Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran

Raziei, Tayeb; Parehkar, Arash

doi:10.1007/s00704-021-03578-0

Cited by 16 publications

(6 citation statements)

References 56 publications

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“…Several numerical models such as Hargreaves-Samani, Blaney-Criddle, ornthwaite, and FAO-56 Penman-Monteith (FAO-56 PM) have been introduced for this purpose, among which the World Meteorological Organization (WMO) model FAO-56 PM can be recommended as the most suitable and a reliable alternative to lysimeter data [3]. Outputs of the FAO-56 PM method are now completely acceptable to be considered as observational ET0 rates in ET0 modeling and generally ET0 related studies [4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

2022

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Evapotranspiration represents the water requirement of plants during their growing season, and its accurate measurement at the farm is essential for agricultural water planners and managers. Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration (ETc) measurement by machine learning (ML) techniques and the least available climatic variables. For this purpose, daily meteorological variables were obtained from three ground meteorological stations in rice cultivation regions of northern Iran during 2003–2016. The ETc rates were calculated by seven meteorological variables, the FAO-56 Penman-Monteith equation, and the regional calibrated rice crop coefficient and considered as the reference data. The MLs, including Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Group Method of Data Handling (GMDH), were utilized for ETc modeling. Different input combinations were applied, based on the use of minimum effective variables as input. Results showed that the models showed the most accurate performances in the input combination of four climatic variables: sunshine duration, maximum temperature, relative humidity, and wind speed. Investigating the accuracy of models in rice growth phases showed that the least estimation error belonged to the initial growing stage, which increased during the mid-season and late-season growing stages. A comparison of the models showed that the GMDH model performed better against the other competitors. For this model, both the Nash-Sutcliffe (NS) coefficient and R2 were greater than 0.98, and the Root Mean Square Error (RMSE) ranged between 0.214 and 0.234 mm per day in all stations. The current approach showed promising results in rice evapotranspiration modeling by only four common meteorological variables and can be reliably applied for indirect measurement of this variable over the rice farms. The studied approach will have research value for rice and other crops in similar/different climatic conditions.

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Section: Introductionmentioning

confidence: 99%

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

2022

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“…The latter approach is heavily reliant on the spatial resolution of weather stations and may yield unreliable estimates in complex terrains [ 9 ]. The application of gridded datasets has also been proposed as a robust approach for estimating ET o in regions experiencing data scarcity [ 9 , 12 , 30 ]. Gridded datasets, with their consistent spatial coverage, bridge data gaps and effectively portray variations in land cover.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, reanalysis datasets have been applied to estimate ET o over Iran [ 9 , 30 ]. They have been also employed to estimate ET o in extreme windy conditions [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Reference evapotranspiration estimation using reanalysis and WaPOR products in dryland Croplands

Veysi,

Nouri,

Jabbari

2024

Heliyon

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“…In South America meteorological station networks are poorly distributed along the high diversity of ecosystems and climates, and the complexity of the topography of its territories (Menne et al ., 2012). Several studies have used reanalysis data as a way to study ET o over large areas and in other regions of the world (Ndiaye et al ., 2020; Rocha Júnior et al ., 2020; Raziei and Parehkar, 2021). Although models can generally efficiently measure meteorological variables such as air temperature or solar radiation, there are often differences in ET o values estimated from weather stations data and from models, due to the poor ability of reanalysis data to estimate surface wind speed and relative humidity (Martins et al ., 2017; Raziei and Parehkar, 2021).…”

Section: Introductionmentioning

confidence: 99%

Trends of reference evapotranspiration and its physical drivers in southern South America

Merino

Gassmann

2022

Intl Journal of Climatology

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Reference evapotranspiration (ETo) is a variable used to characterize the evaporative demand of the atmosphere and its impact on the water balance. During the last decades, significant ETo variabilities have been observed, especially at mid‐latitudes. These variabilities respond mainly to local variations in their physical drivers, such as solar radiation, vapour pressure deficit or wind speed. In this study, the annual and seasonal ETo estimates are generated using the Penman–Monteith method (FAO). Surface weather stations for the Argentine territory and reanalysis data for southern South America of the last four decades (1981–2020) are used. Contributions of both aerodynamic (ETaero) and radiative (ETrad) effects are evaluated to analyse their driving role. Significant positive ETo trends are observed from reanalysis data throughout Argentina, especially on the central east side of the Andes Mountain range with values up to 10 mm·year−1. Most of these ETo changes respond to positive trends in air temperature in the study area, while those in the central Andes also respond to negative trends in dew point temperatures. On the other hand, the increase in energy availability through positive trends in net surface radiation produced a slightly higher ETo in the northern regions of the country. Regional ETo values have shown to be more sensitive to variations in air temperature in the northeastern areas, although changes in humidity and solar radiation could also play a role. In a context of climate change, given that temperature and rainfall are expected to increase in the central and northeastern region of the country and decrease along the eastern side of the Andes Mountains in the coming decades, the characteristics observed over the 1981–2020 period are expected to intensify in the near future.

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Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran

Cited by 16 publications

References 56 publications

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

Reference evapotranspiration estimation using reanalysis and WaPOR products in dryland Croplands

Trends of reference evapotranspiration and its physical drivers in southern South America

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