Hydrology plays a central role in applied and fundamental environmental sciences, but it is
well known to suffer from an overwhelming diversity of models, particularly to simulate …

Digital twins provide insights into physical objects by serving as advanced virtual
representations. Their sensors capture detailed information about an object's functionality …

Glaciers are essential for downstream water resources. Hydrological modeling is necessary
for a better understanding and for future projections of the water resources in these rapidly …

Faced with persistent flooding and water quality challenges, water managers are now
seeking to build digital twins of surface water systems that combine sensor data with online …

Distributed watershed models are increasingly being used to support decisions about
alternative management strategies in the areas of land use change, climate change, water …

Estimation of parameter and predictive uncertainty of hydrologic models has traditionally
relied on several simplifying assumptions. Residual errors are often assumed to be …

Recent research documents that the widely accepted generalized likelihood uncertainty
estimation (GLUE) method for describing forecasting precision and the impact of parameter …

Reliable and precise probabilistic prediction of daily catchment‐scale streamflow requires
statistical characterization of residual errors of hydrological models. This study focuses on …

A novel ensemble-based conceptual-data-driven approach (CDDA) is developed where a
data-driven model (DDM) is used to “correct” the residuals from an ensemble of hydrological …

The paper appraises two approaches for the treatment of heteroscedasticity and
autocorrelation in residual errors of hydrological models. Both approaches use weighted …