Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States
Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States
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年际水文气候变化及其对美国东南部冬季养分负荷的影响
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. Sankarasubramanian
中科院分区：
文献类型：
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作者：
J. Oh;A. Sankarasubramanian
在水文气候文献中，季节性河流的年际变化可以用气候前兆如热带海表温度条件来部分解释。同样，众所周知，水流是估算养分负荷和相关浓度的最重要预测因子。本研究的目的是将这两项发现联系起来，以便利用预测海温的季节前气候预报来预测养分负荷。通过选择美国东南部18个相对不发达的流域，我们将冬季（1 - 2 - 3月，JFM）降水预报与影响该流域JFM水流的降水预报联系起来，建立冬季养分负荷预报。为此，我们考虑了两种不同类型的低维统计模型，在回顾性气候预测的基础上预测未来3个月的养分负荷。对预测模型的分割样本验证表明，至少8个站点观测到的冬季养分负荷的18-45%的年际变化甚至可以在季节开始之前预测。在预测JFM期间观测水质网络（WQN）负荷方面具有很高决定系数（> 0.8）的站点在利用气候预报预测季节总氮（TN）负荷方面表现出显著的技能。将先前的流量条件（12月的流量）作为额外的预测因子并没有增加这些站点的解释方差，但大大降低了预测负荷的均方根误差（RMSE）。将18个站点冬季养分负荷的主导模式联系起来，清楚地说明了与厄尔尼诺-南方涛动（ENSO）条件的关联。这些季节前营养预测在制定前瞻性和适应性营养管理策略中的潜在效用也进行了讨论。
It is well established in the hydroclimatic literature that the interannual variability in seasonal streamflow could be partially explained using climatic precursors such as tropical sea surface temperature (SST) conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that nutrient loadings could be predicted using season-ahead climate forecasts forced with forecasted SSTs. By selecting 18 relatively undeveloped basins in the Southeast US (SEUS), we relate winter (January-February-March, JFM) precipitation forecasts that influence the JFM streamflow over the basin to develop winter forecasts of nutrient loadings. For this purpose, we consider two different types of low-dimensional statistical models to predict 3-month ahead nutrient loadings based on retrospective climate forecasts. Split sample validation of the predictive models shows that 18–45% of interannual variability in observed winter nutrient loadings could be predicted even before the beginning of the season for at least 8 stations. Stations that have very high coefficient of determination (> 0.8) in predicting the observed water quality network (WQN) loadings during JFM exhibit significant skill in predicting seasonal total nitrogen (TN) loadings using climate forecasts. Incorporating antecedent flow conditions (December flow) as an additional predictor did not increase the explained variance in these stations, but substantially reduced the root-mean-square error (RMSE) in the predicted loadings. Relating the dominant mode of winter nutrient loadings over 18 stations clearly illustrates the association with El Nino Southern Oscillation (ENSO) conditions. Potential utility of these season-ahead nutrient predictions in developing proactive and adaptive nutrient management strategies is also discussed.