LINKING LAND USE LAND COVER CHANGE TO GLOBAL GROUNDWATER STORAGE

Groundwater (GW) storage is facing the constant threat of over-exploitation and irreversible depletion, often attributed to agricultural and industrial usage as well as human mismanagement. While several methodologies, varying from well logs to gravity recovery data, have been successfully adopted over the years to track and mitigate GW loss, land cover (LC) has never been deemed a pivotal component. LC or the different types of biomes covering our planet, ranging from forests to grasslands and anthropogenic builts such as croplands, play a significant role in regulating GW resources. LCs and corresponding vegetation character govern the amount and depth of GW uptake, determine the runoff vs percolation rates, and influence local climate. Land use change alters the hydrological connectivity between the surface and subsurface water. Therefore LC is a vital stakeholder in managing GW reserves.

We employed a decision tree based Machine Learning model to (a) identify hydrological and terrestrial drivers affecting GW resources, (b) predict shallow and deep GW storage, (c) rank the drivers according to their impact on GW storage, and (d) understand GW distribution as a function of Land Use Land Cover (LULC) change. The model was developed globally, then extended to basinal scale observations in the Indus, Ganga and Brahmaputra rivers of the Indian subcontinent. Model output has helped to (a) compute the ‘percolation index’ associated with each LC, (b) equate cropland expansion among the 3 basins with shallow and deep GW storage and (c) link LULC-GW change to crop yield. RCP 2.6 crop yield estimates for the 21st century proves detrimental to Indian food and freshwater security, given the strong coupling of LULC change among the 3 basins and how LC change translates to GW storage.