Author Index

* Please note this includes full papers only

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

S

• Sabaghy, S.
  • Random forest classification: A case study of dryland crop cover mapping in the Victorian Mallee using Sentinel-2A, Sentinel-3, and MODIS imagery
    

    Sabaghy, S., Sheffield, K.J. and Robson, S.A.
    https://doi.org/10.36334/modsim.2021.B3.sabaghy

• Saco, P.M.
  • Assessing waterbird breeding conditions using an emulator of wetland inundation regime
    

    Quijano, J., Rodriguez, J., Kuczera, G., Saco, P.M., Sandi, S.G., Carlier, R., Wen, L. and McDonough, K.
    https://doi.org/10.36334/modsim.2021.J1.quijano

• Saft, M.
  • Mapping additional streamflow decline due to shifts in catchment response during the Millennium Drought
    

    Saft, M., Peel, M., Jordan, P., Shepherd, D., Steendam, G., Lett, R. and Peterson, T.
    https://doi.org/10.36334/modsim.2021.K11.saft

  • Observation based gridded annual runoff estimates over Victoria, Australia
    

    Weligamage, H.A.C.G., Fowler, K.J.A., Peterson, T.J., Saft, M. and Peel, M.C.
    https://doi.org/10.36334/modsim.2021.K11.weligamage

  • “Naïve” inclusion of diverse climates in calibration is not sufficient to improve model reliability under future climate uncertainty
    

    Trotter, L., Saft, M., Peel, M.C. and Fowler, K.J.A.
    https://doi.org/10.36334/modsim.2021.J8.trotter

• Saifuddin, R.
  • Application of a delayed logistic equation to a reindeer population in a closed environment
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt

  • The population dynamics of ecosystem engineers and habitat modification
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt2

• Sainsbury, O.
  • The local influence of residential infill on the urban water flows: a multi-city analysis
    

    Moravej, M., Kenway, S., Renouf, M., Urich, C., Sainsbury, O., Bertram, N. and London, G.
    https://doi.org/10.36334/modsim.2021.J4.moravej

• Sanderson, C.
  • Opportunistic emulation of computationally expensive simulations via Deep Learning
    

    Sanderson, C., Pagendam, D., Power, B., Bennett, F.R. and Darnell, R.
    https://doi.org/10.36334/modsim.2021.L1.sanderson

• Sandi, S.G.
  • Assessing waterbird breeding conditions using an emulator of wetland inundation regime
    

    Quijano, J., Rodriguez, J., Kuczera, G., Saco, P.M., Sandi, S.G., Carlier, R., Wen, L. and McDonough, K.
    https://doi.org/10.36334/modsim.2021.J1.quijano

• Sanhueza, C.
  • Freight train scheduling via decentralised multi-agent deep reinforcement learning
    

    Bretas, A.M.C., Mendes, A., Chalup, S., Jackson, M., Clement, R. and Sanhueza, C.
    https://doi.org/10.36334/modsim.2021.M1.bretas

• Sankaranarayanan, N.
  • AeroDAQ: A Workspace based application
    

    Sankaranarayanan, N. and Humphries, R.
    https://doi.org/10.36334/modsim.2021.C2.sankaranarayanan

• Santucci, J-F.
  • Discrete event modeling and simulation of smart parking conflict management
    

    Dominici, A., Capocchi, L., De Gentili, E. and Santucci, J-F.
    https://doi.org/10.36334/modsim.2021.E3.dominici

• Scanlan, J.
  • Disentangling the effects of management and climate on perennial grass pastures and the degradation that follows multi-year droughts
    

    Owens, J., McKeon, G., O’Reagain, P., Carter, J., Fraser, G., Nelson, B. and Scanlan, J.
    https://doi.org/10.36334/modsim.2021.B1.owens

• Schepen, A.
  • Geostatistical merging of weather radar data with a sparse rain gauge network in Queensland
    

    Sharp, C., Schepen, A., Das, S. and Everingham, Y.
    https://doi.org/10.36334/modsim.2021.K6.sharp

• Schulz, J.
  • Trade strategies to mitigate the global impact of regional wheat production shocks
    

    Golding, P., Kapadia, S., Naylor, S., Schulz, J., Maier, H.R., Lall, U. and van der Velde, M.
    https://doi.org/10.36334/modsim.2021.B5.golding

• Seaton, S.
  • Towards a provenance-enabled, reproducible, and extensible machine learning platform by integrating databases, web services, containers, and code repositories in a loosely coupled manner
    

    Singh, R.M., Wilson, P., Gregory, L., Seaton, S. and Malone, B.
    https://doi.org/10.36334/modsim.2021.C4.singh

• Sengupta, A.
  • Generating two-monthly surface water images for the Murray-Darling Basin
    

    Ticehurst, C.J., Teng, J. and Sengupta, A.
    https://doi.org/10.36334/modsim.2021.G5.ticehurst

• Shafqat, M.
  • Model-based machine learning to explore the nexus between COVID-19 and environmental factors in the United States
    

    Munir, T., Hudson, I.L., Cheema, S.A., Muhammad, R., Shafqat, M. and Kifayat, T.
    https://doi.org/10.36334/modsim.2021.H1.munir

• Shanafield, M.
  • Modelling hydrological change due to wildfires
    

    Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H.R., Simmons, C.T., Croke, B.F.W., Jakeman, A.J., Gupta, H.V. and Kavetski, D.
    https://doi.org/10.36334/modsim.2021.J8.partington

• Sharp, C.
  • Geostatistical merging of weather radar data with a sparse rain gauge network in Queensland
    

    Sharp, C., Schepen, A., Das, S. and Everingham, Y.
    https://doi.org/10.36334/modsim.2021.K6.sharp

• Sharples, J.J.
  • Spotfire Utilisation Project: Development of a mapping tool for regions prone to vorticity-driven lateral spread
    

    Badlan, R.L. and Sharples, J.J.
    https://doi.org/10.36334/modsim.2021.G3.badlan

  • Application of neural networks to rate of spread estimation in shrublands
    

    Wadhwani, R., Sutherland, D., Moinuddin, K.A. and Sharples, J.J.
    https://doi.org/10.36334/modsim.2021.G3.wadhwani2

  • Stochastic modelling of wind and its implication for wildfire spread predictions
    

    Masoudian, S., Sharples, J.J., Jovanoski, Z., Towers, I. and Watt, S.
    https://doi.org/10.36334/modsim.2021.G3.masoudian

  • Assessing the potential for pyrocumulonimbus occurrence using simple fire weather indices
    

    Sharples, J.J. and McRae, R.H.D.
    https://doi.org/10.36334/modsim.2021.G3.sharples

• Shaw, M.
  • Modelling to determine key drivers of water quality off sugarcane paddocks in the Great Barrier Reef catchment
    

    Power, B., Shaw, M., Vilas, M.P., Farr, M. and Thompson, M.
    https://doi.org/10.36334/modsim.2021.L1.power

• Sheffield, K.J.
  • Random forest classification: A case study of dryland crop cover mapping in the Victorian Mallee using Sentinel-2A, Sentinel-3, and MODIS imagery
    

    Sabaghy, S., Sheffield, K.J. and Robson, S.A.
    https://doi.org/10.36334/modsim.2021.B3.sabaghy

• Shepherd, D.
  • Mapping additional streamflow decline due to shifts in catchment response during the Millennium Drought
    

    Saft, M., Peel, M., Jordan, P., Shepherd, D., Steendam, G., Lett, R. and Peterson, T.
    https://doi.org/10.36334/modsim.2021.K11.saft

• Shine, D.R.
  • Using causal models to evaluate Force Generation options
    

    Shine, D.R., Knoll, J. and Cao, T.
    https://doi.org/10.36334/modsim.2021.M8.shine

• Sidhu, H.
  • Application of a delayed logistic equation to a reindeer population in a closed environment
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt

  • Modelling of a five reactor Activated Sludge cascade process using ASM #1
    

    Watt, S., Nelson, M., Hai, F. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.L2.watt

  • The population dynamics of ecosystem engineers and habitat modification
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt2

• Silburn, D.M.
  • Revised models for the proportion of suspended sediments in hillslope soil loss – an empirical understanding
    

    Silburn, D.M.
    https://doi.org/10.36334/modsim.2021.L1.silburn

• Silva, R.
  • Does memory help optimise fruit fly foraging at the landscape level?
    

    Newman, J.D., Clarke, A.R., Silva, R. and Parry, H.
    https://doi.org/10.36334/modsim.2021.B7.newman

• Sim, W.
  • Analytic approaches to understanding future Defence workforce needs
    

    Rowe, C., Crockett, A., Rohan-Jones, S., Aljoorfri, S., Tay, N., Coutts, A., Bishop, C., Pay, A., Sim, W., Date, V. and Boyce, J.
    https://doi.org/10.36334/modsim.2021.M8.rowe

• Simmons, C.T.
  • Modelling hydrological change due to wildfires
    

    Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H.R., Simmons, C.T., Croke, B.F.W., Jakeman, A.J., Gupta, H.V. and Kavetski, D.
    https://doi.org/10.36334/modsim.2021.J8.partington

• Singh, R.M.
  • Towards a provenance-enabled, reproducible, and extensible machine learning platform by integrating databases, web services, containers, and code repositories in a loosely coupled manner
    

    Singh, R.M., Wilson, P., Gregory, L., Seaton, S. and Malone, B.
    https://doi.org/10.36334/modsim.2021.C4.singh

• Skinner, A.
  • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
    

    Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
    https://doi.org/10.36334/modsim.2021.H2.lee

• Smairy, M.
  • Impacts of localizing food system on farmers’ livelihood resilience
    

    Loo, P.S., Önal, F., Bax, K., Smairy, M. and Surovets, V.
    https://doi.org/10.36334/modsim.2021.B5.loo

• Smith, S.P.
  • Evaluating instruction quality across narrative modality using measures of real-time cognitive load
    

    Smith, S.P., Blackmore, K., Wark, S. and Nowina-Krowicki, M.
    https://doi.org/10.36334/modsim.2021.M2.smith

• Smith-Gander, C.
  • The Open Maritime Traffic Analysis Dataset
    

    Masek, M., Lam, C.P., Rybicki, T., Snell, J., Wheat, D., Kelly, L., Glassborow, D. and Smith-Gander, C.
    https://doi.org/10.36334/modsim.2021.M9.masek

• Smith-Miles, K.
  • An instance space analysis of combat simulations to understand the impact of force and information advantage on survival ratios
    

    Smith-Miles, K., Bista, S. and Jiang, L.
    https://doi.org/10.36334/modsim.2021.M7.smith-miles

• Snell, J.
  • The Open Maritime Traffic Analysis Dataset
    

    Masek, M., Lam, C.P., Rybicki, T., Snell, J., Wheat, D., Kelly, L., Glassborow, D. and Smith-Gander, C.
    https://doi.org/10.36334/modsim.2021.M9.masek

  • An evolutionary approach to balancing and disrupting real-time strategy games
    

    Snell, J., Masek, M. and Lam, C.P.
    https://doi.org/10.36334/modsim.2021.M8.snell

• Snowden, C.
  • The role of embodied cognition in linguistic mental health analysis
    

    Kernot, D., Van Hooff, M., Lawrence-Wood, E., Glenny, L., Snowden, C., Green, K. and Son, C.
    https://doi.org/10.36334/modsim.2021.H2.kernot

• Sofronov, G.
  • A change-point test for autoregressive processes using a harmonic mean P-value
    

    Ma, L. and Sofronov, G.
    https://doi.org/10.36334/modsim.2021.A1.ma

• Sohel, F.
  • A discrete event simulation for modelling Aljamarat Bridge rituals and evacuation scenarios
    

    Owaidah, A.A., Olaru, D., Bennamoun, M., Sohel, F. and Khan, R.N.
    https://doi.org/10.36334/modsim.2021.I8.owaidah

• Son, C.
  • The role of embodied cognition in linguistic mental health analysis
    

    Kernot, D., Van Hooff, M., Lawrence-Wood, E., Glenny, L., Snowden, C., Green, K. and Son, C.
    https://doi.org/10.36334/modsim.2021.H2.kernot

• Song, Y.J.C.
  • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
    

    Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
    https://doi.org/10.36334/modsim.2021.H2.lee

• Souza, A.L.
  • The dial-a-ride problem in the case of a patient transportation system in Brazil
    

    Bernardo, M., Souza, A.L., Amirghasemi, M., Barthelemy, J. and Perez, P.
    https://doi.org/10.36334/modsim.2021.M1.bernardo

• Specht, A.
  • Enabling integrated modelling systems for environmental prediction at national scale
    

    Specht, A., Guru, S.M. and Morris, B.
    https://doi.org/10.36334/modsim.2021.F3.specht

• Stassen, C.
  • Extreme bushfire projections using a standardised method
    

    Dowdy, A.J., Thatcher, M., Rafter, T., Evans, J., Ye, H., Su, C.-H., Stassen, C., Pepler, A., Bell, S.S. and Steinle, P.
    https://doi.org/10.36334/modsim.2021.G3.dowdy

• Steendam, G.
  • Mapping additional streamflow decline due to shifts in catchment response during the Millennium Drought
    

    Saft, M., Peel, M., Jordan, P., Shepherd, D., Steendam, G., Lett, R. and Peterson, T.
    https://doi.org/10.36334/modsim.2021.K11.saft

• Steinle, P.
  • Extreme bushfire projections using a standardised method
    

    Dowdy, A.J., Thatcher, M., Rafter, T., Evans, J., Ye, H., Su, C.-H., Stassen, C., Pepler, A., Bell, S.S. and Steinle, P.
    https://doi.org/10.36334/modsim.2021.G3.dowdy

• Stewart, L.K.
  • Improving catchment runoff estimates by ameliorating the impacts of SILO pluviometer data discontinuities through expanding the SILO reference database
    

    Stewart, L.K. and Jeffrey, S.J.
    https://doi.org/10.36334/modsim.2021.J13.stewart

• Stewart, R.
  • An assessment framework for classifying determinants of household water consumption and their priorities for research and practice
    

    Cominola, A., Preiss, L., Thyer, M., Maier, H.R., Stewart, R. and Castelletti, A.
    https://doi.org/10.36334/modsim.2021.J3.cominola

• Stewart, R.A.
  • A model based on DOC data for determination of alum dosing for drinking water treatment
    

    Daraei, H., Awad, J., Bertone, E., Stewart, R.A., Chow, C.W.K., Duan, J. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2021.L2.daraei

• Stojanovski, E.
  • Multi-way correspondence analysis approach to examine Nobel Prize data from 1901 to 2018
    

    Alhuzali, T., Beh, E.J. and Stojanovski, E.
    https://doi.org/10.36334/modsim.2021.A1.alhuzali

  • Model-based clustering with mclust R package: Multivariate assessment of mathematics performance of students in Qatar
    

    Alzahrani, A., Beh, E.J. and Stojanovski, E.
    https://doi.org/10.36334/modsim.2021.A1.alzahrani

• Stuart, G.
  • Estimation of road closure risks along the Bruce highway using the AWRA-L water balance model
    

    Vogel, E., Lerat, J., Gericke, L.A., Russell, C.A., Preece, A., Stuart, G., Pipunic, R., Khan, Z. and Donnelly, C.
    https://doi.org/10.36334/modsim.2021.K5.vogel

• Stylianou, S.
  • Factor screening for binary responses from combat simulations
    

    Demirhan, H., Georgiou, S., Gill, A. and Stylianou, S.
    https://doi.org/10.36334/modsim.2021.M5.demirhan

• Su, C.-H.
  • Tropical cyclone-induced extreme winds in climate datasets: East coast of Australia
    

    Bell, S.S., Chand, S.S., Dowdy, A.J., Ramsay, H.A., Deo, A., Su, C.-H., Brown, A. and Ye, H.
    https://doi.org/10.36334/modsim.2021.G4.bell

  • Extreme bushfire projections using a standardised method
    

    Dowdy, A.J., Thatcher, M., Rafter, T., Evans, J., Ye, H., Su, C.-H., Stassen, C., Pepler, A., Bell, S.S. and Steinle, P.
    https://doi.org/10.36334/modsim.2021.G3.dowdy

• Subash, N.
  • Changing crop management improves farm level productivity and profitability for smallholder farmers in northern India
    

    Beletse, Y.G., Laing, A.M., Prestwidge, D.B., Gathala, M.K., Subash, N. and Liedloff, A.C.
    https://doi.org/10.36334/modsim.2021.B8.beletse

• Subramanian, R.
  • Extending the capabilities of applications built using the Workspace architecture
    

    Subramanian, R., Thomas, D.G. and Cummins, S.J.
    https://doi.org/10.36334/modsim.2021.C2.subramanian

• Suleman, M.
  • An ML-based study of extreme weather events incorporating seasonality factor
    

    Khaiter, P. and Suleman, M.
    https://doi.org/10.36334/modsim.2021.F5.khaiter

• Sullivan, A.
  • A water suppression model for wildfires
    

    Hilton, J.E., Sullivan, A., Plucinski, M. and Swedosh, W.
    https://doi.org/10.36334/modsim.2021.G3.hilton

• Surovets, V.
  • Impacts of localizing food system on farmers’ livelihood resilience
    

    Loo, P.S., Önal, F., Bax, K., Smairy, M. and Surovets, V.
    https://doi.org/10.36334/modsim.2021.B5.loo

• Sutherland, D.
  • Application of neural networks to rate of spread estimation in shrublands
    

    Wadhwani, R., Sutherland, D., Moinuddin, K.A. and Sharples, J.J.
    https://doi.org/10.36334/modsim.2021.G3.wadhwani2

  • Improvement of drag model for non-burning firebrand transport in Fire Dynamics Simulator
    

    Wadhwani, R., Sutherland, D., Thorpe, G. and Moinuddin, K.A.
    https://doi.org/10.36334/modsim.2021.G3.wadhwani

• Swedosh, W.
  • Incorporating directionality in the modelling of firebreaks using a two-dimensional dynamic fire spread simulator
    

    Swedosh, W. and Hilton, J.
    https://doi.org/10.36334/modsim.2021.G3.swedosh

  • A water suppression model for wildfires
    

    Hilton, J.E., Sullivan, A., Plucinski, M. and Swedosh, W.
    https://doi.org/10.36334/modsim.2021.G3.hilton

T

• Taimre, T.
  • Estimating tail probabilities of random sums of scale mixture of phase-type random variables
    

    Yao, H. and Taimre, T.
    https://doi.org/10.36334/modsim.2021.A1.yao

• Tay, N.
  • Consideration of enabling and enterprise functions within Defence force design
    

    Boyce, J., Tay, N. and Rowe, C.
    https://doi.org/10.36334/modsim.2021.M8.boyce

  • Analytic approaches to understanding future Defence workforce needs
    

    Rowe, C., Crockett, A., Rohan-Jones, S., Aljoorfri, S., Tay, N., Coutts, A., Bishop, C., Pay, A., Sim, W., Date, V. and Boyce, J.
    https://doi.org/10.36334/modsim.2021.M8.rowe

• Teng, J.
  • Generating two-monthly surface water images for the Murray-Darling Basin
    

    Ticehurst, C.J., Teng, J. and Sengupta, A.
    https://doi.org/10.36334/modsim.2021.G5.ticehurst

• Thatcher, M.
  • Extreme bushfire projections using a standardised method
    

    Dowdy, A.J., Thatcher, M., Rafter, T., Evans, J., Ye, H., Su, C.-H., Stassen, C., Pepler, A., Bell, S.S. and Steinle, P.
    https://doi.org/10.36334/modsim.2021.G3.dowdy

• Thomas, D.G.
  • Workspace Workflow Comparison
    

    Oakes, N. and Thomas, D.G.
    https://doi.org/10.36334/modsim.2021.C2.oakes

  • Extending the capabilities of applications built using the Workspace architecture
    

    Subramanian, R., Thomas, D.G. and Cummins, S.J.
    https://doi.org/10.36334/modsim.2021.C2.subramanian

• Thompson, M.
  • Modelling to determine key drivers of water quality off sugarcane paddocks in the Great Barrier Reef catchment
    

    Power, B., Shaw, M., Vilas, M.P., Farr, M. and Thompson, M.
    https://doi.org/10.36334/modsim.2021.L1.power

• Thorpe, G.
  • Improvement of drag model for non-burning firebrand transport in Fire Dynamics Simulator
    

    Wadhwani, R., Sutherland, D., Thorpe, G. and Moinuddin, K.A.
    https://doi.org/10.36334/modsim.2021.G3.wadhwani

• Thyer, M.A.
  • A method to calibrate daily rainfall-runoff models to monthly streamflow data
    

    Lerat, J., Kavetski, D., McInerney, D. and Thyer, M.
    https://doi.org/10.36334/modsim.2021.J4.lerat

  • Recent learnings towards achieving high quality probabilistic predictions in practical applications of hydrological models
    

    Kavetski, D., McInerney, D., Hunter, J. and Thyer, M.
    https://doi.org/10.36334/modsim.2021.J4.kavetski

  • Modelling hydrological change due to wildfires
    

    Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H.R., Simmons, C.T., Croke, B.F.W., Jakeman, A.J., Gupta, H.V. and Kavetski, D.
    https://doi.org/10.36334/modsim.2021.J8.partington

  • Advances in subseasonal streamflow forecasting: An overview
    

    Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.thyer

  • Improving sub-seasonal streamflow forecasts across flow regimes
    

    McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.mcinerney

  • An assessment framework for classifying determinants of household water consumption and their priorities for research and practice
    

    Cominola, A., Preiss, L., Thyer, M., Maier, H.R., Stewart, R. and Castelletti, A.
    https://doi.org/10.36334/modsim.2021.J3.cominola

  • Uncertainty, sensitivity and scenario analysis: how do they fit together?
    

    Maier, H.R., Guillaume, J.H.A., McPhail, C., Westra, S., Kwakkel, J.H., Razavi, S., van Delden, H., Thyer, M.A., Culley, S.A. and Jakeman, A.J.
    https://doi.org/10.36334/modsim.2021.J5.maier

• Tian, F.-B.
  • CFD solver validations for simulating passively pitching tandem wings in hovering flight
    

    Huang, Q., Mazharmanesh, S., Tian, F.-B., Young, J., Lai, J.C.S. and Ravi, S.
    https://doi.org/10.36334/modsim.2021.A2.huang

• Tian, S.
  • Continental scale downscaling of AWRA-L analysed soil moisture using random forest regression
    

    Yu, Y., Renzullo, L.J. and Tian, S.
    https://doi.org/10.36334/modsim.2021.J10.yu

• Ticehurst, C.J.
  • Generating two-monthly surface water images for the Murray-Darling Basin
    

    Ticehurst, C.J., Teng, J. and Sengupta, A.
    https://doi.org/10.36334/modsim.2021.G5.ticehurst

• Tiong, J.K.R.
  • Causality modelling of simulated temporally indexed events to construct a combat narrative
    

    Tiong, J.K.R. and Chiera, B.A.
    https://doi.org/10.36334/modsim.2021.M7.tiong

• Towers, I.
  • Application of a delayed logistic equation to a reindeer population in a closed environment
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt

  • Stochastic modelling of wind and its implication for wildfire spread predictions
    

    Masoudian, S., Sharples, J.J., Jovanoski, Z., Towers, I. and Watt, S.
    https://doi.org/10.36334/modsim.2021.G3.masoudian

  • The population dynamics of ecosystem engineers and habitat modification
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt2

• Trotter, L.
  • “Naïve” inclusion of diverse climates in calibration is not sufficient to improve model reliability under future climate uncertainty
    

    Trotter, L., Saft, M., Peel, M.C. and Fowler, K.J.A.
    https://doi.org/10.36334/modsim.2021.J8.trotter

• Tularam, G.A.
  • The use of exponential smoothing (ES), Holts and Winter (HW) and ARIMA models in electricity price analysis
    

    Alsaedi, Y. and Tularam, G.A.
    https://doi.org/10.36334/modsim.2021.A1.alsaedi

• Turan, H.H.
  • The closed loop military maintenance workforce: A simulation-based optimisation approach
    

    Withers, K.R., Turan, H.H., Kahagalage, S. and Elsawah, S.
    https://doi.org/10.36334/modsim.2021.M2.withers

• Tuteja, N.
  • Improving sub-seasonal streamflow forecasts across flow regimes
    

    McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.mcinerney

  • Advances in subseasonal streamflow forecasting: An overview
    

    Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.thyer