Study of Fluctuations in the Groundwater Level in Rajasthan: A Spatio-Temporal Approach

Groundwater is a major source of freshwater in many parts of the world. Some regions are excessively dependent on it leading to groundwater consumption faster than it can be naturally replenished and causing water tables to decline unremittingly. Rajasthan is the largest state in the country and water resources supporting the human population are very less. Rajasthan has its water problems more-over, it is undergoing an industrial transformation. The objective of this study is to identify the groundwater critical zones, study the fluctuation of groundwater level and analyse the trend of groundwater level for each district using the Man-Kendall Test. In this study, Inverse Distance Weightage was applied for estimating the attribute values of locations that are within the database using known data values. Then the interpolated data values were extracted for Statistical Analysis using Man-Kendall’s Test. Despite its importance, groundwater is poorly understood and often undervalued. Despite its importance, groundwater is poorly understood and often underestimated. Results show that the groundwater level had been at the most critical in 2010, i.e. -119 m bgl in pre-monsoon and 112 m bgl in post Monsoon. From 1996 to 2016, Mann-Kandell's Trend test illustrated a declining trend in Alwar, Dausa, Jaipur, Jaisalmer, Jhunjhunun, Sawai Madhopur and Sikar districts of


INTRODUCTION
Groundwater accounts for about 20 percent of the world's fresh water. But still less than 1 percent of all the water on Earth, comprising all ocean water and permanent ice. Groundwater refers to the water found beneath the Earth's surface. This water originates mostly from rain, melted snow, and other water that seeps through soil, or the cracks in the rocks. Due to gravity, the water moves downward, below the Earth's surface, until it hits a layer of rock or it can't get through, where it remains there and builds up. This section of water below the surface is called an aquifer. Groundwater can eventually go back up to the surface in the form of springs and wetlands (Chinnasamy et al., 2015). In various places, groundwater discharged naturally into natural springs or contributed to rivers and wetlands. During droughts, groundwater often plays a vital role in sustaining rivers and streams and becomes a valuable buffer. Groundwater is a finite resource, and aquifers can become depleted when extraction rates exceed replenishment, or 'recharge', rates. Like surface water, groundwater can become polluted or contaminated (National Centre for Groundwater Research and Training). The objectives of this study are to find the groundwater critical zones in Rajasthan, identify the seasonal variation of groundwater level and the trend of groundwater level and fluctuation. Rajasthan is a state with low to extremely low rainfall, intense summers with very high temperatures, high diurnal variation of temperatures and low humidity and high evaporation. It is one such Indian state with complex agro-climatic zones and in urgent need of expanding groundwater resources. Moreover, increase in population and urbanization leads to groundwater depletion. Thus, groundwater study plays an important role in assessment, monitoring, planning, development and Integrated Water Resources Management in Rajasthan [1].

A. Study area
The present study focuses on water resources of Rajasthan and its districts. Rajasthan is the largest State in the country with total geographical area of 342,239 km 2 and comprises of 33 districts (Yadav et al., 2016). According to 2011 census, it has a total population of 68,548,437. The state accounts for more than 10% of India's geographical area, supports about 5% of the human population and 20% of the livestock but only possesses 1.2% of the total surface water and 1.7% of the groundwater available in India. The Aravalli hill ranges, running from north east to south-west, divide the state approximately into the western arid and eastern semi-arid regions. Rajasthan's economy has undergone considerable transformation in the recent past, with agriculture (including livestock) providing one-fourth of the state's GDP. Approximately 5.4 million households are engaged in farming, while 60% of the state's population depend on agriculture for their livelihood. Rajasthan is heavily dependent on groundwater for irrigation and about 90% of the drinking water and 60% of the irrigation water is sourced from groundwater supplies. The pressure on groundwater is further growing due to population growth and an increased number of industries. About 80% of the State areas have witnessed groundwater depletion and many towns and villages have experienced a shortage of drinking water, particularly in summer months (Chinnasamy et al., 2015).

International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181 http://www.ijert.org Secondly, data was imported in ArcGIS software and Inverse Distance Weightage (IDW) was applied for estimating the attribute values of locations that are within the range of available data using known data values. The area of Rajasthan was clipped from the interpolated data using Model Builder. These values were extracted to MS-Excel for statistical analysis. The missing data were calculated using the preceding and succeeding data using the following formula: (1) where, a = preceding year data value, b = succeeding year data value The Mann-Kendall (MK) test is a non-parametric method for identifying trends in time-series data. The Mann-Kendall test checks the null hypothesis of no trend versus the alternative hypothesis of the existence of increasing or decreasing trend (Narjary et al., 2014). In XLSTAT, using Trend Analysis tool, Man-Kendall's Test was carried out for the interpolated data. Each data value is compared to all subsequent data values. The initial value of the MK statistic, (tau), is assumed to be 0 (i.e. no trend). If a data value at a later time is higher than a data value of an earlier time, is incremented by 1. On the other hand, if the data value at a later time is lower than a data value sampled earlier, is decremented by 1. The net result of all such increments and decrements yields the final value of . Let x1, x2,…, xn represent n data points, where xj represents the data point at time j. Then, the MK ( ) is given by A high positive value of is an indicator of an increasing trend, and a low negative value indicates a decreasing trend. Sen's slope estimator provides an estimate of the magnitude of the detected trend and is calculated as where xi and xj are data values at time j and k (j > k) respectively. The median of (β) N values of Ti is the Sen's slope estimator.
, if N is even (5) Positive value of indicates an increasing trend, and negative value indicates a decreasing trend in the time series (Narjary et al., 2014). Overall methodology adopted in the study is shown in Fig. 2.

III.
RESULTS AND DISCUSSIONS There is a dynamic balance of water levels in aquifers between ground-water recharge, storage, and discharge. If recharge exceeds discharge, the volume of water in storage will increase and water levels will rise; if discharge exceeds recharge, the volume of water in storage will decrease and water levels will fall. It is because recharge and discharge are not distributed uniformly in space and time, ground-water levels are continuously rising or falling to adjust to the resulting imbalances (Rede, 2012).