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Papers and abstracts listed by session

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A. Applied and computational mathematics

Stream Leaders: Barry Croke and Georgy Sofronov

The Applied and computational mathematics stream focuses on mathematical contributions to modelling and simulation (e.g. based on statistical, stochastic and PDE modelling). This includes development, application and testing of algorithms used in data analysis, model formulation (including component integration), sensitivity analysis and uncertainty quantification. Examples of areas of interest include inverse problems, machine learning, and industrial applications.

  • A3. Contributions from applied mathematics for modelling and simulation

B. Biological systems

Stream Leaders: Malcolm McPhee and Val Snow

Biological Systems welcomes submissions from a wide range of modelling styles: mathematical, mechanistic process-based, agent-based, systems dynamics, and/or data science approaches as applied to biological and agricultural systems. Topics can be inclusive of models and simulation: from descriptions, to development, to applications. Examples of areas of interest include: uncertainty and sensitivity analysis; image analysis; machine learning and artificial intelligence; advances in agent-based modelling of wildlife and pests; livestock, rangelands, pasture and cropping systems; drought resilience, terrestrial and aquatic food webs, and value chain modelling.

  • B2. Agricultural systems
    • Session Organisers: Malcolm McPhee and Rogerio Cichota
    • Use of APSIM Next Generation to identify in-field practices to reduce N leaching under intensive vegetable production systems
      Avendano, F., Cichota, R., Horne, D., Singh, R., Palmer, A. and Bloomer, D.
      https://doi.org/10.36334/modsim.2023.avendano334
    • Simulating crop rotations to improve estimates of nutrient losses using APSIM in HPC
      Cichota, R., Khaembah, E.N., Thomas, S., Lilburne, L., Vickers, S., Omondiagbe, P. and Tait, A.
      https://doi.org/10.36334/modsim.2023.cichota622
    • i-RAT: An interactive rapid assessment tool to assess economic and environmental impacts of different sugarcane irrigation practices
      Collins, B., Attard, S., Banhalmi-Zakar, Z. and Everingham, Y.
      https://doi.org/10.36334/modsim.2023.collins
    • Soil health modelling with APSIM
      Dodd, M.B., Schon, N. and Mackay, A.D.
      https://doi.org/10.36334/modsim.2023.dodd
    • Daily rhythmic behaviour of water buffalo and its effect on their spatial distribution
      Forrest, S.W., Pagendam, D.E., Hoskins, A.J., Drovandi, C., Perry, J., Vanderduys, E. and Bode, M.
      https://doi.org/10.36334/modsim.2023.forrest
    • Invited Paper: Estimating and predicting atmospheric stability for safe agricultural spraying of pesticides
      Grace, D. and Grace, W.
      https://doi.org/10.36334/modsim.2023.grace
    • The potential of using an inverse modelling approach to predict soil PAWC for summer crops in Australia
      He, D., Wang, E. and Verburg, K.
      https://doi.org/10.36334/modsim.2023.he
    • Maximizing wheat grain yield in irrigated mega-environments: Targeting optimal flowering period by selecting optimal sowing date and genotype with appropriate phenological development pattern
      Hu, P., Zheng, B. and Chapman, S.
      https://doi.org/10.36334/modsim.2023.hu675
    • Redesigning a nutrient model to enable faster model development
      Huth, N.I. and Holzworth, D.P.
      https://doi.org/10.36334/modsim.2023.huth
    • Use of SCRUM-APSIM to predict soil water and soil nitrogen dynamics in arable crop rotations
      Khaembah, E.N., Thomas, S., Cichota, R., Sharp, J. and Brown, H.
      https://doi.org/10.36334/modsim.2023.khaembah
    • CattleAssess3D: 3D camera technology integrated with BeefSpecs drafting tool to assist ‘meeting market specifications’
      McPhee, M.J., Walmsley, B.J., Littler, B., Siddell, J.P., Toohey, E., Oddy, V.H., Falque, R., Virgona, A., Vidal-Calleja, T. and Alempijevic, A.
      https://doi.org/10.36334/modsim.2023.mcphee
    • Modelling the future trend of the citrus gall wasp population in southern Australia
      Mo, J.
      https://doi.org/10.36334/modsim.2023.mo
    • Infectious disease spread in free-range egg-laying hens based on empirical mobility patterns and contact networks
      Palmini, A., Jarynowski, A., Welch, M.C., Belik, V., Sibanda, T. and Ruhnke, I.
      https://doi.org/10.36334/modsim.2023.palmini
    • Building trust in continental-scale modelling in agriculture
      Richetti, J., Zheng, B., Navarro Garcia, J. and Lawes, R.
      https://doi.org/10.36334/modsim.2023.richetti
    • Invited Paper: A method to improve the efficiency of calibrating biophysical models for pastures
      Thomas, D.T., Chen, C., Ota, N., Mata, G., Murphy, S.R., Giblin, S. and Beale, P.J.
      https://doi.org/10.36334/modsim.2023.thomas105
    • Cross-scale modelling of cropping systems: from gene/genome to landscape in era of big data
      Wang, E., Brown, H., Trevaskis, B., Zheng, B., Rebetzke, G., Zhao, Z.G., Huth, N.I., He, D., Hyles, J., Glover, M., Malone, B. and Macdonald, B.
      https://doi.org/10.36334/modsim.2023.wang299
    • Simulated efficiency of DMPP applications in reducing N2O emissions from Australian N-fertilized rainfed wheat cropping systems
      Xing, H., Li, G. and Schwenke, G.D.
      https://doi.org/10.36334/modsim.2023.xing579
    • Prediction of wheat and barley phenology through integration of genomic prediction and a crop growth model
      Zheng, B., Brown, H., Zhao, Z.G., Wang, E., Huth, N.I., Dillon, S., Hyles, J., Rathjen, T., Bloomfield, M., Celestina, C., Hunt, J. and Trevaskis, B.
      https://doi.org/10.36334/modsim.2023.zheng289
  • B3. Building spatially-explicit simulation models of biological systems: challenges and success stories
  • B7. Native and modified vegetation
    • Session Organisers: Jinyan Yang, Neville Herrmann and Adam Liedloff
    • Insights from modelling Australia’s tropical savannas
      Liedloff, A. and Cook, G.
      https://doi.org/10.36334/modsim.2023.liedloff
    • A large difference in rain use efficiency among Australian terrestrial ecosystems
      Liu, Z., Guan, H., Batelaan, O. and Grzegorz, S.
      https://doi.org/10.36334/modsim.2023.liu258
    • A modelling framework informs how changes in Mount Bold Reservoir's flood attenuation capacity will affect plant biodiversity
      Newman, J.P., Nicol, J., Kennedy, S., Gehrig, S., Noack, C., von Wielligh, E., Harvy, C., Kildea, T. and van der Linden, L.
      https://doi.org/10.36334/modsim.2023.newman620
    • Developing satellite-derived nitrogen stable isotope ratio grids to globally monitor terrestrial plant nitrogen availability for 1984–2022
      Yang, J., Zhang, H., Guo, Y., Donohue, R.J., McVicar, T.R., Ferrier, S., Müller, W., Lü, X., Fang, Y., Wang, X., Reich, P.B., Han, X. and Mokany, K.
      https://doi.org/10.36334/modsim.2023.yang582

C. Computer science and engineering

Stream Leaders: Min Chen and Dan Ames

Methods for sharing data and computational resources, integrating models, and building simulation systems integrating various disciplines in the open web environment are rapidly changing with the continual development of new information and communications technologies (ICT) including cloud computing, edge computing, blockchain computing, high-performance computing and high-speed Internet. This stream encouraged papers that provide further insights in novel, emerging and advanced ICT, other software technologies and computational methods; and that support decision making to solve comprehensive complex issues in the era of ‘big science’.

This stream was supported and co-led by the International Environmental Modelling and Software Society (iEMSs, https://iemss.org/) and The Open Modeling Foundation (https://www.openmodelingfoundation.org/).

  • C5. Model reusability and reproducible future
    • Session Organisers: Takuya Iwanaga, Jazmin Zatarain Salazar and Fengyuan Zhang
    • Towards scalable and reproducible hydrological modelling with HydroMT: A proof of concept for Australia
      Hegnauer, M., Maguire, S., Eilander, D. and Boisgontier, H.
      https://doi.org/10.36334/modsim.2023.hegnauer
    • "Good enough" principles for reproducibility: Developing pragmatic guidelines for early career scholars
      Iwanaga, T., Salazar, J.Z., Fischer, S.M., Jovanović, R., Liu, N., Zhu, Z. and Zhu, L.-J.
      https://doi.org/10.36334/modsim.2023.iwanaga485
    • Study on sharing and reusing geographic simulation models in web environment
      Xu, K., Chen, M., Yue, S., Wen, Y., Zhang, F., Wang, J. and Lu, G.
      https://doi.org/10.36334/modsim.2023.xu225
    • Provena: A provenance system for large distributed modelling and simulation workflows
      Yu, J., Baker, P., Cox, S.J.D., Petridis, R., Freebairn, A.C., Mirza, F., Thomas, L., Tickell, S., Lemon, D. and Rezvani, M.
      https://doi.org/10.36334/modsim.2023.yu90

D. Economics and finance

Stream Leaders: Chia-Lin Chang, Hamid Yahyaei and Lurion De Mello

The Economics and finance stream welcomed proposals from a wide range of issues pertaining to Innovation and Trade, Risk Management, and impacts of Climate variability/change on financial markets and economies more generally. Examples of topics include any original research and comprehensive review papers at the intersection of economics and finance with commodity markets, international trade, financial risk modelling, and computational finance, and financial markets and climate impact modelling.

E. Energy, integrated infrastructure and urban planning

Stream Leaders: John Boland and Behzad Rismanchi

Australia is in the midst of an energy transition.  The move to Electrify Everything is underway.  This revolution requires a myriad of activities in various areas.  This stream focuses on multiple ways infrastructure networks, systems and services contribute to urban renewal, regional development, better liveability and enhanced productivity. Smart data analytics, resource assessment and forecasting, digital twins, real-time modelling and complex network optimisation are becoming essential instruments for planning, managing, protecting and upgrading these systems. The stream can include submissions covering forecasting of renewables, energy efficient building design, microgrid design, precinct infrastructure, and related topics.

F. Environment and ecology

Stream Leaders: Stefan Reis and Shawn Laffan

Modelling, simulation and software systems play a pivotal role in our understanding of environmental and ecological systems. Complex interactions and relationships require environmental modelling and software tools to underpin and improve decision making in policy and regulatory contexts. Advances in data science, machine learning and approaches to harness big data are key to tackle vast challenges of environmental degradation and global climate change. We encourage the submission of sessions which focus on the development of generic frameworks and integration of models across issues, scales, disciplines and stakeholders. The stream accommodated sessions spanning a scope from advances in modelling, software and simulation, the development and use of advanced software tools, interdisciplinary and transdisciplinary environmental modelling, the integration of models and software tools across issues, scales, disciplines and stakeholders, to the application of novel data science concepts in decision support.

This stream was supported and co-led by the International Environmental Modelling and Software Society (iEMSs, https://iemss.org/).

  • F4. Modelling to evaluate outcomes of environmental watering
    • Session Organisers: Susan Cuddy, Carmel Pollino and Tanya Doody
    • Assessing floodplain ecosystem ecohydrological responses in the Murray-Darling Basin using multiple lines of spatial evidence
      Brooks, S., Doody, T.M. and Gao, S.
      https://doi.org/10.36334/modsim.2023.brooks
    • Reporting on the evaluation of environmental outcomes of delivery of Commonwealth environmental water in the Murray–Darling Basin, Australia
      Cuddy, S.M., Tetreault-Campbell, S., Nolan, M., O'Sullivan, J., Downey, M. and Wignell, E.
      https://doi.org/10.36334/modsim.2023.cuddy688
    • Hydrodynamic and hydrological modelling to assess benefits, risks, and trade-offs from engineered flooding with a limited water resource
      Montazeri, M., McCullough, D.P. and Gibbs, M.S.
      https://doi.org/10.36334/modsim.2023.montazeri
    • Design and implementation of a software tool supporting the Inter-Provincial Water Apportionment Accord in Pakistan
      Perraud, J.-M., Freebairn, A.C., Seaton, S.P., Yu, Y., Podger, G.M., Ahmad, M.D. and Cuddy, S.M.
      https://doi.org/10.36334/modsim.2023.perraud130

G. Global change and natural hazards

Stream Leaders: Jason Evans and Christoph Rudiger

This stream was interested in all aspects of global change and natural hazards and their interactions within the earth system. Topical streams may include modelling of natural hazards such as drought, heatwaves, hail, fires, tropical cyclones, earthquakes, and tsunamis. It also covers modelling of global change issues such as climate change, land degradation (including desertification and plant migration), and the relevance for United Nations sustainable development goals. New model developments and modelling of the phenomena, their impacts on human and natural systems, potential techniques for adaptation, and the use of remote sensing data to address these, are all of interest.

  • G1. Flood and tsunami modelling: techniques and applications
    • Session Organisers: Fazlul Karim, Jin Teng and Catherine Ticehurst
    • The LSG model: A new approach to simulating flood inundation with high accuracy and low computational demand
      Fraehr, N., Wang, Q.J., Wu, W. and Nathan, R.
      https://doi.org/10.36334/modsim.2023.fraehr
    • Machine learning methods for flood prediction: A review of methods, their strengths and limitations
      Karim, F., Armin, M.A., Tychsen-Smith, L., Li, R. and Penton, D.J.
      https://doi.org/10.36334/modsim.2023.karim563
    • Generalizability of deep-learning-based emulation of hydrodynamic flood models
      Li, R., Tychsen-Smith, L., Karim, F., Penton, D.J. and Armin, M.A.
      https://doi.org/10.36334/modsim.2023.li398
    • A large-scale flexible mesh 2D hydrodynamic model for the Cooper Creek floodplain
      Mateo, C.M., Vaze, J., Wang, B., Kim, S.S.H., Marvanek, S., Ticehurst, C., Crosbie, R.S. and Holland, K.
      https://doi.org/10.36334/modsim.2023.mateo
    • Two-monthly maximum water depth for the Murray–Darling Basin: Usage guidance
      Penton, D.J., Teng, J., Ticehurst, C., Marvanek, S., Freebairn, A.C., Vaze, J., Khanam, F. and Sengupta, A.
      https://doi.org/10.36334/modsim.2023.penton
    • Predicting flood inundation extent using remote sensing and machine learning techniques
      Shrestha, D.L., Robertson, D.E., Jin, W. and Ticehurst, C.
      https://doi.org/10.36334/modsim.2023.shrestha
    • Inundation frequency in the Murray–Darling Basin: Past, present and future
      Teng, J., Chiew, F.H.S., Yang, A., Zheng, H., Penton, D.J., Ticehurst, C., Marvanek, S., Vaze, J. and Khanam, F.
      https://doi.org/10.36334/modsim.2023.teng320
  • G5. Modelling to advance resilience towards water related hazards
    • Session Organisers: Wendy Sharples, Ulrike Bende-Michl, Julien Lerat and Elisabeth Vogel
    • Towards a seamless probabilistic flood inundation modelling capability across the disaster response timeline
      Hou, J., Sharples, W., Bahramian, K., Pickett-Heaps, C.A., Woldemeskel, F., Rüdiger, C. and Carrara, E.
      https://doi.org/10.36334/modsim.2023.hou353
    • Beyond extremes: Characterisation of the 2022 Northern Rivers flood
      Lerat, J., Vaze, J., Ticehurst, C., Marvanek, S. and Wang, B.
      https://doi.org/10.36334/modsim.2023.lerat
    • Predicting impact of fires on water quality
      Miotlinski, K., Horwitz, P., Bellhouse, J.A., Blake, D., Silberstein, R., Bath, A., Mitchell, A., Carvalho, A. and Tshering, K.
      https://doi.org/10.36334/modsim.2023.miotlinski
    • Supporting flood risk management by combining integrated modelling and participation
      van Delden, H., Vanhout, R., Radford, D.A., Riddell, G.A., Koks, E.E., Maier, H.R., Zecchin, A.C., Hitchcock, D., Ward, K. and Dandy, G.C.
      https://doi.org/10.36334/modsim.2023.vandelden676
  • G9. The direction of future land surface modelling within Earth system modelling
    • Session Organisers: Christoph Rudiger, Gab Abramowitz and Claire Carouge
    • Influence of lateral flow on land surface fluxes in southeast Australia
      Devanand, A., Evans, J.P., Pitman, A.J., Pal, S., Gochis, D. and Sampson, K.
      https://doi.org/10.36334/modsim.2023.devanand397
    • Adapting JULES for improved hydrological predictions in Australia: Challenges, strategies and future plans
      Tian, S., Rüdiger, C., Renzullo, L.J., Dharssi, I., Marchionni, V., Woldemeskel, F., Frost, A. and Carrara, E.
      https://doi.org/10.36334/modsim.2023.tian575
    • Implementation of a gridded river routing scheme for land surface models and evaluation of streamflow simulations across Australia
      Woldemeskel, F., Rüdiger, C., Khan, Z., Yamazaki, D., Zhang, H., Marthews, T., Hou, J., Dharssi, I. and Su, C.-H.
      https://doi.org/10.36334/modsim.2023.woldemeskel
    • Limited impacts of a permanent inland lake in central Australia on local-to-regional precipitation
      Yang, Z., Ryu, D., Lo, M., Narsey, S., Peel, M.C. and McColl, K.
      https://doi.org/10.36334/modsim.2023.yang280

H. Health and biosecurity

Stream Leaders: Louise Freebairn and Irene Hudson

The Australian bushfires and COVID-19 pandemic brought into sharp focus the importance of data analytic and systems science methods to support evidence-based decision and policy making for health. The Health and biosecurity stream focused on latest developments, applications and challenges for epidemiological and biosecurity modelling, data science and machine learning. Health applications include but are not limited to disease surveillance, communicable diseases, chronic diseases, health services and systems, human behaviour and health, climate change and environmental exposures and health risks.

  • H1. Causal modelling, statistical inference and data science – in digital health, RCTs, precision medicine, global health
    • Session Organisers: Irene Hudson and Salman Cheema
    • A visual examination of Selikoff’s 20-year rule using correspondence analysis and the Cressie-Read family of divergence statistics
      Alzahrani, A., Beh, E.J. and Stojanovski, E.
      https://doi.org/10.36334/modsim.2023.alzahrani
    • Bayesian decision-theoretic analysis of thresholds in Gompertz-mixture models, for robust detection of corona-like viruses in wildlife
      Low-Choy, S., McKinley, T.J., Pulscher, L. and Peel, A.
      https://doi.org/10.36334/modsim.2023.lowchoy656
    • Dynamic health status monitoring using aged care quality indicators for better care: An innovative approach using mixture hidden Markov models
      Silva, S.S.M., Wabe, N. and Westbrook, J.I.
      https://doi.org/10.36334/modsim.2023.silva
    • Visualising instance selection for improved explainability using feature extraction
      Yeo, G.F.A., Hudson, I., Akman, D. and Chan, J.
      https://doi.org/10.36334/modsim.2023.yeo34
  • H2. Data science and simulation modelling methods in health
    • Session Organisers: Louise Freebairn, Geoff McDonnell, Simon Chiu, Mark Heffernan and Ante Prodan
    • Improving access and efficiency in care delivery for patients with spinal cord injury in NSW Australia: A discrete-event dynamic simulation modelling approach
      Assareh, H., Pavlov, V., Adarkar, K., Johnson, J., Fortunato, R., Marial, O. and Middleton, J.
      https://doi.org/10.36334/modsim.2023.assareh
    • Exploring policy options in a system dynamics model of childhood and adolescent obesity
      Chiu, S., Freebairn, L., Occhipinti, J. and Baur, L.
      https://doi.org/10.36334/modsim.2023.chiu524
    • Modelling aspects of the effect of community stigma on the prevalence of anxiety and/or depression
      Hickson, R.I., Rawlinson, A.A., Roberts, M.E. and Faux, N.G.
      https://doi.org/10.36334/modsim.2023.hickson
    • A comprehensive approach to operating theatre scheduling
      Humphreys, P., Spratt, B., Tariverdi, M., Hamilton, A., Cook, D., Burdett, R., Yarlagadda, P. and Corry, P.
      https://doi.org/10.36334/modsim.2023.humphreys
    • Stock and flow modelling of a veterinary teaching hospital: How to better embed clinical teaching into patient flow
      Meler, E., Schull, D., Kelly, S. and Richards, R.
      https://doi.org/10.36334/modsim.2023.meler
    • Managing surgical waiting lists through dynamic priority scoring
      Powers, J., McGree, J.M., Grieve, D., Aseervatham, R., Ryan, S. and Corry, P.
      https://doi.org/10.36334/modsim.2023.powers
  • H3. Methodologies and applications of simulation and decision models for Health Economics and One Health

I. Social systems and modelling processes

Stream Leaders: Kate O'Brien and Oz Sahin

This stream covers all aspects of the human and cultural dimensions of modelling. This includes modelling socio-ecological systems (human-environment interactions), applications or approaches which bring a social-systems lens to modelling, and the process of modelling and associated challenges and best practice. Suitable content for this stream includes model development, data and knowledge management, pedagogical culture, application, case-studies, theory, practice, challenges, opportunities and insights into integration for modelling socio-ecological systems, and for a life-cycle approach to modelling which incorporates input from decision-makers and diverse knowledge sources from model conceptualisation through to application. Submissions that include Indigenous perspectives on modelling were particularly encouraged.

  • I4. How to build and how to kill trust in models, and why it matters
  • I10. Stories of change: working together for a better water future
  • I13. Using resilience and foresight for adaptive management and decision making under deep uncertainty
    • Session Organisers: Vitor Hirata Sanches, Steven Lade, Holger Maier, Seth Westra and Avril Horne
    • Consideration of temporal variability to discover ecologically robust reef futures
      Iwanaga, T., Crocker, R., Anthony, K.R.N. and Robson, B.J.
      https://doi.org/10.36334/modsim.2023.iwanaga490
    • Barossa water security strategy: A demonstration of community leadership, strategic foresight, climate resilience and systems modelling
      Leigh, R., Kingsborough, A., Westra, S., Brettig, P. and Helfgott, A.
      https://doi.org/10.36334/modsim.2023.leigh
    • System dynamics and resilience in the pro-sport athletic department: Towards a capability-based theory
      Melton, D.
      https://doi.org/10.36334/modsim.2023.melton
    • Using participatory foresight processes for strategic water management in Queensland
      Merritt, W.S., Fu, B., Rosello, C., Hamilton, S.H. and Riches, J.
      https://doi.org/10.36334/modsim.2023.merritt
    • A review of quantitative resilience measurements: Gaps in the operationalisation of agency and diversity in resilience metrics
      Sanches, V.H., Crépin, A.S., Dakos, V., Donges, J.F., Guillaume, J.H.A., Haider, J.L., Iwanaga, T., Kwakkel, J.H., Lade, S.J., Quinlan, A.E., Quiñones, R., Rocha, J.C. and Vivas, J.
      https://doi.org/10.36334/modsim.2023.sanches

J. Water resources

Stream Leaders: Jai Vaze and Murray Peel

The Water Resources stream focuses on research into hydrological processes and hydrological modelling tools (landscape and river system) that advance our understanding and management of surface water and groundwater at catchment, regional and continental scales over time scales from hours to decades.

Topics of relevance include (but are not limited to):

  • water balance tools that integrate models and multiple data sources to deliver aggregated national and regional water accounts
  • hydrological modelling frameworks for national and regional water assessments, including those informing environmental flows, flooding and climate change
  • data-driven studies that inform our understanding of hydrological change and dynamics, both historically and under climate change
  • fully coupled surface water, groundwater and river system models (with uncertainty quantification) for development of catchment and basin water management and sharing plans
  • improved understanding of hydrological processes and hydrological modelling methods through model-data fusion (parameterisation, reanalyses and calibration against multiple data sources), system-wide calibration of water balance components (catchment rainfall-runoff, river routing and losses).
  • J3. Ecohydrological modelling
    • Session Organisers: Patricia Saco, Steven Sandi, Jo Owens, Sally Thompson and Jose Rodriguez
    • Identifying groundwater-dependent vegetation in arid zone Australia using imagery time series and singular value decomposition
      Box, P., Brim Box, J., Cobban, D., Lieper, I. and Nano, C.
      https://doi.org/10.36334/modsim.2023.box387
    • NDVI and accumulated antecedent precipitation in the drylands of Mendoza, Argentina
      Brieva, C., Saco, P.M., Rodríguez, J.F. and Sandi, S.G.
      https://doi.org/10.36334/modsim.2023.brieva
    • Ecohydrological option analysis for New South Wales’ coastal regional water strategies: Bega River
      Driver, P.D., Dutta, D., Delagarza, D. and Simons, M.
      https://doi.org/10.36334/modsim.2023.driver148
    • Decoding soil-plant-atmosphere processes by extending in-situ monitoring and experimental data with numerical modelling
      Filipović, V., Krevh, V. and Baumgartl, T.
      https://doi.org/10.36334/modsim.2023.filipovic
    • Mapping groundwater-dependent ecosystems using a phenology matrix and fine-scale remote sensing data
      Gao, S., Castellazzi, P., Pritchard, J.L., Stratford, D. and Doody, T.M.
      https://doi.org/10.36334/modsim.2023.gao605
    • Predicting surface-specific humidity from radiative temperature and ambient weather for evapotranspiration modelling
      Gou, J., Liu, W., Guan, H., Batelaan, O., Bruce, D., Gutierrez, K., Wang, H., Gutierrez, H., Burk, L., Thompson, J. and Woods, J.
      https://doi.org/10.36334/modsim.2023.gou
    • Assessment of mangroves' resilience to land use and climate change in the Pacific Islands
      Jorquera, E., Quijano Baron, J.P., Breda, A., Sandi, S.G., Verdon-Kidd, D., Saco, P.M. and Rodríguez, J.F.
      https://doi.org/10.36334/modsim.2023.jorquera
    • Assessing the contribution of hydrologic and climatic factors on vegetation condition changes in semi-arid wetlands: An analysis of the Narran Lakes
      Liu, M., Jakeman, A.J., Lerat, J., Hamilton, S.H., Jin, H., Croke, B.F.W. and Savage, C.
      https://doi.org/10.36334/modsim.2023.liu198
    • Spatial modelling of understorey evapotranspiration based on the maximum entropy production method
      Liu, W., Guan, H., Bruce, D., Batelaan, O., Keane, R., Keegan-Treloar, R., Gutiérrez-Jurado, K.Y., Thompson, J. and Wang, J.
      https://doi.org/10.36334/modsim.2023.liu287
    • Hydrological connectivity patterns: Assessing the potential impact of water resource development on northern Australian wetlands
      Merrin, L.
      https://doi.org/10.36334/modsim.2023.merrin
    • From modelling to measurements: Bridging gaps in modelling with measured vegetation, evapotranspiration and soil moisture data
      Owens, J., Cleverly, J., Hutley, L.B., Frost, A. and Western, A.W.
      https://doi.org/10.36334/modsim.2023.owens
    • Are terrestrial groundwater-dependent ecosystems dependent on groundwater from localised or regional aquifers?
      Pritchard, J.L. and Bunney, E.
      https://doi.org/10.36334/modsim.2023.pritchard
    • Ecohydrological interactions in coastal wetlands and their resilience to future sea-level rise
      Saco, P.M., Rodríguez, J.F., Breda, A. and Sandi, S.G.
      https://doi.org/10.36334/modsim.2023.saco
    • Hydrodynamic influence on vegetation establishment and biomass storage in coastal wetland systems
      Sandi, S.G., Rodríguez, J.F., Saco, P.M. and Saintilan, N.
      https://doi.org/10.36334/modsim.2023.sandi
    • Modelling habitat suitability under hydrological change in aquatic habitats of northern Australia
      Stratford, D., Linke, S., Merrin, L., Lachish, S., Karim, F. and Kim, S.S.H.
      https://doi.org/10.36334/modsim.2023.stratford
    • Do vegetation changes necessarily intensify hydrological shifts under multiyear droughts?
      Weligamage, H.A.C.G., Fowler, K.J.A., Saft, M., Ryu, D., Peterson, T.J. and Peel, M.C.
      https://doi.org/10.36334/modsim.2023.weligamage
  • J4. Hydrologic non-stationarity and extrapolating models to predict the future

K. Hydroclimate

Stream Leaders: Yongqiang Zhang and Conrad Wasko

This stream focuses on the research fields between climate and hydrology. With continuous climate change in the past several decades and the foreseeable future, our understanding of the hydroclimate continues to evolve, and the complexity in forecasting, predicting, simulating, or attributing change, means many processes, and their interactions, remain not completely understood. However, new data sets, statistical tools, modelling techniques, and advances in computing are all providing us opportunities to improve the understanding of the hydroclimate. Proposals were invited from a wide variety of disciplines that analyse and model all aspects of the hydroclimate, from rainfall, to streamflow, evapotranspiration, groundwater, temperature, and their related hazards. Proposals were encouraged that aimed to improve our process understanding, untangle uncertainties, and attribute changes across all time and spatial scales in the hydroclimate.

  • K4. Hydrological processes under changing climate across temporal scales: data analysis, modeling, and interpretation
  • K5. Hydrometeorological forecasting and decision making: extremes and general advances

L. Water quality

Stream Leaders: Andrew Western, Danlu Guo and Anna Lintern

Poor water quality has social, economic and environmental consequences, and maintaining good water quality is key to sustaining human life. 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. Proposals were invited to 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.

  • L2. Catchment water quality modelling
    • Session Organisers: Danlu Guo and Sandy Elliot
    • Assimilating modelled dissolved inorganic nitrogen loads to monitored data using component-wise iterative ensemble Kalman inversion
      Bennett, F.R., Botha, I., Adams, M.P. and Drovandi, C.
      https://doi.org/10.36334/modsim.2023.bennett42
    • Leveraging high-performance computing facilities for the SWAT water quality catchment model
      Elliott, S., Hoang, L., Dang, T.D., Pletzer, A. and Scott, C.
      https://doi.org/10.36334/modsim.2023.elliott
    • Australia’s water quality trends over two decades
      Guo, D., Minaudo, C., Zhang, Q., Dupas, R., Liu, S., Zhang, K., Bende-Michl, U., Duvert, C. and Lintern, A.
      https://doi.org/10.36334/modsim.2023.guo615
    • Hydroclimatic drivers of stream water quality over 27 years: The role of streamflow, temperature and seasonality
      Lintern, A., Sargent, R., Hagan, J., Wilson, P., Western, A.W., Plum, C. and Guo, D.
      https://doi.org/10.36334/modsim.2023.lintern
    • Contrasting sediment and nutrient export patterns across different hydrological regimes: A case study in the Great Barrier Reef catchments
      Liu, S., Guo, D., Bende-Michl, U., Lintern, A., Waters, D.K. and Wang, Q.
      https://doi.org/10.36334/modsim.2023.liu77
    • Fire, fine sediment, and fractional ground cover: Representation of fire in the Cape York – Great Barrier Reef water quality model
      Pollett, A. and McCloskey, G.L.
      https://doi.org/10.36334/modsim.2023.pollett
    • Modelling total phosphorous and nitrate using a water-age-based approach
      Riazi, Z. and Western, A.W.
      https://doi.org/10.36334/modsim.2023.riazi
    • Identifying and quantifying key sources of nutrient pollution from irrigated agriculture
      Sargent, R., Henry, R., Wong, W.W., Schang, C., Tseng, C.W., Cook, P., Western, A.W., McCarthy, D. and Lintern, A.
      https://doi.org/10.36334/modsim.2023.sargent
    • Invited Paper: A model framework to predict pesticide concentrations in runoff at improved temporal and spatial resolution
      Singh, A. and Hipsey, M.R.
      https://doi.org/10.36334/modsim.2023.singh141
    • Estimating sediment delivery ratios using connectivity index and high-resolution digital elevation model at lower Snowy River area, Australia
      Yang, X., Young, J., Shi, H., Chapman, G., Pulsford, I., Moore, C., Gormley, A. and Thackway, R.
      https://doi.org/10.36334/modsim.2023.yang112
    • How much data should be accumulated for reliable water pollution source identification? Critical time profile discovery and monitoring process design
      Yang, R.Y., Jiang, J.P., Pang, T.R., Zheng, Y. and Yang, Z.H.
      https://doi.org/10.36334/modsim.2023.yang409
    • Uncertainty and its propagation estimation for an integrated water system model: An experiment from water quantity to quality simulations
      Zhang, Y. and Shao, Q.
      https://doi.org/10.36334/modsim.2023.zhang180
    • A comprehensive study of high frequency temporal chaotic behavior of riverine water quality dynamics
      Zhu, M., Jiang, J.P., Tang, S. and Sivakumar, B.
      https://doi.org/10.36334/modsim.2023.zhu640
  • L4. Lake, coastal and marine water quality
    • Session Organisers: Barbara Robson, Mathieu Mongin, Matt Hipsy and Songyan Yu
    • Modelling Trichodesmium optics and buoyancy in the Great Barrier Reef using the eReefs models
      Ani, C.J., Baird, M.E. and Robson, B.J.
      https://doi.org/10.36334/modsim.2023.ani317
    • Application of denoising diffusion models in the augmentation of a regional dataset of inherent optical properties and applications for remote sensing
      Drayson, N., Diakogiannis, F., Cherukuru, N., Blondeau-Patissier, D. and Schroeder, T.
      https://doi.org/10.36334/modsim.2023.drayson
    • Using in-situ hyperspectral reflectance data for cyanobacterial bloom monitoring and forecasting
      Joehnk, K.D., Biswas, T., Anstee, J., Ford, P., Drayson, N., Kerrisk, G. and Malthus, T.
      https://doi.org/10.36334/modsim.2023.joehnk
    • Using remotely sensed data to understand global lake colour variability
      Liu, S., Glamore, W., Liu, Y. and Johnson, F.M.
      https://doi.org/10.36334/modsim.2023.liu327
    • Simulated deposition of aeolian dust on the Australian continental shelf: Preliminary results
      Margvelashvili, N., Skerratt, J.H., Mongin, M., Baird, M.E. and Wild-Allen, K.
      https://doi.org/10.36334/modsim.2023.margvelashvili
    • Invited Paper: Combining dynamic and conceptual models for managing water quality in reservoirs: Guidance from three case studies
      Newman, J.P., Makarewicz, A., Daly, R., Swaffer, B., van der Linden, L. and Harvy, C.
      https://doi.org/10.36334/modsim.2023.newman645
    • Temporal variability of phytoplankton community structure: An individual-based modelling approach
      Ranjbar, M.H., Hamilton, D.P., Pace, M.L., Etemad-Shahidi, A., Carey, C.C. and Helfer, F.
      https://doi.org/10.36334/modsim.2023.ranjbar
    • Modelling the effect of river load reductions on water quality in the Crown of Thorns Starfish outbreak initiation zone of the Great Barrier Reef
      Robson, B.J., Crosswell, J. and Kroon, F.J.
      https://doi.org/10.36334/modsim.2023.robson
    • Black ooze and algae: Modelling the response and restoration of the Coorong
      Sims, C., Hipsey, M.R., Huang, P., Paraska, D. and Zhai, S.
      https://doi.org/10.36334/modsim.2023.sims
    • Integrating field observations and mapping with model outputs (pesticides) to help identify ecologically vulnerable areas and determine suitable field site locations in the Great Barrier Reef
      Skerratt, J.H., Baird, M.E. and Mongin, M.
      https://doi.org/10.36334/modsim.2023.skerratt
    • Mapping water quality in complex estuarine and coastal waters using deep learning models and high-resolution satellite imagery
      Unnithan, S.L.K., Cherukuru, N., Drayson, N. and Ingleton, T.
      https://doi.org/10.36334/modsim.2023.unnithan
    • Calculating pesticide half-lives in reservoirs for model parameterisation
      Waters, D.K. and Silburn, D.M.
      https://doi.org/10.36334/modsim.2023.waters
    • Assessing algal bloom risks from purified recycled water addition to Lake Wivenhoe
      Yu, S., Hamilton, D.P., Okely, P., Smolders, K. and Burford, M.
      https://doi.org/10.36334/modsim.2023.yu532

M. ASOR

Stream Leaders: Melanie Ayre and Simon Dunstall

The Operations Research (OR) stream covered high-quality contributions from across the broad spectrum of OR methods, techniques and applications in academia, defence and industry. Techniques may include (but are not limited to) mixed integer-linear programming, constraint programming, metaheuristics, and modelling and simulation through to more recent approaches in matheuristics, artificial intelligence (AI), machine learning (ML) and data sciences (DS). Applications areas may include (but are not limited to) emergency management and natural hazards, defence, transport, logistics, mining, agriculture and healthcare. Collaboration was encouraged between academia and industry in both session proposals and paper submissions.

  • M1. Area Business Continuity Management
    • Session Organisers: Natt Leelawat, Kenji Watanabe, Akira Kodaka and Jing Tang
    • Effective user interface and user experience design for disaster-related applications: A review
      Bhisitcharoentat, Y., Saengtabtim, K., Leelawat, N. and Tang, J.
      https://doi.org/10.36334/modsim.2023.bhisitcharoentat
    • A business continuity assessment method using Earth observation data: Verification in industrial zones, Thailand
      Kodaka, A., Leelawat, N., Tang, J. and Kohtake, N.
      https://doi.org/10.36334/modsim.2023.kodaka
    • Area-business continuity management web application model test plan
      Tang, J., Leelawat, N., Sornklin, B., Chaweewongpaisal, M., Vikraipaisarn, Y., Bhisitcharoentat, Y., Arayachookiat, P., Meechang, K., Kodaka, A., Iwasaki, Y., Inoue, M. and Watanabe, K.
      https://doi.org/10.36334/modsim.2023.tang226
    • Cascading events simulation for disaster-sensitive metropolitan areas: Resilience enhancement with visualization of consequences of large-scale disasters
      Watanabe, K.
      https://doi.org/10.36334/modsim.2023.watanabe

 

Modelling to support planning for resilience in a changing world