Water storage change assessment in the Seyhan Reservoir (Turkey) using HEC-ResSim model

Ozkaya, Arzu; Zerberg, Yeliz

doi:10.1007/s12517-021-06882-1

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…In the threshold areas of the Çukurova and the Taurus Mountains, summers are hot and dry, and winters are warm and rainy. This region penetrates into the arid-less fertile, excess water climate type [44][45][46].…”

Section: Study Regionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

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Section: Study Regionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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“…As a result, the amount of large particles in turbid sediments increases, mainly particles with a size of 0.25-0.05 mm. The main mass of microaggregates consists of 0.1-0.05 and 0.05-0.01 mm particles, which reduce the density of muddy sediments and increase sediment porosity [6,7].…”

Section: Fig 3 Location Of Selected Reservoirs In Bowl Of South Surkh...mentioning

confidence: 99%

“…Adjusting the flow of rivers with the help of reservoirs is regarded as one of the most critical concerns of integrated water resource utilization worldwide. Special attention is paid to the development of improved methods of reliable and efficient use of existing water reservoirs, scientific justification of the most optimal operating modes, providing consumers with water at the same time during the growing season and increasing the useful volume lost during the operation period [1,5,7].…”

Section: Introductionmentioning

confidence: 99%

Investigations on determination of changes in inactive volume of water reservoirs (case study of South Surkhan water reservoir)

Gapparov,

Mansurov

2023

E3S Web of Conf.

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The article develops the change of the reservoir’s useable volume under the effect of turbidity-sediments, considering the turbid discharges' hydrological regime into the reservoir, and proposes a computation technique. Field research was conducted in the South Surkhan reservoir. Based on the data collected from the field research, an electronic map of the reservoir bowl was made, and morphometric indicators were evaluated, considering the formation of muddy sediments in the reservoir. When the standard water level of the water reservoir is 415 meters, the designed water surface area is 65 km2, and the full volume is 800 mln. m3, and according to the results of field research, the full volume of the water reservoir at the level of the normal moisture level is 491.2 mln. m3, the water surface was 72.45 km2. It showed that the full volume of the reservoir was reduced by 38.6 percent compared to the design volume during the operation period, and the water surface area increased by 7.45 km2. The research results allow to determine the change in the useful volume of the reservoir under the influence of turbidity and sediments, to determine the morphometric indicators, and to use the water reserve effectively.

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“…Finally, the reservoir simulation module is the interface for defining the simulation time and computation interval for simulating the produced alternatives and scenarios. As denoted in the literature, HEC-ResSim has successfully been applied in various time scales and case studies, both for current and climate change conditions [44][45][46][47][48][49][50].…”

Section: Simulation Of Hydropower Generationmentioning

confidence: 99%

Run-Of-River Small Hydropower Plants as Hydro-Resilience Assets against Climate Change

Skoulikaris

2021

Sustainability

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Renewable energy sources, due to their direct (e.g., wind turbines) or indirect (e.g., hydropower, with precipitation being the generator of runoff) dependence on climatic variables, are foreseen to be affected by climate change. In this research, two run-of-river small hydropower plants (SHPPs) located at different water districts in Greece are being calibrated and validated, in order to be simulated in terms of future power production under climate change conditions. In doing so, future river discharges derived by the forcing of a hydrology model, by three Regional Climate Models under two Representative Concentration Pathways, are used as inputs for the simulation of the SHPPs. The research concludes, by comparing the outputs of short-term (2031–2060) and long-term (2071–2100) future periods to a reference period (1971–2000), that in the case of a significant projected decrease in river discharges (~25–30%), a relevant important decrease in the simulated future power generation is foreseen (~20–25%). On the other hand, in the decline projections of smaller discharges (up to ~15%) the generated energy depends on the intermonthly variations of the river runoff, establishing that runoff decreases in the wet months of the year have much lower impact on the produced energy than those occurring in the dry months. The latter is attributed to the non-existence of reservoirs that control the operation of run-of-river SHPPs; nevertheless, these types of hydropower plants can partially remediate the energy losses, since they are taking advantage of low flows for hydropower production. Hence, run-of-river SHPPs are designated as important hydro-resilience assets against the projected surface water availability decrease due to climate change.

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Water storage change assessment in the Seyhan Reservoir (Turkey) using HEC-ResSim model

Cited by 8 publications

References 24 publications

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Investigations on determination of changes in inactive volume of water reservoirs (case study of South Surkhan water reservoir)

Run-Of-River Small Hydropower Plants as Hydro-Resilience Assets against Climate Change

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