IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v80y2016i1p351-365.html
   My bibliography  Save this article

Spatial and temporal water-level variations in the Texas portion of the Ogallala Aquifer

Author

Listed:
  • Romildo Brito Neto
  • Celso Santos
  • Kevin Mulligan
  • Lucia Barbato

Abstract

Groundwater management plays a key role in conserving the volume of available water and maintaining water quality. The goal of this study is to demonstrate how an exploratory data analysis (EDA) technique can be used to identify hazard areas in the Texas (USA) portion of the Ogallala Aquifer where groundwater extraction far exceeds natural recharge. Using data from 9092 observation wells, the study covers the 53-year period from 1960 to 2012. To map changes in the aquifer over time, kriging was used to interpolate a water table elevation surface for each year in the study. Using a GIS, the water table elevation surfaces were overlaid and analyzed to identify those areas with the greatest drawdown. Between 1962 and 2012, the total area where the saturated thickness of the aquifer was greater than 50 m decreased by 21.76 %. A subset of 492 observation wells (those with a complete time series) was selected to analyze trends in the water table elevation using the Mann–Kendall test, and grouped by hierarchical cluster analysis. The Mann–Kendall test, applying a 99 % level of confidence in the hypothesis testing, indicated that 74.39 % of the 492 time series showed a declining trend in the water table. Hierarchical cluster analysis (using dendrograms and heat maps) resulted in the formation of eight groups, highlighting one group of wells (Cluster 3) with a particularly steep decline in the water table over the 53-year time series. When the wells contained in Cluster 3 were overlaid on a map of the change in saturated thickness, the results converge, indicating the hazard area of aquifer decline. This paper highlights an EDA technique to demonstrate that using differing temporal–spatial analytical methods together, as applied in this research, results in a high level of reliability. In a confined aquifer system, mapping changes in water level and hierarchical cluster analysis can be used together to identify those hazard areas where conservation efforts are most needed to slow the rate of aquifer decline. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Romildo Brito Neto & Celso Santos & Kevin Mulligan & Lucia Barbato, 2016. "Spatial and temporal water-level variations in the Texas portion of the Ogallala Aquifer," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(1), pages 351-365, January.
    • Handle: RePEc:spr:nathaz:v:80:y:2016:i:1:p:351-365
    DOI: 10.1007/s11069-015-1971-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-015-1971-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11069-015-1971-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wilkinson, Leland & Friendly, Michael, 2009. "The History of the Cluster Heat Map," The American Statistician, American Statistical Association, vol. 63(2), pages 179-184.
    2. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    3. Matthias Schonlau, 2002. "The clustergram: A graph for visualizing hierarchical and nonhierarchical cluster analyses," Stata Journal, StataCorp LP, vol. 2(4), pages 391-402, November.
    Full references (including those not matched with items on IDEAS)

    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. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    2. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    3. Min-feng Lee & Guey-shya Chen & Shao-pin Lin & Wei-jie Wang, 2022. "A Data Mining Study on House Price in Central Regions of Taiwan Using Education Categorical Data, Environmental Indicators, and House Features Data," Sustainability, MDPI, vol. 14(11), pages 1-15, May.
    4. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    5. M. Almiñana & L. Escudero & A. Pérez-Martín & A. Rabasa & L. Santamaría, 2014. "A classification rule reduction algorithm based on significance domains," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 397-418, April.
    6. Silvia FIGINI & Ron S. KENETT & Silvia SALINI, 2010. "Integrating operational and financial risk assessments," Departmental Working Papers 2010-02, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    7. Onur Doğan & Hakan Aşan & Ejder Ayç, 2015. "Use Of Data Mining Techniques In Advance Decision Making Processes In A Local Firm," European Journal of Business and Economics, Central Bohemia University, vol. 10(2), pages 6821:10-682, January.
    8. Patricia E. N. Lutu & Andries P. Engelbrecht, 2013. "Base Model Combination Algorithm for Resolving Tied Predictions for K -Nearest Neighbor OVA Ensemble Models," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 517-526, August.
    9. Adrien Jamain & David Hand, 2008. "Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 87-112, June.
    10. M. Agovino & A. Rapposelli, 2017. "Regional Performance Trends in Providing Employment for Persons with Disabilities: Evidence from Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 593-615, January.
    11. Adrian Otoiu & Emilia Titan, 2014. "An Alternative Method of Component Aggregation for Computing Multidimensional Well-Being Indicators," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(4), pages 38-52.
    12. Massimiliano Agovino & Maria Ferrara & Antonio Garofalo, 2017. "The driving factors of separate waste collection in Italy: a multidimensional analysis at provincial level," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(6), pages 2297-2316, December.
    13. Wang, Wenjun & Liu, Dong & Liu, Xiao & Pan, Lin, 2013. "Fuzzy overlapping community detection based on local random walk and multidimensional scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6578-6586.
    14. Yi-Chen Chung & Hsien-Ming Chou & Chih-Neng Hung & Chihli Hung, 2021. "Using Textual and Economic Features to Predict the RMB Exchange Rate," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(6), pages 1-8.
    15. Chen-Yang Cheng, 2014. "Indoor localization algorithm using clustering on signal and coordination pattern," Annals of Operations Research, Springer, vol. 216(1), pages 83-99, May.
    16. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    17. Steven Buigut, 2015. "The Effect of Zimbabwe's Multi-Currency Arrangement on Bilateral Trade: Myth Versus Reality," International Journal of Economics and Financial Issues, Econjournals, vol. 5(3), pages 690-700.
    18. Xinhao Luo & Chen Liang & Yongyou Hu, 2019. "Comparison of Different Enhanced Coagulation Methods for Azo Dye Removal from Wastewater," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    19. Arvydas Jadevicius & Simon Huston & Andrew Baum & Allan Butler, 2018. "Two centuries of farmland prices in England," Journal of Property Research, Taylor & Francis Journals, vol. 35(1), pages 72-94, January.
    20. Bőgel, György, 2011. "Az adatrobbanás mint közgazdasági jelenség [The data explosion as an economic phenomenon]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(10), pages 877-889.

    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:spr:nathaz:v:80:y:2016:i:1:p:351-365. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.