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Advancing the SWAT model to simulate perennial bioenergy crops: A case study on switchgrass growth.

Authors :
Dangol, Sijal
Zhang, Xuesong
Liang, Xin-Zhong
Blanc-Betes, Elena
Source :
Environmental Modelling & Software. Dec2023, Vol. 170, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Although the Soil and Water Assessment Tool (SWAT) model has been widely used to assess the environmental impacts of growing perennial grasses for bioenergy production, its utility is limited by not explicitly accounting for shoot and root biomass development. In this study, we integrated the DAYCENT model's grass growth algorithms into SWAT (SWAT–GRASS D) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASS M). Based on testing at eight sites in the US Midwest, we found that SWAT–GRASS M generally outperformed SWAT and SWAT–GRASS D in simulating switchgrass biomass yield and the seasonal development of LAI. Additionally, SWAT–GRASS M can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools. These improvements are critical for credible assessment of agronomic and environmental impacts of growing perennial grasses for biomass production. • We developed the SWAT-GRASS model by integrating the grass growth module from the DAYCENT model into SWAT. • We further enhanced SWAT-GRASS to better represent the effects of LAI on biomass accumulation. • The new model achieved better simulation of switchgrass yield across multiple sites. • The new model can explicitly and reasonably represent root and shoot development of switchgrass. • A new tool for comprehensive assessment of agronomic and environmental impacts of growing switchgrass. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13648152
Volume :
170
Database :
Academic Search Index
Journal :
Environmental Modelling & Software
Publication Type :
Academic Journal
Accession number :
173435287
Full Text :
https://doi.org/10.1016/j.envsoft.2023.105834