Results 171 to 180 of about 3,060 (251)

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

Water 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, Bin Yong, Gerald Corzo, Yang Hong, Ruochen Sun, Xin Tao, Jiahu Wang, Qingyun Duan   +7 more
wiley   +1 more source

Watershed-scale effects of tallgrass prairie reconstruction: 30-Year trends in streamflow, nitrate, and sediment in Walnut Creek, Iowa. [PDF]

J Environ Qual
Anderson ES, Schilling KE, Cole KJ, Wacha KM.   +3 more
europepmc   +1 more source

Reconstructing Long‐Term Reservoir Storage in India Using Hydrological Modeling and Machine Learning

Water Resources Research, Volume 62, Issue 8, August 2026.
Abstract India has one of the largest networks of large dams in the world, where reservoirs play a crucial role in regulating monsoon‐driven water availability for irrigation, hydropower, flood control, and water supply. However, long‐term daily reservoir storage observations are available only from the year 2000 onwards.
Urmin Vegad, Vimal Mishra
wiley   +1 more source

Optimizing pesticide water monitoring: balancing accuracy and efficiency using composite sampling and SEAWAVE-QEX. [PDF]

Environ Monit Assess
Jacobson A, Stone Z, Krueger R, Brain R.
europepmc   +1 more source

Increasing the Spatial and Temporal Resolution of Snow Water Equivalent Estimates Using SNOTEL Data and Low‐Cost Sensing

JAWRA Journal of the American Water Resources Association, Volume 62, Issue 4, August 2026.
ABSTRACT Accurate estimation of snow water equivalent (SWE) is critical for understanding hydrologic processes in snow‐dominated watersheds. This study presents a method for improving the spatial and temporal resolution of SWE estimates using low‐cost snow depth sensors, manual snow sampling, and snow density data from nearby SNOwpack TELemetry (SNOTEL)
Braedon Dority, Jeffery S. Horsburgh
wiley   +1 more source

Asymmetry in snow-water nexus in mountain areas mainly governed by meteorological seasonal changes. [PDF]

Sci Rep
Pimentel R, Notarnicola C.
europepmc   +1 more source

RNSTM: A Network‐Link Approach to Streamflow Temperature Modeling

JAWRA Journal of the American Water Resources Association, Volume 62, Issue 4, August 2026.
ABSTRACT This work presents the development and validation of the River Network Streamflow Temperature Model (RNSTM), which solves the energy balance equations at the air‐water interface within the channels to estimate their temperature. RNSTM considers solar radiation, net longwave radiation, evaporative heat flux, and convective heat transfer ...
Valeria García‐Múnera, Nicolas Velásquez Giron, Ruben D. Molina, Larry J. Weber, Witold F. Krajewski   +4 more
wiley   +1 more source

Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction. [PDF]

Sci Rep
Apak S, Kilinc HC, Yurtsever A, Haznedar H, Ozkan F.   +4 more
europepmc   +1 more source

Estimating Baseline Flow Resistance Parameters for Remote Sensing River Discharge (RSQ) From River Morphology Characteristics

JAWRA Journal of the American Water Resources Association, Volume 62, Issue 4, August 2026.
ABSTRACT This study demonstrates that flow resistance in rivers, quantified via calibration of a modified Manning's equation, can inform its estimation from channel hydrometric information available from regional or global datasets for a limited set (N = 14) of rigorously calibrated river reaches in Alaska and with less certainty in a large set (N ...
David M. Bjerklie, Merritt E. Harlan, Ethan J. Shavers, Craig B. Brinkerhoff, Lawrence V. Stanislawski, J. Toby Minear, Robert W. Dudley   +6 more
wiley   +1 more source

Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning. [PDF]

PLoS One
Zhang Z, Xiao Y, Chen B, Long K, Liang F, Liao J.   +5 more
europepmc   +1 more source