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

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  • 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
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    References listed on IDEAS

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    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.
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