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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

H

  • Hagan, J.
    • 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
  • Haider, J.L.
    • 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
  • Hall, J.
  • Haller-Bull, V.
    • 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
  • Hallgren, W.
    • 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
  • Hameed, T.
    • 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
  • Hamilton, D.P.
    • 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
    • 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
    • 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
  • Han, X.
    • Investigating an extreme drought event with the Bega River System Model
      Simpson, J., Han, X., Dutta, D., Purtle, C. and Craig, A.
      https://doi.org/10.36334/modsim.2023.simpson518
    • Analysis of impact of catchment antecedent moisture conditions on runoff generations
      Tang, Y., Han, X., Cu, T.P. and Dutta, D.
      https://doi.org/10.36334/modsim.2023.tang300
    • 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
  • Hannah, N.
    • A new Python module to convert WRF regional climate projections into CORDEX-compliant datasets
      Tam, E., Di Virgilio, G., Vieira Rocha, C., Choudhury, D., Ji, F., Hannah, N. and Riley, M.
      https://doi.org/10.36334/modsim.2023.tam
  • Harris, D.
    • 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
  • Harvy, C.
    • 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
    • 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
  • Hawes, C.
    • 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
  • He, D.
    • 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
    • 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
  • Helfer, F.
    • 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
  • Helfgott, A.
    • 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
  • Helmstedt, K.J.
    • 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
  • Henderson, B.
    • 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
  • Henry, R.
    • 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
  • Hitchcock, D.
    • 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
  • Hock, K.
    • 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
    • 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
  • Hope, P.
  • Horne, 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
  • Hoskins, A.J.
    • 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
    • A metapopulation model of Little Red Flying Fox population dynamics across Queensland
      Longmuir, D.N.R., Hoskins, A.J. and Hickson, R.I.
      https://doi.org/10.36334/modsim.2023.longmuir
    • 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
  • Hou, J.
    • 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
    • Improving river flood modelling using high-resolution satellite and airborne observations: A case study in the Lower Barwon-Darling River
      Hou, J., van Dijk, A.I.J.M. and Renzullo, L.J.
      https://doi.org/10.36334/modsim.2023.hou96
    • 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
  • Howes, T.
    • 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
  • Hu, P.
    • 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
  • Huddlestone-Holmes, C.
    • 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
  • Hughes, D.
  • Hughes, N.
  • Huh, W.
    • 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
  • Hukkinen, J.I.
    • 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
  • Hunt, J.
    • 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
  • Hutley, L.B.
    • 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
  • Hyles, J.
    • 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
    • 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

I

  • Ingleton, T.
    • 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
  • Inoue, M.
    • 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
  • Irvine, D.J.
    • 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
  • Iwasaki, 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

J

  • Jackson, B.
    • 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
  • Jakeman, A.J.
    • 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
    • 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
    • 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
    • Making good modelling practice common practice
      Hamilton, S.H., Jakeman, A.J. and Elsawah, S.
      https://doi.org/10.36334/modsim.2023.hamilton593
  • Jamieson, R.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
  • Jang, N.-G.
  • Jarynowski, A.
    • 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
  • Javadi, B.
    • An IoT digital twin to create Sydney’s coolest park
      Urich, C., Cressall, B., Pasanen, J., Obst, O., Javadi, B., Kocyigit, M., Thomson, R., Tovey, A., Owen, C. and Pfautsch, S.
      https://doi.org/10.36334/modsim.2023.urich
  • Ji, F.
    • A new Python module to convert WRF regional climate projections into CORDEX-compliant datasets
      Tam, E., Di Virgilio, G., Vieira Rocha, C., Choudhury, D., Ji, F., Hannah, N. and Riley, M.
      https://doi.org/10.36334/modsim.2023.tam
    • 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
    • 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
  • Jiang, J.P.
    • 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
    • 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
  • Jin, H.
    • 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
  • Joehnk, K.D.
    • 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
  • Johnson, C.
  • Johnson, J.
    • 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
  • Jones, E.
    • 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
  • Jorquera, E.
    • 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
  • Jovanović, R.
    • "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

K

  • Kaaronen, R.O.
    • 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
  • Kala, 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
  • Kang, M.-G.
  • Karunaratne, S.
    • Trends and drivers of carbon stocks across rangelands over the decade 2010–2020
      Pasut, C., Karunaratne, S., Malone, B., Shepherd, N. and Zwartz, D.
      https://doi.org/10.36334/modsim.2023.pasut
    • 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
  • Kavetski, D.
    • 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
  • Keane, R.
    • 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
  • Kear, J.
    • 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
  • Keeble, T.P.
    • A seven-day ahead bushfire fuel moisture forecasting system integrating an automated fuel sensor network, weather forecasts and a machine learning model
      Keeble, T.P., Lyell, C.S. and Sheridan, G.
      https://doi.org/10.36334/modsim.2023.keeble
  • Keegan-Treloar, R.
    • 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
  • Kelly, S.
  • Kempt, N.
    • 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
  • Kennedy, 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
  • Kerrisk, G.
    • 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
  • Keyloun, J.W.
    • 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
  • Khan, Z.
    • 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
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    • Simulating hydrological consequences of the 2022 severe drought in the Yangtze River Basin, China
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Modelling to support planning for resilience in a changing world