Yuan Lester L, Mitchell Richard M, Pilgrim Erik M, Smucker Nathan J
Office of Water, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave NW, Mail code 4304T, Washington, DC 20460, USA.
Office of Water, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave NW, Mail code 4304T, Washington, DC 20460, USA.
Sci Total Environ. 2024 Nov 20;952:176032. doi: 10.1016/j.scitotenv.2024.176032. Epub 2024 Sep 3.
Nutrient concentrations in streams vary strongly with flow conditions, and routinely gathered field measurements of nutrients reflect this variability. Diatom assemblage composition has been used in previous studies to infer nutrient concentrations, and because diatoms integrate nutrient concentrations over longer periods of time, diatom inferences may be less susceptible to fluctuations in streamflow. We tested this hypothesis by leveraging differences in the flashiness of streams across a large continental data set. More specifically, we tested whether the variabilities of direct measurements and diatom inferences of dissolved phosphorus and nitrate were greater in flashy versus non-flashy streams. We further considered whether models linking landscape predictor variables to nutrient concentrations yielded consistent results across flashy and non-flashy streams. Our analysis indicated that measured nutrient concentrations were more variable in flashy compared to non-flashy streams and that landscape models identified different important predictors of nutrient concentrations when fit using data from flashy vs. non-flashy streams. In contrast, variabilities of diatom-inferred nutrient concentrations were similar among stream types, as were the important predictor variables (e.g., manure application rates for nitrate and number of wet days for dissolved phosphorus). These analyses indicate that use of diatom-inferred nutrient concentrations can potentially improve efforts to quantify stream nutrient concentrations.
溪流中的养分浓度随水流条件变化很大,常规收集的养分现场测量数据反映了这种变异性。在以往的研究中,硅藻组合成分已被用于推断养分浓度,并且由于硅藻在较长时间内整合养分浓度,因此硅藻推断可能较不易受到溪流流量波动的影响。我们通过利用一个大型大陆数据集里不同溪流的暴涨特性差异来检验这一假设。更具体地说,我们测试了在暴涨溪流与非暴涨溪流中,溶解磷和硝酸盐的直接测量值与硅藻推断值的变异性是否在暴涨溪流中更大。我们还进一步考虑了将景观预测变量与养分浓度联系起来的模型在暴涨溪流和非暴涨溪流中是否产生一致的结果。我们的分析表明,与非暴涨溪流相比,暴涨溪流中实测的养分浓度变异性更大,并且当使用暴涨溪流与非暴涨溪流的数据进行拟合时,景观模型识别出了不同的养分浓度重要预测因子。相比之下,不同类型溪流中硅藻推断的养分浓度变异性相似,重要预测变量也是如此(例如,硝酸盐对应的粪肥施用量和溶解磷对应的雨天数)。这些分析表明,使用硅藻推断的养分浓度可能会潜在地改善量化溪流养分浓度的工作。