Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa Academic Article uri icon

abstract

  • AbstractAccess to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9mm), and highest correlation coefficient (0.53).

published proceedings

  • Environmental Processes

author list (cited authors)

  • Faramarzzadeh, M., Ehsani, M. R., Akbari, M., Rahimi, R., Moghaddam, M., Behrangi, A., ... Oussalah, M.

citation count

  • 1

complete list of authors

  • Faramarzzadeh, Marzie||Ehsani, Mohammad Reza||Akbari, Mahdi||Rahimi, Reyhane||Moghaddam, Mohammad||Behrangi, Ali||Klöve, Björn||Haghighi, Ali Torabi||Oussalah, Mourad

publication date

  • March 2023