Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores

Kumari, N; Srivastava, A; Sahoo, B; Raghuwanshi, NS; Bretreger, D

Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores

Kumari, N Srivastava, A

Sahoo, B Raghuwanshi, NS Bretreger, D

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Natural Resources Research, 2021, 30, (6), pp. 4187-4205
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Kumari, N
dc.contributor.author	Srivastava, A https://orcid.org/0000-0002-3963-265X
dc.contributor.author	Sahoo, B
dc.contributor.author	Raghuwanshi, NS
dc.contributor.author	Bretreger, D
dc.date.accessioned	2022-02-06T21:40:25Z
dc.date.available	2022-02-06T21:40:25Z
dc.date.issued	2021-12-01
dc.identifier.citation	Natural Resources Research, 2021, 30, (6), pp. 4187-4205
dc.identifier.issn	1520-7439
dc.identifier.issn	1573-8981
dc.identifier.uri	http://hdl.handle.net/10453/154224
dc.description.abstract	The increasing demand for water in developing countries, like India, requires efficient water management and resource allocation. This is crucial to accurately assess and predict hydrological processes such as streamflow, drought, and flood. However, simulations of these hydrologic processes from various hydrological models differ in their accuracy. By analyzing different characteristics of hydrological models, selection scores can be used to select the best model for the intended purpose based on their inherit strengths (i.e., some models are better for streamflow prediction). In this study, 13 different criteria were used for the model selection scores including temporal and spatial resolutions, and processes involved. Thereafter, based on different scores, we selected two different hydrological models for streamflow prediction in the Kangsabati River Basin (KRB) in eastern India, namely (1) Génie Rural à 4 paramètres Journalier (GR4J), a conceptual model, and (2) Variable Infiltration Capacity (VIC), a semi-distributed model. The models were calibrated against the daily observed streamflow at upper KRB (Reservoir) and lower KRB (Mohanpur) from 2000 to 2006 and validated during the period from 2008 to 2010. Despite the differences in model structure and data used, both models simulated streamflow at a daily time scale with Nash–Sutcliffe coefficient of 0.71–0.82 for the VIC model and 0.63–0.71 for the GR4J. Due to the simpler structure, parsimonious nature, fewer parameters, and reasonable accuracy, the results suggest that a conceptual rainfall—runoff model like GR4J can be used in data-deficient conditions.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Natural Resources Research
dc.relation.isbasedon	10.1007/s11053-021-09919-0
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0502 Environmental Science and Management, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Geochemistry & Geophysics
dc.title	Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0502 Environmental Science and Management
utslib.for	0914 Resources Engineering and Extractive Metallurgy
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2022-02-06T21:40:23Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	6

Abstract:

The increasing demand for water in developing countries, like India, requires efficient water management and resource allocation. This is crucial to accurately assess and predict hydrological processes such as streamflow, drought, and flood. However, simulations of these hydrologic processes from various hydrological models differ in their accuracy. By analyzing different characteristics of hydrological models, selection scores can be used to select the best model for the intended purpose based on their inherit strengths (i.e., some models are better for streamflow prediction). In this study, 13 different criteria were used for the model selection scores including temporal and spatial resolutions, and processes involved. Thereafter, based on different scores, we selected two different hydrological models for streamflow prediction in the Kangsabati River Basin (KRB) in eastern India, namely (1) Génie Rural à 4 paramètres Journalier (GR4J), a conceptual model, and (2) Variable Infiltration Capacity (VIC), a semi-distributed model. The models were calibrated against the daily observed streamflow at upper KRB (Reservoir) and lower KRB (Mohanpur) from 2000 to 2006 and validated during the period from 2008 to 2010. Despite the differences in model structure and data used, both models simulated streamflow at a daily time scale with Nash–Sutcliffe coefficient of 0.71–0.82 for the VIC model and 0.63–0.71 for the GR4J. Due to the simpler structure, parsimonious nature, fewer parameters, and reasonable accuracy, the results suggest that a conceptual rainfall—runoff model like GR4J can be used in data-deficient conditions.

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http://hdl.handle.net/10453/154224