Abstract. The spatial and temporal controls of preferential flow (PF) during infiltration are still not fully understood. As soil moisture sensor networks allow us to capture infiltration responses in high temporal and spatial resolution, our study is based on a large-scale sensor network with 135 soil moisture profiles distributed across a complex catchment. The experimental design covers three major geological regions (slate, marl, sandstone) and two land covers (forest, grassland) in Luxembourg. We analyzed the responses of up to 353 rainfall events for each of the 135 soil moisture profiles. Non-sequential responses (NSRs) within the soil moisture depth profiles were taken as one indication of bypass flow. For sequential responses maximum porewater velocities (vmax⁡) were determined from the observations and compared with velocity estimates of capillary flow. A measured vmax⁡ higher than the capillary prediction was taken as a further indication of PF. While PF was identified as a common process during infiltration, it was also temporally and spatially highly variable. We found a strong dependence of PF on the initial soil water content and the maximum rainfall intensity. Whereas a high rainfall intensity increased PF (NSR, vmax⁡) as expected, most geologies and land covers showed the highest PF under dry initial conditions. Hence, we identified a strong seasonality of both NSR and vmax⁡ dependent on land cover, revealing a lower occurrence of PF during spring and increased occurrence during summer and early autumn, probably due to water repellency. We observed the highest fraction of NSR in forests on clay-rich soils (slate, marl). vmax⁡ ranged from 6 to 80 640 cm d−1 with a median of 120 cm d−1 across all events and soil moisture profiles. The soils in the marl geology had the highest flow velocities, independent of land cover, especially between 30 and 50 cm depth, where the clay content increased. This demonstrates the danger of treating especially clay soils in the vadose zone as a low-conductive substrate, as the development of soil structure can dominate over the matrix property of the texture alone. This confirms that clay content and land cover strongly influence infiltration and reinforce PF, but seasonal dynamics and flow initiation also have an important impact on PF.

Core Ideas Macropores allow water infiltration into a frozen soil close to 0°C and at high water saturation. Connected biopores channel water beneath the frozen layer. Frozen layer thickness in relation to connected macroporosity is a crucial factor. Infrared thermography can be used to measure frozen layer extent and variability. Water frozen in soil can reduce the soil infiltrability, depending on the water content. We hypothesize that air‐filled macropores control the infiltration of a seasonally frozen soil under high saturation degrees. Sprinkling experiments with different intensities on a seasonally frozen soil were conducted in two winters at high initial water contents. Brilliant Blue FCF (BB) was sprinkled on four plots equipped with soil moisture and temperature probes to mark flow paths. Frozen layer thickness was measured with infrared thermography of soil sections and overlaid with BB images. The frost depth of the experiments was 8 to 15 cm. Infiltration rates showed reduced infiltration compared with unfrozen conditions. By impeding refreezing of the infiltrating water with added NaCl, infiltration rates of 23 to 29 mm h−1 were measured. Without the addition of salt, the infiltration rates decreased to 5 to 10 mm h−1, attributed to pore blockage by refreezing water. Temperature measurements revealed that the frozen layer only thawed close to the soil surface during the experiments. Blue‐stained areas indicated that water was channeled through the frozen layer into the unfrozen soil. In addition, the soil moisture probes below the frozen layer measured an increase in unfrozen water content, whereas total water content in the frozen layer was constant. These observations were explained by a connected air‐filled porosity, such as biopores, which allowed water flow even under high initial water contents. These results illustrate the importance of macroporosity in relation to frost depth in controlling the infiltrability of seasonally frozen soils.

Standard procedures to assess P availability in soils are based on batch experiments with various extractants. However, in most soils P nutrition is less limited by bulk stocks but by strong adsorption and transport limitation. The basic principle of root‐phosphate uptake is to strip phosphate locally from the solid phase by forming a radial depletion zone in the soil solution, optionally enhanced by release of mobilizing substances. Microdialysis (MD), a well‐established method in pharmacokinetics, is capable to mimic important characteristics of P root uptake. The sampling is by diffusional exchange through a semipermeable membrane covering the probes with their sub‐mm tubular structure. Additionally, the direct environment of the probe can be chemically modified by adding, e.g., carboxylates to the perfusate. This study is the first approach to test the applicability of MD in assessing plant available phosphate in soils and to develop a framework for its appropriate use.We used MD in stirred solutions to quantify the effect of pumping rate, concomitant ions, and pH value on phosphate recovery. Furthermore, we measured phosphate yield of top‐soil material from a beech forest, a non‐fertilized grassland, and from a fertilized corn field. Three perfusates have been used based on a 1 mM KNO3 solution: pure (1), with 0.1 mM citric acid (2), and with 1 mM citric acid (3). Additionally, a radial diffusion model has been parametrized for the stirred solutions and the beech forest soil.Results from the tests in stirred solutions were in good agreement with reported observations obtained for other ionic species. This shows the principal suitability of the experimental setup for phosphate tests. We observed a significant dependency of phosphate uptake into the MD probes on dialysate pumping rate and on ionic strength of the outside solution. In the soils, we observed uptake rates of the probes between 1.5 × 10−15 and 6.7 × 10−14 mol s−1 cm−1 in case of no citrate addition. Surprisingly, median uptake rates were mostly independent of the bulk soil stocks, but the P‐fertilized soil revealed a strong tailing towards higher values. This indicates the occurrence of hot P spots in soils. Citrate addition increased P yields only in the higher concentration but not in the forest soil. The order of magnitude of MD uptake rates from the soil samples matched root‐length related uptake rates from other studies. The micro‐radial citrate release in MD reflects the processes controlling phosphate mobilization in the rhizosphere better than measurements based on “flooding” of soil samples with citric acid in batch experiments. Important challenges in MD with phosphate are small volumes of dialysate with extremely low concentrations and a high variability of results due to soil heterogeneity and between‐probe variability. We conclude that MD is a promising tool to complement existing P‐analytical procedures, especially when spatial aspects or the release of mobilizing substances are in focus.

Abstract. The spatial and temporal controls of preferential flow (PF) during infiltration are still not fully understood. Soil moisture sensor networks give the possibility to measure infiltration response in high temporal and spatial resolution. Therefore, we used a large-scale sensor network with 135 soil moisture profiles distributed across a complex catchment. The experimental design covers three major geological regions (Slate, Marl, Sandstone) and two land covers (forest, grassland) in Luxembourg. We analyzed the responses of up to 353 rainfall events for every of the 135 soil moisture profiles. Non-sequential responses within the soil moisture depth-profiles were taken as an indication of PF. For sequential responses wetting front velocities were determined from the observations and compared with predictions by capillary flow. A measured wetting front velocity higher than the capillary prediction was also taken as a proxy for PF. We observed the highest fraction of non-sequential response (NSR) in forests on clay-rich soils (Slate, Marl). Furthermore, these two landscape units showed an increase of NSR with lower initial soil water content and higher maximum rainfall intensity. Wetting front velocities ranged from 6 cm day−1 to 80 640 cm day−1 with a median of 113 cm day−1 across all events and landscape units. The soils in the Marl geology had the highest flow velocities, independent of land cover, especially between 30 and 50 cm depth where the clay content increased. For Marl the median water content change was highest for the deepest soil moisture sensor (50 cm), whereas the other two geologies (Slate, Sandstone) showed a decrease of soil moisture change with depth. This confirms that clay content and vegetation strongly influence infiltration and reinforce preferential flow. Capillary-based soil water flow modelling was unable to predict the observed patterns. This demonstrates the danger of treating especially clay soils in the vadose zone as a low-conductivity layer, as the development of soil structure can dominate over the effect of low-conductive texture.

Abstract. A better understanding of the reasons why hydrological model performance is unsatisfying represents a crucial part of meaningful model evaluation. However, current evaluation efforts are mostly based on aggregated efficiency measures such as Kling–Gupta efficiency (KGE) or Nash–Sutcliffe efficiency (NSE). These aggregated measures provide a relative gradation of model performance. Especially in the case of a weak model performance it is important to identify the different errors which may have caused such unsatisfactory predictions. These errors may originate from the model parameters, the model structure, and/or the input data. In order to provide more insight, we define three types of errors which may be related to their source: constant error (e.g. caused by consistent input data error such as precipitation), dynamic error (e.g. structural model errors such as a deficient storage routine) and timing error (e.g. caused by input data errors or deficient model routines/parameters). Based on these types of errors, we propose the novel diagnostic efficiency (DE) measure, which accounts for these three error types. The disaggregation of DE into its three metric terms can be visualized in a plain radial space using diagnostic polar plots. A major advantage of this visualization technique is that error contributions can be clearly differentiated. In order to provide a proof of concept, we first generated time series artificially with the three different error types (i.e. simulations are surrogated by manipulating observations). By computing DE and the related diagnostic polar plots for the reproduced errors, we could then supply evidence for the concept. Finally, we tested the applicability of our approach for a modelling example. For a particular catchment, we compared streamflow simulations realized with different parameter sets to the observed streamflow. For this modelling example, the diagnostic polar plot suggests that dynamic errors explain the overall error to a large extent. The proposed evaluation approach provides a diagnostic tool for model developers and model users and the diagnostic polar plot facilitates interpretation of the proposed performance measure as well as a relative gradation of model performance similar to the well-established efficiency measures in hydrology.