@phdthesis{Yang2020,
  author    = {Yang, Xiaoqiang},
  title     = {Spatial and temporal analyses of catchment and in-stream nitrate dynamics},
  doi       = {10.25932/publishup-47702},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-477029},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VIII, 146},
  year      = {2020},
  abstract  = {Water quality in river systems is of growing concern due to rising anthropogenic pressures and climate change. Mitigation efforts have been placed under the guidelines of different governance conventions during last decades (e.g., the Water Framework Directive in Europe). Despite significant improvement through relatively straightforward measures, the environmental status has likely reached a plateau. A higher spatiotemporal accuracy of catchment nitrate modeling is, therefore, needed to identify critical source areas of diffuse nutrient pollution (especially for nitrate) and to further guide implementation of spatially differentiated, cost-effective mitigation measures. On the other hand, the emerging high-frequency sensor monitoring upgrades the monitoring resolution to the time scales of biogeochemical processes and enables more flexible monitoring deployments under varying conditions. The newly available information offers new prospects in understanding nitrate spatiotemporal dynamics. Formulating such advanced process understanding into catchment models is critical for model further development and environmental status evaluation. This dissertation is targeting on a comprehensive analysis of catchment and in-stream nitrate dynamics and is aiming to derive new insights into their spatial and temporal variabilities through the new fully distributed model development and the new high-frequency data. Firstly, a new fully distributed, process-based catchment nitrate model (the mHM-Nitrate model) is developed based on the mesoscale Hydrological Model (mHM) platform. Nitrate process descriptions are adopted from the Hydrological Predictions for the Environment (HYPE), with considerable improved implementations. With the multiscale grid-based discretization, mHM-Nitrate balances the spatial representation and the modeling complexity. The model has been thoughtfully evaluated in the Selke catchment (456 km2), central Germany, which is characterized by heterogeneous physiographic conditions. Results show that the model captures well the long-term discharge and nitrate dynamics at three nested gauging stations. Using daily nitrate-N observations, the model is also validated in capturing short-term fluctuations due to changes in runoff partitioning and spatial contribution during flooding events. By comparing the model simulations with the values reported in the literature, the model is capable of providing detailed and reliable spatial information of nitrate concentrations and fluxes. Therefore, the model can be taken as a promising tool for environmental scientists in advancing environmental modeling research, as well as for stakeholders in supporting their decision-making, especially for spatially differentiated mitigation measures. Secondly, a parsimonious approach of regionalizing the in-stream autotrophic nitrate uptake is proposed using high-frequency data and further integrated into the new mHM-Nitrate model. The new regionalization approach considers the potential uptake rate (as a general parameter) and effects of above-canopy light and riparian shading (represented by global radiation and leaf area index data, respectively). Multi-parameter sensors have been continuously deployed in a forest upstream reach and an agricultural downstream reach of the Selke River. Using the continuous high-frequency data in both streams, daily autotrophic uptake rates (2011-2015) are calculated and used to validate the regionalization approach. The performance and spatial transferability of the approach is validated in terms of well-capturing the distinct seasonal patterns and value ranges in both forest and agricultural streams. Integrating the approach into the mHM-Nitrate model allows spatiotemporal variability of in-stream nitrate transport and uptake to be investigated throughout the river network. Thirdly, to further assess the spatial variability of catchment nitrate dynamics, for the first time the fully distributed parameterization is investigated through sensitivity analysis. Sensitivity results show that parameters of soil denitrification, in-stream denitrification and in-stream uptake processes are the most sensitive parameters throughout the Selke catchment, while they all show high spatial variability, where hot-spots of parameter sensitivity can be explicitly identified. The Spearman rank correlation is further analyzed between sensitivity indices and multiple catchment factors. The correlation identifies that the controlling factors vary spatially, reflecting heterogeneous catchment responses in the Selke catchment. These insights are, therefore, informative in informing future parameter regionalization schemes for catchment water quality models. In addition, the spatial distributions of parameter sensitivity are also influenced by the gauging information that is being used for sensitivity evaluation. Therefore, an appropriate monitoring scheme is highly recommended to truly reflect the catchment responses.},
  language  = {en}
}