Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data
Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data
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DOI：
10.1002/lol2.10134
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发表时间：
2019-12-27
影响因子：
7.8
通讯作者：
Zhou, Jiayu
中科院分区：
文献类型：
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作者：
Wagner, Tyler;Lottig, Noah R.;Zhou, Jiayu
水产科学家需要关于未取样湖泊中营养物浓度和藻类生物量指标的可靠、准确的信息，以便了解和预测全球气候和土地利用变化的影响。从历史上看，湖泊和景观特征一直被用作回归模型中的预测变量，以生成营养预测，但往往具有很大的不确定性。改进预测的另一种方法是利用观察到的水的透明度和营养物质之间的关系，这是可能的，因为水的透明度比湖泊营养物质更常见。我们使用了一个联合营养模型，条件预测的总磷，氮，叶绿素a观察到的水的透明度。我们的研究结果表明，当调节水的透明度时，预测误差大幅降低（8-27%;中位数= 23%）。这些模型将提供新的机会，在广泛的空间尺度上预测未取样湖泊的营养盐浓度，减少不确定性。
Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint-nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8-27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.