Assessment of Climate Change Impacts on Reference Evapotranspiration and Simulation of Daily Weather Data Using
            <i>SIMETAW</i>

Ebrahimpour, Meisam; Ghahreman, Nozar; Orang, M.

doi:10.1061/(asce)ir.1943-4774.0000669

Cited by 11 publications

(5 citation statements)

References 34 publications

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“…Climate data are also relevant for agricultural and ecological research [2,3], becoming critical inputs to forecast crop growth and development rates, as well as to model population dynamics of pest and diseases [4]. In addition, the data represent a baseline to assess global and local climate change effects [5].…”

Section: Introductionmentioning

confidence: 99%

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Urdiales

Meza

Gironás

et al. 2018

Water

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Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values.

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Section: Introductionmentioning

confidence: 99%

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Urdiales

Meza

Gironás

et al. 2018

Water

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“…Nowadays, estimation of DPT is of particular interest to researchers working in the area of meteorology and climate. This is because it is one among weather parameters to be considered as an input for climate change impact assessments [8]. In addition, it controls hygroscopic growth of aerosols, aids in estimating the height of cumulus or stratocumulus cloud bases for aviation weather forecasts, and helps in developing systems to enhance predictions of hydro-meteorological variables at basin scale [9,10].…”

Section: Introductionmentioning

confidence: 99%

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Naganna

Deka

Ghorbani

et al. 2019

Water

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Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro–climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP–FFA and MLP–GSA) were authenticated against standard MLP tuned by a Levenberg–Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.

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“…It is also important to identify the spatiotemporal trends of evaporation and evapotranspiration under the changing climate, for use in regional water resources planning (Wang et al, 2011(Wang et al, , 2013Terink et al, 2013;Ebrahimpour et al, 2014;Hosseinzadeh-Talaee et al, 2014;Xing et al, 2014). Therefore, many studies have been done for estimating Eto in South Korea, to contribute to water resources planning, irrigation schedule, and environmental management (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Reference evapotranspiration (Eto) is used to represent the evaporative demand of the atmosphere for a grass reference evapotranspiring surface with abundant water supply, and it is usually estimated using different methods (Allen et al, 1998;Gao et al, 2012;Ngongondo et al, 2013). It is also important to identify the spatiotemporal trends of evaporation and evapotranspiration under the changing climate, for use in regional water resources planning (Wang et al, 2011(Wang et al, , 2013Terink et al, 2013;Ebrahimpour et al, 2014;Hosseinzadeh-Talaee et al, 2014;Xing et al, 2014). Therefore, many studies have been done for estimating Eto in South Korea, to contribute to water resources planning, irrigation schedule, and environmental management (i.e.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of soil evaporation and reference evapotranspirationto climatic variables in South Korea

Aydin¹,

Jung²,

Yang³

et al. 2015

Turk J Agric For

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The quantification of evapotranspiration and soil evaporation is crucial for agricultural water management. The FAO-56 Penman-Monteith and E-DiGOR models were used to compute reference evapotranspiration (Eto) and bare soil evaporation, respectively, at 17 meteorological stations of South Korea, from 1980 to 2009. The same soil parameters were assumed for all stations in order to compare actual soil evaporation (Ea) rates jointly dominated by atmospheric evaporative demand and soil water availability, as well as the size of rainfall events. The sensitivity of Penman-Monteith type equations to the major climatic variables was determined based on 1-year dataset. The long-term mean annual precipitation and Eto calculated at selected stations over the country were 1339.7 mm and 1087.1 mm, respectively. Precipitation showed noticeable interyear fluctuations, and the annual Eto increased gradually during the study period. A strong correlation between pan evaporation (Epan) and Eto was observed (R = 0.808, P < 0.001), based on daily data of 30 years. Similarly, a significant correlation between Epan and potential soil evaporation (Ep) was existent (R = 0.622, P < 0.01). The Ep rates were lower than the Eto rates (Ep = 0.8 × Eto). The magnitude of Ea, as calculated with the model, reached a level of 63% of Ep. On the other hand, Ea accounted for 29.4% to 50.3% of the total precipitation over South Korea. Potential soil evaporation was more sensitive to net radiation, while reference evapotranspiration was mostly affected by the relative humidity. Wind speed was the less effective variable. The contribution of soil heat flux was negligible. The sensitivity of both Ep and Eto to the same climatic variables showed significant differences among seasons and locations. The aridity index ranged from 0.85 to 2.13, and all the study sites could be classified as humid areas. An aridity index of less than 1 appeared about once every 6 to 7 years, based on the station averages.

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Assessment of Climate Change Impacts on Reference Evapotranspiration and Simulation of Daily Weather Data Using SIMETAW

Cited by 11 publications

References 34 publications

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Sensitivity of soil evaporation and reference evapotranspirationto climatic variables in South Korea

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