@article{MacdonaldMerzGuseetal.2022,
  author    = {Macdonald, Elena and Merz, Bruno and Guse, Bj{\"o}rn and Wietzke, Luzie and Ullrich, Sophie and Kemter, Matthias and Ahrens, Bodo and Vorogushyn, Sergiy},
  title     = {Event and catchment controls of heavy tail behavior of floods},
  series = {Water resources research},
  volume    = {58},
  journal   = {Water resources research},
  number    = {6},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2021WR031260},
  pages     = {25},
  year      = {2022},
  abstract  = {In some catchments, the distribution of annual maximum streamflow shows heavy tail behavior, meaning the occurrence probability of extreme events is higher than if the upper tail decayed exponentially. Neglecting heavy tail behavior can lead to an underestimation of the likelihood of extreme floods and the associated risk. Partly contradictory results regarding the controls of heavy tail behavior exist in the literature and the knowledge is still very dispersed and limited. To better understand the drivers, we analyze the upper tail behavior and its controls for 480 catchments in Germany and Austria over a period of more than 50 years. The catchments span from quickly reacting mountain catchments to large lowland catchments, allowing for general conclusions. We compile a wide range of event and catchment characteristics and investigate their association with an indicator of the tail heaviness of flood distributions, namely the shape parameter of the GEV distribution. Following univariate analyses of these characteristics, along with an evaluation of different aggregations of event characteristics, multiple linear regression models, as well as random forests, are constructed. A novel slope indicator, which represents the relation between the return period of flood peaks and event characteristics, captures the controls of heavy tails best. Variables describing the catchment response are found to dominate the heavy tail behavior, followed by event precipitation, flood seasonality, and catchment size. The pre-event moisture state in a catchment has no relevant impact on the tail heaviness even though it does influence flood magnitudes.},
  language  = {en}
}
@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}
}