Estimation of nitrogen runoff loss from croplands in the Yangtze River Basin: A meta-analysis

Zhang, Yufu; Wu, Hao Bin; Yao, Mengya; Zhou, Jia; Wu, Kaibin; Hu, Minpeng; Shen, Hong; Chen, Dingjiang

doi:10.1016/j.envpol.2020.116001

Cited by 40 publications

(9 citation statements)

References 60 publications

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“…This may be because during runoff, rainfall kinetic energy acts on the soil surface. The intensity of rainfall determines the runoff magnitude [26]. Therefore, the soil erosion rate increases with the increase in rainfall intensity [27].…”

Section: Discussionmentioning

confidence: 99%

Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope

Wang,

Jin

et al. 2024

Water

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Rainfall intensity and slope gradient are the main drivers of slope surface runoff and nitrogen loss. To explore the distribution of rainfall runoff and nitrogen loss on the Miyun Reservoir slopes, we used artificial indoor simulated rainfall experiments to determine the distribution characteristics and nitrogen migration paths of surface and subsurface runoff under different rainfall intensities and slope gradients. The initial runoff generation time of subsurface runoff lagged that of surface runoff, and the lag time under different rainfall intensity and slope conditions ranges from 3.97 to 12.62 min. Surface runoff rate increased with increasing rainfall intensity and slope gradient; compared with a rainfall intensity of 40 mm/h, at a slope of 15°, average surface runoff rate at 60 and 80 mm/h increased by 2.38 and 3.60 times, respectively. Meanwhile, the subsurface runoff rate trended upwards with increasing rainfall intensity, in the order 5 > 15 > 10°. It initially increased and then decreased with increasing slope gradient, in the order 5 > 10 > 15°. Total nitrogen (TN) loss concentration of surface runoff shows a decrease followed by a stabilization trend; the concentration of TN loss decreases with decreasing rainfall intensity, and the stabilization time becomes earlier and is most obvious in 5° slope conditions. TN loss concentration in subsurface runoff decreased with increasing rainfall intensity, i.e., 40 > 60 > 80 mm/h. The surface runoff rainfall coefficient was mainly affected by rainfall intensity, a correlation between αs and slope gradients S was not obvious, and the fitting effect was poor. The subsurface runoff rainfall coefficient was mainly affected by slope gradient, the R2 of all rainfall intensities was <0.60, and the fitting effect was poor. The main runoff loss pathway from the Miyun Reservoir slopes was surface runoff, which was more than 62.57%. At the same time, nitrogen loss was subsurface runoff, more than 51.14%. The proportion of surface runoff to total runoff increases with the increase of rainfall intensity and slope, with a minimum of 62.57%, and the proportion of nitrogen loss from subsurface runoff also decreases with increasing rainfall intensity but does not change with slope gradient. The order of different runoff modulus types was mixed runoff (surface and subsurface runoff occur simultaneously) > surface runoff > subsurface runoff. The surface and mixed runoff modulus increased significantly with increasing rain intensity under different rain intensities and slope gradients. Overall, rainfall intensity significantly affected slope surface runoff, and slope gradient significantly affected nitrogen loss.

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Section: Discussionmentioning

confidence: 99%

Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope

Wang,

Jin

et al. 2024

Water

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“…Anthropogenic activities may indirectly affect the population of this species. It has been reported that a considerable amount of nutrients was released into the Yangtze River from croplands due to excessive anthropogenic addition of N ( Wang, 2006 ; Zhang et al , 2021 ). The Yangtze River estuary was identified as the important feeding ground for many fish species including the hairtail ( Deng and Zhao, 1991 ; Chen et al , 1999 ).…”

Section: Methodsmentioning

confidence: 99%

DEB-IBM for predicting climate change and anthropogenic impacts on population dynamics of hairtail Trichiurus lepturus in the East China Sea

Yang

Han

Gorfine

et al. 2022

Conservation Physiology

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The hairtail Trichiurus lepturus supports the largest fisheries in the East China Sea. The stock has fluctuated in the past few decades and this variation has been attributed to human pressures and climate change. To investigate energetics of individuals and population dynamics of the species in responses to environmental variations and fishing efforts, we have developed a DEB-IBM by coupling a dynamic energy budget (DEB) model to an individual-based model (IBM). The parameter estimation of DEB model shows an acceptable goodness of fit. The DEB-IBM was validated with histological data for a period of 38 years. High fishing pressure was largely responsible for the dramatic decline of the stock in middle 1980s. The stock recovered from early 1990s, which coincided with introduction of fishing moratorium on spawning stocks in inshore waters and substantial decrease of fishing efforts from large fisheries companies. In addition, the population average age showed a trend of slight decrease. The model successfully reproduced these observations of interannual variations in the population dynamics. The model was then implemented to simulate the effect of climate change on the population performance under greenhouse gas emission scenarios projected for 2100. It was also used to explore population responses to changing fishing mortalities. These scenario simulations have shown that the population biomass under SSP1-1.9, SSP2-4.5 and SSP5-8.5 would decline by 7.5%, 16.6% and 30.1%, respectively, in 2100. The model predicts that increasing fishing mortality by 10% will cause 5.3% decline of the population biomass, whereas decrease of fishing mortality by 10% will result in 6.8% increase of the biomass. The development of the DEB-IBM provides a predictive tool to inform management decisions for sustainable exploitation of the hairtail stock in the East China Sea.

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“…Greater soil erosion/leaching occurs in the summer when ~45% of annual precipitation occurs (CER = 7.85 and JTR = 8.89 mm day −1 ), compared to only ~13% of precipitation in winter (CER = 2.48 and JTR = 2.38 mm day −1 ). Higher rainfall amounts and intensities were shown to result in greater soil N losses to rivers (Deng et al 2019;Zhang et al 2021). The contribution of atmospheric deposition (wetfall + dryfall) was relatively stable with no signi cant seasonal uctuations (p>0.05).…”

Section: Spatio-temporal Variability Of Water Qualitymentioning

confidence: 99%

Combining the Multivariate Statistics and Dual Stable Isotopes Methods for Nitrogen Source Identification in Coastal Rivers of Hangzhou Bay, China

Zhou

Liu

et al. 2022

Preprint

Self Cite

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Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes (δ15N-NO– 3 and δ18O-NO– 3) and water quality parameters, this study combined the dual nitrate (NO– 3) stable isotope method and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. Further, the dual stable isotope method identified soil N, aquaculture tailwater, domestic wastewater and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine NO– 3 in the Cao’e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and dual stable isotope method showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. Moreover, this study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

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Estimation of nitrogen runoff loss from croplands in the Yangtze River Basin: A meta-analysis

Cited by 40 publications

References 60 publications

Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope

Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope

DEB-IBM for predicting climate change and anthropogenic impacts on population dynamics of hairtail Trichiurus lepturus in the East China Sea

Combining the Multivariate Statistics and Dual Stable Isotopes Methods for Nitrogen Source Identification in Coastal Rivers of Hangzhou Bay, China

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