60 citations as recorded by crossref.

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3. Copula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain R. Mousavian et al. https://doi.org/10.1007/s10291-020-01044-4
4. Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data C. Lorenz et al. https://doi.org/10.3390/rs10111842
5. A stepwise-clustered copula downscaling approach for ensemble analyses of discrete and interactive features in precipitation-extreme variations: a case study for eastern China B. Yu et al. https://doi.org/10.1007/s00382-024-07260-9
6. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. https://doi.org/10.1016/j.jhydrol.2023.129199
7. Copula‐based downscaling of daily precipitation fields M. Lorenz et al. https://doi.org/10.1002/hyp.13271
8. Comparison of Three Techniques to Adjust Daily Precipitation Biases from Regional Climate Models over Germany C. Ibebuchi et al. https://doi.org/10.3390/w14040600
9. A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling R. Mehrotra & A. Sharma https://doi.org/10.1175/JCLI-D-15-0356.1
10. Climate-induced hydrological impact mitigated by a high-density reservoir network in the Poyang Lake Basin N. Dong et al. https://doi.org/10.1016/j.jhydrol.2019.124148
11. Bias correction of daily precipitation for ungauged locations using geostatistical approaches: A case study for the CORDEX‐Africa ensemble M. Lorenz et al. https://doi.org/10.1002/joc.7649
12. A software toolkit for correcting systematic biases in climate model simulations R. Mehrotra et al. https://doi.org/10.1016/j.envsoft.2018.02.010
13. Multivariate stochastic bias corrections with optimal transport Y. Robin et al. https://doi.org/10.5194/hess-23-773-2019
14. Validation of Surface Temperature Derived From MERRA‐2 Reanalysis Against IMD Gridded Data Set Over India P. Gupta et al. https://doi.org/10.1029/2019EA000910
15. A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall A. Mamalakis et al. https://doi.org/10.1002/2016WR019578
16. A comparative study to determine the optimal copula model for the wind speed and precipitation of typhoons M. Um et al. https://doi.org/10.1002/joc.4834
17. A new multi-site multi-variable stochastic model with inter-site and inter-variable correlations, low frequency attributes and stochasticity: A case study in the lower Yellow River basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2021.126365
18. Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework M. Ben Alaya et al. https://doi.org/10.1007/s00382-017-3578-0
19. Satellite-based streamflow simulation using CHIRPS satellite precipitation product in Shah Bahram Basin, Iran S. Mokhtari et al. https://doi.org/10.1007/s11600-021-00724-0
20. Spatiotemporal bias correction of satellite precipitation products using multimodel techniques over temporally coherent clusters in South Peninsular India S. M R et al. https://doi.org/10.1016/j.atmosres.2025.108244
21. Spatiotemporal variability and empirical Copula-based dependence structure of modeled and observed coupled water and energy fluxes M. Soltani et al. https://doi.org/10.2166/nh.2018.163
22. Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges B. Haese et al. https://doi.org/10.1002/2017WR021015
23. Probable Maximum Precipitation: Its Estimation and Uncertainty Quantification Using Bivariate Extreme Value Analysis M. Ben Alaya et al. https://doi.org/10.1175/JHM-D-17-0110.1
24. Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand S. Wuthiwongtyohtin https://doi.org/10.2166/wcc.2020.021
25. Copula-based interpolation methods for air temperature data using collocated covariates F. Alidoost et al. https://doi.org/10.1016/j.spasta.2018.08.003
26. Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter M. Vrac et al. https://doi.org/10.1002/2015JD024511
27. Stepwise cluster ensemble downscaling for drought projection under climate change Y. Wen et al. https://doi.org/10.1002/joc.7977
28. Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations C. Kusumastuti et al. https://doi.org/10.1016/j.hydroa.2024.100193
29. Impact of climate change on water resources of upper Kharun catchment in Chhattisgarh, India N. Kumar et al. https://doi.org/10.1016/j.ejrh.2017.07.008
30. Physics-constrained deep learning bias correction of CMIP6 solar radiation over Africa and its implications for solar power planning in a changing climate P. Adigun et al. https://doi.org/10.1016/j.renene.2025.124658
31. Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction M. Vrac https://doi.org/10.5194/hess-22-3175-2018
32. Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression R. Deo et al. https://doi.org/10.1016/j.renene.2022.12.048
33. Adjusting climate model bias for agricultural impact assessment: How to cut the mustard S. Galmarini et al. https://doi.org/10.1016/j.cliser.2019.01.004
34. Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions R. Maity et al. https://doi.org/10.1175/JHM-D-18-0126.1
35. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. https://doi.org/10.2166/wcc.2023.230
36. A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation M. Cantalejo et al. https://doi.org/10.1038/s41598-024-76848-2
37. TIN‐Copula bias‐correction method for model‐derived maximum temperature in the MENA region G. Lazoglou et al. https://doi.org/10.1002/joc.7364
38. Assessment of enhanced Kohonen self-organizing map, quantile mapping and copula-based bias-correction approaches for constructing basin-scale rainfall forecasts A. Khatun et al. https://doi.org/10.1080/02626667.2022.2109972
39. Bias correction of daily precipitation from climate models, using the Q‐GAM method G. Lazoglou et al. https://doi.org/10.1002/env.2881
40. Correcting systematic biases across multiple atmospheric variables in the frequency domain H. Nguyen et al. https://doi.org/10.1007/s00382-018-4191-6
41. Vine Copula Ensemble Downscaling for Precipitation Projection Over the Loess Plateau Based on High‐Resolution Multi‐RCM Outputs C. Sun et al. https://doi.org/10.1029/2020WR027698
42. Enhancing flash flood susceptibility modeling in arid regions: integrating digital soil mapping and machine learning algorithms Z. Sheikh et al. https://doi.org/10.1007/s12665-025-12140-4
43. Multivariate copula quantile mapping for bias correction of reanalysis air temperature data F. Alidoost et al. https://doi.org/10.1080/14498596.2019.1601138
44. Hydrologic Impacts of Surface Elevation and Spatial Resolution in Statistical Correction Approaches: Case Study of Flumendosa Basin, Italy E. Perra et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001969
45. Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers J. Hnilica et al. https://doi.org/10.5194/hess-23-1741-2019
46. Bias correction of daily precipitation over South Korea from the long-term reanalysis using a composite Gamma-Pareto distribution approach D. Kim et al. https://doi.org/10.2166/nh.2019.127
47. Precipitation Bias Correction: A Novel Semi‐parametric Quantile Mapping Method C. Rajulapati & S. Papalexiou https://doi.org/10.1029/2023EA002823
48. Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings D. Robertson et al. https://doi.org/10.1016/j.jhydrol.2023.129322
49. Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al. https://doi.org/10.5194/hess-29-2023-2025
50. Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products M. Soltani et al. https://doi.org/10.3390/rs13050853
51. Gaussian Copula Method for Bias Correction of Daily Precipitation Generated by a Dynamical Model M. Kim et al. https://doi.org/10.1175/JAMC-D-18-0089.1
52. Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall D. Kim et al. https://doi.org/10.3390/atmos12020191
53. The use of bivariate copulas for bias correction of reanalysis air temperature data F. Alidoost et al. https://doi.org/10.1371/journal.pone.0216059
54. Estimating multisite precipitation by a stepwise NHMM-VAR model considering the spatiotemporal correlations of precipitation amounts X. Zha et al. https://doi.org/10.1016/j.jhydrol.2022.128065
55. Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes P. Ear et al. https://doi.org/10.3390/atmos16040480
56. Future high- and low-flow estimations for Central Vietnam: a hydro-meteorological modelling chain approach Q. Dang et al. https://doi.org/10.1080/02626667.2017.1353696
57. Bias Correction of Climate Model’s Precipitation Using the Copula Method and Its Application in River Basin Simulation G. Lazoglou et al. https://doi.org/10.3390/w11030600
58. Evaluation of a New Statistical Method—TIN-Copula–for the Bias Correction of Climate Models’ Extreme Parameters G. Lazoglou et al. https://doi.org/10.3390/atmos11030243
59. Precipitation-based climate change hotspots across India through a Multi-model assessment from CMIP6 S. Sarkar et al. https://doi.org/10.1016/j.jhydrol.2023.129805
60. Bias Correction of Ocean Bottom Temperature and Salinity Simulations From a Regional Circulation Model Using Regression Kriging J. Chang et al. https://doi.org/10.1029/2020JC017140