Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-635-2017
https://doi.org/10.5194/hess-21-635-2017
Research article
 | 
31 Jan 2017
Research article |  | 31 Jan 2017

Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States

Chengcheng Huang, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, and Xiaogu Zheng

Related authors

Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS experiments
Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, and Naoki Mizukami
EGUsphere, https://doi.org/10.5194/egusphere-2025-38,https://doi.org/10.5194/egusphere-2025-38, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Evaluating an Earth system model from a water manager perspective
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andy Wood
Hydrol. Earth Syst. Sci., 29, 1117–1133, https://doi.org/10.5194/hess-29-1117-2025,https://doi.org/10.5194/hess-29-1117-2025, 2025
Short summary
Skilful probabilistic predictions of UK floods months ahead using machine learning models trained on multimodel ensemble climate forecasts
Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood
EGUsphere, https://doi.org/10.31223/X5X405,https://doi.org/10.31223/X5X405, 2024
Short summary
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279,https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
Short summary
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024,https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025,https://doi.org/10.5194/hess-29-1277-2025, 2025
Short summary
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025,https://doi.org/10.5194/hess-29-1061-2025, 2025
Short summary
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025,https://doi.org/10.5194/hess-29-1033-2025, 2025
Short summary
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025,https://doi.org/10.5194/hess-29-903-2025, 2025
Short summary
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025,https://doi.org/10.5194/hess-29-785-2025, 2025
Short summary

Cited articles

Anderson, E.: Calibration of conceptual hydrologic models for use in river forecasting, Office of Hydrologic Development, US National Weather Service, Silver Spring, MD, 2002.
Anderson, E. A.: “NWSRFS Forecast Procedures”, NOAA Technical Memorandum, NWS HYDRO-14, Office of Hydrologic Development, Hydrology Laboratory, NWS/NOAA, Silver Spring, MD, 1972.
Anderson, E. A.: National Weather Service River Forecast System: Snow accumulation and ablation model, 17 US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1973.
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, 2006.
Arheimer, B., Lindström, G., and Olsson, J.: A systematic review of sensitivities in the Swedish flood-forecasting system, Atmos. Res., 100, 275–284, https://doi.org/10.1016/j.atmosres.2010.09.013, 2011.
Download
Short summary
This study examined the potential of snow water equivalent data assimilation to improve seasonal streamflow predictions. We examined aspects of the data assimilation system over basins with varying climates across the western US. We found that varying how the data assimilation system is implemented impacts forecast performance, and basins with good initial calibrations see less benefit. This implies that basin-specific configurations and benefits should be expected given this modeling system.
Share