A GIS-based framework for quantifying potential shadow casts on lakes applied to a Danish lake experimental facility

We present a Python-based framework for analyzing and quantifying potential shadow casts on lake and reservoir surfaces from the surrounding terrain features. The framework is based on remote sensing data and in this case a detailed Danish nationwide digital elevation model (DEM), which renders not only the physical surface of the terrain, i.e. the topography, but also the elevation of, for instance, buildings and vegetation. We developed a methodological framework encompassing existing computational routines embedded in the open source QGIS platform as well as existing computational packages available for Python, which collectively enable calculation of shadow casts from all elements surrounding a lake or a reservoir surface. Our framework is demonstrated through application to a Danish lake experimental facility but may be used for lakes or reservoirs world-wide to evaluate if shadow casts need to be considered and accounted for in modelling studies by correction of e.g. radiation inputs.