{"title":"Drought reduces nitrogen supply and N2O emission in coastal bays","authors":"","doi":"10.1016/j.watres.2024.122362","DOIUrl":null,"url":null,"abstract":"<div><p>Severe droughts are increasingly prevalent under global climate change, disrupting watershed hydrology and coastal nitrogen cycling. However, the specific effects of drought on nitrogen transport from land to sea and subsequent nitrogen dynamics remain inadequately understood. In this study, we evaluated the consequences of the 2020–2022 drought on nitrogen supply and N<sub>2</sub>O emissions in Xiamen Bay, Southeast China. The results showed that drought significantly reduced annual NH<sub>4</sub><img>N, NO<sub>2</sub><img>N, and NO<sub>3</sub><img>N concentrations in Xiamen Bay by 49.4 %, 32.1 %, and 40.3 %, respectively, compared with the pre-drought year of 2019. The decline in NH<sub>4</sub><img>N concentration was mainly attributed to reduced surface runoff across all seasons. NO<sub>3</sub><img>N and NO<sub>2</sub><img>N concentrations declined only during spring and summer, primarily due to increased potential evapotranspiration (PET) hindering nitrogen supply via groundwater and concurrently enhancing land denitrification. Annual N<sub>2</sub>O emission from Xiamen Bay decreased by 40.0∼72.7 % during the drought, highly correlated with the decline in the concentrations of NO<sub>3</sub><img>N, DIN, and DTN (<em>p</em> < 0.001). Comparative analysis revealed that NO<sub>3</sub><img>N concentration exhibited consistent negative linear regressions with PET and declined as evaporative demand drought conditions worsened across Xiamen Bay, Sansha Bay, and Chesapeake Bay throughout 2010–2022. NH<sub>4</sub><img>N concentration showed a positive regression with river discharge in Xiamen Bay, but negative regressions in the other two bays. Our results indicates that drought reduces N<sub>2</sub>O emission primarily driven by nitrate substrate reduction in the bay. This study provides new insights for predicting coastal nitrogen dynamics and greenhouse gas emissions under global environmental change.</p></div>","PeriodicalId":443,"journal":{"name":"Water Research","volume":null,"pages":null},"PeriodicalIF":11.4000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0043135424012612","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Severe droughts are increasingly prevalent under global climate change, disrupting watershed hydrology and coastal nitrogen cycling. However, the specific effects of drought on nitrogen transport from land to sea and subsequent nitrogen dynamics remain inadequately understood. In this study, we evaluated the consequences of the 2020–2022 drought on nitrogen supply and N2O emissions in Xiamen Bay, Southeast China. The results showed that drought significantly reduced annual NH4N, NO2N, and NO3N concentrations in Xiamen Bay by 49.4 %, 32.1 %, and 40.3 %, respectively, compared with the pre-drought year of 2019. The decline in NH4N concentration was mainly attributed to reduced surface runoff across all seasons. NO3N and NO2N concentrations declined only during spring and summer, primarily due to increased potential evapotranspiration (PET) hindering nitrogen supply via groundwater and concurrently enhancing land denitrification. Annual N2O emission from Xiamen Bay decreased by 40.0∼72.7 % during the drought, highly correlated with the decline in the concentrations of NO3N, DIN, and DTN (p < 0.001). Comparative analysis revealed that NO3N concentration exhibited consistent negative linear regressions with PET and declined as evaporative demand drought conditions worsened across Xiamen Bay, Sansha Bay, and Chesapeake Bay throughout 2010–2022. NH4N concentration showed a positive regression with river discharge in Xiamen Bay, but negative regressions in the other two bays. Our results indicates that drought reduces N2O emission primarily driven by nitrate substrate reduction in the bay. This study provides new insights for predicting coastal nitrogen dynamics and greenhouse gas emissions under global environmental change.
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.