Recent trends in root-zone soil moisture over India using the GLEAM data for the period 1980-2020

Root Zone Soil Moisture (RZSM) plays a critical role in land-atmospheric interactions, water & energy budget, terrestrial evaporation and vegetation health. Unfortunately, the in-situ observations of RZSM are very sparse all over the globe. The present study utilized the RZSM product (based on microwave satellite data) from the GLEAM (Global Land Evaporation Amsterdam Model) to study the trends and spatial variability over India during the period 1980-2020. The annual RZSM climatology map shows that the highest values (>0.35 m³/m³) are found over dense vegetation regions (Western-Ghats, North-East India, and foothills of Himalayas) and low values (<0.2 m³/m³) are found in arid and semi-arid regions of North-West India. The all India annual mean RZSM (area averaged) is 0.285 m³/m³ with the standard deviation of 0.0076 m³/m³ and showing a significant increasing trend (p<0.05) during the period 1980-2020. The analysis found a distinct seasonal variability in RZSM and found the highest RZSM during the southwest monsoon (June-September) season and low values in the pre-monsoon season (March-May) for most of the sub-divisions classified by the India Meteorological Department. The seasonal all India mean RZSM values are 0.23 m³/m³, 0.33 m³/m³, 0.31 m³/m³ during pre-monsoon, monsoon, and post-monsoon seasons respectively, and found a significant increasing trend in all seasons during the study period. The sub-division wise trend (Mann-Kendall test) analysis shows that the pre-monsoon RZSM showed a tremendous increasing trend in most (23 out of 34) of the sub-divisions (except north and northeast India) whereas in monsoon and post-monsoon season only 9 and 12 sub-divisions showed an increasing trend in India respectively. The present study improves our understanding of the regional scale hydrological cycle and the importance of realistic representation of irrigation and land use land cover changes in climate models for better prediction of monsoon and other natural disasters in India.