33 citations as recorded by crossref.

1. The 3‐D Facies and Geomechanical Modeling of Land Subsidence in the Chaobai Plain, Beijing L. Zhu et al. 10.1029/2019WR027026
2. Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area X. Chen et al. 10.1002/2012WR013285
3. Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics P. Li et al. 10.1016/j.jhydrol.2020.124692
4. Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review C. Montzka et al. 10.3390/s121216291
5. A novel sampling-free algorithm for subsurface data assimilation using Gaussian process-derived sensitivities L. Ju et al. 10.1016/j.jconhyd.2021.103884
6. The elusive link between soil physics and catchment hydrology R. Sidle & H. Saito 10.1002/hyp.15200
7. Estimating spatially-variable rate constants of denitrification in irrigated agricultural groundwater systems using an Ensemble Smoother R. Bailey et al. 10.1016/j.jhydrol.2012.08.033
8. Simulating Flood‐Induced Riverbed Transience Using Unmanned Aerial Vehicles, Physically Based Hydrological Modeling, and the Ensemble Kalman Filter Q. Tang et al. 10.1029/2018WR023067
9. Using an ensemble smoother to evaluate parameter uncertainty of an integrated hydrological model of Yanqi basin N. Li et al. 10.1016/j.jhydrol.2015.07.024
10. Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation E. Crestani et al. 10.5194/hess-17-1517-2013
11. 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
12. Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter D. Erdal & O. Cirpka 10.5194/hess-20-555-2016
13. A novel framework for filling data gaps in groundwater level observations P. Oikonomou et al. 10.1016/j.advwatres.2018.06.008
14. On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments A. Thiboult & F. Anctil 10.1016/j.jhydrol.2015.09.036
15. Model-data interaction in groundwater studies: Review of methods, applications and future directions M. Rajabi et al. 10.1016/j.jhydrol.2018.09.053
16. Mapping of hydraulic transmissivity field from inversion of tracer test data using convolutional neural networks. CNN-2T M. Vu & A. Jardani 10.1016/j.jhydrol.2022.127443
17. Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry P. Li et al. 10.1016/j.geoderma.2020.114432
18. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation H. Chang et al. 10.1016/j.advwatres.2016.12.001
19. Detection of potential leakage pathways from geological carbon storage by fluid pressure data assimilation A. González-Nicolás et al. 10.1016/j.advwatres.2015.10.006
20. 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
21. 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
22. An adaptive Gaussian process-based iterative ensemble smoother for data assimilation L. Ju et al. 10.1016/j.advwatres.2018.03.010
23. Advances in understanding river‐groundwater interactions P. Brunner et al. 10.1002/2017RG000556
24. Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation S. Jiang et al. 10.3390/w14050757
25. Data assimilation in groundwater modelling: ensemble Kalman filter versus ensemble smoothers L. Li et al. 10.1002/hyp.13127
26. Experimental sandbox tracer tests to characterize a two-facies aquifer via an ensemble smoother V. Todaro et al. 10.1007/s10040-023-02662-1
27. Groundwater Simulations and Uncertainty Analysis Using MODFLOW and Geostatistical Approach with Conditioning Multi-Aquifer Spatial Covariance Y. Lin et al. 10.3390/w9030164
28. Characterisation of river–aquifer exchange fluxes: The role of spatial patterns of riverbed hydraulic conductivities Q. Tang et al. 10.1016/j.jhydrol.2015.08.019
29. Catchment tomography - An approach for spatial parameter estimation D. Baatz et al. 10.1016/j.advwatres.2017.06.006
30. 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
31. Soil hydrology: Recent methodological advances, challenges, and perspectives H. Vereecken et al. 10.1002/2014WR016852
32. Comparison of Two Ensemble Kalman-Based Methods for Estimating Aquifer Parameters from Virtual 2-D Hydraulic and Tracer Tomographic Tests E. Sánchez-León et al. 10.3390/geosciences10070276
33. Data assimilation in integrated hydrological modelling in the presence of observation bias J. Rasmussen et al. 10.5194/hess-20-2103-2016