The great spatial and temporal variability in hydrological conditions and nitrogen（N）processing introduces large uncertainties to the identification of N sources and quantifying N cycles in plain river network regio...The great spatial and temporal variability in hydrological conditions and nitrogen（N）processing introduces large uncertainties to the identification of N sources and quantifying N cycles in plain river network regions. By combining isotopic data with chemical and hydrologic measurements, we determined the relative importance of N sources and biogeochemical N processes in the Taige River in the East Plain Region of China. The river was polluted more seriously by anthropogenic inputs in winter than in summer. Manure and urban sewage effluent were the main nitrate（NO-3） sources, with the nitrification of N-containing organic materials serving as another important source of NO-3. In the downstream, with minor variations in hydrological conditions, nitrification played a more important role than assimilation for the decreasing ammonium（NH＋4-N） concentrations.The N isotopic enrichment factors（ε） during NH＋4utilization ranged from- 13.88‰ in March to- 29.00‰ in July. The ratio of the increase in δ^18O and δ^15N of river NO-3in the downstream was 1.04 in January and 0.92 in March. This ratio indicated that NO-3assimilation by phytoplankton was responsible for the increasing δ^15N and δ^18O values of NO-3in winter. The relationships between δ^15N of particulate organic nitrogen and isotopic compositions of dissolved inorganic nitrogen indicated that the phytoplankton in the Taige River probably utilized NH＋4preferentially and mainly in summer, while in winter, NO-3assimilation by phytoplankton was dominant.展开更多
The land area in a river network is divided into certain-scale square cells for the sake of precision, and, based on the physical mechanisms of rainfall-runoff processes and runoff pollution, the non-point source poll...The land area in a river network is divided into certain-scale square cells for the sake of precision, and, based on the physical mechanisms of rainfall-runoff processes and runoff pollution, the non-point source pollution from cells is estimated using the export coefficients of different land use types. The non-point source pollution from a land cell should all go into the closest fiver reach, so it is distributed according to the terrain of the plain river network area and the positions of land cells and river network reaches. A relationship between a single land cell and its pollution-receiving reach can be determined using this system. In view of the above, a spatial distribution model of the rainfall runoff and non-point source pollution in reaches of a plain river network area was established. This model can provide technological support for further research on the dynamic effects of non-point source pollution on water quality.展开更多
Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up base...Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.展开更多
为准确评估监测条件有限的平原河网小流域河水水质演变趋势,预知水质变化情况,利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据,基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)和双向长短期记忆神经网络(bi-direc...为准确评估监测条件有限的平原河网小流域河水水质演变趋势,预知水质变化情况,利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据,基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)和双向长短期记忆神经网络(bi-directional long short-term memory,BiLSTM)建立了地表水水质预测模型。利用箱线图和Spearman秩相关系数挖掘水质的时空分布规律,划定中间河段4个站点为重点研究区域,NH3—N和TP为治理重点。通过BOA和双向信息传递机制优化LSTM超参数和模型结构,结果显示,用BOA-BiLSTM模型预测,未来4 h NH_(3)—N浓度的均方根误差(root mean squared error,RMSE)分别为0.2132,0.3689,0.3327和0.3740;未来4 h TP浓度的RMSE分别为0.0246,0.0321,0.0422和0.0334。二者较基准LSTM模型的预测结果分别提升了15.8%,10.6%,10.6%,17.1%和22.6%,3.6%,14.8%,11.8%。以磨石桥NH_(3)—N浓度为例,对比了时序预测与加入上下游数据后的多变量预测结果,发现时序预测对监测参数较少的平原河网具有更强的适用性和更高的预测精度。同时结合研究区域现场勘查和地块分类情况,指出生活源、污水收集及处理设施不完善、雨污合流应为整治重点。当监测参数有限时,本文模型有助于提升对水质异常的监管水平,为环境执法、水环境治理提供数据支撑。展开更多
基金supported by the Mega-projects of Science Research for Water Environment Improvement (No. 2012ZX07101)
文摘The great spatial and temporal variability in hydrological conditions and nitrogen（N）processing introduces large uncertainties to the identification of N sources and quantifying N cycles in plain river network regions. By combining isotopic data with chemical and hydrologic measurements, we determined the relative importance of N sources and biogeochemical N processes in the Taige River in the East Plain Region of China. The river was polluted more seriously by anthropogenic inputs in winter than in summer. Manure and urban sewage effluent were the main nitrate（NO-3） sources, with the nitrification of N-containing organic materials serving as another important source of NO-3. In the downstream, with minor variations in hydrological conditions, nitrification played a more important role than assimilation for the decreasing ammonium（NH＋4-N） concentrations.The N isotopic enrichment factors（ε） during NH＋4utilization ranged from- 13.88‰ in March to- 29.00‰ in July. The ratio of the increase in δ^18O and δ^15N of river NO-3in the downstream was 1.04 in January and 0.92 in March. This ratio indicated that NO-3assimilation by phytoplankton was responsible for the increasing δ^15N and δ^18O values of NO-3in winter. The relationships between δ^15N of particulate organic nitrogen and isotopic compositions of dissolved inorganic nitrogen indicated that the phytoplankton in the Taige River probably utilized NH＋4preferentially and mainly in summer, while in winter, NO-3assimilation by phytoplankton was dominant.
基金supported by the Major Science and Technology Program for Water Pollution Control and Treatment in China (Grant No. 2008X07101-005)
文摘The land area in a river network is divided into certain-scale square cells for the sake of precision, and, based on the physical mechanisms of rainfall-runoff processes and runoff pollution, the non-point source pollution from cells is estimated using the export coefficients of different land use types. The non-point source pollution from a land cell should all go into the closest fiver reach, so it is distributed according to the terrain of the plain river network area and the positions of land cells and river network reaches. A relationship between a single land cell and its pollution-receiving reach can be determined using this system. In view of the above, a spatial distribution model of the rainfall runoff and non-point source pollution in reaches of a plain river network area was established. This model can provide technological support for further research on the dynamic effects of non-point source pollution on water quality.
基金supported by the Major Program of the National Natural Science Foundation of China(Grant No.51190091)the National Natural Science Foundation of China(Grant No.51009045)the Open Research Fund Program of the State Key Laboratory of Water Resources and Hydropower Engineering Science of Wuhan University(Grant No.2012B094)
文摘Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.
文摘为准确评估监测条件有限的平原河网小流域河水水质演变趋势,预知水质变化情况,利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据,基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)和双向长短期记忆神经网络(bi-directional long short-term memory,BiLSTM)建立了地表水水质预测模型。利用箱线图和Spearman秩相关系数挖掘水质的时空分布规律,划定中间河段4个站点为重点研究区域,NH3—N和TP为治理重点。通过BOA和双向信息传递机制优化LSTM超参数和模型结构,结果显示,用BOA-BiLSTM模型预测,未来4 h NH_(3)—N浓度的均方根误差(root mean squared error,RMSE)分别为0.2132,0.3689,0.3327和0.3740;未来4 h TP浓度的RMSE分别为0.0246,0.0321,0.0422和0.0334。二者较基准LSTM模型的预测结果分别提升了15.8%,10.6%,10.6%,17.1%和22.6%,3.6%,14.8%,11.8%。以磨石桥NH_(3)—N浓度为例,对比了时序预测与加入上下游数据后的多变量预测结果,发现时序预测对监测参数较少的平原河网具有更强的适用性和更高的预测精度。同时结合研究区域现场勘查和地块分类情况,指出生活源、污水收集及处理设施不完善、雨污合流应为整治重点。当监测参数有限时,本文模型有助于提升对水质异常的监管水平,为环境执法、水环境治理提供数据支撑。
