Water Quality Assessment of Upper Ganga Canal for Human Drinking

Bahita, Tesfamariam Abreha; Swain, Sarat Chandra; Dayal, Deen; Jha, Pradeep Kumar; Pandey, Ashish

doi:10.1007/978-3-030-51427-3_28

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…Leblanc et al found that there was a sharp decline in groundwater level in the Murray-Darling basin in Australia from 2000 to 2007 due to the extreme drought [17]. The groundwater withdrawals, the artificial recharge, and the land-use nature change can lead to the variation in groundwater level [18]. The investigation performed by Xiao et al showed that the average decline rate of groundwater level during 2007-2013 was higher than during 2001-2006 in Beijing piedmont plain and the decline rate of groundwater level in the agricultural irrigation areas was lowest, compared with those in residential and industrial areas [19].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Cai

Huang

Xu³

et al. 2022

Water

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Many irrigation districts along the Yellow River have been suffering shallow groundwater depression and agriculture-use water shortage. For comprehending response relationships of shallow groundwater level and various factors under different conditions of groundwater buried depth, the hydro-meteorological time series and the agricultural production data in Puyang area of Henan Province, China during 2006–2018 were collected for performing wavelet analysis of the relationship between the groundwater level and the four different factors, such as precipitation, air temperature, water stage of the Yellow River, and well irrigation water amount. It is shown that when the burial depth of groundwater varied from 0–10 m to over 10 m, the groundwater level was related with both the precipitation and air temperature from moderately to weakly and the delayed response times of the groundwater level to them extended from 2–4 months to more than 5 months. The groundwater level maintained a medium correlation with the well irrigation water amount as the burial depth increased, but the lag response time of groundwater level to well irrigation dramatically decreased when the burial depth exceeded 10 m. The dynamic response relationship between the groundwater and the water stage of the Yellow River was mainly affected by the distance away from the Yellow River rather than the burial depth and the influence of the river stage on the groundwater level was limited within the distance approximate to 20 km away from the Yellow River. The findings are expected to provide the reference for groundwater level prediction and groundwater resources protection.

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Section: Introductionmentioning

confidence: 99%

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Cai

Huang

Xu³

et al. 2022

Water

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“…It also adds threat to the food security of the nation. Moreover, owing to the poor adaptabilities and inadequate preparedness, the developing countries like India will have to face detrimental effects of climate change (Aadhar et al 2019;Bahita et al 2021aBahita et al , 2021bGosain et al, 2006;Nandi et al 2024). The problems will be further added by anthropogenic activities (Swain et al 2018b(Swain et al , 2022a(Swain et al , 2022c.…”

Section: Introductionmentioning

confidence: 99%

Application of Turning Point Test to Precipitation Time Series over Seonath River Basin, India

Rath,

Yadu,

Verma

et al. 2024

Int. J. Res. Publ. Rev.

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Precipitation is a very important parameter to estimate climate variability. This paper presents an application of Turning point test to seasonal and annual precipitation data for a period of 30 years over Seonath Basin, a major sub-basin of Mahanadi River Basin. The objective of the application of Turning point test is to catch the randomness of the precipitation time series over the study area. The randomness of precipitation data is checked in general before the trend analysis.The precipitation time series possessing more randomness may give misleading results for trend analysis. Considering this issue, the monthly data was collected for 39 stations, which were categorized into 4 seasons of the year i.e. winter, pre-monsoon, monsoon and post-monsoon. The results reveal for almost all the stations showing insignificant randomness for monsoon, post-monsoon and annual precipitation time series. However, in the winter and pre-monsoon seasons, about half of the stations are showing significant randomness.

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“…Precipitation holds significant importance in the development, planning, and management of water resources projects (Aadhar et al 2019;Dayal et al 2019;Guptha et al 2021Guptha et al , 2022Swain et al, 2015Swain et al, , 2017aSwain et al, , 2017bSwain et al, , 2018aSwain et al, , 2018bSwain et al, , 2022cVerma et al 2016). In India, where over two-thirds of population relies on agriculture directly, rainfall plays a crucial role, particularly due to the Southwest monsoon (Bahita et al 2021a(Bahita et al , 2021bDhal and Swain 2022;Kannan et al 2010;Swain 2014Swain , 2017Swain et al 2019aSwain et al , 2019bSwain et al , 2020aSwain et al , 2021aSwain et al , 2021bSwain et al , 2021cSwain et al , 2022aSharma et al 2020). The majority of annual rainfall is attributed to monsoon rains, making any alterations or fluctuations in precipitation a substantial factor impacting the country's agriculture and food security (Nandi et al 2024;Swain, 2014;Swain et al 2020bSwain et al , 2022bSwain et al , 2022d.…”

Section: Introductionmentioning

confidence: 99%

Enhancing rainfall estimation for water resource management through multiple linear regression approach in Khordha District, Odisha, India

Rath,

Verma,

Yadua

2024

Int. J. Res. Publ. Rev.

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Rainfall estimation holds paramount significance for optimizing water resource utilization and effective management, particularly in countries like India, characterized by an agro-based economy and limited irrigation infrastructure. This paper introduces a multiple linear regression (MLR) model tailored for estimating annual rainfall in Khordha district, Odisha, India. The model is designed to predict rainfall for a given year by leveraging data from the preceding three years.Through rigorous testing on a century-long dataset of annual rainfall, the model demonstrated exceptional performance. By setting the intercept of the MLR model to zero, the derived equation yielded highly accurate results. The predicted values exhibited a robust correspondence with observed data, resulting in a R² value of 0.963. This remarkable R² value underscores the model's suitability for application across the study area. The robustness and accuracy of this model offers valuable insights for enhancing hydro-meteorological assessments and informing strategic water resource management decisions in the region.

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Water Quality Assessment of Upper Ganga Canal for Human Drinking

Cited by 11 publications

References 37 publications

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Application of Turning Point Test to Precipitation Time Series over Seonath River Basin, India

Enhancing rainfall estimation for water resource management through multiple linear regression approach in Khordha District, Odisha, India

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