57 citations as recorded by crossref.

1. Mapped Hydroclimatology of Evapotranspiration and Drainage Runoff Using SMAP Brightness Temperature Observations and Precipitation Information R. Akbar et al. https://doi.org/10.1029/2018WR024459
2. Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. https://doi.org/10.1038/s41598-020-75710-5
3. Multivariate hydrological data assimilation of soil moisture and groundwater head D. Zhang et al. https://doi.org/10.5194/hess-20-4341-2016
4. Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models? Y. Sawada https://doi.org/10.5194/hess-24-3881-2020
5. Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope A. Botto et al. https://doi.org/10.5194/hess-22-4251-2018
6. Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods Y. Fan et al. https://doi.org/10.1016/j.jhydrol.2017.05.010
7. Improving parameter and state estimation of a hydrological model with the ensemble square root filter N. Li et al. https://doi.org/10.1016/j.advwatres.2020.103813
8. Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations J. Zhang et al. https://doi.org/10.1029/2018WR022658
9. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model Z. Yaseen et al. https://doi.org/10.1016/j.jhydrol.2017.09.007
10. Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method Y. Fan et al. https://doi.org/10.1016/j.envsoft.2016.09.012
11. 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. https://doi.org/10.1016/j.advwatres.2019.05.008
12. Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto https://doi.org/10.3389/frwa.2022.948832
13. Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter S. Wang et al. https://doi.org/10.1016/j.jhydrol.2017.09.052
14. TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model W. Kurtz et al. https://doi.org/10.5194/gmd-9-1341-2016
15. Real-time simulation of surface water and groundwater with data assimilation X. He et al. https://doi.org/10.1016/j.advwatres.2019.03.004
16. Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration Y. Sawada & R. Hanazaki https://doi.org/10.5194/hess-24-4777-2020
17. Estimation of hydraulic parameters in a heterogeneous low‐lying farmland near Venice E. Zancanaro et al. https://doi.org/10.1002/hyp.14791
18. Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation K. Wu et al. https://doi.org/10.1117/1.JRS.12.016039
19. Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. https://doi.org/10.1002/wat2.1569
20. Development of a disaggregated multi-level factorial hydrologic data assimilation model F. Wang et al. https://doi.org/10.1016/j.jhydrol.2022.127802
21. The influence of riverbed heterogeneity patterns on river-aquifer exchange fluxes under different connection regimes Q. Tang et al. https://doi.org/10.1016/j.jhydrol.2017.09.031
22. Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model N. Brandhorst & I. Neuweiler https://doi.org/10.5194/hess-27-1301-2023
23. Using ensemble optimal interpolation with dynamic covariance matrices for assimilation of water level observations in a distributed rainfall-runoff-inundation model M. Khaniya et al. https://doi.org/10.1016/j.jhydrol.2025.134733
24. Implicitly Localized MCMC Sampler to Cope With Non-local/Non-linear Data Constraints in Large-Size Inverse Problems J. Brankart https://doi.org/10.3389/fams.2019.00058
25. 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. https://doi.org/10.5194/hess-22-391-2018
26. An adaptive Gaussian process-based iterative ensemble smoother for data assimilation L. Ju et al. https://doi.org/10.1016/j.advwatres.2018.03.010
27. Hydrological reanalysis across the 20th century: A case study of the Amazon Basin S. Wongchuig et al. https://doi.org/10.1016/j.jhydrol.2019.01.025
28. Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT+ O. Mohammadi Igder et al. https://doi.org/10.1016/j.jhydrol.2022.128012
29. Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product C. Emery et al. https://doi.org/10.5194/hess-22-2135-2018
30. Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions T. Chen et al. https://doi.org/10.1016/j.scitotenv.2021.150772
31. Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence Y. Fan et al. https://doi.org/10.1002/2016WR020144
32. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions J. Musuuza et al. https://doi.org/10.3390/rs12050811
33. Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset H. Liu et al. https://doi.org/10.1016/j.jhydrol.2018.11.047
34. Data assimilation in integrated hydrological modelling in the presence of observation bias J. Rasmussen et al. https://doi.org/10.5194/hess-20-2103-2016
35. 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
36. 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. https://doi.org/10.3390/w11102138
37. An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit https://doi.org/10.1175/MWR-D-16-0485.1
38. Land Surface Model Calibration Using Satellite Remote Sensing Data M. Khaki https://doi.org/10.3390/s23041848
39. Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany C. Hung et al. https://doi.org/10.1029/2021WR031549
40. 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. https://doi.org/10.1016/j.advwatres.2017.11.003
41. An application of the ensemble Kalman filter in epidemiological modelling R. Lal et al. https://doi.org/10.1371/journal.pone.0256227
42. Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter Y. Sun et al. https://doi.org/10.1029/2022WR033580
43. Modeling Soil Processes: Review, Key Challenges, and New Perspectives H. Vereecken et al. https://doi.org/10.2136/vzj2015.09.0131
44. Impact of assimilating dam outflow measurements to update distributed hydrological model states: Localization for improving ensemble Kalman filter performance M. Khaniya et al. https://doi.org/10.1016/j.jhydrol.2022.127651
45. State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter H. Zhang et al. https://doi.org/10.5194/hess-21-4927-2017
46. Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows D. Gupta & C. Dhanya https://doi.org/10.1061/(ASCE)HE.1943-5584.0002072
47. Retrieval of Land Surface Model State Variables through Assimilating Screen Level Humidity and Temperature Measurements D. Ren & M. Xue https://doi.org/10.1155/2016/1905076
48. Characterization of conductivity fields through iterative ensemble smoother and improved correlation-based adaptive localization C. Xia et al. https://doi.org/10.1016/j.jhydrol.2024.131054
49. Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources W. Kurtz et al. https://doi.org/10.1016/j.envsoft.2017.03.011
50. Nonparametric Data Assimilation Scheme for Land Hydrological Applications M. Khaki et al. https://doi.org/10.1029/2018WR022854
51. Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. https://doi.org/10.1002/2018WR022546
52. Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Y. Zhang et al. https://doi.org/10.3390/s24010035
53. Assimilation of High‐Resolution Soil Moisture Data Into an Integrated Terrestrial Model for a Small‐Scale Head‐Water Catchment S. Gebler et al. https://doi.org/10.1029/2018WR024658
54. Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. https://doi.org/10.5194/hess-25-3319-2021
55. A data-driven study for evaluating the compressive strength of high-strength concrete Y. Wei et al. https://doi.org/10.1007/s13042-021-01407-4
56. Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter Y. Li et al. https://doi.org/10.3390/rs15071852
57. Nordic contributions to stochastic methods in hydrology D. Rosbjerg et al. https://doi.org/10.2166/nh.2022.137