Accuracy Assessment of the Geospatial Information Agency’s Tidal

Prediction

Khomsin and Danar G. Pratomo

Geomatics Engineering Department, Faculty of Civil Engineering, Environment, and Geo-Engineering,

Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Keywords: Accuracy Assessment, BIG, Tide Prediction, Tide Observation.

Abstract: Bathymetric survey requires tide data to reduce sounding data to the preferability vertical datum. A tidal

observation is performed in the vicinity of the survey area to achieve the tide correction for the depth

measurement. In order to obtain vertical references, it is necessary to conduct a direct tide observation for at

least 15 days period. An in-situ tidal observation takes high operating cost and needs a lot of effort to install

a tide gauge in the survey area. Thus, to reduce the time and the cost for tide observation, tidal prediction data

can be used as an alternative solution. This research attempted to perform the accuracy assessment of the tidal

prediction model from Geospatial Information Agency (BIG). There are 128 tidal stations from BIG which

spread across Indonesia archipelago. These stations provide real time tide observation. Based on the data, BIG

established a tidal prediction model for Indonesia waters. The research examined the BIG tide model with

direct tidal observation data from two locations (Ambon and Cilacap). The results show the accuracy of tidal

prediction from BIG is 0.085m for Ambon and 0.385m for Cilacap. The residual of MSL, HHWL, and LLWL

between tidal prediction and in-situ data in Ambon are -0.022m, -0.063m and +0.020m, and in Cilacap are -

0.147m, -0.122m, and -0.173m, respectively.

1 INTRODUCTION

A hydrographic surveyor has to be able to associate

all measured depths with respect to a vertical datum,

regardless of the water surface variation along the

time of sounding. A water level datum can be a ‘tidal

datum’ when defined in terms of a certain phase of

tide. The datum to which depths on a chart are

referred is known as the chart datum (IHO, 2005). In

Indonesian coastal waters, Mean Sea Level (MSL) is

used for topographic map and Lowest Low Water

Level (LLWL) is used for hydrographic map

(Republik Indonesia, 2011) are computed from

tabulation of the observations of the tide, in this case

the average of the tidal waters everyday over a 19 year

period.

The tide plays an important role both in the land

and sea surveying. In Indonesian’s Constituent No. 4

2011 about Geospatial Information Law, states that

(Republik Indonesia, 2011):

1) The Indonesian base map must consist of

coastline (Article 12),

2) The coastline is a adjoint line between the land

and the sea which is affected by the tides (Article

13, paragraph 1).

3) There are three types of coastlines: a Lowest Low

Water Level (LLWL) is used as nautical chart

reference, Mean Sea Level (MSL) is used as

topographic reference and the Highest High Water

Level (HHWL) (Article 13, paragraph 2).

Tides are the phenomenon of rising and falling of

sea surface caused by the attraction between earth and

celestial bodies such as the Moon and the Sun (IHO,

2005; Triatmojo, 1999; Parker, 2007). Although the

mass of the moon is smaller than the mass of the sun,

but because its distance to the earth is much closer,

the influence of the moon's attraction on the earth is

greater than the influence of the sun. Tidal generating

forces vary inversely as the cube of the distance from

the tide generating object. Gravitational attractive

forces only vary inversely to the square of the

distance between two objects (Thurman, 1994). The

attraction of the moon that affects the tides is 2.2

times greater than the sun attraction.

Khomsin, . and Pratomo, D.

Accuracy Assessment of the Geospatial Information Agency’s Tidal Prediction.

DOI: 10.5220/0008374600650070

In Proceedings of the 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management (ISOCEEN 2018), pages 65-70

ISBN: 978-989-758-455-8

The observation of sea surface variation can be

applied in the following areas (IHO, 2005): a. water

level requirement planning; b. preliminary water level

zoning development; c. control water level station

operation; d. supplemental water level station

installation, operation, and removal; e. data quality

control, processing, and tabulation; f. datum

computation and datum recovery; g. generation of

water level reducers and the final tidal zoning. The

observation of the water level variation is also

essential for supporting hydrographic survey.

However, the development of a tidal station in fields,

either permanent or temporally, is unintelligible. The

complication of developing a tidal station depends on

the topography of the shore and the accessibility of

the survey area, especially if it is located in a remote

area.

The Geospatial Information Agency (BIG) is the

government institution that responsible for providing

geospatial data in Indonesia. One of the responsibility

of BIG is providing tidal data across the country with

an online tide prediction service (BIG, 2018). Yet,

this tide prediction is lack of information related to

the tide data (i.e.: the vertical reference of

downloaded data is unclear). Thus, the data cannot be

directly applied for determining vertical reference

and depth correction in hydrographic survey. This

study aims to examine the accuracy of tidal prediction

data downloaded from tides.big.go.id with in-situ

data in two locations of tidal observations.

Furthermore, the study also analyzed the feasibility of

this tidal prediction for depth correction during the

hydrographic survey.

2 DATA AND METHOD

2.1 Data and Research Area

The research used tidal prediction data downloaded

from tides.big.go.id/pasut. The data are adjusted to

the location and time of in-situ data measurement.

There are two in-situ tidal observation used in the

research: Cilacap and Ambon. The geographical

coordinates of these locations and the date when the

data were taken be seen in Table 1. The aerial image

of the in-situ stations is shown in Fig 1).

2.2 Method

The downloaded tidal prediction is in ASCII format

comprises geographical positions of the station, the

date and the time observation, and the water elevation

(Fig 2). The time is recorded in UTC (Universal Time

Coordinate). The time is converted to the local time,

Table 1: Geographical coordinates of the tidal staff.

No Location Latitude (S) Longitude (E) Date

1 Cilacap 7°44'37.34" 108°59'57.24"

12 July

–

26 July

2017

2 Ambon 3°39'42.34" 128°10'43.05”

14 August

–

28 August

2018

Figure 1: The location of the tidal staff in Cilacap (top) and

Ambon (bottom).

which is in Ambon is UTC + 9 (Eastern Indonesia

Time) and in Cilacap is UTC + 7 (Western Indonesia

Time). The tidal prediction and in-situ data, then,

were plotted overlaid each other to see the difference

between the tidal prediction and in-situ data. The

accuracy of the tidal prediction is represented by Root

Mean Square error (RMSe).

ISOCEEN 2018 - 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management

Table 2: Tidal prediction data in UTC.

Latitude Longitude Date Time Z (m)

-7.743 108,9992 8/14/2018 0:00:00 0,211

-7.743 108,9992 8/14/2018 1:00:00 0,601

-7.743 108,9992 8/14/2018 2:00:00 0,866

-7.743 108,9992 8/14/2018 3:00:00 0,936

-7.743 108,9992 8/14/2018 4:00:00 0,787

-7.743 108,9992 8/14/2018 5:00:00 0,452

-7.743 108,9992 8/14/2018 6:00:00 0,009

-7.743 108,9992 8/14/2018 7:00:00 -0,436

-7.743 108,9992 8/14/2018 8:00:00 -0,777

-7.743 108,9992 8/14/2018 9:00:00 -0,933

-7.743 108,9992 8/14/2018 10:00:00 -0,87

-7.743 108,9992 8/14/2018 11:00:00 -0,607

-7.743 108,9992 8/14/2018 12:00:00 -0,213

The tide analysis was performed using least

square estimation to predict the variation of the water

level over the time which is shown in the following

mathematical equation (Stephenson, 2016):

(1)

where:

y(t) = water level at time t

Z = Mean Sea Level

N = number of tide components, from n=1

to n=N

An = Amplitude of the average harmonic

components

ɷn = angular speed of tidal wave component.

t = time based on GMT.

ψn = Greenwich phase of tidal component n at t =

0 which varies before corrected.

Figure 2: shows the tide prediction and in-situ data

overlaid at the same time reference and the height of

the tide prediction is adjusted to the same reference of

the in-situ data.

Figure 2: The tide prediction (blue) and in-situ (blue) graph

use the same reference.

The difference between the tide prediction and in-

situ data is computed to get the accuracy. Here the

accuracy is represented by root mean square error.

RMSe is a frequently used measure of the difference

between values predicted by a model and the values

observed from the environment that is being

modelled. These individual differences are also called

residuals, and the RMSe serves to aggregate them into

a single measure of predictive power. The RMSe of a

model prediction with respect to the estimated

variable Xmodel is defined as the square root of the

mean squared error (FEMA, 2016):

(2)

where Erms is dimensional RMSe, pi is tidal

prediction data, mi is tidal in-situ observed values the

time i. In this case pi is the tidal heights of BIG tidal

prediction and mi is the tidal heights of in-situ

observation. The accuracy of the amplitude of tidal

constituents can also be computed by Eq. 2.

Vertical references that are often used in

topographic and hydrographic mapping are computed

from the amplitude of each tidal component that has

been determined by least square method based on Eq.

1. According to (Latief, et.al, 2018), highest high

water level and lowest low water level can be

determined by these equations:

𝐻𝐻𝑊𝐿  𝑍0  𝑀2𝑆2𝑃1𝐾2𝑂1𝐾1

(3)

𝐿𝐿𝑊𝐿  𝑍0  𝑀2𝑆2𝑃1𝐾2𝑂1𝐾1

(4)

Accuracy Assessment of the Geospatial Information Agency’s Tidal Prediction

3 RESULTS AND DISCUSSION

The analysis of the accuracy was performed at two

locations which will be explained more detail at the

following section.

3.1 Ambon

The tide prediction from BIG and the tide observation

at Ambon waters can be seen in the tide graph in

Figure 3. Based on the graph, the tidal prediction has

a similar pattern with tidal in-situ (in-situ is blue and

prediction is red in the Fig 3). The residual for each

tidal elevation from time to time can be seen in a

green color. The Erms on Eq. 2, the accuracy of BIG

tidal prediction is ± 8.5 cm. Based on the tidal

constituents produced by least square method in

Table 3 the residual of the amplitude of the 9 (nine)

tidal prediction and in-situ less than 13 cm. The

accuracy of tidal prediction amplitude to in-situ is 5.3

cm computed by Erms. The phase of M2, N2, K2, M4

and MS4 tidal prediction are slower than tidal in-situ

and otherwise for other phases.

Figure 3: Tidal prediction and in-situ graph at Ambon.

According to the results of the tidal component

calculation for data prediction and in-situ, the vertical

reference such as Mean Sea Level (MSL), High

Highest Water Level (HHWL) and Lowest Low

Water Level (LLWL) which are used in the

hydrographic mapping can be computed from Eq. 3

and Eq. 4. The height difference of MSL, HHWL and

LLWL between tidal prediction and in-situ can be

seen on Table 4. It shows that the height residual of

MSL (-2.2 cm), HHWL (-6.3 cm) and LLWL (2 cm)

are very small.

Table 3: Amplitude and Phase of tidal constituents at

Ambon.

Constituents Sy

bol

Phase (degree) Amplitude (m)

In-situ Prediction In-situ Prediction

Main lunar

constituent

234.4557° 230.8995° 0.5657 0.5604

Main solar

constituent

208.3771° 217.7096° 0.1055 0.2322

Lunar

constituent,

due to Earth-

Moon distance

206.3367° 195.3842° 0.1058 0.1178

Soli-lunar

constituent,

due to the

change of

declination

209.7330° 47.8323° 0.0750 0.0646

Soli-lunar

constituent

223.3428° 237.4431° 0.2739 0.1885

Main lunar

constituent

112.9094° 123.5641° 0.1718 0.1343

Main solar

constituent

7.3897° 240.0212° 0.0575 0.0569

Main lunar

constituent

263.2144° 119.0107° 0.0078 0.0001

Soli-lunar

constituent

296.2383° 129.8310° 0.0173 0.0001

Table 4: Vertical references which can be derived from tidal

constituents.

Vertical

Reference

Abbreviation

In-situ

(m)

Prediction

(m)

Residual

(m)

Mean Sea

Level

MSL 1.9337 1.9121 -0.0216

Highest High

Water Level

HHWL 3.2121 3.1489 -0.0632

Lowest Low

Water Level

LLWL 0.6553 0.6752 0.0199

3.2 Cilacap

The same method used for Ambon was performed for

analyzing tidal prediction and in-situ data at the

Cilacap waters. This is described on tidal chart

(Figure 4). From the graph, we can see that the tidal

prediction and in-situ have the same pattern.

However, the amplitudes of some points are very

different. Generally, the height difference of tidal

prediction and in-situ is between -0,5 m and 1 m.

Using Erms formula on Eq. 2, the accuracy of BIG

tidal prediction in Cilacap is ± 38.5 cm. Based on the

tidal constituents produced by least square method in

ISOCEEN 2018 - 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management

Table 5, the residual of the amplitude of the tidal

prediction and in-situ less than 3 cm and the accuracy

of amplitude of tidal prediction to in-situ is 1.4 cm.

The phase of M2, N2, K2, and S2 are faster than tidal

in-situ and otherwise for other phases.

Figure 4: Tidal prediction and in-situ chart at Cilacap.

According to the results of the tidal component

calculation for data prediction and in-situ, the height

difference of MSL, HHWL and LLWL between tidal

Table 5: Amplitude and phase of tidal constituents at

Cilacap.

Constituents Symbol

Phase (degree) Amplitude (m)

In-situ Prediction In-situ Prediction

Average water

level

2.2360 2.0886

Main lunar

constituent

47.1235° 30.7097° 0.4948 0.5024

Main solar

constituent

36.1647° 13.2859° 0.2583 0.2906

Lunar

constituent, due

to Earth-Moon

distance

209.5123° 202.0489° 0.1241 0.1204

Soli-lunar

constituent, due

to the change

of declination

161.1427° 153.9478° 0.1032 0.1111

Soli-lunar

constituent

122.5043° 286.9136° 0.2003 0.1776

Main lunar

constituent

7.7426° 186.8371° 0.1161 0.1162

Main solar

constituent

41.9251° 218.2835° 0.1121 0.1127

Main lunar

constituent

294.0919° 319.4123° 0.0001 0.0002

Soli-lunar

constituent

281.3738° 328.9046° 0.0001 0.0002

shows that the difference of height of MSL (-14.7

cm), HHWL (-12.2 cm) and LLWL (-17.32 cm) are

relatively small.

Table 6: Vertical reference which can be derived from tidal

constituents at Cilacap.

Vertical

Reference

Abbreviation

In-situ

(m)

Prediction

(m)

Difference

(m)

Mean Sea

Level

MSL 2.2360 2.0886 -0.1472

Highest

High

Water

Level

HHWL 3.5207 3.3992 -0.1215

Lowest

Low Water

Level

LLWL 0.9512 0.7780 -0.1732

4 CONCLUSIONS

In this study, an accuracy assessment has performed

to examine the tide model from Geospatial

Information Agency and in-situ observations from

two areas, Cilacap and Ambon. Based on the study,

both data have similar pattern in hourly basis for 15

days period. The residual height of water level

between tidal prediction and in-situ in Ambon is

0.085m and in Cilacap is 0.385m. The differences of

MSL, HHWL, and LLWL between tidal prediction

and in-situ data in Ambon are -0.022m, -0.063m and

+0.020m and in Cilacap are -0.147m, -0.122m and -

0.173m.

ACKNOWLEDGMENTS

The authors would like to thank Geospatial

Information Agency (BIG) for providing the tidal

prediction data.

REFERENCES

A. G. Stephenson, 2016. Harmonic Analysis of Tides Using

Tide Harmonics. URL: https://CRAN.R-project.org/

package=TideHarmonics.

B. Triatmodjo, 1999. Teknik Pantai. Beta Offset.

Yogyakarta.

Accuracy Assessment of the Geospatial Information Agency’s Tidal Prediction

B. B. Parker, 2007. Tidal Analysis and Prediction. NOOA

Special Publication NOS CO-OPS 3.

BIG, 2018. Online Tide Prediction. http://tides.big.go.id/

pasut/.

FEMA, 2016. Guidance for Flood Risk Analysis and

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Tahun 2011 Nomor 49.

ISOCEEN 2018 - 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management