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Research project (§ 26 & § 27)
Duration : 2019-05-01 - 2022-11-30

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.
Research project (§ 26 & § 27)
Duration : 2015-03-01 - 2016-11-30

In a one-years-extension of our previous project “Robust Risk Estimation”, we focus on multivariate aspects and dynamics of extreme events so far not yet covered in this detail. In all our reference applications, i.e., in financial risks of a bank, public health (hospital length of stay and costs), and hydrology (river discharge), there are important questions where these aspects cannot be ignored but rather have to be accounted for. With thin empirical evidence available to this end, misspecification becomes a central issue in the corresponding applications. As a remedy to some extent, we propose to enhance our robust procedures applying robust likelihood techniques to adjust our procedures in a way that they can adapt to minor to moderate model misspecifications. This continues our successful work on theoretical foundation, development and application of robust procedures for risk management of complex systems in the presence of extreme events. In particular this extends the applicability of our software infrastructure in R developed in the current project with its powerful set of diagnostic tools to assess influence and outlyingness of data.
Research project (§ 26 & § 27)
Duration : 2015-03-01 - 2018-12-31

The aim of the project is a comprehensive analysis of drought and low flows in Central Europe (Austria and neighbour regions) under past, present and future conditions. The approach aims to overcome the limitations of current trend analysis and climate projections by a joint analysis of streamflow anomalies with meteorological drivers and tree ring records in the longer past, including the pre-instrumental period. This new understanding of drought generation in the climate and hydrological system will be used (i) to put current extreme low flow conditions into the context of climate change during the past millennium, and (ii) to develop improved models for predicting future drought conditions. The results target government agencies at both state and federal levels and private businesses and the main benefit will come from reduced costs to the government budgets as well as to private stakeholders due to more efficient management decisions.

Supervised Theses and Dissertations