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Related Experiment Video

Updated: Nov 25, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System.

Xiufen Gu1,2, HongGuang Sun2, Yong Zhang3

  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Ninghailu Street, Nanjing, Jiangsu, 210098, China.

Ground Water
|December 19, 2020
PubMed
Summary

Groundwater level fluctuations are influenced by multiple factors, with the best predictors varying by vegetation density and timescale. Understanding these complex relationships is key for effective water resource management.

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Area of Science:

  • Hydrology
  • Environmental Science
  • Geosciences

Background:

  • Groundwater level fluctuations are complexly influenced by surface properties and interactions.
  • Previous research often focused on single factors, neglecting combined impacts on groundwater systems.
  • Accurate quantification of these multivariate relationships remains a challenge.

Purpose of the Study:

  • To explore localized, scale-specific, multivariate relationships between groundwater levels and controlling factors.
  • To investigate the impact of combined hydrologic and meteorological factors on groundwater systems.
  • To analyze groundwater level fluctuations in areas with varying plant densities.

Main Methods:

  • Utilized bivariate wavelet coherence and multiple wavelet coherence analyses.
  • Analyzed groundwater level fluctuations from two wells in the Colorado River riparian zone.
  • Compared findings between areas with low-density and high-density plant stands.

Main Results:

  • For low-density vegetation, barometric pressure and river stage were key at small and large scales, respectively.
  • For high-density vegetation, predictors included barometric pressure, temperature, and river stage, varying with timescale.
  • The optimal predictor set for groundwater head fluctuations is dependent on vegetation cover and hydrological processes.

Conclusions:

  • Identified specific hydrologic and meteorological factors that best explain groundwater level variations across different scales and vegetation densities.
  • Demonstrated that the predictive power of factors is scale- and vegetation-dependent.
  • Provided crucial insights for improving water-resource prediction and management strategies.