Professor Q J Wang

  • Room: Level: 03 Room: 355.07
  • Building: 333 Exhibition St
  • Campus: Other

Research interests

  • (1) Ensemble hydrological forecasting (Floods, short-term and seasonal streamflow, drought)
  • (2) Ensemble weather and climate forecasting (Precipitation, temperature, short-term, seasonal, post-processing)
  • (3) Catchment hydrological modelling (Water balance and runoff, river routing, updating)
  • (4) Bayesian statistical modelling and uncertainty quantification (Hierarchical modelling, MCMC, data transformations, missing and censored data, spatial and temporal models)
  • (5) Ensemble spatial data infilling and interpolating model (ESDIIM) (Precipitation, temperature)
  • (6) Applications of hydrological forecasts to water management (Flood emergency management, water allocation and outlook, environmental watering, drought management, irrigation scheduling)
  • (7) Irrigation (Irrigation systems, on-farm irrigation technologies, salinity, regional planning)


Professor QJ Wang obtained his BE in 1984 from Tsinghua University at Beijing with a “Graduate of Excellence” Award. In Ireland, he completed his MSc in 1987 and PhD in 1990 at University College Galway. QJ worked briefly as a Postdoctoral Fellow with Professor James Dooge at University College Dublin, before returning to University College Galway to take up a Lecturer position. In 1994, QJ came to Australia and joined the University of Melbourne, where he worked as a Lecturer and later as a Senior Lecturer. In 1999, QJ took up a Principal Scientist position at the Victorian Department of Primary Industries, where he led irrigation research. In 2007, QJ joined CSIRO Land and Water as an Office of the Chief Executive Science Leader and Senior Principal Research Scientist. At CSIRO, he built his national and international reputation as a leader in water forecasting research and development. In February 2017, QJ took up the position of Professor of Hydrological Forecasting at the University of Melbourne.

Before joining CSIRO in 2007, QJ’s research interests included statistical hydrology, hydrological modelling and optimisation, irrigation, and regional planning. In CSIRO, QJ built from scratch a globally renowned water forecasting research team. Research by QJ and his team led to a national seasonal streamflow forecasting service operated by the Australian Bureau of Meteorology. The service now provides forecasts for over 300 locations, including major water storages and river systems across Australia. Forecasts issued at the start of each month give probabilities of volumes of streamflow in the next three months ( Research by QJ and his team also led to a new national short-term streamflow forecasting service, which provides daily forecasts of streamflow for the next seven days (

QJ developed a number of cutting-edge mathematical models. Among international applications, the US National Oceanic and Atmospheric Administration is evaluating the Calibration Bridging and Merging (CBaM) method for operational seasonal climate forecasting for the US. QJ has published widely, including many recent journal papers on flood, short term and seasonal streamflow forecasting, and on weather and climate forecasting. See

QJ served on the Queensland Government Chief Scientist’s Science, Engineering and Technology Expert Panel following the devastating 2010-11 Queensland Floods. He was awarded the 2014 GN Alexander Medal by the Institution of Engineers, Australia, and the 2016 CSIRO Medal for Impact from Science. Dr Wang is a co-chair of HEPEX, the peak international community for research and practice of ensemble hydrological forecasting (

At the University of Melbourne, QJ continues his research effort on ensemble forecasting of floods, short-term and seasonal streamflow, and ensemble forecasting of weather, climate and drought. He is interested in analysis of climate and hydrological data, post-processing of forecasts from weather and climate models, catchment water balance and river routing modelling, hydrological model prediction updating and uncertainty quantification, and verification of ensemble forecasts.

QJ is keen to apply his mathematical skill to solving general engineering and science problems. He is particularly interested in formulating and applying Bayesian statistical models, especially hierarchical models, for solving complex practical problems. QJ is also collaborating with colleagues on use of climate and hydrological ensemble forecasts for managing flood and drought hazards and for managing water resources.

Recent publications

  1. Mashford, J.; Song, Y.; Wang, QJ.; Robertson, D. A Bayesian hierarchical spatio-temporal rainfall model. Journal of Applied Statistics. TAYLOR & FRANCIS LTD. 2019, Vol. 46, Issue 2, pp. 217-229. DOI: 10.1080/02664763.2018.1473347
  2. Zhao, T.; Wang, QJ.; Schepen, A. A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs. Agricultural and Forest Meteorology. ELSEVIER. 2019, Vol. 269, pp. 88-101. DOI: 10.1016/j.agrformet.2019.02.003
  3. Wang, QJ.; Zhao, T.; Yang, Q.; Robertson, D. A Seasonally Coherent Calibration (SCC) Model for Postprocessing Numerical Weather Predictions. MONTHLY WEATHER REVIEW. AMER METEOROLOGICAL SOC. 2019, Vol. 147, Issue 10, pp. 3633-3647. DOI: 10.1175/MWR-D-19-0108.1
  4. Acharya, SC.; Nathan, R.; Wang, QJ.; Su, C-H.; Eizenberg, N. An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia. Hydrology and Earth System Sciences. Copernicus Publications. 2019, Vol. 23, Issue 8, pp. 3387-3403. DOI: 10.5194/hess-23-3387-2019
  5. Wang, QJ.; Shao, Y.; Song, Y.; Schepen, A.; Robertson, DE.; Ryu, D.; Pappenberger, F. An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm. Environmental Modelling & Software. Elsevier BV. 2019, Vol. 122, pp. 104550-104550. DOI: 10.1016/j.envsoft.2019.104550
  6. Strazzo, S.; Collins, DC.; Schepen, A.; Wang, QJ.; Becker, E.; Jia, L. Application of a Hybrid Statistical-Dynamical System to Seasonal Prediction of North American Temperature and Precipitation. Monthly Weather Review. AMER METEOROLOGICAL SOC. 2019, Vol. 147, Issue 2, pp. 607-625. DOI: 10.1175/MWR-D-18-0156.1
  7. Zhao, T.; Wang, QJ.; Schepen, A.; Griffiths, M. Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs. Agricultural and Forest Meteorology. ELSEVIER SCIENCE BV. 2019, Vol. 264, pp. 114-124. DOI: 10.1016/j.agrformet.2018.10.001
  8. Schepen, A.; Zhao, T.; Wang, QJ.; Robertson, DE. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments. Hydrology and Earth System Sciences. COPERNICUS GESELLSCHAFT MBH. 2018, Vol. 22, Issue 2, pp. 1615-1628. DOI: 10.5194/hess-22-1615-2018
  9. Nasir, HA.; Zhao, T.; Care, A.; Wang, QJ.; Weyer, E. Efficient River Management using Stochastic MPC and Ensemble Forecast of Uncertain In-flows. IFAC-PapersOnLine. ELSEVIER SCIENCE BV. 2018, Vol. 51, Issue 5, pp. 37-42. DOI: 10.1016/j.ifacol.2018.06.196
  10. Shahrban, M.; Walker, JP.; Wang, QJ.; Robertson, DE. On the importance of soil moisture in calibration of rainfall-runoff models: two case studies. Hydrological Sciences Journal. TAYLOR & FRANCIS LTD. 2018, Vol. 63, Issue 9, pp. 1292-1312. DOI: 10.1080/02626667.2018.1487560
  11. Charles, SP.; Wang, QJ.; Ahmad, M-U-D.; Hashmi, D.; Schepen, A.; Podger, G.; Robertson, DE. Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods. Hydrology and Earth System Sciences. COPERNICUS GESELLSCHAFT MBH. 2018, Vol. 22, Issue 6, pp. 3533-3549. DOI: 10.5194/hess-22-3533-2018
  12. Feikema, PM.; Wang, QJ.; Zhou, S.; Shin, D.; Robertson, DE.; Schepen, A.; Lerat, J.; Bennett, JC.; Tuteja, NK.; Jayasuriya, D. Service and Research on Seasonal Streamflow Forecasting in Australia. World Scientific Series on Asia-Pacific Weather and Climate. World Scientific. 2018, Vol. 10, pp. 157-175. DOI: 10.1142/9789813235663_0010
  13. Bennett, JC.; Wang, QJ.; Robertson, DE.; Schepen, A.; Li, M.; Michael, K. Assessment of an ensemble seasonal streamflow forecasting system for Australia. Hydrology and Earth System Sciences. COPERNICUS GESELLSCHAFT MBH. 2017, Vol. 21, Issue 12, pp. 6007-6030. DOI: 10.5194/hess-21-6007-2017
  14. Zhao, T.; Bennett, JC.; Wang, QJ.; Schepen, A.; Wood, AW.; Robertson, DE.; Ramos, M-H. How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?. Journal of Climate. AMER METEOROLOGICAL SOC. 2017, Vol. 30, Issue 9, pp. 3185-3196. DOI: 10.1175/JCLI-D-16-0652.1
  15. Li, M.; Wang, QJ.; Robertson, DE.; Bennett, JC. Improved error modelling for streamflow forecasting at hourly time steps by splitting hydrographs into rising and falling limbs. Journal of Hydrology. ELSEVIER SCIENCE BV. 2017, Vol. 555, pp. 586-599. DOI: 10.1016/j.jhydrol.2017.10.057
  16. Feikema, PM.; Wang, QJ.; Zhou, S.; Shin, D.; Robertson, DE.; Schepen, A.; Lerat, J.; Bennett, JC.; Tuteja, NK.; Jayasuriya, D. Service and research on seasonal streamflow forecasting in Australia. . 2017, Vol. 10, pp. 157-175.
  17. Shao, Q.; Zhang, L.; Wang, QJ. A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations. Stochastic Environmental Research and Risk Assessment. SPRINGER. 2016, Vol. 30, Issue 6, pp. 1705-1724. DOI: 10.1007/s00477-015-1177-3
  18. Shahrban, M.; Walker, JP.; Wang, QJ.; Seed, A.; Steinle, P. An evaluation of numerical weather prediction based rainfall forecasts. Hydrological Sciences Journal. TAYLOR & FRANCIS LTD. 2016, Vol. 61, Issue 15, pp. 2704-2717. DOI: 10.1080/02626667.2016.1170131
  19. Schepen, A.; Wang, QJ.; Robertson, DE. Application to post-processing of meteorological seasonal forecasting. . Springer. 2016.
  20. Bennett, JC.; Robertson, DE.; Ward, PGD.; Hapuarachchi, HAP.; Wang, QJ. Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments. Environmental Modelling & Software. ELSEVIER SCI LTD. 2016, Vol. 76, pp. 20-36. DOI: 10.1016/j.envsoft.2015.11.006

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