| Abstrakt: |
Commercial Microwave Links (CMLs) can be used as opportunistic and unconventional rainfall sensors by converting the received signal level into path-averaged rainfall intensity. Since in meteorology and hydrology the reliable reconstruction of the rainfall spatial distribution is still a challenging issue, there is a wide-spread 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 on Lambro, a peri-urban catchment 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, due to limited sensitivity of CMLs caused by the coarse quantization step of raw power data. Moreover, it is showed that, when changing the type of rainfall input, a new calibration of model parameters is required. In fact, after the re-calibration of model parameters, CML-driven outputs performances are comparable with RG-driven ones, confirming that the exploitation of a CML network may lead to benefit in hydrological modelling. [ABSTRACT FROM AUTHOR] |
