报告题目：Assessing water supply drought security: Challenges facing stochastic models
报告人：George Kuczera 教授
主持人：孙勋 研究员
报告时间: 2019年11月11日 13:30-14:30
报告地点：闵行校区河口海岸大楼A304
主办单位：华东师范大学地理科学学院、地理信息科学教育部重点实验室
报告人简介：
George Kuczera，澳大利亚纽卡斯尔大学教授，哈佛大学博士。在基于贝叶斯统计方法的水文和水资源的理论探索与应用方面卓有建树，并为城市水资源规划管理做出了重要贡献。研究涉及水文模型的识别校准和优化、基于贝叶斯方法描述模型参数不确定性、洪水和干旱频率和风险评估、随机降雨模型等。累计发表学术论文数百篇，著有WEB of Science高被引论文(2014)，Google Scholar论文总引用过万次，h指数53。曾获澳大利亚工程师学会（IEAust）颁发的G.N Alexander Medal(1999) 和Warren Medal(2000)。
报告内容简介：
Water security is listed as the number one global societal risk for the present generation by the World Economic Forum (2018). The management of water supply systems requires reliable estimation of water availability in a risk-based framework, with stochastic models playing a key role. It is now standard practice for water supply agencies to use stochastic models to generate synthetic hydroclimate sequences that preserve the key statistics contained in the observed/instrumental hydroclimate.
However, such an approach is only adequate for determining current and future water supply risks if the quality and length of the observed/instrumental hydroclimate data is such that it captures the plausible range of impacts associated with climate variability and future climate change. The latest insights from palaeoclimate and climate change research suggest that this is unlikely to be the case because (i) it is highly likely that past (i.e. pre-instrumental record) droughts were more severe than those observed in the ~120 years of instrumental record and (ii) non-stationarity associated with anthropogenic climate change is projected to alter rainfall (and hence streamflow) characteristics
This presentation explores extending the current generation of stochastic hydroclimate models to better represent multi-decadal climate variability and the impact of anthropogenic climate change on future streamflow using climate change covariates. An urban water supply case study will illustrate the practical implications arising from use of the extended stochastic models.