The 7th meeting of the academic forum of the institute of hydrology was held at Tsinghua University

The 7th meeting of the academic forum of the institute of hydrology was held at Tsinghua University on October 16th. The meeting invited two experts to make the report. They were invited by professor wang zhongjing.

Dr Robert Argent, The name of his report is” Water resources ination for water security”.


Dr Robert Argent leads the Australian Bureau of Meteorology's Water Program, delivering a suite of national water data and ination services that include past, present and future assessments of surface and groundwater resources.

Report summaries: Water security has been, and continues to be, a key challenge for Australia, particularly with increasingly variable climate and growing demand. Demands include those from population growth and from the environment, for both water and the important services that water provides. Our water security encompasses a range of time horizons, from days to weeks for many riverine situations, months to years for soil moisture and surface water resources, and years to decades for groundwater resources.  


Professor Q J Wang, The name of his report is” Making weather forecasts real for hydrological ensemble forecasts”.


In 1994, Professor Wang came to Australia and joined the University of Melbourne, where he worked as a lecturer and later as a senior lecturer. In 1999, he took up a principal scientist position at the Victorian Department of Primary Industries, leading irrigation research. In 2007, he joined CSIRO Land and Water as an Office of the Chief Executive Science Leader and senior principal research scientist, leading water forecasting research. In February 2017, He returned to the University of Melbourne as a teaching and research professor.

Report summaries: Hydrological forecasts, with lead times from hours to seasons, are highly valuable for flood emergency management and for water resources management. State-of-the-art forecasting methods aim to (1) produce forecasts that are as accurate as possible, and (2) statistically represent remaining forecast uncertainty in a reliable manner. A useful way to represent forecast uncertainty is to use forecast ensembles.