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Monitoring data for the publication "Fast inundation simulation with RIM2D for urban flood risk assessment and forecasting"
Metadata
| Other contributing persons, institutions or organisations | Krebs, Peter - TU Dresden - ProjectLeader | |
| Other contributing persons, institutions or organisations | Apel, Heike - GFZ Potsdam (ORCID: 0000-0002-8852-652X) - Researcher | |
| Person(s) who is (are) responsible for the content of the research data | Benisch, Jakob - TU Dresden (ORCID: 0000-0002-4782-6024) | |
| Person(s) who is (are) responsible for the content of the research data | Helm, Björn - TU Dresden | |
| Person(s) who is (are) responsible for the content of the research data | Becker, Stephan - TU Dresden | |
| Person(s) who is (are) responsible for the content of the research data | Grummt, Sebastian - TU Dresden | |
| Used research instruments or devices | NIVUS POA / mini sensors, onset HOBO U-20 sensors | |
| Research objects | PhysicalObject: Discharge data from rain events in the sewer network of Dresden | |
| Abstract | Monitoring data used for model calibration and evaluation from the Urban Observatory of the Chair of Urban Water Management | |
| Applied methods and techniques | Water discharge measurement using mean velocity and water level / sewer profile measured by ultrasonic and pressure sensors at three monitoring stations (MS2 / Before MS2 / MS5) | |
| Counties, the data is referencing | GERMANY | de |
| Coordinates of places, the data is referencing | (MS2) "51.004937, 13.844218" | |
| Coordinates of places, the data is referencing | (Before MS2) "13.84344031, 51.00444264 " | |
| Coordinates of places, the data is referencing | (MS5) "13.84466506, 51.00408105" | |
| Regions the data is referencing | Saxony | |
| Additional keywords | pluvial flood, urban inundation, flood modelling, RIM2D, GPU acceleration, heavy precipitation, urban flood risk, flood forecasting | |
| Year or period of data production | 2016-2021 | |
| Publication year | 2023 | |
| Publisher | Technische Universität Dresden | |
| References on related materials | IsPartOf: 123456789/5959 (Handle) | |
| Content of the research data | Dataset: Time Series Data | |
| Holder of usage rights | Technische Universität Dresden | |
| Usage rights of the data | CC-BY-4.0 | |
| Additional precise description of discipline | Water Management | |
| Discipline(s) | Environmental Science and Ecology | de |
| Title of the dataset | Monitoring data for the publication "Fast inundation simulation with RIM2D for urban flood risk assessment and forecasting" |
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Supplementary Material - Fast inundation simulation with RIM2D for urban flood risk assessment and forecasting [1]
Urban pluvial flooding is a growing concern worldwide as consequence of rising urban population and climate change induced increases in heavy rainfall. Easy-to-implement and fast simulation tools are needed to cope with this challenge. This study describes the development of the parsimonious, GPU-accelerated hydraulic model RIM2D for urban pluvial flood simulations. This is achieved by considering the built-up urban area as flow obstacles, and by introducing capacity-based approaches to consider urban drainage by infiltration on pervious surfaces and sewer drainage from roofs and sealed surfaces. The model performance was analyzed by simulating 8 heavy rainfall events in a test area in the city of Dresden, Germany. For these events detailed discharge measurements of sewer discharge are available, providing a unique dataset for evaluating the sewer drainage simulation, which is of high importance for realistic pluvial inundation simulations in urban areas. We show that the model simulates the temporal dynamics of the sewer discharge and the sewer volume within acceptable ranges. Moreover, the erratic variation of the simulated to measured sewer discharge suggests that the deviations from the measurements are caused by the precipitation input rather than the model simplifications. We conclude that RIM2D is a valid tool for urban inundation simulation. Its short simulation runtimes allow probabilistic flood risk assessments and operational flood forecasts.