Enayam Port

Tamil Research Institute
RAPID TECHNO-ECONOMIC FEASIBILITY REPORT FOR
DEVELOPMENT OF COLACHEL PORT AT TAMILNADU
FINAL REPORT
The tidal levels are shown in the table below:
HAT: Highest Astronomical Tide.
The elevation of the highest predicted astronomical tide expected to occur. at 1.05
least once a year
HHWS Highest High Water Spring 1.04
MHHW
Mean Higher High water.
The mean of the higher of the two daily high waters over a long period of 0.83
time. When only one high water occurs on a day, this is taken as a higher high
water .
MLHW
Mean Lower High Water.
The mean of the lower of two daily high waters over a long period of time. 0.67
When only one high water occurs on a day, no value is printed in the MLHW
column, indicating that the tide is diurnal .
MHLW
Mean Higher Low Water.
The mean of the higher of the two daily low waters over a long period of 0.41
time. When only one low water occurs on a day, no value is printed in the
MHLW column, indicating that the tide is diurnal .
MLLW
Mean Lower Low Water.
The mean of the lower of the daily low waters over a long period of time.
When only one low water occurs a day, this is taken as the lower low water
0.25
LLWS Lowest Low Water Spring 0.02
LAT
Lowest Astronomical Tide.
All heights have been taken above the lowest astronomical tide 0.00
GT
Great Diurnal Range
The difference in height between mean higher high water (MHHW) and mean
lower low water (MLLW).
0.58
4. TRANSFORMATION WAVE FROM DEEP WATER TO SHALLOW WATER
Sea states are propagated using a wave propagation model (GUIH-SWAN) from deep water to shallow
water areas in different points of interest.
Firstly a maximum dissimilarity selection algorithm (MDA) has been applied in order to obtain a
representative subset of sea states in deep water areas. The MDA has been selected 200 multivariate sea
states uniformly distributed over data, covering the edges and sampling the variability of deep water
climate.
Secondly this subset has been propagated to shallow water. The propagation model, GUI_SWAN, consists
in a numerical approximation of waves from deep water to shallow water near the coast in order to
evaluate the variability of the wave climate. It is a GUI which facilitates pre-processing and postprocessing
of the information needed to implement the SWAN model (Booij et al. 1999) (Simulating
Waves Nearshore, Cicle III version 40.91).
SWAN is a third-generation wave model, developed at Delft University of Technology that computes
random, short-crested wind-generated waves in coastal regions and inland waters. This model can be
CP1832-FR-AX-02-CP-WaveClimate-Ed1.docx AX02 - 11
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