SWAT-C模型在黄永河流域TOC负荷预测中的应用与评价

Dongho Kim, Jiwon Lee, H. Jeong, Yongsung Kwon, B.Y. Lee, Sangchul Lee
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引用次数: 1

摘要

目的:本研究旨在评估最近开发的SWAT-C水质模型在预测韩国某流域河流总有机碳(TOC)方面的适用性。SWAT-C是土壤和水评估工具(SWAT)的高级版本,用于模拟流域尺度的碳循环。该模型模拟了11年(2010-2020年),包括2年预热期(2010-2011年)、6年校准期(2012-2017年)和3年验证期(2018-2020年)。SWAT-C根据每月流量和TOC负荷进行校准和验证。使用NSE(Nash-Sutcliffe效率)、KGE(Kling Gupta效率)和PBIAS(百分比偏差)对模型性能进行了评估。模拟溶解有机碳(DOC)和颗粒有机碳(POC)负载也显示了下游负载的主要角蛋白类型。结果与讨论:校准结果表明,SWAT-C可以描述具有可接受模型性能的流量和TOC负荷(NSE>0.5,KGE>0.5和PBIAS=<25%)。特别是,SWAT-C很好地模拟了TOC负荷的季节变化。预计TOC负荷在降水量大的夏季会增加,而在其他季节则略低。这种季节性模式可能是由夏季频繁的强降雨事件引起的。结论:本研究表明SWAT-C模型在韩国具有足够的预测精度。因此,SWAT-C将成为监测韩国TOC负荷的有效工具。
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Application and evaluation of SWAT-C model to predict TOC loading in the Hwangryong River Watershed
Objectives : This study aims to assess the applicability of the SWAT-C water quality model recently developed to predict in-stream Total Organic Carbon (TOC) in a watershed within South Korea.Methods : The SWAT-C model was tested in the Hwangryong River Watershed. SWAT-C is an advanced version of Soil and Water Assessment Tool (SWAT) to simulate carbon cycling at the watershed scale. The model was simulated for 11 years (2010-2020) consisting of 2-year warm-up (2010-2011), 6-year calibration (2012-2017), and 3-year validation (2018-2020) periods. SWAT-C was calibrated and validated against monthly streamflow and TOC loads. The model performance was evaluated using NSE (Nash-Sutcliffe efficiency), KGE (Kling-Gupta efficiency), and PBIAS (percent bias) Simulated Dissolve Organic Carbon (DOC) and Particulate Organic Carbon (POC) loads were also presented to see the major caron type loaded to the downstream.Results and Discussion : The calibration results showed that SWAT-C could depict streamflow and TOC loads with acceptance model performances (NSE > 0.5, KGE > 0.5 and PBIAS =< 25%). Especially, the seasonal variations of TOC loads were well simulated by SWAT-C. The TOC load was predicted to increase in the summer season with high precipitation and slightly lower in the other seasons. This seasonal pattern is likely caused by frequent heavy rainfall events in summer.Conclusion : This study demonstrated that SWAT-C model is applicable in South Korea with sufficient prediction accuracy. Thus, SWAT-C would serve as an efficient tool to monitor TOC loads in South Korea.
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