132
9 Hazard Identification
Table 9.3 Space observation themes versus data for a priori risk assessment and benefits
Space observation
themes
Approach
Data for a priori risk
assessment
Benefits from
enhanced data
Visual inspection
Visual comparison of
imagery at different
times
Pre-existing
damages, damage
evolution
Increased efficiency
of site visits (if still
necessary) based on
history
Quantitative
geometry, topography
data
Contour lines
comparison
(volumes,
deformations),
tension cracks, cross
sections definition
Detection of
potentially unstable
volumes, slow
creeping volumes,
deposit and erosion
Enhanced hazard
identification of
existing or potential
phenomena such as
slides, debris flows,
flash-floods, rockfalls
etc. Probability
estimates
Quantitative surface
hydrology data
Water/solid flows,
drainage patterns,
erosion patterns and
deposit areas
definition
Detection of
potential debris
flows, drainage
malfunctions,
overflows
Quantitative slope
cover data
Slopes, orientation,
vegetation cover
health and stress
definition
Detection of
vegetation distress
and its causes.
Permafrost loss
potential. Snow
accumulations
– Existence of low spots (not enough freeboard, leading to over-topping probability increase);
– Presence of erodible material on the slopes;
– Over-steepened slopes;
– Narrow spots on the crest;
– Encroached width, toe erosion, works at toe, etc.;
– Stressed vegetation;
– Easily accessible structure, poorly secured perimeter, residences proximity;
– Lack of maintenance/repairs.
The automated re-evaluation procedure principle makes it possible to use space
or drone observation data to update probabilities of failure and consequences. As
discussed earlier, a preliminary risk assessment estimates a range for the likelihood
of failure of various events. The range can then be split in positive partial contributions
by giving relative weights to the positive observable characteristics, based on results
in the literature, for example as displayed in Table 9.4, which uses a dyke case study.
This leads to determining the dyke’s likelihood of failure and finally to the prioritization for each segment based on positive space-observable characteristics (extant
mitigative measures and features) as shown in Fig. 9.7.
9 Hazard Identification
Table 9.3 Space observation themes versus data for a priori risk assessment and benefits
Space observation
themes
Approach
Data for a priori risk
assessment
Benefits from
enhanced data
Visual inspection
Visual comparison of
imagery at different
times
Pre-existing
damages, damage
evolution
Increased efficiency
of site visits (if still
necessary) based on
history
Quantitative
geometry, topography
data
Contour lines
comparison
(volumes,
deformations),
tension cracks, cross
sections definition
Detection of
potentially unstable
volumes, slow
creeping volumes,
deposit and erosion
Enhanced hazard
identification of
existing or potential
phenomena such as
slides, debris flows,
flash-floods, rockfalls
etc. Probability
estimates
Quantitative surface
hydrology data
Water/solid flows,
drainage patterns,
erosion patterns and
deposit areas
definition
Detection of
potential debris
flows, drainage
malfunctions,
overflows
Quantitative slope
cover data
Slopes, orientation,
vegetation cover
health and stress
definition
Detection of
vegetation distress
and its causes.
Permafrost loss
potential. Snow
accumulations
– Existence of low spots (not enough freeboard, leading to over-topping probability increase);
– Presence of erodible material on the slopes;
– Over-steepened slopes;
– Narrow spots on the crest;
– Encroached width, toe erosion, works at toe, etc.;
– Stressed vegetation;
– Easily accessible structure, poorly secured perimeter, residences proximity;
– Lack of maintenance/repairs.
The automated re-evaluation procedure principle makes it possible to use space
or drone observation data to update probabilities of failure and consequences. As
discussed earlier, a preliminary risk assessment estimates a range for the likelihood
of failure of various events. The range can then be split in positive partial contributions
by giving relative weights to the positive observable characteristics, based on results
in the literature, for example as displayed in Table 9.4, which uses a dyke case study.
This leads to determining the dyke’s likelihood of failure and finally to the prioritization for each segment based on positive space-observable characteristics (extant
mitigative measures and features) as shown in Fig. 9.7.