@misc{Rottler2017,
  type      = {Master Thesis},
  author    = {Rottler, Erwin},
  title     = {Implementation of a snow routine into the hydrological model WASA-SED and its validation in a mountainous catchment},
  doi       = {10.25932/publishup-50496},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-504963},
  school      = {Universit{\"a}t Potsdam},
  pages     = {IV, 64},
  year      = {2017},
  abstract  = {In many regions of the world, snow accumulation and melt constitute important components of the hydrologic cycle. With the objective to improve model performance of the hydrological model WASA-SED (Water Availability in Semi-Arid environments - SEDiments) in catchments affected by snow and ice, a physically-based snow routine has been implemented into the model. The snow routine bases on the energy-balance method of the ECHSE (Eco-hydrological Simulation Environment) software. A first test application has been conducted in two sub-basins of the Is{\´a}bena river catchment (Central Spanish Pre-Pyrenees). Results were validated using satellite-derived snow cover data. Furthermore, a rainfall gauge correction algorithm to restore the liquid precipitation signal of measurements affected by solid precipitation was applied. The snow module proved to be able to capture the dynamics of the snow cover forming during the cold months of the year. The temporary storage of water in the snow cover is able to improve simulations of river discharge. General patterns of the temporal evolution of observed and simulated snow cover fractions coincide. The work conducted only represents a first step in the process of implementation and evaluation of a physically-based snow routine into WASA-SED. Future work is necessary to further improve and test the snow routine and to resolve difficulties that occurred during model applications in the catchment.},
  language  = {en}
}
@article{PilzFranckeBaronietal.2020,
  author    = {Pilz, Tobias and Francke, Till and Baroni, Gabriele and Bronstert, Axel},
  title     = {How to Tailor my process-based hydrological model?},
  series = {Water resources research},
  volume    = {56},
  journal   = {Water resources research},
  number    = {8},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2020WR028042},
  pages     = {24},
  year      = {2020},
  abstract  = {In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semidistributed, process-based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro-meteorological boundary conditions. For instance, we tested the Penman-Monteith and Shuttleworth \& Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore, it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in-depth analysis of process implementations and catchment functioning.},
  language  = {en}
}