USE OF NEAR-REAL-TIME SATELLITE PRECIPITATION DATA AND MACHINE LEARNING TO IMPROVE EXTREME RUNOFF MODELING

Extreme runoff modeling is hindered by the lack of sufficient and relevant ground information and the low reliability of physically based models. This chapter proposes combining 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 an ML model. Two and a half years of near-real-time hourly RS precipitation from the PERSIANN-CCS and IMERG-early run databases and runoff at the outlet of a basin in the tropical Andes of Ecuador were used. 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 above 0.72. On the contrary, there was 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 represent complex precipitation-runoff responses.