.

GDACS approach

The objective of GDACS is to assess the overall impact of tropical storm events on affected societies in order to evaluate the need for international intervention. The goal is to understand, as it is happening, the hazard in as much detail as possible, model the impact on the affected communities and, taking into account local and national coping capacity, estimate the likelihood that the country can cope with the disaster.

To this end, JRC has established partnerships with organisations around the world that can contribute to these objectives, either by providing real-time data, modelling capacity or risk assessment. The main contributors for tropical cyclones include currently the Pacific Disaster Centre (official advisory data), NOAA NESDIS (extreme rainfall) and JRC (wind and storm surge modelling and risk assessment).

Current models

Impact model based on wind speed and population

Based on track information provided by the Pacific Disaster Center, JRC calculates areas around the track affected by high winds. In these areas, GDACS calculates population and critical infrastructure.

In the new methodology the Vulnerability Indicator for Tropical Cyclone described belove has been integrated in the alert model.

JRC developed the Vulnerability Indicator for Tropical Cyclone combining indicators describing the human development (HDI) and the rural populations (Percentage of population in rural areasPopulation living below 10m low elevation in coastal zone). JRC introduced a new Index (Rural Population Index, RPI) for rural population averaging the values of the two chosen indicators for rural population.

The countries were ranked by HDI and RPI using the quartile method and then the scores for the two dimension indices are then aggregated into a composite index using arithmetic mean. The countries were finally assigned to 3 classes, (1) High Vulnerability, (2) Medium Vulnerability, and (3) Low Vulnerability.

Depending on the wind speed, the population in the area, and the vulnerability of the affected countries alert levels are set as follows:

Wind speed

Population

Vulnerability

Alert Level

38 – 73 mph (TS)

< 10M

Low – Medium - High

Green

38 – 73 mph (TS)

> 10M

High

Orange

74 – 110 mph (Cat 1-2)

> 100K or > 10%

Medium – High

Orange

74 – 110 mph (Cat 1-2)

> 1M

High

Red

> 111 mph (Cat 3)

> 100K or > 10%

Medium – High

Red

> 111 mph (Cat 3)

> 1M

Low

Orange

> 131 mph (Cat 4)

> 1M

Low

Red

Storm surge

In addition, GDACS calculates population potentially affected by storm surge, but this is not taken into account in the alert model yet. Storm surge is calculated using HyFlux2, a numerical hydrodynamic code implementing shallow water equations.

The atmospheric forcing included in the storm surge model are the pressure drop and the wind-water friction. The effects of the short waves induced by the wind (wave setup) and the precipitations are not yet implemented in the model. The model has been validated mainly in areas of shallow water (Caribbean, Bay of Bengal). Results in other basins may be inaccurate.

Alert thresholds are set at: Orange >1m, Red >3m.

Extreme rainfall

To account for flooding and other effects of extreme rainfall, GDACS reports on the extreme rainfall calculated  by NOAA NESDIS. The ensemble tropical rainfall potential product (eTRaP) is a high resolution estimate of rainfall based on multiple passive microwave remote sensing images. GDACS considers both the accumulated rainfall (for standing floods) and the rain rates (for landslides and flash floods). The current models (and associated rainfall alert levels) are experimental.

The goal is to provide appropriate alert levels when rainfall causes large landslides and flash floods with humanitarian consequences. The current thresholds are conservative:

  • Total cyclone accummulation (mm):   Green <200mm, Orange >200mm, Red >500mm.
  • Maximum rain rate (mm/h):  Green <17mm/h, Orange >17mm/h, Red >33mm/h. 

Results and limitations

Overall, GDACS has a good record of assessing the impact of tropical cyclones. All red alerts triggered humanitarian intervention or a major national relief effort. GDACS typically provides impact reports 72h before landfall using the official tropical cyclone advisory bulletins. Some cyclones were underestimated because the rainfall and storm surge were not taken into account until 2012.

The main weaknesses identified in a 2008 study [reference] were addressed. A cyclone vulnerability score was implemented to reduce alert scores on resilient countries; a rainfall and storm surge model were developed; and impact of small Island States was calculated differently.

Uncertainty in meteorological forecasts is propagated in GDACS results, causing alert levels to change upwards or downwards. For each advisory, the alert messages use an alert level related to what is to come, ignoring the previous impact. However, the main impact report on the web site shows the alert status of the whole storm, and is modified with each advisory.

Further work

With increasing availability of (real time) data and continuously improving accuracy and detail of models, impact assessment can always improve. GDACS is a collaboration platform open to organisations that have data, models or systems that can significantly contribute towards better impact assessment and new information for emergency responders. JRC, as the scientific lead in GDACS, is interested in exploring integration of such products in the existing impact models.

With regards to tropical cyclones, we’re currently working on:

  • Storm surge impact assessment: a continuous evaluation of the hydrodynamic models in different settings, as well as improved impact assessment, integrated with other hazard components (rain, wind).
  • Extreme rainfall impact assessment: NOAA’s eTRaP models use satellite-based rainfall estimates to forecast extreme rainfall in the next 24h. This data is used in a risk model in development at JRC.
  • Social and mass media analysis for rapid situation awareness.
 Earthquakes and Tsunamis
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GDACS approach

The objective of GDACS is to assess the overall impact of earthquakes (and associated tsunamis) on affected societies. GDACS alert levels aim at classifying earthquakes according to the likelihood that societies can no longer cope at national level and will require humanitarian intervention.

JRC has established partnerships with seismological organisations around the world that provide real-time data on earthquakes parameters, including magnitude, depth and location. These parameters are used to establish an affected area and calculate the population nearby. A country-wide vulnerbility indicator moderates the alert level in order to provide higher alert levels for similar earthquakes in countries that are more vulnerable.

Current models

Earthquakes

Currently, the evaluation of the potential humanitarian impact of earthquakes considers (1) earthquake magnitude (in units reported by source), (2) earthquake depth, (3) population within 100km of epicentre, and (4) national population vulnerability. Elements 1 and 2 are scraped from seismological sources, while elements 3 and 4 are automatically calculated by a GIS based on epicentre location (latitude and longitude), the Landscan population dataset and ECHO's Global Needs Assessment indicator.

The formula for the threshold is calculated as follows. This formula was established based on statistical analysis and minimization of omission and commission errors of historical earthquake disasters.

First, the alert score is calculated:

  • P = sqrt (log10 ( Max (Pop100 / 80000, 1)))
    If Pop100 > 80000 (the cut off population threshold), then P is the square root of the 10 logarithm of Pop100/80000. 
    This means that we assume an exponential relationship between impact and population, with a cut off of 80000 people within an area of 31415,6 km2, or a population density of about 2,5 people per km2.
     
  • M = Max ( magnitude - 4.5, 0 )
    If the magnitude is above 4.5, then M is the magnitude - 4.5.
    This means that we do not consider earthquakes of a magnitude lower than 4.5. Further, we assume - for strong earthquakes - a linear relationship between impact and magnitude.
     
  • V = GNA Vulnerability Index VI for closest country (a value between 1.2 and 3)
    If GNA VI is missing, then V defaults to 1.2.
    The GNA VI is a combination of 9 socio-economic and historical indicators that are computed based on ranking and classification. The value (between 1.2 and 3) is not a quantitative value, but qualitative value.

These factors are combined and weighted as follows:

draft_alert_score = (P * M * V^1.5) / 3

This formula gives a higher weight to the vulnerability of the country, therefore emphasizing that prepared and rich countries with good building standards that can be enforced can better cope with earthquakes than other countries. Equivalent earthquakes are more likely to be a disaster in vulnerable countries.

Subsequently, the draft alert score is modified according to the following rules:

  • If the magnitude is less than 6, no red alerts can be issued. In other words, if the draft_alert_score is larger than 2, it is set to 2.
  • If the earthquake occurred more than 70km deep ('intermediate-depth' earthquakes), the draft_alert_score is reduced by 1.
  • If the earthquake occurred deeper than 300km ('deep-focus' earthquakes), the draft_alert_score is set to 0, or a green alert.

Finally, the alert score is transformed into an alert level according to the following thresholds:

  • Red: more than 2
  • Orange: between 1 and 2
  • Green: less than or equal to 1

At the same time, the tsunami alert score is calculated (see below).

Tsunamis

GDACS tsunami alert calculations are triggered by earthquakes that occur in or near1 water. The logic for the tsunami alert is based on (1) the magnitude of the earthquake, (2) the depth of the earthquake, (3) the maximum wave height at any coast reach by the tsunami. The first two parameters are used to look up a tsunami wave height calculation in the JRC Tsunami Database (containing over 132000 scenarios).

For each earthquake of magnitude exceeding 6.5 occuring in a location with positive water depth (from ETOPO30), the tsunami database is queried for the closest matching scenario. Scenarios have been calculated for 13800 locations covering tsunamogenic areas (from NOAA database) for magnitudes ranging from 6.5M to 9.5M with steps of 0.25M.

If a scenario is available, the maximum wave height at a coast is retrieved. If the maximum wave height is greater or equal than 3m, the tsunam alert is Red; if it the height is greater or equal than 1m, the alert is Orange; otherwise, the alert is Green. These values are then corrected for earthquake depth in the same way as for earthquakes2.

If no scenario has been precalculated (only very few cases), a new calculation is started (taking about 20 minutes), but the alert routine does not take it into account. Rather, the IOC alert Matrix is used (see figure below), based only on magnitude. This fall-back routine, although widely used in tsunami warning centres, results in many false alerts.

1 In 2009, we will extend this to zones near the coast (5km) to account for rupture zones that are partially on land and partially in sea.

2 JRC is working on a better depth correction algorithm, taking into account attenuation using the Okada model.

Results and limitations

Over the years, the GDACS models have been put to the test, with a high user satisfaction. For earthquakes and tsunamis, the most important aspects are timeliness of alerts (as fast as possible) and avoiding false alerts (don’t wake up people if it’s not needed). The first aspect has been improved steadily by forging agreements with regional seismological institutes around the world to push data to GDACS (rather than GDACS scraping data from their web sites). This is most important for timely triggering of tsunami models. The second aspect is ensured by GDACS’s earthquake vulnerability score (which effectively lowers the alert level for countries able to cope with disasters) and the wave-height-based alerting approach for tsunamis (reducing false tsunami alerts by 90%).

Some of the limitations of the approach are:

  • [to be completed]

Further work

With increasing availability of (real time) data and continuously improving accuracy and detail of models, impact assessment can always improve. GDACS is a collaboration platform open to organisations that have data, models or systems that can significantly contribute towards better impact assessment and new information for emergency responders. JRC, as the scientific lead in GDACS, is interested in exploring integration of such products in the existing impact models.

With regards to earthquakes and tsunamis, we’re currently working on:

  • The use of social media for field-based impact assessment in the first 6 hours after an earthquake
  • An upgrade of the tsunami scenario database (Mod 1.2), based on Okada fault deformation and a nested approach for wave propagation.

 

 GDACS Models
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GDACS models

This page is currently in development to report on the status of GDACS models. Please check back later for more complete information.

GDACS approach

The selection and alert level of natural hazards in GDACS is based on automatic impact assessment models. GDACS software continuously scrapes or receives scientific data on natural hazards. Information about the location, strength and other characteristics is then used to calculate an affected area. Different models are used for different disaster types. Subsequently, the potential impact of the event is assessed by calculating the population within the affected area and their vulnerability. All calculations and assessments are done automatically by software, without human intervention.

GDACS uses models for earthquakes, tsunamis and tropical cyclones. For floods, GDACS alerts are manually curated by the Dartmouth Flood Observatory. For volcanoes, GDACS currently reports on VAAC alerts, which are not appropriate for humanitarian intervention. Research and development is continous to improve the global monitoring.