60 citations as recorded by crossref.

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4. 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
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8. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective A. Rasheed et al. 10.1109/ACCESS.2020.2970143
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13. Calibration and uncertainty analysis of integrated SWAT-MODFLOW model based on iterative ensemble smoother method for watershed scale river-aquifer interactions assessment B. Yifru et al. 10.1007/s12145-023-01071-y
14. The importance of state transformations when using the ensemble Kalman filter for unsaturated flow modeling: Dealing with strong nonlinearities D. Erdal et al. 10.1016/j.advwatres.2015.09.008
15. Investigation of Data Assimilation Methods for Soil Parameter Estimation with Different Types of Data Y. Zha et al. 10.2136/vzj2019.01.0013
16. Spatiotemporal sampling strategy for characterization of hydraulic properties in heterogeneous soils D. Yu et al. 10.1007/s00477-020-01882-1
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20. A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity T. Xu et al. 10.1016/j.jhydrol.2020.125681
21. Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter F. Zovi et al. 10.1016/j.jhydrol.2017.02.056
22. A novel sampling-free algorithm for subsurface data assimilation using Gaussian process-derived sensitivities L. Ju et al. 10.1016/j.jconhyd.2021.103884
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24. Losses identification in water distribution networks through EnKF and ES V. Ruzza et al. 10.1080/23249676.2015.1032373
25. Predicting Thermal Performance of an Enhanced Geothermal System From Tracer Tests in a Data Assimilation Framework H. Wu et al. 10.1029/2021WR030987
26. Investigating the pilot point ensemble Kalman filter for geostatistical inversion and data assimilation J. Keller et al. 10.1016/j.advwatres.2021.104010
27. Comparison of Two Ensemble-Kalman Filter Based Methods for Estimating Aquifer Parameters from Real 3-D Hydraulic and Tracer Tomographic Tests E. Sánchez-León et al. 10.3390/geosciences10110462
28. A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+ S. Abbas et al. 10.5194/hess-28-21-2024
29. An iterative normal-score ensemble smoother for dealing with non-Gaussianity in data assimilation L. Li et al. 10.1016/j.jhydrol.2018.01.038
30. Formation compaction vs land subsidence to constrain rock compressibility of hydrocarbon reservoirs C. Zoccarato et al. 10.1016/j.gete.2017.12.002
31. Experimental sandbox tracer tests to characterize a two-facies aquifer via an ensemble smoother V. Todaro et al. 10.1007/s10040-023-02662-1
32. An adaptive Gaussian process-based iterative ensemble smoother for data assimilation L. Ju et al. 10.1016/j.advwatres.2018.03.010
33. On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling D. Yu et al. 10.5194/hess-23-2897-2019
34. Identification of hydraulic conductivity and groundwater contamination sources with an Unscented Kalman Smoother J. Li et al. 10.1007/s00477-024-02761-9
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36. Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter T. Xu & J. Gómez-Hernández 10.1016/j.advwatres.2017.12.011
37. Ensemble Kalman Filter Assimilation of ERT Data for Numerical Modeling of Seawater Intrusion in a Laboratory Experiment V. Bouzaglou et al. 10.3390/w10040397
38. Learning on dynamic statistical manifolds F. Boso & D. Tartakovsky 10.1098/rspa.2020.0213
39. Preconditioned Crank‐Nicolson Markov Chain Monte Carlo Coupled With Parallel Tempering: An Efficient Method for Bayesian Inversion of Multi‐Gaussian Log‐Hydraulic Conductivity Fields T. Xu et al. 10.1029/2020WR027110
40. Non-point contaminant source identification in an aquifer using the ensemble smoother with multiple data assimilation T. Xu et al. 10.1016/j.jhydrol.2021.127405
41. Ensemble smoother with multiple data assimilation for reverse flow routing V. Todaro et al. 10.1016/j.cageo.2019.06.002
42. Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area X. Chen et al. 10.1002/2012WR013285
43. Comparison of ensemble filtering algorithms and null-space Monte Carlo for parameter estimation and uncertainty quantification using CO2sequestration data R. Tavakoli et al. 10.1002/2013WR013959
44. Estimation of eruption source parameters for the 2021 La Soufrière eruption (St Vincent): implications for quantification of eruption magnitude on volcanic islands R. Constantinescu et al. 10.1144/SP539-2023-38
45. Estimation of hydraulic parameters in a heterogeneous low‐lying farmland near Venice E. Zancanaro et al. 10.1002/hyp.14791
46. Assessment of hydraulic conductivity distributions through assimilation of travel time data from ERT-monitored tracer tests E. Crestani et al. 10.1016/j.advwatres.2015.07.022
47. 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
48. Geophysical and hydrological data assimilation to monitor water content dynamics in the rocky unsaturated zone L. De Carlo et al. 10.1007/s10661-018-6671-x
49. Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy Z. Wang et al. 10.1016/j.jhydrol.2024.131320
50. Conditioning Multi-Gaussian Groundwater Flow Parameters to Transient Hydraulic Head and Flowrate Data With Iterative Ensemble Smoothers: A Synthetic Case Study D. Lam et al. 10.3389/feart.2020.00202
51. A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions J. White 10.1016/j.envsoft.2018.06.009
52. Research on groundwater system identification in use of hydrological signals processing technique C. Luo et al. 10.1088/1755-1315/344/1/012101
53. Losses Identification in Uncalibrated Water Distribution Networks: A Case Study V. Ruzza et al. 10.1016/j.proeng.2014.11.451
54. Simultaneous identification of a non-point contaminant source with Gaussian spatially distributed release and heterogeneous hydraulic conductivity in an aquifer using the LES-MDA method W. Zhang et al. 10.1016/j.jhydrol.2024.130745
55. An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions J. Zhang et al. 10.1002/2017WR020906
56. A novel framework for filling data gaps in groundwater level observations P. Oikonomou et al. 10.1016/j.advwatres.2018.06.008
57. Coupled and uncoupled hydrogeophysical inversions using ensemble Kalman filter assimilation of ERT‐monitored tracer test data M. Camporese et al. 10.1002/2014WR016017
58. Impact of sensor failure on the observability of flow dynamics at the Biosphere 2 LEO hillslopes D. Pasetto et al. 10.1016/j.advwatres.2015.04.014
59. Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter Z. Chen et al. 10.1007/s11004-021-09928-y
60. Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction C. Fagandini et al. 10.1007/s00477-024-02800-5