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Author Index

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

U

  • Ugbaje, S.U.
    • 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
  • Uhm, M.
    • 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
    • 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
  • Unnithan, S.L.K.
    • Mapping water quality in complex estuarine and coastal waters using deep learning models and high-resolution satellite imagery
      Unnithan, S.L.K., Cherukuru, N., Drayson, N. and Ingleton, T.
      https://doi.org/10.36334/modsim.2023.unnithan
  • Urich, 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

V

  • van Delden, H.
  • van der Linden, L.
    • Resilient Water Futures for Greater Adelaide – a strategy supported by integrated water system modelling
      van der Linden, L., Kotz, S., Irvine, M. and Greenlee, M.
      https://doi.org/10.36334/modsim.2023.vanderlinden
    • 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
  • van Dijk, A.I.J.M.
    • 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
  • Vanderduys, E.
    • 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
  • Vasco, D.
    • Generalising the raindrop plot for prediction and variable selection: An educational case study
      Vasco, D., Low-Choy, S. and Singh, P.
      https://doi.org/10.36334/modsim.2023.vasco
    • Multiverse experiments for enhancing reproducibility and resilience of statistical modelling via case studies in family wellbeing and biodiversity conservation
      Low-Choy, S., Modecki, K., Vasco, D., Alkhairy, I. and Hallgren, W.
      https://doi.org/10.36334/modsim.2023.lowchoy654
  • Verbraeck, A.
  • Verdon-Kidd, D.
    • 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
    • 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
  • Vickers, S.
  • Vidal-Calleja, T.
    • 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
  • Vieira Rocha, C.
    • 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
  • Vikraipaisarn, Y.
    • Area-business continuity management web application model test plan
      Tang, J., Leelawat, N., Sornklin, B., Chaweewongpaisal, M., Vikraipaisarn, Y., Bhisitcharoentat, Y., Arayachookiat, P., Meechang, K., Kodaka, A., Iwasaki, Y., Inoue, M. and Watanabe, K.
      https://doi.org/10.36334/modsim.2023.tang226
  • Vilas, M.P.
    • 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
  • Vilaseca, F.
  • Virgona, A.
    • 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
  • Vivas, J.
    • 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
  • von Wielligh, E.
    • 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

W

  • Wabe, N.
    • Dynamic health status monitoring using aged care quality indicators for better care: An innovative approach using mixture hidden Markov models
      Silva, S.S.M., Wabe, N. and Westbrook, J.I.
      https://doi.org/10.36334/modsim.2023.silva
  • Walmsley, B.J.
    • 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
  • Wang, C.
    • 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
  • Wang, E.
    • Cross-scale modelling of cropping systems: from gene/genome to landscape in era of big data
      Wang, E., Brown, H., Trevaskis, B., Zheng, B., Rebetzke, G., Zhao, Z.G., Huth, N.I., He, D., Hyles, J., Glover, M., Malone, B. and Macdonald, B.
      https://doi.org/10.36334/modsim.2023.wang299
    • The potential of using an inverse modelling approach to predict soil PAWC for summer crops in Australia
      He, D., Wang, E. and Verburg, K.
      https://doi.org/10.36334/modsim.2023.he
    • 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
  • Wang, H.
    • Predicting surface-specific humidity from radiative temperature and ambient weather for evapotranspiration modelling
      Gou, J., Liu, W., Guan, H., Batelaan, O., Bruce, D., Gutierrez, K., Wang, H., Gutierrez, H., Burk, L., Thompson, J. and Woods, J.
      https://doi.org/10.36334/modsim.2023.gou
    • Impacts of climate extremes on vegetation phenology and productivity in a transect along the Hu Line of China
      Wang, H., Guan, H., Liu, B. and Chen, X.
      https://doi.org/10.36334/modsim.2023.wang19
    • Optimal decision making and control with uncertain events, uncertain physics, or both
      Dunstall, S., Gunasegaram, D., Jiang, C. and Wang, H.
      https://doi.org/10.36334/modsim.2023.dunstall
    • 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
  • Wang, J.
    • Study on the impact of ubiquitous sensing of rainfall data on hydrological simulation
      Wang, J., Khu, S.T. and Mao, Y.N.
      https://doi.org/10.36334/modsim.2023.wang45
    • Spatial modelling of understorey evapotranspiration based on the maximum entropy production method
      Liu, W., Guan, H., Bruce, D., Batelaan, O., Keane, R., Keegan-Treloar, R., Gutiérrez-Jurado, K.Y., Thompson, J. and Wang, J.
      https://doi.org/10.36334/modsim.2023.liu287
    • Impacts of 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
    • Study on sharing and reusing geographic simulation models in web environment
      Xu, K., Chen, M., Yue, S., Wen, Y., Zhang, F., Wang, J. and Lu, G.
      https://doi.org/10.36334/modsim.2023.xu225
  • Wang, L.
  • Wang, L.H.
    • 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
  • Wang, Q.
    • Contrasting sediment and nutrient export patterns across different hydrological regimes: A case study in the Great Barrier Reef catchments
      Liu, S., Guo, D., Bende-Michl, U., Lintern, A., Waters, D.K. and Wang, Q.
      https://doi.org/10.36334/modsim.2023.liu77
  • Wang, X.
    • 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
  • Wang, Z.
    • 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
  • Ward, A.
    • A basic machine learning method for identifying individual biological state and ecological context from movement data
      Schaerf, T.M., Zvezdin, A.V., Welch, M.C., Wilson, A. and Ward, A.
      https://doi.org/10.36334/modsim.2023.schaerf
  • Ward, K.
    • 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
  • Wardell-Johnson, D.H.
    • 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
  • Watanabe, K.
    • Area-business continuity management web application model test plan
      Tang, J., Leelawat, N., Sornklin, B., Chaweewongpaisal, M., Vikraipaisarn, Y., Bhisitcharoentat, Y., Arayachookiat, P., Meechang, K., Kodaka, A., Iwasaki, Y., Inoue, M. and Watanabe, K.
      https://doi.org/10.36334/modsim.2023.tang226
    • Cascading events simulation for disaster-sensitive metropolitan areas: Resilience enhancement with visualization of consequences of large-scale disasters
      Watanabe, K.
      https://doi.org/10.36334/modsim.2023.watanabe
  • Wei, H.S.
    • 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
  • Weligamage, H.A.C.G.
  • Wen, L.
    • 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
  • Westbrook, J.I.
    • Dynamic health status monitoring using aged care quality indicators for better care: An innovative approach using mixture hidden Markov models
      Silva, S.S.M., Wabe, N. and Westbrook, J.I.
      https://doi.org/10.36334/modsim.2023.silva
  • Wignell, E.
    • 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
  • Wilkinson, M.
    • 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
  • Williams, K.J.
    • 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
  • Wilson, A.
    • A basic machine learning method for identifying individual biological state and ecological context from movement data
      Schaerf, T.M., Zvezdin, A.V., Welch, M.C., Wilson, A. and Ward, A.
      https://doi.org/10.36334/modsim.2023.schaerf
  • Wilson, C.
    • 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
  • Wilson, C.S.
    • Statistical analysis of Continuous Haines Index and Fuel Moisture Index for pyrocumulonimbus and non-pyrocumulonimbus bushfires in southeast Australia
      Wilson, C.S., Ma, W., Sharples, J.J. and Evans, J.P.
      https://doi.org/10.36334/modsim.2023.wilson
  • Wilson, L.
  • Wilson, P.
    • 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
  • Woldemeskel, F.
    • 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
    • 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
  • Wong, W.W.
    • Identifying and quantifying key sources of nutrient pollution from irrigated agriculture
      Sargent, R., Henry, R., Wong, W.W., Schang, C., Tseng, C.W., Cook, P., Western, A.W., McCarthy, D. and Lintern, A.
      https://doi.org/10.36334/modsim.2023.sargent
  • Wonink, S.
    • 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
  • Woods, J.
    • Predicting surface-specific humidity from radiative temperature and ambient weather for evapotranspiration modelling
      Gou, J., Liu, W., Guan, H., Batelaan, O., Bruce, D., Gutierrez, K., Wang, H., Gutierrez, H., Burk, L., Thompson, J. and Woods, J.
      https://doi.org/10.36334/modsim.2023.gou
  • Wu, B.
    • 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
  • Wu, M.
    • 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
  • Wyrwoll, P.
    • 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

X

  • Xiang, K.
    • Soil with high plant available water capacity can mitigate the agricultural drought effects on wheat biomass dynamic change
      Xiang, K., Wang, B., Liu, D.L., Huete, A. and Yu, Q.
      https://doi.org/10.36334/modsim.2023.xiang
  • Xiao, S.
    • 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
  • Xu, X.
  • Xu, Z.W.
    • Detecting ecosystem resilience to drought across 119 flux tower stations
      Shao, X.M., Zhang, Y.Q., Tian, J., Ma, N., Zhang, X.Z. and Xu, Z.W.
      https://doi.org/10.36334/modsim.2023.shao343
    • 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
    • Recovery of fire-impacted ecosystems: Detection using a state-of-the-art diagnostic evapotranspiration model
      Xu, Z.W., Zhang, Y.Q. and Ma, N.
      https://doi.org/10.36334/modsim.2023.xu342

Y

  • Yamazaki, D.
    • 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
  • Yang, J.
    • 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
    • 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
    • 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
  • Yang, R.Y.
    • 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
  • Yang, X.N.
    • 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
    • Impacts of drought on crop yield in the semiarid region
      Yang, X.N., Zhang, Y.Q., Tian, J., Zhang, X.Z., Ma, N. and Zhao, Z.G.
      https://doi.org/10.36334/modsim.2023.yang410
  • Yang, Z.H.
    • 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
  • Yazdani, D.
    • 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
  • Yoon, H.N.
    • Microwave data and climate information–based streamflow prediction using the surrogate river discharge model in the Murray–Darling Basin
      Yoon, H.N., Marshall, L.A. and Sharma, A.
      https://doi.org/10.36334/modsim.2023.yoon
  • Young, J.
    • 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
    • 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
  • Yu, Y.
    • 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
    • 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
    • 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

Z

  • Zatarain Salazar, J.
  • Zeng, C.
  • Zhang, H.
    • 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
    • Assessing the impact of bias correction approaches on climate extremes and the climate change signal
      Zhang, H., Chapman, S., Trancoso, R., Toombs, N. and Syktus, J.
      https://doi.org/10.36334/modsim.2023.zhang49
    • 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
    • The impact of bias correction on reference evapotranspiration and its climate change signal
      Zhang, H., Chapman, S., Trancoso, R., Toombs, N. and Syktus, J.
      https://doi.org/10.36334/modsim.2023.zhang48
    • 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
  • Zhang, Q.
    • Australia’s water quality trends over two decades
      Guo, D., Minaudo, C., Zhang, Q., Dupas, R., Liu, S., Zhang, K., Bende-Michl, U., Duvert, C. and Lintern, A.
      https://doi.org/10.36334/modsim.2023.guo615
    • 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
  • Zhang, X.Z.
  • Zhao, Z.G.
    • 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
    • Impacts of drought on crop yield in the semiarid region
      Yang, X.N., Zhang, Y.Q., Tian, J., Zhang, X.Z., Ma, N. and Zhao, Z.G.
      https://doi.org/10.36334/modsim.2023.yang410
    • 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
  • Zheng, B.
    • 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
    • Maximizing wheat grain yield in irrigated mega-environments: Targeting optimal flowering period by selecting optimal sowing date and genotype with appropriate phenological development pattern
      Hu, P., Zheng, B. and Chapman, S.
      https://doi.org/10.36334/modsim.2023.hu675
    • 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
    • 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
  • Zheng, Y.
    • 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
  • Zhu, L.-J.
    • "Good enough" principles for reproducibility: Developing pragmatic guidelines for early career scholars
      Iwanaga, T., Salazar, J.Z., Fischer, S.M., Jovanović, R., Liu, N., Zhu, Z. and Zhu, L.-J.
      https://doi.org/10.36334/modsim.2023.iwanaga485
  • Zou, K.
    • 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

Modelling to support planning for resilience in a changing world