Next Article in Journal
Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania
Previous Article in Journal
Quantitative Assessment of Flow Regime Alteration Using a Revised Range of Variability Methods
Open AccessArticle

Assessing the Influence of Precipitation on Shallow Groundwater Table Response Using a Combination of Singular Value Decomposition and Cross-Wavelet Approaches

1
Key Laboratory of Wetland ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China
2
University of the Chinese Academy of Sciences, Beijing 100049, China
3
School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA
4
Coastal Studies Institute, Louisiana State University, Baton Rouge, LA 70803, USA
5
British Geological Survey, Keyworth, Nottingham NG12 5GG, UK
6
Heilongjiang Province Water Conservancy & Hydropower Investigation, Design and ResearInstitute of the Ministry of Water Resources, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(5), 598; https://doi.org/10.3390/w10050598
Received: 3 April 2018 / Revised: 30 April 2018 / Accepted: 1 May 2018 / Published: 4 May 2018
(This article belongs to the Section Hydrology)
Identifying the spatiotemporal change of the groundwater table to precipitation at the river basin scale can be important for regional water resource management. In this study, we proposed a method that combines singular value decomposition and cross-wavelet approaches to analyze the relationship between groundwater level dynamics and precipitation. The method was applied to the Naoli River Basin, Northeast China. Moreover, the method of continuous wavelet using fast Fourier transform was also used to reveal clearly the relationship between groundwater level and heavy precipitation. The results showed that the major mode of relationship between groundwater and precipitation was divided into four patterns in the study area. In general, the lag time is 27.4 (standard deviation: ±8.1) days in pattern 1, 107.5 (standard deviation: ±13.2) days in pattern 2, 139.9 (standard deviation: ±11.2) days in pattern 3, and 173.4 (standard deviation: ±20.3) days in pattern 4, respectively. In addition, the response of groundwater level dynamics is very sensitive to heavy precipitation in all patterns. Therefore, enhancing the utilization of heavy rainfall and flood resources is an effective way to increase groundwater recharge in this basin. View Full-Text
Keywords: spatiotemporal changes; temporal lag; flood resources; groundwater level; Naoli River Basin spatiotemporal changes; temporal lag; flood resources; groundwater level; Naoli River Basin
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop