loading page

Use of near-real-time satellite precipitation data and machine learning to improve extreme runoff modeling
  • +2
  • Paul Muñoz,
  • Gerald Augusto Corzo Perez,
  • Dimitri Solomatine,
  • Jan Feyen,
  • Rolando Célleri
Paul Muñoz
Universidad de Cuenca

Corresponding Author:[email protected]

Author Profile
Gerald Augusto Corzo Perez
UNESCO-IHE Institute for Water Education
Author Profile
Dimitri Solomatine
UNESCO-IHE Delft Institute for Water Education
Author Profile
Jan Feyen
Katholieke Universiteit Leuven
Author Profile
Rolando Célleri
Universidad de Cuenca
Author Profile

Abstract

Extreme runoff modeling is hindered by the lack of sufficient and relevant ground information and the low reliability of physically-based models. The authors propose to combine precipitation Remote Sensing (RS) products, Machine Learning (ML) modeling, and hydrometeorological knowledge to improve extreme runoff modeling. The approach applied to improve the representation of precipitation is the object-based Connected Component Analysis (CCA), a method that enables classifying and associating precipitation with extreme runoff events. Random Forest (RF) is employed as a ML model. We used 2.5 years of nearly-real-time hourly RS precipitation from the PERSIANN-CCS and IMERG-early run databases (spatial resolutions of 0.04 o and 0.1 o , respectively), and runoff at the outlet of a 3391 km 2-basin located in the tropical Andes of Ecuador. The developed models show the ability to simulate extreme runoff for the cases of long-duration precipitation events regardless of the spatial extent, obtaining Nash-Sutcliffe efficiencies (NSE) above 0.72. On the contrary, we found an unacceptable model performance for a combination of short-duration and spatially-extensive precipitation events. The strengths/weaknesses of the developed ML models are attributed to the ability/difficulty to represents complex precipitation-runoff responses.