50 citations as recorded by crossref.

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2. Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. 10.1038/s41598-020-75710-5
3. Multivariate hydrological data assimilation of soil moisture and groundwater head D. Zhang et al. 10.5194/hess-20-4341-2016
4. Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product C. Emery et al. 10.5194/hess-22-2135-2018
5. Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models? Y. Sawada 10.5194/hess-24-3881-2020
6. Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions T. Chen et al. 10.1016/j.scitotenv.2021.150772
7. Development of a copula‐based particle filter ( C op PF ) approach for hydrologic data assimilation under consideration of parameter interdependence Y. Fan et al. 10.1002/2016WR020144
8. Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. 10.5194/hess-22-4251-2018
9. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions J. Musuuza et al. 10.3390/rs12050811
10. Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods Y. Fan et al. 10.1016/j.jhydrol.2017.05.010
11. Improving parameter and state estimation of a hydrological model with the ensemble square root filter N. Li et al. 10.1016/j.advwatres.2020.103813
12. Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset H. Liu et al. 10.1016/j.jhydrol.2018.11.047
13. Data assimilation in integrated hydrological modelling in the presence of observation bias J. Rasmussen et al. 10.5194/hess-20-2103-2016
14. Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations J. Zhang et al. 10.1029/2018WR022658
15. Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping A. Annis et al. 10.5194/hess-26-1019-2022
16. Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model M. Ciupak et al. 10.3390/w11102138
17. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model Z. Yaseen et al. 10.1016/j.jhydrol.2017.09.007
18. An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit 10.1175/MWR-D-16-0485.1
19. Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method Y. Fan et al. 10.1016/j.envsoft.2016.09.012
20. Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin K. Fang et al. 10.1016/j.advwatres.2019.05.008
21. Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter S. Wang et al. 10.1016/j.jhydrol.2017.09.052
22. HOBE: The Danish Hydrological Observatory K. Jensen & J. Refsgaard 10.2136/vzj2018.03.0059
23. TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model W. Kurtz et al. 10.5194/gmd-9-1341-2016
24. Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model H. Zhang et al. 10.1016/j.advwatres.2017.11.003
25. Real-time simulation of surface water and groundwater with data assimilation X. He et al. 10.1016/j.advwatres.2019.03.004
26. An application of the ensemble Kalman filter in epidemiological modelling R. Lal et al. 10.1371/journal.pone.0256227
27. Modeling Soil Processes: Review, Key Challenges, and New Perspectives H. Vereecken et al. 10.2136/vzj2015.09.0131
28. Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration Y. Sawada & R. Hanazaki 10.5194/hess-24-4777-2020
29. Impact of assimilating dam outflow measurements to update distributed hydrological model states: Localization for improving ensemble Kalman filter performance M. Khaniya et al. 10.1016/j.jhydrol.2022.127651
30. Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation K. Wu et al. 10.1117/1.JRS.12.016039
31. Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. 10.1002/wat2.1569
32. State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter H. Zhang et al. 10.5194/hess-21-4927-2017
33. Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows D. Gupta & C. Dhanya 10.1061/(ASCE)HE.1943-5584.0002072
34. Retrieval of Land Surface Model State Variables through Assimilating Screen Level Humidity and Temperature Measurements D. Ren & M. Xue 10.1155/2016/1905076
35. The influence of riverbed heterogeneity patterns on river-aquifer exchange fluxes under different connection regimes Q. Tang et al. 10.1016/j.jhydrol.2017.09.031
36. Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources W. Kurtz et al. 10.1016/j.envsoft.2017.03.011
37. Nonparametric Data Assimilation Scheme for Land Hydrological Applications M. Khaki et al. 10.1029/2018WR022854
38. Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
39. Assimilation of High‐Resolution Soil Moisture Data Into an Integrated Terrestrial Model for a Small‐Scale Head‐Water Catchment S. Gebler et al. 10.1029/2018WR024658
40. Implicitly Localized MCMC Sampler to Cope With Non-local/Non-linear Data Constraints in Large-Size Inverse Problems J. Brankart 10.3389/fams.2019.00058
41. Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
42. A data-driven study for evaluating the compressive strength of high-strength concrete Y. Wei et al. 10.1007/s13042-021-01407-4
43. Advances in understanding river-groundwater interactions P. Brunner et al. 10.1002/2017RG000556
44. Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy) M. Mazzoleni et al. 10.5194/hess-22-391-2018
45. An adaptive Gaussian process-based iterative ensemble smoother for data assimilation L. Ju et al. 10.1016/j.advwatres.2018.03.010
46. Hydrological reanalysis across the 20th century: A case study of the Amazon Basin S. Wongchuig et al. 10.1016/j.jhydrol.2019.01.025
47. Review of the Kalman-type hydrological data assimilation L. Sun et al. 10.1080/02626667.2015.1127376
48. Catchment tomography - An approach for spatial parameter estimation D. Baatz et al. 10.1016/j.advwatres.2017.06.006
49. Comparison of Two Data Assimilation Methods for Improving MODIS LAI Time Series for Bamboo Forests F. Mao et al. 10.3390/rs9050401
50. Assessing the Performance of Two Hydrologic Models for Forecasting Daily Streamflows in the Cazones River Basin (Mexico) F. González-Leiva et al. 10.4236/ojmh.2016.63014