Articles | Volume 18, issue 11
https://doi.org/10.5194/hess-18-4467-2014
https://doi.org/10.5194/hess-18-4467-2014
Research article
 | 
07 Nov 2014
Research article |  | 07 Nov 2014

Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors

B. Revilla-Romero, J. Thielen, P. Salamon, T. De Groeve, and G. R. Brakenridge

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Cited articles

Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS –global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.
Archer, K. J. and Kimes, R. V.: Empirical characterization of random forest variable importance measures, Comput. Stat. Data Anal., 52, 2249–2260, https://doi.org/10.1016/j.csda.2007.08.015, 2008.
Auret, L. and Aldrich, C.: Empirical comparison of tree ensemble variable importance measures, Chemomet. Intelligent Labor. Syst., 105, 157–170, https://doi.org/10.1016/j.chemolab.2010.12.004, 2011.
Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS –Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141–153, https://doi.org/10.5194/hess-13-141-2009, 2009.
Bontemps, S., Defourny, P., Bogaert, E. V., Arino, O., Kalogirou, V., and Perez, J.R.: GLOBCOVER 2009 – Products Description and Validation Report, available at: http://due.esrin.esa.int/globcover/ (last access: 15 February 2014), 2010.
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Short summary
One of the main challenges in global hydrological modelling is the limited availability of observational data for calibration and model verification. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System (GFDS) for converting the flood detection signal into river discharge values. This work also provides a first analysis of the local factors influencing the accuracy of discharge measurement as provided by this system.