Recent research carried out in CRES has produced a data-based modelling approach to the adaptive forecasting of river flow or levels using rainfall or upstream flow inputs as leading indicators. This approach, which is based on the estimation of a general transfer function model from historical data, has significant advantages over existing flood forecasting methods: for example, on-line adjust...

The assessment of snow, glacier, and rainfall runoff contribution to discharge in mountain streams is of major importance for an adequate water resource management. Such contributions can be estimated via hydrological models, provided that the modeling adequately accounts for snow and glacier melt, as well as rainfall runoff. We present a multiple data set calibration approach to estimate runof...

[1] A novel method is presented for model uncertainty estimation using machine learning techniques and its application in rainfall runoff modeling. In this method, first, the probability distribution of the model error is estimated separately for different hydrological situations and second, the parameters characterizing this distribution are aggregated and used as output target values for buil...

In this paper a Monte Carlo procedure for deriving frequency distributions of extreme discharges starting from a simplified description of rainfall and surface runoff processes and using regional data is presented. The procedure is based on two modules: a stochastic rainfall generator module and a catchment response module. In the rainfall generator module the rainfall storm, i.e. the maximum r...

The use of multiple evapotranspiration (ET) models is critical for exploring the ambiguity in the representations of ET processes. Although ensemble ET models are increasingly used to address this ambiguity, practical issues include: 1) the diversity of process representations, which require different input data and constants; 2) the diversity of nomenclature, terminology and units used in the ...