Remote sensing can be a helpful tool for monitoring crop transpiration (T) for agricultural water management. Since remote sensing provides instantaneous data, upscaling techniques are required to estimate T on a daily scale. This study assesses optimal image acquisition times and four upscaling approaches to estimate daily T. The results indicate that the main errors derive from measurement time and water stress levels, which can be mitigated by choosing a proper upscaling approach.

Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach that is closely linked to direct measurements of soil moisture at a network sites across the UK, to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212) and to remotely sensed satellite estimates.

We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.

The snowmelt event governs the hydrological cycle of sub-arctic areas. In this study, we conducted a tracer experiment on a forested hilltop in Lapland to identify how high-volume infiltration events modify the soil water storage. We found that a strong tracer signal remained in deeper soil layers after the experiment and over the winter, but it got fully displaced during the snowmelt. We propose a conceptual infiltration model that explains how the snowmelt homogenizes the soil water storage.