Abstract
This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the annual cycle of each meteorological variable. The evaluation of its performance was done by deploying its synthetic time series into the physical based hydrological model BROOK90. The weather generator was applied and tested for data from the Anchor Station at the Tharandt Forest, Germany. Additionally its results were compared to the output of another weather generator with spell-length approach for the precipitation series (LARS-WG). The comparison was distinguished into a meteoro-logical and a hydrological part in terms of extremes, monthly and annual sums and averages. Extreme events could be preserved adequately by both models. Nevertheless a general underestimation of rare events was observed. Natural correlations between vapour pressure and minimum temperature could be conserved as well as annual cycles of the hydro-logical and meteorological regime. But the simulated spectrums of extremes, especially, of precipitation and temperature, are more limited than the observed spectrums. While LARS-WG already finds application in practice, the results show that the data derived from the presented weather generator is as useful and reliable as those from the established model for the simulation of the water balance.
Highlights
The planning, construction and management of precipitation related infrastructure like sewer systems, retention areas or dams highly depend on the occurrence and statistical return period of extreme rainfall events [1,2]
The evaluation of its performance was done by deploying its synthetic time series into the physical based hydrological model BROOK90
While LARS-WG data set (LARS)-WG already finds application in practice, the results show that the data derived from the presented weather generator is as useful and reliable as those from the established model for the simulation of the water balance
Summary
The planning, construction and management of precipitation related infrastructure like sewer systems, retention areas or dams highly depend on the occurrence and statistical return period of extreme rainfall events [1,2]. The lag of satisfactory long term observations leads to the development of stochastic models which are able to simulate rainfall without the recognition of atmospheric driven processes [3,4]. Their outcomes, long synthetic rainfall series, fulfil the requirements of the engineers. The main disadvantage of these approaches is their limited capacity to model unobserved states as well as the incomplete preservation of statistical properties They all depend on historical time series, which by definition can not include unobserved extremes of weather variables. Even changes of the climate can be recognized by integrating different scenarios [8,9] or by including a certain expected trend [10]
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