1. KAUTIR (Kerala Agricultural University Theses Information and Retrieval)
Permanent URI for this communityhttp://localhost:4000/handle/123456789/1
Browse
3 results
Search Results
Item Spatio-temporal analysis of groundwater quality in central Kerala(Department of Agricultural Statistics College of Agriculture,vellayani, 2022-02-20) Ardra, K Y; Brigit JosephThe research work titled “Spatio-temporal analysis of groundwater quality in central Kerala” was carried out at the College of Agriculture, Vellayani during the period 2019 to 2021. The objectives set for the study were to analyze the inter-regional variation in groundwater quality parameters over ten years using various multivariate and geostatistical techniques in Alappuzha and Kottayam districts. Also to analyze the interrelationship between the water quality parameters and groundwater levels and to map the locations using the GIS package. 26 location in the Alappuzha district and 22 locations in the Kottayam district was selected for the study. Secondary data on fourteen groundwater quality parameters (Electrical conductivity (EC), pH, Total Hardness (TH), Total alkalinity (TA), anions; CO3, HCO3, SO4, NO3, Cl, F, and cations; Na, K, Ca, and Mg) and groundwater levels were collected from Central Ground Water Board for ten years (2010-2019). On the secondary data of fourteen quality parameters, descriptive statistics such as mean, standard deviation, and coefficient of variation were investigated. The parameters were compared with drinking and agricultural irrigation guidelines defined by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). All parameters in the Alappuzha district, with the exception of fluoride (F-) in the location Thaikkattusseri (F-: 2.86 mg/L), were confirmed to be within BIS and WHO limits. Two parameters, total alkalinity (TA) and potassium (K+) were found to be over the standard limits in the Kottayam District. In Vaikkom, the TA was higher than the norm (BIS: 300 mg/L), while in three locations, Changanasserry, Ezhinjillam, and Thiruvarpu, the K+ was higher than the limit (WHO: 10 mg/L). All of the other parameters were determined to be within the BIS and WHO guidelines. Principal component analysis (PCA) was carried out for the construction of the Principal component weighted Water Quality Index (WQI). In the Alappuzha district, four principal components contributed 82.71 percent of the total variation. In the Kottayam district, three principal components accounted for 86.06 percent of the total variation. Based on the WQI, the locales in Alappuzha districts are grouped into five: very low, low, medium, high, and very high. The Index is found to be increasing with the parameters EC, TH, HCO3, and TA, and the ‘very low’ category had mean value for the parameters 58.8 μS/cm, 15.64 mg/L, 11.28 mg/L, and 7.39 mg/L respectively, the ‘low’ category recorded the mean values 159.95 μS/cm, 44.5 mg/L, 45.52 mg/L, and 35.95 mg/L respectively. The ‘medium category had the mean values 200.17 μS/cm, 69.45 mg/L, 78.52 mg/L, and 55.56 mg/L parameter levels respectively. For the category, ‘high’, mean values were 252.65 μS/cm, 110 mg/L, 164.11 mg/L, and 182.23 mg/L respectively for the parameters. In the category ‘very high’, recorded mean values for these parameters were 297.39 μS/cm, 98.21 mg/L, 116.82 mg/L, and 89.36 mg/L respectively. In the Kottayam district with a CV of 41.38 percent and a mean of 95.09, the estimated WQI ranged from 17.88 in Kattanam to 161.14 in Cherthalai. WQI was found to be below the mean value in 16 of the 26 locations. In the Kottayam district, the estimated WQI varied from 22.04 in Narianganam to 300.48 in Vaikom, with a CV of 83.65 percent and a mean of 94.78. WQI was found to be below the mean value in 15 of the 22 locations. The Index is found to be increasing with the parameters EC, TH, HCO3, and TA, and the ‘very low’ category had mean value for the parameters 94.09 μS/cm, 21.21 mg/L, 17.73 mg/L, and 13.57 mg/L respectively, the ‘low’ category recorded the mean values 241.21 μS/cm, 55.27 mg/L, 36.28 mg/L, and 31.53 mg/L respectively. The ‘medium category had the mean values of 374.02 μS/cm, 106.62 mg/L, 125.79 mg/L, and 89.99 mg/L parameter levels respectively. For the category, ‘high’, mean values were 594.89 μS/cm, 109.34 mg/L, 41.6 mg/L, and 31.61 mg/L respectively for parameters. In the category ‘very high’, recorded mean values for these parameters were 592.78 μS/cm, 211.88 mg/L, 297.5 mg/L, and 201.39 mg/L respectively. The interrelationship between water quality parameters and groundwater level was studied using Pearson's correlation coefficient. In the Alappuzha district, the parameters EC (-0.45), pH (-0.65), TH (-0.5), TA (-0.42), HCO3 (-0.42), and Ca (-0.51) had a significant negative correlation with groundwater level. NO3 (0.33, p-value=0.1) was the only parameter with a positive correlation but was found to be non-significant. In the Kottayam district, a positive-significant correlation was present in parameters pH (0.75), TA (0.22), SO4 (0.59), and Mg (0.6), and a negative significant correlation was present in EC (-0.61), TH (-0.52), HCO3 (-0.48), Na (-0.54) and Ca (-0.46). The remaining parameters were found to have a non-significant correlation. The temporal change was analyzed employing the relative change in the level of each parameter for ten years in selected districts. In the Alappuzha district, a positive temporal increase was observed in all the parameters except pH over ten years. In the Kottayam district, a considerable increase was observed in parameters TH, TA, HCO3, SO4, F, and Mg and a decline in the levels of EC, pH, NO3, Na, K, and Ca. Thematic map based on WQI and groundwater level (GWL) was prepared using ArcGIS software version 10.4 for Alappuzha and Kottayam districts. Based on the thematic maps, spatial distribution and variation of groundwater quality parameters and groundwater level were drawn. In the Alappuzha district, both WQI and GWL maps were categorized into ten classes. Nearing the various water bodies, WQI was recorded higher. The locations bordered by the Pathanamthitta district had comparatively lower WQI. From the GWL map, it was inferred that as the elevation of the district increased, the depth to the water table was increasing. The change observed was according to the topography of the district. In the Kottayam district, locations bordered by Alappuzha showed high WQI value and the locations bordered by the Idukki district had comparatively less WQI. In Kottayam also, depth to the water table showed an increasing trend with the elevation. Spatial and temporal variations in the Alappuzha and Kottayam districts were studied with the help of multivariate and geostatistical techniques. Both methods confirmed the inter-regional and timely disparity of fourteen groundwater quality parameters and groundwater level.Item Geostatistical analysis of groundwater level in Thiruvananthapuram District(Department of Agricultural Statistics, College of Agiculture, Vellayani, 2022) Harinath, A; Pratheesh P GopinathThe research work entitled “Geostatistical analysis of groundwater level in Thiruvananthapuram district” was carried out at the College of Agriculture, Vellayani during 2019-2021. The objective of the study was to analyze the spatiotemporal variations in the groundwater level, identify the relationship between groundwater and climatic factors (i.e., rainfall and temperature), and to prepare the thematic map for the location. To characterize the spatiotemporal fluctuations in groundwater level within the research region, various geostatistical approaches were used. The WRIS [Water Resource Information System] website was used to collect groundwater level data for 29 different locations within the study area for 10 years, from 2008 to 2017. The selection of data points was based on the even spatial distribution such that all the locations in the district are entirely covered. The NASA satellite website data was used to collect the rainfall and temperature data for the 29 distinct sites throughout a ten-year period. The semivariogram models were fitted to assess the spatial continuity of groundwater level. The nugget to sill ratio is also identified for detecting the spatial dependency. In the research region, the kriging interpolation approach was used to assess the spatiotemporal fluctuations in groundwater levels. If the data sets are normally distributed, the kriging interpolation technique will be more successful. Thus, the data points were subjected to exploratory data analysis to test the normality of the data set. The normality of the data sets is found out by Shapiro-Wilk’s normality test. The results showed that the years 2010 and 2017 are not normally distributed as the null hypothesis of the test is rejected. And also, in the case of temperature and rainfall, all the data points were not normally distributed. Thus, for the proper analysis, the log transformation was performed to the data sets which are not normally distributed and proceeded to further steps. The relationship of groundwater and climatic factors were accounted with the correlation analysis. The results showed that the temperature is having more dependency with the groundwater level fluctuation than the rainfall. 88 The semivariogram fitting were done to the groundwater level drop for each location, groundwater level over the years, and for the average groundwater level to identify the spatial and temporal variations in the study area. The drop was found out for each location by taking the difference between the groundwater levels of the years 2008 and 2017. The positive drop refers the depletion in the groundwater level and the negative drop refers the increment in the groundwater level. The nugget to sill ratio explains that the groundwater level drop is having a relatively strong spatial dependence. The three models, Spherical, Exponential and Gaussian models were fitted to the groundwater level for each year. The best fit model was selected by accounting the Adjusted R2 value. The spatiotemporal variation was studied by kriging interpolation method. The thematic maps were created to analyze the groundwater level variations. The maps were created in the ArcGIS 10.4 software. By investigating the maps prepared, the groundwater level depletion is observed severely in the Varkala region, and the Parassala region. The groundwater level at the high ranges like Ponmudi, Bonacaud, and Neyyar regions are maintaining a decent amount of groundwater level. From the PCA biplots prepared, the study concluded that there is a gradual groundwater depletion happening from 2008 to 2017. And from the biplot of years, the temperature is relatively high in 2016, 2017 where the groundwater level is also high. And the temperature is relatively low in 2008, 2009 where the groundwater level is also low. Thus, it can be concluded that the groundwater is having some dependency with the temperature variations which have been detected in the correlation analysis. From the biplot of different locations, it can be analyzed that the Varkala, Sreekariyam, Pothencode, Chengal, Neyyattinkara regions are having high groundwater depth. And Kattakkada, Kallar, Palode, Ariyanadu, Maruthamoola, Peringamala regions are having low groundwater depth. From the research performed, it can be concluded that, most of the locations are having a positive drop in the groundwater, which represents that the groundwater depletion is happening in temporal structure in the study area. The highest depletion in the 89 groundwater is seen in Pothencode, Chengal, Varkala, Neyyattinkara regions. The rate of groundwater level drop is 1.49 meters, which is positive, and can be inferred that there is depletion in the groundwater level. The nugget to sill ratio of the groundwater level drop in the study area is 0.367, which refers that the depletion is moderately spatially dependent. From the correlation analysis, it can be concluded that the temperature is a major factor influencing the groundwater depletion than the rainfall, because there is a positive significant correlation between groundwater and temperature. The groundwater depth of Varkala, Pothencode, Sreekariyam, Neyyattinkara, Chenkal, Kulathoor is high, and at Kattakkada, Palode, Kallar, Ariyanadu have low groundwater depth which can be concluded from PCA biplot of different locationsItem Simulation of salt water intrusion into the coastal aquifers of Kadalundi river basin in Malappuram district using visual modflow(Academy of Climate Change Education and Research Vellanikkara, 2016) Swathy, P S; Sajeena, S