Application of Web 3D GIS for Dam-break Flood Medeling

Cao Zhenyu State Key Laboratory of Information Engineering in

Surveying and Remote Sensing Wuhan University

Wuhan, China scgisczy@163.com

Zhu Juna,Gan Rongchengb a Faculty of Geosciences and Environmental

Engineering Southwest Jiaotong University

vgezj@163.com b Geomatics Center of Sichuan Province

Chengdu, China ganrc2007@163.com

Abstract—This paper introduces the application of web 3D GIS (Geographic information system) for dam-break flood modeling, including dynamic 3D visualization and spatio-temporal analysis. Distributed virtual scene modeling method was firstly introduced. Then the system framework was proposed. Meantime some key technologies including the collaborative workflow, mobile agent computing service and simulation analysis of dam-break flood modeling were also discussed in detail. Finally, authors present a 3D web-based GIS application for simulation analysis of dam-break flood modeling with a case study on Xiaojiaqiao Barrier Lake in Anxian county, Sichuan province, China. Experimental results prove that the scheme addressed in the paper is effective and feasible.

Keywords- web-based GIS; 3D Visualization; dam-break flood routing; simulation analysis; collaborative workflow

I. INTRODUCTION Over the past few decades, large-scale natural and

human-induced disasters have brought significant attention to the importance of disaster response [1]. Examples of such extreme events are earthquakes, hurricanes and flood attacks, which cause extensive human casualties, economic loss, and infrastructure destruction. On May 12, 2008, a magnitude 8.0 earthquake struck China. The earthquake caused 33 barrier lakes, injured over 1000 reservoirs. Engendered by unstable geological conditions, barrier lakes often have no controlled spillway, so it frequently fails catastrophically and leads to downstream flooding, causing high casualties [2]. It was reported that half of landslide dams fail within 10 days based on 63 cases from the literature, and a dam burst often brings significant flooding casualties to the downstream residents [3]. It is always a great challenge for hazard mitigation because proper actions should be performed within a limited time [4, 5].

Web-based 3D GIS is being increasingly used as a very effective tool in disaster management over the last years [6]. Web-based GIS is a centrally managed and distributed computing architecture [7]. Distributed computing is a generic term that includes other terms like Internet, Intranet, the web, network centric and more. It can help us integrate distributed data resources, geographically separated users and heterogamous model software and improve work efficiency. 3D GIS pays attention to offer more intuitive and perceptive expressing methods and means [8]. The multidimensional and dynamic analysis methods have

become the fundamental approaches for exploring spatial problems from all dimensions. The utility of 3D GIS enables the abstract and complicated phenomena to be more actual and intuitive [9]. Web GIS with 3D visualization capability plays a vital role for detailed flood analysis and emergency response. It provides accurate and timely information to the disaster managers and relevant organizations during emergency period for efficient management of disasters.

This paper will focus on how to develop a web-based 3D GIS system, which supports visualization and analysis of dam-break flood routing. The remainder of this paper is organized as follows. Virtual scene modeling method was firstly addressed in section 2. In section 3, system framework of web-based 3D GIS service system was designed. Some key technologies including collaborative workflow control, mobile agent computing service and simulation analysis of dam-break flood routing, were also discussed. Our case study area was introduced and a prototype system was developed in section 4. Rudimentary experimentations results were discussed in section 5. Finally, conclusions were addressed in the final section.

II. VIRTUAL SCENE MODELING Our system pays attention to the multidimensional and

dynamic modeling methods, which can increase understanding of dam-break flood routing issues in distributed virtual shared environment. It aims to reach three main goals: 1) multi-scale, virtual terrain scene to meet different users’ requirements; 2) dynamic 3D representation for dam-break flood routing; 3) dynamic interactive with virtual scene and implementing spatial analysis such as querying spatial position and overlaying thematic maps.

A. Virtual Terrain Scene Modeling Virtual terrain landscape plays an important role in the

virtual geographic environment, which includes the digital elevation model (DEM), 2D imagery data such aerial photography or topographic maps, 2D planning data such as cadastre data or street networks. The virtual environment can serve as interactive, intuitive visualization tools for exploring, analyzing, synthesizing, and simulating multi dimension geo-data and complex geo-phenomena. Figure 1 shows the modeling flowchart of virtual scene.

Figure 1. Modeling flowchart of virtual scene

Moreover, the interactive visualization of the three-dimensional models is of great importance for an in-depth analysis of the data set. From a technical point of view, our system should implement real-time rendering and dynamic scene interaction, which allow users to interactively operate, explore and analyze scene models.

The strategy to simplify complexity scene in a view-dependent fashion is suited well for the real-time rendering of large scenes. View-dependent rendering provides the ability to discard invisible objects in the scene and change level of detail in real-time. The unnecessary calculation resource expenses can be reduced to improve the rendering efficiency [10]. This paper used a view dependent simplification method to improve the rendering efficiency of the scene. The continuous level of detail technique(C-LOD) algorithm was adopted to achieve high frame rates by generating an approximate view-dependent triangulation of large terrain scene. Meanwhile image-based rendering techniques such as the imposter billboard were used to speed up the visualization of 3D object models.

B. 3D visualization of Dam-break Flood Routing Flood can be classified as dynamic and fuzzy boundary

volume objects, which are irregular in shape as opposed to regular objects that can be presented by points, lines, areas as well as small 3D elements. They are quite different from other kinds of information, for their unique properties of multidimensional structural and dynamic change. Dynamic modeling methods based on the particle system can be used to simulate dam-break flood routing, which can be classified as dynamic and fuzzy boundary volume objects. After preprocessing flood routing data, they will be seen as a collection of many minute particles that together represent flood objects. Each particle has many attributes including position, shape, size, color, speed, direction and lifetime. Over a period of time, particles are generated into a system, move and change within the system, and die.

In order to accurately display in virtual scene, it is very important to implement spatial coordinate transformation. Dam-break flood computing model commonly uses the WGS84 projected coordinate system(X, Y, Z). Computing results should be firstly converted to WGS84 geographic coordinate system (Geographic latitude, longitude and elevation). Then we can convert them into the OpenGL world coordinate system (x, y, and z). Meantime, imaged-

based rendering techniques, such as impostors and billboard can be used to improve rendering efficiency, which replaces a complex object by an image that is projected on a transparent quadrilateral. The particle system method is used to represent close scene models, and imposter technique is used to represent distance scene models. Different LOD nodes can switch over automatically by means of one projection error of the screen pixel.

III. SYSTEM FRAMEWORK AND ITS KEY TECHNOLOGIES

A. Design of the System Framework The server-client framework of this system is shown in

Figure 2. It includes server and client. The server layer is the core of our system, and ensures that the whole system is running correctly. On the server side, a series of management toolkits and protocol criterions are defined for sharing, integration and interoperation of all resources and data. It is comprised of four kinds of functional components: data analysis, multimedia support, mobile computing and scene management. They are responsible for data storing, preprocessing, transmitting, re-sampling, re-projection, format transformation, image pyramiding and compression, and geo-spatial data delivery. Moreover, online map service system and mobile agent computing host system are integrated to support distributed data resources sharing service.

On the client side, it provides the interface between the service system and users, which considers the user’s knowledge base, technical capability, role playing, and ages. Its main task is to design useful, transparent, reliable, scalable, and secure applications for users. Meanwhile, local data management and GIS functions are also offered to support different application processing and analysis. By means of these basic services, many applications including virtual scene, cooperative work and simulation analysis are developed for dealing with risk assessment and impact analysis of dam-break in the barrier lake.

Data analysis Scene managementMultimedia support Mobile computing

Service interfaceData management Workflow management

Re-sampling

Re-projection

Index management

Users management Image compression Image pyramiding

Format transformation Data delivery

Server

Online map service

MA Host system

Database

Data management

GIS functions

User interface

Simulation analysis

Virtual scene

Cooperative work

Local database MA Host system

communication

data sharing

Network

Data management

GIS functions

User interface

Simulation analysis

Virtual scene

Cooperative work

Local database MA Host system

Client Client

Figure 2. The server-client framework

B. Collaborative Workflow Control In order to effectively support geographically separated

users implementing collaborative simulation analysis, we designed a collaborative workflow shown as figure 3. We used a workflow process to define different simulation analysis tasks, in which each process node can represent an assessment task. Each work process or state can be seen simultaneously for all participants. Meantime, we also offered video, audio, text chat and other means to support discussion. The whole collaborative workflow includes six important phases.

Applying for chair, and sending collaborative establishment request.

Online members respond, and Work Group was finally formed.

Chair consults with all members for selecting collaborative tasks.

Flood influence analysis in different application conditions

Full break 1/2 break 1/3 break 1/5 break 1/10 break

Allocating collaborative subtasks with corresponding principal.

Applying for collaborative parameters task, obtaining related information.

Checking and adjusting task implementation states.

After finishing all subtasks, risk assessment reports is automatically

Creating Group

Selecting Task

Allocating Task

Finding Information

Finishing Task

Checking Task

risk population dam height dam volume material composition risk cities risk public utility

Figure 3. Control mechanism of collaborative workflow

Firstly, it is creating collaborative workgroup. Every online member can apply for chair role. He can send collaborative establishment request. Other online members may decide if he joins the workgroup. Secondly, after workgroup is finally formed, chair consults with all members for the customization of the risk assessment task. Thirdly, cooperative members may select subtasks to work on according to their own data and professional knowledge. Fourthly, after the completion of the task allocation stage, the sub-task set permissions will be opened, and different cooperative members may obtain and upload information. Fifthly, when we submit the subtask parameter, the parameter information can be checked by the person in charge of this subtask, until the final confirmation. Finally, when all subtasks are completed, the system automatically will generate a risk assessment report of dam-break in Barrier Lake.

C. Mobile Agent Computing Service In order to improve the efficiency of geospatial data

cooperation under distributed, heterogeneous and dynamic environments, a mobile agent computing service was integrated into our system. It is composed of mobile agent, agent host (host’s system), and cooperative agent [11].The agent host is a basic mobile agent service environment existing on each node in the distributed system. It is compatible with the distributed system and does not influence functions of original system. A mobile agent is created and migrates among corresponding host systems to

implementing tasks. Because the agent host and corresponding resources are placed in the same node, the mobile agent can directly access resources in the local system, which effectively avoids the data transmission among the network. Compared to traditional distributed computing systems, the mobile agent based distributed computing model has clear advantages. It can reduce the network loading, enhance communication efficiency, and adapt dynamically to the changing network environment.

D. Simulation Analysis of Dam-break Flood Routing Simulation analysis flowchart of dam-break flood

routing is shown in Figure 4. Three key technologies are involved in implementing the numerical modeling of dam-break flood routing, which include the measurement of dam parameters, simplified calculation model of maximum flux on dam site, computational method for flood routing. Meantime, three factors including the risk population, the risk cities, important or the public utility are considered to assess the consequence influence of the dam-break in the barrier lake. The above factors can be used to assessment of dam-break loss severity.

By means of GIS spatial analysis, dam parameters information of Barrier Lake can be easily extracted from such as remotely sensed data and DEM data. The extracted information of the barrier lake contains the valley width of the dam site (B), the water depth of the dam site (H0), and the volume of the dammed lake (W).

Dam material composition Dam volume Dam height

Risk population Risk cities Public utility

Dam-break loss severity evaluation

Simulation analysis of Dam-break flood routing

Upstream and downstream effects analysis

Measurement of dam parameters Maximum flux on dam site

Numerical model calculation of flood routing

Figure 4. Simulation analysis flowchart of dam-break flood routing

Risk assessment of the dam break in Barrier Lake often considers the most severe condition, namely is the full break of the barrier lake. So the breach size (bm) adopts value equal to B. For the downstream water is shallow before the collapse, Saint-Venant equations are adopted to calculate the instantaneous peak flow at the breach (

mQ ). The calculation of discharge hydrograph is very

complex, and it can be simplified to four parabolas. The empting time of the dammed lake (T) can be calculated formula (2). And the discharge hydrograph can be obtained from H0,W,T, and the half experience data.

32

0827mQ B gH=

(2)

(1)

m

WT KQ

=

The finite-difference solution of Saint-Venant equations are adopted in this study to perform downstream overland flow routing with inputs from dam break flow calculation. The governing equations are depth-averaged continuity and dynamic wave equations.

IV. CASE STUDY Our case study area is Xiaojiaqiao Barrier Lake located

in the Chaping River upstream in Anxian County, Sichuan Province, China. Anxian County is located in the southwest of Mianyang City, Sichuan Province, China, between 31°20′N-31°50′N and 104°00′E-104°45′E, with an area of 1404km2. Xiaojiaqiao dammed lake was located in the Chaping River upstream in Anxian County. Its catchments’ area was about 154.81km2. The water storage of Xiaojiaqiao dammed lake approaches 20 million m3. It was the second largest dammed lake formed in 5.12 Earthquake. For the dam body is constitute of detritus, it was a seriously dangerous dammed lake. The UAV images are collected at May 19, 2008(0.2 m resolution). The IKONOS image is collected at Dec 26, 2008(1m resolution). In this paper, a terrain map covering the study area at a scale of 1: 10 000 was collected from the Bureau of Surveying and Mapping of Sichuan Province. The population, resident region, roads and facilities data are provided by Anxian water authority for inundation analysis.

Figure 5. A snapshot of the client side of the prototype system

To reduce costs and save time, we used open source software and commodity graphics hardware. osgEarth based on OpenSceneGraph (OSG) can generate paged 3D terrain models from digital elevation data and imagery. It can make it easy to help us rapidly model distributed virtual terrain scene. Using Microsoft Visual Studio 2008 .NET, osgEarth and OpenSceneGraph, a prototype system was developed to construct a prototype system. Figure 5 shows the user interface of the prototype system. It can offer several collaborative methods including virtual geographic scene, video, audio, text, mobile computing and so on. Some online data such as Google Earth and Google Map also were integrated to support fundamental geographic information service. Moreover, some basic spatial analysis and dam-

break flood routing visual analysis were provided to support risk assessment of dam-break in Barrier Lake.

In our prototype system, a data registry interface is offered to users provide their resources. Figure 6 is the interface of user data registry service, which can registry data name and type, data path, and attribute description. For all user nodes in the our system, the cooperative agent can specify the resource agent to set up the user management list. This includes user name, password, user IP, and resource attribute. According to different data resource information characteristics, such as the content, quality, terms, position and other characteristics of the geographical space data, corresponding resource identification is set up to describe data and information resources. All data distributed in different user nodes can be registered into a shared resource. All the state information about these resources will be stored in as service data. By means of the above method, we can integrate all data in our system environment and form a virtual uniform database.

Figure 6. The interface of Data information registry service

V. RESULTS AND DISCUSSION Based on the implementing mechanism of collaborative

workflow, distributed multi-users can log in to implement the risk assessment and impact analysis of dam-break in Barrier Lake. Cooperative members may select subtasks to work on according to their own data and professional knowledge. For a specified task (e.g., measurement of the dam volume in barrier lake), one user can set up parameters, choose random polygon, and submit request service. After a series of mobile computing operations, the result is conveyed to the user. The whole procedure is transparent to users. By means of these means, users can obtain dam parameters information of Barrier Lake. For different condition of dam-break, then workgroup can implement simulation analysis of flood routing.

Figure 7. 3D visualization of dam-break flood routing

Figure 7 is a snapshot of 3D visualization of dam-break flood routing on virtual terrain scene. The experimental results have been measured on a HP Compaq 6910P Notebook PC equipped with a 1.8 Ghz Intel core2 processor, 2048 MB of memory and a ATI Mobility Radeon TM X2300 graphics accelerator with 128 MB of graphic memory. The rendering speed ratio is about 50 frames per second. It can support the real-time walkthrough for large scale terrain and the real-time simulation for flood routing in desktop computers.

Figure 8. Flood routing simulation results（5.31 full break）

Figure 8 show the simulation of full failure for Xiaojiaqiao Barrier Lake in May 31, 2008. In the full failure situation, the dam-break flood reaches XiaoBa town at 33 minutes, while reaches SangZao town at 59 minutes. When dam-break flood stabilizes it is 2 hours and 46 minutes. With GIS-assisted inundation potentials analysis, the simulation results can offer us a lot of risk information. For example, the amount of risk population is 13221, and the risk cities include GuanXin village, ZhongXin village, YunFeng village, XiangXi village, ShangQing village, XiaoBa town, SanZao town. These impact analysis results can effectively support the risk assessment of the dam-break in Xiaojiaoqiao Barrier Lake. The evaluation result shows that the level of the Dam-break loss is very severity.

VI. CONCLUSIONS In the GIS field, geo-collaboration can be defined as a

group of people working together either in the same or in different geographical locations or times, to accomplish geo-tasks and solve geo-problems. The study of geo-collaboration involves diverse aspects ranging from participants and organizations to mediated tools, geo-problem contexts and supportive environments [9]. So it is very important to build a web-based 3D GIS service system for intuitive and efficient interactive visualization, which allows distributed users to explore complicated spatial information and conduct collaborative work.

In this paper, we mainly focus on the construction of web–based 3D GIS (Geographic information system) system, which supports visualization and analysis of dam-break flood routing. Distributed virtual scene modeling method was firstly introduced. Then the system framework was proposed, and some key technologies were also

discussed in detail. Finally, we implemented a 3D web-based GIS application for simulation analysis of dam-break flood routing with a case study on Xiaojiaqiao Barrier Lake in Anxian county, Sichuan province, China. The scheme addressed in the paper can efficiently offer intuitive and accurate information to support decision-making in practice applications for the risk assessment of dam-break in Barrier Lake.

ACKNOWLEDGMENT This research is partially supported by the National

Natural Science Foundation Project No. 41001252, Basic Research Program of China, 973 Program No. 2012CB719901.

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