Peak unit discharge estimation based on ungauged watershed parameters

Kayan, Gökhan; Riazi, Amin; Erten, Esra; Türker, Umut

doi:10.1007/s12665-020-09317-4

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…The Cyprus basin lacks a single Q outlet, unlike the Sakarya basin. Therefore, it was divided into 266 sub-basins (Figure 2) using a digital elevation model (DEM) with a spatial resolution of 12 m (Kayan et al, 2021). The Q values of 266 sub-basins can be calculated by the following equation:…”

Section: Q Estimationsmentioning

confidence: 99%

“…The Cyprus basin consists of ephemeral rivers that have not been flowing in some winter seasons. The maximum altitude of the basin is 1981 m above mean sea level, and the maximum hydraulic flow depth is estimated to be 68 km with varying watershed slopes ranging from 2% to 56% (Kayan et al, 2021). The basin's drainage area is roughly 4990 km 2 , encompassing ~54% of Cyprus.…”

Section: Study Area and Data Setsmentioning

confidence: 99%

“…The Cyprus basin lacks a single

Q

outlet, unlike the Sakarya basin. Therefore, it was divided into 266 sub‐basins (Figure 2) using a digital elevation model (DEM) with a spatial resolution of 12 m (Kayan et al, 2021). The

Q

values of 266 sub‐basins can be calculated by the following equation:

{Q}_{SCS}=\frac{{\left({P}_{sta}-0.2{S}_R\right)}^2}{\left({P}_{sta}+0.8{S}_R\right)}

where

{P}_{sta}

is the mean of the daily

P

from four gauge stations in the Cyprus basin ( Figure b).…”

Section: Study Area and Data Setsmentioning

confidence: 99%

See 2 more Smart Citations

A fuzzy logic framework to handle uncertainty in remote sensing‐based hydrological data for water budget improvement across mid‐ and small‐scale basins

Kayan

Türker

Erten

2022

Hydrological Processes

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In recent years, the efficacy of remote sensing (RS) products for water budget (WB) analysis has been widely tested and implemented in global and regional basins. Although RS products provide high temporal and spatial resolution images with near‐global coverage, uncertainty is still a significant problem. Fuzzy logic is a powerful technique for dealing with uncertainty in different engineering problems. In this study, the annual residual error (r$$ r $$) in the WB equation arising from the uncertainties of the RS products was minimized by applying fuzzy correction coefficients to each WB component. For analysis, three different fuzzy linear regression (FLR) models with 14 different sub‐models were used in the two basins having different spatial characteristics, namely Sakarya and Cyprus basins. Although FLR sub‐models produce similar findings in the Sakarya basin, they generated more complex results in the Cyprus basin. This is mainly due to the higher uncertainty of the RS products in the Cyprus basin. The Cyprus basin is too small for some low‐resolution RS‐based products to resolve, and it has a higher leakage error due to across ocean/land boundary. In addition, the general performance of sub‐models is better in the Sakarya basin than that in the Cyprus basin. The best fuzzy sub‐models reduced the error up to 68% and 52% in terms of mean absolute error compared with the non‐fuzzy model in the Sakarya and Cyprus basins, respectively. Further evaluations showed that the best sub‐model precipitation well captured the temporal patterns of gauge observations in both basins. Moreover, they have the best consistency with gauge observations in terms of root mean square error, Kling‐Gupta efficiency, and percent bias in both basins. The results proved that this study will provide valuable insights into WB analysis in ungauged basins by incorporating the fuzzy logic approach into hydrological RS products.

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Section: Q Estimationsmentioning

confidence: 99%

Section: Study Area and Data Setsmentioning

confidence: 99%

“…The Cyprus basin lacks a single

Q

outlet, unlike the Sakarya basin. Therefore, it was divided into 266 sub‐basins (Figure 2) using a digital elevation model (DEM) with a spatial resolution of 12 m (Kayan et al, 2021). The

Q

values of 266 sub‐basins can be calculated by the following equation:

{Q}_{SCS}=\frac{{\left({P}_{sta}-0.2{S}_R\right)}^2}{\left({P}_{sta}+0.8{S}_R\right)}

where

{P}_{sta}

is the mean of the daily

P

from four gauge stations in the Cyprus basin ( Figure b).…”

Section: Study Area and Data Setsmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy logic framework to handle uncertainty in remote sensing‐based hydrological data for water budget improvement across mid‐ and small‐scale basins

Kayan

Türker

Erten

2022

Hydrological Processes

Self Cite

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“…Hence, the analysis of flash floods is crucial to predict and prevent their consequences. Such studies are also necessary for the development of decision support tools for efficient flood mitigation works and for planning infrastructure against flash flood damages, the design of hydraulic structures (Kayan et al, 2021) and watershed management. Flash floods are usually triggered by heavy rainfall events in a short period or/and contribution from sudden snow melt because of high temperature or/and rainfall on the snow (Zhai et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Simulation of flash flood peaks in a small and steep catchment using rain‐on‐grid technique

Godara

Bruland

Alfredsen

2023

J Flood Risk Management

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The frequency of extreme events is increasing as the consequences of climate change. In steep terrains, flash floods with high‐flow velocities induce erosion and sedimentation with potentially disastrous changes of flood path. Hence, the analysis of flash floods in steep terrains in terms of inundation area and flow‐velocity to identify critical points becomes more important. The output of a flood simulation with a traditional hydrologic model provides the flood hydrograph which must be combined with a hydraulic model for downstream consequences. In small and steep catchments, the inflow contribution from every section of the water course can be important to determine where critical conditions may arise. In this study, rain‐on‐grid technique in the hydraulic model Telemac‐2D is used to simulate flash‐flood peaks with spatially distributed precipitation as input in a small and steep catchment in western Norway. Seven events were simulated and sensitivity tests on parameters were conducted. A 200‐year design flood was simulated to show the potential consequences in the catchment. The results show that calibrated models can satisfactorily reproduce peak flows and produce relevant information about water velocities and inundation which decision makers can use for mitigation measures. The paper explores the benefits and limitations through a description of model construction, calibration, and test of sensitivities.

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“…Unfortunately, many rivers do not have hydrometric stations to measure the discharge and water quality. Therefore, determining the discharge of rivers in studies such as determining the riverbed boundary, flood zone, as well as designing aquatic structures require estimating the design discharge based on experimental methods or modelling (Burns et al 2005;Asfaha et al 2015;Waghwala, Agnihotri 2019;Kayan et al 2021). On the other hand, hydrological and hydraulic modelling methods require a set of observational data to calibrate and validate the models (Yazdi, Salehi Neyshabouri 2015).…”

mentioning

confidence: 99%

Prediction of flood discharge and flood flow depth using a hydraulic model and flood marks on the trees in ungauged forested watersheds

Gholami¹

2022

J. For. Sci.

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It is difficult to estimate flood discharges and the flood zones as well as to design hydraulic structures in rivers without using hydrometric stations. Furthermore, using different models to determine the mentioned cases will be accompanied by errors. Therefore, flood marks on the trunks of trees located in the Babolrood riverbed were used to determine the peak discharge, flood flow depth, and flood zone in northern Iran. First, a hydraulic model for the study river was provided using topographic maps with a scale of 1: 1 000, HEC-GeoRAS extension (GIS), and HEC-RAS model. Then, the flood marks of past floods in the form of silt and clay sediments (deposits on the trees in the riverbed) were evaluated and the maximum flood flow depth was determined. Finally, the peak discharge of the past flood was estimated by the trial-and-error method to achieve the flood flow depth in the different river reaches. Then, the hydraulic model using the flow depth data was calibrated in the reaches, and, in the final step, based on the flood marks of other reaches, the model was validated. According to the results, the maximum instantaneous discharge rate of the study flood was 155 m<sup>3</sup>·s<sup>–1</sup> and the maximum flood flow depth was about 2 m. Furthermore, the results showed that the flood mark data in forest lands can be used as a tool for the calibration and validation of hydraulic models. The present methodology is an efficient method for determining the flood peak discharge, spatial variation of the flood depth, and flood zone in forest watersheds without hydrometric stations.

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Peak unit discharge estimation based on ungauged watershed parameters

Cited by 8 publications

References 26 publications

A fuzzy logic framework to handle uncertainty in remote sensing‐based hydrological data for water budget improvement across mid‐ and small‐scale basins

A fuzzy logic framework to handle uncertainty in remote sensing‐based hydrological data for water budget improvement across mid‐ and small‐scale basins

Simulation of flash flood peaks in a small and steep catchment using rain‐on‐grid technique

Prediction of flood discharge and flood flow depth using a hydraulic model and flood marks on the trees in ungauged forested watersheds

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