Assessing the Influence of Precipitation on Shallow Groundwater Table Response Using a Combination of Singular Value Decomposition and Cross-Wavelet Approaches
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Meteorological Data Collection
2.3. Singular Value Decomposition
2.4. Spatial Interpolation
2.5. Cross-Wavelet and Continuous Wavelet Using Fast Fourier Transform
3. Results
3.1. Spatial Mode Recognition between Groundwater and Precipitation
3.2. Temporal Patterns Recognition between Groundwater Level and Precipitation
4. Discussion
4.1. The Practical Value of the Method
4.2. Factors Influencing the Relationship between Groundwater Level and Precipitation
4.3. Uncertainties of This Study
5. Conclusions
- (1)
- The new method can be a cost-effective approach for identifying the spatiotemporal responses of the groundwater table to precipitation, especially in areas where hydrological models are difficult to construct due to a lack of basic data.
- (2)
- The major mode of the relation between groundwater and precipitation was divided into four patterns in the Naoli River Basin. In general, the lag time is 27.4 (std: ±8.1) days, 107.5 (std: ±13.2) days, 139.9 (std: ±11.2) days, and 173.4 (std: ±20.3) days for the patterns 1–4, respectively.
- (3)
- The rapid agricultural development relying on groundwater irrigation has led to an increase of the unsaturated zone thickness, which in turn results in an increase of temporal lags in the groundwater table response to precipitation.
- (4)
- The response of the groundwater table in the studied river basin is very sensitive to heavy rainfall. Thus, enhancing the utilization of the heavy rainfall and flood resources by groundwater may be an effective way to recharge the groundwater. Furthermore, it is possible to make use of the interannual allocation of water resources for the groundwater reservoir to deal with extreme hydrological events of flood and drought.
Author Contributions
Acknowledgments
Conflicts of Interest
References
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SVD Patterns | Phase Angles (°) | Temporal Lags (day) |
---|---|---|
Pattern 1 | 27 (±8) | 27.4 (±8.1) |
Pattern 2 | 106 (±13) | 107.5 (±13.2) |
Pattern 3 | 138 (±11) | 139.9 (±11.2) |
Pattern 4 | 171 (±20) | 173.4 (±20.3) |
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Qi, P.; Zhang, G.; Xu, Y.J.; Wang, L.; Ding, C.; Cheng, C. Assessing the Influence of Precipitation on Shallow Groundwater Table Response Using a Combination of Singular Value Decomposition and Cross-Wavelet Approaches. Water 2018, 10, 598. https://doi.org/10.3390/w10050598
Qi P, Zhang G, Xu YJ, Wang L, Ding C, Cheng C. Assessing the Influence of Precipitation on Shallow Groundwater Table Response Using a Combination of Singular Value Decomposition and Cross-Wavelet Approaches. Water. 2018; 10(5):598. https://doi.org/10.3390/w10050598
Chicago/Turabian StyleQi, Peng, Guangxin Zhang, Y. Jun Xu, Lei Wang, Changchun Ding, and Chunyang Cheng. 2018. "Assessing the Influence of Precipitation on Shallow Groundwater Table Response Using a Combination of Singular Value Decomposition and Cross-Wavelet Approaches" Water 10, no. 5: 598. https://doi.org/10.3390/w10050598
APA StyleQi, P., Zhang, G., Xu, Y. J., Wang, L., Ding, C., & Cheng, C. (2018). Assessing the Influence of Precipitation on Shallow Groundwater Table Response Using a Combination of Singular Value Decomposition and Cross-Wavelet Approaches. Water, 10(5), 598. https://doi.org/10.3390/w10050598