IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p8798-d1159289.html
   My bibliography  Save this article

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

Author

Listed:
  • Juan Zhang

    (College of Geographic and Environmental Science, Tianjin Normal University, Tianjin 300387, China
    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Zhongli Zhu

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Huiqing Hao

    (College of Geographic and Environmental Science, Tianjin Normal University, Tianjin 300387, China
    Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China)

Abstract

This study focused on the impact of anthropogenic activity on magnitude, frequency, and minima of spring discharge. Niangziguan Springs (NS), China, was selected as an example, as its discharge is decreasing due to the combined effects of climate variation and human activity. For exploring the impact of human activity on the spring discharge from climate change, the spring discharges from 1959 to 2015 were divided into two periods: pre-development period (i.e., 1959–1980) and post-development period (i.e., 1981–2015). A polynomial regression model of the spring discharge was developed for the pre-development period. We deduced the model in the post-development period, compared the results with the observed spring discharge, and concluded that the climate variation and human activity caused 6.93% and 32.38% spring discharge decline, respectively. The relationships of spring discharge with Indian Summer Monsoon (ISM), East Asian Summer Monsoon (EASM), E1 Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) were analyzed by wavelet analysis during the two periods. The results illustrated that the monsoons (i.e., ISM and EASM) were dominated by climate factors that affect the NS discharge versus climate teleconnections (i.e., ENSO and PDO). According to different time scales, human activities have had an impact on the periodicity of NS discharge, which altered the periodicities of the spring discharge at inter-annual time scales, but the periodicities at intra-annual and annual time scales have remained the same between the two periods. Under the effects of human activity, the local parameter of non-stationary general extreme value (NSGEV) distribution varied with time. The predicted spring discharge minimum value is supposed to be 4.53 m 3 /s with a 95% confidential interval with an upper boundary of 6.06 m 3 /s and a lower boundary of 2.80 m 3 /s in 2020. The results of this study would benefit the management of spring discharge and water resources.

Suggested Citation

  • Juan Zhang & Zhongli Zhu & Huiqing Hao, 2023. "The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8798-:d:1159289
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/8798/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/11/8798/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    2. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huayao Li & Fawang Zhang & Xinqiang Du & Dezhi Tian & Shan Jiao & Jiliang Zhu & Fenggang Dai, 2023. "Identification of the Pollution Mechanisms and Remediation Strategies for Abandoned Wells in the Karst Areas of Northern China," Sustainability, MDPI, vol. 15(23), pages 1-18, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    2. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    3. Clark, Andrew, 2022. "Causality in the aluminum market," Journal of Commodity Markets, Elsevier, vol. 27(C).
    4. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    5. Vladimír Hönig & Petr Prochazka & Michal Obergruber & Luboš Smutka & Viera Kučerová, 2019. "Economic and Technological Analysis of Commercial LNG Production in the EU," Energies, MDPI, vol. 12(8), pages 1-17, April.
    6. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    7. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    8. Bielak, Łukasz & Grzesiek, Aleksandra & Janczura, Joanna & Wyłomańska, Agnieszka, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Resources Policy, Elsevier, vol. 74(C).
    9. Yishun Liu & Chunhua Yang & Keke Huang & Weiping Liu, 2023. "A Multi-Factor Selection and Fusion Method through the CNN-LSTM Network for Dynamic Price Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    10. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    11. Qi, Yajie & Li, Huajiao & Liu, Yanxin & Feng, Sida & Li, Yang & Guo, Sui, 2020. "Granger causality transmission mechanism of steel product prices under multiple scales—The industrial chain perspective," Resources Policy, Elsevier, vol. 67(C).
    12. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    13. Liu, Kailei & Cheng, Jinhua & Yi, Jiahui, 2022. "Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform," Resources Policy, Elsevier, vol. 75(C).
    14. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    15. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    16. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
    17. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2022. "Gold price forecasting using multivariate stochastic model," Resources Policy, Elsevier, vol. 76(C).
    18. Fang, Shitong & Chen, Keyu & Lai, Zhihui & Zhou, Shengxi & Liao, Wei-Hsin, 2023. "Analysis and experiment of auxetic centrifugal softening impact energy harvesting from ultra-low-frequency rotational excitations," Applied Energy, Elsevier, vol. 331(C).
    19. Feng Jiang & Xue Yang & Shuyu Li, 2018. "Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model," Sustainability, MDPI, vol. 10(7), pages 1-17, June.
    20. Su, Chi-Wei & Wang, Xiao-Qing & Zhu, Haotian & Tao, Ran & Moldovan, Nicoleta-Claudia & Lobonţ, Oana-Ramona, 2020. "Testing for multiple bubbles in the copper price: Periodically collapsing behavior," Resources Policy, Elsevier, vol. 65(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8798-:d:1159289. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.