X-band radar data and predictive management in urban hydrology

Abstract : The main goal of this thesis was to achieve a reliable management tool of storm water storage basins using high resolution X-band radar. It turned out that it required several research developments. The analysed case study includes a retention basin of 10000 m3 located in Val de Marne county downstream of a 2.15 km2 urban catchment. It has a twofold goal: storm water decontamination and flood protection by volume storage. Operationally the management strategies associated with these two aims are conflicting; hence, a predictive management has been set up: a routine exploitation of the basin in the anti-pollution mode, and a switch to the flood protection mode when needed. It should be based a reliable knowledge of short-term rainfall forecasts. A common way to respond to operational needs of the predictive management is to set up a warning system based on the use of radar data. For example, the CALAMAR system relies on the use of single-polarization raw radar data, coming from Meteo-France radar network, being processed with the conventional Z-R conversion methods followed by a calibration with rain gauge. However, the reliability of such warning systems has been subject to debate, often due to a questionable quality of the resulting radar rainfall estimates, compared to local rain gauges. Therefore a new methodology for more meaningful comparison of radar rainfall field products was developed during this PhD project. Being rooted to the multifractal theory, it allows a comparison of the structure and the morphology of rainfall fields in both space and time through scales. It was initially tested on CALAMAR and Meteo-France rainfall products before being applied for results confirmation on initial data from a X band radar, acquired by Ecole des Ponts ParisTech in the framework of the European project RainGain and providing data at higher resolution (up to 100 m in space and 1 min in time). The obtained results not only highlight the crucial influence of raw data processing on the scaling behaviour, but also permit to pre-define the conditions when the CALAMAR optimization may worsen the quality of rainfall estimates. Such conditions would be very difficult to detect with widely used conventional methods, which rely on a very limited number of radar pixels (only those containing rain gauges). Further extensions of the proposed methodology open new horizons for the rainfall data merging. While the scientific literature, notably around the TOMACS experiment in Japan and CASA one in the United States, highlights the operational benefits of higher resolution rainfall measurements thanks to X-band radars, its impact on the performance of hydrological models still remains a subject of debate. Indeed previous research, mainly based on conceptual models remains inconclusive. To overcome these limitations, we used two models relying on two very distinct modelling approaches: CANOE (semi-distributed and conceptual) and Multi-Hydro (fully distributed and physically based research model developed at ENPC). An operational version of CANOE and a new much finer configuration, which increases the sensitivity of the model to spatio-temporal variability of small-scale rainfall, were used. Several extensions of the Multi-Hydro were developed, including an optimization of its resolution, which greatly improves its whole functionality. It appears from this work that by taking into account the spatial and temporal variability of small-scale rainfall, the performance of hydrologic models can be increased up to 20%.Overall, we believe that this dissertation contributes to the development of new, reliable, operational tools to use in their full extent the high-resolution X-band data
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Abdellah Ichiba. X-band radar data and predictive management in urban hydrology. Hydrology. Université Paris-Est, 2016. English. ⟨NNT : 2016PESC1007⟩. ⟨tel-01437464⟩

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