A Study on Drought Characterization and its Propagation Including Terrestrial Water Storage Dynamics under the Influence of Climate Variability

Abstract

River flows in peninsular India rely heavily on seasonal monsoon rainfall, leading to worsened water storage during dry periods or delayed monsoons. This exacerbates inter-state water disputes, particularly in the rain shadow regions of the Western Ghats, where lower rainfall (75.2 mm) and increasing drought frequency due to climate variability and anthropogenic perturbations intensify challenges. Droughts can be caused due to the deficiency of water from a variety of sources/components, including surface water, groundwater, and soil moisture. Accurately measuring and integrating individual water storage components is challenging, making Gravity Recovery and Climate Experiment (GRACE) data exceptionally valuable, as studies show TWS anomalies are well-suited for drought-related research globally. Therefore, it is proposed to use remote sensing data to help understand TWS dynamics and drought conditions in peninsular parts of India. First, this study analyzes changes in TWS across the entire Deccan Plateau (DP) and its RS region in response to climate variability from 2003 to 2016, with a specific focus on assessing the contribution of surface water storage (SWS), specifically reservoir storage, to TWS variability, highlighting the importance of SWS. Responses of TWSA and its different components to rainfall are evaluated using the time lagged correlation. Contribution of individual water storage components to TWSA are also estimated using the component contribution ratio (CCR) approach to identify the major contributors in these regions. Results showed that the increasing trend of TWS in 2003–2009 had reversed in 2010–2016 due to the reduction of climate moistening in both regions. Incorporating SWS and GWS into GLDAS-based TWSA estimation significantly improved its relationship with GRACE-based TWSA, vindicating their importance in both regions. Soil moisture storage and GWS were the two major contributors to TWS variability in these regions, with SWS also having a significant contribution, particularly in the RS region. Second, an Artificial Neural Network (ANN) is used to reconstruct the TWSA data and bridge the 11- month gap between GRACE and GRACE-FO, obtaining a continuous time series of TWSA from 2003 to 2021. The ANN model is developed by considering precipitation, evapotranspiration, reservoir water storage, GWS, SMS, and multivariate ENSO index (MEI) data as predictors and GRACE-based TWSA as target variable. This study also analyzes the influence of SWS and GWS on the accuracy of reconstruction in different sub-divisions, using three different scenarios of inputvariables. The reconstructed TWSA are used to characterize hydrological droughts in 11 sub-divisions, and their links to large-scale climatic oscillations are also analyzed using cross-wavelet transformation analysis. Finally, the propagation relationships among meteorological, hydrological, agricultural, and groundwater droughts are analyzed in South India (SI). Moreover, the influence of large scale teleconnection factors is also analyzed. Results from this study show that agricultural and hydrological droughts have the strongest relationship in SI. Generally, the propagation time is shorter between the agricultural and hydrological droughts, while it is longer for sub-surface region. There is relatively weak link between groundwater and hydrological/agricultural droughts in the SI. All three large-scale climatic factors (ENSO, PSO, and IOD) significantly contribute to drought propagation.