Research

The aim of the study is a consistent estimation of a wide range of low-flow characteristics at observed and unobserved river sites in Austria. For this purpose, novel regionalisation models are to be developed and evaluated with regard to their predictive performance at unobserved sites. A nested model is proposed as the model structure, which takes into account low flow (NQ) of different time scales (year, season, month, minimum observed value) in hierarchical form, in order to obtain consistent estimates of derived mean characteristic values and extreme value statistics. The regionalisation aims at a consistent estimation of natural low flows and uses a data set of at most slightly anthropogenically influenced daily flow series with more than 40 years of observations.

The aim of this project is to assess the impact of summer low flows on the remobilization of pollutants from river sediments. The analyses are carried out for eastern Austria, where agricultural input and the predicted risk of climate warming on low flow and water temperature are particularly high. The innovative combination of data-based models with laboratory experiments and water quality monitoring allows an overall assessment of the sediment-related risk of quality impairment along the water network. The added value of the derived information is presented for three selected catchments and discussed with stakeholders with regard to water management relevance. From this, recommendations for future climate scenarios will be developed.

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.