Spatiotemporal models for streamflow - Evolution and enhancement of statistical space-time methods that extend temporal and spatial statistics for river networks

Spatiotemporal models are a common approach to a wide range of environmental problems. In this context it can be distinguished between a single spatiotemporal model, temporal functions correlated in space or spatial functions correlated in time. Applications of time series extended to a spatial scale are widely used in modelling air pollution, whereas spatial methods extended to a temporal scale can be found in, e.g., soil moisture modelling or interpolation of meteorological variables. However, the application of spatiotemporal models to streamflow is rare. This can be explained by the nested characteristics of streamflow catchments, the tree-wise structure of river networks and spatial and temporal variability of co-variables, such as physiographic catchment characteristics or meteorological variables. Considering these conditions, it is essential to develop and improve spatiotemporal models for streamflow that take the specific spatial and temporal variability into account. Therefore, the project’s aim is to adapt spatiotemporal methods for streamflow and to extend temporal and spatial models to a space-time framework. The main hypothesis of the doctoral proposal is the following: Spatiotemporal models for streamflow can be developed that yield better predictions than (i) point-wise methods in time or space, (ii) and spatiotemporal methods that do not consider the river network topology. To assess this science question in detail, the project is split up into four work packages and each is completed by the submission of a paper to a peer-reviewed journal.