Calibration of empirical equations for estimating reference evapotranspiration in different climates of Iran

Sharafi, Saeed; Ghaleni, Mehdi Mohammadi

doi:10.1007/s00704-021-03654-5

Cited by 15 publications

(17 citation statements)

References 46 publications

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“…According to Sharafi and Ghaleni's (2021a) results, the ET ref were estimated by empirical equations of the PM-FAO 56 for very dry climate, the Hargreaves equation for the semidry climate, and the Penman 1 and 2 equations for the dry and humid climates, respectively (Table 1). At the same time, the Penman-Monteith method is a suitable method in most parts of the country due to its comprehensiveness (Sharafi and Ghalenee, 2021b).…”

Section: Et Ref Evaluationmentioning

confidence: 99%

Predicting Iran’s future agro-climate variability and coherence using zonation‑based PCA

Sharafi

2023

IJAm

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The effects of climate changes on agroecosystems can cause relevant issues. Using principal component analysis (PCA) we determined the 67 agricultural climate indicators (ACI) at 44 of Iran’s synoptic stations under current (1990-2019) and future (2025, 2050, 2075, and 2100) conditions. Based on UNESCO aridity index, the agroecological zonation (AEZ) was used to classify Iran’s regions (very dry, dry, semidry and humid climates). Using the PCA method, the first 5 principal components were determined by including data sets for temperature (winter, spring, summer and autumn maximum and winter minimum temperature), precipitation (winter and summer precipitation), reference evapotranspiration (ETref), and the degree of growth days in spring and winter, which explained about 96 percent of the total variance. For each climate empirical equation for ETref was selected. In order to accurate evaluation of ETref were used The Penman-Monteith based on FAO56 (PM-FAO56) for the very dry climate, the Hargreaves equation for the semidry climate, and the Penman 1 and 2 equations for the dry and humid climates, respectively. According to the results, the first component alone, with an eigenvalue of 41.15, explained more than 74 percent of the total variance. Based on the results of zoning by the PCA outcomes, the stations for 1990-2019 were classified into 7 zones. While 2025, 2050, 2075, and 2100 were classified in 6, 7, 6, and 5 zones, respectively. Under the future climatic conditions of the country, in terms of climatic indicators, the similarity between the stations will increase and the climatic diversity of the country will decline compared to current conditions. The results demonstrated that the PCA method would be valuable for monitoring AEZ in semidry climates at reasonably long periods.

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Section: Et Ref Evaluationmentioning

confidence: 99%

Predicting Iran’s future agro-climate variability and coherence using zonation‑based PCA

Sharafi

2023

IJAm

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“…Furthermore, they concluded that radiation-based equations are best suited to estimate ET 0 in the study region. Also, Tabari et al (2013) and Sharafi and Ghaleni (2021) concluded that the radiation-based models were the best-suited equations, while mass transfer-based equations had the worst performances in Iran. Tabari et al (2012) found the Ritchie equation gives an acceptable performance in modelling crop ET Tabriz, Iran.…”

Section: Discussionmentioning

confidence: 99%

“…They are listed in Table 2 where Equations (4)–(14), (15)–(24), and (25)–(34) are temperature‐, radiation‐, and mass‐transfer‐based equations, respectively. This bundle of equations is widely and commonly used to estimate ET 0 empirically for different climates and regions (Sharafi and Ghaleni, 2021).…”

Section: Methodsmentioning

confidence: 99%

High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

Sobh

Nashwan

Amer

2022

Intl Journal of Climatology

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Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ETo) to generate gridded high-resolution daily ETo estimates over Egypt. The AI algorithms include support vector machine-radial basis function (SVM-RBF), random forest (RF), group method of data handling neural network (GMDH-NN), multivariate adaptive regression splines (MARS), as well as Dynamic Evolving Neural Fuzzy Interference System (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ETo using FAO56 Penman-Monteith equation as a reference estimate. The multi-parameter Kling-Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replication FAO56-PM followed by temperature-and mass-transfer-based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.75) followed by Caprio (median KGE 0.65), and Penman (median KGE 0.52) equations based on station-wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. The generated 0.10°×0.10° daily estimates of ETo enabled the detection of a significant increase of 0.12-0.16 mm/decade in the agricultural-dependent Nile Delta using the modified Mann Kendall test and Sen's slope estimator.

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“…However, establishing these stations can be expensive, particularly in developing countries. The Penman-Monteith equation based on FAO 56 (PM-FAO 56 ) has typically been used as a semi-empirical equation for prediction of ET ref [ [12] , [13] , [14] , [15] ]. In spite of the acceptance of the PM-FAO 56 equation, it requires full datasets of climatic variables.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, researchers studied and evaluated so far ET ref models in various climates [ 15 , [17] , [18] , [19] , [20] ]. The empirical models are usually divided into four categories: models based on temperature, radiation and mass transfer as well as a combination of various models [ 6 , 14 , [21] , [22] , [23] ].…”

Section: Introductionmentioning

confidence: 99%

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

Sharafi¹,

Ghaleni²,

Scholz³

2023

Heliyon

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Calibration of empirical equations for estimating reference evapotranspiration in different climates of Iran

Cited by 15 publications

References 46 publications

Predicting Iran’s future agro-climate variability and coherence using zonation‑based PCA

Predicting Iran’s future agro-climate variability and coherence using zonation‑based PCA

High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

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