Results 231 to 240 of about 208,503 (301)

Event‐Type‐Based Multi‐Dimensional Diagnostics of Process Limitations in Hydrological Models

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Aggregated evaluation metrics and overlooked hydrological process variability in individual streamflow events hinder understanding of how well hydrological processes are encoded in models. This study introduces a novel event‐type‐based multi‐dimensional diagnostic framework to enhance model performance assessment and to identify process ...
Zhenyu Wang, Larisa Tarasova, Ralf Merz
wiley   +1 more source

Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology. [PDF]

Nat Commun
Jennings KS, Collins M, Hatchett BJ, Heggli A, Hur N, Tonino S, Nolin AW, Yu G, Zhang W, Arienzo MM.   +9 more
europepmc   +1 more source

Quantifying and Regionalizing Land Use Impacts on Catchment Response Times With High‐Frequency Observations

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Land use and land cover change (LUCC) can affect the hydrological response time of rivers. However, it is difficult to generate robust and quantitative evidence of this impact at the catchment scale. This lack of evidence also affects the development of rainfall‐runoff models to make ex‐ante predictions.
Anthony C. Ross, Boris Ochoa‐Tocachi, Vivien Bonnesoeur, Braulio Lahuatte, Paola Fuentes, Javier Antiporta, Mauricio F. Villazon, Wouter Buytaert   +7 more
wiley   +1 more source

A multi-task deep learning approach for landslide displacement prediction with applications in early warning systems. [PDF]

Sci Rep
Strnad D, Mongus D, Horvat Š, Šegina E.   +3 more
europepmc   +1 more source

A Diagnostic Framework and Data Inventory to Analyze Human Intervention on Streamflow

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Growing recognition of human impacts on streamflow regimes has driven efforts to integrate water‐management modules into hydrological models to improve simulation accuracy. Yet data constraints often force simplifying assumptions, which may introduce unintended biases and obscure true human influences.
Anav Vora, Ximing Cai
wiley   +1 more source

Research on the robustness of the open-world test-time training model. [PDF]

Front Artif Intell
Pi S, Wang X, Pi J.
europepmc   +1 more source

Investigating Deep Learning Knowledge Transfer in Streamflow Prediction From Global to Local Catchment

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Accurate streamflow prediction is critical for flood forecasting and water resource management, particularly in data‐scarce regions. Deep learning models like Long Short‐Term Memory (LSTM) offer a bridge to hydrologic regionalization utilizing climate data and catchment characteristics to improve behavioral insights and constrain predictive ...
Jamal Hassan Ougahi, John S. Rowan
wiley   +1 more source

Talent-transfer as a catalyst for winter-sport success: a mixed-methods empirical research of china's 2022 olympic campaign. [PDF]

BMC Sports Sci Med Rehabil
Zhang Y, Duan B, Wang J, You Y, Qin L, Xie Y.   +5 more
europepmc   +1 more source

Evaluating the Performance of Uni‐ and Multivariate Bias Correction Techniques: Challenges in Preserving Temporal and Dependence Structures

Water Resources Research, Volume 62, Issue 2, February 2026.
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, Akash Singh Raghuvanshi, Ankit Agarwal   +2 more
wiley   +1 more source