44 citations as recorded by crossref.

1. River discharge and bathymetry estimation from SWOT altimetry measurements K. Larnier et al. https://doi.org/10.1080/17415977.2020.1803858
2. Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale G. Piazzi et al. https://doi.org/10.1029/2020WR028390
3. Observation operators for assimilation of satellite observations in fluvial inundation forecasting E. Cooper et al. https://doi.org/10.5194/hess-23-2541-2019
4. Web data mining for automatic inventory of geohazards at national scale A. Battistini et al. https://doi.org/10.1016/j.apgeog.2013.06.012
5. Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations M. Trudel et al. https://doi.org/10.1016/j.jhydrol.2014.03.072
6. On operational flood forecasting system involving 1D/2D coupled hydraulic model and data assimilation S. Barthélémy et al. https://doi.org/10.1016/j.jhydrol.2018.05.007
7. Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting S. Barthélémy et al. https://doi.org/10.1051/e3sconf/20160718022
8. Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm V. Siqueira et al. https://doi.org/10.1590/2318-0331.011616086
9. Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. https://doi.org/10.5194/hess-25-4435-2021
10. Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. https://doi.org/10.1016/j.advwatres.2017.12.006
11. Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model M. Coustau et al. https://doi.org/10.5194/nhess-13-583-2013
12. Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations A. Van Wesemael et al. https://doi.org/10.1016/j.advwatres.2019.05.025
13. Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods M. Mazzoleni et al. https://doi.org/10.1080/02626667.2018.1430898
14. Efficient Data Assimilation Method for Optimizing Prediction of Flow Field and Sediment Transport in Open Channel M. Ahmed & S. Li https://doi.org/10.1002/rra.70092
15. Ensemble-based data assimilation for operational flood forecasting – On the merits of state estimation for 1D hydrodynamic forecasting through the example of the “Adour Maritime” river S. Barthélémy et al. https://doi.org/10.1016/j.jhydrol.2017.06.017
16. Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts T. Charoensuk et al. https://doi.org/10.1016/j.ejrh.2024.101737
17. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. https://doi.org/10.5194/hess-16-3863-2012
18. A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks L. Li & K. Jun https://doi.org/10.3390/w14091393
19. A hybrid data assimilation and machine learning approach for enhancing operational forecasting in 2D hydrodynamic models C. Cremer et al. https://doi.org/10.2166/hydro.2025.289
20. Operational hydrological data assimilation with the recursive ensemble Kalman filter H. McMillan et al. https://doi.org/10.5194/hess-17-21-2013
21. Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme Y. Li et al. https://doi.org/10.1002/2014WR016667
22. Data Assimilation in Hydrologic Routing: Impact of Model Error and Sensor Placement on Flood Forecasting M. Mazzoleni et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001656
23. Water Surface Elevation Constraints in a Data Assimilation Scheme to Infer Floodplain Topography: A Case Study in the Logone Floodplain A. Shastry & M. Durand https://doi.org/10.1029/2020GL088759
24. Correction of pumping station parameters in a one-dimensional hydrodynamic model using the Ensemble Kalman filter X. Lei et al. https://doi.org/10.1016/j.jhydrol.2018.10.062
25. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting Y. Li et al. https://doi.org/10.1016/j.jhydrol.2014.08.009
26. Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation A. Shastry et al. https://doi.org/10.1016/j.rse.2023.113556
27. Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method J. Shen et al. https://doi.org/10.1007/s00477-015-1074-9
28. On the assimilation of altimetric data in 1D Saint–Venant river flow models P. Brisset et al. https://doi.org/10.1016/j.advwatres.2018.06.004
29. Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. https://doi.org/10.1109/TGRS.2022.3147429
30. Estimating reach-averaged discharge for the River Severn from measurements of river water surface elevation and slope M. Durand et al. https://doi.org/10.1016/j.jhydrol.2013.12.050
31. Modular optimized data assimilation and support vector machine for hydrologic modeling M. Mehrparvar & K. Asghari https://doi.org/10.2166/hydro.2018.009
32. Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems A. Falck et al. https://doi.org/10.3390/rs13214459
33. Research on water level forecasting and hydraulic parameter calibration in the 1D open channel hydrodynamic model using data assimilation L. Kong et al. https://doi.org/10.1016/j.jhydrol.2023.129997
34. Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data R. Paiva et al. https://doi.org/10.1002/2014WR015618
35. Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon R. Paiva et al. https://doi.org/10.5194/hess-17-2929-2013
36. Observation impact, domain length and parameter estimation in data assimilation for flood forecasting E. Cooper et al. https://doi.org/10.1016/j.envsoft.2018.03.013
37. Enhancing SWOT discharge assimilation through spatiotemporal correlations Y. Yang et al. https://doi.org/10.1016/j.rse.2019.111450
38. An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. https://doi.org/10.1029/2022MS003259
39. A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states M. Mure-Ravaud et al. https://doi.org/10.1016/j.envsoft.2016.06.002
40. Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin J. Bai et al. https://doi.org/10.3390/w14244061
41. State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation J. Gong et al. https://doi.org/10.1016/j.jhydrol.2023.129450
42. Utilisation du modèle hydrodynamique 1D Mascaret pour la prévision des crues E. Le Pape et al. https://doi.org/10.1080/27678490.2024.2313198
43. Data‐based mechanistic modelling of tidally affected river reaches for flood warning purposes: an example on the River Dee, UK P. Smith et al. https://doi.org/10.1002/qj.1926
44. River discharge estimation under uncertainty from synthetic SWOT-type observations using variational data assimilation H. Oubanas et al. https://doi.org/10.1051/lhb/2018022