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Surface water and groundwater interaction in the Kosi River alluvial fan of the Himalayan Foreland.

Zafar BegSuneel Kumar JoshiDigvijay SinghSudhir KumarKumar Gaurav
Published in: Environmental monitoring and assessment (2022)
We report the isotopic composition of the surface water and groundwater of the Kosi River fan on the Himalayan Foreland, India. We have collected 65 water samples from surface water (Kosi River (n = 2), streams (n = 9), waterlogging (n = 29), and canal (n = 4)), and groundwater (n = 21) for δ 18 O and δ 2 H analysis during December 2019. We obtained groundwater level data measured at the observation wells from the Central Groundwater Board, India, for 1996 and 2017. The groundwater level varies from 1.0 to 8.1 m below ground level (bgl) and from 0.5 to 9.0 m bgl during 1996 and 2017, respectively. We have used water table fluctuation approach to estimate the recharge rate. The recharge rate in the Kosi Fan varies from 0.7 to 21.4 mm/year from 1996 to 2017. Further, we have used δ 18 O and δ 2 H values of water samples to identify the source and the interaction between surface water and groundwater. The δ 18 O value of groundwater shows a wide variation (from -9.3‰ to -5.6‰) compared to the surface water, i.e., streams (-7.8‰ to -6.4‰) and canals (-6.9‰ to -6.0‰), suggesting mixing in groundwater during recharge processes. Furthermore, we have used a two-component mixing model to assess the fraction contribution from streams and precipitation to groundwater. The estimated fraction contribution from stream water to groundwater ranges from 45 to 83%. We also suggest higher recharge is limited up to the depth of 6 m bgl. We suggest precipitation and surface water actively recharge groundwater. We conclude that marked spatial variation in the isotopic composition of groundwater is mainly due to the local recharge sources and interaction between surface water and groundwater.
Keyphrases
  • drinking water
  • health risk
  • human health
  • water quality
  • heavy metals
  • health risk assessment
  • risk assessment
  • machine learning
  • optical coherence tomography
  • big data