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<p>The present study reported the effectiveness of multivariate statistical tools used to monitor spatio-temporal fluctuations in the water quality of Gharni reservoir. The Gharni reservoir is situated on the Gharni river sub-basin which is a tributary of the main river Manjara located in the Marathwada rainfall deficit area of Maharashtra, India. The water body is periodically sampled at five selected locations between August 2019 and January 2021 to assess water quality of 20 parameters. Different statistical techniques were employed to handle intricate data matrices, including cluster scanning/analysis (CA), factor examination (analysis)/principal component evaluation (analysis) (FA/PCA) for data reduction, and discriminant survey/analysis (DA) for data classification. This method of hierarchical CA categorized the five sampling stations into three groups and seasons into four groups/clusters based on resemblance in the recorded physico-chemical parameter readings. The FA/PCA successfully extracted a total of 14 factors, which accounted for 70% out of the total 20 measured variables. These factors were crucial in explaining 62% of the variability observed in the data. Furthermore, the analysis pointed out the specific components/factors responsible for the alteration in the quality of reservoir water. Additionally, the dominance of individual group was evaluated in relation to the comprehensive differentiability at five distinct sampled locations. The DA yielded 14 parameters with a 99% accuracy rate for assigning correct values. The varifactors (VF) developed in the factor analysis have shown that the variation in quality of surface water was interrelated to two groups. The first group included physico-chemical parameters like dissolved oxygen, temperature, and conductivity, whereas the second group covered nutrients like chlorophyll, phosphorus, and nitrogen and were deposited in the water by soil erosion during rainy season from agricultural land. This case study demonstrates the use of multivariate statistical tools as an excellent exploratory tool for analyzing and interpreting complex data sets. It highlights their effectiveness in assessing water quality and understanding its spatio-temporal variations, ultimately assisting in the management of reservoir water quality.</p>

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Spatio-temporal monitoring of water quality in the Gharni reservoir (India) by multivariate statistical tools: a case study of a reservoir located in a rainfall deficit area

  • 期刊论文

  • 发表日期：

    2025年4月8日

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  • Vijaykumar B. Sutar,
  • Asha T. Landge,
  • Binaya B. Nayak,
  • Preetha Panikkar,
  • Pachampalayam S. Ananthan,
  • Adinath T. Markad

摘要

The present study reported the effectiveness of multivariate statistical tools used to monitor spatio-temporal fluctuations in the water quality of Gharni reservoir. The Gharni reservoir is situated on the Gharni river sub-basin which is a tributary of the main river Manjara located in the Marathwada rainfall deficit area of Maharashtra, India. The water body is periodically sampled at five selected locations between August 2019 and January 2021 to assess water quality of 20 parameters. Different statistical techniques were employed to handle intricate data matrices, including cluster scanning/analysis (CA), factor examination (analysis)/principal component evaluation (analysis) (FA/PCA) for data reduction, and discriminant survey/analysis (DA) for data classification. This method of hierarchical CA categorized the five sampling stations into three groups and seasons into four groups/clusters based on resemblance in the recorded physico-chemical parameter readings. The FA/PCA successfully extracted a total of 14 factors, which accounted for 70% out of the total 20 measured variables. These factors were crucial in explaining 62% of the variability observed in the data. Furthermore, the analysis pointed out the specific components/factors responsible for the alteration in the quality of reservoir water. Additionally, the dominance of individual group was evaluated in relation to the comprehensive differentiability at five distinct sampled locations. The DA yielded 14 parameters with a 99% accuracy rate for assigning correct values. The varifactors (VF) developed in the factor analysis have shown that the variation in quality of surface water was interrelated to two groups. The first group included physico-chemical parameters like dissolved oxygen, temperature, and conductivity, whereas the second group covered nutrients like chlorophyll, phosphorus, and nitrogen and were deposited in the water by soil erosion during rainy season from agricultural land. This case study demonstrates the use of multivariate statistical tools as an excellent exploratory tool for analyzing and interpreting complex data sets. It highlights their effectiveness in assessing water quality and understanding its spatio-temporal variations, ultimately assisting in the management of reservoir water quality.