24 citations as recorded by crossref.

1. Improving multisite precipitation generators based on generalised linear models J. Wessel & R. Chandler https://doi.org/10.5194/ascmo-12-149-2026
2. A sensitivity-based analysis for managing storage capacity of a small agricultural reservoir under drying climate D. Kim et al. https://doi.org/10.1016/j.agwat.2018.10.040
3. Comparison of scenario‐neutral approaches for estimation of climate change impacts on flood characteristics L. Keller et al. https://doi.org/10.1002/hyp.13341
4. Revealing the impacts of climate change on mountainous catchments through high-resolution modelling J. Moraga et al. https://doi.org/10.1016/j.jhydrol.2021.126806
5. Multisite Weather Generators Using Bayesian Networks: An Illustrative Case Study for Precipitation Occurrence M. Legasa & J. Gutiérrez https://doi.org/10.1029/2019WR026416
6. Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model C. Gao et al. https://doi.org/10.1016/j.jhydrol.2020.125337
7. Testing a weather generator for downscaling climate change projections over Switzerland D. Keller et al. https://doi.org/10.1002/joc.4750
8. Impact of different weather generator scenarios on extreme flood estimates in Switzerland E. Kritidou et al. https://doi.org/10.1007/s00477-024-02843-8
9. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment J. Gutiérrez et al. https://doi.org/10.1002/joc.5462
10. Coupled annual and daily multivariate and multisite stochastic weather generator to preserve low- and high-frequency variability to assess climate vulnerability K. Ahn https://doi.org/10.1016/j.jhydrol.2019.124443
11. A Continuous Multisite Multivariate Generator for Daily Temperature Conditioned by Precipitation Occurrence J. Hernández-Bedolla et al. https://doi.org/10.3390/w14213494
12. Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment M. Widmann et al. https://doi.org/10.1002/joc.6024
13. The VALUE perfect predictor experiment: Evaluation of temporal variability D. Maraun et al. https://doi.org/10.1002/joc.5222
14. A climate stress testing method for changes in spatially variable rainfall D. McInerney et al. https://doi.org/10.1016/j.jhydrol.2023.129876
15. Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator N. Peleg et al. https://doi.org/10.1016/j.jhydrol.2019.02.010
16. Improving a stochastic multi-site generation model of daily rainfall using discrete wavelet de-noising: a case study to a semi-arid region S. Hellassa & D. Souag-Gamane https://doi.org/10.1007/s12517-018-4168-0
17. Evaluating the added value of the new Swiss climate scenarios for hydrology: An example from the Thur catchment O. Rössler et al. https://doi.org/10.1016/j.cliser.2019.01.001
18. Parametric gridded weather generator for use in present and future climates: focus on spatial temperature characteristics M. Dubrovsky et al. https://doi.org/10.1007/s00704-019-03027-z
19. Comprehensive evaluation of an improved large‐scale multi‐site weather generator for Germany V. Nguyen et al. https://doi.org/10.1002/joc.7107
20. A gridded multi-site precipitation generator for complex terrain: an evaluation in the Austrian Alps H. Dabhi et al. https://doi.org/10.5194/hess-27-2123-2023
21. A probabilistic assessment of agricultural water scarcity in a semi-arid and snowmelt-dominated river basin under climate change H. Moursi et al. https://doi.org/10.1016/j.agwat.2017.08.010
22. Dynamical and statistical downscaling of a global seasonal hindcast in eastern Africa G. Nikulin et al. https://doi.org/10.1016/j.cliser.2017.11.003
23. Downscaling climate projections over large and data sparse regions: Methodological application in the Zambezi River Basin N. Peleg et al. https://doi.org/10.1002/joc.6578
24. Stochastic generation of multi-site daily precipitation focusing on extreme events G. Evin et al. https://doi.org/10.5194/hess-22-655-2018