Estimation and analysis of the Antecedent Temperature Index–Melt Rate (ATIMR) function using observed data from the Kırkgöze-Çipak Basin, Turkey

Şengül, Selim; İspirli, Muhammet Nuri

doi:10.1016/j.jhydrol.2021.126484

Cited by 4 publications

(11 citation statements)

References 25 publications

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“…Sometimes the theoretical ATIMR curve is not modified, but an additional rate is applied to the melting rate obtained from the ATIMR curve in proportion to the varying rate over time [77,78]. However, the physical meaning of widely used ATIMR functions is important in hydrologic modeling studies [25].…”

Section: Snowmelt Modelmentioning

confidence: 99%

“…Therefore, its estimation and application to a mountainous basin with flow sources of complex composition is noteworthy here [23]. The method provided by Fazel et al (2014) for one snowmelt period at distinct station locations was subsequently developed and applied by Şengül and ˙Ispirli (2021) to create ATIMR curves specific to the Kırkgöze-Çipak Basin using hourly temperatures and snow-water equivalent (SWE) data using error analysis methods recommended by Bombardelli and García (2003) obtained from the three meteorology and snow observation stations [24][25][26]. Their results showed that the application of the ATIMR function using the observed data significantly improves the snowpack simulations, and it is quite useful for runoff simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the annual water volumes in the dam reservoirs built in this region come from the precipitation in the winter months, snowmelt, and the rain falling on the snow cover in the spring. For this reason, conducting hydrological model studies based on snowmelt in the Eastern Anatolia Region of Turkey, where the snow potential is quite high, are of great importance both on a regional, national, and international scale in terms of the planning and economic management of water resources [25,27,28].…”

Section: Introductionmentioning

confidence: 99%

“…model studies based on snowmelt in the Eastern Anatolia Region of Turkey, where the snow potential is quite high, are of great importance both on a regional, national, and international scale in terms of the planning and economic management of water resources [25,27,28].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, which uses these advances in the availability of geospatial data, a continuous hydrological modeling approach is discussed by incorporating the soil moisture account (SMA) algorithm [31] with the snow accumulation and melting algorithm. The Hydrologic Engineering Center's Hydrologic Modeling System [18] was applied to the Kırkgöze-Çipak basin (Figure 1), considering the characteristic behaviors of point and area-based snow-water equivalent simulations by using the most sensitive ATIMR functions calculated on a physical basis [25], and the precipitation distribution algorithms embedded in the model were modified for depicting the actual watershed conditions. The development stages of the model, the determination of the parameters, and the calibration process are explained, and the model results are discussed.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Snowmelt Runoff at the Source of the Mountainous Euphrates River Basin in Turkey for Water Supply and Flood Control Issues Using HEC-HMS Modeling

Şengül

İspirli

2022

Water

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Predicting the runoff from snowpack accumulated in mountainous basins during the melting periods is very important in terms of assessing issues such as water supply and flood control. In this study, the Hydrological Engineering Center–Hydrological Modeling System (HEC-HMS) was used to simulate snowmelt runoff in the Kırkgöze–Çipak Basin that has a complex topography where altitude differences range from 1823 m to 3140 m above the sea level. The Kırkgöze–Çipak Basin, located in eastern Turkey, is a basin where snowfall is highly effective during the cold season. There are three automatic meteorology and snow observation stations and three stream gauge stations in the basin, which are operated especially for the calibration and validation of hydrological parameters at different altitudes and exposures. In this study, the parameters affecting snow accumulation–melting and runoff were investigated using the simulations on an hourly basis carried out over a three-year period for temporal and spatial distribution at the basin scale. Different from previous studies focusing on the rate of snowmelt, the temperature index method, which is calculated with physically-based parameters (R2 = 0.77~0.99), was integrated into the runoff simulations (R2 = 0.84) in the basin. The snowmelt-dominated basin is considered to be the source of the headwaters of the Euphrates River.

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Section: Snowmelt Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Snowmelt Runoff at the Source of the Mountainous Euphrates River Basin in Turkey for Water Supply and Flood Control Issues Using HEC-HMS Modeling

Şengül

İspirli

2022

Water

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Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Gezici,

Katipoğlu,

Şengül

2024

Hydrological Processes

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Water scarcity is a pressing issue, intensified by factors such as population growth and industrialization. Hence, it is crucial to monitor, conserve and analyse groundwater resources, which are essential sources of clean and usable water. This study examines changes in groundwater levels (GWLs) in northeastern Turkey's mountainous and snow‐covered area. The primary objective is to assess the effectiveness of integrated machine learning models, specifically, the extreme learning machine (ELM) technique combined with signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and empirical wavelet transform (EWT) for monthly GWL prediction models. Seventy percent of the accessible data is allocated for training, while 30% is designated for testing. A correlation matrix involving precipitation, temperature, relative humidity and GWL parameters is generated with inputs that possess significant correlations being selected, such as GWLt−1, GWLt−2, RHt, RHt−1 and RHt−2. To evaluate model results, various metrics, including mean squared error, mean absolute error, mean absolute percentage error, mean bias error, bias factor, determination coefficient, Nash‐Sutcliffe efficiency, as well as tools such as box plots, Taylor diagrams and radar charts, are utilized to compare outcomes during the interpretation phase. The results of the analyses show that applying data decomposition methods such as EEMD, VMD and EWT significantly improves the performance of the ELM algorithm in predicting GWLs. VMD‐ELM is the most accurate for GWL forecasting among the approaches examined. The R2 values of the most successful models established in the two wells are 0.993 and 0.905. The outcomes of this research hold significance for decision‐makers and policymakers as it offers informative insights into aquifer surveillance, irrigation strategizing and efficient administration of water resources.

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Snow parameters modeling using remote sensing techniques and HEC-HMS hydrological modeling—case study: Kan Basin

Roohi,

Faeli,

Jamshidi

et al. 2023

Environ Monit Assess

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Estimation and analysis of the Antecedent Temperature Index–Melt Rate (ATIMR) function using observed data from the Kırkgöze-Çipak Basin, Turkey

Cited by 4 publications

References 25 publications

Predicting Snowmelt Runoff at the Source of the Mountainous Euphrates River Basin in Turkey for Water Supply and Flood Control Issues Using HEC-HMS Modeling

Predicting Snowmelt Runoff at the Source of the Mountainous Euphrates River Basin in Turkey for Water Supply and Flood Control Issues Using HEC-HMS Modeling

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Snow parameters modeling using remote sensing techniques and HEC-HMS hydrological modeling—case study: Kan Basin

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