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

Nabavi, E.
- 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

Nair, S.
- Functionalities in eWater Source to address new challenges in hydrological modelling
  Nair, S., Moolman, J., Li, Y. and McCallum, S.
  https://doi.org/10.36334/modsim.2023.nair

Namekawa, M.
- Sequence analysis applying fuzzy graph theory and fuzzy core index method in social network analysis II
  Shinkai, K., Kanagawa, S. and Namekawa, M.
  https://doi.org/10.36334/modsim.2023.shinkai
- Optimal historical volatility for the Dow-Jones industrial average and its application
  Kanagawa, S., Namekawa, M. and Shinkai, K.
  https://doi.org/10.36334/modsim.2023.kanagawa

Nano, C.
- 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

Narsey, S.
- 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

Nasirian, A.
- Using logic-based Benders' decomposition approach for a two-stage integer stochastic staffing and scheduling problem
  Nasirian, A. and Zhang, L.
  https://doi.org/10.36334/modsim.2023.nasirian

Nathan, R.
- The time of emergence of climate change in Australian rivers
  John, A., Nathan, R., Horne, A., Fowler, K.J.A., Stewardson, M., Peel, M.C. and Webb, J.A.
  https://doi.org/10.36334/modsim.2023.john
- Can annual streamflow volumes be characterised by flood events alone?
  Dykman, C., Sharma, A., Wasko, C. and Nathan, R.
  https://doi.org/10.36334/modsim.2023.dykman
- Stochastic centring of BARRA rainfall space-time patterns in a hydrological model
  Scorah, M., Lang, S., Nathan, R. and Cressall, B.
  https://doi.org/10.36334/modsim.2023.scorah
- Catchment dynamics under climate change and the impact on rainfall losses and flooding
  Ho, M., Wasko, C., O'Shea, D., Nathan, R., Vogel, E. and Sharma, A.
  https://doi.org/10.36334/modsim.2023.ho272
- A first step towards the derivation of non-stationary design rainfalls for Australia
  Madhushankha, J.M.L., Wasko, C., Nathan, R. and Johnson, F.M.
  https://doi.org/10.36334/modsim.2023.madhushankha
- Improved rainfall frequency analysis through separation of storm intensity and storm arrival frequency
  O'Shea, D., Nathan, R., Sharma, A. and Wasko, C.
  https://doi.org/10.36334/modsim.2023.oshea
- Australia-wide projections of extreme rainfall and flooding
  Wasko, C., Guo, D., Ho, M., Nathan, R. and Vogel, E.
  https://doi.org/10.36334/modsim.2023.wasko
- 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

Navarro Garcia, J.
- 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
- Modelling climate change impacts on agricultural production using deep learning
  Gao, L., Gao, Y., Chen, Y. and Navarro Garcia, J.
  https://doi.org/10.36334/modsim.2023.gao630
- 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

Neal, B.
- 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

Nelson, M.
- Modelling of the activated sludge process with a stratified settling unit
  Watt, S., Nelson, M., Hai, F. and Sidhu, H.
  https://doi.org/10.36334/modsim.2023.watt

Nelson, S.
- VizumApp: A tool for visualising predictions and uncertainties from spatial models
  Kuhnert, P.M., Nelson, S., Lucchesi, L.R., Chin, B., Pagendam, D.E. and Janardhanan, S.
  https://doi.org/10.36334/modsim.2023.kuhnert

Nemitz, E.
- Assessing the impact of open biomass burning on UK air quality
  Tan, D., Vieno, M., Nemitz, E., Heal, M. and Reis, S.
  https://doi.org/10.36334/modsim.2023.tan177

Nervi, E.
- 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

Neupane, R.
- 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

Neupauer, R.M.
- 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

Newey, L.J.
- Framework to stratify mixed farming systems to estimate soil organic carbon in eastern Australia
  Newey, L.J. and Orgill, S.E.
  https://doi.org/10.36334/modsim.2023.newey

Newman, J.P.
- 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

Newnham, G.
- 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

Ng, T.L.
- 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
- Simulation based decision-making using water and demand forecasts: Hume Dam
  Ng, T.L. and Robertson, D.E.
  https://doi.org/10.36334/modsim.2023.ng392

Nguyen, C.
- Applications of convolutional neural networks and remote sensing data to predict flood extents
  Nguyen, C., Tan, C.W., Daly, E. and Pauwels, V.N.M.
  https://doi.org/10.36334/modsim.2023.nguyen328

Nguyen, D.
- Selecting validation sets based on information entropy
  Nguyen, D. and Watkins, D.
  https://doi.org/10.36334/modsim.2023.nguyen221

Nguyen, D.C.H.
- NARCliM1.5 projections over the southern Murray–Darling Basin, Australia
  Devanand, A., Leonard, M., Westra, S. and Nguyen, D.C.H.
  https://doi.org/10.36334/modsim.2023.devanand483
- Impact of infilling method on calibration of a stochastic weather generator
  Nguyen, D.C.H., Leonard, M., Westra, S. and Dutta, D.
  https://doi.org/10.36334/modsim.2023.nguyen478
- Use of climatic partitions for stochastic rainfall modelling
  Nguyen, D.C.H., Leonard, M. and Westra, S.
  https://doi.org/10.36334/modsim.2023.nguyen480

Nguyen, H.
- 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
- Investigation of the sensitivity of weighting factor parameter in SDEB objective function
  Raut, A., Nguyen, H. and Tuteja, N.
  https://doi.org/10.36334/modsim.2023.raut

Nguyen, T.
- 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
- Spatial autoregressive model for a Dirichlet distribution
  Nguyen, T., Moka, S., Mengersen, K. and Liquet, B.
  https://doi.org/10.36334/modsim.2023.nguyen242

Nicholls, J.
- 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

Nicol, J.
- 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

Nielsen, R.
- 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

Nilchiani, R.
- 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

Nishant, N.
- 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

Nixon, M.
- Role of groundwater in drought-induced hydrological shifts: Numerical modelling to support hypothesis testing
  Fowler, K.J.A., Regan-Beasley, D., Nixon, M. and Walker, G.
  https://doi.org/10.36334/modsim.2023.fowler632

Noack, C.
- 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

Nolan, M.
- 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

Noorduijn, S.
- 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

Noort, 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

Novak, P.
- 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

Noy, I.
- Modelling empowers impact and adaptation assessment of natural hazards
  Wu, J., Noy, I., Wu, Y. and Yang, S.
  https://doi.org/10.36334/modsim.2023.wu263

Oakes, N.
- Experience in using ROS2 under Windows
  Oakes, N. and Hetherton, L.
  https://doi.org/10.36334/modsim.2023.oakes510
- Progress in Workflow comparison
  Oakes, N.
  https://doi.org/10.36334/modsim.2023.oakes127

O'Brien, K.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
- 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

Obst, O.
- 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

Occhipinti, J.
- 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

Oddy, V.H.
- 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

Ofosu, A.
- Exploratory analysis of water quality in a small urbanized watershed using deep learning
  Ofosu, A. and Erechtchoukova, M.G.
  https://doi.org/10.36334/modsim.2023.ofosu

Ogilvie, J.
- Optimising the unpredictability of a ballistic missile
  Comino, E., Ogilvie, J., King, M., Lourey, H. and Wharington, J.
  https://doi.org/10.36334/modsim.2023.comino

Oh, K.-S.
- 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

Okely, 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

Omondiagbe, P.
- 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

O'Neill, M.F.
- Estimating recreational catch
  Mendiolar, M., Filar, J.A., O'Neill, M.F., Martin, T., Teixeira, D., Webley, J. and Holden, M.
  https://doi.org/10.36334/modsim.2023.mendiolar

Onyango, E.
- Towards a participatory systems modelling approach to identify drivers of human health outcomes in food system transitions
  Onyango, E.
  https://doi.org/10.36334/modsim.2023.onyango

Orgill, S.E.
- Framework to stratify mixed farming systems to estimate soil organic carbon in eastern Australia
  Newey, L.J. and Orgill, S.E.
  https://doi.org/10.36334/modsim.2023.newey

O'Shea, D.
- Improved rainfall frequency analysis through separation of storm intensity and storm arrival frequency
  O'Shea, D., Nathan, R., Sharma, A. and Wasko, C.
  https://doi.org/10.36334/modsim.2023.oshea
- Catchment dynamics under climate change and the impact on rainfall losses and flooding
  Ho, M., Wasko, C., O'Shea, D., Nathan, R., Vogel, E. and Sharma, A.
  https://doi.org/10.36334/modsim.2023.ho272

O'Sullivan, J.
- 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

Ota, N.
- 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
- 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

Owen, C.
- 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

Owens, D.
- A snapshot of climate change impacts for Queensland and regions using high-resolution downscaled CMIP6 projections
  Toombs, N., Chapman, S., Trancoso, R., Zhang, H., Owens, D. and Syktus, J.
  https://doi.org/10.36334/modsim.2023.toombs

Owens, J.
- 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

Pace, M.L.
- 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

Pagendam, D.E.
- Towards an AI agronomist: Developing fast and efficient methods for predicting crop growth using machine learning
  Powell, A., Kuhnert, P.M., Pagendam, D.E. and Lawes, R.
  https://doi.org/10.36334/modsim.2023.powell
- 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
- VizumApp: A tool for visualising predictions and uncertainties from spatial models
  Kuhnert, P.M., Nelson, S., Lucchesi, L.R., Chin, B., Pagendam, D.E. and Janardhanan, S.
  https://doi.org/10.36334/modsim.2023.kuhnert
- Calibration and uncertainty analysis of groundwater models assisted by machine learning surrogates
  Janardhanan, S., Pagendam, D.E., MacKinlay, D., Pickett, T. and Kuhnert, P.M.
  https://doi.org/10.36334/modsim.2023.janardhanan
- Stochastic emulators for prediction of noisy physical systems with METHO
  MacKinlay, D., Tong, R., Tsuchida, R., Pagendam, D.E., Janardhanan, S. and Kuhnert, P.M.
  https://doi.org/10.36334/modsim.2023.mackinlay
- 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

Pakeman, R.
- 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

Pal, S.
- 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

Palma, J.H.N.
- 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

Palmer, A.
- 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

Palmer, J.G.
- 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

Palmini, 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

Pandya, V.
- 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

Pang, T.R.
- 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

Paraska, D.
- 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

Park, H.
- 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

Parry, H.
- Digital twins for natural landscape management: Opportunities and challenges
  Dhakal, S. and Parry, H.
  https://doi.org/10.36334/modsim.2023.dhakal

Partington, 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

Pasanen, J.
- 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

Pascal, L.V.
- 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

Pasut, C.
- 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

Paterson, E.
- 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

Pauwels, V.N.M.
- Applications of convolutional neural networks and remote sensing data to predict flood extents
  Nguyen, C., Tan, C.W., Daly, E. and Pauwels, V.N.M.
  https://doi.org/10.36334/modsim.2023.nguyen328

Pavlov, V.
- 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

Peel, A.
- 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

Peel, M.C.
- Assessing climate change impacts on dual-priority water rights in carryover systems at basin scale
  Ren, P., Stewardson, M., Peel, M.C., Turner, M. and John, A.
  https://doi.org/10.36334/modsim.2023.ren357
- The time of emergence of climate change in Australian rivers
  John, A., Nathan, R., Horne, A., Fowler, K.J.A., Stewardson, M., Peel, M.C. and Webb, J.A.
  https://doi.org/10.36334/modsim.2023.john
- A rapid analytical model to represent dual-priority water rights in carryover systems
  Ren, P., Stewardson, M. and Peel, M.C.
  https://doi.org/10.36334/modsim.2023.ren355
- 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
- Variable storage capacity in hydrological models to enhance performance under contrasting climates
  Trotter, L., Saft, M., Peel, M.C. and Fowler, K.J.A.
  https://doi.org/10.36334/modsim.2023.trotter
- Mapping the benefit of multi-variable model calibration for hydrological predictions
  Pool, S., Fowler, K.J.A. and Peel, M.C.
  https://doi.org/10.36334/modsim.2023.pool
- What affects precipitation partitioning on interannual to interdecadal timescale?
  Saft, M. and Peel, M.C.
  https://doi.org/10.36334/modsim.2023.saft
- 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

Peeters, L.J.M.
- 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
- 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
- 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
- Practical guidance for developing causal network topology
  Peeters, L.J.M., Holland, K. and London, A.
  https://doi.org/10.36334/modsim.2023.peeters
- Conceptualising causal networks for environmental impact assessment
  Holland, K., Peeters, L.J.M. and London, A.
  https://doi.org/10.36334/modsim.2023.holland
- 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
- Going beyond Word: Convincing scientists to embrace content management systems for writing science content
  London, A., Tetreault-Campbell, S., Holland, K. and Peeters, L.J.M.
  https://doi.org/10.36334/modsim.2023.london

Pegios, M.
- From surface runoff to streamflow: An application of statistical post-processing for seasonal streamflow forecasting
  Pickett-Heaps, C.A., Sunter, P., Cornish, A., Sharples, W., Pegios, M. and Wilson, C.
  https://doi.org/10.36334/modsim.2023.pickettheaps

Peiris, S.
- 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

Peña-Arancibia, J.L.
- 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

Penney, D.
- 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

Penton, D.J.
- 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
- 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
- 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
- Developing policy analysis tools to measure the inclusivity of water-related policies
  Cuddy, S.M., Penton, D.J., Wahid, S. and Koirala, S.
  https://doi.org/10.36334/modsim.2023.cuddy687
- 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
- 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
- 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

Pepler, A.
- 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

Pereira, G.G.
- Surrogate model for CFD based on machine learning
  Rahman, A., Pereira, G.G., Kilby, P. and Lahur, P.
  https://doi.org/10.36334/modsim.2023.rahman37
- Using Lindenmayer systems for evolutionary design of static mixers
  Howard, D., Kilby, P., Lahur, P. and Pereira, G.G.
  https://doi.org/10.36334/modsim.2023.howard
- Rapid, accurate and convenient fluid dynamical modelling for real-world applications
  Pereira, G.G. and Lahur, P.
  https://doi.org/10.36334/modsim.2023.pereira

Perera, H.
- 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

Perez-Camanyo, J.L.
- Air quality impacts forecasting system for industrial air emissions running in operational mode
  San José, R. and Perez-Camanyo, J.L.
  https://doi.org/10.36334/modsim.2023.sanjose

Perraud, J.-M.
- 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
- 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
- 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

Perry, 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
- 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

Persson, K.M.
- 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

Peter, J.
- 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

Peterson, T.J.
- Can drought regimes undergo shifts?
  Goswami, P., Peterson, T.J., Mondal, A. and Rüdiger, C.
  https://doi.org/10.36334/modsim.2023.goswami501
- Climate-influenced borewells can be statistically identified without groundwater pumping data
  Gupta, M. and Peterson, T.J.
  https://doi.org/10.36334/modsim.2023.gupta
- Runoff non-recovery of ACT water catchments
  Mana, S.C., Peterson, T.J., Lade, S.J., Croke, B.F.W. and Iwanaga, T.
  https://doi.org/10.36334/modsim.2023.mana
- 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

Petheram, C.
- 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
- Space-time velocimetry to estimate discharge during the 2022 River Murray flood
  Gibbs, M.S., Hughes, J.D. and Petheram, C.
  https://doi.org/10.36334/modsim.2023.gibbs
- Linking hydrological and infrastructure models to evaluate the proposed Bradfield Scheme
  Petheram, C., Hughes, J.D., Read, A. and Stokes, C.
  https://doi.org/10.36334/modsim.2023.petheram
- Regionalisation of rainfall-runoff models in southern and northern Australian catchments
  Seo, L., Hughes, J.D. and Petheram, C.
  https://doi.org/10.36334/modsim.2023.seo527
- Digital twin–based river-system model
  Seo, L., Branchaud, D., Petheram, C. and Watson, I.
  https://doi.org/10.36334/modsim.2023.seo610

Petridis, R.
- 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
- Efficiently computing DGGS cell representations of spatial geometries
  Petridis, R., Yu, J., Leighton, B. and Cox, S.J.D.
  https://doi.org/10.36334/modsim.2023.petridis

Pfautsch, S.
- 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

Phatak, A.
- Combining risk forecasts
  Soltyk, S.J., Chan, F., Phatak, A. and Jackson, E.L.
  https://doi.org/10.36334/modsim.2023.soltyk

Philippa, B.
- 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

Phillips, L.
- 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

Pickett, T.
- Calibration and uncertainty analysis of groundwater models assisted by machine learning surrogates
  Janardhanan, S., Pagendam, D.E., MacKinlay, D., Pickett, T. and Kuhnert, P.M.
  https://doi.org/10.36334/modsim.2023.janardhanan

Pickett-Heaps, C.A.
- Improving seasonal streamflow calibration through consideration of raw ensemble spread
  Graham, T.D.J., Wang, Q.J., Pickett-Heaps, C.A., Sharples, W., Wu, W. and Western, A.W.
  https://doi.org/10.36334/modsim.2023.graham171
- From surface runoff to streamflow: An application of statistical post-processing for seasonal streamflow forecasting
  Pickett-Heaps, C.A., Sunter, P., Cornish, A., Sharples, W., Pegios, M. and Wilson, C.
  https://doi.org/10.36334/modsim.2023.pickettheaps
- 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

Pike, K.N.
- 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

Pino, V.
- Framework for interpretable Bayesian soil moisture modelling
  Simmons, J., Pino, V., Graaf, A. and Vervoort, R.W.
  https://doi.org/10.36334/modsim.2023.simmons461

Pinto, R.
- Estimates of recharge for current and future climate scenarios in Victoria using SoilFlux
  Jordan, P., Pinto, R., Tyson, A. and Fawcett, J.
  https://doi.org/10.36334/modsim.2023.jordan
- Forecast of residential demand drivers for Greater Darwin
  Beatty, R., Pinto, R. and Holden, R.
  https://doi.org/10.36334/modsim.2023.beatty

Pitchford, W.
- A strategic planning model for the operation plan in lamb supply chains
  Wang, G., Costa, A., Karunarathne, W., Roughan, M., Miller, S.M. and Pitchford, W.
  https://doi.org/10.36334/modsim.2023.wang57

Pitman, A.J.
- 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

Pletzer, A.
- 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

Plum, C.
- 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

Poddar, S.
- 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

Podger, G.M.
- 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
- 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
- River system modelling to inform Murrumbidgee and NSW Murray regional water strategies
  Dutta, D., Podger, G.M., Trim, A., Cu, T.P., Loonat, N., Matsinos, A., Taylor, N. and Driver, P.D.
  https://doi.org/10.36334/modsim.2023.dutta
- Modelling the Murray-Darling Basin Southern Connected System
  Trim, A., Podger, G.M., Dutta, D. and Puertas, J.M.
  https://doi.org/10.36334/modsim.2023.trim
- 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

Podger, S.
- 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

Podsosonnaya, M.
- Algal bloom modelling based on satellite data
  Podsosonnaya, M., Schreider, M. and Schreider, S.
  https://doi.org/10.36334/modsim.2023.podsosonnaya

Pollett, A.
- 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

Pollino, C.A.
- 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
- 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

Pool, S.
- Mapping the benefit of multi-variable model calibration for hydrological predictions
  Pool, S., Fowler, K.J.A. and Peel, M.C.
  https://doi.org/10.36334/modsim.2023.pool

Poot, P.
- Plant root architecture: A trade-off between tolerance to competitors and potential growth
  Salinas, H., Veneklaas, E., Poot, P., Trevenen, E. and Renton, M.
  https://doi.org/10.36334/modsim.2023.salinas

Post, D.A.
- Impact of vegetation changes on evapotranspiration in northern China
  Li, C.C., Zhang, Y.Q., Chiew, F.H.S., Post, D.A., Yu, Q., Tian, J., Ma, N. and Zhang, X.Z.
  https://doi.org/10.36334/modsim.2023.li363

Potter, N.
- 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
- 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

Powell, A.
- 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
- Towards an AI agronomist: Developing fast and efficient methods for predicting crop growth using machine learning
  Powell, A., Kuhnert, P.M., Pagendam, D.E. and Lawes, R.
  https://doi.org/10.36334/modsim.2023.powell

Powers, J.
- 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

Prasad, A.
- 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
- 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

Prior, D.D.
- Assuring rare earth mineral supply: A literature review and a systems model
  Sahin, O., Elsawah, S., Salim, H., Prior, D.D., Turan, H.H. and Hussain, O.K.
  https://doi.org/10.36334/modsim.2023.sahin

Pritchard, J.L.
- 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
- 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
- Identifying potential groundwater discharge areas using remotely sensed evapotranspiration
  Crosbie, R.S., Taylor, A.R. and Pritchard, J.L.
  https://doi.org/10.36334/modsim.2023.crosbie361

Prober, S.
- 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

Prowse, T.A.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

Pudney, P.
- An optimised strategy for using cooperating missiles for missile defence
  Kapsis, M., Pudney, P., Miller, W. and Freebairn, G.
  https://doi.org/10.36334/modsim.2023.kapsis100
- Determining the best type of defending missile for cooperative missile defence
  Kapsis, M., Pudney, P., Miller, W. and Freebairn, G.
  https://doi.org/10.36334/modsim.2023.kapsis84

Puertas, J.M.
- Modelling the Murray-Darling Basin Southern Connected System
  Trim, A., Podger, G.M., Dutta, D. and Puertas, J.M.
  https://doi.org/10.36334/modsim.2023.trim

Pulscher, L.
- 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

Pulsford, I.
- 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

Purtle, C.
- 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

Pusateri, A.E.
- 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

Qi, L.
- New Zealand's energy transition: Evaluating potential technology trajectories and costs of CO2 emissions in transportation using the NZIES model
  Qi, L.
  https://doi.org/10.36334/modsim.2023.qi

Qin, S.
- Detecting multidecadal variation of short-term drought risk by combining frequency analysis and Fourier transformation methods: A case study in the Yangtze River Basin
  Zou, K., Cheng, L., Zhang, Q., Qin, S., Liu, P. and Wu, M.
  https://doi.org/10.36334/modsim.2023.zou453

Quaine, P.
- Hydrological modelling and multicriteria assessments in reviewing headworks operations
  Quaine, P. and Wilson, K.
  https://doi.org/10.36334/modsim.2023.quaine

Quijano Baron, J.P.
- An ecohydrological approach for modelling and optimisation of vegetation health in dryland wetlands
  Rodríguez, J.F., Saco, P.M., Sandi, S.G., Quijano Baron, J.P., Carlier, R., Kuczera, G. and Wen, L.
  https://doi.org/10.36334/modsim.2023.rodriguez
- 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

Quinlan, A.E.
- 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

Quiñones, R.
- 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

Radford, D.A.
- Integrated natural hazard risk management: A two-week intensive course
  Maier, H.R., Jeanneau, A., Radford, D.A. and He, L.
  https://doi.org/10.36334/modsim.2023.maier62
- 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

Rahman, A.
- 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
- 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
- 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
- Surrogate model for CFD based on machine learning
  Rahman, A., Pereira, G.G., Kilby, P. and Lahur, P.
  https://doi.org/10.36334/modsim.2023.rahman37

Rajamuni, M.M.
- An efficient numerical method for studying noise generation in vortex-induced vibration of bluff bodies
  Rajamuni, M.M., Lai, J.C.S., Ravi, S., Young, J. and Tian, F.
  https://doi.org/10.36334/modsim.2023.rajamuni

Rajanayaka, C.
- Developing an integrated socioeconomic-hydrological model to support catchment-scale water allocation decisions
  Rajanayaka, C., Sağlam, Y., Yang, J., Kees, L. and De Alwis, D.
  https://doi.org/10.36334/modsim.2023.rajanayaka
- 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

Rana, M.
- 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

Randall, L.J.
- RIDGE: Rainfall impact detachment in gully erosion
  Randall, L.J. and Roberts, M.E.
  https://doi.org/10.36334/modsim.2023.randall

Ranjbar, M.H.
- 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

Ranjitkar, P.
- Exploring user intention for dynamic wireless charging facilities: Some insights from a survey in New Zealand
  Majhi, R.C., Ranjitkar, P. and Sheng, M.S.
  https://doi.org/10.36334/modsim.2023.majhi

Rannu, R.P.
- Groundwater recharge estimation through unsaturated zone modelling
  Rannu, R.P., Croke, B.F.W., Merritt, W.S. and Mainuddin, M.
  https://doi.org/10.36334/modsim.2023.rannu

Rao, S.
- '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

Rathjen, T.
- 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

Rau, G.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

Raut, A.
- 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
- Investigation of the sensitivity of weighting factor parameter in SDEB objective function
  Raut, A., Nguyen, H. and Tuteja, N.
  https://doi.org/10.36334/modsim.2023.raut

Raveendran, N.
- Statistical analysis of Australian insurance losses from historical catastrophic disasters
  Sofronova, K. and Raveendran, N.
  https://doi.org/10.36334/modsim.2023.sofronova
- A binary segmentation method for detecting topological domains in Hi-C data
  Raveendran, N. and Sofronov, G.
  https://doi.org/10.36334/modsim.2023.raveendran

Ravi, S.
- An efficient numerical method for studying noise generation in vortex-induced vibration of bluff bodies
  Rajamuni, M.M., Lai, J.C.S., Ravi, S., Young, J. and Tian, F.
  https://doi.org/10.36334/modsim.2023.rajamuni

Rawlinson, A.A.
- 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

Razavi, S.
- Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences
  Razavi, S.
  https://doi.org/10.36334/modsim.2023.razavi

Read, A.
- Linking hydrological and infrastructure models to evaluate the proposed Bradfield Scheme
  Petheram, C., Hughes, J.D., Read, A. and Stokes, C.
  https://doi.org/10.36334/modsim.2023.petheram

Rebetzke, G.
- 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

Reed, P.M.
- Evaluating multiobjective evolutionary algorithms: A real-world benchmarking framework
  Salazar, J.Z., Hadka, D., Reed, P.M., Seada, H. and Deb, K.
  https://doi.org/10.36334/modsim.2023.salazar

Reedman, L.J.
- Investment, capital accumulation and the role of money in national energy economy models used for assessing climate and energy policy
  Khandoker, T., Juniper, J.A. and Reedman, L.J.
  https://doi.org/10.36334/modsim.2023.khandoker

Regan-Beasley, D.
- Role of groundwater in drought-induced hydrological shifts: Numerical modelling to support hypothesis testing
  Fowler, K.J.A., Regan-Beasley, D., Nixon, M. and Walker, G.
  https://doi.org/10.36334/modsim.2023.fowler632

Reich, P.B.
- 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

Reid, J.D.
- Integrated agriculture modelling in a te ao Māori framework
  Reid, J.D., Wilson, M. and Kaye-Blake, W.H.
  https://doi.org/10.36334/modsim.2023.reid

Reis, S.
- Assessing the impact of open biomass burning on UK air quality
  Tan, D., Vieno, M., Nemitz, E., Heal, M. and Reis, S.
  https://doi.org/10.36334/modsim.2023.tan177

Reizinger, K.
- Estimation with pairwise observations
  Chan, F., Mátyás, L. and Reizinger, K.
  https://doi.org/10.36334/modsim.2023.chan555

Remmers, J.O.E.
- Building trust: How modellers navigate dozens of decisions
  Remmers, J.O.E. and Melsen, L.A.
  https://doi.org/10.36334/modsim.2023.remmers

Ren, C.Y.
- Impacts of a severe drought on vegetation and hydrological systems in the Yangtze River Basin, China
  Zhang, Y.Q., Tian, J., Zhang, X.Z., Ma, N., Tang, Z.X., Kong, D.D., Cao, Y.J., Shao, X.M., Wei, H.S., Chen, Y.Y., Wang, J., Wang, L.H., Xu, Z.W., Li, C.C., Yang, X.N. and Ren, C.Y.
  https://doi.org/10.36334/modsim.2023.zhang223

Ren, P.
- Assessing climate change impacts on dual-priority water rights in carryover systems at basin scale
  Ren, P., Stewardson, M., Peel, M.C., Turner, M. and John, A.
  https://doi.org/10.36334/modsim.2023.ren357
- A rapid analytical model to represent dual-priority water rights in carryover systems
  Ren, P., Stewardson, M. and Peel, M.C.
  https://doi.org/10.36334/modsim.2023.ren355

Ren, Z.
- Development of an in-home energy rating tool for existing housing
  Ren, Z., Jian, A. and Chen, D.
  https://doi.org/10.36334/modsim.2023.ren20

Renton, M.
- Plant root architecture: A trade-off between tolerance to competitors and potential growth
  Salinas, H., Veneklaas, E., Poot, P., Trevenen, E. and Renton, M.
  https://doi.org/10.36334/modsim.2023.salinas
- 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

Renzullo, L.J.
- 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
- 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

Reza Nikoo, M.
- 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
- 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

Rezvani, M.
- 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

Riazi, Z.
- 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

Richards, A.
- 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

Richards, R.
- 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

Riches, J.
- 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

Richetti, J.
- 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

Richmond, M.K.
- 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

Riddell, G.A.
- 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

Riley, M.
- 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
- 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

Rios-Ocampo, J.P.
- Modelling process of complex problems from a teaching perspective
  Staby, S.-E. and Rios-Ocampo, J.P.
  https://doi.org/10.36334/modsim.2023.staby625

Rismanchi, B.
- Is behind-the-meter solar an effective way to improve water distribution system resilience?
  Yao, J., Wu, W., Simpson, A.R. and Rismanchi, B.
  https://doi.org/10.36334/modsim.2023.yao238

Rissik, 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

Roberts, J.
- Continuous3D, an end-to-end solution for robotic additive manufacturing
  Lohr, H., King, P., Vargas, A., Chu, C., Roberts, J. and Teixeira, M.
  https://doi.org/10.36334/modsim.2023.lohr

Roberts, M.E.
- 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
- Porous check dams and the MERGE gully erosion model
  Roberts, M.E. and Roots, K.
  https://doi.org/10.36334/modsim.2023.roberts
- RIDGE: Rainfall impact detachment in gully erosion
  Randall, L.J. and Roberts, M.E.
  https://doi.org/10.36334/modsim.2023.randall
- 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

Robertson, D.E.
- Simulation based decision-making using water and demand forecasts: Hume Dam
  Ng, T.L. and Robertson, D.E.
  https://doi.org/10.36334/modsim.2023.ng392
- 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
- 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
- Modelling hydrological impact of remotely sensed vegetation change
  Zheng, H., Robertson, D.E. and Chiew, F.H.S.
  https://doi.org/10.36334/modsim.2023.zheng658
- 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
- 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

Robinson, A.P.
- 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

Robinson, K.
- 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

Robson, B.J.
- 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
- 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
- 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
- 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
- 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

Rocha, J.C.
- 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

Rodrigo, L.M.
- A novel Bayesian variable selection algorithm for multiple linear regression
  Rodrigo, L.M., Kohn, R., Cripps, S. and Cleary, M.J.
  https://doi.org/10.36334/modsim.2023.rodrigo

Rodríguez, J.F.
- 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
- 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
- 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
- An ecohydrological approach for modelling and optimisation of vegetation health in dryland wetlands
  Rodríguez, J.F., Saco, P.M., Sandi, S.G., Quijano Baron, J.P., Carlier, R., Kuczera, G. and Wen, L.
  https://doi.org/10.36334/modsim.2023.rodriguez
- 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

Rojas, A.
- GR4SP: Modelling policy mixes for Victoria’s electricity provision to achieve sustainability transitions
  Rojas, A. and de Haan, F.J.
  https://doi.org/10.36334/modsim.2023.rojas

Rollan, T.A.
- Behavioural survey for cognitive agent modelling in earthquake evacuation
  Rollan, T.A., Marquez, L. and Chua, C.
  https://doi.org/10.36334/modsim.2023.rollan

Roots, K.
- Porous check dams and the MERGE gully erosion model
  Roberts, M.E. and Roots, K.
  https://doi.org/10.36334/modsim.2023.roberts
- INCISION toolbox for the delineation and analysis of incised features within LiDAR data
  Roots, K.
  https://doi.org/10.36334/modsim.2023.roots

Rosello, C.
- 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
- 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

Ross, J.V.
- 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

Roughan, M.
- A strategic planning model for the operation plan in lamb supply chains
  Wang, G., Costa, A., Karunarathne, W., Roughan, M., Miller, S.M. and Pitchford, W.
  https://doi.org/10.36334/modsim.2023.wang57

Rucinski, C.
- 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

Rüdiger, C.
- 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
- 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
- Can drought regimes undergo shifts?
  Goswami, P., Peterson, T.J., Mondal, A. and Rüdiger, C.
  https://doi.org/10.36334/modsim.2023.goswami501
- 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

Rudman, M.
- Modelling the dynamics of fire whirls
  Cunningham, L., Hilton, J. and Rudman, M.
  https://doi.org/10.36334/modsim.2023.cunningham

Ruhnke, I.
- 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

Rumman, R.
- 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

Ryan, 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
- Predicting from functional brain images using partial least squares for Riemannian manifolds
  Ryan, M., Glonek, G., Tuke, J. and Humphries, M.
  https://doi.org/10.36334/modsim.2023.ryan

Ryan, M.H.
- 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

Ryan, S.
- 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

Ryu, D.
- 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
- 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
- A water-level based calibration of rainfall-runoff models using satellite altimetry data
  Jian, J., Ryu, D., Wang, Q.J. and Lee, H.
  https://doi.org/10.36334/modsim.2023.jian531
- Relative performance evaluation of LiDAR and Cartosat DEMs for surface rainwater harvesting site identification
  Kommula, S.P., Lohani, B., Ryu, D. and Winter, S.
  https://doi.org/10.36334/modsim.2023.kommula
- Using data-driven models to identify key drivers of seasonal irrigation water usage
  Gao, Z., Guo, D., Ryu, D. and Western, A.W.
  https://doi.org/10.36334/modsim.2023.gao477

Modelling to support planning for resilience in a changing world