Groundwater monitoring and its spatio-temporal study require installation and management of ground-based observation wells on a large scale. The cost associated with such a study is generally high. An alternative to it is to use remote sensing data to manage groundwater resources in the least cost. There are only a few satellites which can provide gravity-based groundwater data. Gravity Recovery and Climate Experiment (GRACE) is a satellite which measures the change in gravity and is further used to study groundwater fluctuations. In the present study, groundwater fluctuations data (Product of GRACE satellite data) for Haridwar and Delhi region of India has been used to study the temporal and spatial variability using entropy theory. The temporal data from 2003 to 2016 has been used for both regions. The results suggested that the groundwater fluctuations are increasing in both regions of the study area. Results suggested that fluctuation of groundwater was high for the winter season of all years, but in the post-monsoon season, the fluctuation in between Delhi and Hardwar has been detected just about same Seasonal fluctuation in water level for both regions showed a maximum rise of 60 cm in water level and also maximum fall in the same range
Rainfall is the main element of the hydrological cycle and has a direct impact on agriculture sectors. A regular pattern of rainfall results in a healthy crop production, extreme events such as flood and drought, industrial and domestic sectors etc. The present study tried to explore the variability in rainfall pattern with elevation differences in hilly areas, using different measure of entropy indices based on monthly, seasonal and annual scale. The study was carried out for the hilly areas of Uttarakhand. The selected hilly districts of Uttarakhand for this study was Almora, Kashipur, Lansdowne, and Mukteshwar stations. The rainfall data of 116 years from 1901 to 2016 has been used. In the present study, Shannon entropy has been used and variability in rainfall pattern has been done using Mean Marginal Disorder Index (MMDI) and apportionment disorder index. The results suggested that in light of seasonal analysis, post-monsoon season had a high MMDI (0.345) for Lansdowne station followed by Mukteshwar (0.309), and Almora (0.304). However, the highest MMDI (0.340) was recorded for Kashipur during pre-monsoon season, while pre-monsoon season had lowest MMDI for Mukteshwar station followed by Almora and Lansdowne station, although, lowest MMDI was recorded during winter season of Kashipur station. The results revealed that Kashipur and Lansdowne station had high rainfall variability, whereas Almora and Mukteshwar stations had less rainfall variability. The present study revealed that variability in rainfall and rainy days was not uniform everywhere and places at higher elevation has less temporal variability of rainfall patterns and number of rainy days.
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