Dietrich Matthew, Golden Heather E, Christensen Jay R, Lane Charles R, Dumelle Michael
Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, 37830, USA.
Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, 45268, USA.
Environ Sci Technol Lett. 2024 Dec 10;11(12):1406-1412. doi: 10.1021/acs.estlett.4c00938.
Chlorophyll-a (Chl-a) is a commonly used proxy for algal biomass within surface waters, which can be indicative of harmful algal blooms. Excess nutrients, such as nitrogen or phosphorus, promote Chl-a production, often leading to eutrophication. However, little research exists on river nutrients-to-downstream lake Chl-a linkages at large watershed scales and across disparate climatic and physiographic regions. We found a significant positive relationship between measured total nitrogen (TN) and total phosphorous (TP) concentrations in upstream rivers and Chl-a concentrations in downstream lakes at the watershed scale (average area = 99.8 km [35.8-628.6 km], n = 254 watersheds) throughout the conterminous United States (CONUS). Additionally, through spatial logistic regression models, we demonstrate that a small number of explanatory variables (2-3 per model) can accurately predict (77%-86% accuracy, AUC = 0.83-0.91) classifications of high or low riverine TN, TP, or lake Chl-a concentrations throughout the CONUS at the watershed scale. The predictive variables included vegetation type, runoff, tile drainage, temperature, and nitrogen inputs. This work supports the hypothesis that rivers supply nutrients that enhance Chl-a concentrations in downstream lakes and demonstrates the power of parsimonious models combined with spatial autocorrelation to accurately predict classifications of nutrient concentrations and Chl-a across the CONUS.
叶绿素a(Chl-a)是地表水藻生物量常用的替代指标,可指示有害藻华。过量的养分,如氮或磷,会促进叶绿素a的产生,常常导致富营养化。然而,在大流域尺度以及不同气候和地理区域,关于河流养分与下游湖泊叶绿素a之间联系的研究很少。我们发现,在美国本土(CONUS)整个流域尺度(平均面积 = 99.8平方千米[35.8 - 628.6平方千米],n = 254个流域)上,上游河流中测得的总氮(TN)和总磷(TP)浓度与下游湖泊中的叶绿素a浓度之间存在显著正相关关系。此外,通过空间逻辑回归模型,我们证明少量解释变量(每个模型2 - 3个)能够准确预测(准确率77% - 86%,AUC = 0.83 - 0.91)美国本土整个流域尺度上河流中总氮、总磷或湖泊中叶绿素a高浓度或低浓度的分类情况。预测变量包括植被类型、径流、瓦管排水、温度和氮输入量。这项工作支持了河流输送的养分提高下游湖泊叶绿素a浓度这一假说,并证明了简约模型与空间自相关相结合在准确预测美国本土养分浓度和叶绿素a分类方面的作用。