Location: Hydrology and Remote Sensing LaboratoryTitle: SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT
|KARKI, R. - University Of Maryland|
|QI, JUNYU - University Of Maryland|
|GONZALES-BENECKE, C.A. - University Of Oregon|
|MARTIN, T.A. - University Of Florida|
Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/8/2023
Publication Date: 4/13/2023
Citation: Karki, R., Qi, J., Gonzales-Benecke, C., Zhang, X., Martin, T., Arnold, J.G. 2023. SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT. Journal of Environmental Modeling and Software. 164. Article 105705. https://doi.org/10.1016/j.envsoft.2023.105705.
Interpretive Summary: The Soil and Water Assessment Tool (SWAT) model has been widely used to study water resources sustainability. A weakness of the model is the lack of accurate representation of forest ecosystems, which often have a significant impact on watershed hydrology and water quality. Here, we incorporated the Physiological Process in Predicting Growth (3-PG) model into the SWAT model for forest growth simulation. Tested against field data, the enhanced SWAT model can accurately simulate stem, foliage, and coarse root biomass, as well as leaf area index and foliage loss. The new model can also realistically reproduce observed net primary productivity and evapotranspiration. We anticipate the improvements made here will benefit future use of the SWAT model to support sustainable water management at the watershed scale.
Technical Abstract: Forests play a crucial role in hydrologic, nutrient, and carbon cycles, and it is essential to accurately model these vital processes in forest ecosystems in hydrologic models. This study has developed and tested a new forest module for the Soil and Water Assessment Tool (SWAT), based on a process-based forest growth model, the Physiological Process in Predicting Growth (3-PG). The improved SWAT model, referred to as SWAT-3PG, enhances the simulation of biomass assimilation, partitioning, and losses in evergreen, deciduous, and mixed forest systems by dividing assimilated biomass into stem, foliage, and root biomass, as well as simulating tree mortality due to aging and self-thinning. Evaluation of the new forest module at a site-scale for evergreen forests using field-measured data reveals that it can accurately simulate stem, foliage, and coarse root biomass, as well as the leaf area index (LAI) and foliage loss. Deciduous and mixed forest sites were calibrated using remote-sensed LAI, net primary productivity (NPP), and evapotranspiration (ET) datasets. The results demonstrate that the new forest module can accurately replicate LAI, NPP, and ET in deciduous forests, but there were slight difficulties in replicating NPP in mixed forests because SWAT is unable to simulate multiple plant types in a single modeling unit. Improved NPP simulation could be achieved with improved initial parameterization for mixed forest simulations. Sensitivity analysis of SWAT-3PG shows that the model can also be useful in evaluating the impacts of climate and management on forested ecosystems with reduced uncertainty compared to the default model. Additionally, as SWAT 3PG estimates variables such as diameter at breast height (DBH), plant height, and basal area, the new model is valuable to forest managers and growers.