Search

Stream nitrogen concentrations significantly impact nitrogen loads and greenhouse gas emissions, but their spatiotemporal heterogeneity and human influences remain highly uncertain. This study thoroughly explored the spatiotemporal variations in stream nitrogen concentrations in a typical headwater watershed in South China. Spatially distributed measurements were conducted during 2020–2022, and mathematical modeling was implemented based on incorporating these data. More than 4,400 data points were collected for water temperature and concentrations of ammonium nitrogen (NH4‐N), nitrate nitrogen (NOx‐N), dissolved total nitrogen (DTN), total nitrogen (TN), and dissolved oxygen. Results showed that NOx‐N was the largest component of TN, with average concentrations of 1.20 and 1.66 mg L−1, respectively. The stream N2O concentration could be predicted using NH4‐N and NOx‐N concentrations via the Michaelis‐Menten equation. Significant downstream decreases in NH4‐N, NOx‐N, DTN, and TN concentrations were identified in the largest river in the watershed, and clear spatial differences in these nitrogen concentrations existed among the three main rivers. Clear seasonal and annual variations in stream nitrogen concentrations were observed. NH4‐N, NOx‐N, DTN, and TN concentrations correlated with cumulative precipitation from the preceding 8–12 days, while stream N2O concentrations correlated over 13–20 days. Stream N2O concentrations and emissions averaged 12.77 nmol L−1 and 1.12 nmol m−2 s−1, respectively, and were lower in summer than in other seasons. Upstream tea plantations, villages, and adjacent agricultural lands significantly affected nitrogen concentrations, while overflow dams did not. These findings highlight nitrogen cycle's complexity and the need for high‐resolution data to guide effective watershed management. Plain Language Summary: Stream nitrogen concentrations directly influence water nitrogen pollution, eutrophication, and greenhouse gas emissions. Currently, increasingly intensified human activities and climate change have further complicated the already complex spatiotemporal distribution of stream nitrogen concentrations, making it even more uncertain. As a result, in‐depth investigations into the spatiotemporal heterogeneity of stream nitrogen concentrations are urgently needed. In this study, we thoroughly explored the spatiotemporal distribution of stream nitrogen concentrations in a typical headwater watershed in South China, where water quality is significantly influenced by intense human activities, including residential areas, rice and tea cultivation, and the construction of overflow dams. We combined extensive field measurements with mathematical modeling to illustrate the spatiotemporal variations in stream nitrogen concentrations and the corresponding natural and anthropogenic drivers. Our findings revealed that the decreasing trend in stream nitrogen concentrations from upstream to downstream in the largest river could be attributed to upstream human activities, while terrain accounted for the contrasting nitrogen concentrations across different rivers. We also discovered that stream nitrogen concentrations were significantly correlated with cumulative precipitation over the preceding approximately 10 days. Overall, the data and findings from this study have important implications for the management of water environments in headwater watersheds. Key Points: Over 4400 data points are collected to explore spatiotemporal heterogeneity in stream nitrogen concentrations in a headwater watershedStream nitrous oxide concentration can be predicted using stream ammonium and nitrate concentrations via the Michaelis‐Menten equationUpstream tea plantations, villages, and agricultural lands adjacent to streams severely affect stream nitrogen concentrations [ABSTRACT FROM AUTHOR]

Cropland ecosystems are significant emission sources of N2O, but a limited number of studies have focused on the impact of extreme weather events on N2O fluxes from cropland. This present study integrated field observations and model simulations to explore the responses of N2O fluxes to extreme weather events in typical rice and wheat rotation croplands in the middle and lower reaches of the Yangtze River (MLRYR) in China. The findings revealed that the studied rice-wheat rotation cropland exhibited a net source of N2O over the three-year monitoring period, with annual cumulative N2O emissions ranging from 190.4 to 261.8 mg N m−2. N2O emissions during the rice and wheat growing seasons accounted for 29% and 71% of the total yearly emissions, respectively. Extreme heat events led to a 23% to 32% increase in observed N2O emissions from cropland. Observed N2O emissions from irrigated rice fields during extreme precipitation events were 45% lower than those during extreme drought events. In contrast, extreme precipitation events raised observed N2O emissions from rain-fed wheat fields by 36% compared to the multi-year average, while extreme drought events reduced N2O emissions from wheat fields by 20%. Regional simulations indicated that annual cumulative N2O emissions from croplands in the MLRYR are projected to increase from 207.8 mg N m−2 under current climate to 303.4 mg N m−2 in the future. Given the episodic nature and uncertainties associated with N2O emissions from cropland, further validation is necessary for utilizing the model to explore the effects of extreme weather events on N2O in cropland ecosystems. [ABSTRACT FROM AUTHOR]

Lakes are important surface water bodies, and ongoing climate change is a growing threat to the hydrological cycle and water resource availability of lakes in arid regions. Accurately estimating different drivers' contributions to lake water volume can enhance our understanding of lake variations in arid regions. In this study, we combined the land surface model and hydrological model, as well as statistical methods, to analyze the spatiotemporal heterogeneity of lake area changes and the factors affecting these changes during the past decades in Bosten Lake, Ulungur Lake, Ebinur Lake, and Sayram Lake, which are located in a typical dry region in China. The study revealed that the average amounts of river inflow, TWVF, lake ice sublimation, lake surface precipitation, and river outflow in the four lakes were 17.41 × 108 m3 yr−1, 6.60 × 108 m3 yr−1, 0.41 × 108 m3 yr−1, 0.98 × 108 m3 yr−1, and 9.12 × 108 m3 yr−1, respectively. We found that river inflow is the dominant factor affecting changes in open lake areas, while lake surface precipitation is the main factor affecting changes in closed lake areas. Our findings suggest that the main factors dominating the variability of lake water volume differ in different phases and lake types. [ABSTRACT FROM AUTHOR]