Derived flood frequency models can be used to study climate and land use change effects on the flood frequency curve. Intra-annual (i.e., within year) climate variability strongly impacts upon the flood frequency characteristics in two ways: in a direct way through the seasonal variability of storm characteristics and indirectly through the seasonality of rainfall and evapotranspiration which then affect the antecedent catchment conditions for individual storm events. In this paper we propose a quasi-analytical derived flood frequency model that is able to account for both types of seasonalities. The model treats individual events separately. It consists of a rainfall model with seasonally varying parameters. Increased flood peaks, as compared to block rainfall, due to random within-storm rainfall time patterns are represented by a factor that is a function of the ratio of storm duration and catchment response time. Event runoff coefficients are allowed to vary seasonally and include a random component. Their statistical characteristics are derived from long-term water balance simulations. The components of the derived flood frequency model are integrated in probability space to derive monthly flood frequency curves. These are then combined into annual flood frequency curves. Comparisons with Monte Carlo simulations using parameters that are typical of Austrian catchments indicate that the approximations used here are appropriate. We perform sensitivity analyses to explore the effects of the interaction of rainfall and antecedent soil moisture seasonalities on the flood frequency curve. When the two seasonalities are in phase, there is resonance, which increases the flood frequency curve dramatically. We are also able to isolate the contributions of individual months to the annual flood frequency curve. Monthly flood frequency curves cross over for the parameters chosen here, as extreme floods tend to mainly occur in summer while less extreme floods may occur throughout the year.
ASJC Scopus subject areas
- Water Science and Technology