@misc{BaroniZinkKumaretal.2017,
  author    = {Baroni, Gabriele and Zink, Matthias and Kumar, Rohini and Samaniego, Luis and Attinger, Sabine},
  title     = {Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {545},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41917},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419174},
  pages     = {20},
  year      = {2017},
  abstract  = {Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.},
  language  = {en}
}
@article{BaroniZinkKumaretal.2017,
  author    = {Baroni, Gabriele and Zink, Matthias and Kumar, Rohini and Samaniego, Luis and Attinger, Sabine},
  title     = {Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales},
  series = {Hydrology and earth system sciences : HESS},
  volume    = {21},
  journal   = {Hydrology and earth system sciences : HESS},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-21-2301-2017},
  pages     = {2301 -- 2320},
  year      = {2017},
  abstract  = {Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.},
  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}
}