Abstract
Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.
Highlights
Groundwater (GW) is a significant source of domestic, agricultural, and industrial consumption
We evaluated our findings against three datasets, namely: (I) baseflow estimations collected by the US Geological Survey (USGS) stream gauges across the southeast US, where shallow GW impact was detected, especially during winter months; (II) global wetland distributions, where GW is the dominant source of elevated soil moisture states or saturated conditions; (III) distributions of clay-enriched horizons potentially forming perched aquifers resulting in shallow GW
We estimated the high-resolution spatiotemporal distribution of shallow GW based on Soil Moisture Active Passive (SMAP) soil moisture observations at a global scale
Summary
Groundwater (GW) is a significant source of domestic, agricultural, and industrial consumption. It impacts the global hydrologic and energy cycles. Shallow GW, in particular, directly affects evaporation and evapotranspiration with corresponding effects on the biosphere. It profoundly impacts water, energy, and carbon cycles by providing additional water to ecosystems and the land surface. Modeling groundwater and shallow groundwater globally is hindered by computational complexity and the lack of highresolution information on lithological boundaries, soil properties, and knowledge of boundary and initial conditions. Some terrestrial water flux models have tried to simulate the groundwater coupling with the atmosphere at the scales from the watershed [5] to global [6], but they still lack the global datasets with a high spatial and temporal resolution to calibrate and verify results
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