CAREER: Peatland Geomorphology: Quantifying Geomorphological Changes across SE Asia Peatlands

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This project will determine how a process resulting from human activities (peat degradation due to deforestation) influences natural hazards (flooding) and climate change (carbon dioxide emissions). Peatlands are wetlands where waterlogged conditions slow plant decomposition to such an extent that dead plants accumulate to form peat. Under natural conditions peat domes grow over time, storing carbon underground and minimizing the risk of coastal flooding. In contrast, damaged peatlands are compacting, which results in a lowering of the ground elevation and in greenhouse gas emissions. This project proposes to integrate multiple satellite- and ground-based datasets with artificial intelligence tools to address a question of high societal importance: “At what rates will thousands of years of accumulated peat carbon be released to the atmosphere and on what timescale will large coastal areas in southeast Asia become permanently flooded?” Retrospectively, in Louisiana, it was shown that the impact of hurricane Katrina would have been less severe without the preceding decades of peat degradation. This project will proactively quantify when and where flooding will become the most severe in southeast Asia and improve our understanding of peatland dynamics worldwide. Knowledge from this work will enable forecasting future peat decomposition rates and flooding hazards, and refine estimations of carbon dioxide fluxes. These results will inform outreach efforts aimed to empower local communities through education, boost engagement of scientists in public communication, and increase undergraduate diversity through early exposure to hands on research. The goals of this project are to establish the capability to predict the fate of tropical peatlands and their carbon in response to environmental changes and to develop effective ways to engage the population on the importance of peatlands. The central hypothesis of this project is that peat degradation rates are controlled by the landscape morphology and land use history. To test it, this project will rely on three main tasks: 1) identify the controls on peat degradation rates across southeast Asia by combining morphology (Light Detection and Ranging data), deformation (Interferometric Synthetic Aperture Radar data validated by fieldwork), and land use timeseries (multi-spectral optical data); 2) develop a machine learning model accounting for the controls on degradation rates to predict the timing and amplitude of future degradation, carbon dioxide emissions, and the associated flooding; and 3) increase awareness on the importance of peatlands by sharing socially responsive outreach material. The developed techniques will provide a critical framework for worldwide peatland geomorphology studies.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.