Volume 18, No. 2, 2024
Received: 2023/07/08, Accepted: 2024/03/10
Authors:
Haider Fadhil Addab; Ali Al Rammahi;
Abstract:
How to control the salinity of groundwater and drain water at the watershed level has only been the subject of a small number of studies. The time series for stream flow show high fluctuation during the year, and that would affect the quality of the water mass entering the system. So the stream flow fluctuation and water salinity should be studied together to get the whole picture. The purpose of this study is to statistically examine the variation in salt concentration rate over time and between four different flow regimes. Water samples were collected from 2014 to 2019. The analysis was done using R software. Water quality parameters were subjected to statistical approaches such as principal component analysis (PCA), correlation, and water quality indexes. PCA discovered a smaller number of mean 2 variables, showing that 58% of temporal and spatial changes have an impact on drain, ground, and surface water quality. the highest correlation was found between SO4 and total dissolved solids for all flow regimes Multiple water quality indexes were applied to test the water for irrigation purposes. Most of the results show that sampled water from different flow regimes was qualified for testing with these indexes. After that, a linear model was fitted to the data with total dissolved solids as the response. Predictor variables included flow regimes (surface water, groundwater, mixed water, and drain water) and total dissolved solids (TDS) (in mg/l) plus interaction. A model with two-factor repeated measurements and a covariate design was created. TDS was used as the response in a mixed model that was fitted. Flow regime and year plus (flow regime * year) were included in the fixed effect. Tukey's adjustment pairwise shows that differences in salinity values are significant between drain water and surface water.
Keywords:
Total dissolved solids (TDS); Groundwater; ANCOVA, Flow condition, Correlation, PCA, Indexes