[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 …
On the challenges of global entity-aware deep learning models for groundwater level prediction
The application of machine learning (ML) including deep learning models in hydrogeology
to model and predict groundwater level in monitoring wells has gained some traction in …
to model and predict groundwater level in monitoring wells has gained some traction in …
CAMELS-FR dataset: A large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking
O Delaigue, GM Guimarães, P Brigode… - Earth System …, 2024 - essd.copernicus.org
Over the last decade, large-sample approaches, ie based on large catchment sets, have
become increasingly popular in hydrological studies. Efforts were made to assemble and …
become increasingly popular in hydrological studies. Efforts were made to assemble and …
Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain (CAMELS-GB)
G Coxon, N Addor, JP Bloomfield… - NERC …, 2020 - catalogue.ceh.ac.uk
Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great
Britain (CAMELS-GB) - EIDC Part of UKCEH UKCEH logo UKCEH website EIDC EIDC Find …
Britain (CAMELS-GB) - EIDC Part of UKCEH UKCEH logo UKCEH website EIDC EIDC Find …
[PDF][PDF] Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation
Deep learning (DL) models are popular but computationally expensive, machine learning
(ML) models are old-fashioned but more efficient. Their differences in hydrological …
(ML) models are old-fashioned but more efficient. Their differences in hydrological …
A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies
Our understanding and predictive capability of streamflow processes largely rely on high-
quality datasets that depict a river's upstream basin characteristics. Recent proliferation of …
quality datasets that depict a river's upstream basin characteristics. Recent proliferation of …
Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
Machine learning is playing an increasing role in hydrology, supplementing or replacing
physics-based models. One notable example is the use of recurrent neural networks (RNNs) …
physics-based models. One notable example is the use of recurrent neural networks (RNNs) …
LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland
Access to mountainous regions for monitoring streamflow, snow and glaciers is often
difficult, and many rivers are thus not gauged and hydrological measurements are limited …
difficult, and many rivers are thus not gauged and hydrological measurements are limited …
BCUB–a large-sample ungauged basin attribute dataset for British Columbia, Canada
D Kovacek, S Weijs - Earth System Science Data, 2025 - essd.copernicus.org
Abstract The British Columbia Ungauged Basin (BCUB) dataset is an open-source,
extensible dataset of attributes describing terrain, soil, land cover, and climate indices of …
extensible dataset of attributes describing terrain, soil, land cover, and climate indices of …