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Groundwater is gradually becoming scarce in Bangladesh like many other countries around the globe due to various climatic, hydrological, and socio-economic changes. This study aimed to assess the spatiotemporal changes in groundwater availability and sustainability in Bangladesh. The study employed weekly groundwater table (GWT) data from 844 groundwater observation wells of the Bangladesh Water Development Board (BWDB) from 1995 to 2019. Mann-Kendall (M-K) test at 95% significance level and the Sen's slope estimator were used to assess the spatiotemporal variations in GWT, and the reliability-resiliency-vulnerability (RRV) approach was applied for evaluating sustainability. In addition, the yearly trends in RRV indicators and sustainability were estimated. The results revealed the mean GWT is low in the northwest and central regions. The GWT is declining almost all over the country, particularly in the areas where it is already low by 0.548-1.398 m/year. The groundwater showed remarkably poor sustainability for most of the areas. It also showed a declining trend over a large area in the central north. The analysis of results through the scrutiny of irrigated land and population distribution of the country revealed higher abstraction of groundwater for irrigation and domestic supply are the major cause of declining GWT in the northwest and central regions of the country. The study provided an understanding of the changes in groundwater and its sustainability which can be used as a guide for sustainable management of groundwater and contribute to drawing up future groundwater policies.

Groundwater monitoring is essential for sustainable groundwater resource management in a country like Bangladesh, where this precious resource is gradually declining due overextraction. Acquiring groundwater level (GWL) over a large area is time-consuming and expensive. This study proposes an alternative approach to groundwater monitoring using freely available daily groundwater storage (GWS) gridded data of the Global Land Data Assimilation System (GLDAS) with other freely available data, including population, rainfall, temperature, irrigation, elevation for modeling GWL data of Bangladesh with a spatial resolution of 0.25o × 0.25o. This was accomplished by employing multiple linear regression (MLR) and artificial neural networks (ANN), using weekly in-situ GWL data at 844 locations distributed over Bangladesh. The results showed the inability of GWS data to estimate the country's groundwater spatial variability and trend. The relative performance of MLR and ANN models revealed a higher capability of ANN in estimating GWL from GWS and other data with an overall correlation coefficient (R) of 0.95 and mean squared error (MSE) of 0.64. The study revealed population and rainfall have the most decisive influence in determining GWL. The model developed using ANN can be used to estimate GWL at locations where observation data are unavailable and thus monitor GWL for sustainable groundwater management.

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