Comparison of hourly and daily Penman-Monteith grass- and alfalfa-reference evapotranspiration equations and crop coefficients for maize under arid climatic conditions

Ji, Xinglong; Chen, J.M.; Zhao, Wenzhi; Ersi, Kang; Jin, Bowen; Xu, Shiyi

doi:10.1016/j.agwat.2017.06.019

Cited by 21 publications

(9 citation statements)

References 42 publications

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“…Thus, models with less data demand were developed, such as models from categories 2 and 3. These simple models only need temperature data or solar radiation data [31][32][33]. However, they were proposed under specific climate conditions, which may have impact on their applicability for various conditions.…”

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confidence: 99%

“…However, they were proposed under specific climate conditions, which may have impact on their applicability for various conditions. Therefore, understanding the behavior of these models has been a major concern.Many studies have compared the performance of different PET models under various regions and under different climate conditions for historical periods [19,[31][32][33][34][35]. Xu and Singh (2001) [34] compared the accuracy of seven temperature-based equations in the north-western of Canada, and found out that the Blaney-Criddle, Hargreaves, and Thornthwaite models show better performance than others.…”

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confidence: 99%

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Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Jian

Xuan

et al. 2019

Atmosphere

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Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.Atmosphere 2019, 10, 453 2 of 24 management and irrigation planning [21][22][23]. A number of models have been proposed to estimate PET and these models can be divided into four categories: (1) Temperature-based models, such as the Blaney-Criddle model [24]; (2) radiation-based models, including the Makkink model [25]; (3) mass transfer-based models, such as the Rohwer model [26]; (4) combined energy-mass balanced models, such as the Penman-Monteith (PM) model [27]. The PM model is used widely and has been commonly adopted as a reference, owing to the fact that it is a physical-based model and combines both energy and mass balances [28][29][30]. Correspondingly, its requirement of climate variables data is much stricter than other models, including relative humidity and wind speed [27]. These climate data may not be available in many regions or even may not be measured at all. Thus, models with less data demand were developed, such as models from categories 2 and 3. These simple models only need temperature data or solar radiation data [31][32][33]. However, they were proposed under specific climate conditions, which may have impact on their applicability for various conditions. Therefore, understanding the behavior of these models has been a major concern.Many studies have compared the performance of different PET models under various regions and under different climate conditions for historical periods [19,[31][32][33][34][35]. Xu and...

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confidence: 99%

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confidence: 99%

Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Jian

Xuan

et al. 2019

Atmosphere

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“…The ESI [29,30] represents standardized anomalies in the ratio of actual-to-potential ET, (f RET = ET/RET), where ET is actual ET retrieved using the ALEXI two-source energy-balance algorithm [38] and RET is a reference ET computed using a Penman-Monteith formulation for grass [39]. Normalization by RET serves to minimize variability in ET due to seasonal variations in available energy and atmospheric demand, further refining focus on the soil-moisture signal.…”

Section: Esimentioning

confidence: 99%

What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

Enenkel

Farah²,

Hain

et al. 2018

Remote Sensing

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Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers.

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“…The mean values used for ET o,d estimation may misrepresent the evaporative power of the environment during parts of the day and may introduce errors in the calculations. These may worsen under conditions where there are significant changes in solar radiation, wind speed, or vapor pressure deficit during the day (Allen et al, 1994;Allen et al, 1998;Bakhtiari et al, 2017;Ji et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have looked at daily and hourly variation of ET o using the Penman-Monteith method under different climatic conditions (e.g abrupt diurnal changes, wet and hot in monsoon and cold in winter, semiarid dry and arid conditions, Sahelian climate, etc.) (Itenfisu et al, 2003;Irmak et al, 2005;Ji et al, 2017;Djaman et al, 2018a and b), land use/landcover (e.g. agricultural areas, grass or natural vegetation) (Gavian et al, 2008;Perera et al, 2015;dos Santos et al, 2021), and small and large study areas ranging from point scale to continental scale (Bakhtiari et al, 2017;Perera et al, 2015;Djaman et al, 2018a and b;Althoff et al, 2019) in different parts of the world including Australia, Brazil, Iran, Spain, Turkey, USA, and Western Africa.…”

Section: Introductionmentioning

confidence: 99%

Differences in Sum-of-Hourly and Daily Reference Evapotranspiration for a Rice-Wheat Cropping System in Ganga Basin, India

Yetbarek

Ojha

2022

Preprint

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Estimating reference evapotranspiration (ET ) at 24 h timesteps has been considered sufficiently accurate for a long time. However, recent advances in weather data acquisition have made it feasible to apply hourly procedures in ET computation. Hourly timesteps can improve the accuracy of ET estimates, as data averaged daily may misrepresent evaporative power during parts of the day. The objective of the present study is to assess the differences between daily ET computations performed on 24 h (ET ) and hourly (ET ) timesteps for rice-wheat cropping systems in the Ganga Basin, India. The meteorological data for computing reference evapotranspiration were collected from an automatic weather station located in an experimental plot at IIT Kanpur, India. Daily and hourly ET computations were performed according to the FAO-PM (Allen et al, 1998) equation for rice and wheat cropping seasons. Diurnal variations of meteorological parameters resulted in underestimation of ET when the daily time step is considered. No significant difference was observed during wet periods. It is also observed that the hourly estimates of ET were able to capture the abrupt changes in climate variables, while the daily ET fails to get it as it considers the average values only. As a result, the sums of hourly values are more reliable for ET estimates in the Ganga Plains.

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Comparison of hourly and daily Penman-Monteith grass- and alfalfa-reference evapotranspiration equations and crop coefficients for maize under arid climatic conditions

Cited by 21 publications

References 42 publications

Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

Differences in Sum-of-Hourly and Daily Reference Evapotranspiration for a Rice-Wheat Cropping System in Ganga Basin, India

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