@article{MuellerSchuelerZechetal.2022,
  author    = {M{\"u}ller, Sebastian and Sch{\"u}ler, Lennart and Zech, Alraune and Heße, Falk},
  title     = {GSTools v1.3: a toolbox for geostatistical modelling in Python},
  series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union},
  volume    = {15},
  journal   = {Geoscientific model development : an interactive open access journal of the European Geosciences Union},
  number    = {7},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1991-959X},
  doi       = {10.5194/gmd-15-3161-2022},
  pages     = {3161 -- 3182},
  year      = {2022},
  abstract  = {Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.},
  language  = {en}
}
@article{BaroniSchalgeRakovecetal.2019,
  author    = {Baroni, Gabriele and Schalge, Bernd and Rakovec, Oldrich and Kumar, Rohini and Sch{\"u}ler, Lennart and Samaniego, Luis and Simmer, Clemens and Attinger, Sabine},
  title     = {A Comprehensive Distributed Hydrological Modeling Intercomparison to Support Process Representation and Data Collection Strategies},
  series = {Water resources research},
  volume    = {55},
  journal   = {Water resources research},
  number    = {2},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2018WR023941},
  pages     = {990 -- 1010},
  year      = {2019},
  abstract  = {The improvement of process representations in hydrological models is often only driven by the modelers' knowledge and data availability. We present a comprehensive comparison between two hydrological models of different complexity that is developed to support (1) the understanding of the differences between model structures and (2) the identification of the observations needed for model assessment and improvement. The comparison is conducted on both space and time and by aggregating the outputs at different spatiotemporal scales. In the present study, mHM, a process-based hydrological model, and ParFlow-CLM, an integrated subsurface-surface hydrological model, are used. The models are applied in a mesoscale catchment in Germany. Both models agree in the simulated river discharge at the outlet and the surface soil moisture dynamics, lending their supports for some model applications (drought monitoring). Different model sensitivities are, however, found when comparing evapotranspiration and soil moisture at different soil depths. The analysis supports the need of observations within the catchment for model assessment, but it indicates that different strategies should be considered for the different variables. Evapotranspiration measurements are needed at daily resolution across several locations, while highly resolved spatially distributed observations with lower temporal frequency are required for soil moisture. Finally, the results show the impact of the shallow groundwater system simulated by ParFlow-CLM and the need to account for the related soil moisture redistribution. Our comparison strategy can be applied to other models types and environmental conditions to strengthen the dialog between modelers and experimentalists for improving process representations in Earth system models.},
  language  = {en}
}
@article{CarminatiSchneiderMoradietal.2011,
  author    = {Carminati, Andrea and Schneider, Christoph L. and Moradi, Ahmad B. and Zarebanadkouki, Mohsen and Vetterlein, Doris and Vogel, Hans-J{\"o}rg and Hildebrandt, Anke and Weller, Ulrich and Sch{\"u}ler, Lennart and Oswald, Sascha Eric},
  title     = {How the rhizosphere may favor water availability to roots},
  series = {Vadose zone journal},
  volume    = {10},
  journal   = {Vadose zone journal},
  number    = {3},
  publisher = {Soil Science Society of America},
  address   = {Madison},
  issn      = {1539-1663},
  doi       = {10.2136/vzj2010.0113},
  pages     = {988 -- 998},
  year      = {2011},
  abstract  = {Recent studies have shown that rhizosphere hydraulic properties may differ from those of the bulk soil. Specifically, mucilage at the root-soil interface may increase the rhizosphere water holding capacity and hydraulic conductivity during drying. The goal of this study was to point out the implications of such altered rhizosphere hydraulic properties for soil-plant water relations. We addressed this problem through modeling based on a steady-rate approach. We calculated the water flow toward a single root assuming that the rhizosphere and bulk soil were two concentric cylinders having different hydraulic properties. Based on our previous experimental results, we assumed that the rhizosphere had higher water holding capacity and unsaturated conductivity than the bulk soil. The results showed that the water potential gradients in the rhizosphere were much smaller than in the bulk soil. The consequence is that the rhizosphere attenuated and delayed the drop in water potential in the vicinity of the root surface when the soil dried. This led to increased water availability to plants, as well as to higher effective conductivity under unsaturated conditions. The reasons were two: (i) thanks to the high unsaturated conductivity of the rhizosphere, the radius of water uptake was extended from the root to the rhizosphere surface; and (ii) thanks to the high soil water capacity of the rhizosphere, the water depletion in the bulk soil was compensated by water depletion in the rhizosphere. We conclude that under the assumed conditions, the rhizosphere works as an optimal hydraulic conductor and as a reservoir of water that can be taken up when water in the bulk soil becomes limiting.},
  language  = {en}
}