A probabilistic approach for estimating spring discharge facing data scarcity

Abstract Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rasoul Mirabbasi [verfasserIn] Mohammad Nazeri Tahroudi [verfasserIn] Alireza Sharifi [verfasserIn] Ali Torabi Haghighi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Absefid Spring Discharge estimation Copula Bivariate probability Karst spring

Übergeordnetes Werk:	In: Applied Water Science - SpringerOpen, 2013, 14(2024), 2, Seite 16
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:2 ; pages:16

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1007/s13201-023-02071-5

Katalog-ID:	DOAJ091167558

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spelling	10.1007/s13201-023-02071-5 doi (DE-627)DOAJ091167558 (DE-599)DOAJeac4d8c78d7d448da1e1a2e2fc7e072a DE-627 ger DE-627 rakwb eng TD201-500 Rasoul Mirabbasi verfasserin aut A probabilistic approach for estimating spring discharge facing data scarcity 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs are in mountainous and inaccessible areas, long-term observational data are often unavailable. This study proposes a probabilistic method based on bivariate analysis to estimate the discharge of the Absefid spring in Iran. This method constructed the bivariate distribution of the outflows of Absefid (AS) and Gerdebisheh (GS) springs using Copula functions. For this purpose, the fit of 11 different univariate distributions to the discharge data of each spring was tested. The results revealed that the GEV and log-normal distributions best fit the discharge data of GS and AS springs, respectively. In addition, among eight different copula functions, the Joe copula function was selected to construct the bivariate distribution of the discharge data of AS and GS springs. With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. However, the amount of allocated water from this source should be determined by considering the ecological needs of the river downstream of this spring. Absefid Spring Discharge estimation Copula Bivariate probability Karst spring Water supply for domestic and industrial purposes Mohammad Nazeri Tahroudi verfasserin aut Alireza Sharifi verfasserin aut Ali Torabi Haghighi verfasserin aut In Applied Water Science SpringerOpen, 2013 14(2024), 2, Seite 16 (DE-627)64730242X (DE-600)2594789-8 21905495 nnns volume:14 year:2024 number:2 pages:16 https://doi.org/10.1007/s13201-023-02071-5 kostenfrei https://doaj.org/article/eac4d8c78d7d448da1e1a2e2fc7e072a kostenfrei https://doi.org/10.1007/s13201-023-02071-5 kostenfrei https://doaj.org/toc/2190-5487 Journal toc kostenfrei https://doaj.org/toc/2190-5495 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2024 2 16
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callnumber-first	T - Technology
author	Rasoul Mirabbasi
spellingShingle	Rasoul Mirabbasi misc TD201-500 misc Absefid Spring misc Discharge estimation misc Copula misc Bivariate probability misc Karst spring misc Water supply for domestic and industrial purposes A probabilistic approach for estimating spring discharge facing data scarcity
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topic_title	TD201-500 A probabilistic approach for estimating spring discharge facing data scarcity Absefid Spring Discharge estimation Copula Bivariate probability Karst spring
topic	misc TD201-500 misc Absefid Spring misc Discharge estimation misc Copula misc Bivariate probability misc Karst spring misc Water supply for domestic and industrial purposes
topic_unstemmed	misc TD201-500 misc Absefid Spring misc Discharge estimation misc Copula misc Bivariate probability misc Karst spring misc Water supply for domestic and industrial purposes
topic_browse	misc TD201-500 misc Absefid Spring misc Discharge estimation misc Copula misc Bivariate probability misc Karst spring misc Water supply for domestic and industrial purposes
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title	A probabilistic approach for estimating spring discharge facing data scarcity
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title_full	A probabilistic approach for estimating spring discharge facing data scarcity
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title_sort	probabilistic approach for estimating spring discharge facing data scarcity
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title_auth	A probabilistic approach for estimating spring discharge facing data scarcity
abstract	Abstract Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs are in mountainous and inaccessible areas, long-term observational data are often unavailable. This study proposes a probabilistic method based on bivariate analysis to estimate the discharge of the Absefid spring in Iran. This method constructed the bivariate distribution of the outflows of Absefid (AS) and Gerdebisheh (GS) springs using Copula functions. For this purpose, the fit of 11 different univariate distributions to the discharge data of each spring was tested. The results revealed that the GEV and log-normal distributions best fit the discharge data of GS and AS springs, respectively. In addition, among eight different copula functions, the Joe copula function was selected to construct the bivariate distribution of the discharge data of AS and GS springs. With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. However, the amount of allocated water from this source should be determined by considering the ecological needs of the river downstream of this spring.
abstractGer	Abstract Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs are in mountainous and inaccessible areas, long-term observational data are often unavailable. This study proposes a probabilistic method based on bivariate analysis to estimate the discharge of the Absefid spring in Iran. This method constructed the bivariate distribution of the outflows of Absefid (AS) and Gerdebisheh (GS) springs using Copula functions. For this purpose, the fit of 11 different univariate distributions to the discharge data of each spring was tested. The results revealed that the GEV and log-normal distributions best fit the discharge data of GS and AS springs, respectively. In addition, among eight different copula functions, the Joe copula function was selected to construct the bivariate distribution of the discharge data of AS and GS springs. With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. However, the amount of allocated water from this source should be determined by considering the ecological needs of the river downstream of this spring.
abstract_unstemmed	Abstract Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs are in mountainous and inaccessible areas, long-term observational data are often unavailable. This study proposes a probabilistic method based on bivariate analysis to estimate the discharge of the Absefid spring in Iran. This method constructed the bivariate distribution of the outflows of Absefid (AS) and Gerdebisheh (GS) springs using Copula functions. For this purpose, the fit of 11 different univariate distributions to the discharge data of each spring was tested. The results revealed that the GEV and log-normal distributions best fit the discharge data of GS and AS springs, respectively. In addition, among eight different copula functions, the Joe copula function was selected to construct the bivariate distribution of the discharge data of AS and GS springs. With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. However, the amount of allocated water from this source should be determined by considering the ecological needs of the river downstream of this spring.
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title_short	A probabilistic approach for estimating spring discharge facing data scarcity
url	https://doi.org/10.1007/s13201-023-02071-5 https://doaj.org/article/eac4d8c78d7d448da1e1a2e2fc7e072a https://doaj.org/toc/2190-5487 https://doaj.org/toc/2190-5495
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up_date	2024-07-03T18:49:40.327Z
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With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. 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