Evaluation of the Penman–Monteith reference evapotranspiration under limited data and its sensitivity to key climatic variables under humid and semiarid conditions

Koudahe, Komlan; Djaman, Koffi; Adewumi, J. K.

doi:10.1007/s40808-018-0497-y

Cited by 24 publications

(9 citation statements)

References 38 publications

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“…Additionally, for the temperature index, the elasticity of E at to T maxt is significantly greater than that to T mint in the NSPB, mainly because T max was used to calculate several important terms in the E o and E a estimation process, such as net radiation at the crop surface (R n ), the saturation vapor pressure (e s ), and the slops of the saturation vapor pressure versus air temperature curve (∆). Additionally, the values of T max are always greater than T mint at any time of the day and in any season [60]. Furthermore, the variation in the NDVI, which was selected as the indicator of vegetation conditions, has a large influence on the change in E a .…”

Section: Time Windowmentioning

confidence: 99%

Quantifying the Impacts of Climate Change and Vegetation Variation on Actual Evapotranspiration Based on the Budyko Hypothesis in North and South Panjiang Basin, China

Xia

She

et al. 2020

Water

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Actual evapotranspiration (Ea) plays a key role in the global water and energy cycles. The accurate quantification of the impacts of different factors on Ea change can help us better understand the driving mechanisms of Ea in a changing environment. Climate change and vegetation variations are well known as two main factors that have significant impacts on Ea change. Our study used three differential Budyko-type equations to quantify the contributions of climate change and vegetation variations to Ea change. First, in order to establish the relationship between the parameter n, which usually presents the land surface characteristics in the Budyko-type equations, with basic climatic variables and the Normalized Difference Vegetation Index (NDVI), the stepwise linear regression method has been used. Then, elasticity and contribution analyses were performed to quantify the contributions of different numbers of climatic factors and NDVI to Ea change. The North and South Panjiang basin in China was selected to investigate the efficiency of the modified Budyko-type equations and quantify the impacts of climate change and vegetation variations on Ea change. The empirical formal of the parameter n established in this study can be used to simulate the parameter n and Ea for the study area. The results of the elasticity and contribution analyses suggest that climate change contributed (whose average contribution is 149.6%) more to Ea change than vegetation variation (whose average contribution is −49.4%). Precipitation, NDVI and the maximum temperature are the major drivers of Ea change, while the minimum temperature and wind speed contribute the least to Ea change.

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Section: Time Windowmentioning

confidence: 99%

Quantifying the Impacts of Climate Change and Vegetation Variation on Actual Evapotranspiration Based on the Budyko Hypothesis in North and South Panjiang Basin, China

Xia

She

et al. 2020

Water

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“…Solar radiation (Rs) is an important parameter in computing ETo together with other meteorological parameters like wind speed, temperature and relative humidity. Studies have shown strong correlation between solar radiation and ETo [DJAMAN et al 2018;KOSA 2011;KOUDAHE et al 2018;MARTEL et al 2018]. This shows that solar radiation is a dominant factor controlling the evapotranspiration process in these regions.…”

mentioning

confidence: 93%

Performance evaluation of solar radiation equations for estimating reference evapotranspiration (ETo) in a humid tropical environment

Ndulue¹,

Onyekwelu²,

Ogbu³

et al. 2019

Journal of Water and Land Development

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Solar radiation (Rs) is an essential input for estimating reference crop evapotranspiration, ETo. An accurate estimate of ETo is the first step involved in determining water demand of field crops. The objective of this study was to assess the accuracy of fifteen empirical solar radiations (Rs) models and determine its effects on ETo estimates for three sites in humid tropical environment (Abakaliki, Nsukka, and Awka). Meteorological data from the archives of NASA (from 1983 to 2005) was used to derive empirical constants (calibration) for the different models at each location while data from 2006 to 2015 was used for validation. The results showed an overall improvement when comparing measured Rs with Rs determined using original constants and Rs using the new constants. After calibration, the Swartman–Ogunlade (R2 = 0.97) and Chen 2 models (RMSE = 0.665 MJ∙m−2∙day−1) performed best while Chen 1 (R2 = 0.66) and Bristow–Campbell models (RMSE = 1.58 MJ∙m−2∙day−1) performed least in estimating Rs in Abakaliki. At the Nsukka station, Swartman–Ogunlade (R2 = 0.96) and Adeala models (RMSE = 0.785 MJ∙m−2∙day−1) performed best while Hargreaves–Samani (R2 = 0.64) and Chen 1 models (RMSE = 1.96 MJ∙m−2∙day−1) performed least in estimating Rs. Chen 2 (R2 = 0.98) and Swartman–Ogunlade models (RMSE = 0.43 MJ∙m−2∙day−1) performed best while Hargreaves–Samani (R2 = 0.68) and Chen 1 models (RMSE = 1.64 MJ∙m−2∙day−1) performed least in estimating Rs in Awka. For estimating ETo, Adeala (R2 =0.98) and Swartman–Ogunlade models (RMSE = 0.064 MJ∙m−2∙day−1) performed best at the Awka station and Swartman–Ogunlade (R2 = 0.98) and Chen 2 models (RMSE = 0.43 MJ∙m−2∙day−1) performed best at Abakaliki while Angstrom–Prescott–Page (R2 = 0.96) and El-Sebaii models (RMSE = 0.0908 mm∙day−1) performed best at the Nsukka station.

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“…Similarly, [28] adopted weather data from eight meteorological stations to predict reference ETo. Consequently, a study used weather records from eight climatological stations to assess the accuracy of Penman-Monteith equation [29]. In the same approach, [5] referred to use Penman-Monteith equation to calculate the reserved ET and compare with a climate model, this study concluded that forecasting daily ET can be achieved by Penman-Monteith model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Evapotranspiration (ETo) in a Medium Urban Park within a Megacity by Using Artificial Neural Network (ANN) Model

Algretawee

Alshama

2021

Period. Polytech. Civil Eng.

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Evapotranspiration (ETo) is considered a main component of the hydrological cycle. This study was carried out on a medium-size park within a highly urbanized area, close to the center of Melbourne city. The purpose of the study is to calculate the reference evapotranspiration (ETo), particularly at a specified spot in a corner of the park. The hand-held device used to collect data gave consistent results and reduced the need for assumptions. The Penman-Montieth equation was used to calculate the reserved ETo. To build an ETo model, Artificial Neural Network (ANN) was adopted to predict ETo. Three models were built to select the best model, based on the least Root Mean Square Error (RMSE) and the highest coefficient of determination (R2). Results showed a contrast between the observed and predicted magnitudes of ETo. Both of the observed and predicted magnitudes for ETo are higher than most recent studies. Data from the specified location shows a difference in ETo magnitudes relative to the fixed meteorological stations. This study supports that climate change causes increasing magnitudes of reference evapotranspiration ETo.

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Evaluation of the Penman–Monteith reference evapotranspiration under limited data and its sensitivity to key climatic variables under humid and semiarid conditions

Cited by 24 publications

References 38 publications

Quantifying the Impacts of Climate Change and Vegetation Variation on Actual Evapotranspiration Based on the Budyko Hypothesis in North and South Panjiang Basin, China

Quantifying the Impacts of Climate Change and Vegetation Variation on Actual Evapotranspiration Based on the Budyko Hypothesis in North and South Panjiang Basin, China

Performance evaluation of solar radiation equations for estimating reference evapotranspiration (ETo) in a humid tropical environment

Modeling of Evapotranspiration (ETo) in a Medium Urban Park within a Megacity by Using Artificial Neural Network (ANN) Model

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