SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT

This study developed a new process-based forest module for the Soil and Water Assessment Tool (SWAT), based on the Physiological Process in Predicting Growth (3-PG) model (SWAT-3PG). The new model allows for improved biomass assimilation, partitioning (stem, foliage, root), and losses (root turnover...

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Veröffentlicht in:Environmental modelling & software : with environment data news 2023-06, Vol.164, p.105705, Article 105705
Hauptverfasser: Karki, R., Qi, J., Gonzalez-Benecke, C.A., Zhang, X., Martin, T.A., Arnold, J.G.
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Sprache:eng
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Zusammenfassung:This study developed a new process-based forest module for the Soil and Water Assessment Tool (SWAT), based on the Physiological Process in Predicting Growth (3-PG) model (SWAT-3PG). The new model allows for improved biomass assimilation, partitioning (stem, foliage, root), and losses (root turnover, foliage loss, mortality). Evaluation at field-scale showed that SWAT-3PG can replicate the different forest biomass components for evergreen forests well. Testing for deciduous and mixed forests sites using remote-sensed data showed that the model can simulate leaf area index (LAI), net primary productivity (NPP) and actual evapotranspiration (AET) reasonably well and can be used to constrain SWAT-3PG when lack of field data. Sensitivity analysis of SWAT-3PG showed its potential in evaluating the impacts of management and climate on forested ecosystems. SWAT-3PG can also be of importance to forest managers as it can estimate variables such as plant height, diameter at breast height (DBH), and basal area. •The process-based 3-PG model was incorporated in SWAT for tree growth simulation.•Subroutines for evergreen, deciduous, and mixed forests were developed to capture different forest systems.•SWAT-3PG enhances forest simulation with improved biomass assimilation, partitioning, and losses.•SWAT-3PG can be useful to better understand climate and management impacts in forest ecosystems.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2023.105705