Catchment hydrology and water quality are closely related to our society, economic and environment. In the recent decades, our catchments are threatened by emerging issues such as climate change, population growth and urbanization, calling for improved understanding of catchment processes and responses under changes to inform effective management. My work spans multiple topics in surface water hydrology, focusing on understanding and modelling water quality and quantity via data-driven and large-sample approaches, such as the Bayesian space-time models. I am passionate about demonstrating the power of data-driven approaches in improving the understanding and predictive capacity of catchment processes. My current research focuses include understanding the uncertainty in rainfall-runoff and water quality modelling, as well as the spatial and temporal patterns of water quality over large regions. My future work will focus on deepening and adopting this knowledge to improve the prediction of water quality and quantity under a changing climate.
Bios
Dr. Danlu Guo is a Senior Lecturer at the Australian National University and an honorary fellow of the University of Melbourne. Danlu completed her Ph.D. in Hydrology at the University of Adelaide in April 2017. Her research spans across catchment rainfall-runoff modelling, water quality modelling and climate change impact assessment, centring around a common theme of understanding and modelling catchment water resources via data-driven and large-sample approaches. Since 2020, Danlu has been lead CIs for research contracts up to $1M, working with multiple Australian state government agencies. Danlu was awarded the 2022 GN Alexander Medal by Engineering Australia, and the 2019 Early Career Research Award by the Modelling and Simulation Society of Australia and New Zealand (MSSANZ). Danlu currently serves as a lead for the Water Quality Special Interest Group within MSSANZ.
Charuleka Varadharajan is a staff scientist in the Earth and Environmental Sciences area at Berkeley Lab. She is a hydrobiogeochemist and data scientist who has dedicated her career to water sustainability and resilience. Her research focuses on developing innovative data-centric solutions for water-energy applications. She has experience working on a broad range of topics including surface and groundwater quality, water resource resilience to human and natural disturbances, methane cycling, environmental impacts of carbon sequestration and fossil fuel production, and bioremediation. She also leads the Earth AI and data program at LBNL and her research group develops data science capabilities—from machine learning and statistical algorithms, to data management and integration services—for environmental applications.