2019
DOI: 10.3390/w11010142
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Assessing the Potential Impact of Rising Production of Industrial Wood Pellets on Streamflow in the Presence of Projected Changes in Land Use and Climate: A Case Study from the Oconee River Basin in Georgia, United States

Abstract: This study examines the impact of projected land use changes in the context of growing production of industrial wood pellets coupled with expected changes in precipitation and temperature due to the changing climate on streamflow in a watershed located in the northeastern corner of the Oconee River Basin. We used the Soil and Water Assessment Tool (SWAT) for ascertaining any changes in streamflow over time. The developed model was calibrated over a seven-year period (2001–2007) and validated over another seven… Show more

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Cited by 6 publications
(9 citation statements)
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References 60 publications
(93 reference statements)
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“…The prediction of timber harvesting volume is important [12], as is the prediction of the impact of unplanned logging on the price of raw spruce [9]. The problem affects not only most European countries, but also the United States [9] and Canada, both major wood-producing countries.…”
Section: Discussionmentioning
confidence: 99%
“…The prediction of timber harvesting volume is important [12], as is the prediction of the impact of unplanned logging on the price of raw spruce [9]. The problem affects not only most European countries, but also the United States [9] and Canada, both major wood-producing countries.…”
Section: Discussionmentioning
confidence: 99%
“…SWAT-CUPP offers two algorithms, SWAT Parameter Estimator (SPE) and Particle Swarm Optimization (PSO). We used the SPE algorithm (previously Sequential Uncertainty Fitting (SUFI-2)) for model sensitivity analysis, calibration, uncertainty analysis, and validation [5,13]. In SPE, the algorithm maps all uncertainties (parameter, conceptual model, input, etc.)…”
Section: Swat-cup Premium and Swat Parameter Estimator (Spe) Algorithmmentioning
confidence: 99%
“…Discharge was calibrated first since it is the primary controlling variable [54]. After running the one-at-a-time sensitivity analysis and following the literature [5,24,45,61], the model was parameterized, and ranges were assigned. The model was run for three iterations (600 simulations each) for calibration.…”
Section: Model Validationmentioning
confidence: 99%
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