TRAVERSING FRENCH POLYNESIA WITHOUT A LONG STAY VISA
MARQUESAS – TUAMOTUS – SOCIETY ISLANDS

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Interpolating tides between two tidal stations involves estimating tidal heights or times at a location between them based on the data from both stations. Here’s a structured approach:

1. Linear Interpolation (Basic Method)

• If the location of interest is roughly equidistant from both tidal stations and the tide follows a simple trend, a linear interpolation can be applied:

H=H1+(H2−H1)D×dH = H_1 + \frac{(H_2 – H_1)}{D} \times dH=H1​+D(H2​−H1​)​×d

where:

• $H$ = interpolated tide height,
• H1,H2H_1, H_2H1​,H2​ = tide heights at the two stations,
• $D$ = total distance between the stations,
• $d$ = distance from Station 1 to the interpolation point.

2. Time Lag Interpolation

• Tidal waves travel over time, so high/low tide times may shift.
• If the phase lag is known, adjust the time accordingly:

T=T1+(T2−T1)D×dT = T_1 + \frac{(T_2 – T_1)}{D} \times dT=T1​+D(T2​−T1​)​×d

where T1T_1T1​ and T2T_2T2​ are the times of high or low tide at the two stations.

3. Harmonic Analysis (More Accurate)

• Tides are influenced by multiple harmonics (e.g., semi-diurnal, diurnal).
• If harmonic constituents (like M2, S2) are known for the two stations, you can interpolate each harmonic and sum them.
• NOAA and other agencies provide harmonic constants that can be used in tidal prediction models.

4. Nonlinear and GIS-Based Interpolation

• If coastline shape or bathymetry affects tides, use GIS-based methods like:
  • Inverse Distance Weighting (IDW): Weights closer station data more heavily.
  • Kriging: A geostatistical method for more accurate interpolation.
  • Hydrodynamic Models: Computational models (e.g., ADCIRC) simulate tide propagation.