CAREER: Morphodynamics of Delta Channel Networks at the Decadal Scale

River deltas are dynamic landscapes shaped by branching channel networks that transport water, sediment, and nutrients to the coast, supporting communities and ecosystems. These systems are increasingly vulnerable to sea-level rise, reduced sediment supply, and human activities, which can trigger rapid changes such as channel shifts or continuous land loss. While long-term delta evolution is relatively well understood, the short-term processes controlling channel network behavior over decades remain poorly constrained. This CAREER project develops a process-based understanding and predictive framework for delta channel network morphodynamics at timescales relevant to human observation and decision-making. The research integrates field observations, remote sensing, laboratory experiments using 3D-printed channel networks, and modeling approaches that combine physical theory with machine learning. The resulting framework will improve predictions of delta evolution, including in Arctic systems undergoing rapid change. Broader impacts include informing more effective and cost-efficient delta restoration and management strategies, with implications for coastal resilience and infrastructure stability. An integrated education program, including a publicly accessible “Delta Morphology Simulator” and hands-on student design challenges, will enhance public understanding and help train the next generation of scientists and engineers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2544083 | Program: 01002930DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Hongbo Ma | Institution: University of Illinois at Urbana-Champaign, URBANA, IL | Award Amount: $394,428 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544083 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544083.html