@phdthesis{Pilz2020,
  author    = {Pilz, Tobias},
  title     = {Pursuing the understanding of uncertainties in hydrological modelling},
  doi       = {10.25932/publishup-47664},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-476643},
  school      = {Universit{\"a}t Potsdam},
  pages     = {136},
  year      = {2020},
  abstract  = {Hydrological models are important tools for the simulation and quantification of the water cycle. They therefore aid in the understanding of hydrological processes, prediction of river discharge, assessment of the impacts of land use and climate changes, or the management of water resources. However, uncertainties associated with hydrological modelling are still large. While significant research has been done on the quantification and reduction of uncertainties, there are still fields which have gained little attention so far, such as model structural uncertainties that are related to the process implementations in the models. This holds especially true for complex process-based models in contrast to simpler conceptual models. Consequently, the aim of this thesis is to improve the understanding of structural uncertainties with focus on process-based hydrological modelling, including methods for their quantification. To identify common deficits of frequently used hydrological models and develop further strategies on how to reduce them, a survey among modellers was conducted. It was found that there is a certain degree of subjectivity in the perception of modellers, for instance with respect to the distinction of hydrological models into conceptual groups. It was further found that there are ambiguities on how to apply a certain hydrological model, for instance how many parameters should be calibrated, together with a large diversity of opinion regarding the deficits of models. Nevertheless, evapotranspiration processes are often represented in a more physically based manner, while processes of groundwater and soil water movement are often simplified, which many survey participants saw as a drawback. A large flexibility, for instance with respect to different alternative process implementations or a small number of parameters that needs to be calibrated, was generally seen as strength of a model. Flexible and efficient software, which is straightforward to apply, has been increasingly acknowledged by the hydrological community. This work further elaborated on this topic in a twofold way. First, a software package for semi-automated landscape discretisation has been developed, which serves as a tool for model initialisation. This was complemented by a sensitivity analysis of important and commonly used discretisation parameters, of which the size of hydrological sub-catchments as well as the size and number of hydrologically uniform computational units appeared to be more influential than information considered for the characterisation of hillslope profiles. Second, a process-based hydrological model has been implemented into a flexible simulation environment with several alternative process representations and a number of numerical solvers. It turned out that, even though computation times were still long, enhanced computational capabilities nowadays in combination with innovative methods for statistical analysis allow for the exploration of structural uncertainties of even complex process-based models, which up to now was often neglected by the modelling community. In a further study it could be shown that process-based models may even be employed as tools for seasonal operational forecasting. In contrast to statistical models, which are faster to initialise and to apply, process-based models produce more information in addition to the target variable, even at finer spatial and temporal scales, and provide more insights into process behaviour and catchment functioning. However, the process-based model was much more dependent on reliable rainfall forecasts. It seems unlikely that there exists a single best formulation for hydrological processes, even for a specific catchment. This supports the use of flexible model environments with alternative process representations instead of a single model structure. However, correlation and compensation effects between process formulations, their parametrisation, and other aspects such as numerical solver and model resolution, may lead to surprising results and potentially misleading conclusions. In future studies, such effects should be more explicitly addressed and quantified. Moreover, model functioning appeared to be highly dependent on the meteorological conditions and rainfall input generally was the most important source of uncertainty. It is still unclear, how this could be addressed, especially in the light of the aforementioned correlations. The use of innovative data products, e.g.\ remote sensing data in combination with station measurements, and efficient processing methods for the improvement of rainfall input and explicit consideration of associated uncertainties is advisable to bring more insights and make hydrological simulations and predictions more reliable.},
  language  = {en}
}