Nonlinear analysis of groundwater levels: Investigating trends and the impact of El Niño on groundwater drought in a southern region of India

The expansion of groundwater irrigation and the cultivation of water-intensive sugarcane crop, combined with low rainfall, has aggravated groundwater depletion and intensified droughts in a semi-arid region of the Upper Krishna basin, India. Consequently, assessing and managing groundwater resources in this region has become a priority for local authorities. However, this task is challenging due to the limited and inconsistent nature of historical observations, which complicates the balancing of temporal and spatial resolution in groundwater level data. The prevalence of missing values and the lack of sufficient information about their causes further complicate groundwater assessments, leading to potentially inaccurate interpretations. This study employs an iterative singular spectrum analysis (SSA) approach to impute missing groundwater level data from 25 monitoring wells. The reconstructed data is then used to identify nonlinear trends and investigate the impact of strong El Niño events on groundwater drought through cross wavelet transform (XWT) and wavelet coherence (WTC) analyses between 1983 and 2017. The SSA-extracted nonlinear trends revealed short-term deviations in groundwater levels during 1991–2000, 2002–2003, and 2015–2017, which were corroborated by significant cross wavelet power and high wavelet coherence between the Niño 3.4 SST Index and groundwater drought, particularly in low rainfall conditions, indicating stress on the groundwater system. Although the study effectively captures the nonlinear nature of groundwater levels and the influence of climate variability on drought, the complexity of the groundwater system in the region persists due to physical water scarcity and high groundwater extraction for irrigation. This study underscores the importance of imputing missing data and applying nonlinear trend and wavelet analysis to detect short-term deviations caused by severe droughts, driven by strong El Niño events and high irrigation demands.