Please join us for a UW Data Science Seminar featuring UW Civil & Environmental Engineering Master’s student Hernán Querbeson Wednesday, April 1st from 4:30 to 5:20 p.m. PT. The seminar will be held in IEB G109.

“Evaluating the Impact of Precipitation Forcing on Machine Learning Streamflow Modeling”

Abstract: Uruguay relies heavily on hydropower, with three dams along the Río Negro providing nearly half of the country’s electricity. Accurate streamflow modeling is therefore critical for dam operations and energy reliability. Recently, long short-term memory (LSTM) networks have emerged as a leading approach for streamflow modeling, and large-sample datasets such as CARAVAN (which includes meteorological forcings such as time-series for precipitation, temperature, radiation, among others and static attributes of the basins) have enabled their global application. However, CARAVAN’s reanalysis-based precipitation has been shown to reduce models performance in the US compared to other local products. This study presents the evaluation of LSTM-based streamflow modeling in Uruguay, training and evaluating models across eleven basins in the Río Negro under four precipitation forcings: CARAVAN, MSWEP, CHIRPS, and local rain gauges. It assesses how precipitation product choice influences model performance and how hydrological signatures change when precipitation products are combined as model inputs. Results provide insight into the suitability of global precipitation datasets for data-driven streamflow modeling in data-scarce regions, with implications for water resources planning.

Speaker Bio:Hernán Querbes is a Master’s student in the Department of Civil & Environmental Engineering at the University of Washington, specializing in hydrology. He completed his undergraduate degree in Chemical Engineering at Universidad de la República (Uruguay) in 2023. His current research focuses on the application of machine learning techniques, specifically long short-term memory (LSTM) networks, for streamflow modeling.