Abstract: In this data-rich era, hydrologists and other environmental scientists are motivated to measure and model everything, everywhere. Yet, limited time, budgets, and technology constrain the number of variables and resolution that can be measured and modeled; and, furthermore, not all variables and spatiotemporal scales in a system provide useful information. Therefore, broad questions in environmental systems modeling include: What variables, times, and locations are most informative of the relevant processes? And what is the minimum sampling required to achieve robust measurement and modeling?

To address these questions, in this talk, I will discuss the application of data-driven, sparse sensing and modeling methods in ecohydrology. As a first example, we analyze high-frequency (sub-hourly) soil moisture, temperature, and biogeochemical data in the Fourier domain to model the sensitivity of soil respiration to hydroclimatic variability. This method allows us to develop a process-based model that captures variability across timescales in addition to long-term mean values. Secondly, we show that, generally, environmental signals are ​“sparse” in the Fourier domain and this sparsity can be leveraged to reduce temporal sampling requirements orders of magnitude below current state-of-the-practice. Finally, we move beyond the Fourier basis and develop sampling bases tailored to hydrological variables, such as streamflow. This method is applied to predict streamflow in ungauged or poorly gauged basins. Further development and application of these methods promises to improve ecohydrological systems sensing and modeling by reducing sample requirements and identifying a minimal set of variables essential to complete characterization of the dynamics.

Bio: Anthony Parolari is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering at Marquette University. He completed his bachelors and masters degrees in CEE at the University of Michigan, a PhD in CEE with a focus in ecohydrology at the Parsons Laboratory at MIT, and a postdoc at Duke University. He has over 17 years of professional and research experience in hydrology and water resources engineering, including 2 years as a consultant in water infrastructure planning and management. Dr. Parolari’s research investigates the impact of climate on ecosystem processes, including plant productivity, soil biogeochemistry, and surface water quality. This research integrates experiments, data analytics, and computer modeling to improve basic understanding of complex environmental systems that can be leveraged to solve resource management challenges in natural, agricultural, and urban settings.