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

U

• Uhm, M.
  • A flexible family of hypertabastic models
    

    Singh, K., Harrison, N., Eby, W., Tabatabai, M., Bursac, Z., Uhm, M., Bartolucci, A. and Bae, S.
    https://doi.org/10.36334/modsim.2019.I1.singh

V

• Van Dijk, A.I.J.M.
  • A global, near real-time system measuring river, lake, and reservoir dynamics
    

    Hou, J., Van Dijk, A.I.J.M. and Beck, H.E.
    https://doi.org/10.36334/modsim.2019.H5.houJ

  • Burn extent and severity mapping by spectral anomaly detection in the Landsat data cube
    

    Renzullo, L.J., Tian, S., Van Dijk, A.I.J.M., Larraondo, P.R., Yebra, M., Yuan, F. and Mueller, N.
    https://doi.org/10.36334/modsim.2019.H7.renzullo

  • Synthesising multiple observations into annual environmental condition reports: the OzWALD system and Australia’s Environment Explorer
    

    Van Dijk, A.I.J.M. and Rahman, J.
    https://doi.org/10.36334/modsim.2019.J5.vandijk

  • Downscaling runoff products using areal interpolation: a combined pycnophylactic-dasymetric method
    

    Kallio, M., Virkki, V., Guillaume, J.H.A. and Van Dijk, A.I.J.M.
    https://doi.org/10.36334/modsim.2019.K8.kallio

  • Some experiments in automated identification of Australian plants using convolutional neural networks
    

    Boston, T. and Van Dijk, A.I.J.M.
    https://doi.org/10.36334/modsim.2019.A1.boston

• van Klinken, R.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• van Leeuwen, J.
  • Irrigation water resource management: 'IW-QC2' software tool
    

    Awad, J., Pezzaniti, D., Vanderzalm, J., Esu, O.-O. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K9.awad

  • Simulation of the suppressive effect of zinc on cyanobacteria in paper mill wastewater
    

    Burger, H., Awad, J., Hewa, G.A. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K15.burger

• Van Strien, M.
  • Where do we recreate? Comparison of different methods to determine importance of site characteristics for outdoor recreation
    

    Willibald, F., Van Strien, M. and Grêt-Regamey, A.
    https://doi.org/10.36334/modsim.2019.G7.willibald

• Vanderzalm, J.
  • Irrigation water resource management: 'IW-QC2' software tool
    

    Awad, J., Pezzaniti, D., Vanderzalm, J., Esu, O.-O. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K9.awad

• Verburg, K.
  • Using pedotransfer functions to improve the precision of spatially predicted available water capacity
    

    Austin, J.M., Stockmann, U. and Verburg, K.
    https://doi.org/10.36334/modsim.2019.C1.austin

• Verdon-Kidd, D.C.
  • Centennial-scale variability of soil moisture in eastern Australia
    

    Gibson, A.J., Hancock, G.R., Verdon-Kidd, D.C. and Wells, T.
    https://doi.org/10.36334/modsim.2019.K22.gibson

• Vieritz, A.M.
  • Modelling feedlots using the MEDLI model framework
    

    Vieritz, A.M., Lowry, M.M., Sullivan, T.J., Atzeni, M.G., Hope-O'Connor, A.J. and Watts, P.J.
    https://doi.org/10.36334/modsim.2019.C1.vieritz

• Vilas, M.P.
  • Machine learning approaches to improve and predict water quality data
    

    Zhang, Y., Thorburn, P.J., Vilas, M.P. and Fitch, P.
    https://doi.org/10.36334/modsim.2019.D5.zhangYiF

• Virkki, V.
  • Downscaling runoff products using areal interpolation: a combined pycnophylactic-dasymetric method
    

    Kallio, M., Virkki, V., Guillaume, J.H.A. and Van Dijk, A.I.J.M.
    https://doi.org/10.36334/modsim.2019.K8.kallio

• Vogeler, I.
  • Dynamic simulation of crop rotations to evaluate the impact of different nitrogen management strategies on water quality in Southland, New Zealand
    

    Khaembah, E.N., Cichota, R., Zyskowski, R. and Vogeler, I.
    https://doi.org/10.36334/modsim.2019.C1.khaembah

• Vu, T.M.
  • An integrated approach for improved management of an island's scarce water resources under climate change and tourism development
    

    Phan, T.D., Sahin, O., Trinh, H.D., Dao, H.N., Mai, L.S., Pham, T.V., Do, T.P., Vu, T.M. and Capon, S.J.
    https://doi.org/10.36334/modsim.2019.J1.phan

• Vuillaume, J.F.
  • Development of an Atmosphere-Ocean-Geosphere-Coupled Climate Model and its application to the Kanto plain, Japan
    

    Vuillaume, J.F., Fukazawa, S., Goto, K., Hazart, A., Konishi, Y., Onishi, R., Sugiyama, T., Tada, K., Tawara, Y., Tosaka, H. and Takahashi, K.
    https://doi.org/10.36334/modsim.2019.K9.vuillaume

W

• Wakes, S.J.
  • Designing a new single-blade wind turbine using Computational Fluid Dynamics
    

    Wakes, S.J., Cornil, J. and Currie, B.
    https://doi.org/10.36334/modsim.2019.A6.wakes

• Walker, M.
  • Exploring post 2011–12 drought in the Murray–Darling Basin
    

    Nahar, J., Watson, M., Edraki, M., Walker, M. and George, B.
    https://doi.org/10.36334/modsim.2019.K22.nahar

• Walsh, J.
  • Provenance narratives and visualisation to support understanding and trust
    

    Cunningham, A., Nowina-Krowicki, M., Walsh, J., Chung, J., Wark, S. and Thomas, B.H.
    https://doi.org/10.36334/modsim.2019.D1.cunningham

  • Narrative visualisation of simulations: Finding the stories within the data
    

    Walsh, J., Cunningham, A., Wark, S., Nowina-Krowicki, M. and Thomas, B.H.
    https://doi.org/10.36334/modsim.2019.B3.walsh

• Wang, E.
  • Inventory control with partially observable states
    

    Wang, E., Kurniawati, H. and Kroese, D.P.
    https://doi.org/10.36334/modsim.2019.B1.wang

• Wang, L.
  • A hybrid immersed boundary–lattice Boltzmann and finite difference method for bushfire simulation
    

    Wang, L. and Tian, F.-B.
    https://doi.org/10.36334/modsim.2019.B8.wangL

• Wardell-Johnson, D.H.
  • Science for Sustainability: Using Societal Metabolism Analysis to check the robustness of European Union policy narratives in the water, energy and food nexus
    

    Matthews, K.B., Waylen, K.A., Blackstock, K.L., Juarez-Bourke, A., Miller, D.G., Wardell-Johnson, D.H., Rivington, M. and Giampietro, M.
    https://doi.org/10.36334/modsim.2019.J5.matthews

• Wark, S.
  • Narrative visualisation of simulations: Finding the stories within the data
    

    Walsh, J., Cunningham, A., Wark, S., Nowina-Krowicki, M. and Thomas, B.H.
    https://doi.org/10.36334/modsim.2019.B3.walsh

  • Provenance narratives and visualisation to support understanding and trust
    

    Cunningham, A., Nowina-Krowicki, M., Walsh, J., Chung, J., Wark, S. and Thomas, B.H.
    https://doi.org/10.36334/modsim.2019.D1.cunningham

  • Evaluating a virtual human storyteller for improved decision support
    

    Blackmore, K.L., Smith, S.P., Nesbitt, K.V., North, L., Wark, S. and Nowina-Krowicki, M.
    https://doi.org/10.36334/modsim.2019.B2.blackmore

• Warner, A.
  • Comparing remote sensing and tabulated crop coefficients to assess irrigation water use
    

    Bretreger, D., Warner, A. and Yeo, I-Y.
    https://doi.org/10.36334/modsim.2019.C1.bretreger

• Waterer, H.
  • The network maintenance problem on an arc with uncapacitated repair
    

    Charkhgard, P., Kalinowski, T. and Waterer, H.
    https://doi.org/10.36334/modsim.2019.B6.charkhgard

• Waters, D.K.
  • What do we need from catchment water quality models in Australia?
    

    Fu, B., Merritt, W.S., Cuddy, S.M., Jakeman, A.J., Croke, B., Weber, T., Hall, J., Waters, D.K. and Baker, D.
    https://doi.org/10.36334/modsim.2019.K15.fuB

• Watkins, D.
  • An integrated optimisation functionality for Workspace
    

    Potter, D., Hetherton, L., Thomas, D.G., McNaughton, R. and Watkins, D.
    https://doi.org/10.36334/modsim.2019.D2.potter

  • Translation of stranded IP to commercialisable software applications using Workspace
    

    Cleary, P.W., Hetherton, L., Thomas, D.G., Bolger, M. and Watkins, D.
    https://doi.org/10.36334/modsim.2019.D2.cleary

  • Workspace for Industry 4.0
    

    Hetherton, L., Bolger, M., Zhang, Z., Oakes, N., Watkins, D. and Cleary, P.W.
    https://doi.org/10.36334/modsim.2019.D2.hetherton

  • Workspace – a Scientific Workflow System with commercial impact
    

    Oakes, N., Hetherton, L., Bolger, M., Thomas, D.G., Rucinski, C., Watkins, D. and Cleary, P.W.
    https://doi.org/10.36334/modsim.2019.D2.oakes

• Watson, C.
  • “Stopping before you start”: Reducing and preventing initiation of tobacco use in the ACT
    

    Freebairn, L., Kreuger, K., Watson, C., Yang, O., Prodan, A., Graham, K.I., Draper, G., McDonnell, G., Nixon, E. and Atkinson, J-A.
    https://doi.org/10.36334/modsim.2019.I2.freebairn

• Watson, M.
  • Exploring post 2011–12 drought in the Murray–Darling Basin
    

    Nahar, J., Watson, M., Edraki, M., Walker, M. and George, B.
    https://doi.org/10.36334/modsim.2019.K22.nahar

• Watt, S.D.
  • Evaluation of P-Delta instability in hardening oscillators using dynamical systems approach
    

    Pathak, S., Watt, S.D., Khennane, A. and Al-Deen, S.
    https://doi.org/10.36334/modsim.2019.A5.pathak

  • Exploiting ecological non-trophic models in representations of warfare
    

    McLennan-Smith, T.A., Kalloniatis, A.C., Sidhu, H.S., Jovanoski, Z., Watt, S.D. and Towers, I.N.
    https://doi.org/10.36334/modsim.2019.B3.mclennan-smith

  • 2D chaotic flow in competitive exothermic-endothermic reactions
    

    Huang, Z., Watt, S.D., Sidhu, H.S., McIntosh, A.C., Brindley, J., Jovanoski, Z. and Towers, I.N.
    https://doi.org/10.36334/modsim.2019.A5.huang

• Watts, P.J.
  • Modelling feedlots using the MEDLI model framework
    

    Vieritz, A.M., Lowry, M.M., Sullivan, T.J., Atzeni, M.G., Hope-O'Connor, A.J. and Watts, P.J.
    https://doi.org/10.36334/modsim.2019.C1.vieritz

• Waylen, K.A.
  • Science for Sustainability: Using Societal Metabolism Analysis to check the robustness of European Union policy narratives in the water, energy and food nexus
    

    Matthews, K.B., Waylen, K.A., Blackstock, K.L., Juarez-Bourke, A., Miller, D.G., Wardell-Johnson, D.H., Rivington, M. and Giampietro, M.
    https://doi.org/10.36334/modsim.2019.J5.matthews

• Webb, J.A.
  • Future Water: Comparing and contrasting approaches to predicting water quality
    

    Guo, D., Lintern, A., Prodanovic, V., Kuller, M., Bach, P.M., Deletic, A., Shi, B., McCarthy, D., Ryu, D., Webb, J.A., Liu, S. and Western, A.W.
    https://doi.org/10.36334/modsim.2019.K15.guo

• Weber, T.
  • What do we need from catchment water quality models in Australia?
    

    Fu, B., Merritt, W.S., Cuddy, S.M., Jakeman, A.J., Croke, B., Weber, T., Hall, J., Waters, D.K. and Baker, D.
    https://doi.org/10.36334/modsim.2019.K15.fuB

• Wellalage, N.
  • Petty corruption on firm level innovation of MSMEs: Evidence from emerging economies
    

    Wellalage, N.
    https://doi.org/10.36334/modsim.2019.E5.wellalage

• Wells, T.
  • Centennial-scale variability of soil moisture in eastern Australia
    

    Gibson, A.J., Hancock, G.R., Verdon-Kidd, D.C. and Wells, T.
    https://doi.org/10.36334/modsim.2019.K22.gibson

• West, M.B.A.
  • Multi-stock replenishment of naval vessels by specialist replenishment ships
    

    Rowling, S.J., Teo, M. and West, M.B.A.
    https://doi.org/10.36334/modsim.2019.B2.rowling

• West, S.
  • Improving solar power forecasting to reduce regulation Frequency Control Ancillary Services causer pay in the National Electricity Market
    

    Huang, J. and West, S.
    https://doi.org/10.36334/modsim.2019.F1.huangJ2

• Western, A.W.
  • Future Water: Comparing and contrasting approaches to predicting water quality
    

    Guo, D., Lintern, A., Prodanovic, V., Kuller, M., Bach, P.M., Deletic, A., Shi, B., McCarthy, D., Ryu, D., Webb, J.A., Liu, S. and Western, A.W.
    https://doi.org/10.36334/modsim.2019.K15.guo

• Whetton, S.
  • Hydrogen as a future fuel, challenges and opportunities: An overview of modelling approaches
    

    Grozev, G., Ramezani, F., Perez, P., Whetton, S. and Young, M.D.
    https://doi.org/10.36334/modsim.2019.F5.grozev

• Whigham, P.A.
  • Classifying the shape of qPCR expression data
    

    Whigham, P.A., Stanton, J., Mason, C., Rawle, C.B. and Macknight, M.
    https://doi.org/10.36334/modsim.2019.E2.whigham

• White, G.
  • Bushfire emergency response simulation
    

    Bruggemann, T., Ford, J.J., White, G. and Perez, T.
    https://doi.org/10.36334/modsim.2019.A2.bruggemann2

  • Bushfire emergency response uncertainty quantification
    

    Bruggemann, T., Ford, J.J., White, G., Perez, T. and Power, W.
    https://doi.org/10.36334/modsim.2019.A2.bruggemann

• Whitten, S.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• Wilkin, T.
  • Aggregation of dependent criteria in multicriteria decision making problems by means of capacities
    

    Beliakov, G., Bowsell, S., Cao, T., Dazeley, R., Mak-Hau, V., Nguyen, M.-T., Wilkin, T. and Yearwood, J.
    https://doi.org/10.36334/modsim.2019.B3.beliakov

• Willgoose, G.R.
  • Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia
    

    Senanayake, I.P., Yeo, I-Y., Willgoose, G.R., Hancock, G.R. and Bretreger, D.
    https://doi.org/10.36334/modsim.2019.K10.senanayake

• Williams, D.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• Willibald, F.
  • Where do we recreate? Comparison of different methods to determine importance of site characteristics for outdoor recreation
    

    Willibald, F., Van Strien, M. and Grêt-Regamey, A.
    https://doi.org/10.36334/modsim.2019.G7.willibald

• Wong, M.
  • Evolving behaviour trees for automated discovery of novel combat strategy in real-time strategy wargames
    

    Masek, M., Peng Lam, C., Kelly, L. and Wong, M.
    https://doi.org/10.36334/modsim.2019.B4.masek

• Wu, W.
  • Urban flood modelling and planning system (UFMPS)
    

    Xu, W., Syme, B., Huxley, C. and Wu, W.
    https://doi.org/10.36334/modsim.2019.K2.xuW

X

• Xiang, J.
  • ArcWeld: A case study of the extensibility of software applications built using Workspace architecture
    

    Thomas, D.G., Murphy, A.B., Chen, F.F., Xiang, J. and Feng, Y.
    https://doi.org/10.36334/modsim.2019.D2.thomasDG

• Xu, W.
  • Urban flood modelling and planning system (UFMPS)
    

    Xu, W., Syme, B., Huxley, C. and Wu, W.
    https://doi.org/10.36334/modsim.2019.K2.xuW

Y

• Yang, L.
  • Temporal and spatial variation of precipitation in Nansha Islands, South China Sea
    

    Yao, D., Song, X. and Yang, L.
    https://doi.org/10.36334/modsim.2019.A4.yao

• Yang, O.
  • “Stopping before you start”: Reducing and preventing initiation of tobacco use in the ACT
    

    Freebairn, L., Kreuger, K., Watson, C., Yang, O., Prodan, A., Graham, K.I., Draper, G., McDonnell, G., Nixon, E. and Atkinson, J-A.
    https://doi.org/10.36334/modsim.2019.I2.freebairn

• Yao, D.
  • Temporal and spatial variation of precipitation in Nansha Islands, South China Sea
    

    Yao, D., Song, X. and Yang, L.
    https://doi.org/10.36334/modsim.2019.A4.yao

• Ye, N.
  • POMDPs for sustainable fishery management
    

    Filar, J.A., Qiao, Z. and Ye, N.
    https://doi.org/10.36334/modsim.2019.G2.filar

• Ye, T.
  • New understanding of climate change on the Qinghai-Tibetan Plateau and its adapting counter-measures
    

    Shi, P., Ye, T. and Chen, Y.
    https://doi.org/10.36334/modsim.2019.H1.shi

• Yearwood, J.
  • Aggregation of dependent criteria in multicriteria decision making problems by means of capacities
    

    Beliakov, G., Bowsell, S., Cao, T., Dazeley, R., Mak-Hau, V., Nguyen, M.-T., Wilkin, T. and Yearwood, J.
    https://doi.org/10.36334/modsim.2019.B3.beliakov

• Yebra, M.
  • Burn extent and severity mapping by spectral anomaly detection in the Landsat data cube
    

    Renzullo, L.J., Tian, S., Van Dijk, A.I.J.M., Larraondo, P.R., Yebra, M., Yuan, F. and Mueller, N.
    https://doi.org/10.36334/modsim.2019.H7.renzullo

• Yeo, I-Y.
  • Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia
    

    Senanayake, I.P., Yeo, I-Y., Willgoose, G.R., Hancock, G.R. and Bretreger, D.
    https://doi.org/10.36334/modsim.2019.K10.senanayake

  • The effects of SILO & AWRA wind speeds on irrigation depth simulations
    

    Bretreger, D. and Yeo, I-Y.
    https://doi.org/10.36334/modsim.2019.K17.bretreger2

  • Effects of soil data input on catchment streamflow and soil moisture prediction
    

    Binesh, A., Yeo, I-Y. and Hancock, G.R.
    https://doi.org/10.36334/modsim.2019.K24.binesh2

  • Comparing remote sensing and tabulated crop coefficients to assess irrigation water use
    

    Bretreger, D., Warner, A. and Yeo, I-Y.
    https://doi.org/10.36334/modsim.2019.C1.bretreger

• Yetemen, O.
  • The spatio-temporal NDVI analysis for two different Australian catchments
    

    Kumari, N., Yetemen, O., Srivastava, A., Rodriguez, J.F. and Saco, P.M.
    https://doi.org/10.36334/modsim.2019.K3.kumari

  • Aspect-controlled spatial and temporal soil moisture patterns across three different latitudes
    

    Srivastava, A., Yetemen, O., Kumari, N. and Saco, P.M.
    https://doi.org/10.36334/modsim.2019.K6.srivastava

  • Applying rainfall ensembles to explore hydrological uncertainty
    

    Kumari, N., Acharya, S.C., Renzullo, L.J. and Yetemen, O.
    https://doi.org/10.36334/modsim.2019.K14.kumari2

• Yokoi, H.
  • Two-patch model for contact process: migration extends the survival region of species
    

    Nakagiri, N., Sato, K., Yokoi, H. and Tainaka, K.
    https://doi.org/10.36334/modsim.2019.C4.nakagiri

• Young, M.
  • Risk tolerance, investment horizons and debt decisions
    

    Phung, T., Hsu, W., Naylor, M. and Young, M.
    https://doi.org/10.36334/modsim.2019.E5.phung

• Young, M.D.
  • Hydrogen as a future fuel, challenges and opportunities: An overview of modelling approaches
    

    Grozev, G., Ramezani, F., Perez, P., Whetton, S. and Young, M.D.
    https://doi.org/10.36334/modsim.2019.F5.grozev

• Yu, J.
  • Quantifying the contribution of multiple factors to land subsidence using artificial intelligence technology in the typical area of Beijing, China
    

    Cheng, R., Zhu, L., Li, H., Yu, J., Guo, L., Hu, L. and Li, Y.
    https://doi.org/10.36334/modsim.2019.K18.chengR

• Yuan, F.
  • Burn extent and severity mapping by spectral anomaly detection in the Landsat data cube
    

    Renzullo, L.J., Tian, S., Van Dijk, A.I.J.M., Larraondo, P.R., Yebra, M., Yuan, F. and Mueller, N.
    https://doi.org/10.36334/modsim.2019.H7.renzullo

Z

• Zare, F.
  • Constructing customized modeling guidelines: a Participatory Integrated Assessment and Modeling example
    

    Zare, F., Guillaume, J.H.A. and Jakeman, A.J.
    https://doi.org/10.36334/modsim.2019.K1.zare

• Zhang, C.
  • Exploring the relationship between spatiotemporal characteristics of snow cover and ENSO episodes in the Qinghai-Tibetan Plateau
    

    Zhang, C., Liu, F. and Shen, Y.
    https://doi.org/10.36334/modsim.2019.H1.zhangC

• Zhang, J.
  • Multi-objectives optimisation framework: A comparative study on the relationship between urban block typologies and key environmental performance indicators in Tianjin Eco-City
    

    Raharjo, G.P., Heng, C.K., Zhang, J., Sun, X.F., Chao, Y.H. and Shao, M.Q.
    https://doi.org/10.36334/modsim.2019.F4.raharjo

• Zhang, Y.
  • Machine learning approaches to improve and predict water quality data
    

    Zhang, Y., Thorburn, P.J., Vilas, M.P. and Fitch, P.
    https://doi.org/10.36334/modsim.2019.D5.zhangYiF

• Zhang, Y.Q.
  • Long-term vegetation change and its driving factors in a typical catchment of the Loess Plateau
    

    Huang, C.L. and Zhang, Y.Q.
    https://doi.org/10.36334/modsim.2019.K3.huangCL

  • A pixel-based indicator of upland crop waterlogging using remote sensing soil moisture data
    

    Song, P.L., Huang, J.F. and Zhang, Y.Q.
    https://doi.org/10.36334/modsim.2019.K3.song

• Zhang, Z.
  • Workspace for Industry 4.0
    

    Hetherton, L., Bolger, M., Zhang, Z., Oakes, N., Watkins, D. and Cleary, P.W.
    https://doi.org/10.36334/modsim.2019.D2.hetherton

• Zheng, H.
  • Projections of water futures for Australia: an update
    

    Zheng, H., Chiew, F.H.S., Potter, N.J. and Kirono, D.G.C.
    https://doi.org/10.36334/modsim.2019.K7.zhengH

• Zhu, B.
  • Routing strategy using local information based on a two layer cellular automaton model
    

    Zhu, B. and Liem, R.P.
    https://doi.org/10.36334/modsim.2019.F3.zhu

• Zhu, L.
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