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...

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Published in:Applied Water Science
Main Authors: Rasoul Mirabbasi, Mohammad Nazeri Tahroudi, Alireza Sharifi, Ali Torabi Haghighi
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Subjects:
Online Access:https://doi.org/10.1007/s13201-023-02071-5
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author Rasoul Mirabbasi
Mohammad Nazeri Tahroudi
Alireza Sharifi
Ali Torabi Haghighi
author_facet Rasoul Mirabbasi
Mohammad Nazeri Tahroudi
Alireza Sharifi
Ali Torabi Haghighi
author_sort Rasoul Mirabbasi
collection DOAJ
container_title Applied Water Science
description 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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spelling doaj-art-eac4d8c78d7d448da1e1a2e2fc7e072a2025-08-19T22:58:17ZengSpringerOpenApplied Water Science2190-54872190-54952024-01-0114211610.1007/s13201-023-02071-5A probabilistic approach for estimating spring discharge facing data scarcityRasoul Mirabbasi0Mohammad Nazeri Tahroudi1Alireza Sharifi2Ali Torabi Haghighi3Department of Water Engineering, Faculty of Agriculture, Shahrekord UniversityDepartment of Water Engineering, Faculty of Agriculture, Lorestan UniversityWater, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of OuluWater, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of OuluAbstract 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.https://doi.org/10.1007/s13201-023-02071-5Absefid SpringDischarge estimationCopulaBivariate probabilityKarst spring
spellingShingle Rasoul Mirabbasi
Mohammad Nazeri Tahroudi
Alireza Sharifi
Ali Torabi Haghighi
A probabilistic approach for estimating spring discharge facing data scarcity
Absefid Spring
Discharge estimation
Copula
Bivariate probability
Karst spring
title A probabilistic approach for estimating spring discharge facing data scarcity
title_full A probabilistic approach for estimating spring discharge facing data scarcity
title_fullStr A probabilistic approach for estimating spring discharge facing data scarcity
title_full_unstemmed A probabilistic approach for estimating spring discharge facing data scarcity
title_short A probabilistic approach for estimating spring discharge facing data scarcity
title_sort probabilistic approach for estimating spring discharge facing data scarcity
topic Absefid Spring
Discharge estimation
Copula
Bivariate probability
Karst spring
url https://doi.org/10.1007/s13201-023-02071-5
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