Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento 38123, Italy.
Center for Ecohydraulics Research, University of Idaho, Boise, ID 83702, USA.
Sci Total Environ. 2020 Aug 25;732:138390. doi: 10.1016/j.scitotenv.2020.138390. Epub 2020 May 8.
Nitrous oxide (NO) is widely recognized as one of the most important greenhouse gases, and responsible for stratospheric ozone destruction. A significant fraction of NO emissions to the atmosphere is from rivers. Reliable catchment-scale estimates of these emissions require both high-resolution field data and suitable models able to capture the main processes controlling nitrogen transformation within surface and subsurface riverine environments. Thus, this investigation tests and validates a recently proposed parsimonious and effective model to predict riverine NO fluxes with measurements taken along the main stem of the Upper Mississippi River (UMR). The model parameterizes NO emissions by means of two denitrification Damköhler numbers; one accounting for processes occurring within the hyporheic and benthic zones, and the other one within the water column, as a function of river size. Its performance was assessed with several statistical quantitative indexes such as: Absolute Error (AE), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR). Comparison of predicted NO gradients between water and air (ΔNO) with those quantified from field measurements validates the predictive performance of the model and allow extending previous findings to large river networks including highly regulated rivers with cascade reservoirs and locks. Results show the major role played by the water column processes in contributing to NO emissions in large rivers. Consequently, NO productions along the UMR, characterized by regulated flows and large channel size, occur chiefly within this surficial riverine compartment, where the suspended particles may create anoxic microsites, which favor denitrification.
一氧化二氮（NO）是一种被广泛认为的重要温室气体，也是导致平流层臭氧破坏的元凶之一。河流是大气中氮氧化物排放的重要来源之一。为了对这些排放进行可靠的流域尺度估算，需要同时具备高分辨率的现场数据和能够捕捉地表及地下河流水环境中氮转化主要过程的合适模型。因此，本研究对一种最近提出的简洁有效的模型进行了测试和验证，该模型可用于预测密西西比河上游（UMR）干流沿线的河川 NO 通量。该模型通过两个反硝化 Damköhler 数来参数化 NO 排放：一个用于描述潜流区和底栖区的过程，另一个用于描述水柱中的过程，其大小与河流的大小有关。该模型的性能通过多个统计定量指标进行评估，如绝对误差（AE）、纳什-苏特克利夫效率（NSE）、偏度百分比（PBIAS）和均方根误差与实测数据标准差之比（RSR）。将模型预测的水和气之间的 NO 梯度（ΔNO）与现场测量量化的梯度进行比较，验证了模型的预测性能，并允许将先前的发现扩展到大的河网系统，包括具有梯级水库和船闸的高度调节河流。结果表明，水柱过程在大河流中对 NO 排放的贡献很大。因此，受调节水流和大河道尺寸特征影响的密西西比河上游的 NO 产生主要发生在这个表层河流水体中，其中悬浮颗粒可能会产生缺氧微生境，有利于反硝化作用。
