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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

E

  • Egger, F.
    • 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
    • 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
    • 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
  • Einfalt, T.
    • 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
  • Etemad-Shahidi, A.
    • 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
  • Everingham, Y.
    • 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

F

  • Falque, R.
    • 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
  • Fang, Y.
    • 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
  • Farid, A.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
  • Farrell, M.
    • 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
  • Fathi Salmi, E.
    • Building a simulation application for blasting: Case study of software component re-use and customisation in a workflow framework
      Cummins, S.J., Thomas, D.G., Sellers, E., Fathi Salmi, E. and Cleary, P.W.
      https://doi.org/10.36334/modsim.2023.cummins
  • Ferrier, S.
    • 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
  • Fischer, S.M.
    • "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
  • Fletcher, A.
    • Modelling the integration of long fallows into cropping systems for adaptation to climate change in the Mediterranean environment of Western Australia
      Chen, C., Ota, N., Wang, B. and Fletcher, A.
      https://doi.org/10.36334/modsim.2023.chen30
  • Ford, P.
    • 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
  • Forrest, S.W.
    • 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
  • Fortunato, R.
    • 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
  • Foulsham, E.L.
  • Freebairn, A.C.
    • 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
    • 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
    • Understanding river system sensitivity to climate variation
      Shokri, A., Robertson, D.E., Freebairn, A.C. and Potter, N.
      https://doi.org/10.36334/modsim.2023.shokri
    • 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
  • Frost, A.
    • 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
    • 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
  • Frydman, S.
    • Using deep learning methods to create translators between biogeochemical models, improving regional ocean model global integration
      Mongin, M., Phillips, L., Frydman, S. and Jones, E.
      https://doi.org/10.36334/modsim.2023.mongin
  • Fu, Q.
    • 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
  • Furlaud, J.M.
    • Can we use state and transition models to add dynamism to fire risk and behaviour models?
      Furlaud, J.M., Szetey, K., Luxton, S., Newnham, G., Williams, K.J., Prober, S. and Richards, A.
      https://doi.org/10.36334/modsim.2023.furlaud

G

  • Gamage, P.
    • 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
  • Gandomi, A.H.
    • Improving the performance of vertical slotted breakwater and modeling its hydrodynamic behavior by genetic programming
      Gandomi, M., Khorshidi, M.S., Reza Nikoo, M., Chen, F. and Gandomi, A.H.
      https://doi.org/10.36334/modsim.2023.gandomi
    • Enhancing empirical modelling in environmental science with knowledge discovery and genetic programming
      Khorshidi, M.S., Gandomi, M., Reza Nikoo, M., Yazdani, D., Chen, F. and Gandomi, A.H.
      https://doi.org/10.36334/modsim.2023.khorshidi
  • Gandomi, M.
    • Improving the performance of vertical slotted breakwater and modeling its hydrodynamic behavior by genetic programming
      Gandomi, M., Khorshidi, M.S., Reza Nikoo, M., Chen, F. and Gandomi, A.H.
      https://doi.org/10.36334/modsim.2023.gandomi
    • Enhancing empirical modelling in environmental science with knowledge discovery and genetic programming
      Khorshidi, M.S., Gandomi, M., Reza Nikoo, M., Yazdani, D., Chen, F. and Gandomi, A.H.
      https://doi.org/10.36334/modsim.2023.khorshidi
  • Gary, M.S.
    • 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
  • Gautam, S.
    • 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
  • Gaydon, D.
  • Gehrig, S.
    • 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
  • Gierus, L.
    • 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
  • Gilbert, J.
    • 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
  • Gilmour, J.
    • 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
  • Glover, M.
    • 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
  • Gnawali, K.
  • Golchin, M.
    • Building a one-vs-all classifier for spatial prediction of detected pathogens
      Maskell, P., Ryan, M., Karawita, A., Hickson, R.I. and Golchin, M.
      https://doi.org/10.36334/modsim.2023.maskell
    • Using spatially explicit models to determine seasonal differences in space use and behaviour of feral buffalo in the Northern Territory
      Pike, K.N., Golchin, M., Perry, J., Vanderduys, E. and Hoskins, A.J.
      https://doi.org/10.36334/modsim.2023.pike
  • Gonzalez, D.
    • Transfer learning models for predicting spatiotemporal dynamics of groundwater levels
      Gao, Y., Gao, L., Gonzalez, D., Fu, G., Chen, Y. and Navarro Garcia, J.
      https://doi.org/10.36334/modsim.2023.gao634
    • Levelised cost distribution curves and global sensitivity analysis for assessing managed aquifer recharge scheme viability under uncertainty
      Gonzalez, D., Guillaume, J.H.A., Peeters, L.J.M. and Wyrwoll, P.
      https://doi.org/10.36334/modsim.2023.gonzalez
  • Gormley, A.
    • 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
  • Gou, J.
    • 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
  • Grasso, S.V.
    • 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
  • Green, D.
    • Changing climate and the uncertainties in allocating water for consumptive and environmental needs in highly developed catchments
      Savadamuthu, K., McCullough, D.P. and Green, D.
      https://doi.org/10.36334/modsim.2023.savadamuthu
    • Least-cost optimisation of common energy infrastructure for multi-industry low emissions hubs: Implications of hydrogen demand
      Green, D., Foster, J., Havas, L., Graham, P.W., Hayward, J. and Khandoker, T.
      https://doi.org/10.36334/modsim.2023.green
  • Griebler, C.
    • 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
  • Guan, H.
    • 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
    • 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
    • Impacts of climate extremes on vegetation phenology and productivity in a transect along the Hu Line of China
      Wang, H., Guan, H., Liu, B. and Chen, X.
      https://doi.org/10.36334/modsim.2023.wang19
    • 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
  • Guillaume, J.H.A.
    • 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
    • Identifying factors influencing water planning: Benefits of a capability approach?
      Rosello, C., Guillaume, J.H.A., Pollino, C.A. and Jakeman, A.J.
      https://doi.org/10.36334/modsim.2023.rosello103
    • Engaging users in critical appraisal of computer model software
      Rosello, C., Guillaume, J.H.A., Taylor, P., Cuddy, S.M., Pollino, C.A. and Jakeman, A.J.
      https://doi.org/10.36334/modsim.2023.rosello628
    • Levelised cost distribution curves and global sensitivity analysis for assessing managed aquifer recharge scheme viability under uncertainty
      Gonzalez, D., Guillaume, J.H.A., Peeters, L.J.M. and Wyrwoll, P.
      https://doi.org/10.36334/modsim.2023.gonzalez
  • Guo, Y.
    • Discovering relationships among economic variables using machine learning techniques
      Guo, Y., Li, J., Lo, T., Zhu, Z., Lee, G. and Toscas, P.
      https://doi.org/10.36334/modsim.2023.guo139
    • 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
  • Gupta, H.
    • Virtual hydrological laboratories: Developing the next generation of conceptual models to support decision-making under change
      Thyer, M., Gupta, H., Westra, S., McInerney, D., Maier, H.R., Kavetski, D., Jakeman, A.J., Croke, B.F.W., Simmons, C.T., Shanafield, M., Partington, D. and Tague, C.
      https://doi.org/10.36334/modsim.2023.thyer
  • Guthrie, E.
  • Gutierrez, H.
    • 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
  • Gutierrez, K.
    • 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
  • Gutiérrez-Jurado, K.Y.
    • Modelling flows from the past to inform flows for the future
      Penney, D., Savadamuthu, K., Van Der Wielen, M. and Gutiérrez-Jurado, K.Y.
      https://doi.org/10.36334/modsim.2023.penney
    • 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

Modelling to support planning for resilience in a changing world