Wu Yan-hao; Zhou Nian-qing; Wu Zi-jun; Lu Shuai-shuai; Cai Yi

doi:10.19637/j.cnki.2305-7068.2022.03.004

doi: 10.19637/j.cnki.2305-7068.2022.03.004

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出版历程
- 收稿日期: 2022-05-18
- 录用日期: 2022-07-27
- 刊出日期: 2022-09-15

Carbon, nitrogen and phosphorus coupling relationships and their influencing factors in the critical zone of Dongting Lake wetlands, China

1.
Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
2.
State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China

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Corresponding author: nq.zhou@tongji.edu.cn

摘要

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Abstract: Wetland is a transition zone between terrestrial and aquatic ecosystems, and is the source and sink of various biogenic elements in the earth’s epipelagic zone. In order to investigate the driving force and coupling mechanism of carbon (C), nitrogen (N) and phosphorus (P) migration in the critical zone of lake wetland, this paper studies the natural wetland of Dongting Lake area, through measuring and analysing the C, N and P contents in the wetland soil and groundwater. Methods of Pearson correlation, non-linear regression and machine learning were employed to analyse the influencing factors, and to explore the coupling patterns of the C, N and P in both soils and groundwater, with data derived from soil and water samples collected from the wetland critical zone. The results show that the mean values of organic carbon (TOC), total nitrogen (TN) and total phosphorus (TP) in groundwater are 1.59 mg/L, 4.19 mg/L and 0.5 mg/L, respectively, while the mean values of C, N and P in the soils are 18.05 g/kg, 0.86 g/kg and 0.52 g/kg. The results also show that the TOC, TN and TP in the groundwater are driven by a variety of environmental factors. However, the concentrations of C, N and P in the soils are mainly related to vegetation abundance and species which influence each other. In addition, the fitted curves of wetland soil C-N and C-P appear to follow the power function and S-shaped curve, respectively. In order to establish a multivariate regression model, the soil N and P contents were used as the input parameters and the soil C content used as the output one. By comparing the prediction effects of machine learning and nonlinear regression modelling, the results show that coupled relationship equation for the C, N and P contents is highly reliable. Future modelling of the coupled soil and groundwater elemental cycles needs to consider the complexity of hydrogeological conditions and to explore the quantitative relationships among the influencing factors and chemical constituents.
- Dongting Lake /
- Wetland critical zone /
- Carbon /
- Nitrogen and phosphorus /
- Driving factors /
- Coupling mechanisms

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Figure 1. Soil sampling sites in the lake wetland critical zone of the Poyang Lake region, Southern China; LS – Lishui; YJ – Yuanjiang; ZS – Zishui; XJ – Xiangjiang

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Figure 2. Construction process of artificial neural network

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Figure 3. Spatial distribution of soil C, N, P in wetland critical zones (a, c, e belongs to P1 section, b, d, f belongs to P2 section)

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Figure 4. Temporal and spatial changes of TOC, TN and TP in groundwater in wetland critical zone

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Figure 5. Characteristics TOC, TN and TP in surface water in wetland critical zone

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Figure 6. Correlation coefficient between C, N, P contents and physical and chemical factors in soil and groundwater of wetland critical zone

*P<0.05, **P<0.01, ***P<0.001

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Figure 7. The 1:1 relationship between measured and fitted values of soil C based on machine learning and nonlinear fitting (where A and B are scatterplots based on BP neural network and nonlinear regression fitting respectively)

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Figure 8. Prediction of soil C content and relative error results in wetland critical zone

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Table 1. Physical and chemical properties of wetland critical zones on different monitoring profiles

Profile	Soil pH	W(%)	Groundwater pH	DO(mg/L)	Eh(mV)	T(°C)
P1	5.8^a	33.2^a	7.1^a	2.5^a	2.6^a	19.3^a
P2	6.8^b	31.6^a	6.5^a	3.1^b	33.4^b	18.8^a
Note: Letters in the table indicate significant differences in different places in the same column (P < 0.05).

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Table 2. Hydraulic conductivity of soil layers at depth in wetland critical zone (after Wei et al., 1989)

profile P1		profile P2
Depth	Type of soil layer	Depth	Type of soil layer
0–3 m	Silt clay，K: 1.2×10⁻⁶–6.0×10⁻⁵ cm/s	0–0.8 m	Sandy soil，K: 6.0×10⁻⁵–6.0×10⁻⁴ cm/s
3–4 m	Clay，K: < 1.2×10^-6 cm/s	0.8–8 m	Silt clay，K: 1.2×10⁻⁶–6.0×10⁻⁵ cm/s
4–10 m	Silt clay，K: 1.2×10⁻⁶–6.0×10⁻⁵ cm/s	8–10 m	Clay，K: < 1.2×10⁻⁶ cm/s

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Table 3. Statistical characteristics of C, N and P in different soil profiles

Profile	Soil C			Soil N			Soil P
Profile	Range （g/kg）	Mean （g/kg）	Variation coefficients （%）	Range （g/kg）	Mean （g/kg）	Variation coefficients （%）	Range （g/kg）	Mean （g/kg）	Variation coefficients （%）
P1	5.81–37.24	20.34^a	47.54	0.23–1.89	0.90^a	37.31	0.19–1.03	0.52^a	34.67
P2	1.80–32.19	15.86^b	42.10	0.08–1.56	0.82^b	35.11	0.15–0.74	0.52^a	30.57
Average		18.05	44.82		0.86	36.21		0.52	32.62
Note: Different letters represent significant differences in the mean values of each profile (p<0.05), and the same letters represent non-significant differences.

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Table 4. Statistical characteristics of TOC, TN and TP in groundwater of different profiles

profile	Groundwater TOC			Groundwater TN			Groundwater TP
profile	Range （mg/L）	Mean （mg/L）	Variation coefficients （%）	Range （mg/L）	Mean （mg/L）	Variation coefficients （%）	Range （mg/L）	Mean （mg/L）	Variation coefficients （%）
P1	0.49–3.83	1.94^a	56.24	2.81–7.74	4.22^a	27.45	0.20–0.78	0.44^a	22.08
P2	0.29–2.81	1.24^b	43.54	1.17–6.73	4.16^a	33.33	0.11–0.88	0.55^b	50.13
Avrage		1.59	49.89		4.19	30.39		0.49	36.11
Note: Different letters represent significant differences in the mean values of each profile (p < 0.05), and the same letters represent non-significant differences.

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Table 5. C-N and C-P fitting model results

Equation	C-N model summary			C-P model summary
Equation	R²	F	Significance	R²	F	Significance
Linear	0.481	65.864	0	0.302	30.745	0
Logarithmic	0.412	49.701	0	0.303	30.844	0
Inverse	0.229	21.087	0	0.27	26.291	0
Quadratic	0.481	32.469	0	0.302	15.177	0
Cubic	0.481	21.337	0	0.331	11.382	0
Composite	0.537	82.291	0	0.34	36.501	0
Power	0.752	143.159	0	0.521	61.65	0
S-curve	0.549	86.602	0	0.571	73.243	0
Growth	0.537	82.291	0	0.34	36.501	0
Exponential	0.537	82.291	0	0.34	36.501	0
Logistic	0.537	82.291	0	0.34	36.501	0

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Table 6. Model evaluation indicators

Model	Sample size	RMSE	MRE	MAE
BP Neural Network	23	3.43	0.27	3.03
Non-linear regression	23	3.18	0.21	2.61

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