Scientific and operational value of alternative datasets in hydrological science

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Applied hydrological science strongly relies on hydrometeorological information, and especially on measurements of precipitation end temperature. However, time series of relevant hydrometeorological variables are plagued with problems such as short temporal horizons, missing data, errors, and various biases. This limits, sometimes severely, the ability to adequately represent the spatial and temporal variability of the hydrological processes at the basin scale. New datasets have been proposed to palliate the lack of direct observations and the less than optimal coverage in time and space. Available datasets consists of the traditional station-based data and of three alternative datasets: gridded datasets, reanalyses of observations and dynamically generated data. The scientists and industrial partners involved in this project propose to: Investigate the scientific and operational value of alternative datasets in hydrological science. More precisely, this project intends to address the following specific objectives: 1-Compare traditional and new datasets with respect to their ability at providing useful information for key hydrological applications and research. and, for given hydrologic problems: 2-Further explore the advantages and drawbacks of observationally-based datasets 3-Estimate the added-value of dynamically generated datasets in hydrological studies The methodology is centered around five research themes that will compare the different datasets with respect to their ability at providing relevant information for hydrological modelling, extreme analysis, as well as provide insights on the optimization of observation networks and on the added-value of complex physically-based hydrology models.