A Bayesian Hierarchical Approach for Simulating Multi-time Scale Hydrological Variability for Water Resource Planning. 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 ....A Bayesian Hierarchical Approach for Simulating Multi-time Scale Hydrological Variability for Water Resource Planning. 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.
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A new strategy for design flood estimation in a nonstationary climate. Evidence suggests that global warming will result in an increase in the frequency and/or magnitude of heavy rainfall, leading to flooding with potentially devastating consequences. This study provides a renewed focus on design flood estimation that takes into account a changing climate where assumptions of stationarity are no longer tenable.