Results 271 to 280 of about 4,088,927 (359)
Abstract Global environmental changes have posed threats to ecosystems worldwide. Safeguarding terrestrial ecosystem health in particular is fundamental to achieving global sustainability targets, yet land degradation, carbon depletion and climate extremes continue to undermine resilience due to climate change and human activities.
Baijun Shang +3 more
wiley +1 more source
Gazing into the flames: A guide to assessing the impacts of climate change on landscape fire. [PDF]
Clarke H +7 more
europepmc +1 more source
Modeling Geoid and Dynamic Topography From Tomography‐Based Thermo‐Chemical Mantle Convection
Abstract Mantle convection causes the most important contribution to the geoid and dynamic topography. With mantle density inferred from high‐resolution tomography models and numerical methods solving the governing equations of viscous mantle flow, the modeled geoid can fit the observations well.
Ronghua Cui +2 more
wiley +1 more source
Projections of future climate for U.S. national assessments: past, present, future. [PDF]
Basile S +10 more
europepmc +1 more source
PrecipFusionNet: A Unified Deep Learning Model for Improving Numerical Precipitation Prediction
Abstract Numerical weather prediction (NWP) often suffers from substantial biases when forecasting extreme rainfall. Traditional corrections tend to underuse spatial information, while deep learning approaches typically struggle with data imbalance for rare events.
Ziyi Zhang, Huiling Yuan
wiley +1 more source
Skillful multiyear prediction of flood frequency along the US Northeast Coast using a high-resolution modeling system. [PDF]
Zhang L +10 more
europepmc +1 more source
Abstract Global Climate Models (GCMs) are essential for simulating past and future climates but suffer from systematic biases and coarse resolution, limiting direct applications. Bias correction (BC) and downscaling, using dynamical or statistical methods, address these issues. Quantile mapping (QM)‐based BC is widely used, yet it distorts dependencies,
Sachidananda Sharma +2 more
wiley +1 more source
Abstract The rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself.
Scott L. Painter, Georgia Destouni
wiley +1 more source
Future North Atlantic tropical cyclone intensities in modified historical environments. [PDF]
Lalo N +10 more
europepmc +1 more source

