River discharge estimation from remote sensing

River discharge is the key variable for many scientific and operational applications related to water resources management, flood risk mitigation and for water cycle change and climate studies. Collection, archiving and distribution of river discharge data, coming from the currently operating network, are globally scarce and limited.
Satellite sensors are considered the new source to monitor river discharge, thanks to the repeated, uniform and global measurements for a long time-span due to the large number of satellites launched during the last twenty years. The integration of satellite with in-situ data seems the best solution for the still open issue regarding the monitoring of freshwater.
Among the satellite sensors, radar altimeters and optical sensors demonstrated their capability to estimate river discharge. The recent advances in radar altimetry technology offer important information for water level monitoring of rivers, and the increased accuracy of the sensors encourages its use as a validation tool for many applications from simple routing approaches [1], to complex hydraulic models [2,3]. Moreover, the multi-mission approach [4,5], i.e. interpolating different altimetry river crossings, has potential to overcome the limitations due to the poor spatial-temporal sampling. Alternatively, optical sensors, thanks to their frequent revisit time (nearly daily) and large spatial coverage, could support the evaluation of river discharge variations. Studies conducted on the Moderate Resolution Imaging Spectroradiometer (MODIS) demonstrated that the reflectance variation can be a proxy of discharge and can be used not only for river discharge estimation [6,7] but also for forecasting purposes [8].
Attempts to merge both the sensors, optical and altimeter, have been also investigated [9]to improve the evaluation of river discharge, by using physical approach [10] and machine learning approach [11].

Click here to see the video of LOGYTalk about the river discharge estimation by satellite data and here to download the presentation)

Contact: angelica.tarpanelli@irpi.cnr.it

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