Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors
- 1European Commission Joint Research Centre, Ispra, Italy
- 2Utrecht University, Faculty of Geosciences, Utrecht, the Netherlands
- 3University of Colorado Boulder, Boulder, USA
Abstract. One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values.
The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers.
Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.