Evaluation and calibration of Blaney–Criddle equation for estimating reference evapotranspiration in semiarid and arid regions

Heydari, Mohammad Mehdi; Tajamoli, Ali; Ghoreishi, Seyyed Hojjat; Darbe-Esfahani, Masoud Khodabakhshi; Gilasi, Hadi

doi:10.1007/s12665-014-3809-1

Cited by 17 publications

(11 citation statements)

References 31 publications

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“…The Hargreaves-Samani equation was used to calculate the modern AI used in this study 57 and therefore was also used for the future projections. It has been shown to perform nearly as well as the common FAO Penman method but requires substantially less parametrisation with more readily available data 62 . Future projected precipitation was then divided by modelled future potential evapotranspiration to provide future AI values across Australia.…”

Section: Methodsmentioning

confidence: 99%

Identifying threshold responses of Australian dryland rivers to future hydroclimatic change

Larkin

Ralph

Tooth

et al. 2020

Sci Rep

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Rivers provide crucial ecosystem services in water-stressed drylands. Australian dryland rivers are geomorphologically diverse, ranging from through-going, single channels to discontinuous, multichannelled systems, yet we have limited understanding of their sensitivity to future hydroclimatic changes. Here, we characterise for the first time the geomorphology of 29 dryland rivers with catchments across a humid to arid gradient covering >1,800,000 km 2 of continental eastern and central Australia. Statistical separation of five specific dominantly alluvial river types and quantification of their present-day catchment hydroclimates enables identification of potential thresholds of change. Projected aridity increases across eastern Australia by 2070 (RCP4.5) will result in ~80% of the dryland rivers crossing a threshold from one type to another, manifesting in major geomorphological changes. Dramatic cases will see currently through-going rivers (e.g. Murrumbidgee, Macintyre) experience step changes towards greater discontinuity, characterised by pronounced downstream declines in channel size and local termination. Expanding our approach to include other river styles (e.g. mixed bedrockalluvial) would allow similar analyses of dryland rivers globally where hydroclimate is an important driver of change. Early identification of dryland river responses to future hydroclimatic change has far-reaching implications for the ~2 billion people that live in drylands and rely on riverine ecosystem services. Rivers are lifelines in climatically variable and water-stressed drylands, the dry subhumid through hyperarid environments that cover 40-50% of the Earth's land surface and host ~28% of the world's population 1,2. Dryland rivers are fundamentally important for human populations, providing a plethora of provisioning, regulating, supporting and cultural ecosystem services 1,3. Yet dryland rivers exist in marginal environments and are threatened by declines in water availability due to the impacts of climate change (e.g. decreased rainfall, increased temperature and evapotranspiration, and greater climatic variability) and other human activities (e.g. river regulation, flow diversion and abstraction, and land use change) 4-6. Rivers are not static conduits of water, sediment and nutrients, but adjust dynamically to a suite of internal and external drivers. Among various external drivers (e.g. tectonic activity, sea level fluctuations, climate), research has shown that late Quaternary hydroclimatic changes have driven substantial geomorphological changes to many dryland rivers globally, including during the mid to late Holocene [e.g. 7-10 ]. Indeed, in tectonically stable settings such as continental Australia, and in reaches where rivers are free from significant bedrock influence, hydroclimatic changes are the principal driver of river response and resulting channel-floodplain geomorphology. To date, however, assessment of the potential likelihood and pathways of hydrological and geomorphological changes in dryland rivers due...

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Section: Methodsmentioning

confidence: 99%

Identifying threshold responses of Australian dryland rivers to future hydroclimatic change

Larkin

Ralph

Tooth

et al. 2020

Sci Rep

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“…The model provides a standard with which to compare the evapotranspiration capability under various climatic conditions, and it has been successfully applied at scales from a single basin to all of China Gao et al 2006;Heydari et al 2014;Meng and Mo 2012;Zhang et al 2007b;Zheng et al 2009). The Penman-Monteith ET 0 (Allen et al 1998) is calculated at daily scale according the following equation:…”

Section: Calculation Of Etmentioning

confidence: 99%

The spatiotemporal variation of reference evapotranspiration and the contribution of its climatic factors in the Loess Plateau, China

Liang

Bai

et al. 2016

Environ Earth Sci

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Reference evapotranspiration (ET 0 ) is an important parameter of agricultural activity and hydrometeorological studies. In this study, we used the Food and Agriculture Organization Penman-Monteith equation to evaluate ET 0 , and to investigate the change point of ET 0 in the Loess Plateau region from 1960 to 2013. The results showed that a change point was detected at approximately the year 1990 for annual ET 0 series from 108 meteorological stations using Cramer's statistical test. The annual ET 0 decreased significantly (p \ 0.05) by -1.22 mm year -1 from 1960 to 1990, especially during the summer months, which contributed the most to the total annual reduction, while it increased significantly (p \ 0.001) by 1.15 mm year -1 from 1991 to 2013, with the spring months contributing the most. The ET 0 and its trend in the five integrated management divisions of the Loess Plateau have significant spatial heterogeneity. The highest and lowest ET 0 were found in the third and fifth divisions from 1960 to 2013. The ET 0 decreased significantly in the first division and increased in the fourth divisions (1960-1990 and 1991-2013, respectively). Using differential equations to quantitatively evaluate the contribution of various factors, the wind speed was mostly responsible for the variability in the ET 0 trend from 1960 to 1990, followed by solar radiation and vapor pressure. The positive effect of air temperature on ET 0 trend was offset by the other three factors, and the combined effects of the four climatic variables led to the decrease in the ET 0 trend. However, the rapidly increasing air temperature became the predominant factor in the change in the ET 0 trend after 1990. A spatiotemporal variation of predominant contribution to the ET 0 trend was identified. The temperature dominant region changed from the third to the first division for 1960-1990 and 1991-2013. Radiation did not change. Vapor pressure changed from the first to the fourth and the wind speed changed from the forth to the third. This study could contribute to a better understanding of the response of the spatial and temporal variation of ET 0 to increased climate change. Additionally, this research also provides scientific support to regional planning and management.

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“…Among the numerous empirical methods to estimate ETo, nine models were selected that had the best performance under different climates on the basis of previous investigations [1,[46][47][48][49][50], and the results were compared with the FPM. ETo is the reference crop evapotranspiration (mm/day), R n is the net radiation (MJ/m 2 /day), G is the soil heat flux (MJ/m 2 /day), γ is the psychrometric constant (kPa/ • C), e s is the saturation vapor pressure (kPa), e a is the actual vapor pressure (kPa), ∆ is the slope of the saturation vapor pressure-temperature curve (kPa/ • C), T is the average daily air temperature ( • C), u is the mean daily wind speed at 2 m (m/s), H is the elevation (m), ϕ is the latitude (rad), T min is the minimum air temperature ( • C), T max is the maximum air temperature ( • C), RH is the average relative humidity (%), n is the actual duration of sunshine (hr), R s is the solar radiation (MJ/m 2 /day), R a is the extraterrestrial radiation (MJ/m 2 /day), λ is the latent heat of vaporization (MJ/kg), and C T , I, K T , a, b, P, a t , and T x are empirical coefficients Genetic algorithm (GA), genetic programming (GP), and gene expression programming (GEP) are different kinds of data-driven machine learning techniques to find an optimum solution for complex problems by artificial intelligence (AI).…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

et al. 2019

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In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day −1 ) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day −1 , and Kerman (KE), RMSE = 0.4177 mm day −1 , respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R 2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R 2 < 0.50) due to its poor performance under such extreme conditions. Atmosphere 2019, 10, 311 2 of 15 sediment [9], predicting velocity in compound channels [10], characterizing risks in water supply systems [11], and modeling evaporation [12].Although the GEP has been developed in water resources studies, the application of this technique for ETo modeling is limited. Some of the successful applications of the GEP to estimate ETo can be listed as follows.Parasuraman et al. [3] modeled ETo using only ground temperature and net radiation. Irmak and Kamble [13] investigated evapotranspiration data assimilation with the GA and soil, water, atmosphere, and plant (SWAP) model for on-demand irrigation. The data assimilation methodology obtained from the present research can be considered a practical tool at the field scale for scheduling the irrigation estimated by remote sensing-based evapotranspiration. The results showed that the GA was effectively able to determine the terms included in the fitness function, and parameters were predicted reasonably, especially if only four variables were included. Traore and Guven [14] developed regional-specific numerical models of ETo using the GEP in Sahel. Statistically, the GEP was an effectual modeling tool for the successful computation of ETo under the study area. The results indicated that, using the GEP model, it would be possible to formulate an accurate and applicable numerical equation for each region by irrigation systems for the manual computation of ETo under the study area, where sufficient meteorological variable is often missing. Shiri [15] claimed that GEP outperforms empirical model to calculate ETo in hyper arid regions over Iran. Traore et al. [16] found that GEP is a robust tool to model ETo in in Jiangsu province, China. Mattar [17] demonstrated that GEP is a more accurate method than empirical equations to ...

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Evaluation and calibration of Blaney–Criddle equation for estimating reference evapotranspiration in semiarid and arid regions

Cited by 17 publications

References 31 publications

Identifying threshold responses of Australian dryland rivers to future hydroclimatic change

Identifying threshold responses of Australian dryland rivers to future hydroclimatic change

The spatiotemporal variation of reference evapotranspiration and the contribution of its climatic factors in the Loess Plateau, China

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

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