Characterization of the eastern Suva Planina Mt. karst aquifer (SE Serbia) by time series analysis and stochastic modelling

被引:1
|
作者
Petrovic, Branislav [1 ]
Marinovic, Veljko [1 ]
Stevanovic, Zoran [1 ]
机构
[1] Univ Belgrade, Fac Min & Geol, Ctr Karst Hydrogeol, Dept Hydrogeol, Belgrade, Serbia
关键词
Karst aquifer characterization; Time series analysis; Stochastic modelling; SE Serbia; SPECTRAL-ANALYSES; HYDROGRAPHS; WATER;
D O I
10.1007/s12665-023-10911-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper deals with karst aquifer characterization using time series and stochastic analysis and modelling as well as groundwater quality analyses. Such a methodological approach was applied on the eastern Suva Planina Mt. karst aquifer by evaluating data for the period of 2015-2018, which included monitoring and analysis of major karst springs Mokra and Divljana and their quantity and quality parameters. Quantitative characterization of these karst springs utilised for water supply of Nis city, has shown high karstification degree and very well retention capacity of both, Mokra and Divljana sub-systems with slightly lower storativity of the Divljana sub-system. In addition, simulation models have proved that precipitation is the main driving force of the hydraulic behaviour of the karst groundwater circulation and discharge in this area. Qualitative characterization has shown very small variation of chemical parameters, with expected karst groundwater fingerprint, and isotopic analysis confirmed that water replenishment is relatively fast. Results also speculate that these two springs actually represent discharging points of one aquifer that is fragmented into two interconnected sub-systems. The study proves the necessity for karst groundwater characterization to manage this important natural resource in a sustainable way.
引用
收藏
页数:17
相关论文
共 8 条
  • [1] Characterization of the eastern Suva Planina Mt. karst aquifer (SE Serbia) by time series analysis and stochastic modelling
    Branislav Petrović
    Veljko Marinović
    Zoran Stevanović
    Environmental Earth Sciences, 2023, 82
  • [2] Quantitative and Geochemical Characterization of the Mokra Karst Aquifer (SE Serbia) by Time Series Analysis and Stochastic Modelling
    Petrovic, B.
    Marinovic, V.
    EUROKARST 2022: ADVANCES IN THE HYDROGEOLOGY OF KARST AND CARBONATE RESERVOIRS, 2023, : 49 - 55
  • [3] TIME SERIES ANALYSIS, MODELLING AND ASSESSMENT OF OPTIMAL EXPLOITATION OF THE NEMANJA KARST SPRINGS, SERBIA
    Jemcov, Igor
    Petric, Metka
    ACTA CARSOLOGICA, 2010, 39 (02) : 187 - 200
  • [4] Application of short time series analysis for the hydrodynamic characterization of a coastal karst aquifer: the Salento aquifer (Southern Italy)
    Balacco, Gabriella
    Alfio, Maria Rosaria
    Parisi, Alessandro
    Panagopoulos, Andreas
    Fidelibus, Maria Dolores
    JOURNAL OF HYDROINFORMATICS, 2022, 24 (02) : 420 - 443
  • [5] Temporal and spatial characterization of sediment transport through a karst aquifer by means of time series analysis
    Jukic, Damir
    Denic-Jukic, Vesna
    Kadic, Ana
    JOURNAL OF HYDROLOGY, 2022, 609
  • [6] Karst Aquifer Characterization by Means of Its Karstification Degree and Time Series Analysis (Case: Ngerong Spring in Rengel Karst, East Java']Java, Indonesia)
    Mujib, Muhammad Asyroful
    Adji, Tjahyo Nugroho
    Haryono, Eko
    Naufal, Muhammad
    Fatchurohman, Hendy
    INDONESIAN JOURNAL OF GEOSCIENCE, 2024, 11 (01): : 45 - 60
  • [7] Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling
    Asma El Amri
    Soumaia M’nassri
    Nessrine Nasri
    Hanen Nsir
    Rajouene Majdoub
    Environmental Science and Pollution Research, 2022, 29 : 43300 - 43318
  • [8] Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling
    El Amri, Asma
    M'nassri, Soumaia
    Nasri, Nessrine
    Nsir, Hanen
    Majdoub, Rajouene
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (28) : 43300 - 43318