How do varying nitrogen fertilization rates affect crop yields and riverine N2O emissions? A hybrid modeling study

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-02-03 DOI:10.1016/j.watres.2025.123242
Diego Panique-Casso , Nelson F. Pacheco-Bueno , Marie Anne Eurie Forio, Peter Goethals, Long Ho
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Abstract

Headwater streams in agricultural areas constitute significant sources of nitrous oxide (N2O) due to nutrient enrichment; however, their emissions are often overlooked in current environmental impact assessments. This scarcity highlights the importance of developing advanced decision tools to evaluate these contributions and create effective mitigation strategies. Our study establishes the first integrated modeling framework that combines a process-based model SWAT+ with a linear mixed model (LMM) to predict N2O emissions from a headwater agricultural river system in Belgium under diverse climate change and fertilization scenarios. In particular, the calibrated and validated SWAT+ model was used to simulate streamflow, nutrient transport, and crop yields under these scenarios, from which, together with biochemical data collected from sampling campaigns, riverine N2O emissions were predicted via LMM. Our results revealed hydrologically driven patterns in riverine N2O emissions, with peak emissions in winter and spring, driven by precipitations enhancing shallow subsurface flows, carrying leached nutrients from fields to the river, and fueling N2O emissions. These phenomena were intensified under climate change scenarios, especially during combined wetter and hotter winters and springs, which elevated headwater N2O emissions by 40 %. Moreover, when coupling these conditions with a 20 % increase in fertilizer rates, riverine N2O emissions would be boosted by 83 %. These findings underscore the importance of integrating land-surface and river processes, to effectively quantify the feedback loop between river nutrient enrichment and climate change under the influence of agricultural practices, and to support comprehensive mitigation strategies under the warming climate.

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不同的氮肥施用量如何影响作物产量和河流N2O排放?混合模型研究
农业地区的水源由于营养物质的富集而成为氧化亚氮的重要来源;然而,在目前的环境影响评估中,它们的排放往往被忽视。这种稀缺性突出了开发先进决策工具以评估这些贡献和制定有效缓解战略的重要性。本研究建立了首个综合建模框架,将基于过程的模型SWAT+与线性混合模型(LMM)相结合,用于预测不同气候变化和施肥情景下比利时水源农业河流系统的N2O排放。特别地,使用校准和验证的SWAT+模型模拟了这些情景下的河流流量、养分运输和作物产量,并结合从采样活动中收集的生化数据,通过LMM预测了河流N2O排放。我们的研究结果揭示了河流N2O排放的水文驱动模式,冬季和春季排放达到峰值,降水增强了浅层地下流,将浸出的营养物质从农田带到河流,并促进了N2O的排放。这些现象在气候变化情景下加剧,特别是在冬季和春季湿热混合的情况下,水源N2O排放量增加了40%。此外,当这些条件与肥料用量增加20%相结合时,河流N2O排放量将增加83%。这些发现强调了整合陆地表面和河流过程的重要性,以有效量化农业实践影响下河流养分富集与气候变化之间的反馈回路,并支持气候变暖下的综合缓解战略。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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