Abstract: Flow recession analysis, relating discharge Q and its time rate of change -dQ/dt, has been widely used to understand catchment-scale flow dynamics. However, data points in the plot of -dQ/dt versus Q typically form a wide point cloud due to noise and hysteresis, and it is still unclear what information we can extract from the data points and how to understand the information. In this study, we utilize a machine learning tool to capture the point cloud using the past trajectory of discharge. Our results show that most of the data points can be captured using 5 days of past discharge. While analyzing the machine learning model structure and the trained parameters is a daunting task, we show that we can learn the catchment scale flow recession dynamics from what the machine-learned. We analyze patterns learned by the machine and explain and hypothesize why the machine-learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage-discharge relationship can be estimated based on the attractor.
Biographical Note: Dr. Peter A. Troch is a professor of surface water hydrology at the Department of Hydrology and Atmospheric Sciences of the University of Arizona and the science director of Biosphere 2. He is editor of Water Resources Research, editor of special issues in Advances in Water Resources and Journal of Hydrometeorology, and was editor of Hydrological Processes. Dr. Troch has published 180 papers in refereed international journals dealing with (flash) flood forecasting, catchment classification and similarity, landslide and debris flow modeling, remote sensing applications in hydrology and data assimilation, climate variability and climate change impacts on water availability, and the role of vegetation on hydrologic partitioning at catchment scales. His current research involves seasonal, decadal, and climate predictions of water availability in semi-arid river basins, as well as developing research infrastructure to investigate Critical Zone processes across climate gradients. Dr. Troch has received several awards and honors for his research contributions. He is the recipient of the European Geosciences Union’s John Dalton Medal and holds the Agnese Nelms Haury Chair in Environment and Social Justice. He is elected Fellow of the Galileo Circle of the College of Science at the University of Arizona. In 2015, he was elected Fellow of the American Geophysical Union (AGU).
|Time (Eastern time)
||Student presentations – 3 slides, 3 minutes, Q&A
||Online via zoom
||Break; Seminar preparation
||Seminar: Hysteretic behavior of flow recession dynamics:
Application of machine learning and learning from the machine