As the new generation of artificial intelligence (AI) continues to evolve, weather big data and
statistical machine learning (SML) technologies complement each other and are deeply …

The approximately century-long instrumental record of precipitation over land reflects a
single sampling of internal variability. Thus, the spatiotemporal evolution of the observations …

Predictions of a warmer climate over the Great Lakes region due to global change generally
agree on the magnitude of temperature changes, but precipitation projections exhibit …

Rainfall is an important driver of many Earth surface and subsurface processes such as
floods, groundwater recharge, or plants growth. Models are used to investigate the physical …

We present the application of a parametric stochastic weather generator within a
nonstationary context, enabling simulations of weather sequences conditioned on …

The spatial structure of climatic variables synthesized by a weather generator has
considerable impact on the modeling of hydrological variability; however, in most cases, it …

Over half a century ago, the Harvard Water Program introduced the field of operational or
synthetic hydrology providing stochastic streamflow models (SSMs), which could generate …

Despite many concerns about climate change impacts on rainfed crops, few studies have
been conducted on the yield variations of different crops arising from climate changes in …

Synthetic rainfall simulations from stochastic models are commonly used for water resource
management, as they are able to provide a wider range of meteorological conditions than …

Stochastic weather generators combined with hydrological models have been proposed for
continuous synthetic simulation to estimate return periods of extreme floods. Yet, this …