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https://doi.org/10.5194/egusphere-2025-1708
https://doi.org/10.5194/egusphere-2025-1708
30 Apr 2025
 | 30 Apr 2025

Improving Streamflow Simulation through Machine Learning-Powered Data Integration and Its Implications for Forecasting in the Western U.S.

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

Abstract. Accurate streamflow forecasts are crucial but remain challenging for the arid Western United States (U.S.). Recently, machine learning methods such as long short-term memory (LSTM) have exhibited high accuracy in streamflow simulation and strong abilities to integrate observations to enhance performance. This study evaluated an LSTM-based data integration approach that incorporates streamflow (Q) and snow water equivalent (SWE) observations to improve streamflow estimations across different lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over hundreds of basins in the Western U.S. Integrating Q at the daily scale provided the greatest improvements, increasing the median Kling-Gupta Efficiency (KGE) of 646 basins from 0.80 to 0.96 when integrating 1-day lagged Q, and remaining at 0.89 even with a 10-day lag. Integrating Q at the monthly scale also enhanced streamflow estimations, though to a lesser extent than at the daily scale, with the median KGE rising from 0.80 to 0.86 when integrating 1-month lagged streamflow. The next most notable improvement resulted from integrating SWE at the monthly scale, where the median KGE improved to 0.86 when integrating 1-month lagged SWE. Furthermore, SWE integration showed greater benefits at the monthly scale in snow-dominated basins during snowmelt season, which was beneficial for spring-summer flow estimations. However, integrating SWE at the daily scale did not show improvements. These results highlight the potential of this LSTM-based data integration approach for both short-term and long-term streamflow forecasting due to its performance, automation and efficiency.

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Journal article(s) based on this preprint

21 Oct 2025
Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S.
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
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

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1708', Anonymous Referee #1, 02 Jun 2025
    • AC1: 'Reply on RC1', Yuan Yang, 04 Aug 2025
  • RC2: 'Comment on egusphere-2025-1708', Anonymous Referee #2, 23 Jun 2025
    • AC2: 'Reply on RC2', Yuan Yang, 04 Aug 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1708', Anonymous Referee #1, 02 Jun 2025
    • AC1: 'Reply on RC1', Yuan Yang, 04 Aug 2025
  • RC2: 'Comment on egusphere-2025-1708', Anonymous Referee #2, 23 Jun 2025
    • AC2: 'Reply on RC2', Yuan Yang, 04 Aug 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (08 Aug 2025) by Xing Yuan
AR by Yuan Yang on behalf of the Authors (16 Aug 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (18 Aug 2025) by Xing Yuan
RR by Anonymous Referee #2 (26 Aug 2025)
RR by Anonymous Referee #1 (02 Sep 2025)
ED: Publish as is (04 Sep 2025) by Xing Yuan
AR by Yuan Yang on behalf of the Authors (09 Sep 2025)

Journal article(s) based on this preprint

21 Oct 2025
Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S.
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
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
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

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Latest update: 21 Oct 2025
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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 days, 1–6 months) and timescales (daily and monthly) over Western U.S. 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 U.S.
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