1.Introduction
The water time(multte) refers to the Korean traditional calendar system to count one lunar month with 15 days expressing tidal phenomenon according to tidal dynamics. The water time calendar system was structured in 15-day cycle in a lunar month with the ancient knowledge that tide changes regularly in line with the lunar synodic month and has been passed down by tradition so far.
The cycle includes the tidal phenomenon with two rounds of rising tide and two rounds of ebb tide. And neap tide with weak current and spring tide with strong current appear every 15 days in turn. In a month, two rounds of neap and spring tides appear respectively. And these phenomena is called water time in one month cycle. This traditional and conventional system of water time in Korea shows ancestors’, especially ancient fishermen’s wisdom of live by the sea(Park, 1985).
Regarding existing literature on water time, one preceding study organized and unified the names regarding water time, which had been passed down by tradition(Park, 1985). Another study also looked at the relationship between water time and ecosystem as well as water time and red tide(Jang, 2009). Study is also active in the marine product industry, examining differences in small octopus catch (Jung and Kim, 2001; Oh et al., 2012). Study on tidal phenomenon has also been active(Cho and Kang, 2012; Kim and Kim, 2013).
As shown in Figure 1, risk is imposed on navigators and vessel traffic services operators who manage many inbound and outbound ships in a specific water time. However, study has been insufficient regarding fishing activities and maritime traffic patterns according to water time.
In this recognition, this study seeks to analyze ships traffic characteristics according to water time in order to provide the necessary data for efficient traffic management development.
2.Method of research
2.1.Scope of study area
This study utilized the AIS(Automatic Identification System) actual measurement date for 365 days from January 1 to December 31, 2013. The analysis object region was Mokpogu where vessels navigating Mokpo Port pass through, as shown in the Figure 2(Gang et al., 2014).
2.2.Procedure of study
To analyze maritime traffic volume according to water time, 1 year amount of solar calendar data were converted into lunar calendar. Table 1 describes water time terms coming down by tradition in the East Sea area, South Sea area and West Sea area, respectively. Park(1985) analyzed the relation between water time and tidal phenomenon in a statistical manner and united the mutually different local water time names. Since local people are accustomed to live by following the orally-transmitted water time cycle, the traditional water time system of West Sea – 7 water time event system (day 1 and day 16 in lunar month) - was applied. If the last date of a month is 29, 6 water times corresponding to day 30 in a lunar month was omitted to align the data.
3.Current status
3.1.Fishing industry status
Table 2 shows the number of registered fishing boats according to fishing industry segment. Around the Mokpo coast, there are 44 ships for coastal stow net fishery; 29, for shore stow net fishery; and 399, for shore mixed fishery, etc.
3.2.Stow net wake
Stow net ships and gill net ships are the main part of fishing industry by following water time. Stow net, in particular, is to fix fishing fear firmly not to be drifted away in tide and rely on the tidal force to coercively push fish in the net. Thus, stow net ships is largely affected by water time compare to other types of fishing, stow net ships move in or out of ports by following water time. Figure 3, 4shows only stow net ships’ inbound and outbound wakes on the 2nd-3rd water time. It shows more outbound ships than inbound ships. Also Figure 5, 6 shows only stow net ships’ inbound and outbound wakes on the 12th-13th water time. It shows more inbound ships than outbound ships. Accordingly, these figures demonstrate the differentiated patterns of maritime traffic according to water time.
4.Maritime traffic characteristics according to water time
4.1.Variation index according to water time
Table 3 shows the number of ships moving in and out of the Mokpo port according to water time for 1 year. Also the table shows the variation indexes(the average daily maritime traffic for each water time ÷ the annual average daily maritime traffic) during water time, which were 1.07 and 0.93 for the 12th and 15th water time. In turn, the maritime traffic for the 12th water is about 14.6 % higher than of the maritime traffic for the 15th water.
Figure 7 shows the variation index of the numbers of outbound ships and inbound ships according to each water time. The trends of changes in ship inbound and outbound variation indexes move to the mutually opposite directions. While there were more outbound traffic on the 2nd-3rd water time than on other water time, inbound traffic was larger on the 12th-13th water time. Outbound traffic on the 2nd-3rd water times was larger than on the 7th water time by about 23-24 %. Inbound traffic on the 12th-13th water times was more often than on the 9th water time by approximate 29-33 %. This seems the effect of ships moving in line with water time as in Figure 2-5, to show the mutually opposed trends in outbound and inbound variation indexes according to each water time.
4.2.Hourly variation index according to water time
Figure 8 shows hourly variation indexes (The average hourly maritime traffic for each water time ÷ the annual average hourly maritime traffic) of outbound ships according to water time. It can be noticed that there were more outbound ships at 6 than other timeslots. Especially at 6 on the 10th water time, there were 98.7 % more outbound ships than those on the 4th water time. Figure 9 shows inbound ships’ variation indexes of each timeslot according to water time. There were more inbound ships at around 17 than other time of a day. Especially at 17 on the 12th water time, the number of outbound ships is larger than on the 9th water time by 83.4 %.
The relationship of hourly variation index(v) according to time(h) was analyzed with the curve fitting. As a result, an appropriate model was found in terms of the sum of 8 sine functions as described in the equation (1). Table 4 shows the comparison of goodness of fit of fourier series model and sum of sine functions model. The coefficientof determination(R2) of the sine function model exceeded 0.95, indicating that the hourly variation index(v) according to time(h) was well explained by the sum of sine function.
It seems that this is because diverse sine functions worked together complicatedly, which represented the 2 rounds of ebbs and risings a day appearing in the intervals of 6 hours; the intensity of tides and the currents change accompanying neap and spring tides; human daily bio-cycle of departing in the morning and returning in the evening,and many others.
5.Conclusion
In line with the changes in intensity of tide as shown in neap and spring tides according to water time, maritime activities including fishery are diversified. This study is significant in analyzing the maritime traffic characteristics quantitatively in the context of the traditional water time system.
In this study, the differences of maritime traffic volume in each water time were compared by applying the variation index. As a result, it was found herein that the number of outbound ships was larger on the 2nd-3rd water times than the 7th water times by 23-24 %. And the number of inbound ships was higher on the 12th-13th water times than the 9th water time by 29-33 %. Such a result seems because of the effect of ships moving in line with specific water time. So the inbound and outbound variation indexes were found to move in mutually opposite directions according to water time.
It was also found that the number of outbound ships was higher at 6 on the 10th water time than the 4th water time by 98.7 % whereas the number of inbound ships was larger at 17 on the 12th water time than the 9th water time by 83.4 %. From these findings, it is noted that time hour and water time have an effect on ships’ inbound and outbound.
The hourly variation index of inbound and outbound ships according to time, in particular, was found to change in the form of sine function model. It is deemed necessary to understand the ship traffic patterns according to water time for safe traffic management including especially large ships such as new ships and deep draft ships. This study is expected to serve as a necessary basic material for development of maritime traffic management according to water time.
