The use of remote sensing-derived water surface data for hydraulic model calibration

Domeneghetti, Alessio; Tarpanelli, Angelica; Brocca, Luca; Barbetta, Silvia; Moramarco, Tommaso; Castellarin, Attilio; Brath, Armando, 2014, The use of remote sensing-derived water surface data for hydraulic model calibration, Remote sensing of environment 149 (2014): 130–141. doi_10.1016/j.rse.2014.04.007,
URL: http://www.cnr.it/prodotto/i/298251

Considering a large satellite dataset (i.e. ~. 16. years of ERS-2 and ENVISAT observations) we investigate the reliability of remotely-sensed data for calibrating a quasi-two dimensional (quasi-2D) hydraulic model of a ~. 140. km reach of the middle-lower portion of the Po river (Northern Italy), for which detailed topographical information and in-situ hydrometric data are available. In particular, we refer to traditional and remotely-sensed hydrometric data for_ 1) evaluating if ERS-2 and ENVISAT data can be used for model calibration; 2) assessing whether remotely-sensed water elevation data can integrate traditional hydrometric data and improve the reliability of the hydraulic model. Satellite overpasses are generally characterized by low frequencies and the accuracy of remotely-sensed water surface levels is still limited. Nevertheless, the results of our analysis indicate that for medium-to-large rivers ERS-2 and ENVISAT satellite data can effectively enhance our knowledge of the average streamflow regime of a given reach_ they can be directly used in calibration, and their integration with in-situ data may significantly enhance the representation of the hydraulic behaviour of the study river. Considering our study reach, if compared to the model implemented on the basis of in-situ data only the hydraulic model parameterized on the basis of satellite and in-situ hydrometric data provides a better reproduction of average flow conditions, and it also results in the most accurate representation of the maximum water profile observed during a major flood occurred in October 2000. © 2014 Elsevier Inc.

Data from https://intranet.cnr.it/people/