Modelling of the land surface water-, energy-, and carbon balance provides insight into the behaviour of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large-scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyse the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS-1D was simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in-depth analysis of the soil SHPs and derived soil characteristics was performed to analyse why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time-integrated behaviour of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby et al. (1984) for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter-comparison studies to avoid artefacts originating from the choice of PTF rather from different model structures.

In his seminal paper on solution of the infiltration equation, Philip (1957) proposed a gravity time, tgrav, to estimate practical convergence time of his infinite time series expansion, TSE. The parameter tgrav refers to a point in time where infiltration is dominated equally by capillarity and gravity derived from the first two (dominant) terms of the TSE expansion. Evidence that higher order TSE terms describe the infiltration process better for longer times. Since the conceptual definition of tgrav is valid regardless of the infiltration model used, we opted to reformulate tgrav using the analytic approximation proposed by Parlange et al. (1982) valid for all times. In addition to the roles of soil sorptivity (S) and saturated (Ks) and initial (Ki) hydraulic conductivities, we explored effects of a soil specific shape parameter β on the behavior of tgrav. We show that the reformulated tgrav (notably tgrav= F(β) S^2/(Ks - Ki)^2 where F(β) is a β-dependent function) is about 3 times larger than the classical tgrav given by tgrav, Philip= S^2/(Ks - Ki)^2. The differences between original tgrav, Philip and the revised tgrav increase for fine textured soils. Results show that the proposed tgrav is a better indicator for convergence time than tgrav, Philip. For attainment of the steady-state infiltration, both time parameters are suitable for coarse-textured soils, but not for fine-textured soils for which tgrav is too conservative and tgrav, Philip too short. Using tgrav will improve predictions of the soil hydraulic parameters (particularly Ks) from infiltration data as compared to tgrav, Philip.

Remotely-sensed Solar Induced chlorophyll Fluorescence (SIF) is a novel promising tool to retrieve information on plants’ physiological status due to its direct link with the photosynthetic process. At the same time, narrow band Vegetation Indices (VIs) such as the MERIS Terrestrial chlorophyll index (MTCI), and the Photochemical Reflectance Index (PRI), as well as broad band VIs like the Normalized Difference Vegetation Index (NDVI), have been widely used for crop stress assessment. A match between these remote sensing products and the spatial distribution of soil units is expected; nevertheless, an in-depth analysis of such relationship has been rarely performed so that additional studies are required. In this contribution, we aimed at the comparison in the use of normalized SIF (SIF = SIF/PAR; computed with the Spectral Fitting Method, SFM) and VIs (MTCI, PRI and NDVI) for heat stress assessment in corn, sugar beet and potato at the beginning and towards the end of a heatwave occurring in Selhausen, Germany, 2018. Data were acquired with the HyPlant airborne sensor, which is a high performance imaging spectrometer with around 0.30 nm of spectral resolution in the Oxygen absorption bands. We compared different plots located in the upper (poorer soil characteristics for agriculture such as water holding capacity and content of coarse sediments) or lower landscape terraces; we also evaluated the different remote sensing products in comparison with site specific geophysics-based soil maps. At the beginning of the heat wave we found that, compared with VIs, SIF data showed a clearer differentiation of the stress conditions at a terrace level in potato and sugar beet. However, towards the end of the wave a significant decrease of MTCI and NDVI contrasted with higher SIF in sugar beet and corn. Nonetheless, those crops (sugar beet and corn) did not show significant SIF differences between terraces. A significant spatial match was found between SIF and geophysics-derived soil spatial patterns (p = 0.004-0.030) in fields where NDVI was more homogeneous (p = 0.028-0.499, respectively). This suggests the higher sensitivity of SIF to monitor heat stress compared with common VIs.