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Coupled data-driven and process-based model for fluorescent dissolved organic matter prediction in a shallow subtropical reservoir
Environmental Modelling & Software ( IF 4.8 ) Pub Date : 2021-04-06 , DOI: 10.1016/j.envsoft.2021.105053
Xinchen Wang , Hong Zhang , Edoardo Bertone , Rodney A. Stewart , Sara P. Hughes

Monitoring and understanding the dissolved organic matter (DOM) cycle in a drinking water reservoir is crucial to water authorities, since most water treatment practices aim to remove DOM to prevent the formation of potentially harmful disinfection by-products. A vertical profiling system (VPS) installed in reservoirs can continuously detect the fluorescent DOM (fDOM) and determine the fDOM transport process. Although the VPS can interprete fDOM concentrations, water treatment operators still collect and rely upon DOM datasets that are manually sampled throughout the year. A long-term historical database provides an opportunity to develop a three-dimensional fDOM prediction model. In the present study, we collected and analysed VPS and sampling data and developed and assessed an innovative coupled data-driven and process-based model. These models were able to forecast future fDOM in both temperate and extreme weather conditions. Modelling scenario analysis concluded that deeper layers of the reservoir as well as areas close to the riverine zone had higher fDOM concentrations than any other zones during storm events. Simulated fDOM can be a proxy for dissolved organic carbon concentration. The model also determined that inflow creeks were predominant fDOM sources during storm events and continuing winds transported the fDOM from bottom to surface water layers. This study has implications for reservoir and water treatment plant operators seeking to gain a better understanding of the DOM cycle in a reservoir and to more efficiently manage DOM removal.



中文翻译:

耦合数据驱动和基于过程的浅亚热带储层荧光溶解有机物预测模型

监视和了解饮用水水库中的溶解性有机物(DOM)循环对水务管理部门至关重要,因为大多数水处理实践都旨在去除DOM,以防止形成潜在有害的消毒副产物。安装在储层中的垂直轮廓分析系统(VPS)可以连续检测荧光DOM(fDOM)并确定fDOM的运输过程。尽管VPS可以解释fDOM浓度,但水处理运营商仍会收集并依赖全年手动采样的DOM数据集。长期历史数据库为开发三维fDOM预测模型提供了机会。在本研究中,我们收集并分析了VPS和采样数据,并开发和评估了创新的数据驱动和基于过程的耦合模型。这些模型能够在温带和极端天气条件下预测未来的fDOM。建模方案分析得出的结论是,在暴风雨期间,水库深层以及靠近河流带的区域的fDOM浓度高于其他任何区域。模拟的fDOM可以替代溶解的有机碳浓度。该模型还确定,暴风雨期间流入小河是fDOM的主要来源,持续的风将fDOM从底部转移到地表水层。这项研究对寻求更好地了解水库中DOM循环并更有效地管理DOM去除的水库和水处理厂运营商具有重要意义。建模方案分析得出的结论是,在暴风雨期间,水库深层以及靠近河流带的区域的fDOM浓度高于其他任何区域。模拟的fDOM可以替代溶解的有机碳浓度。该模型还确定,暴风雨期间流入小河是fDOM的主要来源,持续的风将fDOM从底部转移到地表水层。这项研究对寻求更好地了解水库中DOM循环并更有效地管理DOM去除的水库和水处理厂运营商具有重要意义。建模方案分析得出的结论是,在暴风雨期间,水库深层以及靠近河流带的区域的fDOM浓度高于其他任何区域。模拟的fDOM可以替代溶解的有机碳浓度。该模型还确定,暴风雨期间流入小河是fDOM的主要来源,持续的风将fDOM从底部转移到地表水层。这项研究对寻求更好地了解水库中DOM循环并更有效地管理DOM去除的水库和水处理厂运营商具有重要意义。该模型还确定,暴风雨期间流入小河是fDOM的主要来源,持续的风将fDOM从底部转移到地表水层。这项研究对寻求更好地了解水库中DOM循环并更有效地管理DOM去除的水库和水处理厂运营商具有重要意义。该模型还确定,暴风雨期间流入小河是fDOM的主要来源,持续的风将fDOM从底部转移到地表水层。这项研究对寻求更好地了解水库中DOM循环并更有效地管理DOM去除的水库和水处理厂运营商具有重要意义。

更新日期:2021-04-13
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