Extreme droughts, heat waves, fires, hurricanes, floods, and landslides cause the largest
losses in the United States, and globally, from natural hazards linked to weather and climate …

We present a general Bayesian hierarchical framework for conducting nonstationary
frequency analysis of multiple hydrologic variables. In this, annual maxima from each …

Floods often affect large regions and cause adverse societal impacts. Regional flood hazard
and risk assessments therefore require a realistic representation of spatial flood …

Identifying the climatic drivers of an ecological system is a key step in assessing its
vulnerability to climate change. The climatic dimensions to which a species or system is …

We present a semi-Bayesian hierarchical modeling framework for conducting space–time
frequency analysis of precipitation extremes over a large domain. In this framework, the data …

Abstract A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of
daily streamflow is presented that uses the spatial dependence induced by the river network …

The quantitative validation of rainfall statistics obtained from space-borne sensors, and the
evaluation of the associated uncertainty are hindered by i) the limited coverage of rain …

Stochastically generated streamflow time series are used for various water management
and hazard estimation applications. They provide realizations of plausible but as yet …

Estimating the dependence structure of concurrent extremes is a fundamental issue for
accurate assessment of their occurrence probabilities. Identifying the extremal dependence …

Water managers need non-stationary tools to better characterize precipitation extremes.
Statistical approaches based on extreme value theory (EVT) are increasingly being used …