Model linkage to assess forest disturbance impacts on water quality: A wildfire case study using LANDIS(II)-VELMA

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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Abstract

Wildfires in western US forests increased over the last two decades, resulting in elevated solid and nutrient loadings to streams, and occasionally threatening drinking water supplies. We demonstrated that a linked LANDIS (LANDscape DIsturbance and Succession)-VELMA (Visualizing Ecosystem Land Management Assessments) modeling approach can simulate wildland fire effects on water quality using the 2002 Colorado Hayman Fire. Utilizing LANDIS-II's forest landscape model to simulate forest composition and VELMA's eco-hydrologic model to simulate pre- and post-fire water quantity and quality, the best calibration performance yielded a Nash-Sutcliffe Efficiency (NSE) of 0.621 during 2000–2006 (most optimal annual - 0.921) in comparison to North American Land Data Assimilation System (NLDAS) runoff. Pre-fire modeled runoff, nitrate, and surface water temperature (SWT) correlated with observations. Simulated post-fire runoff (229%) and SWT (20.6%) were elevated relative to pre-fire, with nitrate concentrations 34 times greater than the aquatic life threshold (0.01 mg N/L).

利用模型联系评估森林干扰对水质的影响:利用 LANDIS(II)-VELMA 进行野火案例研究
在过去二十年里,美国西部森林的野火有所增加,导致溪流的固体和营养负荷增加,有时甚至威胁到饮用水供应。我们以 2002 年科罗拉多海曼大火为例,证明了 LANDIS(景观干扰与演替)-VELMA(可视化生态系统土地管理评估)建模方法可以模拟野火对水质的影响。利用 LANDIS-II 的森林景观模型模拟森林组成,并利用 VELMA 的生态水文模型模拟火灾前后的水量和水质,在 2000-2006 年期间,与北美土地数据同化系统(NLDAS)径流相比,最佳校准性能产生了 0.621 的纳什-萨特克利夫效率(NSE)(最理想的年度为 0.921)。模拟的火灾前径流、硝酸盐和地表水温度(SWT)与观测结果相关。模拟的火灾后径流(229%)和 SWT(20.6%)与火灾前相比有所升高,硝酸盐浓度比水生生物阈值(0.01 毫克 N/L)高出 34 倍。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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