Collaborative Research: CAIG: Data Science Frontiers in Advancing Predictive Understanding of Landscapes and Erosional Extremes under Changing Climatic Scenarios

The amplification of extreme events, more frequent flooding and intense fires caused by climate change, combined with escalating anthropogenic alterations like deforestation and changing land-use practices, have led to a period of rapid transformation across landscapes. These transformations unfold over a range of space and time scales, from catastrophic rapid events due to landslides and debris flows to longer-term impacts due to bank erosion and shifting rivers in response to increased sediment and streamflow, affecting the environment, ecosystem services, and our society.

Field observations of landscapes over the range of space and time scales needed for studying landscape reorganization are not available, and in cases of rapid change, such as after landslides and post-fire debris flows, real-time high-resolution observations are limited and expensive. Our research will capitalize on a unique data set of experimental landscapes where "nature" was left alone to structure and evolve landscapes under prescribed uplift and precipitation rates.

The data was collected at the lab at very high resolution in space and time (0.5 mm and 5 mins) over the full evolution of the landscape (~10 h) under different conditions (steady and transient) of precipitation and uplift. These data sets offer a unique opportunity to study the workings of landscapes and the emergence of erosional extremes in response to change using novel data analytics methodologies, such as process-relevant and physics-informed Machine Learning/AI techniques.

The overall goal of this research is to advance our predictive understanding of landscapes and identify the most relevant geomorphic variables driving erosion under various climatic forcings, including the emergence of highly erosional events. It is expected that this effort will inform the development of predictive models that can be used for landscape planning and management.

The specific objectives of this research are: (1) Extract the local geomorphic transport laws driving landscape evolution under different climatic forcing using the large-scale experimental data and novel physics-informed explainable Artificial Intelligence (xAI) methods, (2) Quantify the imprint of structural connectivity (neighborhood dependence) on emergent behavior and nonlocal transport laws using geomorphologically-inspired Graph Neural Networks that acknowledge the landscape connectivity emerging from the flow accumulation process, and (3) Leverage the transferability of ML models (Transfer Learning) for identification of areas of extreme erosion where fewer observations are available, with a specific application to post-fire hazard assessment.

This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the National Discovery Cloud for Climate initiative within the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering.

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.