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Computing related content is introduced in school curricula all over the world, placing new requirements on school teachers and their knowledge. Little attention has been paid to fostering the skills and attitudes required to teach the new content. This involves not only traditional computing topics, such as algorithms or programming, but also the role of technology in society as well as questions related to ethics, safety and integrity. As technology develops at a fast rate, so does the content to be taught. Learning computing content through isolated in-service training initiatives is by no means enough, but rather, teachers need to develop confidence to independently and continuously explore what is new, what is relevant and how to include digital competence in their teaching. Teachers' self-efficacy is hence of crucial importance. In a previous article [13] we described the development of a self-efficacy scale for teachers, focusing on digital competence as described in EU's framework DigComp 2.0. In this paper, we extend that work by analysing 530 teachers' responses collected in Autumn 2017 during a series of workshops and other professional development events. Our goal was to collect baseline data, painting a picture of teachers' current self-efficacy levels in order to facilitate follow-up studies. In addition, our results also point out challenging areas, consequently providing important insight into what topics and themes should be emphasized in professional development initiatives.

Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simula-tions. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipita-tion estimates based on point rain gauge measurement inter-polation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of meas-urement stations. In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpola-tion by using the operational hydrometeorological sensor network in the Thur river basin in north-eastern Switzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by MeteoSwiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment-average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km2, beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the perfor-mance indices. An evaluation based on precipitation inten-sity thresholds indicated a decreasing performance for higher precipitation intensities. The results of this study emphasise the benefits of dense and adequately distributed rain gauge networks. ABSTRACT. Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of measurement stations.In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpolation by using the operational hydrometeorological sensor network in the Thur river basin in north-eastern Switzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by MeteoSwiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment-average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km 2 , beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the performance...