Snow plays a crucial role in regulating water availability in eco-hydrological systems. Its spatial distribution is key for understanding melting dynamics, particularly in forests where snow amounts vary on small spatial scales (<3 m) compared to open areas. Uncrewed Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR) measurements can quantify the snow distribution in forests at sufficiently high spatial resolution. Such datasets showed that snow distribution can be aggregated into spatial patterns featuring similar snow dynamics. However, there is no suitable dataset available to investigate whether these patterns persist throughout different seasons and how they differ between sites. This study introduces a new dataset comprising UAV-based LiDAR surveys, a dense automatic snow depth sensor network, and additional ground measurements, covering three seasons at two forested sites. The identification of snow distribution patterns from LiDAR data in one season is achieved using a clustering workflow, first presented by Geissler et al. (2023a). Identified patterns are subsequently used for spatially extrapolating observed time series of snow depth and snow water equivalent from a few locations. The results show that snow patterns are influenced by site-specific factors such as wind or radiation but are persistent over time. A comparison with physics-based snow model simulations underlines the added value of the presented observation-based snow products allowing a spatiotemporally continuous analysis of discrepancies. This study therefore advances our understanding of using forest snow patterns to create high-resolution, spatiotemporally continuous snow products with reduced measurement or computational effort.

Understanding the transport processes and travel times of pollutants in the subsurface is crucial for an effective management of drinking water resources. Transport processes and soil hydrologic processes are inherently linked to each other. In order to account for this link, we couple the process-based hydrologic model RoGeR with StorAge Selection (SAS) functions. We assign to each hydrological process a specific SAS function (e.g. power law distribution function). To represent different transport mechanisms, we combined a specific set of SAS functions into four transport model structures: complete-mixing, piston flow, advection-dispersion and advection-dispersion with time-variant parameters. In this study, we conduct modelling experiments at the Rietholzbach lysimeter, Switzerland. All modelling experiments are benchmarked with HYDRUS-1D. We compare our simulations to the measured hydrologic variables (percolation and evapotranspiration fluxes and soil water dynamics) and the measured water stable isotope signal (18O) in the lysimeter seepage for a period of ten years (1997-2007). An additional virtual bromide tracer experiment was used to benchmark the models. Additionally, we carried out a sensitivity analysis and provide Sobol indices for soil hydrologic model parameters and SAS parameters. Our results show that the advection-dispersion transport model produces the best results. And thus, advective-dispersive transport processes play a dominant role at Rietholzbach lysimeter. Our modelling approach provides the capability to test hypotheses of different transport mechanisms and to improve process understanding and predictions of transport processes. Overall, the combined model allows a very effective simulation of combined flux and transport processes at various temporal and spatial scales.

The rapid expansion of impermeable surfaces in cities has a major impact on urban hydrology. Infiltration of rainwater is reduced and water runs off faster with higher runoff peaks. Urban trees as stormwater management tools are becoming more relevant to reduce flood risks in addition to other ecosystem services. An in-situ field experiment to measure throughfall on Norway maple ( Acer platanoides) and small-leaved lime ( Tilia cordata) was conducted to determine the interception of solitary urban trees with different degrees of surface sealing in the city of Freiburg, Germany. The relationships between rainfall characteristics, tree morphological traits, and the interception behavior were investigated with eight trees per species. 76 recorded rainfall events were evaluated from April to September 2021. Average interception values were higher for small-leaved lime (70.3 ± 6.6%) than for Norway maple (54.8 ± 10.3%) and hence much higher than in a typical forested environment. The average interception loss of all recorded events was 2.58 ± 0.60 mm for Norway maple and 3.73 ± 0.29 mm for small-leaved lime. For both tree species, significant linear correlations were found between the relative interception and other factors like rainfall depths, the leaf area index (LAI), and the plant area index (PAI) (adj.R 2 > 0.45). In contrast to Norway maple, small-leaved lime also showed significant relationships of several tree morphological parameters with the interception (adj.R 2 > 0.43). LAI, which also effects the interception, of both tree species significantly decreased with the degree of surface sealing. Our results provide a better understanding of the interception process of solitary trees for different urban sites and allows to parameterize interception based on measurable properties. However, further field experiments with various tree species need to be conducted to obtain a larger database for typical parameters in models and to support urban planners in managing stormwater runoff.

Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially when additional components such as vegetation add complexity. To increase our understanding of the spatio-temporal variability of snow and to validate snow models, we need reliable observation data at similar spatial and temporal scales. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are limited either to one point in space or in time. We present a new, extensive dataset of snow variability in a sub-alpine forest in the Alptal, Switzerland. The core dataset consists of a dense sensor network, repeated high-resolution LiDAR data acquired using a fixed-wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, we determine four distinct spatial clusters of similar snow depth dynamics. These clusters are characterized and further used to derive daily snow depth and snow water equivalent (SWE) maps. The results underline the complex relation of topography and canopy cover towards snow accumulation and ablation. The derived products are the first to our knowledge that provide daily, high-resolution snow depth and SWE based almost exclusively on field data. They are therefore ideally suited for the validation of distributed snow models. Our approach can be applied to other project areas and improve our understanding of the spatio-temporal variability of snow in forested environments.

Process understanding of the interaction between streamflow, groundwater and water usages under drought are hampered by a limited number of gauging stations, especially in tributaries. Recent technological advances facilitate the application of non-commercial measurement devices for monitoring environmental systems. The Dreisam River in the South-West of Germany was affected by several hydrological drought events from 2015 to 2020, when parts of the main stream and tributaries fell dry. A flexible longitudinal water quality and quantity monitoring network was set up in 2018. Among other measurements it employs an image based method with QR codes as fiducial marker. In order to assess under which conditions the QR-code based water level loggers (WLL) deliver data according to scientific standards, we present a comparison to conventional capacitive based WLL. The results from 20 monitoring stations reveal that the riverbed was dry for > 50 \% at several locations and even for > 70 \% at most severely affected locations during July and August 2020, with the north western parts of the catchment being especially concerned. Thus, the highly variable longitudinal drying patterns of the stream reaches could be monitored. The image-based method was found to be a valuable asset for identification of confounding factors and validation of zero level occurrences. Nevertheless, a simple image processing approach (based on an automatic thresholding algorithm) did not compensate for errors due to natural conditions and technical setup. Our findings highlight that the complexity of measurement environments is a major challenge when working with image-based methods.