63 citations as recorded by crossref.

1. A Combined Optimization‐Assimilation Framework to Enhance the Predictive Skill of Community Land Model C. Zhang et al. https://doi.org/10.1029/2021WR029879
2. Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques D. Lu & D. Ricciuto https://doi.org/10.5194/gmd-12-1791-2019
3. Multiobjective adaptive surrogate modeling‐based optimization for parameter estimation of large, complex geophysical models W. Gong et al. https://doi.org/10.1002/2015WR018230
4. A Dynamic Baseline Calibration Procedure for CGE models J. Ziesmer et al. https://doi.org/10.1007/s10614-022-10248-4
5. Surrogate-based flowsheet model maintenance for Digital Twins B. Palotai et al. https://doi.org/10.1016/j.dche.2025.100228
6. A multi-objective adaptive surrogate modelling-based optimization algorithm for constrained hybrid problems R. Sun et al. https://doi.org/10.1016/j.envsoft.2021.105272
7. Reprint of: Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model J. Jefferson et al. https://doi.org/10.1016/j.cageo.2015.11.002
8. Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2016.12.008
9. Assessing MODIS carbon and water fluxes in grasslands and shrublands in semiarid regions using eddy covariance tower data Y. Li et al. https://doi.org/10.1080/01431161.2020.1811915
10. Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China L. Liu et al. https://doi.org/10.5194/hess-23-3335-2019
11. A review of parallel computing applications in calibrating watershed hydrologic models M. Asgari et al. https://doi.org/10.1016/j.envsoft.2022.105370
12. Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method Z. Di et al. https://doi.org/10.1016/j.atmosres.2019.04.011
13. Adaptive learning of effective dynamics for online modeling of complex systems I. Kičić et al. https://doi.org/10.1016/j.cma.2023.116204
14. Diet composition uncertainty determines impacts on fisheries following an oil spill H. Morzaria-Luna et al. https://doi.org/10.1016/j.ecoser.2018.05.002
15. Optimization of high resolution COSMO model performance over Switzerland and Northern Italy A. Voudouri et al. https://doi.org/10.1016/j.atmosres.2018.05.026
16. Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework G. Karagiannis et al. https://doi.org/10.3390/computation10050072
17. Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting Y. Gan et al. https://doi.org/10.1175/BAMS-D-15-00104.1
18. Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology A. Karmalkar et al. https://doi.org/10.1007/s00382-019-04617-3
19. Improving runoff simulation in the Western United States with Noah-MP and VIC models L. Su et al. https://doi.org/10.5194/hess-28-3079-2024
20. Machine learning in agricultural and applied economics H. Storm et al. https://doi.org/10.1093/erae/jbz033
21. On the applicability of surrogate‐based Markov chain Monte Carlo‐Bayesian inversion to the Community Land Model: Case studies at flux tower sites M. Huang et al. https://doi.org/10.1002/2015JD024339
22. Integrating fundamental model uncertainty in policy analysis J. Ziesmer et al. https://doi.org/10.1016/j.seps.2023.101591
23. Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers H. Wang & J. Xiao https://doi.org/10.3390/rs13040794
24. Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. https://doi.org/10.1029/2020WR028740
25. Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model Z. Di et al. https://doi.org/10.1007/s11430-016-9021-6
26. A review of the global soil property maps for Earth system models Y. Dai et al. https://doi.org/10.5194/soil-5-137-2019
27. Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models J. Li et al. https://doi.org/10.1002/2015MS000583
28. Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1 D. Xu et al. https://doi.org/10.5194/gmd-15-5021-2022
29. Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area Z. Di et al. https://doi.org/10.1007/s00382-017-3729-3
30. Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm C. Monteil et al. https://doi.org/10.5194/hess-24-3189-2020
31. A data-driven preference learning approach for multi-objective vehicle routing problems in last-mile delivery Z. Nourmohammadi et al. https://doi.org/10.1016/j.trc.2025.105101
32. Integration of Adaptive Emulators and Sensitivity Analysis for Enhancement of Complex Hydrological Models V. Budamala & A. Baburao Mahindrakar https://doi.org/10.1007/s40710-020-00468-x
33. Sensitivity analysis of the convective‐scale AROME model to physical and dynamical parameters M. Wimmer et al. https://doi.org/10.1002/qj.4239
34. Ten strategies towards successful calibration of environmental models J. Mai https://doi.org/10.1016/j.jhydrol.2023.129414
35. Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations Y. Qian et al. https://doi.org/10.1029/2018JD028927
36. A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. https://doi.org/10.3390/rs17030408
37. Process refinement contributed more than parameter optimization to improve the CoLM's performance in simulating the carbon and water fluxes in a grassland Y. Li et al. https://doi.org/10.1016/j.agrformet.2020.108067
38. Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling J. Li et al. https://doi.org/10.1002/joc.5428
39. MULTI-OBJECTIVE PARAMETER OPTIMIZATION AND UNCERTAINTY ASSESSMENT IN A LAND SURFACE MODEL BY MULTIPLE SATELLITES’ OBSERVATIONS S. NOMURA & Y. SAWADA https://doi.org/10.2208/jscejhe.77.2_I_1351
40. Multi-Objective Optimization of Laminated Functionally Graded Carbon Nanotube-Reinforced Composite Plates Using Deep Feedforward Neural Networks-NSGAII Algorithm D. Ly et al. https://doi.org/10.1142/S0219876221500651
41. A heterogeneous computing accelerated SCE-UA global optimization method using OpenMP, OpenCL, CUDA, and OpenACC G. Kan et al. https://doi.org/10.2166/wst.2017.322
42. Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6) R. Pathak et al. https://doi.org/10.3389/fclim.2021.670740
43. Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model J. Jefferson et al. https://doi.org/10.1016/j.cageo.2015.07.001
44. Multiobjective ensemble surrogate-based optimization algorithm for groundwater optimization designs M. Wu et al. https://doi.org/10.1016/j.jhydrol.2022.128159
45. An adaptive surrogate modeling-based sampling strategy for parameter optimization and distribution estimation (ASMO-PODE) W. Gong & Q. Duan https://doi.org/10.1016/j.envsoft.2017.05.005
46. How parameter specification of an Earth system model of intermediate complexity influences its climate simulations Y. Shi et al. https://doi.org/10.1186/s40645-019-0294-x
47. Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization D. Lu et al. https://doi.org/10.1002/2017MS001134
48. Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon S. Chinta & C. Balaji https://doi.org/10.1007/s00382-020-05288-1
49. Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters C. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.135424
50. Optimization of parameters in the surface-layer scheme of the Weather Research and Forecasting (WRF) model for near-surface wind and temperature J. Lee et al. https://doi.org/10.1016/j.atmosres.2026.109034
51. Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method C. Zhang et al. https://doi.org/10.3390/w12040943
52. Metaheuristic algorithms for groundwater model parameter inversion: Advances and prospects J. Chen & Z. Dai https://doi.org/10.1016/j.deepre.2024.100009
53. Evaluation of parameter interaction effect of hydrological models using the sparse polynomial chaos (SPC) method H. Wang et al. https://doi.org/10.1016/j.envsoft.2019.104612
54. Enhance the prediction of complex hydrological models by pseudo-simulators V. Budamala & A. Baburao Mahindrakar https://doi.org/10.1080/10106049.2019.1629646
55. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. https://doi.org/10.1016/j.jhydrol.2023.129944
56. Snow phenology-guided regionalization for parameter optimization: enhancing runoff simulation in an alpine basin Y. Shi et al. https://doi.org/10.1016/j.jhydrol.2026.135595
57. Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model Y. Qian et al. https://doi.org/10.1007/s00382-023-06977-3
58. In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics Y. Yang et al. https://doi.org/10.1029/2018WR024178
59. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. https://doi.org/10.3390/w11050910
60. Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization H. Baki et al. https://doi.org/10.1175/JAMC-D-21-0184.1
61. A stepwise surrogate model for parameter calibration of the Variable Infiltration Capacity model: the case of the upper Brahmaputra, Tibet Plateau H. Gu et al. https://doi.org/10.2166/hydro.2020.010
62. An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media J. Chen et al. https://doi.org/10.1029/2021WR030595
63. Water and heat coupling processes and its simulation in frozen soils: Current status and future research directions G. Hu et al. https://doi.org/10.1016/j.catena.2022.106844