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Improving the representation of groundwater processes in a large-scale water resources model

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Relation http://oar.icrisat.org/12124/
https://doi.org/10.1080/02626667.2023.2208755
https://doi.org/10.1080/02626667.2023.2208755
 
Title Improving the representation of groundwater processes in a large-scale water resources model
 
Creator Baron, H E
Keller, V D J
Horan, R
MacAllister, D J
Simpson, M
Jackson, C R
Houghton-Carr, H A
Rickards, N
Garg, K K
Sekhar, M
MacDonald, A
Rees, G
 
Subject Water Resources
India
 
Description This study explores whether incorporating a more sophisticated representation of groundwater, and human–groundwater interactions, improves predictive capability in a large-scale water resource model. The Global Water Availability Assessment model (GWAVA) is developed to include a simple layered aquifer and associated fluxes (GWAVA-GW), and applied to the Cauvery River basin in India, a large, human-impacted basin with a high dependence on groundwater. GWAVA-GW shows good predictive skill for streamflow upstream of the Mettur dam: Kling-Gupta efficiency ≥ 0.3 for 91% of sub-catchments, and improved model skill for streamflow prediction compared to GWAVA over the majority of the basin. GWAVA-GW shows some level of predictive skill for groundwater levels over seasonal and long-term time scales, with a tendency to overestimate depth to groundwater in areas with high levels of groundwater
pumping. Overall, GWAVA-GW is a useful tool when assessing water resources at a basin scale, especially in areas that rely on groundwater.
 
Publisher Taylor and Francis Group
 
Date 2023-03-21
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_attribution
 
Identifier http://oar.icrisat.org/12124/1/HYDROLOGICAL%20SCIENCES%20JOURNAL_01-22_2023.pdf
Baron, H E and Keller, V D J and Horan, R and MacAllister, D J and Simpson, M and Jackson, C R and Houghton-Carr, H A and Rickards, N and Garg, K K and Sekhar, M and MacDonald, A and Rees, G (2023) Improving the representation of groundwater processes in a large-scale water resources model. HYDROLOGICAL SCIENCES JOURNAL. 01-22. ISSN 2150-3435