Abstract
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.
Highlights
Flooding is the most prevalent natural hazard at the global scale, often with dire humanitarian and economic effects
As a first step we analysed the relationship between the satellite signal and the in situ-observed discharge to have an initial understanding of the performance between the two data sets (Sect. 4.1)
Variables included in the analysis are daily mean river discharge, river width, upstream catchment area, potential flood hazard area, land cover, leaf area index (LAI), climatic zones, presence of large floodplains, flooded forest and wetlands, river ice and hydrologic structure
Summary
Flooding is the most prevalent natural hazard at the global scale, often with dire humanitarian and economic effects. According to the International Disaster Database (EM-DAT), an average of 175 flood events per year occurred globally between 2002 and 2011, affecting an average of 116.5 million people, and causing economic losses of USD 25.5 billion. According to MunichRe (2014), the costliest natural catastrophe worldwide in terms of overall economic losses in 2013 was the flooding in southern and eastern Germany and neighbouring states in May and June, with estimated damages of USD 15.2 billion. In June of the same year, flooding in India claimed 5000 lives, with a further 2 million affected (MunichRe, 2014; EM-DAT). The Global Assessment Report (UNISDR, 2011) states that the proportion of world population living in flood-prone river basins increased by 114 % over four decades from 1970 to 2010. While economic losses due to river floods have increased over the last 50 years, the number of casualties has decreased.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
