engleski

Increase of hydrological model skill for the mountainous basins by integration of remote sensing data

The insufficient density of observation stations network in mountain basins is often a limiting factor for reliable modelling of hydrological processes. Therefore, remotely sensed precipitation products and snow-covered areas supplement measured ground data in inaccessible regions. The dissertation will evaluate the quality of two remotely sensed precipitation products, CHIRPS and ERA5, based on a comparison with ground measured precipitation. Simple empirical equations based on the degree-day method are often used to model snow and ice melting. The dissertation will compare two approaches to the calculation of runoff in mountain basins: (a) a physically-based hydrological model that uses the empirical degree-day method and (b) a new approach based on an artificial neural network that, in addition to the standard ground measurements, additionally uses remotely sensed snow cover and snow depth measurements at meteorological stations, which are often not used by existing models.

Hydrological Modelling, Mountainous Basins, Snow-Rain Runoff Regime, Remote Sensing, Artificial Neural Networks

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