A Bayesian Hierarchical Approach for Simulating Multi-time Scale Hydrological Variability for Water Resource Planning

对未来干旱风险的评估取决于随机模型提供的水文输入模拟。目前的模型仅限于使用当地观测的水文数据模拟单一时间尺度的变率。这些数据仅提供了有限的关于长期气候变化的信息，而气候变化是造成长期严重干旱的原因。拟议的研究将开发一个新的贝叶斯框架，用于模拟水文数据的多时间尺度变化。这将使模拟长期可变性的动态过程能够在不确定性框架内使用辅助信息加以确定。这将为水资源规划者提供更准确的长期干旱风险评估。

Assessments of future drought risks are dependent on simulations of hydrological inputs provided by stochastic models. The current models are limited to simulating variability at a single time scale using only local observed hydrological data. This data has only limited information on the long-term climate variability which is the cause of long-term severe droughts. The proposed research will develop a new Bayesian framework for simulating multi-time scale variability in hydrological data. This will enable the dynamic processes which simulate long-term variability to be identified using auxiliary information in an uncertainty framework. This will provide water resource planners with more accurate assessments of long-term drought risks.