Urban Drainage; Remote sensing; Microwave attenuation; Statistical system emulators; Uncertainty analysis; Model structure deficits
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Bianchi Blandine, Rieckermann Jörg, Berne Alexis (2013), Quality control of rain gauge measurements using telecommunication microwave links, in Journal of Hydrology
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Schleiss Marc A., Rieckermann Jörg, Berne Alexis (2013), Quantification and modeling of wet-antenna attenuation for commercial microwave links, in IEEE Geoscience and Remote Sensing Letters
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Bianchi Blandine, A Variational Approach to Retrieve Rain Rate by Combining Information from Rain Gauges, Radars, and Microwave Links, in JOURNAL OF HYDROMETEOROLOGY
Engineers design and operate combined or separate sewer systems to provide sanitation and flood protection for our cities. However, the rapid drainage of urban areas and the emissions of untreated wastewater from com-bined sewer overflows increase the stress to receiving waters. This is expected to be even more pronounced in the future, because of emerging toxic contaminants, such as pesticides, and because of climate change which will probably increase volumes and variability of rainfall. Consequently there is an urgent need for mitigation measures and to better use our existing infrastructure. However, promising solutions, such as optimal operation through real-time control of drainage networks (RTC), or the integrated optimization of sewers, wastewater treatment plants and rivers through Integrated Modeling (IM), are limited by two factors: poor rainfall data and the computational costs of complex simulation models. To minimize receiving water pollution and at the same time maintain current flood pro-tection levels, we must therefore, first, provide high-resolution rainfall data beyond the point estimates of traditional rain gauges and the inaccurate information from rainfall radars. Second, we need fast simulation models and novel techniques of uncertainty analysis to increase the credibility of model predictions.The goals of our project are therefore to reduce the uncertainty in predicted wet weather flows with better rainfall information and to decrease the computational requirements of sewer flow prediction models. Specifi-cally, we will i) use commercial microwave links (MWLs) from telecommunication networks as virtual rain gauges and ii) develop algorithms for data assimilation to predict probabilistic rainfall fields that combine various types of rainfall information. In parallel, we will investigate novel methods to iii) identify model structure deficits and quantify associated uncertainty and iv) develop efficient emulators of hydrodynamic sewer flow models that will be used, to-gether with the probabilistic rainfall fields, to reduce the uncertainty of predicted sewer flows.In the future, such efficient emulators, together with improved rainfall information based on MWLs, will allow for a better operation of urban drainage systems, for example through RTC or IM, and for improved uncertainty assess-ment of the involved simulation models. Accurate rainfall data with a spatial resolution of a few hundred square me-ters and in time steps of few seconds or minutes would advance not only urban hydrology, but will allow for com-pletely new types of analyses in a variety of fields such as meteorology, hydrology, diffuse pollution and insurances.To reach these goals, we need an interdisciplinary project team of hydrometeorologists, engineers and ap-plied mathematicians for two distinct reasons. First, the knowledge of hydrometeorologists and engineers is needed to process the information from MWL and to calibrate and validate the estimated high-resolution rainfall fields using monitored runoff and detailed sewer flow modeling. Second, both disciplines need the contributions of applied mathe-maticians to rigorously account for the associated uncertainties and to develop fast emulators that decrease the simula-tion time of complex computer models.