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|Title:||ANALYSIS OF HIGH FREQUENCY GROU-ND WATER- DATA|
|Authors:||Deshpande, S. M.|
HIGH FREQUENCY GROUND WATER- DATA
DIGITAL WATER LEVEL RECORDERS
|Abstract:||In the State of Maharashtra the groundwater levels are monitored at discrete times since 1974. This network has been supplemented with continuous monitoring of groundwater levels by Digital Water Level Recorders (DWLR) through newly constructed piezometers since 1998. The enormous discrete and continuous data of groundwater levels of Aurangabad district have been used for performing different types of analysis, like adequacy of network design, estimation of level of accuracy of past data and groundwater resources, optimisation of frequency of observation in DWLR and stochastic modelling of hourly groundwater level data. Simpler tools have been developed and used for the conversion of retrieved DWLR data and different analyses. Network design analysis has been attempted by using coefficient of variation-based technique. The available network of 141 observation wells was found adequate where as the supplemented network was much denser, for 5% error in estimation of average groundwater levels. The level of accuracy of the past data has been estimated with a meagre error of 6%, showing the reliability of the old system data. Similarly the average error in estimation of groundwater resource is around 7%, which is commensurate with the typical hydrogeological conditions of the study area. The optimisation of frequency of observation has been attempted by computing the summation error from the smallest feasible interval. The results showed that the present frequency of observation i.e. six hour within district is optimum. Simulation of hourly groundwater levels of four piezometers by stochastic modelling indicate diurnal periodicity in the data. The periodicities in mean are explained by 8, 6, 2 and 5 significant harmonics for Peerbavada, Dhavalapuri, Waghalgaon and Pimperkheda respectively. The periodicities in standard deviation are explained by 11, 11, 8 and 9 significant harmonics respectively. The dependent stochastic component was explained by AR(2) model for Peerbavada, and AR(1) for all others.|
|Appears in Collections:||MASTERS' DISSERTATIONS (Hydrology)|
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