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It is important to let map users know the
limitations involved in the preparation of these maps:
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The topographic data (from the
USGS DEM model for Puerto Rico) is old (in some cases by as much as 40
years). In some locations (a good example is the Punta Miquillo/Punta Picua
area in Rio Grande) the changes to the topography have been enormous. The
same holds in the Isabela area, where coastal dunes, 10 or more meters
high, appear in the USGS DEM data, but those dunes are no longer there,
having being mined decades ago. This fact, together with the computational
grid cell size of approximately 90 meters, has to be considered in assessing
the reliability of the maps. Hopefully, in the near future we may have
available high-resolution LIDAR data taken in December of 2000 along a
1 km wide coastal strip. The use of this LIDAR data hinges on the processing
of it to obtain “bare earth elevations”, and in the determination of an
accurate geoid surface model for Puerto Rico.
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As mentioned above, the SHOALS
data contains, at some locations, elevations above MSL that correspond
to the vegetation cover at the site. The same for nearshore infrastructure
like for example, the city of Aguadilla. These erroneous elevations will
present a solid barrier to the inland penetration of the sea. Although
if the infrastructure is well built it will really act as a barrier to
the inland flood. The same holds for any sand dunes present in the data,
whether the dune is still there or not. It will act as a solid barrier
since no erosion, or scouring, is contemplated in the model.
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The high-resolution computational
grid used in the tsunami simulations, the grid where the tsunami runup
phase was computed – Grid C, had a constant Manning friction coefficient,
n, all over it. The value used, 0.025, corresponds to water flow over slime-covered
cobbles, gravel, and grass (see http://wwwrcamnl.wr.usgs.gov/sws/fieldmethods/Indirects/nvalues/
for a good visual description of Manning’s n values). This is the default
value of the model and it is the same value used in the numerical simulation
of the 1918 Puerto Rico tsunami (Mercado and McCann, 1998), where the comparison
of model results and runup observations (see report by Reid and Taber,
1919, on the Internet site) was acceptable. It is obvious that the value
of ‘n’ will vary significantly all over the island, and especially in developed
areas. The value adopted in this study will be valid only up to a certain
limited distance from the MSL shoreline. For this reason, it is felt that
the extensive inland flooding observed in some coastal regions of the island
represents quite conservative results. For this reason, and in order to
somewhat compensate for this, the inland limit of the flood was taken at
the 1 meter elevation (above Mean Sea Level) contour of the inland flood.
That is, in low lying areas, where the inland flood peaks at some distance
inland and then decreases, the digitization of the inland limit of flooding
was done along the 1 m flood elevation contour. In future studies, when
more up-to-date, higher resolution, and more accurate topography becomes
available, one could run the model with variable ‘n’ values based on land-use
information available in GIS format.
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As explained above, the flood
results shown do not discriminate between earthquake scenarios of different
return periods. As will be described in the final report about the seismology
of the region upon which the tsunami scenarios were based, different earthquake-prone
regions around the island have different expected earthquake return periods.
The practical approach adopted here, which is typical in the field of hazard
mitigation, is that since the uncertainty in return periods is so large
then we better be prepared for the worst case scenario. We can leave for
another study refinements of this adopted approach. Since the original
flood data for each earthquake has been stored, if necessary we could prepare
flood maps based on the region of the earthquake epicenter.
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In the calculation of the initial
sea surface deformation in the earthquake source region, computed using
the Masinha and Smylie model, we had the option of using either a mean
(over the fault length) slip magnitude or the maximum magnitude (occurring
somewhere along the fault length), both output by the methodology used
by Dr. Huerfano. In this study we adopted the maximum value as applicable
all along the fault length. This is the first time this type of maps are
being prepared for Puerto Rico and we wanted to err on the conservative
side, leaving for a later re-evaluation the use of smaller values of the
slip. As expected, this will also add to the conservatism of the results.
[Note: There are nowadays more sophisticated, and possibly reliable, models
to estimate the initial sea surface condition than the Masinha and Smylie
model. But they require seismic data not available to us.]
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For the same reason, the fault
depth below the bottom of the ocean was assumed to be as small as the algorithm
used to compute the initial sea surface deformation allowed. The algorithm
tends to become unstable if fault depths below the ocean bottom are assumed
less than approximately 200 meters. But this threshold varies, and so we
did computations with different values and adopted the smaller one allowed.
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Finally, it should be stated
that there is the possibility that a tsunami may propagate upriver and,
in some cases, overflow its banks. This is something that has been observed
in the past at many locations. This possibility has not been addressed
in this study. People living near river banks in the proximity to the coastline
should be careful about the use of these maps since they will not show
this possibility.
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There are other assumptions
involved in the methodology used to obtain the fault parameters, but these
are made in the study by Dr. Huerfano.
These maps have been distributed
to local emergency response agencies in both paper and CD versions. All
of this information is also available in GIS Shape files format. The results
show that western and northern Puerto Rico will be the most affected zones,
with the hazard being smaller in the eastern and southern zones.
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