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A comprehensive evaluation of wavelet functions to analyse the groundwater storage trends

Publication Type : Conference Paper

Publisher : EGU General Assembly 2020

Url :

Year : 2020

Abstract : India, the country which is highly dependent on groundwater for its drinking and irrigation requirements (88% and 85% respectively), is already facing an acute water crisis. The groundwater storage in major cities is predicted to reach absolute zero by the end of 2020 (CWMI report 2018). While the demand is projected to increase double fold than the supply by 2030, the need for better understanding the behaviour of groundwater storage is very important to come up with better management policies. Analysing the presence of non-parametric linear trend in groundwater studies has been well recognised as it clearly reveals the detail of declining groundwater storage. For this endeavour, methods like Theil-Sen Slope estimator (SS), to detect linear trend, has often been applied with the assumption of stationary. However, highly complex, dynamic and non-linear behaviour of groundwater systems require alternate methods besides SS to improve our understanding in the cases where groundwater system exhibits non-stationarity in the trend. Recently wavelet based method has been explored for the trend analysis of several hydro-climatic variables including the groundwater storage. Wavelet being empirical in nature still requires further investigation as the selection of particular wavelet function carries subjectivity. In this study, we made an attempt to comprehensively analyse the use of different wavelet function in the groundwater storage trend analysis and to further reduce the uncertainty to select the best suitable wavelet function. To demonstrate our approach, the groundwater data collected from two contrasting river basin (i.e., Beas in the Himalayas and Godavari in the Deccan plateau) which has high distress for declining storage, were used. In the overall context, the focus of the study was to overcome the mis-conclusions due to the survivor biases caused by data gaps while predicting the actual long term groundwater storage trend.

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