| 1. |
- Breinl, Korbinian, et al.
(författare)
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A joint modelling framework for daily extremes of river discharge and precipitation in urban areas
- 2015
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Ingår i: Journal of Flood Risk Management. - : Wiley. - 1753-318X.
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Tidskriftsartikel (refereegranskat)abstract
- Human settlements are often at risk from multiple hydro-meteorological hazards, which include fluvial floods, short-time extreme precipitation (leading to ‘pluvial’ floods) or coastal floods. In the past, considerable scientific effort has been devoted to assessing fluvial floods. Only recently have methods been developed to assess the hazard and risk originating from pluvial phenomena, whereas little effort has been dedicated to joint approaches. The aim of this study was to develop a joint modelling framework for simulating daily extremes of river discharge and precipitation in urban areas. The basic framework is based on daily observations coupled with a novel precipitation disaggregation algorithm using nearest neighbour resampling combined with the method of fragments to overcome data limitations and facilitate its transferability. The framework generates dependent time series of river discharge and urban precipitation that allow for the identification of fluvial flood days (daily peak discharge), days of extreme precipitation potentially leading to pluvial phenomena (maximum hourly precipitation) and combined fluvial–pluvial flood days (combined time series). Critical thresholds for hourly extreme precipitation were derived from insurance and fire service data.
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| 2. |
- Breinl, Korbinian, et al.
(författare)
-
Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards
- 2015
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 22:3, s. 334-347
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Tidskriftsartikel (refereegranskat)abstract
- Stochastic weather generators simulate synthetic weather data while maintaining statistical properties of the observations. A new semi-parametric algorithm for multi-site precipitation has been published recently by Breinl et al. (2013), who used a univariate Markov process to simulate precipitation occurrence at multiple sites for two small rain gauge networks. Precipitation amounts were simulated in a two-step process by first resampling observations and then sampling and reshuffling of parametric precipitation amounts. In the present study, the precipitation model by Breinl et al. (2013, J. Hydrol. 498: 23–35) is implemented in a weather generation framework for daily precipitation and temperature. It is extended to a considerably larger gauge station network of 19 stations and further improved to reduce the duplication of historical records in the simulation. Autoregressive-moving-average models (ARMA) are used to simulate mean daily temperature at three sites. Power transformations reduce the bias of simulated temperature extremes. Precipitation amounts are simulated by means of hybrid distributions consisting of a Weibull distribution for low precipitation amounts and a generalized Pareto distribution (GPD) for moderate and extreme precipitation amounts. The proposed weather generator is particularly suitable for assessing hydrometeorological hazards such as flooding as it reproduces the spatial variability of precipitation very well and can generate unobserved extremes.
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