Geostatistical analysis of groundwater level in Thiruvananthapuram District (Record no. 289661)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 06142nam a22002297a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 630.31 |
| Item number | HAR/GE PG |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Harinath, A |
| 245 ## - TITLE STATEMENT | |
| Title | Geostatistical analysis of groundwater level in Thiruvananthapuram District |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Vellayani |
| Name of publisher, distributor, etc | Department of Agricultural Statistics, College of Agiculture |
| Date of publication, distribution, etc | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 73p. |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | M Sc |
| 520 ## - SUMMARY, ETC. | |
| Abstract | The 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 locations |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Agricultural Statistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Ground water |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Temperature fluctuation |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Rainfall distribution |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Semivariogram models |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Pratheesh P Gopinath (Guide) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://krishikosh.egranth.ac.in/handle/1/5810190250 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Item type | Theses |
| 546 ## - LANGUAGE NOTE | |
| Language note | English |
| Not for loan | Collection code | Permanent location | Current location | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Not For Loan | Reference Book | KAU Central Library, Thrissur | KAU Central Library, Thrissur | Theses | 2022-07-07 | 630.31 HAR/GE PG | 175331 | 2022-07-07 | Theses |