The importance of lake-specific characteristics for water quality across the continental United States
The importance of lake-specific characteristics for water quality across the continental United States
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DOI：
10.1890/14-0935.1
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发表时间：
2015-06-01
影响因子：
5
通讯作者：
Weathers, Kathleen C.
中科院分区：
文献类型：
--
作者：
Read, Emily K.;Patil, Vijay P.;Weathers, Kathleen C.
湖泊水质受当地和区域驱动因素的影响，包括湖泊物理特征、水文、景观位置、土地覆盖、土地利用、地质和气候。在这里，我们展示了景观湖沼学框架内使用随机森林算法在全国范围内，空间上明确的数据集，美国环境保护局的2007年国家湖泊评估的假设检验的效用。对于1026个湖泊，我们测试了空间尺度上水质驱动因素的相对重要性，水文连通性在调节水质驱动因素中的重要性，以及空间尺度和连通性在五个重要的湖泊水质指标（总磷，总氮，溶解有机碳，浊度和电导率）的响应变量中的重要性如何不同。通过对不同空间尺度下水质预测因子的影响进行建模，我们发现湖泊特有的特征（例如，（如水深、沉积物面积与体积之比）对解释水质很重要（解释了54-60%的方差），而区域化方案的有效性远低于湖泊具体指标（解释了28-39%的方差）。流域尺度的土地利用和土地覆盖解释了45-62%的方差，森林覆盖和农业土地利用是最重要的流域尺度预测因子。水质驱动因素并不是独立运作的;在某些情况下，水文连通性（上游地表水特征的存在）介导了区域尺度驱动因素的影响。例如，对于有上游湖泊的湖泊的水质，区域分类方案的预测效果远低于湖泊特定变量，而上游没有湖泊或没有地表水流入的湖泊则相反。在美国大陆的规模，电导率的解释驱动程序在更大的空间尺度比其他水质响应。目前利用区域化计划指导水质标准的管理做法可以通过考虑湖泊的具体特点加以改进，这些特点是美国大陆范围内水质的最重要预测因素。空间范围和高质量的上下文数据可用于此分析，使这项工作前所未有的应用景观湖沼学理论的水质数据。此外，湖泊形态对其他水质控制的重要性已被证明与水生科学家和管理人员有关。
Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.