Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-709-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-709-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Image-based classification of stream stage to support ephemeral stream monitoring
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Garrett McGurk
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Anahita Jensen
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Fred Martin Ralph
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
Morgan C. Levy
Scripps Institution of Oceanography, University of California San Diego, San Diego, California, USA
School of Global Policy and Strategy, University of California San Diego, San Diego, California, USA
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Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
Hydrol. Earth Syst. Sci., 29, 5453–5476, https://doi.org/10.5194/hess-29-5453-2025, https://doi.org/10.5194/hess-29-5453-2025, 2025
Short summary
Short summary
We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 d, 1–6 months) and timescales (daily and monthly) over Western US basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western US.
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph
The Cryosphere, 18, 121–137, https://doi.org/10.5194/tc-18-121-2024, https://doi.org/10.5194/tc-18-121-2024, 2024
Short summary
Short summary
We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
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Short summary
Intermittent streams are vital to ecosystems and water supply, but are hard to monitor and increasingly affected by climate change. To address this, we used field camera images from 2017 to 2023 at a stream in northern California to train a machine learning model that classifies streamflow as dry, low, or high. This low-cost method enables monitoring of changing intermittent stream conditions and supports water management in data-scarce regions.
Intermittent streams are vital to ecosystems and water supply, but are hard to monitor and...