Two types of monthly water balance models at basin scale are used: PE models use precipitation and potential évapotranspiration (PET) as their observed input data, whereas P models need only precipitation. Calibration proceeds by comparing model runoff and observed runoff. Calibration is entirely automatic with the exclusion of subjective elements. All models differ only by their actual évapotr...

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude altitude of meteorological station, month, average temperatures, among others) areas Galicia (Spain). The were trained, validated queried using data three sta...

This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and ...

Calibration and prediction in conceptual rainfallrunoff (CRR) modelling is affected by the sampling and measurement uncertainty in the forcing/response data and by the structural error of the model conceptualisation. The Bayesian Total Error Analysis methodology (BATEA) offers a robust approach to deal with these multiple sources of uncertainty. The core idea is to pose the model calibration as...