LAUSR

Conceptual lumped rainfall-runoff models are frequently used for various environmental problems. To put them into practice, both the model calibration method and data series of the area-averaged precipitation and air temperature are needed. In the case when data from more than one measurement station are available, first the catchment-averaged meteorological data series are usually obtained by some method, and then they are used for calibration of a lumped rainfall-runoff model. However, various optimization methods could easily be applied to simultaneously calibrate both the aggregation weights attributed to various meteorological stations to obtain a lumped meteorological data series and the rainfall-runoff model parameters. This increases the problem dimensionality but allows the optimization procedure to choose the data that are most important for the rainfall-runoff process in a particular catchment, without a priori assumptions. We test the idea using two conceptual models, HBV and GR4J, and three mutually different, relatively recently proposed Evolutionary Computation and Swarm Intelligence optimization algorithms, that are applied to three catchments located in Poland and northwestern USA. We consider two cases: with and without the model error correction applied to the rainfall-runoff models. It is shown that for the calibration period, joint optimization of the weights used to aggregate the meteorological data and the parameters of the rainfall-runoff model improves the results. However, the results for the validation period are inconclusive and depend on the model, error correction, optimization algorithm, and catchment.