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Transactions in GIS. 2022;26:1299–1317. wileyonlinelibrary.com/journal/tgis

1299

DOI: 10.1111/tgis.12904

RESEARCH ARTICLE

Design and development of a web- based

interactive twin platform for watershed

management

Yinguo Qiu

| Hongtao Duan

| Hui Xie

| Xiaokang Ding

Yaqin Jiao

1,2

Key Laboratory of Watershed Geographic

Sciences, Nanjing Institute of Geography

and Limnology, Chinese Academy of

Sciences, Nanjing, China

School of Marine Technology and

Geomatics, Jiangsu Ocean University,

Lianyungang, China

Correspondence

Hongtao Duan, Key Laboratory of

Watershed Geographic Sciences, Nanjing

Institute of Geography and Limnology,

Chinese Academy of Sciences, Nanjing

210008, China.

Email: htduan@niglas.ac.cn

Funding information

National Natural Science Foundation of

China, Grant/Award Number: 42101433;

Natural Science Foundation of Jiangsu

Province, Grant/Award Number:

BK20201100; Major Science and

Technology Program for Water Pollution

Control and Treatment, Grant/Award

Number: 2017ZX07603001

Abstract

There are limitations to traditional digital watershed plat-

forms in terms of realistic visualization of geospatial elements

and powerful decision support for watershed management.

To advance the status quo, a web- based digital twin is de-

signed and implemented in this article that can: (1) realize

virtual simulation of geographic elements on the browser

side; (2) present the current situation and excessive informa-

tion of the watershed water environment; and (3) provide

decision support for integrated watershed management.

Multiple 3D modeling methods are adopted cooperatively

for total- factor virtual simulation of geographic elements,

and a browser- side data loading scheme is designed for dy-

namic loading and cull rendering of 3D models. Additionally,

schemes for spatiotemporal modeling of multisource data,

analysis of multitype data, and scientific computation of

mathematical models are proposed to support precise wa-

tershed management, making the platform practical. The

implemented digital twin is applied in the Chaohu Lake

Watershed, demonstrating that it can realize both stunning

visual effects and practical decision- support functions.

1 | INTRODUCTION

With the continuous growth in population and the rapid development of industry and agriculture, rivers are experi-

encing various degrees of pollution. As a result, the safety of drinking water and social and economic development

are facing serious problems (Abdallah & Rosenberg, 2019; Qiu, Xie, Sun, & Duan, 2019; Wang et al., 2015). The

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prevention and control of river water pollution has drawn the attention of all levels of government, and it has become

a consensus that the fine management of watersheds is the only route toward eliminating river water pollution (Duan

et al., 2020; Villa et al., 2020). To achieve this goal, fast acquisition, intelligent analysis, and vivid visualization of mul-

tisource and heterogeneous watershed data have aroused significant interest from academic researchers at home

and abroad (Chen et al., 2020; Do, Lo, Chiueh, & Thi, 2012; Karamouz, Nokhandan, Kerachian, & Maksimovic, 2009;

Tuna, Arkoc, & Gulez, 2013; Wang et al., 2018; Zeinalzadeh & Rezaei, 2017; Zhang et al., 2019). With this back-

ground, digital watersheds have been considered a powerful means for watershed planning and management. Use of

digital watersheds can collect and manage all kinds of watershed information, combining synthetically several mod-

ern technologies: GIS, virtual reality, high- performance computing, etc. (Dong, Ma, & Yang, 2011; Eidson et al., 2010;

Shi, Chen, Li, & Wang, 2020; Yan, Jiang, Xie, Wang, & Li, 2019; Yuan, Xia, Yuan, Chen, & Zhang, 2012).

The connotation of a digital watershed includes three aspects in essence: (1) efficient acquisition and man-

agement of massive spatiotemporal data; (2) establishment of a virtual geographic environment (VGE); and (3)

precise and intelligent management of watersheds. For the first aspect, the key task is to construct an integrated

watershed environmental monitoring network and design a spatiotemporal data model. For the second aspect, the

main task is to realize a multidimensional and multiscale representation of fundamental geographic information

and multiple monitoring data. For the third aspect, it is necessary to gain answers to four important questions:

whether there exists an abnormality of the watershed water environment, which water quality index should be

reduced in order to achieve a particular standard of water quality, where (which river segment) the specific water

quality index should be reduced, and how much the specific index should be reduced in the target river segment

(these are called the “3W1H” questions for short).

Considerable attention has been paid to the construction of monitoring networks of watershed water envi-

ronments (Karamouz et al., 2009; Menon, Divya, & Ramesh, 2012; Varekar, Karmakar, Jha, & Ghosh, 2015; Verma

& Chaudhary, 2012; Zennaro et al., 2009) and the 3D visualization of multisource watershed data (Bhimani &

Spolentini, 2017; Harman, Brown, & Johnson, 2017; Harman, Brown, Johnson, Rinderle- Ma, & Kannengiesser, 2015;

Japs, Kaiser, & Kharatyan, 2020; Schito, Jullier, & Raunal, 2019). To efficiently describe the spatial distribution

characteristics of the watershed water environment, more attention has been paid to the design and optimization

schemes of monitoring networks, determining both the numbers and locations of monitoring sites on the basis

of basic characteristics of watersheds (Karamouz et al., 2009) (e.g., point and diffuse pollution sources; Varekar

et al., 2015). After the acquisition of water environmental data, it is also important to transfer the monitored data

efficiently and reliably, and several monitoring systems (Menon et al., 2012; Verma & Chaudhary, 2012; Zennaro

et al., 2009) have been implemented based on wireless sensor networks to realize continuous and remote data

monitoring based on wireless communication protocols.

Past research has indicated that people can obtain more knowledge from 3D simulation scenes than tradi-

tional 2D scenes (Bhimani & Spolentini, 2017; Harman et al., 2015, 2017; Japs et al., 2020; Schito et al., 2019). For

example, if one is in a virtual simulation scene, the impact of climate change can be understood more intuitively

than from reading newspapers or watching TV programs. Consequently, the construction of 3D virtual simulation

watershed scenes has received considerable attention from relevant scholars, in order to express answers to the

“3W1H” questions by way of human– computer interaction (HCI). Early studies on 3D visualization methods for

watershed data focused mainly on the 3D representation of watershed terrain, exploiting digital elevation models

(DEMs) and high- resolution remote sensing images (Jenson & Domingue, 1988; Mark, 1983; Zhu, Zhao, Zhong,

& Sui, 2004). The main advantage of this category of methods of 3D model generation is that the 3D terrain of

a large area can be represented rapidly. The data volume of the generated 3D models, however, is usually large,

putting considerable pressure on data visualization. To improve the rendering efficiency of terrain data without

affecting the visual effect, a tile pyramid of terrain and remote sensing images is usually built by organizing terrain

and remote sensing images in layers and blocks, adopting the level- of- detail (LOD) method; terrain blocks can

then be scheduled and rendered (Losasso & Hoppe, 2004; Zhu, Gong, Du, & Zhang, 2005). Limited by scale, it is

difficult for 3D terrain models generated based on DEMs to express the local detailed features of watersheds. To

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enhance the visual and interactive quality of digital watershed platforms, the technology to establish a watershed

VGE has drawn broad attention (Carmona & Froehlich, 2011; Chen, Lu, & Wu, 2004; Cheng, Li, Nie, & Li, 2018;

Fan & Li, 2017; Gao, Yuan, & Gan, 2018; Huang, Mao, Xu, Li, & Lei, 2006; Lin, Chen, & Lü, 2012; Lin et al., 2013;

Lü, Chen, Yuan, & Zhou, 2017; Piccand, Noumeir, & Paquette, 2008; Xie et al., 2011; Zhu, 2014). Fine models

of some elements (e.g., buildings and water conservancy facilities) were commonly overlaid on the basis of a

3D terrain model (Chen et al., 2004; Huang et al., 2006), so that the detailed features of watersheds can be ex-

pressed. To balance the fidelity and loading speed of 3D models, multiple models with different details are usually

built for each entity and invoked on demand (Carmona & Froehlich, 2011; Piccand et al., 2008; Xie et al., 2011).

Another common solution is to classify spatial entities according to importance; the greater the importance of

an entity, the higher the accuracy of its corresponding 3D models (Cheng et al., 2018; Zhu, 2014). The methods

mentioned by Piccand et al. (2008), Carmona and Froehlich (2011), Xie et al. (2011), Zhu (2014), and Cheng

et al. (2018) can obtain good effects of 3D visualization under limited hardware conditions, but the modeling

process, especially the procedure of precision model construction, is mainly completed manually and commonly

inefficiently. Furthermore, traditional 3D visualization schemes focus mainly on elements of shape rules and are

not suitable for modeling most categories of watershed factors (e.g., vegetation, farmlands, and rivers). In the

past decade, along with the rapid development of laser radar and unmanned aerial vehicle (UAV) technologies,

watershed VGEs can be established rapidly (Fan & Li, 2017; Gao et al., 2018). However, the core purpose of

these methods is to realize total- factor virtual visualization of watersheds, while spatial analysis and incremental

updating of multisource elements in VGEs have been ignored. To address this deficiency, several algorithms for

geo- simulation, geo- analysis, and geo- collaboration have been proposed and applied in a VGE (Lin et al., 2012,

2013; Lü et al., 2017), and remarkable progress has been made.

Actually, the biggest difference between digital watershed platforms and other kinds of digital platforms is

that the aforementioned “3W1H” questions are important for digital watershed platforms to improve their prac-

ticability. Accordingly, mathematical models for answering “3W1H” questions are essential for VGEs of water-

sheds. In the existing research, nevertheless, this kind of mathematical model is rarely integrated because of the

difficulty of fusing various types of models and multidimensional data (e.g., models for calculating the pollutant

capacity of lake areas and allowable fluxes of rivers, 2D results of model calculations, and 3D scenes, etc.). As a

result, the practicability of digital watershed platforms is still less than ideal.

In summary, the main emphases of traditional research in terms of digital watersheds focus mainly on the

monitoring of water environmental data and the visualization of watershed elements in specific hardware and

software environments, while little work has been done on the topic of refined watershed management in VGEs.

In response, an interactive twin watershed platform is designed and implemented in this article. This platform

can realize vivid visualization and intelligence analysis of multisource watershed data, as well as precise decision-

making for intelligent management of watersheds. The novelty of this platform can be summarized as follows.

1. Vivid simulation and 3D spatial analysis of geographic factors are realized without special requirements of

hardware and software environment. To realize fluent running of the platform, 3D models of watershed

elements are organized and stored based on a quad- tree structure, and methods of cull rendering (CR),

LOD, and web- based asynchronous loading are applied in a coordinated fashion during the process of

dynamic rendering of virtual geographic scenes.

2. Abnormal information on water environment can be automatically diagnosed. Spatiotemporal data models and

intelligent analysis algorithms are constructed for the multisource monitoring data of watershed water environ-

mental data (WWED), and both the current situation and excess information on the watershed water environ-

ment can be sensed rapidly. Accordingly, the first question in “3W1H” (i.e., whether there exists an abnormality

of the watershed water environment) can be answered.

3. Precision watershed management is realized in a real- scene 3D environment. A set of mathematical models are

developed and coupled for the precise reduction of water pollution mandated by water quality targets. As a

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