1 research outputs found
Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia
Groundwater depletion in South Asia’s Himalayan,
transboundary
Indus-Ganges-Brahmaputra-Meghna (IGBM) rivers basin is among the highest
globally. Given the high irrigation demand and population, groundwater
sustainability requires an improved understanding of groundwater systems
for the accurate prediction of groundwater levels (GWLs). However,
the prediction of groundwater system behaviors is a significant challenge
since it is dominated by spatiotemporal and subsurface depth-dependent
drivers. Earlier studies that address the challenges are mainly based
on the short spatial and temporal extent and/or do not separate the
renewable (i.e., shallow) vs nonrenewable (i.e., deeper) groundwater
signals. Here, we first identified the variable importance of spatial
and depth-dependent drivers on GWL in the IGBM basin. Our results
indicate a greater influence of anthropogenic factors (i.e., widespread
pumping and increased population) in most parts of the IGBM basin,
except in the precipitation-dominated basin of the Brahmaputra. Our
next purpose was to delineate a multifactorial approach for GWL prediction
using the two most used machine learning models (i.e., support vector
machine and feed-forward neural network) in the literature. In general,
the machine learning model outputs show a good match in comparison
to the GWL from the observation wells (n = 2303 distributed
across India and Bangladesh) with some limitations in areas with increased
groundwater irrigation. We separately compared the results from shallow
(35 m) observation wells, emphasizing the
significance
of deep groundwater pumping. Our approach highlights the importance
of spatiotemporal to multidepth factors in GWL prediction and can
be adopted in other parts of the globe to predict GWLs