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• W4226180533 startingPage "2093" @default.
• W4226180533 abstract "Abstract. Commercial microwave links (CMLs) can be used as opportunistic and unconventional rainfall sensors by converting the received signal level into path-averaged rainfall intensity. As the reliable reconstruction of the spatial distribution of rainfall is still a challenging issue in meteorology and hydrology, there is a widespread interest in integrating the precipitation estimates gathered by the ubiquitous CMLs with the conventional rainfall sensors, i.e. rain gauges (RGs) and weather radars. Here, we investigate the potential of a dense CML network for the estimation of river discharges via a semi-distributed hydrological model. The analysis is conducted in a peri-urban catchment, Lambro, located in northern Italy and covered by 50 links. A two-level comparison is made between CML- and RG-based outcomes, relying on 12 storm/flood events. First, rainfall data are spatially interpolated and assessed in a set of significant points of the catchment area. Rainfall depth values obtained from CMLs are definitively comparable with direct RG measurements, except for the spells of persistent light rain, probably due to the limited sensitivity of CMLs caused by the coarse quantization step of raw power data. Moreover, it is shown that, when changing the type of rainfall input, a new calibration of model parameters is required. In fact, after the recalibration of model parameters, CML-driven model performance is comparable with RG-driven performance, confirming that the exploitation of a CML network may be a great support to hydrological modelling in areas lacking a well-designed and dense traditional monitoring system." @default.
• W4226180533 created "2022-05-05" @default.
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• W4226180533 date "2022-04-27" @default.
• W4226180533 modified "2023-10-16" @default.
• W4226180533 title "Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment" @default.
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• W4226180533 doi "https://doi.org/10.5194/hess-26-2093-2022" @default.
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