Effect of using multiple stream gauging stations on calibration of hydrologic parameters and estimation of hydrograph of ungauged neighboring basin

Akay, Hüseyın; Koçyiğit, Müsteyde Baduna; Yanmaz, A. Melih

doi:10.1007/s12517-018-3642-z

Cited by 21 publications

(6 citation statements)

References 23 publications

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“…The popular regionalization methods are physical similarity, spatial proximity, and regression 5 . Previously, many studies have been conducted and compared the performance of the regionalization methods to predict total streamflow or direct runoff 6 in the ungauged catchment with various hydrological models (WASMOD 7 , VIC 8 , SWAT 9 , GR4J 10 , HMETS 10 , MOHYSE 10 , and HEC-HMS 11 ) for different regions. Some of them showed that distance-based (spatial proximity, physical similarity) outperformed regression methods 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

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Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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“…Ayrıca, El-Shamy tarafından önerilen taşkın potansiyelinin değerlendirilmesi yaklaşımında aynı grupta değerlendirilen alt havzalar, bu çalışma ile uyarlanan yönteme göre de bir önceliklendirmeye tabii tutulabilir. [42,43] daha iyi tahmin edilebilir [44,45]. Benzerlik oranı, havzaların drenaj alanlarının yanı sıra uzunluk, genişlik, drenaj ağı, relief ve eğim vb.…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

“…Bu büyüklüklerin havzanın ilgili morfometrik büyüklükleri ile doğrudan ifade edilebileceği gözden kaçırılmamalıdır. Birbirinden farklı hidrolojik özelliklere sahip havzalarda hidrolojik parametrelerin ölçüm alınan havzalardan ölçüm alınmayan havzalara doğrudan transferi, büyüklükler arasında bazı benzerlikler kurularak [42,43] daha iyi tahmin edilebilir [44,45]. Benzerlik oranı, havzaların drenaj alanlarının yanı sıra uzunluk, genişlik, drenaj ağı, relief ve eğim vb.…”

Section: Sonuçlar (Conclusion)unclassified

Morfometrik parametreler yardımıyla havzada muhtemel taşkın riskinin tahmin edilmesi: Akçay Havzası örneği

Koçyiğit¹,

Akay²

2018

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Computation of morphometric parameters of sub basins of Akçay Basin  Evaluation of flood potential of sub basins of Akçay Basin  Evaluation of the location of Yunuslar HEPP Figure A. (a) Hydrologic soil groups, (b) sub-basins and stream orders of Akçay Basin Purpose: In this study, the Akçay Basin located in Western Black Sea Region is divided into 11 sub basins and a prioritization method is employed to evaluate the flash flood potential of those sub basins with the usage of the morphometric parameters computed from geometrical parameters of those sub basins, the drainage network and the stream orders. Theory and Methods: The Akçay Basin was divided into sub basins and the geometrical parameters of those sub basins, the drainage network and the stream orders specified by the Strahler method were determined by using ArcGIS 10.1. Thus, the morphometric parameters of each sub basin were computed, sub basins were ranked by using those values according to their flash flood potentials and then prioritized. Moreover, a graphical method was used to investigate the flash flood potentials of the sub basins. Results: It was found out that less than half of the sub basins had high flash flood potential so check dams could be constructed at the outlets of those sub basins to mitigate flooding in the area. Furthermore, existing river bridge at the outlet of one of the sub basins with medium flash flood potential should be protected against excessive scouring at the piers due to those flash floods. Conclusion: According to the prioritization of the sub basins, it was found out that the locations of the Yunuslar HEPP was appropriate to collect water from sub basins with high and medium flash flood potentials. Graphical method showed that all sub basins had medium flash flood potential.

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“…Possible future flood discharges for ungauged basins can be estimated by transferring, regionalizing hydrologic parameters from gauged neighbouring basins or remote sensing and field measurement techniques. However, it is not recommended to use transferred or spatially distributed hydrologic parameters in the computation of design discharge as the spatial heterogeneities in the considered watershed may not be captured (Akay, Baduna Koçyiğit, & Yanmaz, 2018;Baduna Koçyiğit, Akay, & Yanmaz, 2017;Zhang et al, 2013). Unit hydrograph of a watershed can also be estimated using morphometric parameters, which can directly reflect the characteristics of a watershed.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Scour Failure Risk of a River Bridge Located in an Ungauged Basin

Akay

2021

BJRBE

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In this study, scour failure risk of the Çatalzeytin Bridge located in the Western Black Sea Basin, Turkey, was assessed for possible future flood events and appropriate scour countermeasures were considered based on economic and constructability considerations. Waterway adequacy in the spans of the bridge and scour criticality around bridge foundations considered for risk calculations in HYRISK were estimated by hydrological and hydraulic analyses of the watershed and stream. Since the watershed of the bridge is ungauged, geomorphological instantaneous unit hydrograph concept was adopted to estimate the peak discharges with various return periods to be used in hydraulic modelling. Monte Carlo simulation results indicated that most of the simulated peak discharges were in the 95% confidence interval. Hydraulic model results from HECRAS indicated that waterway adequacy and scour criticality were critical for discharges with 200 and 500-year return periods. Scour failure risk of the Çatalzeytin Bridge was classified as high and it was proposed to reduce the risk by constructing partially grouted riprap as the most feasible alternative that would consequently increase the expected lifespan of the bridge. Following this methodology, river bridges may be prioritized based on the risk analysis.

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Effect of using multiple stream gauging stations on calibration of hydrologic parameters and estimation of hydrograph of ungauged neighboring basin

Cited by 21 publications

References 23 publications

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Morfometrik parametreler yardımıyla havzada muhtemel taşkın riskinin tahmin edilmesi: Akçay Havzası örneği

Mitigation of Scour Failure Risk of a River Bridge Located in an Ungauged Basin

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