Scientific background

From Rasor Wiki
(Difference between revisions)
Jump to: navigation, search
(Shake Map)
(Shake Map)

Revision as of 00:33, 11 May 2017



Exposure is the condition of being affected by something. People, properties, systems, or other territorial elements that are subject to potential losses from natural hazards are thereby called exposed elements. Exposed elements in RASOR are classified following a precise taxonomy and they are characterized trough a series of attributes. Some of the attributes are used to describe them in physical terms and determine consequently their physical vulnerability; other attributes describe their economic value or their importance and can be used to perform other analyses (economic, social, functional etc.), in addition to the physical damage.


Exposed elements in RASOR are organized in categories. Each category represents a class of homogeneous exposed elements, i.e. elements which could be characterized using the same family of attributes. As an example, the attribute "Number of storeys above ground" can be used to characterize a building but it is useless for a road or a crop.

Category name Description
Buildings Roofed and walled structures used as a place for people to live, work, do activities, store things, etc.
Facilities Point element of the built environment that cannot be assimilated to a building (e.g. airports, communication antennas, fuel facilities, wells, tanks, pumping stations, electric power substations)
High potential loss facilities Dams, nuclear power plants, military installations
Road network Road sections, bridges and tunnels
Railway network Railway sections, bridges and tunnels
Light rail network Light rail sections, bridges and tunnels
Lifelines Pipelines (oil, gas, potable and waste water), electric and communication lines
Agricultural sites Areas devoted to agricultural production, such as farmlands, orchards, olive groves, vineyards
Areas of natural interest Protected areas and natural reserves or in general locations with recognized natural, ecological and/or cultural values
Population Population distribution at census block
Land cover Areal description of the territory in terms of land cover, according to Corinne Land Cover Classification (level III)
Hydraulic and geological defenses Hydraulic and geological defensive measures, such as levees for flood or boulder catching nets for debris falling


Buildings in RASOR are characterized trough a series of physical attributes, inherited from GEM [1] and HAZUS[2] taxonomies such as the number of floor, the material or the occupancy class in addition to a series of economic, human and social attributes such as the economic value per square meter or the percentage of people present in the building. The list of the attributes and their description is reported in the following table.

In addition to this list there are also 14 attributes related to number and characteristics of people present in the buildings. These attributes are the same of those of the population with the exception that, in this case, they all refer to the building unit rather than to the census track or other administrative units (e.g. "% of females in the building", rather than "% of females").

Attribute name Description Format / Example of possible values
Lateral load resisting system material Material from which the Lateral Load-Resisting System is primarily constructed[3]. It contains information on the material type (e.g. masonry, concrete, steel) which can be specified describing also the material technology, which is specific for each material type (e.g. for masonry: adobe blocks, fired clay hollow bricks, dressed stones etc.; for concrete: pre-cast concrete, cast in place concrete etc.)
  • generic steel
  • concrete generic reinforcement - generic technology
  • masonry unreinforced - adobe_blocks technology - no mortar
  • heavy wood
Lateral load resisting system type Type of structural system that provides resistance against horizontal forces through vertical and horizontal components[4]. It describes the type (e.g. moment frame; post and beam wall) which be be specified describing also the ductility.
  • moment frame - ductility unknown
  • wall - non ductile
  • post and beam - equipped with base isolation and or energy dissipation devices
  • no lateral load resisting system
Number of storeys above ground Number of storeys or floors above ground, including the ground floor and floors above[5].
  • 1
Number of storeys below ground Number of storeys below the level of the primary entrance, otherwise described as the number of basements or basement levels. This does not include the ground floor. If it is known that there are no storeys below ground floor, the number is zero[6].
  • 0
Year of construction Date of construction which usually refers to the year in which the building was completed. If the building consists of structures that vary in age, use the earliest date. If the structure of the building has been retrofitted in a manner that improves its seismic performance, enter the date of the retrofit instead of the date of construction[7].
  • 1970
Ground floor elevation Height of the ground floor level of the building above grade in meters (grade is the level of the ground at the perimeter of the building)[8].
  • 1
Slope of the ground Angle (in degrees) from horizontal across the length of the building footprint, in the direction where the greatest difference in level of grade is observed. A flat site would be zero (0) degrees[9].
  • 12
Building usage The type of activity (function) that the building is used for.
  • Generic residential
  • Wholesale trade
  • School
Basement replacement cost per m2 Replacement cost of the structure (in dollars) per square meter for storeys below ground
  • 200
Building maintenance level Level of maintenance of the building described through 5 qualitative classes (from very poor to very good)
  • very poor
  • medium
Building position within a block The position of a building in relation to other neighbouring buildings, in terms of the number of adjoining buildings and their location relative to the building under consideration. Adjoining is defined in this table as spaced apart a distance less than 4% of the height of the lower building[10].
  • Detached building
  • Adjoining buildings on two sides
Content replacement cost per m2 Replacement cost of the building content (in dollars) per square meter for storeys above ground
  • 500
Exterior walls coverage materials Material that covers most of the exterior walls of the building[11].
  • glass
  • vegetative
  • wooden
Effective area of building or block respect to footprint (%) Built-up area of the spatial feature.
  • 90
Floor connections Declares the presence of floor connections that transfer in-plane forces of floor diaphragms to the lateral load-resisting structure of the building (e.g. walls), and also restrain outward displacements of walls. These connections apply to suspended floor diaphragms only, they do not apply to floors at ground floor level[12].
  • Floor-wall diaphragm connection not provided
  • Floor-wall diaphragm connection present
Floor system material Material from which the floor is primarily constructed. Do not include materials that are non-structural or that are not part of the structure of the floor[13]. It contains information on the material type (e.g. masonry, concrete, wood) which can be specified describing also the methods of construction, which is specific for each material type (e.g. for masonry: vaulted, shallow-arched etc.; for concrete: cast-in-place beamless reinforced, cast-in-place beam-supported reinforced etc.)
  • generic masonry floor
  • wood-based sheets on joints or beams
  • cast-in-place beamless reinforced concrete floor
Foundation depth Depth of foundations in meters
  • 5
Foundation type The type of foundation system, classified according to depth and whether it has lateral load-resisting capacity [14].
  • Shallow foundation, no lateral capacity
  • Deep foundation, with lateral capacity
Plan irregularity Type of structural irregularity that is observed in the plan (horizontal plane) of the building. If more than one plan irregularity is observed, both can be reported. The primary plan irregularity is that deemed to be the most significant in terms of affecting the building’s seismic performance, and the secondary plan irregularity is that deemed to be the next most significant[15].
  • re-entrant corner (primary) and torsion eccentricity (secondary)
  • re-entrant corner
Potential users Number of potential users in public buildings
  • 200
Replacement cost of the cars in the building Replacement cost of the cars present in the building (in dollars)
  • 30000
Roof connections Declares the presence of a)Connections that enable the roof diaphragm to transfer horizontal shear forces induced by an earthquake or wind to the lateral load-resisting structure of the building and to prevent walls from falling away from the diaphragm OR b)connections from the roof to the vertical structure of the building to prevent wind uplift or lift-off[16].
  • roof-wall diaphragm connection present
  • roof tie-down not provided
Roof coverage Material that covers the roof[17].
  • clay or concrete tile
  • vegetative
  • wooden
Roof maintenance level Level of maintenance of the building described through 3 qualitative classes (poor, average, good)
  • poor
  • average
Roof shape The shape and angle of the roof on the building[18].
  • monopitch
  • curved
  • flat
Roof system material Material of the roof system. Roof system is the structure that supports roof covering and environmental loading (such as rain and snow)[19].
  • masonry
  • metal
  • earthen
Seismic design code Declares if a seismic code has been adopted or not.
  • built before adoption of seismic building codes
  • built after adoption of seismic building codes
Shape of the building footprint Shape of the projection of the exterior edge of the building at grade onto the horizontal plane[20].
  • square, solid
  • square, with an opening in plan
  • L-shape
Split level presence Declares if the floor levels are staggered (1) or not (0).
  • 0
  • 1
Structure replacement cost per m2 Replacement cost of the building structure (in dollars) per square meter for storeys above ground
  • 400
Vertical irregularity Type of structural irregularity that is observed in the elevation or section of the building (vertical plane) If more than one vertical irregularity is observed, both can be reported. The primary vertical irregularity is that deemed to be the most significant in terms of affecting the building’s seismic performance, and the secondary vertical irregularity is that deemed to be the next most significant
  • setback (primary) and short column (secondary)
  • pounding potential
Number of people working in commercial or industrial buildings Number of people in the staff regularly employed by the considered business or industry. Note: This field should be completed only for buildings having a proper building usage i.e. commercial, industrial or mixed.
  • 50


Facilities are classified according to HAZUS[21] taxonomy through the fields "Facility type" and "Component anchorage". In addition information on the replacement cost is added to perform the economic loss. The list of the attributes and their description is reported in the following table:

Attribute name Description Format / Example of possible values
Facility type Description of the type of facility.
  • railway fuel facility w/ backup power
  • generic railway fuel facility
  • large wastewater treatment plant
Replacement cost Replacement cost (in dollars) of the whole considered facility.
  • 1000000
Component anchorage Declares if the facility's non structural components are seismically anchored or not, or if it is not relevant
  • anchored components
  • unanchored components
  • not relevant

High potential loss facilities

High potential loss facilities, such as dams, nuclear power plants and military installations are classified according to HAZUS[22] taxonomy through the fields "High potential loss facility type". In addition information on the replacement cost is added to perform the economic loss. The list of the attributes and their description is reported in the following table.

Attribute name Description Format / Example of possible values
High potential loss facility type Description of the type of facility.
  • nuclear power facilities
  • generic dam
  • earthen dam
Replacement cost Replacement cost (in dollars) of the whole considered high potential loss facility.
  • 1000000

Road network

Road network elements are classified in RASOR through the following list of attributes. Road element type classification follows the HAZUS[23] taxonomy.

Attribute name Description Format / Example of possible values
Name Road name

free value

Number of lanes Number of traffic lanes per direction.
  • 2
Replacement cost per meter Replacement cost (in dollars) of one meter of the road element.
  • 250
Road element type Type of road element (road segment, tunnel or bridge).
  • road bored/drilled tunnel
  • generic continuous steel road bridge
Track relevance Hierarchical level of importance of the track.
  • motorway
  • local road
  • national road
Road surface It describes if the road is paved or unpaved.
  • paved
  • unpaved

Railway network

Railway network elements are classified in RASOR through the following list of attributes. Railway element type classification follows the HAZUS[24] taxonomy.

Attribute name Description Format / Example of possible values
Name Railway name

free value

Replacement cost per meter Replacement cost (in dollars) of one meter of the railway element.
  • 250
Railway element type Type of railway element (railway segment, tunnel or bridge).
  • railway track
  • railway bridge continuous steel conventional design
Railway relevance Hierarchical level of importance of the railway.
  • local
  • national

Light rail network

Light rail network elements are classified in RASOR through the following list of attributes. Light rail element type classification follows the HAZUS[25] taxonomy.

Attribute name Description Format / Example of possible values
Name Light rail name

free value

Replacement cost per meter Replacement cost (in dollars) of one meter of the light rail element.
  • 250
Light rail element type Type of light rail element (light rail segment, tunnel or bridge).
  • lightrail cut and cover tunnel
  • wood lightrail bridge


Lifelines are classified according to HAZUS[26] taxonomy trough the fields "Lifeline type", "Undergrounding", "Ductility", "Intersection with the river network". Other fields have been added to allow to perform economic and systemic analyses. The list of the attributes and their description is reported in the following table.

Attribute name Description Format / Example of possible values
Lifeline relevance Hierarchical level of importance of the considered section respect with the whole network. Tree levels are defined. For electric power networks the three levels correspond to high (first level), medium (second level) and low (third level) voltage lines. For all the other lifelines for which it is proper to define a hierarchy, it is suggested to use only the first level as transmission and the third one as distribution levels.
  • First level - transmission
  • Second level
  • Third level - distribution
Lifeline type Description of the type of lifeline.
  • waste water pipes
  • generic electric power distribution circuits
  • telephone cable
Pipe diameter Measure of the pipe diameter in mm
  • 180
Undergrounding It describes if the lifeline is graved or exposed/aerial.
  • buried
  • exposed
  • unknown undergrounding
Replacement cost per m Replacement cost (in dollars) of one meter of the lifeline element.
  • 20
Intersection with the river network It describes if the lifeline crosses or not any element of the river network.
  • intersection
  • no intersection
Ductility It describes if the pipeline is ductile or brittle.
  • brittle pipelines
  • ductile pipelines
  • not relevant

Agricultural sites

Agricultural sites are classified according to a taxonomy mainly based on FAO - Indicative Crop Classification (ICC) [27] The list of the attributes and their description is reported in the following table.

Attribute name Description Format / Example of possible values
Cultivation type Type of cultivation.
  • root bulb or tuberous vegetables
  • sorghum
  • generic cereals
Producer cost Cost of production in dollars of one ton of the considered product [$/ton].
  • 180
Production Production of a certain agricultural product measured in tons per areal unit of harvest [tons/harvest]
  • 10

Areas of natural interest

Areas of natural interest are classified in RASOR through the following list of attributes. IUCN classificationfollows the classification developed by the International Union for the Conservation of Nature (IUCN)[28] .

Attribute name Description Format / Example of possible values
Name Name of the area

free value

Administrative level Level at which the management of the area is exerted.
  • national
  • local
  • international
IUCN class Classification of areas of natural interest according with the classification developed by the International Union for the Conservation of Nature (IUCN).
  • national park
  • strict nature reserve
  • natural monument or feature
Restoration cost per hectare Restoration cost (in dollars) of one hectare of the considered natural area.
  • 10000
Number of flora species present in the area Number of the different flora species present in the area
  • 1500
Number of fauna species present in the area Number of the different fauna species present in the area
  • 100
Number of protected flora species in the area Number of the different flora species present in the area, which are protected by law.
  • 10
Number of protected fauna species in the area Number of the different fauna species present in the area, which are protected by law.
  • 2


Population attributes describe the amount of people at census block and characterize them according with the common available census indicators related to age, sex and social conditions in general. The characteristics inserted in RASOR are those usually required in literature to evaluate social vulnerability from natural disasters (Cutter et al., 2003) [1]

Attribute name Description Format / Example of possible values
Number of total people Total amount of people present in the area. This number does not necessarily coincide with resident people, but it can account also for people working and in general using the area.
  • 10000
Mean level of school enrolment People's average number of years of schooling
  • 10
Mean number of components per family -
  • 3
Mean salary People's average salary (in dollars)
  • 1000
 % of females -
  • 58
 % of illitterates -
  • 5
 % of migrant workers -
  • 30
 % of people belonging to ethnic minorities -
  • 10
 % of people in school age -
  • 30
 % of people with less than 5 years -
  • 15
 % of people with more than 65 years -
  • 20
 % of special need population Percentage of people having "a physical or mental impairment that substantially limits one or more of the individual's major life activities, such as caring for one's self, performing manual tasks, walking, seeing, hearing, speaking, breathing, learning and working"[29].
  • 1
 % of unemployed -
  • 5
 % of commuting people in day time Percentage of people (workers and students) who move to reach working/studying places in day time
  • 30

Land cover

Land cover information is classified according with the third level of the CORINE Land Cover classification[30].

Attribute name Description Format / Example of possible values
Land cover Land cover class, according with CORINE classification.
  • Water bodies
  • Continuous urban fabric
  • Airports

Hydraulic and geological defenses

Hydraulic and geological defenses are classified in RASOR according to the type.

Attribute name Description Format / Example of possible values
Type of hydraulic and geological defensive measure It considers different types of levee according to the material of construction for hydraulic measures, and active and passive rockfall defences for geological measures.
  • Reinforced concrete levee
  • Generic levee
  • Rockfall barriers

Level of significance of attributes

The significance of the attributes used to describe each category of exposed elements depends on:

  • the hazard (earthquake, flood...)
  • the type of impact (physical, economic, social..)


Level of significance Colour
100 - mandatory
80 - very important
60 - important
40 - moderately significant
20 - additional

Attribute Hazard Impact
Earthquake Flood Hurricane Landslide Tsunami Volcano Windstorm
Replacement cost of the cars in the building Physical
Replacement cost of the cars in the building Physical



Ground Shaking

The maximum possible seismic shaking, expressed as Peak Ground Acceleration (PGA) or Peak Ground Velocity (PGV) is the main parameter estimated during seismic hazard assessment, in which it is associated to a probability of occurrence in a specified period of time. The estimation of PGA or PGV probabilities is the result of a complex process considering many variables arising from a critical analysis of all available scientific studies for the given area. However a User tool for the generation of worst case seismic shaking scenarios due to single earthquake sources has been rated as a useful tool and is provided in the RASOR platform.

The user can choose two different engines. Presently the two simulation engine are available: ShakeMap by US Geological Survey and OpenQuake by GEM Foundation

Shake Map

The ShakeMap tool in RASOR is provided to the User to simulate PGA/PGV scenarios due to single earthquake sources. We will use the ShakeMap simulation software developed by the US Geological Survey, to calculate the PGA and PGV and MMI caused by an earthquake.

ShakeMap employs Ground Motion Prediction Equations (GMPEs) to calculate the spatial variations of ground motion due to a seismic wave radiating from an earthquake source. The quality of the PGA/PGV maps calculated for each scenario is dependent from the robustness of the GMPE calculated for the region. In many countries affected by strong seismicity, scientific studies have generated empirical regional GMPEs based on an analysis of a large number of instrumental records from past earthquakes. The ShakeMap software can be used globally, since it contains GMPEs taken from the scientific literature. Future releases will provide the user with the possibility to modify the GMPE relative to his region through RASOR.

Technical background in seismology is recommended to use this tool.

An example of a Shakemap for the 2012 Emilia main shock is shown in the following figure:

Example of a Shakemap for the 2012 Emilia main shock.

Co-seismic ground displacement

The generation of a co-seismic ground deformation scenario is important to simulate the effects of earthquakes in a given area to take appropriate prevention measures.

Co-seismic ground deformation scenarios are simulations of the ground deformation due to a certain amount of slip occurring on a given fault plane. They are obtained through forward modeling using Okada (1985) [2] formulations. The implementation of the algorithm was provided by INGV.

The User selects a fault from a fault database, or directly defines a fault plane if none is already available, defines the dislocation parameters (based on past earthquake magnitudes or just his judgment), and runs the code to generate the simulated ground movements in the three cartesian components: North and East planar displacements, and Up vertical displacements, referred to the pre-earthquake state. The three raster layers can be generated at any user-defined pixel resolution.

Seismically triggered landslide

The transient stress induced by the passage of the seismic waves can trigger gravitational mass movements, depending on the amplitude and frequency of the seismic waves and on the degree of instability of the mass. In mountainous terrains, earthquake-triggered landslides often cause strong damage, and especially in mountainous areas it is very important to simulate triggered landslide scenarios for various earthquake magnitudes and distances.

In the RASOR platform a tool is be implemented to generate regional triggered landslide scenarios using the sliding-block method developed by Newmark and Ambraseys. Using this tool the user will be able to calculate the Newmark displacements over an area, and isolate the areas where the amount of displacement is judged to be critical (i.e. able to trigger a landslide). The user can also classify the displacement values according to hazard categories, e.g. low hazard (0-1 cm of displacement), moderate (1-5 cm), high (5-15 cm), very high (>15 cm). The critical displacements and the hazard categories are region-dependent, and vary with landslide type, depth of sliding surface, rock/soil properties, strength of material. While the tool contains standard categories for a first-order scenario generation, they can be overridden by the user if more accurate local estimates are available. In the following section a description of the algorithm is given.

Input spatial datasets:

  1. Digital Elevation Model, needed to calculate Slope angle → thrust angle map (α)
  2. Geotechnical properties of lithological units:
    • Cohesion (c’) map
    • Frictional angle (ϕ’) map
    • Unit dry weight (γ) map
    • Unit wet weight (γw) map
  3. Peak Ground Acceleration map (pre-existing or calculated on the fly)
  4. Map of distances from triggering earthquake (R), to be calculated on the fly

Note: if a map showing the actual distribution of geotechnical properties is not available for the area (most common case), it must be generated by the user from a geological map, by attributing to each lithological unit geotechnical parameters taken from reference databases (e.g.

The PGA map and the map of earthquake distance may be calculated in the tool or already defined from previous analysis.

Input data to define scenario:

  1. User-defined landslide parameters:
    • Thickness of the failure slab measured along the slope normal (t)
    • Proportion of the slab thickness that is saturated (m)
  2. Location of the reference earthquake or fault. If only eq is provided: latitude, longitude, depth of hypocenter and moment magnitude). If fault geometry is available (from a database or user-defined): lat, lon of center top, width, length, top depth, strike, dip, rake and moment magnitude.

Algorithm steps

Slope/Thrust angle determination (α)

A slope map is generated from the DEM. The thrust angle (dip angle of the basal slide surface) is considered equal to the slope angle.

Calculation of the static factor of safety (FS)

Using friction angle, cohesion, unit weights and slope angle maps, the FS is calculate for each map pixel according to:

FS = (\frac{c'}{\gamma \cdot t \cdot sin\alpha})+(\frac{tan\phi'}{tan\alpha}) -(\frac{\gamma w \cdot m \cdot tan\phi'}{\gamma tan\alpha})

where: t = slope-normal thickness of the failure slab, and m = proportion of the slab thickness that is saturated; γ and γw are the dry and wet soil unit weights, respectively.

Calculation of the critical acceleration (ac)

Using the FS map and the slope map the critical acceleration, which represents the seismic landslide susceptibility, is calculated for each map pixel.

 ac = (FS-1)g \ sin \alpha\,

where g = gravity acceleration

Calculation of the AriasIntensity (Ia)

For the given fault and earthquake, the PGA map, as well as the distance map (R) are calculated. The Arias intensity is calculated only for map pixels where the PGA > ac, using the magnitude (Mw) and the distance (R):

log I_a = Mw - 2logR - 4.1 \, (Jibson 1993) [3]

where: Mw is the moment magnitude and R is the earthquake source distance in Km.

Calculation of Newmark displacement (Dn)

The Newmark displacement represents the permanent displacement of the idealized sliding block. Dn can be estimated through an empirical function relating critical acceleration (dynamic slope stability) and Arias intensity (ground shaking intensity):

Log Dn = 2.401 log I_a - 3.481 log a_c - 3.230 \pm 0.656 \,	(Jibson 2007) [4]

Since it has been shown that different empirical relations can best represent areas with different crustal attenuation properties (Chousianitis et al., 2014) [5] the tool must provide the possibility for the user to define ad hoc parameters where available.

Calculation of seismic landslide hazard classes

The Newmark displacement is an index of seismic slope performance, but its significance in terms of actual seismic landslide hazard depends on the model assumptions and on the landslide type and depth, soil and crustal properties, etc.

To convert the calculated Dn values into failure probabilities (hazard) for a given region, the Dn values should be calibrated using actual seismic landslide occurrence during one or more earthquakes. This calibration will generate an empirical relationship relating the landslide probability to Dn, as the one produced for California by Jibson et al., 2000 [6]:

 P(f)=0.335[1 - exp(-0.048 Dn^{1.565})]\,

As shown below different hazard classes have been defined in different studies and over different regions:

Hazard class (Probability) Dn (cm) (Jibson & Michael, 2009) [7] Dn (cm) (Rodriguez-Peces et al, 2012) [8] Dn (cm) (Majidi et al., 2013) [9]
Very high (P>10%) >15 >5 >5
High (P=7-10%) 5-15 5-15 2-5
Moderate (P=4-7%) 1-5 2-5 0.1-2
Low (P=0-4%) <1 <2 <0.1

The tool should then contain default hazard categories for a first-order scenario generation, but the user should be allowed to define a different regional classification scheme.

Post-event stress-loaded seismic faults

During the earthquake occurrence, the fault rupture process suddenly releases a portion of the stress accumulated along the fault plane during the inter-seismic period. A part of the released Coulomb stress is redistributed throughout the crust in the vicinity of the mainshock, and where this stress is added to the pre-existing stress level, aftershocks are normally triggered. In some cases the transferred stress may be so large to trigger the rupture on a nearby large fault, generating an aftershock of magnitude similar or slightly lower than the mainshock. In many cases these large aftershocks generate considerable damage, impacting on structures already weakened by the previous events.

While deterministic estimates of earthquake triggering are not feasible, and probabilistic estimates have not yet reached sufficient robustness, the knowledge of the amount of positive stress transferred on a receiving fault in the area surrounding a large earthquake is still a useful piece of information during the response phase. For instance a high level of stress on a given fault can prompt the generation of a shaking scenario which can be used to guide the implementation of further safety measures or the optimal location of temporary shelters. The latter will be best located where the transferred stress is negative (i.e. the total stress on the fault plane is reduced), while the areas where the transferred stress increases the total stress on the fault plane should be avoided.

The stress calculation module needs as input a fault model with a fault slip distribution. It requires the user to select one or more receiving faults on which the differential stress is calculated. These receiving faults should be selected from a fault database of defined by the User on the fly. INGV will provide the necessary software written in IDL. A User with a geophysical background is preferred to run this tool. The outputs of the module are polygon shapefiles for each of the selected receiving faults. Each shapefile describes the fault plane as subdivided into several rectangular patches, as in the fault model product. In this case however, one of the parameters is the differential Coulomb stress on the relative patch of the fault plane, as shown in the following table:

ID Length Width Top_d Strike Dip Coorde Coordn Rake Δ CFF(MPa)
... 1000.0 1000.0 0.0 251 90 609800 4897189 -170 0.1657
... 1000.0 1000.0 0.0 251 90 608854 4896863 -170 0.1615
... ... ... ... ... ... ... ... ... ...

An example of the GIS output of the module is shown in the following figure:

Stress transfer scenario for the May 20, 2012, Emilia earthquake. The mainshock rupture is the empty rectangle; the colour scale represents the Coulomb stress transferred to the three nearest receiving faults. Rupture of the fault no.2 occurred 9 days after the mainshock and caused increased casualties and damage.

Riverine Flood


The WFlow hydrological model (Schellekens, 2011) is a distributed hydrological model that requires little calibration effort and maximizes the use of available spatial data. It has proven to perform well in data-scarce regions where only global data can be used. WFLOW is part of the OpenStreams initiative, see:

More information can be found on:


The “model engine” used to generate flood hazard maps is Subgrid (Stelling, 2012), part of the 3Di project ( Subgrid solves full shallow water equations and is especially suited for the simulation of overland-flow, forced by specific runoff and/or discharge/water level boundary conditions. Models are discretized, using a DEM, friction layer and optional a 1 dimensional cross-section layer and/or levee-delineation.

More information about SubGrid can be found on:

Coastal Flood


Delft3D is an open source modeling suite composed of several modules, grouped around a mutual user interface, while being capable to interact with one another. Delft3D-FLOW is one of these modules. It is a multi-dimensional (2D or 3D) hydrodynamic (and transport) simulation program which calculates non-steady flow and transport phenomena that result from tidal and meteorological forcing on a rectilinear or a curvilinear, boundary fitted grid. For RASOR, only the 2D functionality is used.

More information about the Delft3D modeling suite can be found on:

RunUp Model


Land deformation is caused by natural and anthropogenic processes acting with variable spatial and temporal scales and intensities.

The natural processes include the still ongoing, long term crustal response to the ice cap melting occurred after the Last Glacial Maximum, the compaction processes of recently deposited sediments, local and regional tectonics and volcanic activity, and other processes, as landslides and sinkholes.

The anthropogenic processes, also influenced by global climate changes, include the subsidence induced by underground fluid withdrawal (oil, gas or water), the effects of soil consolidation related to infrastructure or building construction, the underground mining operations.

The most common cause of strong anthropogenic land subsidence is the excessive pumping of underground fluids (hydrocarbons, water).. The withdrawal of fluids from the sediments causes a decrease of the pore-fluid pressure and of the pore volume, eventually leading to sediment compaction. For ground waters, the abstraction rate at the global scale has at least tripled over the past 50 years and is now increasing at an annual rate of 1-2% (UN, 2012). Lowlands, especially near coastal areas, are often deeply affected by land subsidence induced by aquifer depletion, due to strong pumping and the presence of favourable sediments.

The knowledge of the temporal and spatial trends of land subsidence is an essential ingredient of any model aiming to support the management of the underground resource. Additionally, in regions with very high subsidence rates (tens of cm/yr), the subsidence will increase the inundable areas during floods even in the mid term (years). In RASOR Land subsidence measurements calculated using multitemporal InSAR methods providing high resolution measurements (e.g. Persistent Scatterers) are available. The land subsidence maps are generated outside of the system by RASOR partners and ingested as external layers. Since the dynamics of the subsidence causes may be highly variable, the deformation signal is often non-linear, and the complete displacement time series are provided, together with the mean (linear) velocity for quick reference. Where several SAR datasets are available, the product could be released as Cartesian displacement and velocity components (North, East, Up or East and Up).

Typical format for this product is a point shapefile with the following attribute table:

ID Lat Lon East North Height error Mean Velocity Coh. St.Dev. Date 1 Date 2 ...
XXXX 41.575 15.125 702580.00 50000125.00 14.0 1.5 0.10 2.7 0.0 9.2 12.3

ID = unique identifier for each ground measurement point or cell

Lat, Lon in WGS 84

East, North in UTM local zone

Height error

Mean Vel. = Velocity estimated through a linear fit to the displacements in the columns Date1 to Date n

Coh.= Multitemporal coherence

St. Dev.= standard deviation to the linear model

Date 1 to Date n = ground displacement values in mm

This product may require several updates, to monitor the subsidence evolution.



A wind field is generated from a user-defined or historical hurricane track by a module called WES. The Wind Enhance Scheme (WES) was initially developed by the UK Met Office following the theory from Holland (1980, 2008). Improvements to the method have been made by Deltares over the last years, making the program more robust and improving the consistency of the results. The output of WES can be used as input for Delft3D-FLOW, i.e. for storm surge simulation.

WES generates a tropical cyclone wind field by computing surface winds and pressure around the specified location of a tropical cyclone center, for a given maximum wind speed. Other cyclone properties that are originally input the WES are either fixed (the radius of maximum wind is set to 75 km) or derived from the maximum wind speed (the pressure drop is related to the wind speed following formula 4 in Matsui et al (2011)). In future RASOR implementations these parameters could also be specified by the user or taken from cyclone forecasts from the Joint Typhoon Warning Center (JTWC).


To generate a rainfall field from a hurricane track, a model called R-CLIPER is employed. This model was developed by Robert Tuleya and co-workers from NOAA (Tuleya et al, 2007). The model was implemented by Deltares a separate module that takes the same input as WES. This has the advantage that the same FEWS model adapter can be used for both models. The output is a NetCDF or ASCII grid time series of hourly rainfall on a regular grid. This rainfall is input to the hydrological model WFLOW


Physical vulnerability refers to the potential for physical impacts on the built environment and the natural resources. Physical damages are directly linked to hazard intensity and the characteristics of the element at risk. For this reason physical vulnerability is always hazard-dependent and element-dependent. In construction science, physical damages are related to the intensity of the hazard through the vulnerability curves or fragility curves. The RASOR platform uses only vulnerability curves, so - whenever fragility curves are available - a suitable procedure for transforming them into vulnerability curves must be adopted. Vulnerability functions are organized in libraries. Each library can contain functions addressing several categories of hazard, exposed elements, forcing, and target. Some libraries are available to the users as default libraries, and they are described in the following sub-paragraphs. Furthermore, the user can create one or more libraries by duplicating and modifying the default ones.

Default Vulnerability Libraries


Hazus library for seismic risk
Hazus library for flood risk

Modified Hazus


The Manciola library contains a collection of flood vulnerability functions for land use categories. The name of the library refers to the main author of a literary review (Manciola et al. 2003, La mappatura delle aree inondabili, in Italian).


Direct Impact

Systemic Impact


TanDEM-X Mission

The main objective of the TanDEM-X mission is the generation of a global digital elevation model (DEM) with satellite SAR interferometry. This is achieved by extending the TerraSAR-X synthetic aperture radar (SAR) mission by the nearly identical satellite TanDEM-X. Flying in close formation only a few hundred meter apart, the two satellites are imaging the terrain below them simultaneously from different angles. These images are processed into accurate elevation maps with a 12 meter spacing and an absolute vertical accuracy better than 10 meter. Since 2010 both satellites have been operated to map all land surfaces at least twice and difficult terrain up to four times and more. While data acquisition for the DEM generation is already concluded it is expected to complete the processing of the global DEM in the second half of 2016. The TanDEM-X mission is financed and implemented as a public-private partnership between DLR German Aerospace Center and Airbus Defence and Space. DLR is responsible for providing TanDEM-X data to the scientific community ([31]), while Airbus Defence and Space is responsible for commercial marketing of the data ([32]).

DEM generation

The TanDEM-X DEM produced at DLR German Aerospace Center is a final, global Digital Elevation Model of the land masses of the Earth. Due to the X-Band interferometric SAR characteristics, the model includes elevated objects such as buildings and vegetation. Therefore, the TanDEM-X DEM is a Digital Surface Model (DSM). It is derived from at least two SAR acquisitions and has a nominal spacing of 0.4 arcseconds (12 meter). In the first step of the generation process all operational SAR acquisitions are processed to single DEM scenes having an extent of about 30km x 50km. The remaining offsets and tilts of the DEM scenes are in the range of some few meters and are compensated in the follow-on DEM calibration step. These corrected DEM scenes are eventually fused into the final DEM product tiles. Besides the elevation data itself additional information layers, such as height error maps, amplitude images, water indication masks, coverage maps and consistency masks are generated. To assure the final DEM quality measures to external height reference data (e.g. SRTM, ICESAT validation points, kinematic GPS tracks) are calculated automatically for every individual DEM tile. Furthermore, all tiles are subject to a detailed manual inspection. Finally, a quality status and remark is assigned and the tiles are stored in DLR’s archive.

DEM editing

Due to the X-Band interferometric SAR characteristics, the DEM includes elevated objects such as buildings and vegetation. Water bodies are not edited. Therefore, it is only of limited use for the different risk assessment tasks within RASOR. In order to achieve sufficient usability for the different tasks the provided DEM has to be adapted accordingly. The DEM is edited in an automated post-processing step to fill invalid values, to remove bridges, to smooth noisy DEM values while preserving linear structures and to assign water bodies a constant height. A most accurate water mask is a prerequisite for editing water bodies and rivers. The starting point is the water indication mask as provided with the unedited DEM. By evaluating the DEM in combination with the amplitude image and the height error map the WAM can be further improved gaining higher accuracy and a higher level of detail. The edited water mask is subsequently used to consistently edit water body and river heights. The assigned height is derived from the surrounding shore line. As the DEM heights are averaged from several acquisitions it is assumed, that this height refers to a mean water level. A more accurately edited DEM may be gained by manual editing and sophisticated algorithms. This could comprise the removal of vegetation and buildings, the consistent flow of rivers and the avoidance of misclassifications in the water mask.


  1. Cutter, S.L., Boruff, B.J. and Shirley, L.W. (2003) Social vulnerability to environmental hazards. Social Science Quarterly, 84 (2), 242–261.
  2. Okada, Y. (1985), Surface deformation due to shear and tensile faults in a half-space, Bulletin of the Seismological Society of America, 75 (4), 1135 - 1154
  3. Jibson, R. W. (1993). Predicting earthquake-induced landslide displacements using Newmark's sliding block analysis. Transportation research record, 9-9.
  4. Jibson, R. W. (2007). Regression models for estimating coseismic landslide displacement. Engineering Geology, 91(2), 209-218.
  5. Chousianitis, K., Del Gaudio, V., Kalogeras, I., & Ganas, A. (2014). Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece. Soil Dynamics and Earthquake Engineering, 65, 11-29.
  6. Jibson, R. W., Harp, E. L., & Michael, J. A. (2000). A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58(3), 271-289.
  7. Jibson, R. W., & Michael, J. A. (2009). Map Showing Seismic Landslide Hazards in Anchorage, Alaska. US Department of the Interior, US Geological Survey.
  8. Rodríguez-Peces, M. J., García-Mayordomo, J., & Martínez-Díaz, J. J. (2012). Slope instabilities triggered by the 11th May 2011 Lorca earthquake (Murcia, Spain): comparison to previous hazard assessments and proposition of a new hazard map and probability of failure equation. Bulletin of Earthquake Engineering, 1-16.
  9. Majidi, R. F., Mahdavifar, M. R., & Kashanchi, M. (2013). Estimating the Strength Parameters of Geological Formations Using Fuzzy Sets and its Application in Generating Seismic–Landslide Hazard Maps. In Earthquake-Induced Landslides (pp. 807-815). Springer Berlin Heidelberg.
Personal tools