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Article

Extending a Large-Scale Model to Better Represent Water Resources without Increasing the Model’s Complexity

1
UK Centre for Ecology & Hydrology, Wallingford OX10 8BB, UK
2
National Institute of Hydrology, Roorkee 247667, India
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Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India
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International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502324, India
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Civil Engineering Department, Indian Institute of Technology, Roorkee 247667, India
*
Authors to whom correspondence should be addressed.
Academic Editors: Kumaraswamy Ponnambalam and Jamshid Mousavi
Water 2021, 13(21), 3067; https://doi.org/10.3390/w13213067
Received: 10 September 2021 / Revised: 21 October 2021 / Accepted: 30 October 2021 / Published: 2 November 2021
The increasing impact of anthropogenic interference on river basins has facilitated the development of the representation of human influences in large-scale models. The representation of groundwater and large reservoirs have realised significant developments recently. Groundwater and reservoir representation in the Global Water Availability Assessment (GWAVA) model have been improved, critically, with a minimal increase in model complexity and data input requirements, in keeping with the model’s applicability to regions with low-data availability. The increased functionality was assessed in two highly anthropogenically influenced basins. A revised groundwater routine was incorporated into GWAVA, which is fundamentally driven by three input parameters, and improved the simulation of streamflow and baseflow in the headwater catchments such that low-flow model skill increased 33–67% in the Cauvery and 66–100% in the Narmada. The existing reservoir routine was extended and improved the simulation of streamflow in catchments downstream of major reservoirs, using two calibratable parameters. The model performance was improved between 15% and 30% in the Cauvery and 7–30% in the Narmada, with the daily reservoir releases in the Cauvery improving significantly between 26% and 164%. The improvement of the groundwater and reservoir routines in GWAVA proved successful in improving the model performance, and the inclusions allowed for improved traceability of simulated water balance components. This study illustrates that improvement in the representation of human–water interactions in large-scale models is possible, without excessively increasing the model complexity and input data requirements. View Full-Text
Keywords: large-scale model; hydrology; groundwater; reservoirs; Cauvery; Narmada large-scale model; hydrology; groundwater; reservoirs; Cauvery; Narmada
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MDPI and ACS Style

Horan, R.; Rickards, N.J.; Kaelin, A.; Baron, H.E.; Thomas, T.; Keller, V.D.J.; Mishra, P.K.; Nema, M.K.; Muddu, S.; Garg, K.K.; Pathak, R.; Houghton-Carr, H.A.; Dixon, H.; Jain, S.K.; Rees, G. Extending a Large-Scale Model to Better Represent Water Resources without Increasing the Model’s Complexity. Water 2021, 13, 3067. https://doi.org/10.3390/w13213067

AMA Style

Horan R, Rickards NJ, Kaelin A, Baron HE, Thomas T, Keller VDJ, Mishra PK, Nema MK, Muddu S, Garg KK, Pathak R, Houghton-Carr HA, Dixon H, Jain SK, Rees G. Extending a Large-Scale Model to Better Represent Water Resources without Increasing the Model’s Complexity. Water. 2021; 13(21):3067. https://doi.org/10.3390/w13213067

Chicago/Turabian Style

Horan, Robyn, Nathan J. Rickards, Alexandra Kaelin, Helen E. Baron, Thomas Thomas, Virginie D. J. Keller, Prabhas K. Mishra, Manish K. Nema, Sekhar Muddu, Kaushal K. Garg, Rishi Pathak, Helen A. Houghton-Carr, Harry Dixon, Sharad K. Jain, and Gwyn Rees. 2021. "Extending a Large-Scale Model to Better Represent Water Resources without Increasing the Model’s Complexity" Water 13, no. 21: 3067. https://doi.org/10.3390/w13213067

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