In the past L.G. Lanza has collaborated on articles with A. Molini. One of their most recent publications is I. Remote sensing in hydrologyRemote sensing in hydrology: Some downscaling and uncertainty issues. Which was published in journal Physics and Chemistry of the Earth.

More information about L.G. Lanza research including statistics on their citations can be found on their Copernicus Academic profile page.

L.G. Lanza's Articles: (2)

I. Remote sensing in hydrologyRemote sensing in hydrology: Some downscaling and uncertainty issues

AbstractSome issues in the field of remote sensing application in hydrology are discussed in this editorial paper based on several contributions presented at Session HS11 ‘Remote Sensing in Hydrology’ during the XXI General Assembly of EGS in The Hague. These include downscaling problems, reliability estimates, and the assessment of the uncertainty associated with the retrieval of hydrological variables at various scales of concern. Calibration and validation issues are among the most pressing problems, for a major difficulty confronting many hydrological applications is still that of providing the available sophisticated models with very detailed initial and boundary conditions, from extensive high resolution monitoring campaigns.

Improving the accuracy of tipping-bucket rain records using disaggregation techniques

AbstractWe present a methodology able to infer the influence of rainfall measurement errors on the reliability of extreme rainfall statistics. We especially focus on systematic mechanical errors affecting the most popular rain intensity measurement instrument, namely the tipping-bucket rain-gauge (TBR). Such uncertainty strongly depends on the measured rainfall intensity (RI) with systematic underestimation of high RIs, leading to a biased estimation of extreme rain rates statistics. Furthermore, since intense rain-rates are usually recorded over short intervals in time, any possible correction strongly depends on the time resolution of the recorded data sets. We propose a simple procedure for the correction of low resolution data series after disaggregation at a suitable scale, so that the assessment of the influence of systematic errors on rainfall statistics become possible. The disaggregation procedure is applied to a 40-year long rain-depth dataset recorded at hourly resolution by using the IRP (Iterated Random Pulse) algorithm. A set of extreme statistics, commonly used in urban hydrology practice, have been extracted from simulated data and compared with the ones obtained after direct correction of a 12-year high resolution (1 min) RI series. In particular, the depth–duration–frequency curves derived from the original and corrected data sets have been compared in order to quantify the impact of non-corrected rain intensity measurements on design rainfall and the related statistical parameters. Preliminary results suggest that the IRP model, due to its skill in reproducing extreme rainfall intensities at fine resolution in time, is well suited in supporting rainfall intensity correction techniques.

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