Modeling with Digital Ocean and Digital Coast

The oceans are an important part of the earth that is a treasure house of resources and an important regulator of the global environment. There is a great part of the human being living in the coastal region all over the world. Oceans and coastal regions are changing at faster rates, over broader scales, than ever before and in fundamentally new ways. Digital analysis based on multisource data can greatly improve the cognition about the oceans and coasts, which are from diverse observing approaches such as satellites, airplane, ship, high frequency ground wave radar, buoys (moored and drifting) and land-based stations. This chapter briefly describes the concepts of the digital ocean and digital coast (DO&DC), discusses the modeling and visualization technologies for the realization of the DO&DC system, and notes the important roles of the DO&DC in digital earth development.

Digital Earth is an integrated approach building scientific infrastructure. The ocean data is a typical big data, which can be seen from the data volume, velocity, variety, and value perspectives. The Digital Earth systems provide a three-dimensional visualization and integration platform for ocean big data, which include ocean management data, in situ observation data, remote sensing observation data and model output data. Based on the analysis on the characteristic of ocean big data, this chapter studies the ocean big data acquire and integration technology that is based on the Digital Earth system. Firstly, the construction of the Digital Earth based three-dimensional ocean big data integration environment is discussed. Then, the ocean management data integration technology is presented which is realized by general database access, web service and ActiveX control. Third, the in situ data stored in database tables as records integration is realized with a three-dimensional model of the corresponding observation apparatus display in the Digital Earth system using a same ID code. In the next two parts, the remote sensing data and the model output data integration technologies are discussed in detail. The application in the Digital Ocean Prototype System of China shows that the method can effectively improve the efficiency and visualization effect of the data.

This chapter mainly focuses on the network service technology for ocean data application. This chapter starts with an introduction of two classes of traditional network service composition technology, all of which have limitations. To satisfy the increasing requirements of the user, the chapter introduces a new type of dynamic service combination technology. Then, this chapter introduces the modeling process of the technology, the network data service validation process and the implementation process of network data services. This new technology enables the rapid extension of the ocean information system and allows the system to rapidly design an individual response to user demand. Thus, the prospect of this application is notably good.

The mechanism of flood forecasting is a complex process, which involves precipitation, drainage-basin characteristics, land use/cover types, and runoff discharge. Because of the complexity of flood forecasting, hydrological models and statistical models need to be developed for flood frequency analysis, river runoff prediction, and flood forecasting. In this chapter, Soil Conservation Service (SCS) Curve Number (CN) model is applied for river runoff prediction in the Oak Ridges Moraine (ORM) area, southern Ontario. The historical data for the past several decades (river gauging, precipitation, ground water, census and land use) are used to model the relationship among the stream runoff, precipitation and hydrological-geographical features to apply SCS CN model for river runoff prediction.

Because of the complexity of flood forecasting, hydrological models and statistical models need to be developed for flood frequency analysis, and flood forecasting. In this chapter, the performance of LP3, GEV, PL and GP models was investigated for flood frequency analysis to find the most suitable flood frequency analysis method in the Oak Ridges Moraine (ORM) area, southern Ontario. Historical data of river runoff peak discharge for the past several decades are used to model the relationship between flood discharge and return period by fitting a theoretical statistical distribution for each gauging station. The correlation coefficients of observations and model fitted lines are compared to evaluate the performance of each flood frequency analysis method. The comparison of different flood frequency analysis methods suggest that there are no significant differences of the LP3, GEV, PL and GP models for less than approximate 10-year return period flood frequency analysis in the ORM area. And there are no significant differences of the LP3, GEV, PL and GP models for greater than approximate 10-year return period flood frequency analysis in the ORM area.

The application of flood forecasting models requires the efficient management of large spatial and temporal datasets, involving data acquisition, storage, processing, analysis and display of model results. Difficulty in linking data, analysis tools, and models is one of the barriers to be overcome in developing an integrated flood forecasting system. The current revolution in technology and the online availability of spatial data facilitate Canadians’ need for information sharing in support of decision making. This need has resulted in studies demonstrating the suitability of the web as a medium for implementation of flood forecasting. Web-based Spatial Decision Support Services (WSDSS) provides comprehensive support for information retrieval, model analysis and extensive visualization functions for decision-making support and information services. This chapter develops a prototype WSDSS that integrates models, analytical tools, databases, graphical user interfaces, and spatial decision support services to help the public and decision makers to easily access flood and flood-threatened information. Flood WSDSS helps to mitigate flood disasters through river runoff prediction, flood forecasting, and flood information (flood discharge, water level and flood frequency) dissemination. The ultimate aim of this system is to improve access to flood model results by the public and decision makers.

This chapter focuses on data and system modeling for ocean and coast disasters. At first, the ocean and coast disaster monitoring data are introduced. According to its characteristics, we analyse how to organize data in the data warehouse. Then, we discuss how to use an ocean stereo monitoring data management system for data management. Next, according to the characteristics of ocean disaster factors and the software architecture, this chapter introduces the elements of ocean disasters and a variety of expressions and implementations. The chapter ends with the introduction of the storm surge disaster analysis model system and the sea level rising disaster analysis model system, including the data needed by the system, the software structure design and the function realization process.

Coastal zones are a junction between the land and the ocean. The dynamic nature of the coast makes it difficult to clearly define the borders of coastal zones. Sometimes these zones are referred to as tidewater areas, extending from the coast to approximately 10 miles inland. Ketchum defined a coastal zone as the band of dry land and adjacent ocean space (water and submerged land) in which terrestrial processes and land uses directly affect oceanic processes and uses and vice versa (Ketchum BH, The water’s edge: critical problems of the Coastal zone. MIT Press, Cambridge, MA, 1972). Coastal zones are one of the most ecologically valuable and biodiverse regions in the world. They provide food, freshwater and other marine products for human beings, as well as play an important role in the local environment. However, human activities and global climate change have given rise to natural disasters that threaten coastal zones environmental security. In particular, the effect of industrial projects on coastal zones is worsening. Thus, it is important to study these areas to provide guidance for coastal resource management and environmental protection. Numerous researchers have attempted to monitor coastal zone dynamics based on remote sensing data. In this chapter, we summarize various topics concerning the application of remote sensing in coastal zones. We focus on describing several new methods, including coastline automatic extraction, intertidal zone identification, coastal wetland classification and coastal invasive plant detection, using remote sensing.

Firstly, this chapter introduces the digital ocean and digital coast applications from the standpoint of ocean ecological environmental monitoring, shoreline and island management, El Nio phenomenon and the Sea-level rising monitoring, fisheries and public service and so on. Then, the practice in the program of China offshore digital ocean information infrastructure is introduced. The achievements laid the technical foundation for the development of China’s digital ocean, the sustainable development of China’s ocean industry.