Periodic behavior of groundwater level fluctuations in residential areas

Almedeij, Jaber; Al-Ruwaih, F.

doi:10.1016/j.jhydrol.2006.01.013

Cited by 47 publications

(27 citation statements)

References 13 publications

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“…... (4) ... (5) If the p-value is small enough, the trend is quite unlikely to be caused by random sampling. The Z values are approximately normally distributed, and a positive Z value larger than 1.96 (based on normal probability tables) denotes a significant increasing trend at the significance level of 0.05, whereas a negative Z value lower than -1.96 shows a significant decreasing trend.…”

Section: Fig 1: Location Map Of Study Area Datamentioning

confidence: 99%

Trend Analysis of Groundwater Level Using Non-Parametric Tests in Alluvial Aquifers of Uttar Pradesh, India

Kumar

Chandniha

Lohani

et al. 2018

Curr. World Environ

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In the present study, groundwater level trends have been evaluated using the non-parametric methods i.e., Modified Mann-Kendall (MMK) and Sen's slope estimator during the period 1998 to 2012 at 13 locations in 4 districts of Lucknow division namely Hardoi, Laxmipur, Lucknow and Sitapur of Uttar Pradesh, India. The entire trend analysis has been verified at a significance level of 5 percent. The groundwater level trend analysis has shown negative values for 7 locations covering 54 percent area and positive values for 6 locations covering 46 percent area in pre-monsoon season. However, in post monsoon season, 4 locations covering 31 percent area exhibitednegative and 9 locations covering 69 percent area revealedpositive trends. The difference in the water level trends in two different seasons may be attributed to the recharge by rainfall in post-monsoon season.

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Section: Fig 1: Location Map Of Study Area Datamentioning

confidence: 99%

Trend Analysis of Groundwater Level Using Non-Parametric Tests in Alluvial Aquifers of Uttar Pradesh, India

Kumar

Chandniha

Lohani

et al. 2018

Curr. World Environ

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“…Sustainability 2016, 8, page-page 2 preceding precipitation, proximity to the river, and pumping and recharge activities [8][9][10][11]. Moving average, which is the cumulative precipitation during a certain past period, was introduced as a more refined expression for precipitation and better captures the characteristics of GWL and its variations than precipitation [8,9].…”

Section: Study Regionsmentioning

confidence: 99%

“…Other components that affect GWL variation are the duration and intensity of preceding precipitation, proximity to the river, and pumping and recharge activities [8][9][10][11]. Moving average, which is the cumulative precipitation during a certain past period, was introduced as a more refined expression for precipitation and better captures the characteristics of GWL and its variations than precipitation [8,9]. Guttmann [9] and Murray [12] both used the moving average and showed that the moving average is closely related to the variation in GWL over time.…”

Section: Introductionmentioning

confidence: 99%

Comparative Influences of Precipitation and River Stage on Groundwater Levels in Near-River Areas

et al. 2015

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Abstract:The sustainable performance of foundations of various urban buildings and infrastructures is strongly affected by groundwater level (GWL), as GWL causes changes in the stress state within soil. In the present study, the components affecting GWL were investigated, focusing on the effects of precipitation and river stage. These components were analyzed using a six-year database established for hydrological and groundwater monitoring data. Five study regions for which daily measured precipitation, river stage, and GWL data were available were compared. Different periods of precipitation, geographical characteristics, and local surface conditions were considered in the analysis. The results indicated that key influence components on GWL are different depending on the hydrological, geological, and geographical characteristics of the target regions. River stage had the strongest influence on GWL in urban areas near large rivers with a high ratio of paved surface. In rural areas, where the paved surface area ratio and soil permeability were low, the moving average showed a closer correlation to GWL than river stage. A moving average-based method to predict GWL variation with time was proposed for regions with a low ratio of paved surface area and low permeability soils.

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“…Periodogram is a tool of spectrum inference based on the frequency domain of time series (Almedeij and Al-Ruwaih, 2006). The steps for periodogram analysis are as follows: first, the amplitude of waves and initial phase computed by harmonic wave methods based on a series of test periods (or fictitious period, artificial period, possible period) are given and then a curve-period gram is obtained.…”

Section: Periodogram Analysis Of Stationary Time Seriesmentioning

confidence: 99%

Crytic period analysis model of hydrological process and its application

Wang

Lin

Qian

2009

Hydrological Processes

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Abstract:It is difficult to analyse the crytic period of the hydrological process, because hydrological time series is probably characterized by heteroscedasticity. To find out the crytic period, a model is constructed as follows: (1) after using zero-mean transformation for the data, to do Augmented Dickey-Fuller stationary test for the sequence, to build the corresponding AR(p) model and then to do ARCH effects test and white noise test for residual series; (2) for those time series that cannot pass through ARCH test, using logarithm transformation to reduce the heteroscedasticity, and then to redo step (1) until they pass through ARCH test and stationary test; (3) using periodogram analysis to determine all the possible the prime periods and further to put forward three kinds of tests to determine significance level of those prime periods. As examples, the hydrological processes of streamflow from 1784 to 1997 for the gauging stations of Alaer and Xinquman along Tarim River are analysed. After reducing their heteroscedasticity, AR(4) and AR(2) models are developed, respectively. Our results show that the streamflows from the two gauging stations have the same cryptic period of 42Ð7 years. Furthermore, the reliability for the crytic period model is testified by variance analysis, which shows that the crytic period model is useful and reliable.

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Periodic behavior of groundwater level fluctuations in residential areas

Cited by 47 publications

References 13 publications

Trend Analysis of Groundwater Level Using Non-Parametric Tests in Alluvial Aquifers of Uttar Pradesh, India

Trend Analysis of Groundwater Level Using Non-Parametric Tests in Alluvial Aquifers of Uttar Pradesh, India

Comparative Influences of Precipitation and River Stage on Groundwater Levels in Near-River Areas

Crytic period analysis model of hydrological process and its application

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