Next Article in Journal
A Study on Interaction between Overfall Types and Scour at Bridge Piers with a Moving-Bed Experiment
Next Article in Special Issue
Distributed-Framework Basin Modeling System: IV. Application in Taihu Basin
Previous Article in Journal
Thixotropic Behavior of Reconstituted Debris-Flow Mixture
Previous Article in Special Issue
Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling
Open AccessArticle

A Comparative Assessment of Hydrological Models in the Upper Cauvery Catchment

1
UK Centre for Ecology & Hydrology, Wallingford OX10 8BB, UK
2
Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India
3
Divecha Centre for Climate Change, Indian Institute of Science, Bangalore 560012, India
4
International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502324, India
5
Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore 560012, India
*
Author to whom correspondence should be addressed.
Water 2021, 13(2), 151; https://doi.org/10.3390/w13020151
Received: 2 December 2020 / Revised: 24 December 2020 / Accepted: 4 January 2021 / Published: 11 January 2021
(This article belongs to the Special Issue Modelling Hydrologic Response of Non­-homogeneous Catchments)
This paper presents a comparison of the predictive capability of three hydrological models, and a mean ensemble of these models, in a heavily influenced catchment in Peninsular India: GWAVA (Global Water AVailability Assessment) model, SWAT (Soil Water Assessment Tool) and VIC (Variable Infiltration Capacity) model. The performance of the three models and their ensemble were investigated in five sub-catchments in the upstream reaches of the Cauvery river catchment. Model performances for monthly streamflow simulations from 1983–2005 were analysed using Nash-Sutcliffe efficiency, Kling-Gupta efficiency and percent bias. The predictive capability for each model was compared, and the ability to accurately represent key catchment hydrological processes is discussed. This highlighted the importance of an accurate spatial representation of precipitation for input into hydrological models, and that comprehensive reservoir functionality is paramount to obtaining good results in this region. The performance of the mean ensemble was analysed to determine whether the application of a multi-model ensemble approach can be useful in overcoming the uncertainties associated with individual models. It was demonstrated that the ensemble mean has a better predictive ability in catchments with reservoirs than the individual models, with Nash-Sutcliffe values between 0.49 and 0.92. Therefore, utilising multiple models could be a suitable methodology to offset uncertainty in input data and poor reservoir operation functionality within individual models. View Full-Text
Keywords: Cauvery; hydrological modelling; VIC; SWAT; GWAVA; ensemble modelling; water resources Cauvery; hydrological modelling; VIC; SWAT; GWAVA; ensemble modelling; water resources
Show Figures

Figure 1

MDPI and ACS Style

Horan, R.; Gowri, R; Wable, P.S.; Baron, H.; Keller, V.D.J.; Garg, K.K.; Mujumdar, P.P.; Houghton-Carr, H.; Rees, G. A Comparative Assessment of Hydrological Models in the Upper Cauvery Catchment. Water 2021, 13, 151. https://doi.org/10.3390/w13020151

AMA Style

Horan R, Gowri R, Wable PS, Baron H, Keller VDJ, Garg KK, Mujumdar PP, Houghton-Carr H, Rees G. A Comparative Assessment of Hydrological Models in the Upper Cauvery Catchment. Water. 2021; 13(2):151. https://doi.org/10.3390/w13020151

Chicago/Turabian Style

Horan, Robyn; Gowri, R; Wable, Pawan S.; Baron, Helen; Keller, Virginie D.J.; Garg, Kaushal K.; Mujumdar, Pradeep P.; Houghton-Carr, Helen; Rees, Gwyn. 2021. "A Comparative Assessment of Hydrological Models in the Upper Cauvery Catchment" Water 13, no. 2: 151. https://doi.org/10.3390/w13020151

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop