Wetlands help regulate how water and nutrients move through the environment, but most large-scale Earth system models (ESMs) still cannot properly represent small-scale surface ponding (i.e., when water temporarily pools on the land). This ponding affects energy flow, water availability, and greenhouse gas emissions like carbon dioxide and methane. To address this, we developed a new modeling approach for simulating surface water ponding within the GFDL Earth System Model. Our approach tracks water movement between ponds and soil, includes how water freezes and thaws, and uses machine learning to represent small features like hills and slopes within larger model grid cells. It also simulates how excess water flows across land.

Our results show that surface ponding increases evaporation and lowers ground temperatures, which influences local climate during floods and droughts. We are expanding this work to include deeper water bodies and more realistic terrain. We compare model results with field data and satellite observations to improve accuracy. We also study how ponding affects carbon emissions in northern wetlands. By combining our new ponding approach with an existing soil carbon model, we aim to better understand how wetlands respond to environmental changes, helping us improve predictions of climate impacts on ecosystems.