65 research outputs found

    Estimating flow and transport parameters in the unsaturated zone with pore water stable isotopes

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    The first author was funded by the DFG Research Group: From Catchments as Organised Systems to Models based on Functional Units (FOR 1598). The second author was funded by the DFG project Coupled soil-plant water dynamics – Environmental drivers and species effects (contract numbers: GE 1090/10-1 and WE 4598/2-1). The isotope data in the precipitation for Roodt were provided by FNR/CORE/SOWAT, project of the Luxembourg Institute of Science and Technology – LIST. Sampling of the isotope profiles was made possible by the support of the CAOS Team and Begona Lorente Sistiaga, Benjamin Gralher, Andre Böker, Marvin Reich and Andrea Popp. Special thanks to Britta Kattenstroth and Jean Francois Iffly for their technical support in the field and Barbara Herbstritt for her support in the laboratory. For Roodt, soil texture and hydraulic parameter information were provided by Conrad Jackisch and Christoph Messer (KIT, Karlsruhe, Germany) and hydraulic conductivity data were provided by Christophe Hissler and JĂ©rĂŽme Juilleret (LIST). Pore water isotope and soil moisture data for Hartheim were provided by Steffen HolzkĂ€mper and Paul Königer. Temperature and precipitation data for Hartheim were provided by the Chair of Meteorology and Climatology, University of Freiburg.Peer reviewedPublisher PD

    Finding behavioral parameterization for a 1-D water balance model by multi-criteria evaluation

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    Evapotranspiration is often estimated by numerical simulation. However, to produce accurate simulations, these models usually require on-site measurements for parameterization or calibration. We have to make sure that the model realistically reproduces both, the temporal patterns of soil moisture and evapotranspiration. In this study, we combine three sources of information: (i) measurements of sap velocities; (ii) soil moisture; and (iii) expert knowledge on local runoff generation and water balance to define constraints for a “behavioral” forest stand water balance model. Aiming for a behavioral model, we adjusted soil moisture at saturation, bulk resistance parameters and the parameters of the water retention curve (WRC). We found that the shape of the WRC influences substantially the behavior of the simulation model. Here, only one model realization could be referred to as “behavioral”. All other realizations failed for a least one of our evaluation criteria: Not only transpiration and soil moisture are simulated consistently with our observations, but also total water balance and runoff generation processes. The introduction of a multi-criteria evaluation scheme for the detection of unrealistic outputs made it possible to identify a well performing parameter set. Our findings indicate that measurement of different fluxes and state variables instead of just one and expert knowledge concerning runoff generation facilitate the parameterization of a hydrological model

    Estimates of tree root water uptake from soil moisture profile dynamics

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    Root water uptake (RWU), as an important process in the terrestrial water cycle, can help us to better understand the interactions in the soil–plant–atmosphere continuum. We conducted a field study monitoring soil moisture profiles in the rhizosphere of beech trees at two sites with different soil conditions. We present an algorithm to infer RWU from step-shaped, diurnal changes in soil moisture. While this approach is a feasible, easily implemented method for moderately moist and homogeneously textured soil conditions, limitations were identified during drier states and for more heterogeneous soil settings. A comparison with the time series of xylem sap velocity underlines that RWU and sap flow (SF) are complementary measures in the transpiration process. The high correlation between the SF time series of the two sites, but lower correlation between the RWU time series, suggests that soil characteristics affect RWU of the trees but not SF

    Soil moisture: variable in space but redundant in time

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    Soil moisture at the catchment scale exhibits a huge spatial variability. This suggests that even a large amount of observation points would not be able to capture soil moisture variability. We present a measure to capture the spatial dissimilarity and its change over time. Statistical dispersion among observation points is related to their distance to describe spatial patterns. We analyzed the temporal evolution and emergence of these patterns and used the mean shift clustering algorithm to identify and analyze clusters. We found that soil moisture observations from the 19.4 km2 Colpach catchment in Luxembourg cluster in two fundamentally different states. On the one hand, we found rainfall-driven data clusters, usually characterized by strong relationships between dispersion and distance. Their spatial extent roughly matches the average hillslope length in the study area of about 500 m. On the other hand, we found clusters covering the vegetation period. In drying and then dry soil conditions there is no particular spatial dependence in soil moisture patterns, and the values are highly similar beyond hillslope scale. By combining uncertainty propagation with information theory, we were able to calculate the information content of spatial similarity with respect to measurement uncertainty (when are patterns different outside of uncertainty margins?). We were able to prove that the spatial information contained in soil moisture observations is highly redundant (differences in spatial patterns over time are within the error margins). Thus, they can be compressed (all cluster members can be substituted by one representative member) to only a fragment of the original data volume without significant information loss. Our most interesting finding is that even a few soil moisture time series bear a considerable amount of information about dynamic changes in soil moisture. We argue that distributed soil moisture sampling reflects an organized catchment state, where soil moisture variability is not random. Thus, only a small amount of observation points is necessary to capture soil moisture dynamics

    How can we model subsurface stormflow at the catchment scale if we cannot measure it?

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    Subsurface stormflow (SSF) can be a dominant run‐off generation process in humid mountainous catchments (e.g., Bachmair & Weiler, 2011; Blume & van Meerveld, 2015; Chifflard, Didszun, & Zepp, 2008). Generally, SSF develops in structured soils where bedrock or a less permeable soil layer is overlaid by a more permeable soil layer and vertically percolating water is deflected, at least partially, in a lateral downslope direction due to the slope inclination. SSF can also occur when groundwater levels rise into more permeable soil layers and water flows laterally through the more permeable layers to the stream (“transmissivity feedback mechanism”; Bishop, Grip, & O'Neill, 1990). The different existing terms for SSF in the hydrological literature such as shallow subsurface run‐off, interflow, lateral flow, or soil water flow reflects the different underlying process concepts developed in various experimental studies in different environments by using different experimental approaches at different spatial and temporal scales (Weiler, McDonnell, Tromp‐van Meerveld, & Uchida, 2005). Intersite comparisons and the extraction of general rules for SSF generation and its controlling factors are still lacking, which hampers the development of appropriate approaches for modelling SSF. But appropriate prediction of SSF is essential due to its clear influence on run‐off generation at the catchment scale (e.g., Chifflard et al., 2010; Zillgens, Merz, Kirnbauer, & Tilch, 2005), on the formation of floods (e.g., Markart et al., 2013, 2015) and on the transport of nutrients or pollutants from the hillslopes into surface water bodies (Zhao, Tang, Zhao, Wang, & Tang, 2013). However, a precise simulation of SSF in models requires an accurate process understanding including, knowledge about water pathways, residence times, magnitude of water fluxes, or the spatial origin of SSF within a given catchment because such factors determine the transport of subsurface water and solutes to the stream. But due to its occurrence in the subsurface and its spatial and temporal variability, determining and quantifying the processes generating SSF is a challenging task as they cannot be observed directly. Therefore, it is logical to ask whether we can really model SSF correctly if we cannot measure it well enough on the scale of interest (Figure 1). This commentary reflects critically on whether current experimental concepts and modelling approaches are sufficient to predict the contribution of SSF to the run‐off at the catchment scale. This applies in particular to the underlying processes, controlling factors, modelling approaches, research gaps, and innovative strategies to trace SSF across different scales

    Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

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    Transpiration is a key process in the hydrological cycle, and a sound understanding and quantification of transpiration and its spatial variability is essential for management decisions as well as for improving the parameterisation and evaluation of hydrological and soil–vegetation–atmosphere transfer models. For individual trees, transpiration is commonly estimated by measuring sap flow. Besides evaporative demand and water availability, tree-specific characteristics such as species, size or social status control sap flow amounts of individual trees. Within forest stands, properties such as species composition, basal area or stand density additionally affect sap flow, for example via competition mechanisms. Finally, sap flow patterns might also be influenced by landscape-scale characteristics such as geology and soils, slope position or aspect because they affect water and energy availability; however, little is known about the dynamic interplay of these controls. We studied the relative importance of various tree-, stand- and site-specific characteristics with multiple linear regression models to explain the variability of sap velocity measurements in 61 beech and oak trees, located at 24 sites across a 290 km2 catchment in Luxembourg. For each of 132 consecutive days of the growing season of 2014 we modelled the daily sap velocity and derived sap flow patterns of these 61 trees, and we determined the importance of the different controls. Results indicate that a combination of mainly tree- and site-specific factors controls sap velocity patterns in the landscape, namely tree species, tree diameter, geology and aspect. For sap flow we included only the stand- and site-specific predictors in the models to ensure variable independence. Of those, geology and aspect were most important. Compared to these predictors, spatial variability of atmospheric demand and soil moisture explains only a small fraction of the variability in the daily datasets. However, the temporal dynamics of the explanatory power of the tree-specific characteristics, especially species, are correlated to the temporal dynamics of potential evaporation. We conclude that transpiration estimates on the landscape scale would benefit from not only consideration of hydro-meteorological drivers, but also tree, stand and site characteristics in order to improve the spatial and temporal representation of transpiration for hydrological and soil–vegetation–atmosphere transfer models

    Form and function in hillslope hydrology : Characterization of subsurface flow based on response observations

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    The phrase form and function was established in architecture and biology and refers to the idea that form and functionality are closely correlated, influence each other, and co-evolve. We suggest transferring this idea to hydrological systems to separate and analyze their two main characteristics: their form, which is equivalent to the spatial structure and static properties, and their function, equivalent to internal responses and hydrological behavior. While this approach is not particularly new to hydrological field research, we want to employ this concept to explicitly pursue the question of what information is most advantageous to understand a hydrological system. We applied this concept to subsurface flow within a hillslope, with a methodological focus on function: we conducted observations during a natural storm event and followed this with a hillslope-scale irrigation experiment. The results are used to infer hydrological processes of the monitored system. Based on these findings, the explanatory power and conclusiveness of the data are discussed. The measurements included basic hydrological monitoring methods, like piezometers, soil moisture, and discharge measurements. These were accompanied by isotope sampling and a novel application of 2-D time-lapse GPR (ground-penetrating radar). The main finding regarding the processes in the hillslope was that preferential flow paths were established quickly, despite unsaturated conditions. These flow paths also caused a detectable signal in the catchment response following a natural rainfall event, showing that these processes are relevant also at the catchment scale. Thus, we conclude that response observations (dynamics and patterns, i.e., indicators of function) were well suited to describing processes at the observational scale. Especially the use of 2-D time-lapse GPR measurements, providing detailed subsurface response patterns, as well as the combination of stream-centered and hillslope-centered approaches, allowed us to link processes and put them in a larger context. Transfer to other scales beyond observational scale and generalizations, however, rely on the knowledge of structures (form) and remain speculative. The complementary approach with a methodological focus on form (i.e., structure exploration) is presented and discussed in the companion paper by Jackisch et al.(2017)
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