- Email: [email protected]
Visualization of an Oxygen-deficient Bottom Water Circulation in Osaka Bay, Japan
Estuarine, Coastal and Shelf Science (2000) 50, 81–84 Article No. ecss.1999.0534, available online at http://www.idealibrary.com on
Visualization of an Oxygen-deficient Bottom Water Circulation in Osaka Bay, Japan H. Takahashia, Y. Hosokawa, K. Furukawa and H. Yoshimura Port and Harbour Research Institute, MOT, Japan, 3-1-1, Nagase, Yokosuka 239-0826, Japan Received 9 December 1998 and accepted in revised form 22 March 1999 A visualization system that can analyse integrated images of time and spatially dependent data has been developed. The system was used to analyse coastal environmental monitoring data sets obtained from Osaka Bay, Japan. The visualization of water temperature, salinity, and dissolved oxygen (DO) in the spring, summer and autumn revealed an oxygen deficient water circulation in the inner part of the bay. The circulation had a strong correlation with vertical stratification. In addition, the speed of the oxygen deficient water mass was c. 1·2 cm s 1, and the mass circulated counter-clockwise in the inner part of the bay. 2000 Academic Press Keywords: estuarine circulation; water quality distribution; dissolved oxygen; stratification; environmental data visualization; 3-D contour surface
Introduction In the past, much research has been done in Osaka Bay (Figure 1) to study its water quality, circulation and other environmental data (e.g. Nakatsuji et al., 1994; Tanimoto & Hoshika, 1997). A proper visualization and interpretation of large amounts of temporal and spatial data are important for a better understanding of the bay environment. A barrier to making such visualizations and interpretations of an environmental database is the lack of data points for building a good interpolation image for space and time. For example, environmental monitoring schemes have been set up by regional governments in the major bays of Japan. One of the schemes in Tokyo Bay, Japan is a monthly two-layer sampling at 31 points. This information is too sparse in time and space for building a smooth visualization of time dependent variations of the obtained values. This is why a snapshot image of a two-dimensional contour line plot should be used to interpolate the monitoring data (Figure 2) for a useful image. An independent two-dimensional sparse data structure has two major negative effects on the observation of the data. One is the invisibility of the time-dependent variation of the data, and the other is the difficulty of examining the interrelationship Full sized figures, tables and animations are stored on the CDROM accompanying this article. Use a Web browser to access the start page ‘ default.htm ’ and follow the links. The help file ‘ help.htm ’ provides answers for some common problems. a E-mail: [email protected]
0272–7714/00/010081+04 $35.00/0
between data elements. For example, if a snapshot of an oxygen deficient water distribution is shown, it is difficult to understand the three-dimensional movement of the water mass. It is also difficult to compare this with other environmental factors, e.g. temperature, salinity and stratification. This paper has two aims. Firstly, a new visualization system will be presented. The visualization system enables us to spatially analyse time varying phenomena as a visual image. The system uses an interpolation system to build the image. Secondly, an example of the application is presented using the Osaka Bay data. The system demonstrates that dissolved oxygem (DO) distribution and anoxic water circulation in the bay changes according to the inner bay’s density stratification.
Data visualization system Data interpolation strategy The system requires: densely interpolated data for time and space; high quality control of the sampled data; and a universal co-ordinate system for data visualization. The system must employ a good interpolation scheme, because only sparse data is available in the field data sets. For example, 10 observations at 10-day intervals, for the 12 points shown in Figure 2, can be interpolated with a two-step scheme. First, there is the generation of a finer mesh system using linear interpolation. The interpolation gives detailed 2000 Academic Press
82 H. Takahashi et al.
figures of an instantaneous snapshot of the environmental condition. Next, each 10 day snapshot is linearly interpolated in time to produce daily information. The linear interpolation of the time variables enables an application of this interpolation to irregularly sampled data sets. The two-step interpolation results in a base data set for visualization with sufficient density. Functions of the system To create a good visualization image, the visualization system can: build a database system with three dimensions for space and time; enable arithmetic calculation within stored database items; and create video images. The system can then achieve the following images. Arbitrary cut image. The system can slice images in any surface as shown in Figure 3 (vertical cutting and oblique cutting). This is a very basic visualization technique. This technique is used in Animation 1 to visualize surface and bottom dissolved oxygen distributions. Differential value visualization. Time and space gradients can also be traced with this system because the density of data is sufficient for such a calculation. For example, the spatial density gradients provide a good indication of density stratification, or the spatial salinity gradients provide a good indication of a salt wedge shape. These capabilities are helpful in the analysis of the flow acceleration in the domain and boundary layers. This technique is used in Animation 1 to visualize the stratification index that is a gradient of sigma-t. Three-dimensional contour surface image. The cutting surface does not necessarily have to be flat. As shown in Figure 4, the contour can be traced in threedimensional images. The surface which have same value for a factor (contour surface) will be a good indicator of the factor’s convection and diffusion. This technique is used in Animation 2 to visualize the movement of anoxic water. Osaka Bay environmental monitoring data Data set The obtained data set for Osaka Bay has: six monitoring factors (temperature, salinity, density, chlorophyl a, DO, and turbidity); data samples for every 1 m interval from surface to bottom; 20 sampling points shown in Figure 1; 20 samplings during April 1997–
October 1997 (the minimum interval was 4 days, and the maximum interval was 27 days). The data set (Figure 1) was interpolated with a fine mesh structure of dimensions 872 on the horizontal axis, and a 1 m interval on the vertical axis. In addition, the data was linearly interpolated to produce daily information. A database was created for differential value image and contour surface image. The outputs were converted to two or three-dimensional images and animations to show time dependent variables. Output of image and new findings Interrelation analysis for density stratification and DO distribution. A change of density stratification is said to trigger a change in DO distribution (e.g. Culberson & Piedrahita, 1996). This density stratification is also found in the Osaka Bay data (Animation 1). A viewpoint was set from north-west to south-east, and mesh co-ordinates were distorted for a better visualization. The image of the interrelationship between DO and density stratification is shown by a vertical cross-section of spatial density gradient data and DO distribution mapped on the same scene. In the animation, there is a density stratification image on the left, a surface DO distribution image at the top right, and a bottom DO distribution image at the bottom right. The density stratification is indicated by a spatial differentiation of the density using our analytical system. On 11 July 1997, there was a weak stratification, and a well-mixed DO distribution between the surface and the bottom. The mean surface DO concentration was c. 8·4 mg l 1. On 15 July 1997, a strong stratification developed, and the mean surface DO concentration was 12·8 mg l 1 and the bottom DO concentration was close to 0 mg l 1 at the northern part of the bay. This animation visualizes the development of anoxic water at the bottom layer synchronized with the appearance of strong stratification. Anoxic water mass circulation. To get a clearer view of the development of anoxic water, the contour surface was traced for low DO (3 mg l 1) (Animation 2). The same visualization co-ordinates were employed (viewpoint from south to north). The anoxic water mass moved counter-clockwise in the inner part of the bay with speed of c. 1·2 cm s 1. The anoxic water movement detected here agreed with the data obtained from Nakatuji et al. (1994) using salinity distribution in the bay. This is the first field data set that directly shows the horizontal anoxic water movement. Nakamura and Nishimura (1988) tried to explain the development of
An oxygen-deficient bottom water circulation in Osaka Bay 83
the anoxic water mass in a vertical one-dimensional DO model. Their study did not consider the horizontal transportation of the bottom water. However, Yamane et al. (1998) pointed out the importance of vertical mixing in estuarine circulation using a threedimensional water circulation model. Our findings from the DO data support the results of Yamane et al. (1998). The anoxic water mass moved in the same direction as in the estuarine circulation. A twodimensional contour surface as Animation 1 does not sufficiently show the movement because of complexity of DO distribution, but three-dimensional contour surface as Animation 2 can clearly show the horizontal movement. Conclusions The visualization system presented here has the capability of analysing three-dimensional environmental monitoring data with time-series data. The example of data monitoring in Osaka Bay showed the system has good efficiency, especially for a relational analysis between a horizontal DO distribution and a vertical stratification. The anoxic water circulation indicated by the contour surface helps us understand the global view of circulation in the bay. Nevertheless, this system only provides a tool for helping us understand the natural
environment. The next step is to study the physics of the circulation of anoxic water in detail using the system. Acknowledgements The authors would like to thank Mr H. Katho, Kansai International Airport Co., Ltd. for providing a valuable field data set, Mr H. Chiba, The Numerical Algorithms Group, Japan K. K. for helping the system integration, and anonymous referees for constructive comments during the review process. References Culberson, S. D. & Piedrahita, R. H. 1996 Aquaculture pond ecosystem model: temperature and dissolved oxygen prediction—mechanism and application. Ecological Modelling 89, 231–258. Nakamura, Y. & Nishimura, H. 1988 Development mechanics of anoxic water mass in coastal zone. Proceedings of Coastal Engineering, JSCE 35, 802–806 (in Japanese). Nakatsuji, K., Fujiwara, T. & Kurita, H. 1994 An estuarine system in semi-enclosed Osaka Bay in Japan. In Flux Changes in Estuaries (Dyer, K., ed.), Olsen & Olsen, pp. 79–84. Tanimoto, T. & Hoshika, A. 1997 Transport of total suspended matter, particulate organic carbon, organic nitrogen and phosphorus in the inner part of Osaka Bay. Journal of Oceanography 53, 365–371. Yamane, N., Teraguchi, T. & Nakatsuji, K. 1998 Development mechanics of anoxic water mass in enclosed bay. Proceedings of Coastal Engineering, JSCE 45, 961–965 (in Japanese).
84 H. Takahashi et al.
F 1. Map of Osaka Bay, Japan. Sampling points from April 1997 to October 1997 are shown as 20 plots.
F 4. Contour surfaces of environmental data and its development image to visualize the mass transportation.
F 2. Ordinary environmental monitoring data distribution and its visualization. (Left: three-dimensional distribution of environmental data; right: cross sectional contour line image of the data.)
A 1. Vertical cross-section of spatial density differentiation data and DO distribution at Osaka Bay, Japan from April 1997 to October 1997. Viewpoint set from north-west to south-east. (Left: stratification index; top right: surface DO concentration; and bottom right: bottom DO concentration.)
F 3. A vertical (right) and oblique (left) cutting image of environmental data.
A 2. Contour surface of DO concentration surface (3 mg l 1) at Osaka Bay, Japan from April 1997 to October 1997. Viewpoint set from south to north.
Visualization of an Oxygen-deficient Bottom Water Circulation in Osaka Bay, Japan H. Takahashia, Y. Hosokawa, K. Furukawa and H. Yoshimura Port and Harbour Research Institute, MOT, Japan, 3-1-1, Nagase, Yokosuka 239-0826, Japan Received 9 December 1998 and accepted in revised form 22 March 1999 A visualization system that can analyse integrated images of time and spatially dependent data has been developed. The system was used to analyse coastal environmental monitoring data sets obtained from Osaka Bay, Japan. The visualization of water temperature, salinity, and dissolved oxygen (DO) in the spring, summer and autumn revealed an oxygen deficient water circulation in the inner part of the bay. The circulation had a strong correlation with vertical stratification. In addition, the speed of the oxygen deficient water mass was c. 1·2 cm s 1, and the mass circulated counter-clockwise in the inner part of the bay. 2000 Academic Press Keywords: estuarine circulation; water quality distribution; dissolved oxygen; stratification; environmental data visualization; 3-D contour surface
Introduction In the past, much research has been done in Osaka Bay (Figure 1) to study its water quality, circulation and other environmental data (e.g. Nakatsuji et al., 1994; Tanimoto & Hoshika, 1997). A proper visualization and interpretation of large amounts of temporal and spatial data are important for a better understanding of the bay environment. A barrier to making such visualizations and interpretations of an environmental database is the lack of data points for building a good interpolation image for space and time. For example, environmental monitoring schemes have been set up by regional governments in the major bays of Japan. One of the schemes in Tokyo Bay, Japan is a monthly two-layer sampling at 31 points. This information is too sparse in time and space for building a smooth visualization of time dependent variations of the obtained values. This is why a snapshot image of a two-dimensional contour line plot should be used to interpolate the monitoring data (Figure 2) for a useful image. An independent two-dimensional sparse data structure has two major negative effects on the observation of the data. One is the invisibility of the time-dependent variation of the data, and the other is the difficulty of examining the interrelationship Full sized figures, tables and animations are stored on the CDROM accompanying this article. Use a Web browser to access the start page ‘ default.htm ’ and follow the links. The help file ‘ help.htm ’ provides answers for some common problems. a E-mail: [email protected]
0272–7714/00/010081+04 $35.00/0
between data elements. For example, if a snapshot of an oxygen deficient water distribution is shown, it is difficult to understand the three-dimensional movement of the water mass. It is also difficult to compare this with other environmental factors, e.g. temperature, salinity and stratification. This paper has two aims. Firstly, a new visualization system will be presented. The visualization system enables us to spatially analyse time varying phenomena as a visual image. The system uses an interpolation system to build the image. Secondly, an example of the application is presented using the Osaka Bay data. The system demonstrates that dissolved oxygem (DO) distribution and anoxic water circulation in the bay changes according to the inner bay’s density stratification.
Data visualization system Data interpolation strategy The system requires: densely interpolated data for time and space; high quality control of the sampled data; and a universal co-ordinate system for data visualization. The system must employ a good interpolation scheme, because only sparse data is available in the field data sets. For example, 10 observations at 10-day intervals, for the 12 points shown in Figure 2, can be interpolated with a two-step scheme. First, there is the generation of a finer mesh system using linear interpolation. The interpolation gives detailed 2000 Academic Press
82 H. Takahashi et al.
figures of an instantaneous snapshot of the environmental condition. Next, each 10 day snapshot is linearly interpolated in time to produce daily information. The linear interpolation of the time variables enables an application of this interpolation to irregularly sampled data sets. The two-step interpolation results in a base data set for visualization with sufficient density. Functions of the system To create a good visualization image, the visualization system can: build a database system with three dimensions for space and time; enable arithmetic calculation within stored database items; and create video images. The system can then achieve the following images. Arbitrary cut image. The system can slice images in any surface as shown in Figure 3 (vertical cutting and oblique cutting). This is a very basic visualization technique. This technique is used in Animation 1 to visualize surface and bottom dissolved oxygen distributions. Differential value visualization. Time and space gradients can also be traced with this system because the density of data is sufficient for such a calculation. For example, the spatial density gradients provide a good indication of density stratification, or the spatial salinity gradients provide a good indication of a salt wedge shape. These capabilities are helpful in the analysis of the flow acceleration in the domain and boundary layers. This technique is used in Animation 1 to visualize the stratification index that is a gradient of sigma-t. Three-dimensional contour surface image. The cutting surface does not necessarily have to be flat. As shown in Figure 4, the contour can be traced in threedimensional images. The surface which have same value for a factor (contour surface) will be a good indicator of the factor’s convection and diffusion. This technique is used in Animation 2 to visualize the movement of anoxic water. Osaka Bay environmental monitoring data Data set The obtained data set for Osaka Bay has: six monitoring factors (temperature, salinity, density, chlorophyl a, DO, and turbidity); data samples for every 1 m interval from surface to bottom; 20 sampling points shown in Figure 1; 20 samplings during April 1997–
October 1997 (the minimum interval was 4 days, and the maximum interval was 27 days). The data set (Figure 1) was interpolated with a fine mesh structure of dimensions 872 on the horizontal axis, and a 1 m interval on the vertical axis. In addition, the data was linearly interpolated to produce daily information. A database was created for differential value image and contour surface image. The outputs were converted to two or three-dimensional images and animations to show time dependent variables. Output of image and new findings Interrelation analysis for density stratification and DO distribution. A change of density stratification is said to trigger a change in DO distribution (e.g. Culberson & Piedrahita, 1996). This density stratification is also found in the Osaka Bay data (Animation 1). A viewpoint was set from north-west to south-east, and mesh co-ordinates were distorted for a better visualization. The image of the interrelationship between DO and density stratification is shown by a vertical cross-section of spatial density gradient data and DO distribution mapped on the same scene. In the animation, there is a density stratification image on the left, a surface DO distribution image at the top right, and a bottom DO distribution image at the bottom right. The density stratification is indicated by a spatial differentiation of the density using our analytical system. On 11 July 1997, there was a weak stratification, and a well-mixed DO distribution between the surface and the bottom. The mean surface DO concentration was c. 8·4 mg l 1. On 15 July 1997, a strong stratification developed, and the mean surface DO concentration was 12·8 mg l 1 and the bottom DO concentration was close to 0 mg l 1 at the northern part of the bay. This animation visualizes the development of anoxic water at the bottom layer synchronized with the appearance of strong stratification. Anoxic water mass circulation. To get a clearer view of the development of anoxic water, the contour surface was traced for low DO (3 mg l 1) (Animation 2). The same visualization co-ordinates were employed (viewpoint from south to north). The anoxic water mass moved counter-clockwise in the inner part of the bay with speed of c. 1·2 cm s 1. The anoxic water movement detected here agreed with the data obtained from Nakatuji et al. (1994) using salinity distribution in the bay. This is the first field data set that directly shows the horizontal anoxic water movement. Nakamura and Nishimura (1988) tried to explain the development of
An oxygen-deficient bottom water circulation in Osaka Bay 83
the anoxic water mass in a vertical one-dimensional DO model. Their study did not consider the horizontal transportation of the bottom water. However, Yamane et al. (1998) pointed out the importance of vertical mixing in estuarine circulation using a threedimensional water circulation model. Our findings from the DO data support the results of Yamane et al. (1998). The anoxic water mass moved in the same direction as in the estuarine circulation. A twodimensional contour surface as Animation 1 does not sufficiently show the movement because of complexity of DO distribution, but three-dimensional contour surface as Animation 2 can clearly show the horizontal movement. Conclusions The visualization system presented here has the capability of analysing three-dimensional environmental monitoring data with time-series data. The example of data monitoring in Osaka Bay showed the system has good efficiency, especially for a relational analysis between a horizontal DO distribution and a vertical stratification. The anoxic water circulation indicated by the contour surface helps us understand the global view of circulation in the bay. Nevertheless, this system only provides a tool for helping us understand the natural
environment. The next step is to study the physics of the circulation of anoxic water in detail using the system. Acknowledgements The authors would like to thank Mr H. Katho, Kansai International Airport Co., Ltd. for providing a valuable field data set, Mr H. Chiba, The Numerical Algorithms Group, Japan K. K. for helping the system integration, and anonymous referees for constructive comments during the review process. References Culberson, S. D. & Piedrahita, R. H. 1996 Aquaculture pond ecosystem model: temperature and dissolved oxygen prediction—mechanism and application. Ecological Modelling 89, 231–258. Nakamura, Y. & Nishimura, H. 1988 Development mechanics of anoxic water mass in coastal zone. Proceedings of Coastal Engineering, JSCE 35, 802–806 (in Japanese). Nakatsuji, K., Fujiwara, T. & Kurita, H. 1994 An estuarine system in semi-enclosed Osaka Bay in Japan. In Flux Changes in Estuaries (Dyer, K., ed.), Olsen & Olsen, pp. 79–84. Tanimoto, T. & Hoshika, A. 1997 Transport of total suspended matter, particulate organic carbon, organic nitrogen and phosphorus in the inner part of Osaka Bay. Journal of Oceanography 53, 365–371. Yamane, N., Teraguchi, T. & Nakatsuji, K. 1998 Development mechanics of anoxic water mass in enclosed bay. Proceedings of Coastal Engineering, JSCE 45, 961–965 (in Japanese).
84 H. Takahashi et al.
F 1. Map of Osaka Bay, Japan. Sampling points from April 1997 to October 1997 are shown as 20 plots.
F 4. Contour surfaces of environmental data and its development image to visualize the mass transportation.
F 2. Ordinary environmental monitoring data distribution and its visualization. (Left: three-dimensional distribution of environmental data; right: cross sectional contour line image of the data.)
A 1. Vertical cross-section of spatial density differentiation data and DO distribution at Osaka Bay, Japan from April 1997 to October 1997. Viewpoint set from north-west to south-east. (Left: stratification index; top right: surface DO concentration; and bottom right: bottom DO concentration.)
F 3. A vertical (right) and oblique (left) cutting image of environmental data.
A 2. Contour surface of DO concentration surface (3 mg l 1) at Osaka Bay, Japan from April 1997 to October 1997. Viewpoint set from south to north.