iEMSs biennial conference
A4. Tools and software for participatory modeling and decision support Red Cedar, Tuesday 10:30 Ehsan Ghane Development and application of a DRAINMOD-based decision-support tool for optimizing the performance of saturated buffers 10:45 Enrico Pisoni Unveiling the origins of PM2.5 pollution in European urban environments: a comprehensive analysis 11:00 Gregorio Toscano Advancing Watershed Management: A Multiobjective …
Plenary Session Big Ten, Tuesday, 08:30 KEYNOTE Barbara RobsonLeader, Tropical Marine Water Quality and Impacts team, Australian Institute of Marine Science Assessing model “fitness for purpose” in the context of long-term change How do you know whether a model is “fit for purpose”? I propose a framework that considers not only the accuracy of the …
[Wednesday, 26 June]
Session C2, 1 pm (Room 106) – due to one cancelled talk, we were able to move the two remaining talks after the coffee break up and have a new schedule for this session published. Please check the schedule and note that session C2 will conclude with the final talk before the coffee break.
Workshop C3 (Centennial Room), has the room all afternoon, but will formally start after Coffee break at 3:30 pm.
Workshop F2 (Room Change) – The Workshop was originally scheduled for the Lincoln Room, but has been moved to the Centennial Room to make use of the remote connection facilities. It will run at the planned time from 1:00 – 3:00 p.m.
[Tuesday, 25 June]
Workshop A3, which was planned to be held in the Lincoln Room at 3:30 had to be cancelled due to the session organiser being unable to attend the conference.
[General notes]
Please note, that due to some sessions not receiving any or only few submissions, and some session organisers being unable to attend the conference, not all of the original sessions open for submissions will be run. Abstracts submitted to these sessions have been reallocated (usually) within the same stream, in cooperation with session organisers. Such changes affect the following sessions:
Finally, please note that as originally indicated, the “Zero”-Sessions were only for submissions where authors where unsure about the best topical fit, all submissions under these sessions were evaluated and assigned to the best suited topical session.
Please check other sessions if you had originally requested one of these sessions in your submission, or get in touch with us using the conference_editor@iemss.org email address.
Equally, workshops WS A2 and WS F1 were cancelled on request of the organisers.
Questions that will be raised and discussed, preferably in groups of 5-7 persons, with the aim of improving Participatory Modelling (PM) in practice include:
Workshop participants will learn to use a new method of Particle Swarm Optimization (PSO) with parameters and objective functions grouped by simulated processes and outcomes. The MultiGroup PSO method (MG-PSO) and related workflow will be introduced and demonstrated with a case study. We will introduce the Ages distributed watershed model principles and parameters to be applied to an agricultural watershed.
Ages is deployed as a Model-as-a-Service using the Cloud Services Innovation Platform (CSIP) with an API extension for calibration. All software is open source.
The MG-PSO workflow will be demonstrated with a new a graphical user interface MG-PSO-GUI utilizing the Python PSO library, a package available through PyPI. Attendees can participate in a hands-on exercise using the parameter sensitivity calibration tools. The case study uses Ages simulations of the South Fork Iowa River Watershed with different parameter groupings. Participants will learn how to set up calibration strategies, run a simple calibration, and explore the graphical output options. Deployment options for different models and platforms will also be discussed. Participants may explore ways to extend and customize the workflow, methods, and graphical interface for other uses.
Participants are encouraged to bring your own laptop preloaded with Python 3.10
Further information on the software is available at: https://pypi.org/project/mg-pso-gui/ [1]
Motivation
Humans have made profound and irreversible changes to the Earth. Because Anthropocene systems are highly interdependent and dynamically evolving, often with accelerating rates of cultural and technological evolution, the ensuing family of societal challenges (e.g., climate change and impacts, renewable energy, adaptive infrastructure, disasters, pandemics, food insecurity, biodiversity loss, sustainability, resilience and equity) must be framed and addressed in an integrated manner. To catalyze the required societal transformations, an evolutionary, system-of-systems (SoS) convergence paradigm is needed to coordinate strategic interventions across multiple systems and scales.
Description
The need for coordination and integration across multiple systems to address multiple societal challenges is clear, but almost every aspect of the scientific, technological and educational enterprise works against achieving what is most urgently needed. Describing how the Anthropocene systems coevolved, and briefly illustrating how the ensuing societal challenges became tightly integrated across multiple spatial, temporal and organizational scales, we propose an evolutionary, system-of-systems approach as a convergence paradigm for the entire family of interdependent societal challenges of the Anthropocene. This convergence paradigm requires that social scientists, environmental scientists and engineers collectively and systematically decompose, characterize and then re-combine the geophysical, biophysical, sociocultural and sociotechnical systems needed to address these large-scale challenges. The workshop will cover the following primary elements:
• Basic Research on the nested evolutionary sequence of geophysical, biophysical, sociocultural and sociotechnical systems
• SoS Framework including process-level models, systems-level models and the use of systems modeling language (SySML) and hetero-functional graph theory (HFGT) to couple the various models into a common computational framework
• SoS Pedagogy that develops a common scientific language building on the linguistic foundations of SySML and HFGT
• SoS Decision-Support System focusing on stakeholder engagement, equity, participatory modeling, scenarios and deep uncertainty
• Application to City-Regions to inegratively address the family of societal challenges at the urban and regional scale
Organizers: John Little, Sondoss El Sawah, Tony Jakeman and Amro Farid
The ISESS community (www.isess.net) meets at iEMSS 2024 to explore the latest advancements in environmental software systems using high throughput data streams and artificial intelligence to create virtual replicas of environmental systems. We will discuss how Digital Twins can help us better understand and model the behavior of complex environmental systems, from climate change to biodiversity and food security. During this workshop, we will delve into the following topics: (a) The concept of Digital Twins and its applications in environmental sciences, (b)High throughput data streams and their role in creating accurate Digital Twins, (c) Artificial intelligence and machine learning techniques for analyzing and predicting environmental phenomena, (d) Case studies of successful Digital Twin implementations in environmental monitoring and management, (e) Future outlook and potential applications of Digital Twins in environmental sciences.
Exascale computers are capable of 10^18 floating point operations per second – the equivalent power of a billion laptops. This scale of computational power is achieved primarily through massive parallelism, in particular, though not exclusively, through GPU programming. There are embarrassingly parallel problems that modellers of complex, coupled social-environmental systems can trivially address through parallel computing architectures, not least in calibration over multidimensional parameter spaces. However, large-scale computing architectures such as exascale computers also provide opportunities to model systems in greater detail and with larger spatial extent, albeit that interactions among system components typically put upper bounds on the performance improvements that can be realized through parallelism. In the ‘ExAMPLER’ project, funded by the UK’s Engineering and Physical Sciences Research Council, we have been exploring how access to exascale computing could transform the use of agent-based modelling in policy-relevant contexts. This workshop builds on earlier visioning work to develop insights into how this transformative change could be realized through reimagined software architectures and institutions supporting high-performance computing use. The project has a website at https://exascale.hutton.ac.uk/
As the field of environmental modeling becomes increasingly reliant on more data-driven approaches, it is essential to equip students with the skills to effectively interpret and analyze complex environmental models. This workshop presents a framework that integrates data science techniques and environmental modeling principles to enable educators to bring modern tools into their existing curriculum and inspire students to tackle real-world environmental challenges.
Participants will explore strategies for designing effective learning objectives, assessment methods, and practical exercises for environmental modeling. Through a guided exercise, participants will learn how to access, clean, and process large-scale environmental data using Python in Google Colab notebooks. Groups of participants will then practice applying the framework to their environmental domain.
In this workshop, educators, researchers, and practitioners can exchange their experiences, best practices, and innovative methods for effectively utilizing modeling software in environmental education. By equipping participants with the skills and tools needed to leverage environmental modeling to improve student learning, the workshop aims to empower the next generation of environmental modelers to make informed decisions and contribute meaningfully to environmental sustainability.
Workshop Objectives:
1. Discuss the importance of environmental modeling in understanding and addressing complex environmental challenges.
2. Share best practices and innovative approaches for integrating modeling software into the classroom.
3. Apply data science techniques to environmental datasets for modeling and analysis.
4. Discuss the benefits and challenges of using modeling software for student learning and engagement.
5. Identify strategies to overcome barriers and promote adopting modeling software in environmental education.
Making models FAIR (Findable, Accessible, Interoperable, and Reusable) is a widely shared goal among modeling scientists. Initiating and sustaining the scientific practices needed to realize this goal will be challenging, however. It requires additional time, effort, and a conceptual shift to move beyond developing models to meet specific research or policy aims, to developing models that also can be used or extended by others.
Community-wide standards can help to enable FAIR-aligned practices in modeling social and environmental systems. However, it is equally important to create incentives for organizations and individual researchers to engage in these practices as well as software tools and clear guidance that make it easier to make a model FAIR. The Open Modeling Foundation (OMF) was established recently as a federation of modeling science organizations (including iEMSs) to promote community-wide standards for best practices in modeling. It also endeavors to incentivize FAIR practice through a combination of recognition, rewards, and requirements. Model users need to be able to identify models that meet FAIR standards, and developers who create models that meet such standards need to be professionally recognized.
In this workshop, representatives of OMF Working Groups will offer brief introductory presentations about how the modeling community can establish the necessary feedbacks between incentives and standards for FAIR-aligned practice. These presentations will be aimed at stimulating round table discussions among attendees to this workshop. Special emphasis will be on establishing a professional culture to support FAIR practice among early career researchers who will become the next generation of modeling scientists. We invite researchers and practitioners from all experience levels to join us in the discussion on incentives to stimulate FAIR practices in modeling (both inside and outside academia).
Organizers: Michael Barton (Arizona State University), Allen Lee (Arizona State University), Serena Hamilton (Australian National University), Iestyn Woolway (Bangor University), Min Chen (Nanjing Normal University), Andrew Bell (Boston University)), Charlotte Till (Arizona State University)
US EPA’s PMF5 (Positive Matrix Factorization v. 5) tool is being upgraded and replaced by a new open-source application, which includes detailed workflows and graphical analysis notebooks, and in the future will include a self-contained desktop application. Used worldwide, PMF5 provides scientific support for developing and implementing environmental standards and forensics. By using a range of environmental data, this tool determines how much each source contributed to observed exposure concentrations. The new tool provides a matrix factorization workflow that includes two different algorithms for calculating source apportionment solutions where there is input data uncertainty. The tool intends to replicate the functionality and graphical components of PMF5 but with enhanced and modern algorithms and techniques. The workshop would guide user’s through the details of the code through Jupyter notebooks, such as running pre-processing, model training, and post-processing steps, describe the details of the algorithms, and provide the users with the skills to run this tool on their own data.
Model design choices (here encompassing many aspects including formulation, system boundaries, scales, scenarios, objectives, quantitative and qualitative methods of uncertainty assessment) have far-reaching effects on the whole modelling process – from formulation to construction, participation and monitoring. These choices naturally affect a model’s input data requirements, assumptions made, simulated outcomes and their uncertainty. Design choices and their transparency also influence the ability for participatory partners and stakeholders to develop trust in a model’s outputs and therefore the success of the model to support policy development or decision making. Thus, participatory modelling should comprise “cradle to grave” decisions (design to practice) and their justification.
This workshop accompanies the iEMSs 2024 presentation sessions on water quality modeling. It will attempt to develop a framework and outline an associated set of methods that will assist the modeling community to assess and communicate the effects of design choices on model output quantities of interest, with a focus on uncertainty management and assessment, and decision support tools for policy and its implementation. The framework should be generically applicable regardless of model style (e.g., modelling for different spatial and temporal resolution) and purpose (e.g., for forecasting, reporting and planning). But in the workshop, we will focus on models addressing various sources (urban, industrial, agricultural, point and diffuse/distributed) causing social, economic and environmental consequences of poor water quality.
The outcome of this workshop is expected to be a Position Paper on the current and future designs of water quality modeling for managing uncertainty. For those wishing to contribute to the workshop it is proposed that their primary ideas be submitted as an abstract and presented as a talk in the accompanying water quality sessions so as to make more efficient use of time in the workshop. The organizers will lead a discussion structured around the topic and invite audience participation on key subtopics. There will be an overview talk on the topic in the sessions on water quality.
Environmental justice encompasses the fair and equitable distribution of environmental benefits and burdens, focusing on marginalized communities disproportionately affected by environmental hazards. This session proposes to highlight the significance of environmental modeling and software in addressing environmental justice issues and will provide a platform for researchers, practitioners, policymakers, and community leaders to discuss the role of modeling and software in understanding, analyzing, and advocating for environmental justice. Through engaging discussions and case studies, participants will explore the potential of modeling tools in advancing equity, inclusion, and sustainable development.
Session Objectives:
• Understand the intersection of environmental modeling, software, and environmental justice.
• Discuss the importance of incorporating environmental justice principles in modeling and software development.
• Explore innovative approaches to integrate environmental justice considerations in modeling processes and outcomes.
• Share case studies and best practices for utilizing modeling and software for addressing environmental justice issues.
• Identify challenges and opportunities in applying modeling and software tools to promote environmental justice.
• Discuss strategies for collaboration between academia, communities, and policymakers in using modeling for environmental justice advocacy.
Potential Topics to Be Covered:
• Introduction to environmental justice and its relationship with modeling and software.
• Incorporating environmental justice principles into modeling and software development.
• Case studies of modeling applications for addressing environmental justice issues.
• Assessing environmental justice disparities through modeling and data analysis.
• Collaborative modeling approaches involving communities and stakeholders.
• Tools and software for visualizing and communicating environmental justice data.
• Ethical considerations and challenges in using modeling for environmental justice.
• Innovative modeling techniques to address complex environmental justice problems.
• Evaluating the effectiveness and impact of modeling in advancing environmental justice goals.
• Policy implications and recommendations for integrating modeling into environmental justice advocacy.
Islands serve as early warning systems and special places where complex interactions between natural, physical, and social systems can be observed and monitored. Cultural and natural heritages located in islands are increasingly at risk from coastal hazards. Detecting and evaluating these complex interactions have become a critical step to significantly reduce the adverse impacts of coastal floods, also considering a wide variety of forcings (including storm surge, earthquakes, global atmospheric processes) and multiple risk factors (wildfires, precipitation, wind intensity). In this session, we would look at different methodologies and case studies of risk analysis at cultural and natural sites in islands exposed to coastal flooding under local and global wave energy and sea-level rise scenarios at different planning horizons. Using recent data and analytical tools from many island communities, the hazards and threats but also strategies for mitigation, adaptation, and risk reduction will be described. We welcome contributions on various case studies exploring the barriers and limitations to implementation, along with creative strategies for further collaborative research, testing, and capacity building are shared.
The primary goal of this workshop is to develop a common, open approach to deriving and representing air pollution source/receptor (S/R) relationships that would allow the results of multi-model global and regional atmospheric chemistry experiments, such as those conducted by TF HTAP and others, to be easily compared and incorporated into integrated assessment models (IAMs) or other reduced-complexity models (RCMs) for air quality.
The workshop will use a set of invited presentations and group discussion to:
• survey the methods currently used to derive and represent air pollution S/R relationships in IAMs and other RCMs for air quality
• develop recommendations for the design of a common, open framework that would allow S/R relationships from different complex atmospheric chemistry and transport models to be incorporated into IAMs and RCMs
• develop recommendations for the design of multi-model experiments under the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) and other cooperative global atmospheric chemistry modeling efforts needed as inputs to this open framework
TF HTAP is an expert group under the LRTAP Convention (or UNECE Air Convention) (see http://htap.org). Formed in 2005, TF HTAP has organized a series of multi-model studies to improve our understanding of the influence of air pollutant emissions in each region of the world on air pollution impacts (health, ecosystem, climate, …) in other regions of the world. Traditionally, these multi-model studies have used complex global and regional atmospheric chemistry models running a series of sensitivity simulations to derive a set of S/R relationships between regions of the world.
Air pollution S/R relationships have been developed for use in a number of IAMs or RCMs using similar approaches to that used in the TF HTAP experiments. However, the S/R relationships in a particular IAM or RCM are usually derived from one set of simulations with a single complex atmospheric chemistry model. Through a common, open framework, we hope to make it possible for existing and future IAMs and RCMs to incorporate information from the large ensembles of model results produced by TF HTAP and others. Access to these ensemble results will allow IAMs and RCMs to compare the implications of choosing different complex models or ensemble averages and to update the S/R relationships as complex models and the environmental conditions simulated continue to evolve.
Hydroinformatics has offered novel solutions for water-related challenges that can meaningfully provide integration between big data, system science, and computational intelligence. The goal of this session is to provide an active forum to discuss scientific ideas and technical solutions to the advancement of:
(i) predictive and analytical models based on computational intelligence, machine learning, and data science,
(ii) workflow automation techniques through the use of scientific computing, crowdsourced data, sensing, and web services,
(iii) methods for the analysis of big datasets, including remote sensing, time series, and synthetic/real-time monitoring data, and (iv) AI-enabled digital twins for water resources decision making.
Applications could cover any area of water resources such as rainfall-runoff modelling, sedimentation and flood/stormwater modelling, water system analytics, analysis of meteorological and hydrologic data, coupling numerical weather prediction with hydrologic model. Contributions from early-stage researchers, students, women, and minorities are especially welcome.
Artificial intelligence (AI) is rapidly transforming the field of environmental science. AI tools and software libraries are now being used to model and analyze environmental systems, communicate environmental information to the public, and develop new environmental policies and regulations. This session proposes an in-depth exploration of AI tools and software libraries pertinent to environmental modeling, analysis, and communication. This session will explore the latest advances in AI for environmental science, and discuss how these tools can be used to address some of the most pressing environmental challenges facing the world today. The proposed session will blend theoretical learning with practical demonstrations, supplemented by relevant case studies. The session invites studies that focus on the latest advances in AI for environmental modeling and analysis, AI tools for simulating environmental systems, predicting environmental impacts, and identifying environmental risks. The session will also explore how AI can be used to communicate environmental information to the public. This will include a discussion of AI tools for creating interactive visualizations, developing educational materials, and engaging the public in environmental decision-making. The ultimate objective is to foster a deeper understanding of the intersection between AI and environmental science, promoting its use for future research and initiatives.
The recent release of OpenAI’s ChatGPT, Google’s Bard, and other related tools has brought artificial intelligence (AI) to the forefront of many conversations. These tools can perform many tasks from writing computer code to composing an essay, but how AI will be used for environmental modelling is still in development. There have been examples where AI has shown exciting promise for accurate and efficient environmental models. At the same time, the range of environmental applications is broad and the related data is often sparse and/or non-uniform. Furthermore, environmental models can be used to inform costly and consequential decisions making their results long-lasting and raising a question of trustability. These attributes pose challenges to the use of AI for environmental modelling and decision making – challenges that applications such as natural language processing do not face. On the upside, environmental applications have associated process knowledge which could be leveraged. We invite researchers to present on their own experiences and/or vision regarding AI (including machine learning) for environmental modelling and decision making. We encourage topics on the successes, challenges, and possibilities of using AI for environmental applications. We also encourage topics related to the trust (or lack thereof) in AI for environmental applications and if/how that trust can be increased including (but not limited to) the use of Explainable AI and Knowledge-Infused AI.
Models are useful tools for geographic and environmental research in analyzing global/regional geohypotheses, and supporting decision/policy making. To date, numerous models have been developed to simulate different geographic phenomena and processes to solve various problems. Due to the complexity of geographic and environmental problems, there is an increasing need to support collaborative modeling with experts from different disciplines. However, the heterogeneity of geographic and environmental models hinder model exchanging or integration to better represent reality and answer broader research questions. Open modeling and simulation could significantly help increase the transparency of research and reusability of models, promoting collaboration of researchers and scholars across domains.
This session aims to bring scholars worldwide together to explore the theories and approaches of open modeling and simulation in geographic and environmental research. This session focuses on research about model standard design, application paradigm, and system implementation for open modeling and simulation in relevant domains. Potential topics include (but are not limited to) the following:
– Model standard design;
– Model reusability and reproducibility;
– Service-oriented simulation framework or system design;
– Participatory modeling or collaborative modelling;
– Data-exchanging in model integration;
– Model application in specific domain (e.g., hydrology, soil, atmosphere, etc.)
Developing surrogate models from results of environmental process models requires a progression of operations that cover the entire spectrum from process model calibration to surrogate model evaluation. Each step of this pipeline can make or break the final product, and can be implemented in numerous ways. This session focuses on innovative approaches to build the pipeline, applying these new methods, and quantifying the propagation of uncertainty from input data to process models to surrogate model results. Specific case studies may include simulation of crop growth, water quality and quantity, carbon sequestration and greenhouse gas emissions, and other environmental processes.
Potential topics for discussion within the session include, but are not limited to:
– Selection of objective functions;
– Evaluation of model metrics;
– Data splitting for cross-validation;
– Geospatial sampling and clustering;
– Parameter space dimensionality/complexity;
– Parameter non-uniqueness;
– A priori & a posteriori sensitivities;
– Error and uncertainty propagation.
This session explores integrating data science into social and environmental assessments for sustainable development. Attendees will learn about cutting-edge methodologies, innovative applications, and best practices through presentations and interactive discussions. The session covers the role of data science in enhancing the effectiveness of impact assessments using advanced analytics, machine learning, and AI to process large datasets, reveal patterns, and make predictions.
We will discuss integrating geospatial analysis, remote sensing, advanced analytics, machine learning, and AI for environmental assessments. This combination allows for better identification of impacts and risks associated with land use, infrastructure, and climate change, enabling real-time monitoring and assessment to support decision-making. Additionally, the session will examine natural language processing and sentiment analysis for evaluating social impacts and stakeholder perspectives, fostering a more inclusive and participatory process. This helps develop targeted mitigation strategies and effective communication of assessment results.
Lastly, we will address ethical considerations, including data privacy, quality, and representation. Participants will contribute to guidelines and best practices for responsible and effective incorporation of data science in social and environmental assessments.
Water infrastructure systems such as reservoirs and dams, pump stations, storage tanks, and distribution networks are important infrastructure to provide water to residential, commercial, industrial and agricultural users to maintain their daily activities. Billions of dollars per year are spent on construction, expanding, augmenting and operation (e.g., pumping) of these systems. The planning, design, operation and rehabilitation of these systems follows a complex engineering decision-making process that often requires the use of simulation models and optimization techniques to ensure a range of system conditions are met and objectives are optimized. This process is further complicated as 1) these decision-making conditions are changing in both short- and long-term, and 2) non-conventional water (e.g., harvested stormwater and rooftop rainwater) and energy (e.g., behind-the-meter solar) sources have been considered. We invite contributions from any field investigating the planning, design, operation, rehabilitation, and simulation modelling and optimization of water infrastructure systems and the analysis of water-energy nexus in these systems that will contribute to improved management of these systems. Interdisciplinary studies addressing these issues are welcome.
Streamflow forecasts from the U.S. National Water Model are generated on an hourly basis for various forecast periods ranging from 18 hours to 30 days for over 1.8M stream reaches throughout the nation. The resulting massive dataset is continuing to grow at a rate that makes it increasingly challenging to easily extract useful information or so-called “actionable intelligence” from the data. This problem is being addressed by researchers that are part of The Cooperative Institute for Research to Operations (CIROH) through funding from the National Oceanic and Atmospheric Administration (NOAA). Research efforts include exploring improved means and methods for archiving, querying, and transferring data through Google Big Query, Amazon Web Services, and other cloud providers, as well as improved methods and tools for exploring current and historical forecasts, tracking warnings and potential floods. This session is intended to support presentations on advances in hydroinformatics related to the NWM specifically, but also encourages presentations on related and similar systems. Anticipated presentations will showcase cloud services-based data access, visualization tools, web and mobile applications for forecast analysis, and related.
The U.S. National Water Model (NWM) has made significant advances in continental scale forecasting on its 1.8M stream reaches since its launch in 2016. The Cooperative Institute for Research to Operations (CIROH) is a research consortium of >20 universities that is actively improving the NWM through funding from the National Oceanic and Atmospheric Administration (NOAA), to make the model more accurate, flexible, and maintainable. A key goal of this effort is restructuring the model to operate on a “NextGen” modelling framework that is a modular and open-source platform for building complex integrated models using the Basic Model Interface (BMI) standard for coupling different types of models together. This approach allows the NWM to incorporate new and improved modeling capabilities more easily – including enabling the NWM to incorporate new models for snowmelt, groundwater, and reservoir operations. This session is an opportunity for researchers working on the NWM directly as part of CIROH, as well as others with an interest in continental-scale integrated modeling efforts, to present and discuss relevant advances in the modeling framework, methods, and approaches. Presentations are invited on all related topics with special emphasis on the integration of flood inundation modeling, groundwater, snowmelt, and other components in the NWM. Techniques for model validation, post processing and bias correction, and related presentations are also encouraged.
We cannot envisage the future generation of models without thinking about the next generation of professionals (including model producers and consumers) who have the mindset and capacity for realising this vision and beyond. Education plays a central role in shaping these mindsets. Yet, there is a clear lack of ‘pedagogy culture’ in the field, which manifests in the lack of debate, investigation and evaluation concerning how methods are taught. In the absence of research-based insights, teachers are left to rely on a network of peers, scattered research literature, and much trial-and-error for developing their teaching resources and practices. In addition, the pandemic and consequent rapid shift to online teaching have generated new ways of thinking, practices and tools inside and outside classrooms.
This session aims to promote a scholar-based approach for teaching complex systems modelling. We welcome theoretical and empirical studies from various fields of applications related to complex modelling (e.g. environment, health, business…etc). Examples of contributions include:
• Pedagogical advances related to systems modelling, including assessment design approaches
• Case studies sharing specific methods and tools used in teaching systems modelling
• Good examples of teaching resources, such as learning lessons, vignettes and rubrics
• Reflective work from a teacher as well as a learner perspective gleaning insight into ‘what worked’, ‘what did not work’, and why.
Participatory modeling and community engagements are critically important for countries and global society to address complex challenges relating to natural resources, hazards, and environments. Reasons include:
(1) the local knowledge and other sources of knowledge provided;
(2) the preferences, feasibilities, and science/policy feedbacks contributed – potentially extending across scales of governance and across time, space, communities, and cultures; and
(3) the accountability and political will generated – also potentially extending across scales of governance and community.
This session examines the policy, governance, and societal conditions that affect, and are affected by, participatory processes and stakeholder engagements. A key question is how participatory modeling processes can function as integrated and cohesive components of governance systems so that their outcomes transcend across scales of governance and community. We are also interested in the approaches, knowledgebases, or tools that can provide greater transparency and accountability to science/policy governance and outcomes.
Our session seeks to improve the outcomes of governance processes and decision making – across a multiplicity of scales – through participatory modeling and a diversity of stakeholder engagements. We are particularly interested in presentations and case studies relating to climate adaptation and sustainability.
In many parts of the world, agricultural production contributes to a significant part of the greenhouse gas production, material discharges through intensive farming and the ongoing decline of biodiversity. At the social and political level, thus a need to transform agriculture towards ecological sustainability and multifunctional landscapes has been recognized. Many models and methods have been developed by scientists that address specific parts of this problem, e.g. hydrological and biodiversity models to describe changes in ecosystems, agent-based models simulating socio-economic processes of the agricultural system or multi-criteria optimization identifying optimal land-use allocations. However, to guarantee practical relevance of modelling results for a successful transformation of agriculture, stakeholders should be integrated in the modelling process. Hence, in this session we seek recent methods and applications of modelling/optimization approaches with strong stakeholder involvement that aim at solving real-world problems in agricultural landscapes. Submissions focused on applications are encouraged; they may be related to water and agri-environmental management, land use allocations, biodiversity protection, and the adoption of sustainable technology and management. Methodological advancements may include but are not limited to the genres of game theory, evolutionary and hybrid algorithms, operations research, hierarchical optimization, or agent-based modelling.
Narratives are effective forms of communication that serve to reflect and influence identity and emotions, provide simplifications of social-ecological systems, and build visions of the past, present and futures of communities. Narratives – their embedded emotional cues, beliefs, values, mental models and schema – interact with sources of knowledge, ways-of-life, social and biophysical contexts. Narratives powerfully affect societal approaches to climate adaptation and pathways for sustainability.
Our session will focus on methods that can be used to:
(1) Elicit and analyze narratives and their influence on decision-making and participatory processes.
(2) Identify benefits and pitfalls to participatory processes; and roles for narratives in improving facilitation.
(3) Create Records of Engagement and Decision-making (RoED) to improve facilitation, and for broader, longer-term, dissemination/use of the efforts and outcomes of participatory processes.
Biocultural evolution provides a foundation for contextualizing/evaluating narratives and patterns of thought and/or (in)action. Social science frameworks – including those relating to values, norms, sources of knowledge, biases, heuristics – are also of relevance.
Our session seeks to improve participatory modeling and stakeholder engagements through a focus on narratives, narrative components and RoED, including as they may be applied to climate adaptation and sustainability issues.
Advances in data science, machine learning, and artificial intelligence are accelerating at an exponential pace. Potentials for application in social-ecological systems modeling already include: increasing the efficiency of computational processes, complex model emulation, and data mining for unique scenarios. While these
application areas have the potential to increase performance of model application, some conceptual tensions exist for applications in participatory modeling, in which processes of engagement, communication, learning, and trust-building are emphasized in addition to questions of model fidelity, realism, and efficiency. In addition, AI-based models can exhibit problems such as spurious results or algorithmic bias. In this session, we invite papers that illuminate ways in which data science, machine learning, and artificial intelligence can support the specific goals of participatory modeling.
These may include: how these techniques could support the function of models to act as effective boundary objects between diverse perspectives, increase the accessibility and transparency of complex models, reduction of barriers (for example technical skills) to collaborative modeling processes, facilitate multi-scalar problem formulation, or aid in the interpretability or communication of model output. We also invite papers that address how PM could be used to contribute to the creation of more responsible AI models.
We propose a session that will bring together researchers and experts from various disciplines to discuss and examine the latest tools and software for participatory decision-making, modeling, and design. In particular, the session encourages submissions discussing advances in openly developed software and tooling for participatory modeling and decision support across various fields.
These include environmental and climate policymaking, resource planning, and climate change adaptation. The use of computational tools to support participatory modeling and decision making are now commonplace, enabling knowledge elicitation and organization and promoting a diversity of stakeholder participation. Recent issues around research reproducibility and transparency have led to a general preference for openly developed computational tools to address, or otherwise mitigate, concerns around the trifecta of reproducibility, reusability, and replicability. Improvements to the accessibility and usability of said software can also enhance the exploration of complex environmental issues and allow for a more holistic and inclusive knowledge base to be developed. The goal of this session is to explore new and upcoming computational tools for participatory modeling, as well as understand their strengths, limitations, and challenges. Understanding how to best use available computational tools allows for a more holistic and inclusive knowledge base to be developed, enabling practitioners to tackle complex environmental problems more effectively.
We welcome submissions that report on new software, or the integration of AI and machine learning into existing tools, across all aspects of the participatory process. Software to enhance uncertainty communication to help stakeholders understand and navigate uncertainty inherent in complex environmental problems are also of interest.
People’s interactions with models are increasing as data access and modelling techniques evolve rapidly. Ten years after the publication of the position paper Modelling with Stakeholders by Voinov and Bousquet (2010), and Voinov et al. update in 2016, claims to revisit concepts, techniques, and technologies that support a participatory approach in modelling. This session aims to incite academic discussions, oral presentations, paper/workshop showcases, and symposiums that provoke the use of participatory modelling (or modelling with stakeholders) as key to tackling current challenges in different disciplines. The session will be an invitation to different disciplines and researchers, practitioners or modellers from a wide range of fields that use modelling and simulation to imagine people’s participation in the centre and human-interface-technology interaction as key to thinking more than the next generation of participatory modelling. All new technologies, software, Virtual reality/Augmented reality VR/AR platform uses, dashboards, simulation games, tools, modelling techniques, case studies, evaluation, and models will be welcome in this session. Opening session ideas will be led by the paper presented: Modelling with Stakeholders 2.0: more than a next-generation (Avendano-Uribe, B.E. 2023).
Scenarios are well-established scientific tools in sustainability sciences to depict and to develop ideas of plausible and/or desirable future development pathways, and to foster anticipatory knowledge. They help to gain a better understanding of the complexity and the uncertainties of systems as well as to estimate the effects of decisions and different influencing variables. In addition, they provide a platform for integrating the knowledge, approaches and objectives of different actors involved in their creation. The co-creation of scenarios with stakeholders, experts and scientists in participatory processes becomes increasingly important for identifying trade-offs and synergies between different aspects of sustainability and for developing feasible and socially acceptable solutions e.g. regarding the implementation of land management or climate mitigation strategies. Objective of this session is to present and to discuss methods and case studies for the application of simulation models that integrate social and environmental components in participatory scenario processes. Focus is on research related to sustainability science. This may include questions of identifying the relevant scale level and domain of modelling, incorporation of stakeholder knowledge and qualitative scenario components into models in an iterative manner as well as communication of model results and inherent uncertainties.
Environmental sustainability is one of the greatest challenges in the 21st century, and the recent IT and data revolution offers unprecedented opportunities for potential breakthroughs in our ability to understand and manage complex systems, interactions, and sustainability. However, these possibilities are largely not realized, primarily because the technologies to produce big data are not matched by the technologies to utilize them. How we work is actually preventing us from taking full advantage of new opportunities; although the internet offers a connected platform for problem solving, we largely use it as static storage and pipelines. Although big data from ‘networks of networks’ offer a unique opportunity for understanding, characterizing, and modeling processes and system connectivity at unprecedented scales, we treat it as an extra data source for problem solving at our local sites or systems.
In this session, we invite presentations on intelligent environmental and water resources science and engineering, with a focus on new paradigms of data use, modeling, software, and innovative applications. In particular, we invite presentations related to or involving:
• Data-enabled science and modeling
• Cyber-enabled discovery and learning
• Connected problem-solving
• Data- / technology-mediated collaboration
• Multiscale, complex system modeling
• Intelligent water resources software systems and innovative applications
• Smart water resources management and
This session aims to bring together researchers from diverse disciplines who have studied or are interested in the environmental impacts on the transmission of diseases. The session will focus on exploring various modelling approaches used to understand the complex interplay between planetary health and environmental sustainability. It will cover a wide range of topics, from theoretical and empirical modelling to the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques in disease transmission studies. By sharing knowledge and experiences, participants will gain insights into the best practices for accurate and insightful modelling in the context of planetary health and environmental sustainability.
Topics Covered, but not limited to:
1. Planetary Health and Environmental Sustainability: Understanding the interconnectedness of environmental factors and human health, and their relevance in policy development.
2. Disease Transmission Modelling: Discussions on the strengths and limitations of different modelling approaches and techniques in the context of planetary health, and their implications for decision-making.
3. AI and ML Techniques: Showcase of AI and ML applications in understanding planetary health and sustainability challenges
4. Spatial and Temporal Modelling: Utilizing spatial and temporal models to identify hotspots, patterns, and trends in disease spread
The concept of a circular bioeconomy offers a transformative solution that promotes sustainable production, minimizes waste, and maximizes resource efficiency. This conference session aims to explore the key aspects of food and agricultural systems within the framework of a circular bioeconomy, bringing together experts, modellers, practitioners, and researchers to share insights, discuss innovative strategies, and foster collaborations.
This conference session will provide a comprehensive exploration of the concept of a circular bioeconomy and its relevance to food and agricultural systems focusing on the need for a systems approach. We will delve into the challenges and opportunities presented by this transformative model and showcase successful case studies, cutting-edge research, and practical applications that are driving the transition towards a more sustainable and resilient future. The session will comprise keynote speeches, and a panel discussion.
Suggested topics:
1. Sustainable Agriculture and Resource Efficiency
2. Waste Management and Valorization
3. Circular Food Systems
4. Policy, Economics, and Stakeholder Engagement
The session invites original contributions on a wide range of applications of modeling and analytic techniques to a broader domain of planetary health through the lens of attaining sustainable development goals. Possible techniques may include, but are not limited to, exploratory data analysis, dataset summarization, explainable feature engineering, hybrid models and joint prediction and explanation, proxy models, intelligent data analysis and its combination with process-based simulations and computational modeling, confirmatory analysis. Hybrid frameworks and techniques, success stories of their application across spatial and temporal scales as well as lessons learned from the undertaken projects are of special interest.
This session gathers the knowledge of frontiers in socio-environmental systems to identify, discuss and evaluate the potential of socio-behavioural, infrastructure and technology interventions over different sectors -e.g., buildings, transport, and food- and various geographical scales toward climate-resilient cities. We aim to discuss the novel practices and methods in socio-environmental modelling and explore how energy demand transitions can contribute to rapid and deep climate change mitigation towards 1.5°C. This session covers a wide range of approaches and methods to simulate human-environment interactions and (qualitatively and quantitively) assess the impacts of low energy demand interventions on climate change, human health and wellbeing.
Socio-Environmental Systems (SES) modelling involves developing and/or applying models to investigate complex problems arising from interactions among human (i.e. social, economic) and natural (i.e. biophysical, ecological, environmental) systems. SES modelling can be used to support multiple goals, such as informing decision making and actionable science and promoting learning, education and communication. Elsawah et al. (2020) identified eight grand challenges for accelerating the use and impact of SES modelling. These eight grand challenges are gaps in our knowledge, methods and data that limit the certainty that can be ascribed to addressing the SES issues.
They are:
1) Holistic uncertainty assessment and management,
2) Bridging epistemologies across disciplines,
3) Scales and scaling in SES modelling,
4) Creating models that combine qualitative and quantitative methods and data sources,
5) Elevating SES models adoption and impact on policy,
6) Capturing structural changes in SES,
7) Human dimension in SES modelling, and
8) Leveraging new data types and sources.
This session aims to progress the community dialogue about tackling these challenges by bringing together recent innovation, and identifying pressing issues where we need to accelerate research efforts. We have particular interest in submissions tackling the interactions among the challenges, such as approaches for dealing with the uncertainty related to contested knowledge and plurality of perspectives.
The concept of a circular bioeconomy offers a transformative solution that promotes sustainable production, minimizes waste, and maximizes resource efficiency. This conference session aims to explore the key aspects of food and agricultural systems within the framework of a circular bioeconomy, bringing together experts, modellers, practitioners, and researchers to share insights, discuss innovative strategies, and foster collaborations. This conference session will provide a comprehensive exploration of the concept of a circular bioeconomy and its relevance to food and agricultural systems focusing on the need for a systems approach. We will delve into the challenges and opportunities presented by this transformative model and showcase successful case studies, cutting-edge research, and practical applications that are driving the transition towards a more sustainable and resilient future. The session will comprise keynote speeches, and a panel discussion.
Suggested topics:
1. Sustainable Agriculture and Resource Efficiency
2. Waste Management and Valorization
3. Circular Food Systems
As modelling shifts away from the traditional focus on the model itself and towards the role of model development in linking science, decision-making and social learning, we are seeing a new wave of research on what constitutes good modelling practice. Novel practices and processes in modelling are further motivated by increasing mainstream use of models in high stakes policy contexts, new sources of data for validation and model building, and understanding of the subjective, social, and behavioural phenomena underpinning modelling. This session encourages a broad view on the topics of problem framing, model co-design, system identification and conceptualisation, uncertainty, and validation with a focus on evaluating the quality of modelling, models, and model predictions.
We welcome methodological contributions and empirical explorations of these issues from a variety of perspectives, including:
• Efficient and effective modelling
• Credibility, salience, and legitimacy
• Usability, reliability and feasibility
• Responsible, coherent and transparent use of modelling to support policy evidence bases
• New techniques for model validation and benchmarking
• Processes and criteria for establishing model quality
• Organisational processes and institutions to support good modelling practice
• Modelling standards
Red tides, blue-green algae, and cyanobacteria are examples of harmful algal blooms (HABs) that can have severe impacts on human health, aquatic ecosystems, and the economy. Too much nitrogen and phosphorus in the water causes algae to grow faster than ecosystems can handle. Significant increases in algae harm water quality, food resources and habitats, and decrease the oxygen that fish and other aquatic life need to survive. Large growths of algae are called algal blooms and they can severely reduce or eliminate oxygen in the water, leading to illnesses in fish and the death of large numbers of fish. Some algal blooms are harmful to humans because they produce elevated toxins and bacterial growth that can make people sick if they come into contact with polluted water, consume tainted fish or shellfish, or drink contaminated water.
HABs are a major environmental problem around the world, in all of the 50 states, and notably so in the Great Lakes Region
This session will offer presentations from agencies, academia, and the private sector to highlight the following:
1) HAB issues currently being experienced in the Great Lakes Region
2) HAB process description uncertainty and research being done to reduce that uncertainty
3) HAB predictive modeling research and development
Sustainable food-energy-water systems and emerging technologies raise challenges for modelling agricultural systems. We must move beyond modelling uniform fields of annual crops to address the issues at the core of this Congress. Models must be designed to deal with complex production systems: which vary spatially (e.g. widely-spaced agroforestry, skip-row cropping); where management exacerbates heterogeneity (e.g. residue management in oil palm plantations, nutrient transfers by livestock); or where the boundaries of the spatial unit are fluid in time (e.g. land that is a community resource).
Agricultural models and their software need to be continually improved to accommodate these needs. We encourage submissions that focus on new/improved methods/approaches (rather than examples of usage) in order that the agricultural modellers can learn as a community.
Artificial Intelligent (AI) and Machine Learning (ML) have transformed industries, offering new possibilities for process-based modelling (PBM). This session explores the synergies between AI, ML, and PBM, showcasing their enhanced capabilities, challenges, and real-world implementations across diverse domains. Experts will share insights, experiences, and use cases in this evolving field.
Key Topics Covered:
• Integrating AI and ML techniques in PBM: Challenges and opportunities
• Data-driven approaches for PBM and analysis
• Case studies on AI and ML-aided modelling in specific domains
• Techniques for feature engineering and model selection
• Enhancing system performance through AI and ML optimization
• Predictive modelling and decision support systems in complex environments
• Explainable AI (XAI) in PBM to enable and build trust in the results
Why Attend:
• Insights from esteemed experts in AI, ML, and PBM
• Discover real-world applications and practical impact in various domains
• Network with researchers, practitioners, and industry professionals
• Engage in interactive sessions for deeper understanding and collaboration
Who Should Attend:
• Researchers and academicians
• Professionals involved in system analysis, optimization, and decision-making
• Data scientists and engineers interested
• Policy makers and regulators
Join us to gain valuable knowledge, engage in discussions, and explore new frontiers in AI and ML-driven PBM and applications
In many countries, the prioritisation of water for industry (e.g. irrigation, mining) has resulted in significant and often irreversible decline of the riverine system providing and delivering that water. In response, some countries have sought to rebalance water allocation in several ways – including reducing demand through efficiencies in water use, and explicit allocation of water to the environment through what is often termed ‘environmental flows’ or ‘environmental water’. This is a multi-disciplinary issue with temporal, spatial, cultural and symbiotic complexities – and models and assessment frameworks play a key role in being able to navigate these complexities. This session welcomes papers that describe models and modelling approaches that attempt to quantify the outcomes of environmental watering on aquatic and riparian ecosystems. Papers that describe models that attempt to integrate outcomes across ecosystem niches, especially welcome.
This session will include presentations related to the automation of numerous tasks that environmental modelers perform in conducting modeling studies when deploying one or more existing models. Environmental modeling is generally performed by deploying one or more existing models that require key tasks such as model calibration, input scenario building, data provisioning for input parameterization, setting up data exchange between components in a model as well as between models, model output analysis, and provisioning of computational and IT infrastructures. Automating these tasks as much as possible increases modeling efficiency, brings consistency between modeling studies, and enables less experienced modelers to perform modeling studies.
Example workflows include but are not limited to
(1) building and archiving/caching of input scenarios such as future climate, demographics/migrations, and land use at national and global scales,
(2) Preprocessing and caching of historic climate, meteorologic, and land use data,
(3) Calibrating models in advance at various scales for large spatial extents,
(4) Preparing and caching of spatially localized model input templates,
(5) Developing workflows to handle data exchanges within and between models, and
(6) Setting up elastic computational infrastructures to handle modeling at various spatial/temporal resolutions and spatial extents (matching modeling tasks to memory, storage, and computational resources on-the-fly).
Geographic and environmental models, as well as corresponding systems, are constantly proposed and developed to solve various environmental problems. They are often too specialized and complex to be applied directly by non-expert users or reproduced for further improvements. Intelligent modeling is a general concept with a distinguishing feature that reduces the difficulty to apply, customize, and integrate domain models by means of data-driven, knowledge-driven, or hybrid of them with the support of efficient computing. Owing to the emerging big data, knowledge graph, domain model, and cloud computing, it is an excellent opportunity to design intelligent modeling methods and implement easy-to-use systems for wider users to solve environmental problems.
This session welcomes scholars worldwide to share their inspiring ideas, intelligent modeling methods and easy-to-use systems in promoting the intelligentization of geographic and environmental modeling. The potential topics include, but are not limited to, intelligent modeling methods, integrating modeling frameworks, and cloud-based or cloud-native systems for geographic and environmental modeling.
Environmental modelling plays a critical role in assessing the impact of human activities on natural systems and guiding environmental management decisions. Optimization methods, including recent evolutionary computation methods, have been increasingly used to improve the accuracy and efficiency of environmental modelling, and they have shown great potential in solving complex socioeconomic problems.
This session will bring together researchers and practitioners to discuss recent advances and applications of optimization-based environmental modelling. We welcome contributions related to a wide range of environmental modelling topics, including water quality management, air quality management, energy, and resource management, land use planning, ecosystem management, and biodiversity conservation.
The session will provide a forum for exchanging ideas and experiences, identifying common challenges and opportunities, and fostering collaborations among the participants.
Topics of interest include but are not limited to:
• Optimization methods for environmental modelling and decision-making
• Multi-objective optimization for environmental management and planning
• Optimization-based approaches for water quality management and watershed planning
• Optimization-based models for air quality management and emissions control
• Optimization techniques for energy and resource management
DMTES aims to approach and to promote the interaction between the Environmental Sciences community and the Data Mining/Data Science community and related fields, such as Artificial Intelligence, Statistics, Intelligent Decision Making Support Systems or other fields, all providing methodologies to extract added value from data, so that actionable knowledge to support further decision-making is generating. We invite submissions of papers and presentations about applications of data science, data mining and related methodologies to environmental problems. In this edition we encourage works oriented to explainable AI, digital transformation and H2030 agenda for Sustainable Development Goals (SDG). Which is the real insertion of data into the high level decision-making processes ? How data mining and intelligent decision support systems can help to work for the SDG ? How relevant is explainability to bridge the gap between data and decisions
Applications related with, but not limited to water, air quality and natural resources management to ecological footprint and circular economy or environmental policies are welcomed, as works addressing issues related to data quality, cybersecurity, explainability of models, and role in decision support processes
New or improved techniques or methods are welcomed, as well as innovative applications, including heterogenous sources of information, like classical data, images, open text, semmantic data, georeferenced data, data streams.
Poor water quality has social, economic and environmental consequences, and maintaining good water quality is key to sustaining human life and ecosystem health. While we still need to understand fundamental water quality processes, we increasingly have a need to model new and emerging water treatment systems, emerging chemicals, the impacts of climate change and land and water management on water quality, and interactions between socio-economic systems and water quality. We invite contributions that focus on monitoring, modelling and analyses of all aspects of water quality across all environments including natural, agricultural, urban, peri-urban catchments, as well as rivers, groundwater, lakes, estuaries and other receiving waters. We also welcome contributions that use modelling to address contemporary issues in water quality modelling, such as uncertainty, scale choices, long-term trends and decision scaling.
Fire is a global phenomenon influencing ecosystem patterns, carbon stocks and fluxes, and atmospheric composition, contributing to local and regional air pollution with substantial health effects. Uncontrollable and extreme wildfires exacerbate climate change, contributing significant greenhouse gasses to the atmosphere, and have substantial impacts on public health including birth-outcomes, cardiorespiratory disease and neurodevelopment. Increasingly detailed observational data of different aspects of fire regimes have opened opportunities for improving our understanding of fire drivers and impacts from local to global scales.
Atmospheric conditions such as moisture strongly determine the probability of fire occurrence, but vegetation properties and human land management activities are widely recognised as important factors. A new UNEP report, Spreading like Wildfire, finds that climate change and land-use change are making wildfires worse and anticipates a global increase of extreme fires even in areas previously unaffected. In most cases, emissions from fire activity have substantial transboundary effects as pollution may travel long distances and thus affect public health far from the source, requiring modelling to better understand the relationships between sources and receptors.
We welcome contributions with a modelling and simulation focus on topics related to air pollution and human health impacts, particularly from wildfire as well as preventive activities. In addition, we invite submissions aiming to improve our understanding and capabilities of modelling interactions between fire, land surface, and air pollution. Contributions may cover a wide range of topics, including data integration methods related to remote sensing, in situ observations, Health Impact Assessments and local-to-regional scale modelling. We are especially interested in studies focusing on the importance of climate, land-use, atmospheric and vegetative conditions on fire occurrence across scales, the impacts of fire on properties of land and atmosphere, or feedbacks between fire, land and atmosphere and studies focusing on impacts of fires on public health and ecological systems incl. ecoystem services.
Climate change and future land development are expected to alter physical, chemical, and biological aspects of freshwater and marine systems. This session focuses on modeling of water systems, and can include modeling of pollutants, such as excess nutrients and their effects on harmful algal blooms and eutrophication, as well as fate, transport, and effects of toxics and emerging contaminants. We are interested in how these models can be linked to socioeconomic endpoints, how they can include management responses, and how they can be operationalized for environmental management. There is particular interest in how water models and modeling of water scenarios can be set in the context of the nexus of food, water, energy, health, and biodiversity to support systems assessment.
This session reflects on decades of integrated environmental assessments and the modeling that has supported this. It covers lessons learned and points the way forward for modeling in support of integrated assessments with a focus on four topics.
1. The changing environmental assessment landscape
• 50 years of worldwide environment-related assessments backed by models
• the institutional / governance aspects of modeling including where responsibilities and capacity for modeling for decision support sit, what policy processes they feed into, what type of capacity is needed, and how is capacity mobilized or developed
2. Eight grand challenges in socio-environmental modelling: the upshot of three years of international attention
Reflects and builds on the eight SES challenges presented by El Sawah et. al 2020
3. Challenges and opportunities in combining (too) many models in integrated assessments
Looking forward to the upcoming UN Global Environment Outlook, GEO-7 and other global/regional assessments , insights from the use of multiple models, from multiple teams, will be presented.
4. The role of models in the era of contested knowledge
The work of the UK-based organization, Sense about Science, providing guidance for evidence-based science especially to promote trust in models will be presented, together with the effective practices identified in the 2022 seminar on Integrated assessments for environmental policy in a ‘post-truth society’ hosted by PBL & TIAS
This stream through its various sessions seeks to bring together academic experts, action researchers and practitioners to explore recent developments in participatory decision making, modelling, design and research to solve complex problems of today. We will focus on questions related to the latest trends in participatory research, what role AI and Machine Learning can play in advancing participatory methods, how to organise, support and promote stakeholder participation, as well as how diversity among stakeholder groups can help and related areas.
Environmental modelling of the environmental fate of a variety of contaminants in and across all environmental media is a powerful tool for regulatory and management strategies. Topics in this stream may cover aspects of modelling the chemical and physical transformation of pollutants in air, water and soil, the impact of pollution on human and ecosystem health, biodiversity and the integrated assessment of potential synergies and unintended consequences of technical, behavioural and nature-based solutions.
This stream will cover a range of approaches including open integrated system, and computational intelligence methods in environmental modeling, e.g., computational workflow development, data analytics, and hybrid models of AI and environmental informatics.
Modelling complex environmental and agricultural systems inevitably leads to a problem of the design or structure of the system and the identification of the “true values” of numerous parameters that affect model predictions. This stream will include topics related to the methods, tools, and applications in systems design, parameter identification, and evaluation of uncertainty of model predictions. We particularly welcome sessions and submissions that address the impact of a lack of knowledge about the systems design or parameters on likely outcomes in agricultural or environmental problems.
The stream will cover novel approaches to earth system modelling, software frameworks for predictions of complex interrelationships of environmental factors and their consequences for the planetary health. This includes approaches to simulation of human-environment interactions and integration of environmental and social determinants to assess risks to human health, quantitative and qualitative methods for sustainability assessment, and approaches to monitoring progress towards attainment of Sustainable Development Goals at the global, national, regional, and local levels.
Fast advances in remote sensing techniques, in-situ observation systems, and information and communication technologies have contributed to the proliferation of Big Data on environmental systems. Big Data brings about new opportunities for a better understanding of complex systems through new forms of information processing, storage, retrieval, and analytics. Machine learning, which refers to computer algorithms that automatically learn from data, can advance our prediction capability on complex systems with less human intervention. Collectively, Big Data and ML techniques have shown great potential for data-driven decision-making, operation research, and process optimization in planning and managing environmental systems. We encourage the submission of papers that provide insights into novel data solutions, software technologies, and computational and machine-learning methods to guide human activities on environmental systems, including, but not limited to, environmental systems planning, management, and operations.