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Related Concept Videos

Variability: Analysis01:11

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Evaluating climate variability and pumping effects in statistical analyses.

Timothy D Mayer1, Roger D Congdon

  • 1U.S. Fish and Wildlife Service, 911 NE 11th Ave., Portland, OR 842-3835, USA. tim_mayer@fws.gov

Ground Water
|March 1, 2008
PubMed
Summary
This summary is machine-generated.

Ground water levels in southern Nevada are sensitive to both climate variability and human pumping. Wet years significantly impact groundwater recharge more than dry years, affecting spring discharge.

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Last Updated: Jul 7, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Hydrology
  • Environmental Science
  • Statistical Analysis

Background:

  • Groundwater resource development is nearing sustainability limits, making it crucial to understand factors influencing water levels.
  • Distinguishing between natural climate variability and anthropogenic impacts on groundwater is challenging.

Purpose of the Study:

  • To identify and differentiate climate and anthropogenic effects on regional groundwater levels and spring discharge in southern Nevada.
  • To understand aquifer system response to climate by analyzing the importance of various climate parameters.

Main Methods:

  • Employed statistical methods including trend analysis, cluster analysis, and time series analysis with seasonal decomposition.
  • Analyzed regional groundwater levels and spring discharge data from southern Nevada.
  • Examined the relationship between climate parameters, pumping, and groundwater system responses.

Main Results:

  • Groundwater levels show greater responsiveness to wet years than dry years.
  • Cluster analysis and time series decomposition distinguished between climate-driven recharge and pumping/evapotranspiration effects.
  • Higher elevation springs demonstrated sensitivity to minor groundwater level fluctuations.

Conclusions:

  • Groundwater levels and spring discharge are influenced by a combination of climate variability and anthropogenic pumping.
  • Aquifer systems in arid regions like the southwestern US respond differently to wet and dry periods.
  • Spring discharge is a sensitive indicator of changes in groundwater levels, even small ones.