Time-series modeling of reservoir effects on river nitrate concentrations
Time-series modeling of reservoir effects on river nitrate concentrations
复制标题
水库对河流硝酸盐浓度影响的时间序列模型
DOI：
10.1016/j.advwatres.2009.04.002
复制
发表时间：
2009
影响因子：
4.7
通讯作者：
Kung
中科院分区：
文献类型：
--
作者：
Andrea L. Schoch;K. Schilling;Kung
塞洛维尔水库位于美国爱荷华州得梅因市以北约10公里处，是得梅因河的一个24.1公里的蓄水池。用于饮用水供应的得梅因河的地表水因硝酸盐氮而受损。选取1977-2006年30年水库上下游月平均硝酸盐浓度数据进行时间序列分析。我们的目标是：(1)建立一个模型，描述水库下游的硝酸盐浓度作为进入水库的浓度的函数；(2)使用该模型提前1个月预测下游水质。结果表明，使用传递函数方法可以有效地模拟下游硝酸盐，该方法将当前和前一个月的流入浓度作为输入变量。流入浓度使用AR（20）模型建模，其中高阶模型与其他人注意到的时间相关性一致。传递函数模型表明，水库使硝酸盐浓度降低了22±6%，大大超过了先前的估计。利用该模型预测的月硝酸盐含量几乎都在其实际实测值的95%预测区间内，并且受流量变化的影响不大。
Saylorville Reservoir is a 24.1km2impoundment of the Des Moines River located approximately 10km north of the City of Des Moines, Iowa, USA. Surface water from the Des Moines River used for drinking water supply is impaired for nitrate–nitrogen. Monthly mean nitrate concentration data collected upstream and downstream of the reservoir for a 30-year period (1977–2006) were selected for time-series analysis. Our objectives were to (1) develop a model describing nitrate concentrations downstream of the reservoir as a function of the concentrations entering the reservoir and (2) use the model to provide a 1-month ahead forecast for downstream water quality. Results indicated that downstream nitrate can be effectively modeled using a transfer function approach that utilized inflow concentrations during the current and previous month as input variables. Inflow concentrations were modeled using an AR(20) model, with the higher order model consistent with temporal correlation noted by others. The transfer function model suggested that the reservoir is reducing nitrate concentrations by 22±6%, a reduction that greatly exceeds previous estimates. Monthly nitrate forecasted with the model were nearly all within a 95% prediction interval of their actual measured values and did not appear greatly affected by flow variations.