Author Index

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

A

• Abbasi, B.
  • Optimal ordering decisions for red blood cells at hospitals under blood-type substitutions
    

    HosseiniFard, Z., Khatami, M., Abbasi, B. and Houtum, G.
    https://doi.org/10.36334/modsim.2023.hosseinifard

• Abdollahi, A.
  • Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale
    

    Abdollahi, A. and Yebra, M.
    https://doi.org/10.36334/modsim.2023.abdollahi

• Abdollahian, M.A.
  • Data-driven risk mitigation: Water quality forecasting during extreme events
    

    Rumman, R., Martin, J., van der Sterren, M., Chen, C., Abdollahian, M.A. and Hughes, D.
    https://doi.org/10.36334/modsim.2023.rumman

• Accary, G.
  • Physics-based modelling of junction fires: Effects of some topographical parameters in intersecting fires
    

    Hassan, A., Accary, G., Sutherland, D. and Moinuddin, K.
    https://doi.org/10.36334/modsim.2023.hassan

• Adams, M.P.
  • Unlocking ensemble ecosystem modelling for simulation of high-dimensional networks: From days to minutes
    

    Vollert, S.A., Drovandi, C. and Adams, M.P.
    https://doi.org/10.36334/modsim.2023.vollert

  • Mathematical modelling demonstrates how students can get stuck in unproductive learning regimes
    

    Egger, F., Lodge, J.M., Adams, M.P., Birkett, G.R., Monsalve-Bravo, G.M., Howes, T. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2023.egger512

  • A light-hearted approach to a serious problem: Building “educated trust” in models
    

    O'Brien, K.R., Adams, M.P., Egger, F., Maxwell, P., Weber, T., Maier, H.R., Vilas, M.P., Shaw, M., Turner, R., Birkett, G.R., Hamilton, D.P., Langsdorf, H. and Baird, M.E.
    https://doi.org/10.36334/modsim.2023.obrien

  • 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

  • Planning research and development in poor data and urgent decision-making contexts as an adaptive management problem
    

    Pascal, L.V., Adams, M.P., Chadès, I. and Helmstedt, K.J.
    https://doi.org/10.36334/modsim.2023.pascal

• Adarkar, K.
  • 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

• Addy, S.
  • Cross-scale analysis of social-ecological systems: Policy options appraisal for delivering NetZero and other environmental objectives in Scotland
    

    Matthews, K.B., Blackstock, K.L., Wardell-Johnson, D.H., Miller, D.G., Tavana, M., Thomson, S., Moxey, A., Nielsen, R., Baggaley, N., Loades, K., Paterson, E., Pakeman, R., Hawes, C., Stockan, J., Stutter, M., Addy, S. and Wilkinson, M.
    https://doi.org/10.36334/modsim.2023.matthews

• Ahammed, F.
  • Stormwater drainage systems design under variable climatic conditions
    

    Ahammed, F.
    https://doi.org/10.36334/modsim.2023.ahammed

• Ahmad, I.
  • Spatiotemporal variation of water resources system vulnerability in the Upper Yangtze River, China
    

    Zhang, F., Wang, L., Shi, X., Zeng, C., Ahmad, I., Wang, G., Thapa, S. and Xu, X.
    https://doi.org/10.36334/modsim.2023.zhang394

• Ahmad, M.D.
  • Pathway to impact: Sustainable Development Investment Portfolio
    

    Wahid, S., Cuddy, S.M., Ahmad, M.D., Mainuddin, M. and Almeida, A.C.
    https://doi.org/10.36334/modsim.2023.wahid

  • Water allocation planning in the Indus Basin Irrigation System in Pakistan: Using scientific tools to build trust between stakeholders
    

    Ahmad, M.D., Cuddy, S.M., Podger, G.M., Yu, Y. and Perraud, J.-M.
    https://doi.org/10.36334/modsim.2023.ahmad252

  • High spatiotemporal resolution remotely sensed timeseries actual evapotranspiration estimates for irrigation management in salinity-affected areas of the southern Indus basin
    

    Ahmad, M.D., Peña-Arancibia, J.L. and Yu, Y.
    https://doi.org/10.36334/modsim.2023.ahmad184

  • 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

• Ahmed, A.A.M.
  • Disaggregation of annual water demand in the southern Murray–Darling Basin
    

    Ahmed, A.A.M., Wang, Q.J., Western, A.W., Graham, T.D.J. and Wu, W.
    https://doi.org/10.36334/modsim.2023.ahmed574

• Ahmed, K.
  • Data-driven artificial intelligence–based streamflow projections under climate change: Review of methods and application for Melbourne's supply system
    

    Jahanbani, H. and Ahmed, K.
    https://doi.org/10.36334/modsim.2023.jahanbani

• Aili, A.
  • Modeling of radiative free cooling for data centers
    

    Long, W. and Aili, A.
    https://doi.org/10.36334/modsim.2023.long52

• Akman, D.
  • 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

• Akoluk, D.
  • Exploring the impact of modelling assumptions on distributive justice using JUSTICE
    

    Biswas, P., Akoluk, D., Zatarain Salazar, J., Kwakkel, J.H. and Verbraeck, A.
    https://doi.org/10.36334/modsim.2023.biswas

• Alam, J.
  • The Source Murray Model for NSW regional water strategies
    

    Alam, J., Hardy, M., Korn, A., Podger, G.M. and Dutta, D.
    https://doi.org/10.36334/modsim.2023.alam

• Albornoz, F.
  • A growth feedback model with limiting resources gives rise to behaviours of mutualism, parasitism, and competition between a plant and a mycorrhizal fungus
    

    Grasso, S.V., Ryan, M.H., Albornoz, F. and Renton, M.
    https://doi.org/10.36334/modsim.2023.grasso

• Aldrich, C.
  • Change detection in drill core images based on local binary pattern
    

    Gu, X., Cook, N.J., Metcalfe, A.V. and Aldrich, C.
    https://doi.org/10.36334/modsim.2023.gu67

  • Texture feature extraction for change detection in drill core images: A comparative study
    

    Gu, X., Cook, N.J., Metcalfe, A.V. and Aldrich, C.
    https://doi.org/10.36334/modsim.2023.gu69

• Alempijevic, A.
  • 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

• Alkhairy, I.
  • Multiverse experiments for enhancing reproducibility and resilience of statistical modelling via case studies in family wellbeing and biodiversity conservation
    

    Low-Choy, S., Modecki, K., Vasco, D., Alkhairy, I. and Hallgren, W.
    https://doi.org/10.36334/modsim.2023.lowchoy654

• Allen, D.E.
  • GANs through the looking glass: How real is the fake financial data created by Generative Adversarial Neural Nets?
    

    Allen, D.E., Mushunje, L. and Peiris, S.
    https://doi.org/10.36334/modsim.2023.allen

• Allen, K.
  • Past, present, and future droughts in the Murray–Darling Basin
    

    Higgins, P.A., Palmer, J.G., Andersen, M.S., Turney, C.S.M., Johnson, F.M., Allen, K., Verdon-Kidd, D. and Cook, E.R.
    https://doi.org/10.36334/modsim.2023.higgins559

  • Identifying stochastic model parameter non-stationarity over multi-centennial timescales
    

    Armstrong, M., Kiem, A.S., Kuczera, G., Vance, T. and Allen, K.
    https://doi.org/10.36334/modsim.2023.armstrong537

• Almeida, A.C.
  • Kamala Basin strategy to support water resources management and basin planning in Nepal
    

    Almeida, A.C., Foran, T., Penton, D.J., Gnawali, K., Wahid, S. and Cuddy, S.M.
    https://doi.org/10.36334/modsim.2023.almeida

  • Pathway to impact: Sustainable Development Investment Portfolio
    

    Wahid, S., Cuddy, S.M., Ahmad, M.D., Mainuddin, M. and Almeida, A.C.
    https://doi.org/10.36334/modsim.2023.wahid

• Alonso, J.
  • Can local information impact in SWAT catchment scenario modelling?
    

    Nervi, E., Alonso, J., Vervoort, R.W. and Baethgen, W.
    https://doi.org/10.36334/modsim.2023.nervi672

  • Modelling afforestation scenarios in SWAT: Towards an improved representation of evapotranspiration
    

    Alonso, J., Badano, L., Vilaseca, F., Nervi, E. and Vervoort, R.W.
    https://doi.org/10.36334/modsim.2023.alonso

  • Visualisation platform demo for experimental catchment data
    

    Nervi, E., Alonso, J., Vervoort, R.W. and Baethgen, W.
    https://doi.org/10.36334/modsim.2023.nervi545

• Alsterberg, C.
  • Estimation of the water balance and water yield in the Lagan River catchment, Sweden, using the Australian Water Resources Assessment Landscape Model
    

    Bjerkén, A., Alsterberg, C., Klante, C. and Persson, K.M.
    https://doi.org/10.36334/modsim.2023.bjerken

• Alzahrani, A.
  • 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

• Alzubaidi, S.
  • Modelling collective states and individual-level interactions in small sheep flocks
    

    Welch, M.C., Alzubaidi, S. and Schaerf, T.M.
    https://doi.org/10.36334/modsim.2023.welch468

• Anderlieste, M.
  • WaterSENSE: Update on implementing water use monitoring and assessment services
    

    Wonink, S., Jackson, B., Brombacher, J., Vervoort, R.W., Einfalt, T., Anderlieste, M., Chambel Leitão, P. and Noort, M.
    https://doi.org/10.36334/modsim.2023.wonink

• Andersen, M.S.
  • Resolving temporal-scale differences between paleoclimate reconstructions and a groundwater model for recharge estimation
    

    Sharifazari, S., Andersen, M.S., Johnson, F.M., Palmer, J.G. and Turney, C.S.M.
    https://doi.org/10.36334/modsim.2023.sharifazari

  • Past, present, and future droughts in the Murray–Darling Basin
    

    Higgins, P.A., Palmer, J.G., Andersen, M.S., Turney, C.S.M., Johnson, F.M., Allen, K., Verdon-Kidd, D. and Cook, E.R.
    https://doi.org/10.36334/modsim.2023.higgins559

  • Bridging the data gap: A spatially resolved reconstruction of drought in the South Pacific Islands
    

    Higgins, P.A., Palmer, J.G., Andersen, M.S., Turney, C.S.M. and Johnson, F.M.
    https://doi.org/10.36334/modsim.2023.higgins276

• Andersen, T.
  • Strategic decisions for routing autonomous vehicles: When to reoptimise?
    

    Andersen, T., Sankupellay, M., Belward, S., Myers, T. and Chen, C.
    https://doi.org/10.36334/modsim.2023.andersen

• Andrys, J.
  • Evaluation of precipitation extremes in ERA5-driven regional climate simulations
    

    Ji, F., Di Virgilio, G., Nishant, N., Tam, E., Evans, J.P., Kala, J., Andrys, J., Thomas, C. and Riley, M.
    https://doi.org/10.36334/modsim.2023.ji

• Ani, C.J.
  • 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

  • Connectivity modelling at local scales identifies sources and sinks of coral recruitment within reef clusters
    

    Ani, C.J., Cresswell, A., Haller-Bull, V., Gilmour, J. and Robson, B.J.
    https://doi.org/10.36334/modsim.2023.ani324

• Anjomshoa, H.
  • A data-driven preventive surgery scheduling in flexible operating rooms using stochastic optimization
    

    Sadegh Zadeh, H., Anjomshoa, H., Zhang, L. and Fackrell, M.
    https://doi.org/10.36334/modsim.2023.sadeghzadeh

• Anstee, J.
  • 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

• Anthony, K.R.N.
  • 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

  • Developing synthetic datasets for reef modelling
    

    Crocker, R., Robson, B.J., Iwanaga, T. and Anthony, K.R.N.
    https://doi.org/10.36334/modsim.2023.crocker

• Araujo, R.P.
  • Capturing qualitative reef dynamics across decadal timescales
    

    Brown, K., Jenner, A.L., Araujo, R.P. and Corry, P.
    https://doi.org/10.36334/modsim.2023.brown

• Arayachookiat, P.
  • 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

• Arjomand, E.
  • Assessing the potential impacts of well integrity failure on groundwater resources in Australia
    

    Doble, R., Mallants, D., Huddlestone-Holmes, C., Peeters, L.J.M., Kear, J., Turnadge, C., Wu, B., Noorduijn, S. and Arjomand, E.
    https://doi.org/10.36334/modsim.2023.doble

• Armin, M.A.
  • 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

• Armstrong, M.
  • Climate risk assessments in New South Wales using publicly available stochastic climate data
    

    Armstrong, M., Beecham, R., Dutta, D. and Higgins, P.A.
    https://doi.org/10.36334/modsim.2023.armstrong115

  • Sampling NARCliM factored stochastic data for infrastructure risk assessment
    

    Beecham, R., Smith, C., Sugiyanto, M., Dutta, D. and Armstrong, M.
    https://doi.org/10.36334/modsim.2023.beecham

  • Identifying stochastic model parameter non-stationarity over multi-centennial timescales
    

    Armstrong, M., Kiem, A.S., Kuczera, G., Vance, T. and Allen, K.
    https://doi.org/10.36334/modsim.2023.armstrong537

• Arnold, A.D.
  • Real-time problem solving at the Lunar South Pole
    

    Gilchrist, M.J. and Arnold, A.D.
    https://doi.org/10.36334/modsim.2023.gilchrist

• Arnold, S.
  • Soft sensing of water quality parameters in indoor shrimp farming using machine learning models
    

    Rahman, A., Arnold, S. and Emerenciano, M.
    https://doi.org/10.36334/modsim.2023.rahman28

• Aryal, S.K.
  • Drought propagation across the Murray–Darling Basin
    

    Aryal, S.K., Zheng, H. and Zhang, Y.Q.
    https://doi.org/10.36334/modsim.2023.aryal

  • Water yield assessment for major river basins of Afghanistan using GR4J and GR4JSG models
    

    Karim, F., Penton, D.J., Aryal, S.K., Chen, Y. and Wahid, S.
    https://doi.org/10.36334/modsim.2023.karim636

• Aseervatham, R.
  • 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

• Ashbolt, S.C.
  • Planned development of a modelling information system for Melbourne's water supply system
    

    Kularathna, M.D.U.P., Ashbolt, S.C., Jahanbani, H. and Vu, K.
    https://doi.org/10.36334/modsim.2023.kularathna

  • The role of water resource planning models in integrated water management: A Melbourne Water case study
    

    Ashbolt, S.C. and Kularathna, M.D.U.P.
    https://doi.org/10.36334/modsim.2023.ashbolt

• Assareh, H.
  • 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

• Athukorala, R.
  • 'Stat on pixels': An automated counting method for selective fluorescent-stained microplastics using Nile Red dye
    

    Athukorala, R., Lynch, S.K., Johnson, C., Suzzi, A.L., Rao, S. and Foulsham, E.L.
    https://doi.org/10.36334/modsim.2023.athukorala548

  • Disentangling hydrological mixtures
    

    Athukorala, R., Simmons, J., Cripps, S. and Vervoort, R.W.
    https://doi.org/10.36334/modsim.2023.athukorala550

• Attard, S.
  • 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

  • Development of an online weather and irrigation forecast decision support tool using an action learning process
    

    Sexton, J., Philippa, B., Melville, B., Schepen, A., Attard, S., Davis, M. and Everingham, Y.
    https://doi.org/10.36334/modsim.2023.sexton

• Austin, K.
  • Using multi-replicate source modelling to create a probabilistic storage forecasting tool
    

    Austin, K., Neal, B., Guthrie, E., Scorah, M., Matthews, K. and Jordan, P.
    https://doi.org/10.36334/modsim.2023.austin

• Avendano, B.
  • The use of game-based participatory modelling to facilitate social and infrastructure resilience planning
    

    Avendano, B.
    https://doi.org/10.36334/modsim.2023.avendano224

• Avendano, F.
  • 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

B

• Badano, L.
  • Modelling afforestation scenarios in SWAT: Towards an improved representation of evapotranspiration
    

    Alonso, J., Badano, L., Vilaseca, F., Nervi, E. and Vervoort, R.W.
    https://doi.org/10.36334/modsim.2023.alonso

• Bae, S.
  • Evaluating the reliability of ChatGPT answers for safety management in chemical handling facilities
    

    Lee, D.G., Bae, S. and Baek, J.B.
    https://doi.org/10.36334/modsim.2023.lee384

  • Trends in brain injury among United States female students linked to consumer products
    

    Le, T.D., Cook, A., Keyloun, J.W., Ledlow, G., Pusateri, A.E., Uhm, M., Bae, S. and Singh, K.P.
    https://doi.org/10.36334/modsim.2023.le254

  • Impact of distance to travel in cervical cancer outcome: National Cancer Database Study
    

    Bae, S., Pandya, V., Park, H., Wang, Z., Wang, C., Wang, H., Uhm, M., Huh, W. and Singh, K.P.
    https://doi.org/10.36334/modsim.2023.bae

  • Estimate for risk reduction rate using fuzzy-AHP and Bayesian network
    

    Lee, M.J., Bae, S. and Baek, J.B.
    https://doi.org/10.36334/modsim.2023.lee303

• Baek, J.B.
  • Study on the appropriateness of detector installation in chemical supply facilities for semiconductor manufacturing
    

    Oh, K.-S., Ha, S.Y. and Baek, J.B.
    https://doi.org/10.36334/modsim.2023.oh108

  • Estimate for risk reduction rate using fuzzy-AHP and Bayesian network
    

    Lee, M.J., Bae, S. and Baek, J.B.
    https://doi.org/10.36334/modsim.2023.lee303

  • Optimization of dilution ventilation layout design of isolation building using CFD
    

    Kim, M.C., Lee, M.J. and Baek, J.B.
    https://doi.org/10.36334/modsim.2023.kim378

  • Evaluating the reliability of ChatGPT answers for safety management in chemical handling facilities
    

    Lee, D.G., Bae, S. and Baek, J.B.
    https://doi.org/10.36334/modsim.2023.lee384

• Baethgen, W.
  • Can local information impact in SWAT catchment scenario modelling?
    

    Nervi, E., Alonso, J., Vervoort, R.W. and Baethgen, W.
    https://doi.org/10.36334/modsim.2023.nervi672

  • Visualisation platform demo for experimental catchment data
    

    Nervi, E., Alonso, J., Vervoort, R.W. and Baethgen, W.
    https://doi.org/10.36334/modsim.2023.nervi545

• Baggaley, N.
  • Cross-scale analysis of social-ecological systems: Policy options appraisal for delivering NetZero and other environmental objectives in Scotland
    

    Matthews, K.B., Blackstock, K.L., Wardell-Johnson, D.H., Miller, D.G., Tavana, M., Thomson, S., Moxey, A., Nielsen, R., Baggaley, N., Loades, K., Paterson, E., Pakeman, R., Hawes, C., Stockan, J., Stutter, M., Addy, S. and Wilkinson, M.
    https://doi.org/10.36334/modsim.2023.matthews

• Bahramian, K.
  • Streamflow regime shifts in a changing climate: A case study from Victoria, Australia
    

    Bende-Michl, U., Thomas, S., Bahramian, K., Kociuba, G., Sharples, W., Pepler, A. and Tolhurst, G.
    https://doi.org/10.36334/modsim.2023.bende-michl

  • 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

• Baird, M.E.
  • 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

  • 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

  • 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

  • A light-hearted approach to a serious problem: Building “educated trust” in models
    

    O'Brien, K.R., Adams, M.P., Egger, F., Maxwell, P., Weber, T., Maier, H.R., Vilas, M.P., Shaw, M., Turner, R., Birkett, G.R., Hamilton, D.P., Langsdorf, H. and Baird, M.E.
    https://doi.org/10.36334/modsim.2023.obrien

• Bakar, S.
  • An operational framework to automatically evaluate the quality of weather observations from third-party stations
    

    Shao, Q., Li, M., Dabrowski, J.J., Bakar, S., Rahman, A., Powell, A. and Henderson, B.
    https://doi.org/10.36334/modsim.2023.shao114

• Baker, C.M.
  • Developing an agent-based simulation model to evaluate biosecurity inspection policies
    

    Baker, C.M., Le, T.P., Waring, T.K. and Robinson, A.P.
    https://doi.org/10.36334/modsim.2023.baker

  • A partially observed approach to invasive species management: To monitor or control?
    

    Waring, T.K., Somers, V., McCarthy, M. and Baker, C.M.
    https://doi.org/10.36334/modsim.2023.waring

• Baker, D.
  • Role of modelling and monitoring hub in fostering interagency collaboration
    

    Loonat, N., Baker, D., Joyner, A. and Couriel, E.
    https://doi.org/10.36334/modsim.2023.loonat

• Baker, P.
  • 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

• Balocchi, F.
  • Integrated modelling of forest growth and hydrologic processes for forest management
    

    Yang, J., White, D., Palma, J.H.N., Meason, D., Balocchi, F. and Rajanayaka, C.
    https://doi.org/10.36334/modsim.2023.yang393

• Banhalmi-Zakar, Z.
  • 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

• Barca, J.C.
  • A review of UAV swarm-based detection of a dynamic contamination plume
    

    Borazjani, S., Kennedy, J., Barca, J.C. and Crase, S.
    https://doi.org/10.36334/modsim.2023.borazjani

• Barton, C.M.
  • Earth systems to Anthropocene systems: An evolutionary, system-of-systems, convergence paradigm for interdependent societal challenges
    

    Little, J.C., Kaaronen, R.O., Hukkinen, J.I., Xiao, S., Sharpee, T., Farid, A.M., Nilchiani, R. and Barton, C.M.
    https://doi.org/10.36334/modsim.2023.little

• Batelaan, O.
  • 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

  • Using expert participation to evaluate the accuracy and variability of hand-drawn water table mapping
    

    Marshall, S.K., Batelaan, O. and Peeters, L.J.M.
    https://doi.org/10.36334/modsim.2023.marshall

  • 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

  • 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

  • Increasing our understanding of the adjoint state method of model sensitivity calculation
    

    Turnadge, C., Neupauer, R.M., Batelaan, O., Crosbie, R.S. and Simmons, C.T.
    https://doi.org/10.36334/modsim.2023.turnadge

• Bath, A.
  • 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

• Battaglia, M.
  • Integrated modelling of forest growth and hydrologic processes for forest management
    

    Yang, J., White, D., Palma, J.H.N., Meason, D., Balocchi, F., Rajanayaka, C., Dawes, W. and Battaglia, M.
    https://doi.org/10.36334/modsim.2023.yang393

• Baumgartl, T.
  • 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

• Baumgartner, P.
  • Indoor movement data analytics based on machine learning for manufacturing assembly line
    

    Rana, M., Smith, D.V., Rahman, A. and Baumgartner, P.
    https://doi.org/10.36334/modsim.2023.rana

  • Data-driven probabilistic logic programming applied to movement analytics in manufacturing
    

    Tartaglia, E. and Baumgartner, P.
    https://doi.org/10.36334/modsim.2023.tartaglia

• Baur, L.
  • 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

• Bayer, P.
  • Modelling the impact of climate change on global groundwater temperatures
    

    Benz, S.A., Irvine, D.J., Rau, G.C., Bayer, P., Menberg, K., Blum, P., Jamieson, R.C., Griebler, C. and Kurylyk, B.L.
    https://doi.org/10.36334/modsim.2023.benz

• Bayraktarov, E.
  • The sensitivity of species distribution models to new data and the utility of putting uncertainty on the map
    

    Clemens, R., Schwenke, A., Bayraktarov, E. and Laffan, S.W.
    https://doi.org/10.36334/modsim.2023.clemens

• Beale, P.J.
  • 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

• Beatty, R.
  • Forecast of residential demand drivers for Greater Darwin
    

    Beatty, R., Pinto, R. and Holden, R.
    https://doi.org/10.36334/modsim.2023.beatty

• Beecham, R.
  • Climate risk assessments in New South Wales using publicly available stochastic climate data
    

    Armstrong, M., Beecham, R., Dutta, D. and Higgins, P.A.
    https://doi.org/10.36334/modsim.2023.armstrong115

  • Sampling NARCliM factored stochastic data for infrastructure risk assessment
    

    Beecham, R., Smith, C., Sugiyanto, M., Dutta, D. and Armstrong, M.
    https://doi.org/10.36334/modsim.2023.beecham

• Beh, E.H.Y.
  • Modelling Lake Bonney: The salty problem child of the Lock 3 weir pool at River Murray South Australia
    

    Beh, E.H.Y.
    https://doi.org/10.36334/modsim.2023.beh

• Beh, E.J.
  • 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

• Belik, V.
  • 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

• Beling, E.
  • Combining salinity and water management modelling for the Hawkesbury Nepean Valley
    

    Simpson, J., Podger, S., Li, C., Nicholls, J., Kumandur, K., Dunne, P., Beling, E. and Coen, F.
    https://doi.org/10.36334/modsim.2023.simpson120

• Bellhouse, J.A.
  • Applying the Bureau of Meteorology’s national hydrological projections dataset to assess risks to future water supply scheme reliability
    

    Bellhouse, J.A., Schopf, J. and Turner, M.
    https://doi.org/10.36334/modsim.2023.bellhouse

  • 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

• Belward, S.
  • Strategic decisions for routing autonomous vehicles: When to reoptimise?
    

    Andersen, T., Sankupellay, M., Belward, S., Myers, T. and Chen, C.
    https://doi.org/10.36334/modsim.2023.andersen

• Bende-Michl, U.
  • Streamflow regime shifts in a changing climate: A case study from Victoria, Australia
    

    Bende-Michl, U., Thomas, S., Bahramian, K., Kociuba, G., Sharples, W., Pepler, A. and Tolhurst, G.
    https://doi.org/10.36334/modsim.2023.bende-michl

  • What is the flood risk in Australia under future climate?
    

    Khan, Z., Bende-Michl, U., Srikanthan, S., Vogel, E., Sharples, W., Peter, J., Hope, P., Dowdy, A. and Wilson, L.
    https://doi.org/10.36334/modsim.2023.khan432

  • 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

  • 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

  • A guide to future climate projections for water resource management in Western Australia
    

    Schopf, J., Kitsios, A., McCallum, S., Turner, M., Bende-Michl, U. and Hall, J.
    https://doi.org/10.36334/modsim.2023.schopf

• Bennett, B.
  • Streamflow-based evaluation highlights discrepancies in stochastic rainfall model performance
    

    Nguyen, T., Bennett, B. and Leonard, M.
    https://doi.org/10.36334/modsim.2023.nguyen509

• Bennett, D.
  • How ‘green’ is my hydrogen? A comparative study of land, water, and wastewater footprints of renewable hydrogen production
    

    Thomas, H., Bennett, D., Peeters, L.J.M. and Holland, K.
    https://doi.org/10.36334/modsim.2023.thomas391

• Bennett, F.R.
  • Parameter sensitivity analysis of the MERGE quasi-steady state gully erosion model
    

    Bennett, F.R., Singh, A. and Roberts, M.E.
    https://doi.org/10.36334/modsim.2023.bennett128

  • 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

• Bennett, J.C.
  • Understanding residual risks of forecast-informed river system operations
    

    Robertson, D.E., Ng, T.L., Shrestha, D.L. and Bennett, J.C.
    https://doi.org/10.36334/modsim.2023.robertson

  • Building a modern, all-purpose hydrological forecasting system
    

    Matala, A., Hunter, J., Robinson, K. and Bennett, J.C.
    https://doi.org/10.36334/modsim.2023.matala

  • Better accounting of droughts in long-range inflow predictions with TULIP
    

    Horsley, D., Bennett, J.C., Schepen, A. and Robertson, D.E.
    https://doi.org/10.36334/modsim.2023.horsley

  • Realised added value in rainfall trends and variance from regional climate models
    

    Teng, J., Bennett, J.C., Charles, S., Chiew, F.H.S., Ji, F., Potter, N., Fu, G. and Thatcher, M.
    https://doi.org/10.36334/modsim.2023.teng316

• Benz, S.A.
  • Modelling the impact of climate change on global groundwater temperatures
    

    Benz, S.A., Irvine, D.J., Rau, G.C., Bayer, P., Menberg, K., Blum, P., Jamieson, R.C., Griebler, C. and Kurylyk, B.L.
    https://doi.org/10.36334/modsim.2023.benz

• Bhisitcharoentat, Y.
  • 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

  • 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

• Bhosale, B.
  • Wavelet-curvelet-contourlet based remote sensing data mining model
    

    Bhosale, B.
    https://doi.org/10.36334/modsim.2023.bhosale

• Birand, A.
  • Modelling eradication potential of a newly developed gene drive strategy in mice using spatially explicit agent-based simulations
    

    Birand, A., Gierus, L., Cassey, P., Ross, J.V., Prowse, T.A.A. and Thomas, P.Q.
    https://doi.org/10.36334/modsim.2023.birand

• Birkett, G.R.
  • A light-hearted approach to a serious problem: Building “educated trust” in models
    

    O'Brien, K.R., Adams, M.P., Egger, F., Maxwell, P., Weber, T., Maier, H.R., Vilas, M.P., Shaw, M., Turner, R., Birkett, G.R., Hamilton, D.P., Langsdorf, H. and Baird, M.E.
    https://doi.org/10.36334/modsim.2023.obrien

  • Mathematical modelling demonstrates how students can get stuck in unproductive learning regimes
    

    Egger, F., Lodge, J.M., Adams, M.P., Birkett, G.R., Monsalve-Bravo, G.M., Howes, T. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2023.egger512

• Bishop, T.
  • A framework for the fusion of earth observation and soil data to constrain soil organic carbon model parameters
    

    Ugbaje, S.U., Pagendam, D.E., Karunaratne, S., Bishop, T., Mishra, U., Gautam, S. and Farrell, M.
    https://doi.org/10.36334/modsim.2023.ugbaje

• Biswas, P.
  • Exploring the impact of modelling assumptions on distributive justice using JUSTICE
    

    Biswas, P., Akoluk, D., Zatarain Salazar, J., Kwakkel, J.H. and Verbraeck, A.
    https://doi.org/10.36334/modsim.2023.biswas

• Biswas, T.
  • 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

• Bjerkén, A.
  • Estimation of the water balance and water yield in the Lagan River catchment, Sweden, using the Australian Water Resources Assessment Landscape Model
    

    Bjerkén, A., Alsterberg, C., Klante, C. and Persson, K.M.
    https://doi.org/10.36334/modsim.2023.bjerken

• Blackstock, K.L.
  • Cross-scale analysis of social-ecological systems: Policy options appraisal for delivering NetZero and other environmental objectives in Scotland
    

    Matthews, K.B., Blackstock, K.L., Wardell-Johnson, D.H., Miller, D.G., Tavana, M., Thomson, S., Moxey, A., Nielsen, R., Baggaley, N., Loades, K., Paterson, E., Pakeman, R., Hawes, C., Stockan, J., Stutter, M., Addy, S. and Wilkinson, M.
    https://doi.org/10.36334/modsim.2023.matthews

• Blake, D.
  • 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

• Blondeau-Patissier, D.
  • 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

• Bloomer, D.
  • 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

• Bloomfield, M.
  • 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

• Blum, P.
  • Modelling the impact of climate change on global groundwater temperatures
    

    Benz, S.A., Irvine, D.J., Rau, G.C., Bayer, P., Menberg, K., Blum, P., Jamieson, R.C., Griebler, C. and Kurylyk, B.L.
    https://doi.org/10.36334/modsim.2023.benz

• Blumson, D.
  • Modelling the impacts of non-kinetic factors on combat effectiveness: The role of deception
    

    Hock, K., Staby, S.-E., Gary, M.S., Kosowski, L., Blumson, D., Cao, H.T., Elsawah, S., Kempt, N. and Richmond, M.K.
    https://doi.org/10.36334/modsim.2023.hock

  • Moving beyond attrition in modelling land combat
    

    Staby, S.-E., Blumson, D., Cao, T., Elsawah, S., Gary, M.S., Hock, K., Kosowski, L. and Richmond, M.K.
    https://doi.org/10.36334/modsim.2023.staby373

• Bode, M.
  • 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

• Bogomolov, T.
  • Markov chain modelling of causality in anomalous and non-anomalous combat event series
    

    Tiong, J.K.R., Bogomolov, T. and Chiera, B.A.
    https://doi.org/10.36334/modsim.2023.tiong

• Boisgontier, H.
  • 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

• Boland, J.
  • Probabilistic forecasting for solar energy
    

    Boland, J.
    https://doi.org/10.36334/modsim.2023.boland

• Bolger, M.
  • The importance of self-awareness to digital twins
    

    Hetherton, L., Bolger, M., Xie, P., Zhang, Z. and Rucinski, C.
    https://doi.org/10.36334/modsim.2023.hetherton

  • Workspace and the Mixed Reality Lab: Where scientific workflows meet Industry 4.0
    

    Watkins, D., Bolger, M. and Zhang, Z.
    https://doi.org/10.36334/modsim.2023.watkins

• Bolt, A.
  • Building trust in hazard model uncertainties
    

    Huston, C., Kuhnert, P.M., Bolt, A. and Davis, J.
    https://doi.org/10.36334/modsim.2023.huston454

• Bolz, P.
  • Bias-correcting regional climate models for rainfall erosivity projections: How much does it matter?
    

    Bulovic, N., McIntyre, N., Trancoso, R., Bolz, P. and Shaygan, M.
    https://doi.org/10.36334/modsim.2023.bulovic618

  • Evaluating downscaled CMIP5 and CMIP6 for rainfall erosivity projections
    

    Bulovic, N., McIntyre, N., Trancoso, R., Bolz, P. and Shaygan, M.
    https://doi.org/10.36334/modsim.2023.bulovic428

• Borazjani, S.
  • A review of UAV swarm-based detection of a dynamic contamination plume
    

    Borazjani, S., Kennedy, J., Barca, J.C. and Crase, S.
    https://doi.org/10.36334/modsim.2023.borazjani

• Botelho, D.
  • Developing an assessment framework to guide investment in water models
    

    Gibbes, B., Botelho, D., Singh, A., Rissik, D. and Hipsey, M.R.
    https://doi.org/10.36334/modsim.2023.gibbes

• Botev, Z.
  • A continuous optimization algorithm for column subset selection
    

    Mathur, A., Moka, S. and Botev, Z.
    https://doi.org/10.36334/modsim.2023.mathur

• Botha, I.
  • 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

• Box, P.
  • 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

  • Agent-based model of Macrobrachium sp. migration in the Daly River, Northern Territory, Australia
    

    Box, P., Brim Box, J. and Novak, P.
    https://doi.org/10.36334/modsim.2023.box390

• Branchaud, D.
  • Advancing environmental management through digital twin technology: A demonstration and future outlook for land and water resource development in Australia
    

    Branchaud, D., Seo, L., Petheram, C., Fu, Q. and Watson, I.
    https://doi.org/10.36334/modsim.2023.branchaud

  • Digital twin–based river-system model
    

    Seo, L., Branchaud, D., Petheram, C. and Watson, I.
    https://doi.org/10.36334/modsim.2023.seo610

• Breda, A.
  • 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

  • The influence of different objective functions in GR4J model-on-model performance for streamflow forecasting application
    

    Nguyen, H., Tuteja, N., Perera, H., Raut, A., Hameed, T., Neupane, R. and Breda, A.
    https://doi.org/10.36334/modsim.2023.nguyen546

  • 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

• Bremner, S.
  • Assessing the impact of Australian bushfires on rooftop photovoltaic energy production
    

    Isaza, A., Kay, M., Evans, J.P., Prasad, A. and Bremner, S.
    https://doi.org/10.36334/modsim.2023.isaza

  • Estimation of long-term solar power fluctuations across Australia using high-resolution regional climate projections
    

    Poddar, S., Evans, J.P., Kay, M., Prasad, A. and Bremner, S.
    https://doi.org/10.36334/modsim.2023.poddar

• Brettig, P.
  • 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

• Bridgart, R.
  • A new tool for determining potential biomass use for bioenergy in New South Wales
    

    Perraud, J.-M., Bridgart, R., Carney, C. and Ximenes, F.
    https://doi.org/10.36334/modsim.2023.perraud379

• Brieva, C.
  • 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

• Brim Box, J.
  • 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

  • Agent-based model of Macrobrachium sp. migration in the Daly River, Northern Territory, Australia
    

    Box, P., Brim Box, J. and Novak, P.
    https://doi.org/10.36334/modsim.2023.box390

• Brombacher, J.
  • WaterSENSE: Update on implementing water use monitoring and assessment services
    

    Wonink, S., Jackson, B., Brombacher, J., Vervoort, R.W., Einfalt, T., Anderlieste, M., Chambel Leitão, P. and Noort, M.
    https://doi.org/10.36334/modsim.2023.wonink

• Brookes, K.
  • Strategic assessment of proposed river infrastructure, with Clarence River – Bellingen Pipeline, as an example
    

    Driver, P.D., Taylor, N., Carey, B., Cu, T.P., Brookes, K. and Harris, D.
    https://doi.org/10.36334/modsim.2023.driver209

• Brooks, S.
  • 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

• Brown, H.
  • 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

  • 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

  • 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

• Brown, K.
  • Capturing qualitative reef dynamics across decadal timescales
    

    Brown, K., Jenner, A.L., Araujo, R.P. and Corry, P.
    https://doi.org/10.36334/modsim.2023.brown

• Browne, C.
  • If this, then that, then what? A generative process for overcoming implicit bias in the initial phases of participatory modelling
    

    Browne, C. and Nabavi, E.
    https://doi.org/10.36334/modsim.2023.browne

• Bruce, D.
  • 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

  • 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

• Bryan, B.A.
  • Socio-ecological system dynamics modelling for sustainability assessment at the local scale
    

    Szetey, K., Moallemi, E.A. and Bryan, B.A.
    https://doi.org/10.36334/modsim.2023.szetey

• Bryant, C.
  • Modelling the probability of lightning-caused ignitions in Australia
    

    Zhao, L., Yebra, M. and Bryant, C.
    https://doi.org/10.36334/modsim.2023.zhao580

• Bulovic, N.
  • Bias-correcting regional climate models for rainfall erosivity projections: How much does it matter?
    

    Bulovic, N., McIntyre, N., Trancoso, R., Bolz, P. and Shaygan, M.
    https://doi.org/10.36334/modsim.2023.bulovic618

  • Evaluating downscaled CMIP5 and CMIP6 for rainfall erosivity projections
    

    Bulovic, N., McIntyre, N., Trancoso, R., Bolz, P. and Shaygan, M.
    https://doi.org/10.36334/modsim.2023.bulovic428

• Bulumulla, C.
  • Modelling self-evacuation archetypes to improve wildfire evacuation traffic simulations: A regional case study
    

    Singh, D., Bulumulla, C., Strahan, K., Gilbert, J., Gamage, P., Marquez, L. and Lemiale, V.
    https://doi.org/10.36334/modsim.2023.singh54

• Bunney, E.
  • 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

• Burdett, R.
  • 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

• Burford, M.
  • 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

  • Flow deficits in northern Australian estuaries: Implications for water extraction
    

    Egger, F., Burford, M., Weber, T. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2023.egger513

• Burk, L.
  • 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

• Burns, G.
  • Exploring future climate impacts on water allocations in the southern Murray–Darling Basin
    

    Fowler, K.J.A., Horne, A., Haddock, L., Burns, G. and Zhang, Z.
    https://doi.org/10.36334/modsim.2023.fowler638

  • A practical approach to assessing climate change impacts on snow cover and streamflow in southeast Australia
    

    Burns, G., Fowler, K.J.A. and Horne, A.
    https://doi.org/10.36334/modsim.2023.burns

• Butler, K.
  • Bottlenecks, feedbacks and thresholds in wildfire suppression
    

    McCarthy, N.F., Duff, T., Butler, K. and Deutsch, S.
    https://doi.org/10.36334/modsim.2023.mccarthy