@article{ReichMikolajBlumeetal.2021,
  author    = {Reich, Marvin and Mikolaj, Michal and Blume, Theresa and G{\"u}ntner, Andreas},
  title     = {Field-scale subsurface flow processes inferred from continuous gravity monitoring during a sprinkling experiment},
  series = {Water resources research : WRR / American Geophysical Union},
  volume    = {57},
  journal   = {Water resources research : WRR / American Geophysical Union},
  number    = {10},
  publisher = {Wiley},
  address   = {New York},
  issn      = {0043-1397},
  doi       = {10.1029/2021WR030044},
  pages     = {18},
  year      = {2021},
  abstract  = {Field-scale subsurface flow processes are difficult to observe and monitor. We investigated the value of gravity time series to identify subsurface flow processes by carrying out a sprinkling experiment in the direct vicinity of a superconducting gravimeter. We demonstrate how different water mass distributions in the subsoil affect the gravity signal and show the benefit of using the shape of the gravity response curve to identify different subsurface flow processes. For this purpose, a simple hydro-gravimetric model was set up to test different scenarios in an optimization approach, including the processes macropore flow, preferential flow, wetting front advancement (WFA), bypass flow and perched water table rise. Besides the gravity observations, electrical resistivity and soil moisture data were used for evaluation. For the study site, the process combination of preferential flow and WFA led to the best correspondence to the observations in a multi-criteria assessment. We argue that the approach of combining field-scale sprinkling experiments in combination with gravity monitoring can be transferred to other sites for process identification, and discuss related uncertainties including limitations of the simple model used here. The study stresses the value of advancing terrestrial gravimetry as an integrative and non-invasive monitoring technique for assessing hydrological states and dynamics.},
  language  = {en}
}
@article{BlumeSchneiderGuentner2021,
  author    = {Blume, Theresa and Schneider, Lisa and G{\"u}ntner, Andreas},
  title     = {Comparative analysis of throughfall observations in six different forest stands},
  series = {Hydrological processes},
  volume    = {36},
  journal   = {Hydrological processes},
  number    = {3},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0885-6087},
  doi       = {10.1002/hyp.14461},
  pages     = {21},
  year      = {2021},
  abstract  = {Throughfall, that is, the fraction of rainfall that passes through the forest canopy, is strongly influenced by rainfall and forest stand characteristics which are in turn both subject to seasonal dynamics. Disentangling the complex interplay of these controls is challenging, and only possible with long-term monitoring and a large number of throughfall events measured in parallel at different forest stands. We therefore based our analysis on 346 rainfall events across six different forest stands at the long-term terrestrial environmental observatory TERENO Northeast Germany. These forest stands included pure stands of beech, pine and young pine, and mixed stands of oak-beech, pine-beech and pine-oak-beech. Throughfall was overall relatively low, with 54-68\% of incident rainfall in summer. Based on the large number of events it was possible to not only investigate mean or cumulative throughfall but also its statistical distribution. The distributions of throughfall fractions show distinct differences between the three types of forest stands (deciduous, mixed and pine). The distributions of the deciduous stands have a pronounced peak at low throughfall fractions and a secondary peak at high fractions in summer, as well as a pronounced peak at higher throughfall fractions in winter. Interestingly, the mixed stands behave like deciduous stands in summer and like pine stands in winter: their summer distributions are similar to the deciduous stands but the winter peak at high throughfall fractions is much less pronounced. The seasonal comparison further revealed that the wooden components and the leaves behaved differently in their throughfall response to incident rainfall, especially at higher rainfall intensities. These results are of interest for estimating forest water budgets and in the context of hydrological and land surface modelling where poor simulation of throughfall would adversely impact estimates of evaporative recycling and water availability for vegetation and runoff.},
  language  = {en}
}
@article{HeistermannBogenaFranckeetal.2022,
  author    = {Heistermann, Maik and Bogena, Heye and Francke, Till and G{\"u}ntner, Andreas and Jakobi, Jannis and Rasche, Daniel and Schr{\"o}n, Martin and D{\"o}pper, Veronika and Fersch, Benjamin and Groh, Jannis and Patil, Amol and P{\"u}tz, Thomas and Reich, Marvin and Zacharias, Steffen and Zengerle, Carmen and Oswald, Sascha},
  title     = {Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site W{\"u}stebach},
  series = {Earth System Science Data (ESSD)},
  volume    = {14},
  journal   = {Earth System Science Data (ESSD)},
  publisher = {Copernicus},
  address   = {Katlenburg-Lindau},
  issn      = {1866-3516},
  doi       = {10.5194/essd-14-2501-2022},
  pages     = {2501 -- 2519},
  year      = {2022},
  abstract  = {Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 W{\"u}stebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land-atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.},
  language  = {en}
}
@article{TrautmannKoiralaCarvalhaisetal.2022,
  author    = {Trautmann, Tina and Koirala, Sujan and Carvalhais, Nuno and G{\"u}ntner, Andreas and Jung, Martin},
  title     = {The importance of vegetation in understanding terrestrial water storage variations},
  series = {Hydrology and Earth System Sciences},
  volume    = {26},
  journal   = {Hydrology and Earth System Sciences},
  number    = {4},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-26-1089-2022},
  pages     = {1089 -- 1109},
  year      = {2022},
  abstract  = {So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff ( Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.},
  language  = {en}
}