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

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

Wu YH, Zhou NQ, Wu ZJ, et al. 2022. Carbon, nitrogen and phosphorus coupling relationships and their influencing factors in the critical zone of Dongting Lake wetlands, China. Journal of Groundwater Science and Engineering, 10(3): 250-266 doi:  10.19637/j.cnki.2305-7068.2022.03.004
Citation: Wu YH, Zhou NQ, Wu ZJ, et al. 2022. Carbon, nitrogen and phosphorus coupling relationships and their influencing factors in the critical zone of Dongting Lake wetlands, China. Journal of Groundwater Science and Engineering, 10(3): 250-266 doi:  10.19637/j.cnki.2305-7068.2022.03.004

doi: 10.19637/j.cnki.2305-7068.2022.03.004

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

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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

    Figure  2.  Construction process of artificial neural network

    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)

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

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

    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

    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)

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

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

    ProfileSoil
    pH

    W(%)
    Groundwater
    pH

    DO(mg/L)

    Eh(mV)
    T(°C)
    P15.8a33.2a7.1a2.5a2.6a19.3a
    P26.8b31.6a6.5a3.1b33.4b18.8a
    Note: Letters in the table indicate significant differences in different places in the same column (P < 0.05).
    下载: 导出CSV

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

    profile P1profile P2
    DepthType of soil layerDepthType of soil layer
    0–3 mSilt clay,K: 1.2×10−6–6.0×10−5 cm/s0–0.8 mSandy soil,K: 6.0×10−5–6.0×10−4 cm/s
    3–4 mClay,K: < 1.2×10-6 cm/s0.8–8 mSilt clay,K: 1.2×10−6–6.0×10−5 cm/s
    4–10 mSilt clay,K: 1.2×10−6–6.0×10−5 cm/s8–10 mClay,K: < 1.2×10−6 cm/s
    下载: 导出CSV

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

    ProfileSoil CSoil NSoil P
    Range
    (g/kg)
    Mean
    (g/kg)
    Variation coefficients
    (%)
    Range
    (g/kg)
    Mean
    (g/kg)
    Variation coefficients
    (%)
    Range
    (g/kg)
    Mean
    (g/kg)
    Variation coefficients
    (%)
    P15.81–37.2420.34a47.540.23–1.890.90a37.310.19–1.030.52a34.67
    P21.80–32.1915.86b42.100.08–1.560.82b35.110.15–0.740.52a30.57
    Average18.0544.820.8636.210.5232.62
    Note: Different letters represent significant differences in the mean values of each profile (p<0.05), and the same letters represent non-significant differences.
    下载: 导出CSV

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

    profileGroundwater TOCGroundwater TNGroundwater TP
    Range
    (mg/L)
    Mean
    (mg/L)
    Variation coefficients
    (%)
    Range
    (mg/L)
    Mean
    (mg/L)
    Variation coefficients
    (%)
    Range
    (mg/L)
    Mean
    (mg/L)
    Variation coefficients
    (%)
    P10.49–3.831.94a56.242.81–7.744.22a27.450.20–0.780.44a22.08
    P20.29–2.811.24b43.541.17–6.734.16a33.330.11–0.880.55b50.13
    Avrage1.5949.894.1930.390.4936.11
    Note: Different letters represent significant differences in the mean values of each profile (p < 0.05), and the same letters represent non-significant differences.
    下载: 导出CSV

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

    EquationC-N model summaryC-P model summary
    R2FSignificanceR2FSignificance
    Linear0.48165.86400.30230.7450
    Logarithmic0.41249.70100.30330.8440
    Inverse0.22921.08700.2726.2910
    Quadratic0.48132.46900.30215.1770
    Cubic0.48121.33700.33111.3820
    Composite0.53782.29100.3436.5010
    Power0.752143.15900.52161.650
    S-curve0.54986.60200.57173.2430
    Growth0.53782.29100.3436.5010
    Exponential0.53782.29100.3436.5010
    Logistic0.53782.29100.3436.5010
    下载: 导出CSV

    Table  6.   Model evaluation indicators

    ModelSample sizeRMSEMREMAE
    BP Neural Network233.430.273.03
    Non-linear regression233.180.212.61
    下载: 导出CSV
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  • 收稿日期:  2022-05-18
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