Groundwater is a significant resource for water uses. Groundwater depletion is becoming a global issue for water sustainability. Many places of Globe are suffering to decrement of groundwater. India is overexploiting this resource for irrigation, urbanization and industrialization. In this study evaluation of groundwater storage potential is taken into consideration for Bankura district, West Bengal. Periodical data observed from November 2007 to January 2017 is perceived for study purposes. Broadly remote sense method is used in this study. In the present work Gravity Recovery and Climate Experiment (GRACE) technique is applied for predicting groundwater changes. With the inclusion of Global Land Data Assimilation Systems (GLDAS), performance predictor implies good results. Estimation shows groundwater depletion is amounting to be an average rate of 0.35cm/year. September 2011 experienced maximum positive groundwater changes with equivalent thickness of 17.6722 cm, whereas in June 2013 the substantial change of depletion is found to be -24.16828 cm. Both observed and estimated groundwater changes are compared and have established the value of correlation coefficient as 0.827.
Precipitation is an important phenomenon which contributes in the constant supply of water over entire earth. Atmospheric water accounts for less than 0.001% of total water yet it is responsible for the constant supply throughout the globe. It is important to know the distribution of precipitation along with space to know the pattern of precipitation spatially. In order to know this spatial pattern five different geospatial interpolation techniques totaling to 20 different models are applied for 30 years (1988 - 2018) of monthly average precipitation. These models are compared to know which one of these gives the best resemblance of the phenomena. Six performance measures, MAE, MBE, MSE, RMSE, ME and R2 are used to compare the different models. The model for which error is minimum (close to zero) and efficiency is maximum (close to unity) are preferable. After application of various models, it was found that IDW technique with weight parameter of 3 gives the best result with MBE of -0.1397, MAE of 2.9372, MSE of 13.0708, RMSE of 3.6154, ME of 0.7842 and R2 of 0.7744. Other models that performed well were Universal kriging and RBF. After evaluating the best model, error in the estimation of data by that model was also carried out to know the locations where error is intense. It is seen that where the precipitation is intense the errors associated increases. Temporal variation of rainfall is equally important to know have a clearer picture about the pattern of precipitation spatially as well as with seasonally. Therefore, after figuring out the best model, temporal variation of precipitation was also determined showing monthly variation of rainfall. So, after plotting spatial and temporal variation of precipitation it becomes easier for us to determine the precipitation at places which are not gauged.