Shin-ichi Aoki

Effect of Regulation of the Inlet Channel on Tides and Water Levels in Lake Hamana, Japan

Hamanako is an economically and culturally important tidal lake on the Pacific coast of Honshu, Japan. Between 1954 and 1973, entrance works were constructed to regulate the channel to the lake. In a previous paper, the tidal records from Maisaka just inside the lake, prior to regulation, were analyzed to explain the responses of the lake to the ocean tide and other water level changes. The present paper compares water level responses with those following regulation of the entrance. The principal differences are that at Maisaka the tidal range increased up to 1965, following the first stages of regulation, but it has been constant since then. In the inner part of the lake, regulation has increased the tidal range but reduced its variability and has reduced the mean water level in the lake. The southern sand shoal divides the lake into two tidal basins, with Maisaka lying in the smaller outer basin. Studies of the inner basin have shown that its tidal range continues to increase. A hydrodynamic model has been used to show that this increase is primarily due to scouring of the inner channel through the southern shoal. The model has also shown that the constancy of the Maisaka tidal amplitude since 1965 is not a static equilibrium, but is a balance between the effects of the outer entrance channel scouring and increasing the Maisaka tide, offset by the inner channel scouring and reducing the Maisaka tide.

Shoreline Monitoring Method Using Neural Network for Adaptive Management

Continuous shoreline monitoring is necessary for coastal management. It is however difficult to conduct shoreline measurements frequently because of high cost and hard work. In this research, a short term prediction method using the neural network was investigated as a tool of shoreline monitoring for the adaptive coastal management. The neural network could reproduce the measured shoreline position with a high accuracy of 6.2m for a maximum change of 50m. A combination use of the neural network and shoreline measurement is an effective method for the continuous monitoring although the development of the neural network needs field data for the network construction and its performance is affected by quality and quantity of the data.