@article{LescesenSrajBasarinetal.2022,
  author    = {Lescesen, Igor and Sraj, Mojca and Basarin, Biljana and Pavic, Dragoslav and Mesaros, Minucer and Mudelsee, Manfred},
  title     = {Regional flood frequency analysis of the sava river in south-eastern Europe},
  series = {Sustainability},
  volume    = {14},
  journal   = {Sustainability},
  number    = {15},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2071-1050},
  doi       = {10.3390/su14159282},
  pages     = {19},
  year      = {2022},
  abstract  = {Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.},
  language  = {en}
}
@article{ThiekenApelMerz2015,
  author    = {Thieken, Annegret and Apel, Heiko and Merz, Bruno},
  title     = {Assessing the probability of large-scale flood loss events: a case study for the river Rhine, Germany},
  series = {Journal of flood risk management},
  volume    = {8},
  journal   = {Journal of flood risk management},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1753-318X},
  doi       = {10.1111/jfr3.12091},
  pages     = {247 -- 262},
  year      = {2015},
  abstract  = {Flood risk analyses are often estimated assuming the same flood intensity along the river reach under study, i.e. discharges are calculated for a number of return periods T, e.g. 10 or 100 years, at several streamflow gauges. T-year discharges are regionalised and then transferred into T-year water levels, inundated areas and impacts. This approach assumes that (1) flood scenarios are homogeneous throughout a river basin, and (2) the T-year damage corresponds to the T-year discharge. Using a reach at the river Rhine, this homogeneous approach is compared with an approach that is based on four flood types with different spatial discharge patterns. For each type, a regression model was created and used in a Monte-Carlo framework to derive heterogeneous scenarios. Per scenario, four cumulative impact indicators were calculated: (1) the total inundated area, (2) the exposed settlement and industrial areas, (3) the exposed population and 4) the potential building loss. Their frequency curves were used to establish a ranking of eight past flood events according to their severity. The investigation revealed that the two assumptions of the homogeneous approach do not hold. It tends to overestimate event probabilities in large areas. Therefore, the generation of heterogeneous scenarios should receive more attention.},
  language  = {en}
}