Using wavelet based de-noising to identify the trend of ground water level (case study: Ardabil plain)

Document Type : Research paper

Author

Department of Civil Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran.

Abstract

Ardabil plain)
Groundwater is an important source of fresh water to meet the demands of growing industries such as agriculture, fisheries, mining, and manufacturing and the municipal water demands due to rise in population in different parts of the world. Efficient management of groundwater is an essential task in different regions, especially in arid and semi-arid climates that faces chronic shortage of fresh water. Thus, the detection of trends in groundwater levels is very essential in order to constantly monitor the levels of the ground water table.
Nowadays, there are different statistical methods for trend analyzing hydrological time series such as t-test, regression analysis, Pearson correlation coefficient, the Spearman’s Rho, Sen’s slope, Wald–Wolfowitz and most commonly used method of Mann–Kendall (MK). Mann-Kendall (MK) test was introduced and developed by Mann (1945) and Kendall (1975), respectively. The advantage of this method is that it does not follow any specific statistical distribution. It has been widely used to analyze the monotonic trends in hydrological time series. The MK1 test for trend detection assumes that the sample data are serially independent, even though a few hydrological series show significant serial correlation. An alternative MK test is to remove first the serial correlation such as lag-1 auto regression or higher-order process from the time series prior to application of the test that its name is MK2. MK3 test is to remove all the serial correlation from the time series prior to application of the test.The objectives of the present study are: (1) to identify the trends and magnitude of trends in groundwater levels on monthly time scales with three variations of Mann-Kendall test include: (i) Mann–Kendall without autocorrelation, (ii) Mann–Kendall with lag-1 autocorrelation and trend-free pre-whitening, (iii) Mann–Kendall wi

Keywords

References

Adamowski J., Chan H.F. (2011). A Wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28-40.
Almasri A., Locking H. and Shukar G. (2008). Testing for climate warming in Sweden during 1850–1999 using wavelet analysis. J. Appl. Stat., 35, 431–443.
Araghi A., Mousavi Baygi M., Adamowski J., Malard J., Nalley D. and Hasheminia S.M. (2015) Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data. Atmos. Res., 155, 52-72.
Brocque A., Kath J. and Smith K. (2018). Chronic groundwater decline: A multi-decadal analysis of groundwater trends under extreme climate cycles. 561, 976-986.
Daneshvar Vousoughi F., Dinpashoh Y. and Alami M.T. (2011). The Effect of Drought on Groundwater Alignment in Two Recent Decades (Case Study: Ardebil Plain). J. Water and Soil Sci., 4(21), 165-179 [In Persian].
Donoho, D.H. 1995. De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41 (3): 613–617.
      Gibrilla A., Anornu G. and Adomako D. (2018). Trend analysis and ARIMA modelling of recent groundwater levels in the White Volta River basin of Ghana. Groundwater Sustain. Dev., 6, 150-163.
Hartigan, J.A. and Wong, M.A. 1979. Algorithm AS 139: A k-means clustering algorithm. Applied Statistics, 28:8–100.
Kahya E., and Kalayci S. (2004). Trend analysis of streamflow in Turkey, J. Hydrol., 289, 128-144.
Kendall, M.G. (1975). Rank Correlation Measures. Charles Griffin Inc. London.
Kohonen, T. The Self organizing map, Neurocomputing., 1998. 21, 1-6.
Kumar S., Merwade V., Kam J. and Thurner K. (2009). Streamflow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains. J. Hydrol., 374: 171-183.
Lee J.Y., Yi M.J., Moon S.H., Cho M., Won J.H., Ahn K.H. and Lee J.M. (2007). Causes of the changes in groundwater levels at Daegu, Korea: the effect of subway excavations. Bull Eng. Geol. Environ., 66, 251-258.
Mann, H.B. 1945. Non-parametric test against trend. Econometrica, 13: 245-259.
Nalley D., Adamowski J. and Khalil B. (2012). Using discrete wavelet transforms to analyze trends instream flow and precipitation in Quebec and Ontario (1954–2008). J. Hydrol., 475, 204-228.
Nourani V., Alami M. T. and Aminfar M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intel., 22, 466–472.
Nourani V., Alami, M. T and Daneshvar Vousoughi, F. (2015). Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling, J. Hydrol., 524, 255–269.
Panda K., Mishra A., Jena S.K., James B.K. and Kumar A.  (2007). The influence of drought and anthropogenic effects on groundwater levels in Orissa, India. J. Hydrol. Process., 343, 140-153.
Partal T. and Kahya E. 2006. Trend analysis in Turkish precipitation data. Hydrol. Process., 20: 2011-2026.
Paul S., Datta D. and Sarkar P. K. (2011). Determination of trend of temporal trend of annual precipitation by stationary wavelet components in Northern part of Maharashtra. J. Environ. Sci., 2, 1–11.
Rashid, Md. M., Beecham, S. and Chowdhury, R.K. (2015). Assessment of trends in point rainfall using Continuous Wavelet Transforms. Adv.Water Resour., 82, 1-15.  
Shahid, S., and Hazarika, M.K. (2009). Groundwater drought in the northwestern districts ofBangladesh. Water Resour. Manage., 24: 1989-2006.
Tan C., Huang B., Liu K., Chen H., Liu F., Qiu J., and Yang J. (2017). Using the wavelet transform to detect temporal variations in hydrological processes in the Pearl River, China. Quat. Int., 440, Part B, 10, 52-63
Wang H., Zhang M., Zhu H., Dang X., Yang Z. and Yin L. (2012). Hydro-climatic trends in the last 50 years in the lower reach of the Shiyang River Basin, NW China. Catena 95, 33–41.
Xu K., Milliman J.D., and Xu H. (2010). Temporal trend of precipitation and runoff in major Chinese Riverssince 1951. Global. Planet.Change., 73, 219-232.
Yazdani, MR. Morteza kh.Q. (2010). Hydrological Drought Analysis (Groundwater) in Mobarakeh Lanjan Region. 2ed Nat. Conf. on Drought Effects and Management Tools, Esfahan, Iran [In Persian].
Zahmatkesh Q. AlaviPanah K. Zehtabian Q.R. (2002). Study of Oscillations of shallow Underground Watersheds of Playa Parsian Case Study Semnan, J. Desert, 2, 15-30 [In Persian].
Zhang W., Yan Y., Zeng J., Li L., Dong X., and Cai H. (2009). Temporal and spatialvariability of annual extreme water level in the Pearl River Delta region, China. Global. Planet. Change., 69, 35-47.
Hydrogeology
Volume 5, Issue 1
September 2020
Pages 61-72

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