A comparison of regionalization methods in monsoon dominated tropical river basins

The present study evaluated five regionalization methods: global averaging; regression; spatial proximity; behavioral similarity and artificial neural network (ANN) for Soil and Water Assessment Tool (SWAT), using data from 24 river basins in monsoon dominated tropical river basins of peninsular Ind...

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Autores principales:	Pramod Soni, Shivam Tripathi, Rajesh Srivastava
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	hydrology regionalization tropical river basins ungauged basins Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/442aadd09bd94cf29fb0a86f0e59c6c9
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id	oai:doaj.org-article:442aadd09bd94cf29fb0a86f0e59c6c9
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spelling	oai:doaj.org-article:442aadd09bd94cf29fb0a86f0e59c6c92021-11-05T19:02:16ZA comparison of regionalization methods in monsoon dominated tropical river basins2040-22442408-935410.2166/wcc.2021.298https://doaj.org/article/442aadd09bd94cf29fb0a86f0e59c6c92021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/1975https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354The present study evaluated five regionalization methods: global averaging; regression; spatial proximity; behavioral similarity and artificial neural network (ANN) for Soil and Water Assessment Tool (SWAT), using data from 24 river basins in monsoon dominated tropical river basins of peninsular India. Regionalization was performed for each basin using the remaining 23 basins. The performance of the calibration and thus the regionalization method is limited by the unreliable or erroneous data at the basins. Overall, we found that the regression method outperforms other regionalization methods in terms of predicting the daily as well as peak discharges. It was found that despite showing a better R2 in training, testing and validation, the ANN method performed poorly probably due to a lower number of training data. Therefore, it is suggested that the ANN should be avoided for regionalization in the absence of sufficient training data. Moreover, the regression equations developed in the present study can be utilized to predict SWAT parameters of basins located in the vicinity of the study area. However, the basins located far away from the group of catchments or having diverse characteristics should be avoided for regionalization. HIGHLIGHTS Overall, the regression-based method showed comparatively better performance both in terms of precision and accuracy.; Simpler regression methods are better than complex ANNs when the number of gauged basins are limited.; The performance of the regionalization method is limited by the unreliable or erroneous data at the basin.;Pramod SoniShivam TripathiRajesh SrivastavaIWA Publishingarticlehydrologyregionalizationtropical river basinsungauged basinsEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 1975-1996 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hydrology regionalization tropical river basins ungauged basins Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	hydrology regionalization tropical river basins ungauged basins Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Pramod Soni Shivam Tripathi Rajesh Srivastava A comparison of regionalization methods in monsoon dominated tropical river basins
description	The present study evaluated five regionalization methods: global averaging; regression; spatial proximity; behavioral similarity and artificial neural network (ANN) for Soil and Water Assessment Tool (SWAT), using data from 24 river basins in monsoon dominated tropical river basins of peninsular India. Regionalization was performed for each basin using the remaining 23 basins. The performance of the calibration and thus the regionalization method is limited by the unreliable or erroneous data at the basins. Overall, we found that the regression method outperforms other regionalization methods in terms of predicting the daily as well as peak discharges. It was found that despite showing a better R2 in training, testing and validation, the ANN method performed poorly probably due to a lower number of training data. Therefore, it is suggested that the ANN should be avoided for regionalization in the absence of sufficient training data. Moreover, the regression equations developed in the present study can be utilized to predict SWAT parameters of basins located in the vicinity of the study area. However, the basins located far away from the group of catchments or having diverse characteristics should be avoided for regionalization. HIGHLIGHTS Overall, the regression-based method showed comparatively better performance both in terms of precision and accuracy.; Simpler regression methods are better than complex ANNs when the number of gauged basins are limited.; The performance of the regionalization method is limited by the unreliable or erroneous data at the basin.;
format	article
author	Pramod Soni Shivam Tripathi Rajesh Srivastava
author_facet	Pramod Soni Shivam Tripathi Rajesh Srivastava
author_sort	Pramod Soni
title	A comparison of regionalization methods in monsoon dominated tropical river basins
title_short	A comparison of regionalization methods in monsoon dominated tropical river basins
title_full	A comparison of regionalization methods in monsoon dominated tropical river basins
title_fullStr	A comparison of regionalization methods in monsoon dominated tropical river basins
title_full_unstemmed	A comparison of regionalization methods in monsoon dominated tropical river basins
title_sort	comparison of regionalization methods in monsoon dominated tropical river basins
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/442aadd09bd94cf29fb0a86f0e59c6c9
work_keys_str_mv	AT pramodsoni acomparisonofregionalizationmethodsinmonsoondominatedtropicalriverbasins AT shivamtripathi acomparisonofregionalizationmethodsinmonsoondominatedtropicalriverbasins AT rajeshsrivastava acomparisonofregionalizationmethodsinmonsoondominatedtropicalriverbasins AT pramodsoni comparisonofregionalizationmethodsinmonsoondominatedtropicalriverbasins AT shivamtripathi comparisonofregionalizationmethodsinmonsoondominatedtropicalriverbasins AT rajeshsrivastava comparisonofregionalizationmethodsinmonsoondominatedtropicalriverbasins
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A comparison of regionalization methods in monsoon dominated tropical river basins

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