[HTML][HTML] Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
A vine copula‐based ensemble projection of precipitation intensity–duration–frequency curves at sub‐daily to multi‐day time scales
Precipitation intensity–duration–frequency (IDF) curves play a crucial role in the design and
planning of urban infrastructure to reduce the risk of urban flooding and rainfall‐triggered …
planning of urban infrastructure to reduce the risk of urban flooding and rainfall‐triggered …
Evaluating vegetation vulnerability under compound dry and hot conditions using vine copula across global lands
With global warming, climate extremes are intensifying, particularly compound dry and hot
events (CDHEs), which would severely impact vegetation growth. However, quantitatively …
events (CDHEs), which would severely impact vegetation growth. However, quantitatively …
Predicting hydrological drought with Bayesian model averaging ensemble vine copula (BMAViC) model
Streamflow deficit (hydrological drought) poses a large risk to water resources management,
agricultural production, water supply, hydropower generation, and ecosystem services …
agricultural production, water supply, hydropower generation, and ecosystem services …
Streamflow forecasting method with a hybrid physical process-mathematical statistic
The complex topology of river networks and the numerous factors influencing streamflow
make it challenging to forecast streamflow in large river basins. Improving the accuracy of …
make it challenging to forecast streamflow in large river basins. Improving the accuracy of …
A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement
R Xu, S Hu, H Wan, Y Xie, Y Cai, J Wen - Journal of Environmental …, 2024 - Elsevier
Deep learning methods exhibited significant advantages in mapping highly nonlinear
relationships with acceptable computational speed, and have been widely used to predict …
relationships with acceptable computational speed, and have been widely used to predict …
[HTML][HTML] Assessment of physical schemes for WRF model in convection-permitting mode over southern Iberian Peninsula
F Solano-Farias, MGV Ojeda, D Donaire-Montaño… - Atmospheric …, 2024 - Elsevier
Convection-permitting models (CPMs) enable the representation of meteorological variables
at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant …
at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant …
Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years
Runoff is a crucial water cycle component that contributes to the water resources to sustain
human life. Historical trends in runoff, when examining climate change scenarios, provide …
human life. Historical trends in runoff, when examining climate change scenarios, provide …
[HTML][HTML] Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Z Cui, S Guo, H Chen, D Liu, Y Zhou… - Hydrology and Earth …, 2024 - hess.copernicus.org
The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-
BMA methods have been widely used to quantify flood forecast uncertainty. This study …
BMA methods have been widely used to quantify flood forecast uncertainty. This study …