Challenges of hydrological analysis for water resource development in semi-arid mountainous regions: case study in Iran

Hishinuma, Shiro; Takeuchi, Kuniyoshi; Magome, Jun

doi:10.1080/02626667.2013.853879

Cited by 9 publications

(3 citation statements)

References 56 publications

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“…The situation is more severe in mountainous basins [6] because of the inaccessibility of most of these sites for local observations. It is the reason why water budget analyses in such basins are not as easy as in other gauged basins [7,8]. However, most of the large rivers (e.g., the Ganga, the Indus, the Sutlej, the Brahmaputra, the Mekong, the Yellow) in the world originate from the mountains and are perennial in nature as they are constantly fed by snow and glaciers.…”

Section: Introductionmentioning

confidence: 99%

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Marahatta

Devkota

Aryal

2021

Water

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The soil and water assessment tool (SWAT) hydrological model has been used extensively by the scientific community to simulate varying hydro-climatic conditions and geo-physical environment. This study used SWAT to characterize the rainfall-runoff behaviour of a complex mountainous basin, the Budhigandaki River Basin (BRB), in central Nepal. The specific objectives of this research were to: (i) assess the applicability of SWAT model in data scarce and complex mountainous river basin using well-established performance indicators; and (ii) generate spatially distributed flows and evaluate the water balance at the sub-basin level. The BRB was discretised into 16 sub-basins and 344 hydrological response units (HRUs) and calibration and validation was carried out at Arughat using daily flow data of 20 years and 10 years, respectively. Moreover, this study carried out additional validation at three supplementary points at which the study team collected primary river flow data. Four statistical indicators: Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), ratio of the root mean square error to the standard deviation of measured data (RSR) and Kling Gupta efficiency (KGE) have been used for the model evaluation. Calibration and validation results rank the model performance as “very good”. This study estimated the mean annual flow at BRB outlet to be 240 m3/s and annual precipitation 1528 mm with distinct seasonal variability. Snowmelt contributes 20% of the total flow at the basin outlet during the pre-monsoon and 8% in the post monsoon period. The 90%, 40% and 10% exceedance flows were calculated to be 39, 126 and 453 m3/s respectively. This study provides additional evidence to the SWAT diaspora of its applicability to simulate the rainfall-runoff characteristics of such a complex mountainous catchment. The findings will be useful for hydrologists and planners in general to utilize the available water rationally in the times to come and particularly, to harness the hydroelectric potential of the basin.

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Section: Introductionmentioning

confidence: 99%

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Marahatta

Devkota

Aryal

2021

Water

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“…About 67% of the basin area is mountainous and 33% is plain. This basin has a surface area of 67,112 km 2 (Hishinuma et al 2014).…”

Section: Study Area and Datamentioning

confidence: 99%

Hydrological drought analysis in response to climate change based on a novel hybrid machine learning algorithm

Morshed-Bozorgdel

Hosseini

Farzin

2022

Preprint

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For the first time, a combination of metaheuristic algorithms and machine learning is used for hydrological drought analysis under climate change conditions and applications. The new framework is used by a novel hybrid machine learning model named the least-squares support vector machine-African vulture optimization algorithm (LSSVM-AVOA). The performance of the proposed hybrid algorithm was compared with three algorithms, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machines (LSSVM). Using the technique for order of preference by similarity to the ideal solution (TOPSIS) method showed that LSSVM-AVOA with a score of 0.98 could be employed to improve the LSSVM modeling results. Three global climate models (GCM), including ACCESS-ESM1-5 (A1), CanESM5 (C5), and MRI-ESM2-0 (M2) during the historical period 1985–2014 and four scenarios, including SSP126, SSP245, SSP245-cov-fossil (SCF), SSP585 in future from 2021 to 2050 was considered for predicting discharge at Karun basin (Sepid Dasht Sezar, Bakhtiari Tang Pang, Sezar Tang Pang, Taleh Zang and Gotvand stations). Using new hybrid algorithm, the prediction results showed that discharge in most scenarios and stations decreased between − 0.81% and − 7.83% (except Sezar Tang Pang and Gotvand station). The standardized runoff index (SRI) results of hydrological drought analysis showed by the SSP585 scenario it seems that for the next first 10 years, a mild drought period can occur in this basin. Also, in the future period for SSP126 scenario by the first five years in the next 30 years, none drought is predicted.

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“…Karun and Karkheh basins, the food baskets of Iran, contain the largest rivers in the country (Karun and Karkheh rivers). These two basins are critically important in terms of agricultural production, water resources, and energy supply (Ahmad and Giordano, 2010;Hishinuma et al, 2014;Marjanizadeh et al, 2010). In recent years, the occurrence of multi-year droughts and devastating floods caused by climate change and different anthropogenic interventions in the basins has made the limited water resources in the region even more vulnerable (Afkhami et al, 2007;Peyravi et al, 2019;Vaghefi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran

Rahmati¹,

Bakhtar²,

Shayeghi³

et al. 2021

Preprint

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Access to spatio-temporally consistent precipitation data is a key prerequisite for hydrological studies, especially in data-scarce regions. Different global precipitation products offer an alternative way to estimate precipitation over areas with inadequate gauge distributions. However, before use of the datasets, the accuracy of these global estimations must be carefully studied at local and regional scale. This study evaluated 14 global precipitation products against gauge observations 2003-2012 in Karun and Karkheh basins in southwest Iran. Different categorical and statistical indices, including Kling-Gupta Efficiency (KGE), bias, correlation coefficient, and variability ratio, at varying spatial and temporal resolution were used to evaluate the products. KGE results at both daily and monthly time steps suggested that TMPA-3B42V7.0 and MERRA-2 outperformed all other products, while CMORPH-BLDV1.0 and PERSIANN-CDR was the best-performing product at daily and monthly time steps, respectively. ERA5-Land showed the highest positive bias compared with in-situ observations, particularly for mountainous southeastern parts of Karun basin. Overall, bias-adjusted products obtained by merging ground-based observations in the estimations outperformed the unadjusted versions. The spatial distribution of statistical error metrics indicated that almost all products showed their greatest uncertainties for mountainous regions, due to complex precipitation processes in these regions.These results can significantly contribute to various horological and water resources planning measures in the study region, including early flood warning systems, drought monitoring, and optimal dam operations.

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Challenges of hydrological analysis for water resource development in semi-arid mountainous regions: case study in Iran

Cited by 9 publications

References 56 publications

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Hydrological drought analysis in response to climate change based on a novel hybrid machine learning algorithm

Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran

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