Predictive performance is one of the most important issues for practical applications of
ensemble hydroclimatic forecasts. While different forecasting studies tend to use different …

Ten years, 16 fully coupled global models, and hundreds of research papers later, the North
American Multimodel Ensemble (NMME) monthly-to-seasonal prediction system is looking …

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …

Multivariate functional principal component analysis (MFPCA) is a widely used tool for
modeling and prognosis of degradation signals. However, MFPCA usually assumes …

Using the global climate model outputs without any adjustment may bring errors in water
resources and climate change investigations. This study tackles the critical issue of bias …

Highly accurate monthly rainfall predictions can provide early warnings for rain-related
disasters, such as floods and droughts, and allow governments to make timely decisions …

Streamflow predictions are vital for detecting flood and drought events. Such predictions are
even more critical to Sub-Saharan African regions that are vulnerable to the increasing …

Variations in monthly precipitation are associated with climate extremes having significant
socio‐economic and eco‐environmental impacts. Knowledge of monthly precipitation …

Subseasonal to seasonal (S2s) precipitation forecasts provide great potential for
hydrological forecasting at an extended range. The study proposed a support vector …

Gaussian process (GP), as a pivotal offshoot of machine learning (ML), has garnered
significant attention in recent years due to its exceptional advantages in tackling high …