Nonlinear empirical modeling to estimate phosphorus exports using continuous records of turbidity and discharge
Nonlinear empirical modeling to estimate phosphorus exports using continuous records of turbidity and discharge
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使用连续的浊度和排放记录来估计磷出口的非线性经验模型
DOI：
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
F. Moatar
中科院分区：
文献类型：
--
作者：
C. Minaudo;R. Dupas;C. Gascuel;O. Fovet;P. Mellander;P. Jordan;M. Shore;F. Moatar
我们测试了一种经验建模方法，使用相对低成本的浊度和排放量连续记录作为代理，以亚小时时间步估计磷(P)浓度，以估计负荷。该方法考虑了风暴事件的非线性和滞后效应以及水文条件的变异性。研究人员利用来自欧洲四个不同农业流域的全磷和活性磷的高频记录来测试该方法的磷负荷。这些模型是根据每周的采样数据结合每年每小时调查的10次风暴（每周+调查）进行校准的，然后用于重建所有风暴事件期间的磷浓度，以计算年负荷。对于总P，结果表明，这种建模方法可以在有限的不确定性（≈−10%±15%）下估计年负荷，比基于浊度的简单线性回归、基于插值的周+数据而没有风暴事件重建、或基于周序列或月序列的流量加权计算的估计更可靠。对于无功P，基于非线性模型的负荷不确定性与基于简单线性回归的风暴事件重建的不确定性相似（≈20%±30%），低于未进行周或月序列风暴重建的不确定性，但大于基于插值的周+数据的不确定性（≈- 15%±20%）。这些经验模型表明，当使用连续代理时，我们可以从不连续的P时间序列中估计可靠的P输出，这对于在高频调查中完成时间序列数据集非常有用，即使是在较长的时间内。
We tested an empirical modeling approach using relatively low‐cost continuous records of turbidity and discharge as proxies to estimate phosphorus (P) concentrations at a subhourly time step for estimating loads. The method takes into account nonlinearity and hysteresis effects during storm events, and hydrological conditions variability. High‐frequency records of total P and reactive P originating from four contrasting European agricultural catchments in terms of P loads were used to test the method. The models were calibrated on weekly grab sampling data combined with 10 storms surveyed subhourly per year (weekly+ survey) and then used to reconstruct P concentrations during all storm events for computing annual loads. For total P, results showed that this modeling approach allowed the estimation of annual loads with limited uncertainties (≈ −10% ± 15%), more reliable than estimations based on simple linear regressions using turbidity, based on interpolated weekly+ data without storm event reconstruction, or on discharge weighted calculations from weekly series or monthly series. For reactive P, load uncertainties based on the nonlinear model were similar to uncertainties based on storm event reconstruction using simple linear regression (≈ 20% ± 30%), and remained lower than uncertainties obtained without storm reconstruction on weekly or monthly series, but larger than uncertainties based on interpolated weekly+ data (≈ −15% ± 20%). These empirical models showed we could estimate reliable P exports from noncontinuous P time series when using continuous proxies, and this could potentially be very useful for completing time‐series data sets in high‐frequency surveys, even over extended periods.
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DOI：
10.5194/hess-2015-545
发表时间：
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期刊：
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