Short online conferences (max. 1 hour; every two months) should bring the OpenSense community the idea of the newest opportunistic sensor data achievements concerning opening data, data processing algorithms, and applications. During the conference, two speakers will be invited (20 min talk + 10 min discussion).
The fifth conference willtake place on 30 July 2024, 9:00 CET.
Martin Fencl will present on Microwave link data for urban water management
The contribution will present the possibilities of using commercial microwave link (CML) data in the field ofurban drainage. It will summarize the main findings obtained during ten years of our research and present a pilot study in the city of Olomouc, where CML data was provided in near real time to the water utility to optimize wastewater treatment plant performance. The contribution will show what this data bring to modelling runoff from urban catchments of different sizes. How they can be combined with standard observations (rain gauges, flow meters) and if they can be used with existing physical models or with ad-hoc constructed data-driven models.
Greta Cazzaniga and Andrijana Todorovic will present on Hydrological application of OS data: The Lambro catchment case study
The spatial variability ofprecipitation is a source of major uncertainty in hydrological modelling. Opportunistic sensors (OS), such as commercial microwave links (CML), mayprovide a significant contribution to the characterization of rainfall spatial variability. Indeed, OS rainfall data can complement observations collected from traditional sensors as networks of raingauges or radars, and contribute to obtaining the fine-scale rainfall data needed for accurate hydrological modelling. OS rainfall data, especially the CML-based ones, have been used to simulate runoff. However, these studies mainly focused on small urban catchments, whereas applications to larger peri-urban or rural catchments are scarce. Our research presents a pioneering attempt to apply CML-sensed rainfall data for runoff simulations in the peri-urban Lambro catchment in Italy. After the pre-processing of the OS data, the CML-based rainfall is evaluated from the standpoint of hydrological modelling by comparing model performance to that ofthe model driven by other rainfall data, obtained from raingauges and radar. Based on our results, we provide recommendations for applications of CML-sensed rainfall for large-scale hydrological modelling.
Join Zoom Meeting
https://tuwien.zoom.us/j/64842384214
Meeting ID: 648 4238 4214
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