Key Laboratory of Nonpoint Pollution Control, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Blackland Research and Extension Center, Texas A&M University, 720 East Blackland Rd., Temple, TX, 76502, USA.
J Environ Manage. 2019 Nov 15;250:109477. doi: 10.1016/j.jenvman.2019.109477. Epub 2019 Aug 31.
Although the real-time monitoring technique has been widely applied due to the improvement of sensors, development of traditional sampling methods is still worth of being discussed due to the economically feasibility. Currently, extreme events (e.g. extreme rainfall caused by climate change) play a relatively important role in nutrient export. However, impacts of extreme events on the optimization of sampling strategy is still not well addressed despite the uncertainty of different frequency sampling programs has been sufficiently discussed in previous studies. Therefore, the corresponding impact of extreme events impact on the optimization of sampling strategy was investigated by examining temporal (i.e., inter-annual and seasonal) variations of available data. Uncertainty of nutrient flux estimates under different sampling frequencies was explored by subsampling daily monitoring data. Results showed that uncertainty in flux estimates differed between nitrogen and phosphorus. The relative error (RE) in annual TN flux estimates ranged from -4.2% to 2.4% (once per three-day) to -21.4-31.1% (monthly sampling), while the RE in annual TP flux estimates varied from -14.1% to 8.2% (once per three-day) to -65.9%-163.4% (monthly sampling). Biweekly and weekly sampling routines are considered the optimal sampling program for total nitrogen (TN) and for total phosphorus (TP) when the extreme events impact were not been considered. The uncertainty of flux estimates with different sampling frequencies increased with the increasing extreme events. High level of uncertainty occurred in years with the most extreme events in 2012 (RE: 21.4-69.0% for TN, 33.3-96.6% for TP), while the lowest can be found in 2011 (RE: 0-20.7% for TN, 0-48.3% for TP) (with the fewest extreme events). In addition, uncertainty in TN and TP flux estimates was generally greater during summer season than during other seasons. These results highlighted the important role of extreme events in nutrient export. Approximately half of the annual TN and TP flux occurred in some extreme days that only accounted for less than 20% in the same year. The onset of these extremes of nutrient export was likely due to the stormflow with addition of external fertilizer and the direct discharge of surface ponding water from paddy fields during special periods of time. These results would be helpful for the optimization of sampling strategy.
尽管由于传感器的改进,实时监测技术已经得到了广泛应用,但由于经济可行性的原因,传统采样方法的发展仍然值得探讨。目前,极端事件(例如气候变化引起的极端降雨)在养分输出中起着相对重要的作用。然而,尽管先前的研究已经充分讨论了不同频率采样方案的不确定性,但极端事件对采样策略优化的影响仍未得到很好的解决。因此,通过检查可用数据的时间(即年际和季节性)变化,研究了极端事件对采样策略优化的相应影响。通过对每日监测数据进行子采样,探讨了不同采样频率下养分通量估计的不确定性。结果表明,氮磷养分通量估计的不确定性不同。每年 TN 通量估计的相对误差(RE)范围为-4.2%至 2.4%(每三天一次)至-21.4%至-31.1%(每月采样),而每年 TP 通量估计的 RE 范围为-14.1%至 8.2%(每三天一次)至-65.9%至-163.4%(每月采样)。当不考虑极端事件的影响时,双周和每周采样方案被认为是总氮(TN)和总磷(TP)的最佳采样方案。随着极端事件的增加,不同采样频率下通量估计的不确定性也会增加。在 2012 年极端事件最多的年份,通量估计的不确定性最高(RE:TN 为 21.4%至 69.0%,TP 为 33.3%至 96.6%),而在 2011 年极端事件最少的年份,不确定性最低(RE:TN 为 0%至 20.7%,TP 为 0%至 48.3%)。此外,夏季 TN 和 TP 通量估计的不确定性通常大于其他季节。这些结果强调了极端事件在养分输出中的重要作用。大约一半的年 TN 和 TP 通量发生在一些极端的日子里,这些日子在同一年中只占不到 20%。这些养分输出极端事件的发生可能是由于暴雨期间的地表径流带来的外部肥料和特殊时期稻田表面积水的直接排放所致。这些结果将有助于优化采样策略。