Professor Q J Wang
- (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 (http://www.bom.gov.au/water/ssf). 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 (http://www.bom.gov.au/water/7daystreamflow).
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 (http://www.hepex.org).
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.
- Zhao T, Bennett JC, Wang Q, Schepen A, Wood AW, Robertson DE, Ramos MH. How suitable is quantile mapping for postprocessing GCM precipitation forecasts?. Journal of Climate. American Meteorological Society. 2017, Vol. 30, Issue 9.
- Shao Q, Zhang L, Wang Q. 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.
- Shahrban M, Walker J, Wang Q, Seed A, Steinle P. An evaluation of numerical weather prediction based rainfall forecasts. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES. International Association of Hydrological Sciences Press. 2016, Vol. 61, Issue 15.
- Bennett JC, Robertson DE, Ward PGD, Hapuarachchi HAP, Wang Q. Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments. Environmental Modelling and Software. Elsevier Science. 2016, Vol. 76.
- Schepen A, Wang Q, Everingham Y. Calibration, bridging, and merging to improve GCM seasonal temperature forecasts in Australia. Monthly Weather Review. American Meteorological Society. 2016, Vol. 144, Issue 6.
- Zongxing L, Qi F, Wang Q, Song Y, Aifang C, Jianguo L. Contribution from frozen soil meltwater to runoff in an in-land river basin under water scarcity by isotopic tracing in northwestern China. Global and Planetary Change. Elsevier BV. 2016, Vol. 136.
- Zongxing L, Qi F, Wang Q, Yanlong K, Aifang C, Song Y, Yongge L, Jianguo L, Xiaoyan G. Contributions of local terrestrial evaporation and transpiration to precipitation using δ18O and D-excess as a proxy in Shiyang inland river basin in China. Global and Planetary Change. Elsevier BV. 2016, Vol. 146.
- Zhao T, Schepen A, Wang Q. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach. Journal of Hydrology. Elsevier Science. 2016, Vol. 541.
- Li M, Wang Q, Bennett JC, Robertson DE. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting. Hydrology and Earth System Sciences. Copernicus GmBH. 2016, Vol. 20, Issue 9.
- Schepen A, Zhao T, Wang Q, Zhou S, Feikema P. Optimising seasonal streamflow forecast lead time for operational decision making in Australia. Hydrology and Earth System Sciences. Copernicus GmBH. 2016, Vol. 20, Issue 10.
- Zongxing L, Qi F, Wang Q, Song Y, Jianguo L, Yongge L, Yamin W. Quantitative evaluation on the influence from cryosphere meltwater on runoff in an inland river basin of China. Global and Planetary Change. Elsevier BV. 2016, Vol. 143.
- Bennett JC, Wang Q, Li M, Robertson DE, Schepen A. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model. Water Resources Research. American Geophysical Union. 2016, Vol. 52, Issue 10.
- Zongxing L, Qi F, Song Y, Wang Q, Yang J, Yongge L, Jianguo L, Xiaoyan G. Stable isotope composition of precipitation in the south and north slopes of Wushaoling Mountain, northwestern China. Atmospheric Research. Elsevier BV. 2016, Vol. 182.
- Zongxing L, Qi F, Wang Q, Song Y, Hongyi L, Yongge L. The influence from the shrinking cryosphere and strengthening evopotranspiration on hydrologic process in a cold basin, Qilian Mountains. Global and Planetary Change. Elsevier BV. 2016, Vol. 144.
- Li M, Wang Q, Bennett JC, Robertson DE. A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts. Hydrology and Earth System Sciences. Copernicus GmBH. 2015, Vol. 19, Issue 1.
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