Evaluation of the satellite-based global flood Detection System for measuring river discharge: Influence of local factors

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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.

Figures

  • Figure 1. Location of selected stations (398) and corresponding river basins (109). TRMM and AMSR-E brightness temperature product extents are also provided.
  • Table 1. Number of catchments by continent and range of upstream areas for the located stations.
  • Table 2. Climate and land cover type of the 322 sites selected for the calibration and validation, aggregated by continent, climate and land cover.
  • Figure 2. Example of a measurement site: Caracarai station (Rio Branco catchment, Brazil). The blue rectangles outline the measurement pixels and the background image is from 2014 (Google; Landsat, DigitalGlobe).
  • Figure 3. (a) Scatterplot for the Senanga station (long 23.25 degree, lat −16.116 degree) in the Zambezi River (Africa). Monthly mean for March from 1998 up to 2002. (b) Validation hydrograph for 2003–2004 and skill scores for Senanga. The (monthly) rating equations were used to calibrate the signal into discharge units. Different rating equations were used for different months.
  • Figure 4. Location of stations and R skill score between in situobserved discharge and satellite signal (4 days and 4 pixels average). Globally, 169 sites have R > 0.3, of which 42 have R > 0.5.
  • Figure 5. Nash–Sutcliffe efficiency of the validation (n= 332 stations). Globally, 154 stations have NSE> 0, of which 80 stations have NSE> 0.50.
  • Figure 6. (a) Relationship between R obtained from the validation of satellite-measured discharge and the maximum river width for each location. (b) Relationship between the same R score and the presence of significant floodplains, flooded forest and wetlands. The horizontal dotted line shows the R = 0.3 and R = 0.7 threshold, and the vertical line is the river width equal to 1 km.

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CITATION STYLE

APA

Revilla-Romero, B., Thielen, J., Salamon, P., De Groeve, T., & Brakenridge, G. R. (2014). Evaluation of the satellite-based global flood Detection System for measuring river discharge: Influence of local factors. Hydrology and Earth System Sciences, 18(11), 4467–4484. https://doi.org/10.5194/hess-18-4467-2014

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