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Impact Forecasting has developed a catastrophe flood model for Czechia to estimate insurance losses. The model is built on a dataset of 12,066 years of daily rainfall and temperature data for the European area, representing the current climate (LAERTES-EU). This dataset was used as input to the rainfall–runoff model, resulting in a series of daily river channel discharges. Using analyses of global and regional climate models dealing with the impacts of climate change, this dataset was adjusted for the individual RCP climate scenarios in Europe. The river channel discharges were then re-derived using the already calibrated rainfall–runoff models. Based on the changed discharges, alternative versions of the standard catastrophe flood model for the Czechia were created for the various climate scenarios. In outputs, differences in severity, intensity, and number of events might be observed, as well as the size of storms. The effect on the losses might be investigated by probable maximum losses (PML) curves and average annual loss (AAL) values. For return period 1 in 5 years for the worst-case scenario, the differences can be up to +125 percent increase in insurance losses, while for the return period 1 in 100 years it is a −40 percent decrease. There is no significant effect of adaptation measures for the return period 1 in 100 years, but there is a −20 percent decrease in the return period 1 in 5 years.

Leaf area index (LAI) belongs among the catchment characteristics widely used in hydrological models but still associated with great uncertainties. In a mountain forest catchment, the leaf area affects retention and evapotranspiration loss, and it could be significantly modified by forestry practices. In this study, LAI in mature stands of Norway spruce (Picea abies) and European beech (Fagus sylvatica) was analysed in headwater catchments of the Jizera Mountains (Czech Republic) between 2012 and 2016. A comparison evaluation of LAI in harvested site with dominant herbaceous vegetation was taken into account by applying direct ground investigation what was compared with hemispherical canopy photography (Gap light analyser GLA-V2) and satellite remote sensing (Sentinel-2 mission). While the direct ground measurement includes only the foliage (leaves or needles), the Gap light analysis is affected by trunks and branches, and the remote sensing techniques by herbaceous understory. The results of the Gap light analyser underestimated the ground based LAI values by 52-76 per cent, and satellite interpretations by 29-73 per cent. The remote sensing is capable to provide effective information on the distribution of LAI within the time and space. However, in a catchment scale, the satellite detection underestimated average LAI values approx. by 42-62 per cent. Changes in the observed rainfall interception reflected well the LAI variation.

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