@article{UllrichHegnauerNguyenetal.2021,
  author    = {Ullrich, Sophie Louise and Hegnauer, Mark and Nguyen, Dung Viet and Merz, Bruno and Kwadijk, Jaap and Vorogushyn, Sergiy},
  title     = {Comparative evaluation of two types of stochastic weather generators for synthetic precipitation in the Rhine basin},
  series = {Journal of hydrology},
  volume    = {601},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam [u.a.]},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2021.126544},
  pages     = {16},
  year      = {2021},
  abstract  = {Stochastic modeling of precipitation for estimation of hydrological extremes is an important element of flood risk assessment and management. The spatially consistent estimation of rainfall fields and their temporal variability remains challenging and is addressed by various stochastic weather generators. In this study, two types of weather generators are evaluated against observed data and benchmarked regarding their ability to simulate spatio-temporal precipitation fields in the Rhine catchment. A multi-site station-based weather generator uses an auto-regressive model and estimates the spatial correlation structure between stations. Another weather generator is raster-based and uses the nearest-neighbor resampling technique for reshuffling daily patterns while preserving the correlation structure between the observations. Both weather generators perform well and are comparable at the point (station) scale with regards to daily mean and 99.9th percentile precipitation as well as concerning wet/dry frequencies and transition probabilities. The areal extreme precipitation at the sub-basin scale is however overestimated in the station-based weather generator due to an overestimation of the correlation structure between individual stations. The auto-regressive model tends to generate larger rainfall fields in space for extreme precipitation than observed, particularly in summer. The weather generator based on nearest-neighbor resampling reproduces the observed daily and multiday (5, 10 and 20) extreme events in a similar magnitude. Improvements in performance regarding wet frequencies and transition probabilities are recommended for both models.},
  language  = {en}
}