31 citations as recorded by crossref.

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3. GFPLAIN and Multi-Source Data Assimilation Modeling: Conceptualization of a Flood Forecasting Framework Supported by Hydrogeomorphic Floodplain Rapid Mapping A. Annis & F. Nardi https://doi.org/10.3390/hydrology8040143
4. Study of multi-source river discharge data assimilation in collaboration with EKF and MIKE11HD Z. Yinqin et al. https://doi.org/10.1080/02626667.2025.2492884
5. Exploiting the proliferation of current and future satellite observations of rivers G. Schumann & A. Domeneghetti https://doi.org/10.1002/hyp.10825
6. A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products M. Ghazali et al. https://doi.org/10.1080/02626667.2018.1558365
7. Chained hydrologic-hydraulic for flood modeling by assimilating SAR-derived flood extent and FFSAR-processed altimetry data T. Nguyen et al. https://doi.org/10.1016/j.jhydrol.2025.134013
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10. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. https://doi.org/10.1029/2022RG000788
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12. Potential in improving monthly streamflow forecasting through variational assimilation of observed streamflow A. Mazrooei et al. https://doi.org/10.1016/j.jhydrol.2021.126559
13. Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping A. Annis et al. https://doi.org/10.5194/hess-26-1019-2022
14. Towards Digital Twin in Flood Forecasting with Data Assimilation Satellite Earth Observations—A Proof-of-Concept T. Nguyen et al. https://doi.org/10.3390/rs18050685
15. Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation J. Travert et al. https://doi.org/10.5194/nhess-26-2387-2026
16. Data assimilation of flood maps in a 2D flood model for different performance measures J. Travert et al. https://doi.org/10.1007/s10596-026-10452-3
17. A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation A. Dasgupta et al. https://doi.org/10.1029/2020WR027859
18. Improving indoor air flow and temperature prediction with local measurements based on CFD-EnKF data assimilation W. Qian et al. https://doi.org/10.1016/j.buildenv.2022.109511
19. Assimilation of Satellite Flood Likelihood Data Improves Inundation Mapping From a Simulation Library System H. Hooker et al. https://doi.org/10.1002/met.70104
20. Comparative performance evaluation of DeepONet architectures for dam-break hydrodynamic simulations L. Gu & X. Lai https://doi.org/10.1016/j.wse.2026.01.004
21. Water quality modelling based on 4-dimensional variational assimilation method in the Nanfei River, Lake Chaohu Basin^* L. Xijun & H. Guojian https://doi.org/10.18307/2021.0513
22. Optimizing measurement schemes to improve indoor airflow and temperature CFD–EnKF joint simulation W. Qian et al. https://doi.org/10.1016/j.buildenv.2023.111070
23. A Data Assimilation-Informed Deep Learning Framework for Transient Turbulent Urban Wind Field Prediction: Integrating Sparse High-Fidelity Data and Low-Resolution Simulations J. Huang et al. https://doi.org/10.1016/j.scs.2026.107407
24. AI-Empowered Latent Four-dimensional Variational Data Assimilation for River Discharge Forecasting K. Wang et al. https://doi.org/10.1109/JSTARS.2025.3611136
25. Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations T. Nguyen et al. https://doi.org/10.1109/TGRS.2023.3338296
26. Hydrometeorological Extreme Events in Africa: The Role of Satellite Observations for Monitoring Pluvial and Fluvial Flood Risk M. Gosset et al. https://doi.org/10.1007/s10712-022-09749-6
27. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges S. Grimaldi et al. https://doi.org/10.1007/s10712-016-9378-y
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31. On the Impacts of Observation Location, Timing, and Frequency on Flood Extent Assimilation Performance A. Dasgupta et al. https://doi.org/10.1029/2020WR028238