Land use and climate variability amplifies watershed nitrogen exports in coastal China

Huang, Yaling; Huang, Jinliang; Ervinia, Ayu; Duan, Shuiwang; Kaushal, Sujay S.

doi:10.1016/j.ocecoaman.2018.02.024

Cited by 30 publications

(4 citation statements)

References 73 publications

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“…The Jiulong River watershed (JRW) in southeastern China (Figure a) is a good testbed for studying the biogeochemical processes of nutrients under a subtropical monsoon climate. − Long-term studies in this area have accumulated an adequate amount of nutrient data for practicing DL-based modeling. The JRW has a total area of 14 740 km 2 , with an average annual temperature of 20.9 °C and an average annual precipitation of 1600 mm.…”

Section: Methodsmentioning

confidence: 99%

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions

Xiong

Zheng

Chen

et al. 2022

Environ. Sci. Technol.

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Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32–0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall–runoff relationships. The multi-watershed flux model projects 0.60–12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7–20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.

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Section: Methodsmentioning

confidence: 99%

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions

Xiong

Zheng

Chen

et al. 2022

Environ. Sci. Technol.

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“…The Jiulong River Watershed (JRW) has experienced severe water quality degradation over the last 20 years (Huang et al, 2021;Huang and Huang, 2019). In previous studies, long-term hydroclimatic data have been used to explore bacterial spatiotemporal patterns and drivers, as well as how these patterns and causes are affected by typhoons, high precipitation, and human activity in subtropical areas (Xie et al 2023) In this study, we aimed to explore the effects of land-use and hydrologic regime patterns on E. coli and fecal coliforms in the Jiulong River watershed.…”

Section: Introductionmentioning

confidence: 99%

Coupled effects of land-use patterns and hydrological regimes on fecal indicator bacteria in a coastal watershed

Huang,

Wang,

Xie

et al. 2023

Preprint

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Monitoring and assessing fecal indicator bacteria (FIB) can inform aquatic ecosystem health and management. However, knowledge of the patterns of fecal coliforms and the underlying mechanisms in rivers is still limited. In this study, we aimed to investigate the coupled effects of land-use patterns and hydrological regimes on FIB patterns in a subtropical coastal watershed, the Jiulong River Watershed (JRW). Water samples were collected quarterly for nutrient and FIB measurements across the watershed, from October 2020 to July 2021. The results indicate that Escherichia coli and Fecal E. coli concentrations peaked in summer and autumn, with significantly higher concentrations observed in urban watersheds than in natural watersheds. Redundancy analysis further revealed that of the four sampling seasons, the relative contribution of land use to FIB characteristics in the JRW was the highest in autumn, whereas that of water quality was the highest in the other three seasons. Water flow showed the lowest relative contribution in all the seasons and a positive correlation between high nutrient concentrations and E. coli counts exhibited. This study enhances our understanding on the coupled effects of seasonal hydrological regimes and land-use patterns on FIB characteristics, which might provide implications for mitigating the risk of E. coli contamination in similar human-impacted watersheds.

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“…For example, more N is discharged into rivers from cropland [ 9 ] and building land [ 10 ], while less N is discharged into rivers from grassland [ 11 ] and woodland [ 12 ], and woodland can even absorb N from water [ 13 ]. N content in water coincides highly in time and space with agricultural activities [ 6 ], and the significant seasonal and interannual variation [ 14 ] also reflects N is related to land use type. Land is an important resource for socioeconomic development [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of changes in land use structure on nitrogen input in the Pingzhai Reservoir watershed, a karst mountain region

Wang

Zhou

et al. 2023

Heliyon

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Land use and climate variability amplifies watershed nitrogen exports in coastal China

Cited by 30 publications

References 73 publications

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions

Coupled effects of land-use patterns and hydrological regimes on fecal indicator bacteria in a coastal watershed

Effects of changes in land use structure on nitrogen input in the Pingzhai Reservoir watershed, a karst mountain region

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