Results 121 to 130 of about 19,564 (237)

Global River Discharge Projections From a Large Multi‐Model Ensemble of CMIP6 and ISIMIP3b Simulations

open access: yesEarth's Future, Volume 14, Issue 8, August 2026.
Abstract Rivers are central to the global hydrological cycle, supporting ecosystems and human water use. River discharge, one of the best observed hydrological variables, is expected to change under anthropogenic warming with potentially devastating consequences.
P. B. Seubert   +4 more
wiley   +1 more source

Multi‐Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Despite the necessity for accurate flood prediction, many regions lack sufficient river discharge observations. Although numerous models for daily river discharge prediction exist, achieving high accuracy, interpretability, and efficiency under data‐scarce conditions remains a major challenge.
Mizuki Funato, Yohei Sawada
wiley   +1 more source

Applying Transfer Learning for Street‐Scale Nuisance Flood Forecasting in Coastal‐Urban Environments

open access: yesWater Resources Research, Volume 62, Issue 8, August 2026.
Abstract An important challenge with Machine Learning (ML) is its transferability; that is, whether an ML model trained on one set of data can be applied to a second set of data without requiring full retraining of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained ...
Binata Roy   +6 more
wiley   +1 more source

Diagnosing Cryospheric Runoff Dynamics: A Distributed Differentiable Hydrological Model With Global Transfer Learning

open access: yesWater Resources Research, Volume 62, Issue 8, August 2026.
Abstract Accurate hydrological prediction in alpine regions remains challenging due to complex cryospheric processes and limited observational records. Traditional hydrological models are often subject to structural uncertainty, whereas purely data‐driven deep learning (DL) models may lack physical interpretability under non‐stationary climate ...
Jun Mei   +7 more
wiley   +1 more source

Serosurvey of Crimean-Congo Hemorrhagic Fever Virus in dromedary camels (<i>Camelus dromedarius</i>) in Egypt. [PDF]

open access: yesFront Vet Sci
Elmahallawy EK   +8 more
europepmc   +1 more source

Assessing the Origin of the COVID19 Pandemic Is Still Important

open access: yesFASEB BioAdvances, Volume 8, Issue 8, August 2026.
ABSTRACT The COVID19 pandemic, caused by the betacoronavirus SARS‐CoV‐2, now ranks among the world's deadliest plagues, and certainly one of the most significant public health crises in the last 100 years. Despite more than 6 years of investigating the pandemic, it has been difficult to assess the origin of the disease in the City of Wuhan, China.
Jason D. Bannan
wiley   +1 more source

Allies or adversaries? A reflection on the relationship between case officers and parent/carers

open access: yesSupport for Learning, Volume 41, Issue 3, August 2026.
Abstract The relationship between Special Educational Needs and Disabilities (SEND) case officers and parents/carers is central to the effective implementation of inclusive education policy, yet it is often characterised by tension, mistrust and conflicting expectations. By critically reflecting on whether SEND case officers and parents/carers function
Pooja Sharma
wiley   +1 more source

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