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Estimating the Average Magnitude of Pumping Surrounding Monitoring Wells Using Signal Processing
https://orcid.org/0000-0002-2906-6244
The groundwater level observed from a monitoring well is a mixed signal resulting from different stimulations. It is challenging to obtain a specific component from this mixed signal. This is the case for obtaining the pumping component from a monitoring well in an area with many pumping wells. This study developed a signal processing method to extract the average magnitude of pumping (AMP) from the observed groundwater signals. AMP represents the variation over time in the groundwater level caused by pumping surrounding a monitored well. It is obtained from a digital filtering procedure. The use of a Gaussian high-pass filter removes the long-term trends from the groundwater signal and reveals a high-frequency signal that is usually hidden. After applying frequency analysis and verifying it by fast Fourier transformation (FFT), the pumping frequency was identified. The pumping frequency is set as the center frequency for a band-pass filter to obtain AMP. To validate and understand the factor influencing AMP, a three-dimensional (3D) numerical model was constructed to simulate artificial groundwater fluctuations. The synthetic case study showed that the pumping rate and the distance between the pumping wells and the monitoring well are factors that control AMP. We further applied this methodology to evaluate AMP for three monitoring wells in Tuku Township, located in the Chou-Shui Chi alluvial fan in central Taiwan. The results showed a negative correlation between the AMP variation and rainfall events, describing long-term trends for pumping and the influence of pumping on the groundwater level variation with time. Furthermore, the results revealed the scale of the AMP at each monitoring well, and the area with a higher AMP had a higher drawdown magnitude daily. Finally, the average pumping rate of a region can be estimated using AMP.
Estimating the Average Magnitude of Pumping Surrounding Monitoring Wells Using Signal Processing
https://orcid.org/0000-0002-2906-6244
The groundwater level observed from a monitoring well is a mixed signal resulting from different stimulations. It is challenging to obtain a specific component from this mixed signal. This is the case for obtaining the pumping component from a monitoring well in an area with many pumping wells. This study developed a signal processing method to extract the average magnitude of pumping (AMP) from the observed groundwater signals. AMP represents the variation over time in the groundwater level caused by pumping surrounding a monitored well. It is obtained from a digital filtering procedure. The use of a Gaussian high-pass filter removes the long-term trends from the groundwater signal and reveals a high-frequency signal that is usually hidden. After applying frequency analysis and verifying it by fast Fourier transformation (FFT), the pumping frequency was identified. The pumping frequency is set as the center frequency for a band-pass filter to obtain AMP. To validate and understand the factor influencing AMP, a three-dimensional (3D) numerical model was constructed to simulate artificial groundwater fluctuations. The synthetic case study showed that the pumping rate and the distance between the pumping wells and the monitoring well are factors that control AMP. We further applied this methodology to evaluate AMP for three monitoring wells in Tuku Township, located in the Chou-Shui Chi alluvial fan in central Taiwan. The results showed a negative correlation between the AMP variation and rainfall events, describing long-term trends for pumping and the influence of pumping on the groundwater level variation with time. Furthermore, the results revealed the scale of the AMP at each monitoring well, and the area with a higher AMP had a higher drawdown magnitude daily. Finally, the average pumping rate of a region can be estimated using AMP.
Estimating the Average Magnitude of Pumping Surrounding Monitoring Wells Using Signal Processing
https://orcid.org/0000-0002-2906-6244
The groundwater level observed from a monitoring well is a mixed signal resulting from different stimulations. It is challenging to obtain a specific component from this mixed signal. This is the case for obtaining the pumping component from a monitoring well in an area with many pumping wells. This study developed a signal processing method to extract the average magnitude of pumping (AMP) from the observed groundwater signals. AMP represents the variation over time in the groundwater level caused by pumping surrounding a monitored well. It is obtained from a digital filtering procedure. The use of a Gaussian high-pass filter removes the long-term trends from the groundwater signal and reveals a high-frequency signal that is usually hidden. After applying frequency analysis and verifying it by fast Fourier transformation (FFT), the pumping frequency was identified. The pumping frequency is set as the center frequency for a band-pass filter to obtain AMP. To validate and understand the factor influencing AMP, a three-dimensional (3D) numerical model was constructed to simulate artificial groundwater fluctuations. The synthetic case study showed that the pumping rate and the distance between the pumping wells and the monitoring well are factors that control AMP. We further applied this methodology to evaluate AMP for three monitoring wells in Tuku Township, located in the Chou-Shui Chi alluvial fan in central Taiwan. The results showed a negative correlation between the AMP variation and rainfall events, describing long-term trends for pumping and the influence of pumping on the groundwater level variation with time. Furthermore, the results revealed the scale of the AMP at each monitoring well, and the area with a higher AMP had a higher drawdown magnitude daily. Finally, the average pumping rate of a region can be estimated using AMP.
Estimating the Average Magnitude of Pumping Surrounding Monitoring Wells Using Signal Processing
J. Hydrol. Eng.
Ouédraogo, Abdoul Rachid (author) / Hsu, Shaohua Marko (author) / Wang, Yetmen (author)
2023-04-01
Article (Journal)
Electronic Resource
English
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