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Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications

June 2020
Earth-Science Reviews 207(3):103223

DOI:10.1016/j.earscirev.2020.103223

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CC BY-NC-ND 4.0

Authors:

Min Chen

Nanjing Normal University

Alexey Voinov

University of Twente

Daniel P Ames

Brigham Young University

Show all 15 authorsHide

Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field.

Conceptual framework for open web-distributed integrated geographic modelling and simulation.

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User cases of open web-distributed integrated geographic modelling and simulation at different levels.

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Key points of the integrated modelling environment.

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An example of a conceptual model.

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An example of a logical modelling process.

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Contents lists available at ScienceDirect

Earth-Science Reviews

journal homepage: www.elsevier.com/locate/earscirev

Position paper: Open web-distributed integrated geographic modelling and

simulation to enable broader participation and applications

Min Chen

a,k,l

, Alexey Voinov

b,m

, Daniel P. Ames

, Albert J. Kettner

, Jonathan L. Goodall

Anthony J. Jakeman

, Michael C. Barton

g,n

, Quillon Harpham

, Susan M. Cuddy

, Cecelia DeLuca

Songshan Yue

a,k,l

, Jin Wang

a,k,l

, Fengyuan Zhang

a,k,l

, Yongning Wen

a,k,l

, Guonian Lü

a,k,l,⁎

Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China

Center on Persuasive Systems for Wise Adaptive Living (PERSWADE), Faculty of Engineering and IT, University of Sydney, Sydney, Australia

Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT, USA

Community Surface Dynamics Modelling System (CSDMS), Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder, CO, USA

Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA

Institute for Water Futures, Fenner School of Environment and Society, Australian National University, Canberra, Australia

School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

HR Wallingford, Oxfordshire, UK

CSIRO Land and Water, Canberra, Australia

NOAA Earth System Research Laboratory, Boulder, USA

State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, China

Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China

Faculty of Engineering Technology, University of Twente, Netherlands

Center for Social Dynamics and Complexity, Arizona State University, Tempe, AZ, USA

ARTICLE INFO

Keywords:

Open

Web-distributed

Integrated geographic modelling

Geographic simulation

Geographic research

ABSTRACT:

Integrated geographic modelling and simulation is a computational means to improve understanding of the

environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible

to conduct open, extensible integrated geographic modelling across a network in which resources can be ac-

cessed and integrated, and further distributed geographic simulations can be performed. This open web-dis-

tributed modelling and simulation approach is likely to enhance the use of existing resources and can attract

diverse participants. With this approach, participants from diﬀerent physical locations or domains of expertise

can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated

modelling and simulation systems, highlighting the associated development challenges when moving to an open

web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design

perspective, with potential use cases provided. The four components of this conceptual framework - a set of

standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed

simulation environment - are also discussed in detail with the goal of advancing this emerging ﬁeld.

1. Introduction

The geographic environment is the surface on which human socie-

ties exist and thrive (Churchill and Friedrich, 1968;Matthews and

Herbert, 2008). It is a comprehensive system consisting of natural, so-

cial, cultural, and economic factors and their interactions (Lin et al.,

2013a). Geographic modelling and simulation have been extensively

used to better understand the geographic environment and improve

decision making (Demeritt and Wainwright, 2005).

The objectives of geographic modelling are generally to analyze and

better understand the evolving processes and interactions among the

factors that constitute the geographic environment, and to build in-

terpretable models that serve decision-makers (Wei and Chen, 2005;Xu

and Chen, 2017). In short, geographic modelling is a representation of

geographic entities, events, interactions and their logical consequences

(Smyth, 1998). Following the sequence of “representation-simulation-

prediction”, geographic simulation can be regarded as an application

step of geographic modelling (Batty, 2011), and it can be conducted to

https://doi.org/10.1016/j.earscirev.2020.103223

Received 28 June 2019; Received in revised form 7 May 2020; Accepted 13 May 2020

⁎

Corresponding author at: Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China.

E-mail address: gnlu@njnu.edu.cn (G. Lü).

Earth-Science Reviews 207 (2020) 103223

Available online 06 June 2020

(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

reﬂect and predict speciﬁc geographic patterns and processes (Lin and

Chen, 2015;Zhang et al., 2016;Goodchild, 2018;Rossi et al., 2019).

Geographic modelling and simulation can contribute to geographic

research and decision making; for instance, numerical geographic ex-

periments can be conducted instead of real-world geographic experi-

ments which can be costly, time consuming, or practically infeasible

(Lin et al., 2013b;Lin et al., 2015;Chen and Lin, 2018;Chen et al.,

2018b). Doing so, the inﬂuences of changes to geosystem factors can be

assessed (e.g., Benenson and Torrens, 2004).

To date, researchers worldwide have developed numerous geo-

graphic simulation models for diﬀerent applicable areas, at diﬀerent

spatial and temporal scales, and for diﬀerent processes (e.g., hydro-

logical [e.g., Liu et al., 2014, 2016;Lai et al., 2016, 2018;Zhu et al.,

2019;Salas et al., 2020], atmospheric [e.g., Zhang et al., 2014;Yan

et al., 2016;Ning et al., 2019], geomorphological [e.g., Shobe et al.,

2017;Barnhart et al., 2018;Reichenbach et al., 2018;Batista et al.,

2019;Rossi et al., 2019;Broeckx et al., 2020]). However, these are

typically single-domain and single-scale models, and as such, they have

limited capacity for simulating comprehensive geographic phenomena

(Lu, 2011;Harpham et al., 2014;Gianni et al., 2018). For example,

when studying the socioenvironmental impacts of intense precipitation

in a watershed on areas located downstream, several dynamic processes

are involved. These include precipitation, inﬁltration, soil saturation,

surface and subsurface runoﬀ, streamﬂow, and ﬂow-routing, over the

background of a static physical environment (slope, elevation, river

network, landcover, etc.) and human structures. Furthermore, in com-

prehensive decision-making, social settings should also be considered,

which might include, for example, the distribution of endangered

groups and individuals and potential evacuation strategies. Thus, it is

diﬃcult to incorporate all the relevant physical and socioenvironmental

process dynamics comprehensively in a single model. Such a model

would require a wide variety of disciplines and could quickly fall be-

hind the latest developments in each discipline; indeed, such a model

would likely be too cumbersome to maintain. From this perspective,

integrated modelling provides a potentially useful reference to enable

comprehensive simulations (e.g., Oxley et al., 2004;Peckham et al.,

2013;Peckham, 2014). As a type of integrated modelling (EPA, 2007,

2008a, 2008b), integrated geographic modelling can be deﬁned as

employing a set of interdependent resources (e.g., geographic simula-

tion models, geographic data) that together form an appropriate geo-

graphic modelling system.

Focusing on this research topic, and bearing in mind the trend to-

wards open science (e.g., Woelﬂe et al., 2011;Nosek et al., 2015), this

article lays out a vision for an open web-distributed integrated geo-

graphic modelling and simulation approach that encourages wide par-

ticipation and combines diﬀerent disciplines in one framework. Here,

the term “open” implies that (1) modelling and simulation resources

(models, data, and even computational resources) can be openly

shared, discovered, and accessed among communities; (2) integrated

modelling and simulation tasks can be openly performed using these

open resources; and (3) the open community can grow and expand

organically through a well-deﬁned extensibility paradigm. Moreover,

the term “web-distributed” reﬂects a technical feature associated with

achieving the target of openness in an internet-based environment.

The motivation and content of this paper draws from an early design

concept of Open Geographic Modelling and Simulation Systems

(OpenGMS), later modiﬁed and extended through a series of workshops

and conference sessions on open modelling (Table 1). These events

were organized to explore an international open science and commu-

nity around forming an ecosystem of reusable and interoperable models

for studying complex interactions between humans and the environ-

ment. This paper mainly focuses on supporting openness through the

accessibility and usability of geographic modelling and simulation re-

sources as web-distributed services, thereby introducing a roadmap for

implementation.

The remainder of this article is structured as follows. Section 2

summarizes several existing integrated modelling and simulation sys-

tems and their openness levels, along with a discussion of their corre-

sponding development challenges. A conceptual framework is proposed

in Section 3 from a system design perspective that includes four com-

ponents: (i) a set of standards, (ii) a resource sharing environment, (iii)

a collaborative integrated modelling environment, and (iv) a dis-

tributed simulation environment. Section 4 provides use cases based on

the combination of diﬀerent components. To move toward im-

plementation, each component and its development roadmaps are dis-

cussed in detail in Section 5. Finally, conclusions and suggestions for

further research are presented in Section 6.

2. Existing integrated modelling and simulation systems

2.1. Features of the existing integrated modelling and simulation systems

Beginning in the early 1990s, bolstered by continually improving

database management systems, model management strategies and

corresponding decision-support systems have undergone accelerated

development (e.g., Dolk, 1993;Dolk and Kottemann, 1993;Oxley et al.,

2004). Integrated modelling at this time was mainly at the operational

level, and models were integrated or linked through hard-coded ap-

proaches (Dolk and Kottemann, 1993). Later, more logical and se-

mantically clear chains were developed that enabled model assembly

and integration; thus, more component-based integrated modelling

approaches and corresponding modularized model solutions were in-

troduced (Argent, 2004;Argent et al., 2006). Table 2 lists some well-

known component-based systems/tools (Table 2). These component-

based systems are characterized by object-oriented design methods,

including the encapsulation of analytical codes and computational ap-

plication programming interfaces (APIs) to standardize interoperability

among model components. While these software systems have lowered

many barriers to model integration, it remains diﬃcult to integrate

models across diﬀerent hardware and software systems, computational

environments, and system architectures (Granell et al., 2013a), and

there are still barriers to model sharing among the existing “model

clusters” (Zhang et al., 2019).

Recently, the development of Service Oriented Architecture (SOA)

and cloud computing has promoted web-based (including service-based

and resource-based) model sharing technologies (e.g., Wen et al., 2006;

Feng et al., 2009;Fook et al., 2009;Castronova et al., 2013a;Granell

et al., 2013b;Wen et al., 2013;Wen et al., 2017), related web-based

simulation resource management systems (e.g., HydroShare,

[Horsburgh et al., 2016;Morsy et al., 2017]) and distributed model

integration strategies (e.g., Yue et al., 2016;Belete et al., 2017) have

emerged. Object Modelling Systems (OMS) upgraded its OMS3 release

to scale models by capitalizing on cloud infrastructure and SOAs and

launched its Cloud Services Innovation Platform (CSIP) (David et al.,

2013). Meanwhile, the Open Geospatial Consortium (OGC) adopted

OpenMI 2.0 as a standard to improve the sharing of models, and re-

searchers have extended this standard for the integration of models

using service-based modelling (e.g., Castronova et al., 2013b;Buahin

and Horsburgh, 2015;Harpham et al., 2019). The Community Surface

Dynamics Modelling System (CSDMS) developed pymt, an open source

python package that provides the tools needed to run and couple

models that expose the Basic Model Interface (BMI) (Hutton and Piper,

2020). Besides preforming simulation utilizing pymt on a HPC or

desktop, cloud based access to Jupyter Notebooks make it possible to

couple and run models in the pymt framework through the web.The

Open Geographic Modelling and Simulation System (OpenGMS) has

provided a platform where users can explore and share resources re-

lated to geographic modelling and simulation, thus forming an open

community where researchers can reuse resources for geographic ex-

ploration online (e.g., Wen et al., 2013;Zhang et al., 2019;Chen et al.,

2019;Wang et al., 2020). Clearly, model sharing and integration over

the web is a growing ﬁeld, particularly in environmental and

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

geographic modelling, allowing integrated modelling to be conducted

in unique and innovative ways, spanning the boundaries of software,

hardware, research domains, and even crossing sociopolitical bound-

aries (Granell et al., 2013a). Com Using the three criteria for “Open”

described above, i.e. (1) open resource sharing, (2) open integrated

modeling and simulation and (3) open community, Table 3 lists som

typical web-based systems/tools and shows the extent to which they

support openness.

Despite the many achievements of these systems, only a few of them

fully support openness in integrated geographic modeling and simula-

tion, a key ‘open’ criteria. This demonstrates and highlights the urgent

need to address this gap.

2.2. Challenges with open integrated geographic modelling and simulation

To move toward open integrated geographic modelling and simu-

lation, relevant challenges need to be carefully analyzed before de-

signing an appropriate architecture. Other studies have reviewed some

of the challenges related to integrated modelling. For example, Voinov

and Cerco (2010) discussed the heterogeneity of models and related

data transformation; Kelly et al. (2013) presented the challenges with

choosing model integration methods; Sutherland et al. (2014) analyzed

the challenges associated with universally applying integrated model-

ling technologies from a required systematic basis, and Elsawah et al.

(2020) highlighted the eight key challenges to overcome in socio-en-

vironmental systems modeling. This article focuses on the challenges

associated with the open web approach.

2.2.1. From a resource perspective

The fundamental challenge from a resource perspective is de-

termining how to properly describe a wider range of modelling and

simulation resources to bridge diﬀerent resource users and providers. If

providers can construct clear and concise descriptions of their re-

sources, then users can reuse these resources more eﬀectively and

correctly in a given network (Harpham and Danovaro, 2015). However,

openness will inevitably introduce an even wider array of variation, and

traditional standards cannot bridge all of the possible variations and

gaps. It is diﬃcult to design standards that can carefully balance ﬂex-

ibility with depth and breadth of detail. Standards that seek to cover

every eventuality will be too complicated to use; and standards that are

too speciﬁc will solve few integration problems.

2.2.2. From a resource provider perspective

Resource providers are responsible for providing geographic simu-

lation models, data and servers for online reuse and integration. Several

challenges exist for resource providers who wish to participate in open

modelling tasks. Here, we summarize these challenges based on the

processes that occur before, during and after sharing.

First, motivation is a determining factor that stimulates people to

act. Rewarding provider(s) is a key element in motivating people or

institutions to share resources. Therefore, designing a suitable business

model to provide the incentive for the implementation of a vision is a

challenge. An incentive should not be overly complex but should

provide encouragement and thus enhance the sustainability of the re-

source sharing and reuse communities.

A second challenge is determining how to make resource sharing as

convenient for resource providers as it is for users. From this perspec-

tive, the user experience is an important factor that aﬀects the inten-

tions of resource providers. Usable and user-friendly tools are still

needed to facilitate tasks such as model encapsulation, data prepara-

tion, and serve sharing in a standardized way.

Last, honoring ownership and copyright policies is another chal-

lenge. Although various types of licenses (e.g., permissive and copyleft)

have been designed for open source projects from a legal perspective,

more strategies are needed to protect providers’ intellectual property.

For example, while many open source software codes are provided

under well-established open source licenses such as MIT, BSD, GPL or

MPL, a lack of awareness (or disregard for license conditions) may still

result in infringements of intellectual property.

2.2.3. From a resource user perspective

Resource users are practitioners using modelling and simulation

resources in a web environment. There are two main categories of users

to consider: (i) experts, who are knowledgeable about certain aspects of

the topic, but not necessarily about all of the various processes and

scales, and (ii) general stakeholders, individuals and groups, who may

be impacted by the system considered, but might know less about it

from a scientiﬁc perspective, though they could have ample indigenous

and intuitive knowledge about the topic. Obviously, these two types of

users will possess diﬀerent sets of user requirements, the handling of

which may be a signiﬁcant challenge.

A second challenge is ﬁnding the most suitable resources among the

numerous resources available online. When simulation resources (in-

cluding models, data, and servers) are openly shared, it can be daunting

for users to ﬁnd resources easily and timely when the bulk of typical or

customized resources are widely available by diﬀerent resource provi-

ders.

The third challenge is properly using resources in the web en-

vironment to complete open integrated geographic modelling and si-

mulation tasks, compared to the usage of centralized systems. Several

points should be considered, including how to access and reuse re-

sources through the network, how to perform collaborative modelling

tasks following typical modelling processes, and how to manage in-

tegrated simulation processes when the resources are distributed on the

web. This includes, for example, data management and transfer through

the web and the real-time monitoring of online servers during model

execution.

3. A conceptual framework for open web-distributed integrated

geographic modelling and simulation

As previously mentioned, some typical characteristics distinguish

open web-distributed integrated geographic modelling and simulation

systems from other modelling systems. First, with open web-distributed

systems, resources can be shared and accessed through the web for wide

reuse. Second, entire geographic model integration and simulation

Table 1

Related international events.

Date Address Topic Form

2017/8/17-19 Nanjing, China International Workshop on Open Geographical Modelling and Simulation Workshop

2018/6/24-28 Fort Collins, USA The 9th International Congress on Environmental Modelling and Software (iEMSs 2018) – Open Socio-environmental

Modelling and Simulation

Session

2018/6/29-30 Colorado, USA Open Modelling Foundation: An international alliance for scientiﬁc computational modelling standards Workshop

2019/5/18-20 Nanjing, China The 1st Regional Conference on Environmental Modelling and Software (Asian Region) Conference

2019/12/2 Canberra, Australia The 23rd International Congress on Modelling and Simulation (MODSIM2019) – Cloud and web applications for

environmental data analysis and modelling

Session

2019/12/4 Canberra, Australia Workshop of Open Modelling Foundation: Standards for Model Documentation Workshop

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

processes can be implemented and adjusted along with distributed re-

sources through the web environment. Finally, users can join in geo-

graphic exploration and idea exchange more easily with lower thresh-

olds than they face with some centralized or closed systems.

To achieve these goals, a conceptual framework is proposed (Fig. 1).

The conceptual framework for open web-distributed integrated

geographic modelling and simulation consists of four main components:

(i) standards and speciﬁcations for resources, (ii) a resource sharing

environment, (iii) a collaborative integrated modelling environment,

and (iv) a distributed simulation environment. The main functions are

introduced sequentially in this section, and the detailed implementation

road maps are illustrated in Section 5.

Table 2

Some typical component-based systems (in no particular order).

Name Features References

The Community Surface Dynamics Modeling System

(CSDMS)

CSDMS is a diverse community of experts promoting the modelling of earth

surface processes by developing, supporting, and disseminating integrated

software modules that predict the movement of ﬂuids, and the ﬂux

(production, erosion, transport, and deposition) of sediment and solutes in

landscapes and their sedimentary basins.

Peckham et al. (2013)

Spatial Modelling Environment (SME) SME is an integrated environment for high performance spatial modelling

which transparently links icon-based modelling tools with advanced

computing resources to support dynamic spatial modelling of complex

systems

Maxwell and Costanza (1997a, 1997b)

Dynamic Information Architecture System (DIAS) DIAS is a ﬂexible, extensible, object-based framework for developing and

maintaining complex multidisciplinary simulations of a wide variety of

application contexts.

Simunich et al. (2002),Hummel and

Christiansen (2002)

Common Component Architecture (CCA) CCA supports parallel and distributed computing as well as local high-

performance connections between components in a language-independent

manner.

Kumfert et al. (2006),Bernholdt et al.

(2006)

Earth System Modelling Framework (ESMF) ESMF is based on the principle that complicated applications are broken into

smaller components with standard calling interfaces. A model component

that implements the ESMF standard interface can communicate with the

ESMF shell and inter-operate with other models.

Hill et al. (2004),Collins et al. (2005),

DeLuca et al. (2012)

Object Modelling System (OMS) OMS allows model construction and model application based on components.

OMS v3.+ is a highly interoperable and lightweight modelling framework for

component-based model and simulation development on multiple platforms.

Skrlisch et al. (2005),Ahuja et al.

(2005)

Open Modelling Interface (OpenMI) The OpenMI compliant components can run simultaneously and share

information at each timestep making model integration feasible at the

operational level.

Moore and Tindall (2005),Gregersen

et al. (2005, 2007),Harpham et al.

(2014)

FluidEarth The FluidEarth platform is based on the concept of writing a ‘wrapper’ for

software codes, and on providing a generic linking mechanism so that any

model can be linked to any other.

Harpham et al. (2014)

System for Environmental and Agricultural Modeling;

Linking European Science and Society

(SEAMLESS)

The SEAMLESS project developed science and a computerized framework for

integrated assessment of agricultural systems and the environment.

Janssen et al. (2011),Van Ittersum et al.

(2008)

FRAMES A feed forward modelling framework, employs the component-based

approach and incorporates data dictionaries for data exchange. Wrappers are

written for each component to read and write data to the dictionaries. The

framework then manages transfer of data between components during

runtime through an inter-component communication API.

Whelan et al. (2014)

Common Modelling Protocol (CMP) CMP deﬁnes a transport protocol and describes a message based mechanism

for packing and unpacking data, executable entry points, and a set of deﬁned

messages to transfer variables and events from one model and/or component

to others involved in a simulation.

Moore et al. (2007)

BioMA/APES The focus of BioMA is to run integrated modelling products against spatial

databases. It is a direct result from the previous component-based framework

called APES, which is aimed to estimate the biophysical behavior of

agricultural production systems in response to the interaction of weather, soil

and agro-technical management options.

Donatelli et al. (2010)

The Invisible Modelling Environment (TIME) TIME simpliﬁes the task by providing a high level, metadata driven

environment for automating common tasks, such as creating user interfaces

for models, or optimizing model parameters. This reduces the learning curve

for new developers while the use of commercial programming languages

gives advanced users unbridled ﬂexibility.

Stenson et al. (2011)

The Library of Hydro-Ecological Modules (LHEM) LHEM (http://giee.uvm.edu/LHEM) was designed to create ﬂexible

landscape model structures that can be easily modiﬁed and extended to suit

the requirements of a variety of goals and case studies.

Voinov et al. (2004)

JGrass-NewAge JGrass-NewAge is a system for hydrological forecasting and modelling of

water resources at the basin scale. It has been designed and implemented to

emphasize the comparison of modelling solutions and reproduce hydrological

modelling results in a straightforward manner.

Formetta et al. (2014)

Science and Policy Integration for Coastal System

Assessment (SPICOSA)

The multi-disciplinary project SPICOSA used a common, component-based

simulation framework for environmental modelling.

de Kok et al. (2015)

Tarsier The framework facilitates fast, powerful model development by providing a

system for implementing separate model elements as autonomous modules,

which may then be tightly and ﬂexibility integrated. It is object-oriented,

with integration of modules achieved through the sharing of common objects

(and was the precursor of TIME)

Watson and Rahman (2004)

Artiﬁcial Intelligence for Ecosystem Services (ARIES) A web application to assess ecosystem services and illuminate their values to

humans in order to make environmental decisions easier and more eﬀective

Villa et al. (2009),Bagstad et al. (2013)

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

First, the precepts of open web-distributed integrated geographic

modelling and simulation should be founded on the standards and

speciﬁcations for resources in the network. Using standards and speci-

ﬁcations will help to standardize heterogeneous resources, including

models, data, and server resources, thus facilitating resource sharing

abilities and knowledge exchange capabilities for a broader user group.

Standards and speciﬁcations will also beneﬁt model interoperability

between modelling platforms during the entire integrated modelling

and distributed simulation processes (https://csdms.colorado.edu/

wiki/Interoperability). Some standards and speciﬁcations have been

formulated for speciﬁc domains. For example, Crosier et al. (2003)

presented a six-stage method for describing environmental models on

the web, and Grimm et al. (2006, 2010) and Müller et al. (2013) pro-

posed the ODD standard for agent-based models. Projects such as Hy-

droShare used the Open Archive Initiative’s Object Reuse and Exchange

(ORI-ORE) standard to describe their hydrological models and data

(Lagoze et al., 2007;Tarboton et al., 2014;Horsburgh et al., 2016), and

Schema.org and Geoscience Cyberinfrastructure for Open Discovery in

the Earth Sciences (GEOCODES) of the USA-NSF supported EarthCube

program are engaged in developing data standards and web standards

for resources. However, the standards and speciﬁcations for broadly

describing geographic modelling resources are still under discussion

(Harpham and Danovaro, 2015). Several issues may need to be con-

sidered in the process of design: (1) What should such a standard in-

clude? (2) What are the minimal requirements? (3) How will modelers

who meet this standard be recognized? (4) How can model developers/

scientists be incentivized to meet these standards? (5) How should these

standards be reviewed, adopted, and disseminated? Many of these de-

sign challenges have not yet been adequately addressed, but at least,

resource standards and speciﬁcations should be formulated by ana-

lyzing the features of both resources and usage.

Second, the resource sharing environment should support the open

sharing of various types of reusable resources. Sharing and reusing si-

mulation resources can bridge the gap between resource providers and

resource users, avoid wasting resources (Granell et al., 2013a), and

beneﬁt integrated modelling and simulation (e.g., Frakes and Kang,

2005;Laniak et al., 2013;Belete et al., 2017). In such an environment,

strategies are needed to support resource sharing and reuse, including

standardized model services generation, simulation resource discovery,

design of resource sharing modes, and authentication and access con-

trol methods. A standardized model services generation strategy aims to

reduce the heterogeneity of diﬀerent model resources. From this per-

spective, sharing geographic simulation models as services is a feasible

way to improve the eﬃciency of model reuse on the web (Lu et al.,

2019). Simulation resource discovery strategies foster identifying and

accessing individual and suitable resources (including models, data,

and servers). The design of resource tracking and control strategies is

intended to provide protection for resources and their providers, with

the objective of ensuring security and privacy for networked resources.

The design of a simulation resource-sharing mode aims to promote

communication through virtual communities or networks, to facilitate

use and provide feedback and to encourage diﬀerent resource providers

to contribute their resources (Zare et al., 2020).

Third, the collaborative modelling environment supports building

integrated models as a team through the internet, by taking full ad-

vantage of existing shared resources. The collaborative modelling en-

vironment proposed in this paper is intended to provide a workspace for

integrated modelling tasks suitable for geographically distributed ex-

perts, who each may represent diﬀerent domain speciﬁc research ex-

pertise, to conduct speciﬁc modelling tasks. At a minimum, the colla-

borative modelling environment should support the basic function of

integrated modelling; that is, it can support combining resources to-

gether to build a computational solution. In this environment, the

modelling workﬂow can be parsed into several stages, e.g., from con-

ceptual to logical modelling and then to computational modelling (as

explained in more detail in Section 5.3). The conceptual modelling

Table 3

Web-based platforms/systems

Open resource sharing Open integrated modelling and

simulation

Open community Reference

Esri ArcGIS Online √(part, commercial-based) √(part, commercial-based) √(part) https://www.esri.com/en-us/arcgis/products/arcgis-online/

overview

CyberGIS √ √ Li et al. (2013),Nyerges et al. (2013)

OpenGMS √ √(part) √ Chen et al. (2013),Chen et al. (2019),Zhang et al. (2019),

Wang et al. (2020)

HydroShare √ √(part) √ Tarboton et al. (2014),Horsburgh et al. (2016),Gan et al.

(2020)

SWATShare √ √(part) √ Rajib et al. (2014, 2016)

CSDMS √ √(part) √ Peckham et al. (2013),Peckham and Goodall (2013)

OpenMI √(Part) √(part) √ Moore and Tindall (2005),Gregersen et al. (2007),Harpham

et al. (2019)

(Hydrologic Information System) HIS √ √ Goodall et al. (2010), Castronova et al. (2013)

AWARE √(part) Granell et al. (2010)

eHabitat √(part) Dubois et al. (2013)

Group On Earth Observations (GEOSS) Platform √ Christian (2005),Butterﬁeld et al. (2008),Giuliani et al.

(2013)

Geospatial Data Cloud √ https://www.gscloud.cn/

National Special Environment and Function of Observation and Research Station

Shared Service Platform

√http://www.crensed.ac.cn/portal/

Tethys Platform: e.g., SWATOnline √ √ Swain (2015),Swain et al. (2015)

The Hydrologic and Water Quality System (HAWQS) √ Yen et al. (2016)

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

process can be regarded as a step in parsing the geographic problem to

be solved and categorizing the relationships among diﬀerent geo-

graphic entities and processes. The logical modelling process can use

tools such as process-ﬂow diagrams, UMLs or ﬂow charts to describe the

inner structure (e.g., nested and combined sub model component

structures) and behavior (e.g., when to run which sub model) of the

integrated models. The computational modelling ﬁnally forms an in-

tegrated computational solution combined with appropriate resources.

A mapping schema with rules needs to be developed to advance the

mapping process from the conceptual model to the logical model and

then to the computational model. To generate a real computational

model, existing shared resources must be connected by resource cou-

pling strategies. The new model that is built during this step could then

also be reused in resource sharing environments. Within the entire

process, collaborative-mode design strategies are necessary to facilitate

open web-distributed geographic modelling among distributed users to

investigate comprehensive geographic challenges. As such, partici-

pants, even if they have no modelling resources at hand, can work

collaboratively through the web to design new geographic conceptual

models, analyze the logic underlying each geographic process, and link

diﬀerent model services and data resources together to form an in-

tegrated model.

Finally, the distributed simulation environment can be regarded as a

workspace for implementing integrated geographic computational

models. As resources that form the integrated model may be distributed

in the internet, the distributed simulation environment should be

designed to support the execution and control of all geographic simu-

lation processes with distributed resources. From this perspective, the

strategies for distributed execution of resources should be considered

ﬁrst. Then, network-oriented monitoring and visualization must be in-

cluded to help users control the simulation processes and understand

the results. To ensure the quality of modelling and simulation, online

assessment (e.g., calibration, validation, goodness of ﬁt) is also needed,

and if the results are not satisfactory, optimization (e.g., replace re-

sources, adjust simulation processes) may be required. Last but equally

important, to support broad participation, involvement of (e.g., deci-

sion makers and others interested in the geographic problems being

addressed) with open discussions on creating decision-making tools and

strategies must be part of the design process.

4. Use cases of open web-distributed integrated geographic

modelling and simulation systems

The process for understanding a system normally starts with the

cognition of its use cases (Goodchild, 2008, 2012). To improve the

recognition of open web-distributed integrated geographic modelling

and simulation systems, this section focuses on illustrating some use

cases in diﬀerent application scenarios. Based on the combination of the

diﬀerent components of the proposed conceptual framework, the main

use cases can be illustrated as shown in Fig. 2.

Fig. 1. Conceptual framework for open web-distributed integrated geographic modelling and simulation.

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

4.1. Online resource sharing

Online resource sharing is a basic use case. Enabling modelling and

simulation resources to be searchable, accessible, interoperable, reu-

sable and able to be integrated through the internet is a worthwhile

eﬀort that can allow widespread usage (Wang, 2010;Goodall et al.,

2011;Wang et al., 2013;Harpham et al., 2017;Lu et al., 2019).

Combined with standards and a resource sharing environment, at the

very least, the duplication of eﬀorts would be reduced, thus saving

resources. When an individual or team wants to conduct a speciﬁc si-

mulation, they could employ these resources directly for their research

without investing in the redevelopment of a set of models, or spending

resources on software installation or hardware preparation.

4.2. Reliable and reproducible research support

Accelerating transparent resource reuse is a meaningful way to

support reliable and reproducible research (Sui, 2014;Essawy et al.,

2018). With the open sharing approach, the operating steps of simu-

lation resources can be tracked and accessed through the internet.

Consequently, others would be able to follow the steps in previously

reported simulations, ensuring that they could interactively repeat the

experiments and improve the reliability of the initial research—not just

read the reported results in scientiﬁc publications or project reports.

Making available operating steps of simulation resources would be

beneﬁcial for both resource promotion and trust enhancement.

4.3. Comprehensive geographic modelling by multiple participants

The collaborative integrated modelling environment allows the in-

tegrated modelling process to be discussed and coordinated by dis-

tributed experts and stakeholders from a wide variety of domains as a

team. Collaboration is meaningful to scientiﬁc research, which involves

complex problems, rapidly changing technology, and dynamic knowl-

edge growth (Hara et al., 2003). For an integrated geographic model-

ling study, participants may be physically distributed, and not all have

detailed, individual process knowledge of all processes that are in-

volved in the comprehensive modelling scenario. For example, when

modelling air pollution for the Yangtze River Delta, a meteorologist

may have expertise on the meteorological conditions and processes, an

air pollution expert may know how to analyze pollutant sources, and a

geomorphologist may know well how to incorporate and adequately

model the underlying interacting surfaces. Even though they may be

located in diﬀerent parts of the world, with the collaborative integrated

modelling environment, and the previous described two components,

such a team could collaboratively employ and integrate a set of mod-

elling resources from the internet to represent such comprehensive

geographical phenomena. These experts might even replace or adjust

components to explore diﬀerent solutions and improve the results

without physically meeting.

4.4. Open geographic exploration with broader resources and participants

An open web-distributed strategy would eﬀectively provide chances

for both experts and general stakeholders to engage in geographic ex-

ploration tasks (Chen et al., 2019). Foldit (Cooper et al., 2010), for

example, was developed to encourage the public to engage in protein

assembly tasks. The unexpected success of this approach shows that

involving the public has the potential to solve extremely complex

problems (Cooper et al., 2010;Khatib et al., 2011). Such crowdsour-

cing-based research methods have been increasingly applied in bioin-

formatics (Good and Su, 2013). Geographic research includes topics of

great concern to stakeholders who care about the changing of geo-

graphic environment around them. When modelling and simulation

process and results can be accessed openly, general stakeholders will

have more opportunities to conveniently explore the geographic en-

vironment according to their interests. These stakeholders could com-

bine a variety of resources to explore geographic processes or conduct

diﬀerent geographic simulations. More geographic knowledge could

also be collected and contributed from stakeholders to improve the

overall understanding of complex geographic processes (Haklay, 2013;

Bergez et al., 2013;Johnson et al., 2015). Sometimes, geographic

Fig. 2. User cases of open web-distributed integrated geographic modelling and simulation at diﬀerent levels.

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

simulations, especially microscale simulations, require more precise

and real-time data (Sagintayev et al., 2012;Eisman et al., 2017;Sun

et al., 2018;Barker and Macleod, 2019); thus, volunteered geographic

information (VGI)-based data can be collected and used with the dis-

tributed simulations, thus beneﬁtting stakeholders by making them

more aware of the local environments (e.g., investigating sound pol-

lution around their house). By doing this, the action caused by en-

vironmental awareness can be refreshed, and the data can be con-

tinually collected for additional simulations (Chen et al., 2017).

5. Detailed road maps for each component in the conceptual

framework

5.1. Standards and speciﬁcations

In the geographic domain, large quantities of geographic simulation

models and data resources exist, and they have been developed and

shaped by diﬀerent disciplines (Lu et al., 2019). The heterogeneity of

these resources is not only due to the intrinsic properties of the re-

sources themselves but also due to the methods used to describe the

corresponding metadata, semantics, spatial references, etc. (Yue et al.,

2015). Moreover, these models and data resources may have been

created and used in diﬀerent operating systems (e.g., Windows, MacOS,

and Linux) (Belete et al., 2017). The heterogeneity of these resources

may lead to diﬃculties in: (1) reusing shared resources, (2) integrating

shared resources (Jiang et al., 2017), and (3) sharing ideas among

modelers (Heuschele et al., 2017). Therefore, standards and speciﬁca-

tions need to be established before resources are shared and integrated.

With the continued emergence of shared resources on the web,

classiﬁcation and metadata standardization is important, thereby al-

lowing users to discover, locate and access their target resources. If

these resources are classiﬁed properly, then they can be easily found

and accessed. Metadata speciﬁcations provide a way to describe these

simulation resources in a standardized and unambiguous way for reuse

and interoperation. In addition to classiﬁcation and metadata, other

standards and speciﬁcations should also be designed for each resource

type, as shown in Fig. 3.

Beyond classiﬁcation and metadata, at least two other types of

standards for model resources should be considered. First, diﬀerent

models have diﬀerent data requirements, which can be represented

using model-related data interfaces. Model-related data interfaces

mainly describe the input/output (I/O) of models, including any limits

on the amounts of I/O data, and the related semantic and spatial re-

ference information of the I/O data. Second, diﬀerent models have

diﬀerent behavior interfaces. The behavior interfaces refer to the in-

ternal module structures and the external commands needed to invoke

models and model features. For example, complex integrated geo-

graphic simulation models may consist of sub-modules; thus, these in-

tegrated models may have their own methods for assembling these

modules. Moreover, diﬀerent models may have diﬀerent invocation

methods (e.g., EXE ﬁles and JAR ﬁles have diﬀerent invocation

methods) and invocation sequences (e.g., the execution of one model

may depend on the output of another model). Additionally, some

models may require external input to continue running, and so on.

These heterogeneous model behaviors may need to be described in a

standard way to help users implement these models after they are

shared as resources. Therefore, standards to describe model-related

data interfaces and behavior interfaces are important to support “model

standardization”.

For data resources, due to the heterogeneity of multisource geo-

graphic data and the potential variety of model data requirements,

barriers still could exist between geographic simulation models and the

related data resources (Lü et al., 2015). To prepare a model with cor-

rected data resources, in addition to classiﬁcation and metadata stan-

dards, a data expression standard should be proposed that can uni-

versally describe data resources. Yue et al. (2016) suggested that a data

expression standard should include the data structure, data semantics,

units, spatial references, etc., thus providing a solid basis for model

invocation and data exchange. Some examples are the data re-

presentation model (DRM) of the Source for Environmental Data Re-

presentation & Interchange (SEDRIS) project (http://www.sedris.org/)

and the universal data eXchange (UDX) model of the OpenGMS plat-

form (Yue et al., 2015).

Server resources, which can be distributed in the network, are the

hosts of model(s) and data resources. The server capacity and perfor-

mance are crucial factors in model invocation and data scheduling. To

describe a server, both a software description, which includes the op-

erating environment, library dependencies, etc., and a hardware de-

scription, which includes disk capacity, memory size, CPU perfor-

mance, etc., should be considered when designing standards for server

resources (Wen et al., 2017).

5.2. Resource sharing environment

A resource sharing environment aims to bridge the gaps between

geographic simulation resource providers and users. To create this en-

vironment, there are at least four key items that should be considered:

resource sharing, resource discovery, resource tracking and control, and

share-oriented aided design (see Fig. 4).

Fig. 3. Standards and speciﬁcations for geographic modelling and imulation

resources. Fig. 4. Key points to resource sharing environment.

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

5.2.1. Resource sharing

Supported by resource standards and speciﬁcations, the goal of re-

source sharing is to provide carriers (e.g., portals), strategies (e.g.,

model service generation strategies) and tools to help resource provi-

ders share their resources.

Because web services support remote reuse and allow more parti-

cipants to join conveniently (Zhang et al., 2019), we are primarily in-

terested in sharing models as web services.In this case, standardized

encapsulation, service deployment and publishing, service access and

invocation, and model execution monitoring and control must all be

considered to support resource sharing. Standardized encapsulation

refers to methods for wrapping model resources to form universal ser-

vices in the network. Castronova et al. (2013a) and Qiao et al. (2019)

provided paradigms based on the Web Processing Service (WPS) pro-

tocol. Zhang et al. (2019) presented a service-oriented strategy for

model wrapping in the OpenGMS platform on the web, CSDMS devel-

oped the BMI interfaces to wrap models such that they can be coupled

in a framework (Peckham et al., 2013), and the Open Modelling In-

terface 2.0 (OpenMI) (Donchyts et al., 2010) and Web Process Standard

2.0 (Müller and Pross, 2015), have been designed and implemented to

"wrap" models to expose them as web services. Service deployment and

publishing involve methods designed for deploying and publishing

models as services. Several related studies can be referenced to design

such methods. For instance, Rubio-Loyola et al. (2011) presented a

scalable service deployment method for an open software-deﬁned net-

work infrastructure; Smaragdakis et al. (2014) presented a scalable and

distributed solution for optimizing resource deployment in the network;

and Wen et al. (2017) proposed a service-oriented deployment strategy

for sharing geo-analysis models in a web environment. Service access

and invocation are aimed at providing methods or tools such that users

can access and invoke model services; these include both user interface

(UI) and software development kit (SDK) approaches. Execution mon-

itoring and control require methods or tools to help users obtain real-

time model status at runtime and interact with the models. MonPaas

(Calero and Aguado, 2015), a service-oriented monitoring method in

which each cloud consumer is allowed to customize the monitoring

metrics, can be used as a reference.

For data resource sharing, structured expressions for heterogeneous

data, and control and optimization of data transmission are important.

A structured expression of heterogeneous data aims to provide methods

for universally describing heterogeneous data; such expressions beneﬁt

users’ understanding and communication and are crucial for further

data conversion and model integration. Chen et al., 2009;Yue et al.

(2015) presented the UDX model to describe data structurally, enabling

users to better understand the data. With UDX model, Wang et al.

(2018) designed data processing services to support model running and

data conversion in the web environment. Transmission control is ex-

pected to be designed to enhance security and ensure completeness and

traceability during the data transmission process; while transmission

optimization is intended to optimize the eﬃciency of data transfer over

the network. To guarantee security and respect ownership, digital wa-

termarking is one method to help control this issue for data (Shih,

2017). Jiao et al. (2018) presented a method to ensure data com-

pleteness during transmission. Zhang et al. (2017) designed a method to

trace the provenance of data being used. Regarding methods for en-

hancing data transmission eﬃciency, many spatial data transmission

algorithms have been developed and can be used as references (e.g.,

Falls et al., 2014;Bhattacharya and Jilani, 2015).

When sharing a server resource, at a minimum, the structured ex-

pression of heterogeneous servers and service-oriented environment

conﬁgurations need to be considered. A structured description method

for heterogeneous servers aims to describe server features, including

the hardware environments (CPU types, memory sizes, etc.), operating

systems (versions of Windows, Linux, MacOS, etc.) and software en-

vironments (e.g., Geospatial Data Abstraction Library (GDAL) or

Python). A service-oriented environment conﬁguration initially

provides basic methods for conﬁguring hardware and software en-

vironments to support server sharing. After server resources are shared,

methods should also be provided to support the remote conﬁguration of

hardware and software in the server environment according to the

deployed service requirements. For instance, if a computer is shared as

a server, after it is registered on the web, when a model with a diﬀerent

hardware/software requirement than those for which the server has

been conﬁgured needs to be deployed on the server, the server owner

(or the users, if given suﬃcient permissions) should be able to conﬁgure

the server with the suitable hardware/software.

5.2.2. Resource discovery

Finding suitable models, data or servers is a challenge for resource

users (Goodall et al., 2011). Resource discovery, which is an supporting

aspect of resource sharing, provides methods for making queries and

locating target resources. Two steps may be involved in resource dis-

covery: relationship and index building and matching rule design.

Index building involves building a storage structure that can be

searched eﬃciently for target resources. Relationship building explores

the diﬀerent relationships among resources and links them; then, these

resources can be queried based on the developed relationship network.

For example, FigShare (Singh, 2011) creates diﬀerent featured cate-

gories for their online shared data resources, and each resource is

equipped with a Digital Object Identiﬁer (DOI), allowing it to be

tracked and searched. Chen et al. (2018) designed a data model to

capture the relationships among geographic simulation models, actors

(agencies and researchers), and application scenarios by considering

their evolution processes.

Matching rule design involves building rules to support matches

between simulation resource keywords and user requirements. Search

engines, such as Google, and related scientiﬁc research tools, such as

Stanford CoreNLP (Manning et al., 2014), have designed matching al-

gorithms. When combined with resource classiﬁcation and metadata,

these matching algorithms can be referenced when building matching

rules.

5.2.3. Resource tracking and control

Resource tracking and control, which aims to track the usage pro-

cesses of resources and enable their security, has drawn increased at-

tention in open web-distributed resource sharing (Gordon et al., 2003;

Rong et al., 2013;Sicari et al., 2015). Resource tracking and control can

be realized to some extent through usage tracking and information

recording, and authentication and access control.

Usage tracking and information recording tasks are designed to

make records of the usage process of resources as well as to store in-

formation related to the used resources. Tracking the usage process can

provide a clear idea of the activities for which resources are used, e.g., a

model is deployed on server A, and data are transferred through server

B to Server C. Recording related information (e.g., authorship, con-

tributors, copyrights) can provide resource context during the evolution

process, and can thus help protect intellectual contributions.

Authentication and access control tasks are designed to improve the

security of resources after they are provided as services. Such tasks may

involve multiple methods to help to identify actors securely, enable

access to allow actors, and use practices that prevent abuse. From this

perspective, technologies related to network security (e.g., local net-

work, private cloud), usage category assignment (e.g., free use, com-

mercial use, or private use) and illegal usage control (e.g., cracking and

decompilation) could be employed as references.

5.2.4. Sharing mode design

Encouraging resource owners to make their resources available to

communities is another challenge in resource sharing processes (Bartol

and Srivastava, 2002;Bassi et al., 2003;Chard et al., 2012) and in-

volves at least two points. The ﬁrst point is related to community

building - forming teams that include resource owners, users and

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

related stakeholders. A sustainable community is crucial for the

achievement of open simulation resource sharing. Organizations such

as CUAHSI (Universities Allied for Water Research), CSDMS, Earth-

Cube, Unidata, CyberGIS, and SWITCH-ON (European Union Hydro-

logic data sharing) have established their respective communities to

ensure sustainable development. In summary, these strategies include:

(1) Governance and community organization (e.g., working groups,

initiatives, committees); (2) participation rules, rights, rewards and

responsibilities; (3) promotion of both communities and resources

through multi-channel (e.g., workshops, training); (4) attention at-

traction (e.g., publishing related news and cutting-edge technologies;

(5) use experience enhancement (e.g., providing user friendly tools);

and (6) feedback channels design (e.g., comments and citation reports).

Among these strategies, an important technical point is how to provide

diﬀerent kinds of tools (e.g., resource wrapping tools, resource pub-

lishing tools, resources invoking tools) to satisfy a diverse group of

participants. User friendliness in design is a basic criterion. Because

open integrated platforms attract many users with diﬀerent back-

grounds and usage habits, it is challenging to ﬁnd a balance among a

wide range of demands.

5.3. Collaborative integrated modelling environment

After resources are shared in the open web environment, modelers

can create integrated compositions of geographic models using the

shared resources in the network. In the evolution of geographic re-

search, solving comprehensive geographic problems is an active re-

search area (Fu et al., 2015;Fu and Pan, 2016). Due to the complexity

of geographic environments, especially those that connect the human

and natural environments (Chen et al., 2013;Hamilton et al., 2015),

modelers from diﬀerent disciplines may have diﬀerent conceptions of

geographic phenomena or processes. Thus, collaboration has been

emphasized in comprehensive geographic research and integrated

modelling (e.g., Wu et al., 2015;You and Lin, 2016;Basco-Carrera

et al., 2017;Evers et al., 2017;Harpham et al., 2014;Yue et al., 2020;

Bandaragoda et al., 2019), and is especially valuable in the open web-

distributed approach. Such collaboration fosters communication and

cooperation, helps in forming common understandings among multiple

researchers through the web, and further guides the integrated geo-

graphic modelling process. The design of a collaborative integrated

modelling environment should consider: the design of the modelling

solution, the modelling process and the collaborative modelling mode

(Fig. 5). The modelling process is the core of integrated geographic

modelling; the modelling solution guides the detailed modelling pro-

cesses; and the collaborative mode provides implementation strategies

to support the entire integrated modelling process in a collaborative

way through the web.

5.3.1. Modelling solution design

The modelling solution, which is a critical foundation for integrated

geographic modelling, can be regarded as a decomposition and analysis

process for the complex geographic problems to be analyzed. It can also

help translate the modelling purpose into a model description. Before

considering integrated modelling for comprehensive geographic phe-

nomena or processes, ﬁrst, the research questions should be determined

and decomposed. For example, to better understand the growth process

of plants in certain areas, precipitation, photosynthesis and soil nutrient

cycling processes may need to be decomposed, so experts in the related

domains can be invited to participate. Then, the modelling workﬂow is

analyzed and developed to describe the overall process of model

building. During this step, diﬀerent modelling tasks can be apportioned

to diﬀerent experts, and modelling roles can be assigned.

5.3.2. Modelling process design

Because there are currently no uniﬁed steps in describing a general

integrated modelling process, in this article, we divide the modelling

process into conceptual modelling, logical modelling and computa-

tional modelling.

Conceptual modelling provides an abstract idea of the integrated

geographic models to be developed. Because conceptual models will lay

a foundation for model idea communication among the diﬀerent par-

ticipants in the integrated modelling process, it is meaningful to de-

velop such concepts and express them in simple, understandable ways

to help to reach a consensus on the modelling topics. Clark et al. (2015)

summarized the modelling conception of process-based hydrologic

models using Structure for Unifying Multiple Modelling Alternatives

(SUMMA). In this respect, geographic scenarios, which involve multiple

geographic phenomena and processes, can be regarded as suitable

media for expressing geographic conceptual models (Lu et al., 2018).

Based on geographic scenarios and combined with expression methods

(e.g., graphs, script descriptions), geographic conceptual models can be

built to match the conceptual scenarios (e.g., Chen et al., 2011). Fig. 6

shows an example of a conceptual model based on geographic scenarios

to represent a forest ﬁre. But we note that not all conceptual models

need to be represented in such a vivid way. Within this possible ap-

proach, during the conceptual modelling process, geographic objects

and the relationships among these objects should be clariﬁed and ex-

pressed. For example, when modelling a forest ﬁre, concepts such as

wind (speed and direction), trees (species and density) and air (factors

such as humidity) may need to be considered and expressed. Moreover,

relationships such as the eﬀects of wind speed and direction and tree

density on ﬁre spreading also need to be expressed. In addition to the

expression of geographic objects and relationships, constraints are also

important for reﬂecting natural geographical laws and knowledge. For

example, trees should generally be planted on the ground, not in the air;

thus, when building a conceptual model, constraints based on general

geographic knowledge should be included from a knowledge-base to

help to guide the building of a realistic conceptual model.

Logical modelling involves modelling the inner structures (e.g.,

nested and combined submodel component structures) and behavior

(e.g., when to run a submodel) of the integrated models, based on the

developed conceptual model. In this stage, ﬁrst, the geographic pro-

cesses or subprocesses represented by the geographic conceptual

models, need to be mapped to the corresponding logical components.

For example, a conceptual model of hydrological processes may include

several subprocesses, such as precipitation, evapotranspiration and in-

ﬁltration. These sub-processes and their relationships need to be ex-

pressed by logical components and their associations. GoldSim (https://

www.goldsim.com/web/home/) is an example that uses inﬂuence

diagrams and their links to represent the logical subprocesses and their

relationships of an integrated model. Second, these organized speciﬁc-

Fig. 5. Key points of the integrated modelling environment.

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

logic components need to be conﬁgured with content, such as declaring

which types or classes of models should be used to represent a corre-

sponding process. Finally, the logical model, which is the product of the

logical modelling process, needs to be expressed structurally. Thus, it

can be associated with the real accessible resources to simulate the

represented geographic processes in subsequent steps. Fig. 7 is an ex-

ample of one method to accomplish such logical modelling.

Computational modelling can be regarded as the process of con-

ﬁguring distributed resources on the networks to generate an execu-

table integrated model workﬂow based on the guidance from the de-

signed conceptual models and logical models. In this stage, ﬁrst, the

appropriate model resources on the distributed networks need to be

matched and bound to the corresponding logical components. These

may be diﬀerent services that are deployed and published on diﬀerent

server resources. Second, the matched model behaviors and input/

output data need to be clariﬁed and conﬁgured before their invocation.

For example, model data assimilation generally requires external inputs

for further computations; thus, such behaviors must be declared so that

the model operates correctly. Additionally, the methods for resource

coupling also need to be designed to generate a real computational

model. Such designs may include methods for model-model coupling

(e.g., upscaling and downscaling, spatio-temporal feature type adapta-

tions and transformations), model-data preparation (e.g., model-spe-

ciﬁc data preprocessing, see Yue et al., 2018 which provides a loose

data resource conﬁguration strategy for web-based model services) and

model-server compatibility (e.g., server environment selection and

conﬁguration to ﬁt the model). Fig. 8 shows an example of this type of

computational modelling.

Fig. 6. An example of a conceptual model.

Fig. 7. An example of a logical modelling process.

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

5.3.3. Collaborative modelling mode design

Collaborative modelling mode design provides a series of methods

and tools that allow a team of researchers to perform modelling tasks

together and share their knowledge over a network. This concept may

involve several methods, such as task decomposition and ﬂow for-

mulation, role assignment and management, and collaborative process

control.

Task decomposition and ﬂow formulation are aimed at dividing the

full set of open web-distributed integrated geographic modelling tasks

into subtasks and forming a complete modelling workﬂow. For ex-

ample, when studying pollution in a speciﬁc area, the modelling pro-

cess can be divided into several tasks, including hydrology process

modelling, meteorological process modelling, eﬀects on humans and

ecology, their costs and responses, and data preparation or acquisition

of server resources. These tasks can be linked to form a modelling

workﬂow. To implement these tasks collaboratively, role assignment

and management may require diﬀerent kinds of roles that must be si-

multaneously managed. For example, meteorologists, health experts,

economists and hydrologists may be assigned diﬀerent roles when

conducting diﬀerent modelling tasks with which they are familiar.

Moreover, the process of modelling may need to be collaboratively

controlled (e.g., progress monitoring, task optimization and role sche-

duling) during the entire geographic modelling process.

5.4. Distributed simulation environment

The distributed simulation environment, which includes distributed

execution and control, and collaborative simulation and evaluation,

enables integrated computational models to operate in networks and

helps users conduct and optimize collaborative simulations (see Fig. 9).

5.4.1. Distributed execution and control

The results of integrated geographic modelling are computational

models consisting of several submodules that can be invoked through

distributed networks. Due to the complexity of the internet, key aspects

for model distributed execution and control as indicated below need to

be considered.

Process monitoring includes monitoring the operating process of

computational models and the corresponding submodules through the

web, the model execution status (e.g., the progress of model invocation,

log information and exception information), and related server re-

sources (e.g., memory and CPU utilization).

Distributed control involves developing the controlling strategies to

handle the entire execution process and interrelated resources in a

distributed network, such as invoking each submodule based on the

order determined during the modelling process and performing data

dispatching among distributed servers.

Runtime optimization provides methods to improve the operating

performance of the integrated computational models. This may include

Fig. 8. An example of a computational modelling process.

Fig. 9. Key points of the distributed simulation environment.

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

methods that optimize the server node selection (e.g., selecting the

most suitable server nodes to participate in the collaborative simula-

tion) or optimize the data transmission eﬃciency (e.g., data compres-

sion and block transmission).

Exception processing notiﬁes users of potential mistakes or errors

during an interrupted execution process. Error and warning logs pro-

vide a direct way of capturing exceptions that occur during computa-

tional model invocation. A good logging system can report exceptions

in a timely manner. Then, exception-processing solutions can be de-

signed and employed to handle these. For example, if a time-out occurs

when requesting certain resources, the logging system may record this

exception. Then, corresponding solutions, such as requesting the same

model services from another server resource, could be employed to

circumvent this exception.

5.4.2. Collaborative simulation and evaluation

Collaborative simulation and evaluation are critical processes when

applying the results of integrated geographic modelling in a web-dis-

tributed system. In this stage, to achieve comprehensive and colla-

borative geographic simulations, the following factors should be con-

sidered.

First, synchronous expression and interactive analysis are necessary.

To evaluate the quality of integrated geographic modelling in the open

web-distributed environment, multiple users may need to work inter-

actively and share their knowledge to analyze and optimize the mod-

elling results (e.g., through discussion, comparison validation and vi-

sualization) in a distributed manner. In this case, the simulation

processes and results need to be expressed synchronously to diﬀerent

experts in the network for exploration. For example, an expert may

adjust some parameters before simulation, and another expert may

perform some operations (e.g., a cutting analysis of the ground layers)

involving the simulation and visualization results. Others may need to

be made aware of these changes synchronously, and then provide their

comments and suggestions to improve the next round of simulations.

Some examples of this can be found in Xu et al. (2011) and Zhu et al.

(2016).

Second, parameter calibration and model evaluation is another key

part in this aspect.Based on evaluating the model output, the model

parameters should be calibrated accordingly to improve the quality of

the results. Model evaluation includes uncertainty analyses, model

veriﬁcation and model validation (Matott et al., 2009;Eker et al.,

2019). Uncertainty analysis of models is more important in integrated

geographic modelling because uncertainty may increase due to model

integration (Jakeman et al., 2006;Voinov and Cerco, 2010;Koo et al.,

2020). This might complicate model calibration since the parameters of

sub models must be calibrated while comparing data to the output of

the integrated model. Model veriﬁcation focuses on the correctness of

model results, while the model validation ensures that the results are as

expected. New online tools are needed to support both collaborative

calibration and evaluation over the web.

Third, a model might need to be adjusted during simulation. At least

two types of adjustments may be considered after initial model execu-

tion. First, if the results are not satisfactory, it is necessary to determine

how best to adjust the model to improve its results and understand

whether certain sub models have to be replaced or ﬁxed, or whether

additional sub-models need to be considered and integrated. Tools need

to be designed to support the convenient replacement or extension of

sub models for further use. Conversely, if the integrated model performs

suﬃciently well, the simulation process itself can still be improved for

the next rounds of simulation by choosing alternative, better per-

forming servers that provide the same sub models as services.

Finally, a simulation-based open discussion and decision support

will contribute to model application and dissemination. More stake-

holders (e.g., the public and decision makers) may become involved in

open web-distributed integrated geographic modelling and simulation

and provide their own contributions. For example, to create speciﬁc

simulations requiring real-time environmental data, the public may

participate and provide local environmental data to improve the si-

mulation results (crowd-sourcing). Moreover, given diﬀerent simula-

tion solutions and results, decision makers may perform comparative

analyses with modelers and simulators to design better solutions. All

these tasks are expected to be supported and online tools (e.g., con-

sultation tools, analysis tools, and report making tools) are needed to

facilitate broad participation.

6. Conclusions

Comprehensive geographic exploration and understanding calls for

interdisciplinary, multi-scale, and collaborative eﬀorts. Open web-dis-

tributed modelling and simulation is an emerging and exciting area of

scientiﬁc research aimed at supporting such modelling eﬀorts. It can

encourage more participants to become active in geographic research

by removing obstacles to both resource sharing and collaborative

modelling and simulation. It may learn from the experiences of ‘big

data’ to usher in a ‘big model’ era. This article envisions such an open

web-distributed approach to geographic modelling and simulation by

drawing on and synthesizing past literature, and by presenting a con-

ceptual framework to organize key research topics in this emerging

ﬁeld. From this perspective, we have arrived at ﬁve key conclusions.

First, open web-distributed modelling and simulation will introduce

an increasing number of modelling and simulation resources that can

contribute to both resource reusability and comprehensive problem-

solving. Eﬀorts are still needed to be made to form a limited number of

enabling standards and speciﬁcations that can be used across topic

domains so that this growth in modelling and simulation resources can

be eﬀectively inventoried, organized, and integrated for geographic

simulations. For example, model document standards and service op-

eration standards for models are still under exploration.

Second, for open research communities, convenience will aﬀect the

participation of both resource providers and users in continued ex-

changes. Designing highly-usable ways to prepare and apply model and

data resources is crucial for the long-term success. Recent research has

made progress regarding the usability of web-distributed modelling

systems with proposed UIs, although most work has focused on model

communication standards and semantics. More work that speciﬁcally

focusses on the user experience is needed to enable broad adoption and

participation in these systems.

Third, there is a research gap in enabling a wide variety of potential

models to be successfully integrated into compositions caused by a lack

of focus on the diﬀerent conceptualizations and representations of

geographic space and time across component standards. For example,

the geographic models that could be considered for such integrated

systems produce outputs that include a wide variety of spatial feature

types, such as grids, points and meshes (Chen et al., 2018a). Although

the implementations of standards such as OpenMI oﬀers low-level

ﬂexibility in interpolating among feature types when implemented in

diﬀerent time-step schemes, and some discrete global grids have been

developed to express of grid nodes, edges, and cells in a uniform way to

support spatial data organization, pattern simulation, and the visuali-

zation of spatial data (e.g., Lin et al., 2018), more work is still required

to make this truly generic, practical and eﬃcient.

Fourth, in a web environment, the distributed execution of the sub

models within an integrated model calls for safe, secure, and highly-

eﬃcient computational and message passing methods. For the servers

that provide resource services, safety control is important not only for

the server itself, but also for the entire simulation process. Based on

multiple servers, execution eﬃciency must also be addressed through

advanced technologies such as parallel-computing, secure message

passing, and fault-tolerant model orchestration strategies. Considerable

progress has been made regarding these topics, but more work is

needed to ensure consistency of reproducibility of model simulations in

web-execution environments. Many challenges remain, such as,

M. Chen, et al. Earth-Science Reviews 207 (2020) 103223

handling web resources that are upgraded, or deprecated and oﬄine;

conducting comprehensive comparisons of software environment and

platform dependencies for diﬀerent servers; and tracking the workﬂows

of the operations in modelling and simulation processes to enable re-

producible research.

Finally, beyond geography, this paper also provides a framework to

structure the necessary activities required to support general web

modelling activities. This framework can combine various existing in-

tegrated modelling tools and systems (e.g. CSDMS, OpenMI, and

OpenGMS). We argue that having such a framework for organizing and

focusing current eﬀorts in the community, as well as identifying re-

search gaps that must be overcome to implement the web-based mod-

elling vision discussed, is needed to advance the ﬁeld. To this end, an

Open Modelling Foundation (OMF, https://openmodelingfoundation.

org/) has been established to actively promote international standards

and best practices to improve the modelling culture, and make models

more accessible, reliable and reproducible. We expect that broad

community involvement will help demonstrate the beneﬁts of the fra-

mework to providers and users and encourage these stakeholders to

incorporate their innovations and oﬀerings. In this paper we have fo-

cused on the technical aspects of the framework to demonstrate that it

is possible and practical, and that it can advance the science and

practices of open web-distributed integrated modelling and the corre-

sponding practices. This is not to discount the equally challenging so-

cial and cultural aspects of changing the practice of modelling and fa-

cilitate the adoption of open web-distributed integrated modelling

principles. Many future developments will be required from both the

technical and social perspectives to implement this vision and we an-

ticipate that research outputs in this area will continue to grow ex-

ponentially. We hope that the implementation roadmap presented in

this paper can serve the community as an organizational structure to

motivate and drive future research and development in this ﬁeld.

Declaration of Competing Interest

None

Acknowledgements

We express our sincere thanks to Prof.David Tarboton and other

anonymous reviewers for their valuable comments. Chen was supported

by the NSF for Excellent Young Scholars of China (No. 41622108), the

National Basic Research Program of China (973 Program) (No.

2015CB954103). Lü and Chen were supported by the Key funding of

NSF of China (No. 41930648), and the Priority Academic Program

Development of Jiangsu Higher Education Institutions (No.

164320H116). Kettner was supported by the CSDMS project funded by

The US National Science Foundation (grant 1831623). Goodall was

supported by theUS National Science Foundationthrough award

numberOAC-1664061.

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Simulation Software in the Design and AI-Driven Automation of All- Terrain Farm Vehicles and Implements for Precision Agriculture

Article

Full-text available

Apr 2025

Precision agriculture depends on the automation and mechanization of agricultural equipment and vehicles in a variety of terrains, which increases productivity and sustainability. This review presents a comparative analysis of significant simulation software used in designing and developing automated agricultural systems, emphasizing their methodologies and significance in advancing farm technology. Artificial intelligence (AI) and machine learning (ML) methods are modeled, optimized, and integrated using key technologies such as MATLAB/Simulink, SolidWorks, ANSYS, AirSim, and Gazebo. The results demonstrate how these technologies improve agricultural automation's real-time decision-making, predictive maintenance, and system accuracy. Case studies illustrate their practical application in simulating all-terrain farm vehicles and specialized implements. The best tools for simulating autonomous navigation are AirSim and Gazebo, although MATLAB/Simulink is particularly adept at system-level AI modeling. This study takes a new approach to improving design, control, and environmental interactions by combining many modeling tools. This makes it easier to make agricultural automation systems that last longer and work better. It is suggested that future studies investigate the relationship between agricultural automation, AI, and simulation in greater detail to propel precision agriculture forward.

Maximum Flow Model With Multiple Origin and Destination and Its Application in Designing Urban Drainage Systems

Article

Full-text available

Jan 2025

In this paper, a multi-origin and multi-destination maximum flow model is proposed by integrating the objective programming method with graph theory. Through Model 1, low-cost design of drainage pipeline networks can be achieved. For instance, in the experiments, certain pipelines such as Pipeline 9, Pipeline 20, and Pipeline 27 can be excluded during the initial construction phase while still maintaining effective drainage performance and reducing costs. Model 2 enables the monitoring of drainage capacity at various nodes within the existing drainage system. For example, under a specific rainfall intensity, it was observed that certain nodes, such as Nodes 9~16, Node 45, and Node 48, exhibit poor drainage capacity, necessitating precautionary measures. Model 3 focuses on the expansion planning of the drainage system, ensuring its normal operation under certain rainfall intensities. The results based on a specific area in Changchun City demonstrate that the proposed models not only possess strong practical application value in the design of urban drainage systems but also have broader applicability in other fields such as traffic flow and logistics supply chain planning. This study opens up new avenues for model construction and enriches the practical application effects of mixed-integer programming models.

Interoperability for ecosystem service assessments: Why, how, who, and for whom?

Article

Full-text available

Mar 2025

Despite continued, rapid growth in the literature, the fragmentation of information is a major barrier to more timely and credible ecosystem services (ES) assessments. A major reason for this fragmentation is the currently limited state of interoperability of ES data, models, and software. The FAIR Principles, a recent reformulation of long-standing open science goals, highlight the importance of making scientific knowledge Findable, Accessible, Interoperable, and Reusable. Critically, FAIR aims to make science more transparent and transferable by both people and computers. However, it is easier to make data and models findable and accessible through data and code repositories than to achieve interoperability and reusability. Achieving interoperability will require more consistent adherence to current technical best practices and, more critically, to build consensus about and consistently use semantics that can represent ES-relevant phenomena. Building on recent examples from major international initiatives for ES (IPBES, SEEA, GEO BON), we illustrate strategies to address interoperability, discuss their importance, and describe potential gains for individual researchers and practitioners and the field of ES. Although interoperability comes with many challenges, including greater scientific coordination than today's status quo, it is technically achievable and offers potentially transformative advantages to ES assessments needed to mainstream their use by decision makers. Individuals and organizations active in ES research and practice can play critical roles in creating widespread interoperability and reusability of ES science. A representative community of practice targeting interoperability for ES would help advance these goals.

Research on Service-Oriented Sharing and Computing Framework of Geographic Data for Geographic Modeling and Simulation

Article

Full-text available

Dec 2024

Geographic data are the foundation of geographic model construction, and any stage of their acquisition, processing, and analysis may have an impact on the efficiency and quality of geographic modeling and simulation. With the advent of the era of big data, a large number of data resources are generated in the field of geographic information. However, due to the heterogeneity of geographic data and the security of data usage, massive geographic data resources are difficult to fully explore and utilize, resulting in the formation of data islands. This paper proposes a service-oriented geographic data-sharing and computing framework, which provides users with a complete set of geographic data access and application processes (such as data acquisition, processing, configuration, etc.), so as to reduce the difficulty of using data and improve the efficiency of data sharing. The framework mainly consists of three core components: (1) the “Data service container” can publish data resources as data services to provide a consistent data access interface; (2) the “Workspace” provides a series of methods and tools for users to develop data-computing solutions; and (3) the “Data-computing engine” is responsible for performing computing tasks such as data processing and configuration. Finally, a case of runoff simulation using the SWAT model is designed, in which the whole process of data sharing, acquisition, calculation, and application is realized, so as to verify the validity of the proposed framework.

Facilitating sensitivity analysis of hydrological models through knowledge-driven configuration and distributed online model services

Article

May 2025
J HYDROL

Hydrological models (HMs) are essential for understanding the complexities of the water cycle and runoff dynamics. Sensitivity analysis (SA), an essential component of HMs, plays a key role in identifying the parameters that have the greatest impact on model outcomes. It helps to simplify the complexity of hydrological systems and provides a comprehensive understanding of the underlying physical processes. However, the complexity of HMs and the diversity of SA methods pose significant challenges for researchers, making the SA configuration process intricate and requiring substantial computational resources. To address these challenges, we propose a comprehensive strategy that integrates knowledge-driven configuration services with distributed online model services. First, we establish a rule-based knowledge repository and a case-based knowledge repository. These repositories provide general configuration guidance and similar SA case recommendations, respectively, to support decision-making in critical SA steps. This ensures that the configuration of SA is accurate and reliable. Secondly, we encapsulate HMs as web services and leverage distributed computing resources to optimize execution efficiency. Then, we integrate the HM services with the SA modules to achieve a complete SA experiment. Based on this strategy, we finally developed a prototype system that offers a user-friendly tool for conducting SA with enhanced computational performance and streamlined workflow. The watershed-scale HM, SWAT, was used to test the effectiveness of the prototype system. The results demonstrate that this strategy enables more comprehensive analysis and improves decision-making through configuration guidance, and holds promise for enhancing the reliability and efficiency of SA in hydrological modeling.

Position paper: Domain knowledge-driven intelligentization of watershed modeling and scenario analysis for precise watershed management

Article

Full-text available

Apr 2025

Intelligent determination of proper spatial extents for input data during geographical model workflow building

Article

Feb 2025
ENVIRON MODELL SOFTW

The spatial extent required for geographical model inputs depends on the model and input data characteristics, often differing from the user-defined area of interest (AOI). For example, a DEM input for stream network extraction should cover the upstream catchment area of the AOI. Determining proper spatial extents is crucial for both modeling accuracy and efficiency but is often complex and tedious , especially for workflows which may raise chain effect on varying spatial extents among diverse inputs. Few methods currently address this issue. This paper proposes an intelligent approach to automate spatial extent determination during geographical model workflow building, adapting to the user-defined AOI. The approach combines knowledge rules and heuristic modeling with advanced geoprocessing. Implemented in a prototype system, a case study on digital soil mapping for arbitrary-shaped AOI was conducted to validate the effectiveness of the approach, showing that it provides users with easy-to-use and accurate geographical modeling across broad applications.

Exploring the Feasibility of Utilizing Blockchain in Cloud Computing and AI-Enabled Building Information Modeling for Enhancing Data Exchange in Construction Supply Chain Management

Conference Paper

Full-text available

Jan 2025

Construction Supply Chain Management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted Building Information Modeling (BIM)-a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, Artificial Intelligence (AI) can be integrated to handle this data's magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.

A component based modular treatment of the soil-plant-atmosphere continuum: the GEOSPACE framework (v.1.2.9)

Preprint

Full-text available

Jan 2025

The soil-plant-atmosphere continuum (SPAC) system is a complex and interconnected network of physical phenomena, encompassing heat transfer, evapotranspiration, precipitation, water absorption, soil water flow, substance transport, and gas exchange. These processes govern the exchange of energy, matter, and water within the SPAC system. To better understand and model SPAC interactions, interdisciplinary approaches are essential due to the inherent complexity of the system. Instead of relying on a single monolithic model, we propose a component-based modeling approach, where each component addresses a specific aspect of the system. Object-oriented programming (OOP) is adopted as the foundational framework for this approach, providing flexibility and adaptability to accommodate the ever-changing nature of the SPAC system. The Soil Plant Atmosphere Continuum Estimator in GEOframe (GEOSPACE) is presented in this paper, in particular the one-dimensional development, GEOSPACE-1D. The framework is a tool designed to facilitate robust, reliable and transparent simulations of SPAC interactions. It embraces the principles of open-source software and modular design, aiming to promote open, reusable, and reproducible research practices. By implementing the OOP, GEOSPACE-1D breaks down the complexity of SPAC modeling into smaller, self-contained structures, each responsible for a specific scientific or mathematical concept. This modular architecture adheres to the "open to extensions, closed to modifications" philosophy, enabling easy model extension without disrupting existing components. Equations are implemented in an abstract manner, emphasizing the use of common interfaces over concrete classes, a hallmark of contemporary OOP. GEOSPACE-1D adopts a generic programming framework, where distinct classes adhere to a common interface. This compartmentalization serves two critical purposes: validating individual processes against analytical solutions and facilitating the integration of novel processes into the system. The paper emphasizes the significance of modeling the coupling between infiltration and evapotranspiration for accurate hydrological simulations. It explores the interplay between plant transpiration, soil evaporation, and soil moisture dynamics, highlighting the need to account for these interactions in SPAC models. The paper concludes by underlining the importance of modularity, transparency, and openness in SPAC modeling, principles that underlie the development of GEOSPACE-1D and its components. Overall, GEOSPACE-1D represents a promising approach to SPAC modeling, providing a flexible and extensible framework for studying complex interactions within the Earth's Critical Zone. It is worth recalling that the fundamental premise of GEOSPACE-1D is not to create a single soil-plant-atmosphere model, but to establish a system that allows the creation of a series of soil-plant-atmosphere models, adapted to the specific needs of the user's case study.

A Framework for Assessing the Computational Reproducibility of Geo-Simulation Experiments

Article

Mar 2025
ENVIRON MODELL SOFTW

Eight grand challenges in socio-environmental systems modeling

Article

Full-text available

Jan 2020

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

Geography: A Very Short Introduction

Book

Mar 2008

Geography: A Very Short Introduction charts modern geography's development from its historical roots in exploring foreign lands and mapping the regions of the world. Spanning both physical and human geography, the discipline today is unique as a subject which can bridge the divide between the sciences and the humanities, and between the environment and our society. Using wide-ranging examples from global warming and oil, to urbanization and ethnicity, this Very Short Introduction paints a broad picture of the current state of geography, its subject matter, concepts and methods, and its strengths and controversies.

Participatory intercomparison strategy for terrestrial carbon cycle models based on a service-oriented architecture

Article

May 2020
FUTURE GENER COMP SY

Terrestrial carbon cycle models are important tools for simulating carbon exchange; however, there are still significant uncertainties in the simulation results of different models. Model-data intercomparison has therefore been widely recognized as an effective approach for evaluating model performance and acquiring a more reliable understanding of the terrestrial carbon cycle. Although considerable efforts have been made in establishing model intercomparison projects (MIPs), existing MIPs still experience limitations in supporting teams of researchers working collaboratively online and ensuring the reproducibility of model experiments. This article proposes a participatory intercomparison strategy based on a service-oriented architecture (SOA), which aims to offer a web-based platform for researchers to construct participatory intercomparison (PIC) projects. The three fundamental components of a PIC project are the PIC topic, PIC instance, and PIC task. The PIC topic is used to help participants co-design backgrounds, goals, and comparison protocols. The PIC instance is used to help participants provide models, observations and benchmark data as reusable services. The PIC task is used to help participants formulate comparison workflows and acquire customized comparison results. Using the proposed strategy, a PIC project can be easily created and maintained by a group of geographically distributed participants. Reusability of models and data can be achieved through the proposed service-based wrapping method, and the reproducibility of model-data comparison experiments can be achieved through the workflow-based comparison method. A web platform named “P-MIP” was implemented and tested that helps researchers collaborate online, and a demonstrative PIC project was constructed to verify the feasibility and capability of the proposed strategy.

Integrating hydrologic modeling web services with online data sharing to prepare, store, and execute hydrologic models

Article

May 2020
ENVIRON MODELL SOFTW

Web based applications, web services, and online data and model sharing technology are becoming increasingly available to support hydrologic research. This promises benefits in terms of collaboration, computer platform independence, and reproducibility of modeling workflows and results. In this research, we designed an approach that integrates hydrologic modeling web services with an online data sharing system to support web-based simulation for hydrologic models. We used this approach to integrate example systems as a case study to support reproducible snowmelt modeling for a test watershed in the Colorado River Basin, USA. We demonstrated that this approach enabled users to work within an online environment to create, describe, share, discover, repeat, modify, and analyze the modeling work. This approach encourages collaboration and improves research reproducibility. It can also be adopted or adapted to integrate other hydrologic modeling web services with data sharing systems for different hydrologic models.

A data sharing method in the open web environment: Data sharing in hydrology

Article

Apr 2020
J HYDROL

Data sharing plays a fundamental role in providing data resources for geographic modeling and simulation. Although there are many successful cases of data sharing through the web, current practices for sharing data mostly focus on data publication using metadata at the file level, which requires identifying, restructuring and synthesizing raw data files for further usage. In hydrology, because the same hydrological information is often stored in data files with different formats, modelers should identify the required information from multisource data sets and then customize data requirements for their applications. However, these data customization tasks are difficult to repeat, which leads to repetitive labor. This paper presents a data sharing method that provides a solution for data manipulation based on a structural data description model rather than raw data files. With the structural data description model, multisource hydrological data can be accessed and processed in a unified way and published as data services using a designed data server. This study also proposes a data configuration manager to customize data requirements through an interactive programming tool, which can help in using the data services. In addition, a component-based data viewer is developed for the visualization of multisource data in a sharable visualization scheme. A case study that involves sharing and applying hydrological data is designed to examine the applicability and feasibility of the proposed data sharing method.

A global sensitivity analysis approach for identifying critical sources of uncertainty in non-identifiable spatially distributed environmental models: A holistic analysis applied to SWAT for input datasets and model parameters

Article

Feb 2020
ENVIRON MODELL SOFTW

Reflective communication to improve problem-solving pathways: Key issues illustrated for an integrated environmental modelling case study

Article

Feb 2020
ENVIRON MODELL SOFTW

The pathway of a modelling project is commonly described as an adaptively adjusted chain of steps at which various decisions are made. Communication and documentation about these decisions are crucial to enabling reflection and adapting the pathway to changing circumstances, such that well-informed planning is required. Project decision making, however, often remains a black box; it is rare to find reporting of dead ends, alternative decisions, and changes in decisions during the project. This paper uses an integrated environmental management case study in Iran to demonstrate the importance of reflective documentation and communication within the pathway. We show how a pathway diagram, incorporating some 14 symbols depicting steps, decision forks, options (selected and alternatives), actions, communication and documentation, can illustrate the role of communication within the project and identify lessons learnt. We also encourage further work on application of agile project management and social science techniques to improve modelling practices.

An open-data open-model framework for hydrological models’ integration, evaluation and application

Article

Apr 2020
ENVIRON MODELL SOFTW

To tackle fundamental scientific questions regarding health, resilience and sustainability of water resources which encompass multiple disciplines, researchers need to be able to easily access diverse data sources and to also effectively incorporate these data into heterogeneous models. To address these cyberinfrastructure challenges, a new sustainable and easy-to-use Open Data and Open Modeling framework called Meta-Scientific-Modeling (MSM) is developed. MSM addresses the challenges of accessing heterogeneous data sources via the Open Data architecture which facilitates integration of various external data sources. Data Agents are used to handle remote data access protocols, metadata standards, and source-specific implementations. The Open Modeling architecture allows different models to be easily integrated into MSM via Model Agents, enabling direct heterogeneous model coupling. MSM adopts a graphical scientific workflow system (VisTrails) and does not require re-compiling or adding interface codes for any diverse model integration. A study case is presented to illustrate the merit of MSM.

Landslide mobilization rates: A global analysis and model

Article

Nov 2019
EARTH-SCI REV

Landslides are a main driver of landscape evolution and a dominant sediment source in many regions worldwide. Nevertheless, their role in sediment mobilization and denudation remains poorly quantified, especially at the global scale. Based on an extensive literature review, we compiled measured contemporary landslide mobilization rates (i.e. the average annual volume of hillslope material displaced per unit area; LMR, [m³/km²/y]) from 116 study areas worldwide. Using this dataset, we calibrated and validated a multiple regression model that simulates global patterns of LMR at decadal timescales. This model explains about 62% of the observed variance in LMR based on topography, seismicity and lithology. By applying this model at the global scale and accounting for uncertainties due to observation and prediction errors, we estimate that annually about 56 billion m³ (∼100 gigaton) is mobilized by landslides worldwide. Intercontinental and interregional differences are large with Asia accounting for 68% (38 billion m³) of the global LMR. The Central and Southeast Asian mountain ranges (including the Himalaya and Karakoram) account for 50% (28 billion m³) of this rate, while covering only 2% of the Earth’s terrestrial surface. Comparisons of our simulated LMR with other data and models from literature suggest that, in highly tectonically active regions, around 70% of the mobilized landslide volumes are due to earthquake-triggered landslides. At the global scale, the contribution of coseismic landsliding to the total landslide mobilization rate is likely in the order of 30–40%. Using available databases of measured catchment sediment yield (SY), we also conducted a statistical comparison between measured SY and estimated LMR for nearly 3000 catchments in Europe and Africa. We observe highly significant relationships between LMR and SY for Europe (R²: 0.35, p < 0.001) and Africa (R²: 0.2, p < 0.001). Furthermore, landslide mobilization rates are typically larger than the corresponding SY. Overall, this study provides a first estimation of the global patterns of LMR. The results confirm the importance of landsliding as a dominant process of landscape denudation and sediment mobilization, but also illustrate the huge regional variations that characterize this process as well as the importance of sediment dynamics within catchments.

Introductory Overview: The OpenMI 2.0 Standard for Integrating Numerical Models

Article

Oct 2019
ENVIRON MODELL SOFTW

The purpose of this paper is to introduce, explain and promote the Open Modelling Interface (OpenMI) version 2.0 standard for coupling environmental numerical models (simulations of environmental processes). It is intended to be accessible to readers of all levels of experience. During recent decades it has been recognised that the environment is made up of a complex set of interconnected processes. Therefore, understanding the environment requires not only understanding of the processes in isolation, but also the interactions between these processes. Traditional methods of simulating such environmental interactions have included passing the outputs of one numerical model into another or creating a single ‘super-model’ covering a variety of processes. OpenMI provides a standard method which could be applied to independent numerical model components allowing them to exchange data and therefore influence one another. This is achieved without fundamental changes to the core of the components themselves.

Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications

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