RESUMO
The aim of this paper is to analyse the differences in the long-term regimes of extreme precipitation and floods across the Alpine-Carpathian range using seasonality indices and atmospheric circulation patterns to understand the main flood-producing processes. This is supported by cluster analyses to identify areas of similar flood processes, both in terms of precipitation forcing and catchment processes. The results allow to isolate regions of similar flood generation processes including southerly versus westerly circulation patterns, effects of soil moisture seasonality due to evaporation and effects of soil moisture seasonality due to snow melt. In many regions of the Alpine-Carpathian range, there is a distinct shift in flood generating processes with flood magnitude as evidenced by a shift from summer to autumn floods. It is argued that the synoptic approach proposed here is valuable in both flood analysis and flood estimation.
RESUMO
In environmental modelling, estimating the confidence level in conceptual model parameters is necessary but difficult. Having a realistic estimation of the uncertainties related to the parameters is necessary i) to assess the possible origin of the calibration difficulties (correlation between model parameters for instance), and ii) to evaluate the prediction confidence limits of the calibrated model. In this paper, an application of the Metropolis algorithm, a general Monte Carlo Markov chain sampling method, for the calibration of a four-parameter lumped urban stormwater quality model is presented. Unlike traditional optimisation approaches, the Metropolis algorithm identifies not only a "best parameter set", but a probability distribution of parameters according to measured data. The studied model includes classical formulations for the pollutant accumulation during dry weather period and their washoff during a rainfall event. Results indicate mathematical shortcomings in the pollutant accumulation formulation used.