A-sınıfı Buharlaşma Kabından Olan Buharlaşma Miktarının Penman ve Priestley-Taylor (PT) Modelleri ile Tahmini

Sezer, Çağlar Özkan; Öztekin, Tekin; Sezer, Esra Kutlu

doi:10.20289/zfdergi.414100

Cited by 5 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The r (NSE) values for the Penman and Priestly-Taylor methods were 1.07 (0.48) and 1.05 (0.42), respectively. These results are consistent with those of [12], who also used these methods on measured data at the Samsun station. They noted that the Penman method tends to overestimate E pan compared to the Priestly-Taylor method.…”

Section: A Evaporation Estimation By Penman and Priestley-taylor Methodssupporting

confidence: 91%

“…Some recent studies on evaporation can be mentioned into three groups. The first group of studies were concerned about suggesting the empirical methods with needing less input variables that makes the best estimation for their study areas [5][6][7][8][9][10][11][12][13][14]. The second group compares the performance of artificial intelligence models, such as artificial neural networks and fuzzy logic with that of empirical equations, highlighting the potential of these models [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Priestly-Taylor Coefficient Evaluation for Konya Closed Basin

Cicibıyık,

Şarlak,

Üstün

2024

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Measurement of evaporation in the field is difficult and expensive; thus, the empirical evaporation estimation methods have been developed. However, these estimation methods have both advantages and disadvantages. The main disadvantage is that their coefficients were determined by the climatic conditions of the study areas. One of these methods is Penman. The Penman method, accepted as a reference, has reached the closest estimations to the measurement of evaporation in the field of the different parts of the world. However, it needs lots of measured climatic data. The Priestley-Taylor method was derived to reduce the measured data needs of the Penman method. Priestly and Taylor represented the variables such as saturated and actual vapor pressures and wind speed with coefficient of 1.26. The researchers have continued to study on the calibration of the  coefficient for their studies’ area since this method has been known to underestimate evaporation value in areas where advection is effective. The present study consists of two stages. First, evaporation was tried to be estimated with these two methods by using the measured climatic data of five meteorological stations in the Konya Closed Basin. Estimated values were evaluated making comparison with the pan measurements. Although slightly higher values were estimated from the pan measurements with each method, the Penman method was found to be relatively more consistent on the basis of statistical indicators. Second,  coefficient was obtained as 1.28 for the study area by using three artificial intelligence-based optimization algorithms. The Penman method was used for comparison in this stage. It was concluded that there was no need for any calibration of the  coefficient and the original one was found to be valid for the study area as well.

show abstract

Section: A Evaporation Estimation By Penman and Priestley-taylor Methodssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Priestly-Taylor Coefficient Evaluation for Konya Closed Basin

Cicibıyık,

Şarlak,

Üstün

2024

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…It was observed that the all-former methods provided successful results, although the performance was better in the GEP method in the simulations. In a similar manner, Sezer et al [21] estimated the daily and monthly evaporation losses for class A pans in Samsun with the Kohler-Nordenson-Fox (KNF) and Christiansen models during the 2012-2013 time period. The results show that the EP values were acquired using the KNF model with a higher performance and the Christiansen model overestimated the evaporation.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Dis

2023

Sustainability

View full text Add to dashboard Cite

The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (PBias = −1.65%, MAE = 1.27 mm d−1, RMSD = 1.6 mm d−1, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, PBias = −6.44%, MAE = 3.58 mm d−1, RMSD = 4.2 mm d−1, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques.

show abstract