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Wissenschaftliche Beiträge

Hier finden Sie die Ergebnisse und Berichte des Projekts KARE.

Near surface roughness estimation: A parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages
Near surface roughness estimation: A parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type.

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Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks
Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables.

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