Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

Abstract This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ung...

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Autores principales:	Amirhosein Mosavi, Mohammad Golshan, Bahram Choubin, Alan D. Ziegler, Shahram Khalighi Sigaroodi, Fan Zhang, Adrienn A. Dineva
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e1a8336badf426394d966be223e76a8
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spelling	oai:doaj.org-article:9e1a8336badf426394d966be223e76a82021-12-02T18:03:07ZFuzzy clustering and distributed model for streamflow estimation in ungauged watersheds10.1038/s41598-021-87691-02045-2322https://doaj.org/article/9e1a8336badf426394d966be223e76a82021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87691-0https://doaj.org/toc/2045-2322Abstract This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.Amirhosein MosaviMohammad GolshanBahram ChoubinAlan D. ZieglerShahram Khalighi SigaroodiFan ZhangAdrienn A. DinevaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amirhosein Mosavi Mohammad Golshan Bahram Choubin Alan D. Ziegler Shahram Khalighi Sigaroodi Fan Zhang Adrienn A. Dineva Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
description	Abstract This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.
format	article
author	Amirhosein Mosavi Mohammad Golshan Bahram Choubin Alan D. Ziegler Shahram Khalighi Sigaroodi Fan Zhang Adrienn A. Dineva
author_facet	Amirhosein Mosavi Mohammad Golshan Bahram Choubin Alan D. Ziegler Shahram Khalighi Sigaroodi Fan Zhang Adrienn A. Dineva
author_sort	Amirhosein Mosavi
title	Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
title_short	Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
title_full	Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
title_fullStr	Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
title_full_unstemmed	Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
title_sort	fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9e1a8336badf426394d966be223e76a8
work_keys_str_mv	AT amirhoseinmosavi fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds AT mohammadgolshan fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds AT bahramchoubin fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds AT alandziegler fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds AT shahramkhalighisigaroodi fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds AT fanzhang fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds AT adriennadineva fuzzyclusteringanddistributedmodelforstreamflowestimationinungaugedwatersheds
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Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

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