Reference Crop Evapotranspiration Research Articles

The Relationship between Reference Crop Evapotranspiration Change Characteristics and Meteorological Factors in Typical Areas of the Middle of the Dry-Hot Valley of Jinsha River

The Relationship between Reference Crop Evapotranspiration Change Characteristics and Meteorological Factors in Typical Areas of the Middle of the Dry-Hot Valley of Jinsha River

Short-Term Forecasting of Daily Reference Crop Evapotranspiration Based on Calibrated Hargreaves–Samani Equation at Regional Scale

Short-Term Forecasting of Daily Reference Crop Evapotranspiration Based on Calibrated Hargreaves–Samani Equation at Regional Scale

A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques

Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.

The U.S. Midwest and High Plains aquifer-fed croplands are previously unrealized hotspots of extreme evaporative demand exposure

Abstract Total evaporative demand or atmospheric thirst is a primary determinant of agroecosystems’ water use and an indispensable input to scientifically based irrigation design and management. However, despite its extensive use to represent agricultural environments, it has not been assessed for its extreme behavior. Prolonged exposure to extreme evaporative demand conditions a.k.a thirstwaves can be especially stressful for agricultural output, water use, and management, but remains uninvestigated owing to lack of meaningful metrics for quantifying and reporting “extreme thirst exposure”. In this letter, I present spatial (county-level) and temporal (1981-2021) changes in exposure to extreme thirst during the agricultural growing season across the conterminous U.S. (CONUS). Using a fully physical metric of evaporative demand, i.e., standardized short crop reference evapotranspiration (ETo), I define two novel measures: cumulative extreme thirst exposure (thirstcum) and average extreme thirst anomaly (thirstanom) to represent the seasonal-level severity of thirstwaves. Both metrics showed significant spatiotemporal variation with long-term averages of 12 mm (thirstcum) and 0.66 mm d-1 (thirstanom) for CONUS. Distinct spatial patterns were revealed for extreme thirst exposure that had little in common with those observed for total ETo. Spatially, hotspots of high extreme thirst exposure were co-located with the Midwest and High Plains aquifer regions, that account for 64% of total acreage and 28% of irrigated acreage nationally, respectively. Critical for food and water security, these regions have experienced the highest extreme thirst exposure nationally, hence necessitating reevaluation of regional disparities in water stress. While thirstcum and thirstanom have increased by 5.6 mm and 0.21 mm d-1 on an average in CONUS, worsening of extreme thirst exposure is especially concerning for High Plains aquifer region (12.6 mm and 0.54 mm d-1, respectively). Emergence of previously unrealized hotspots in regions critical for water security uncover potential pitfalls for planning and adaptation that may result from overlooking extreme measures of evaporative demand.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Green and Blue Water Footprints of Major Crops in the Wami/Ruvu Basin, Tanzania: Implications for Water Scarcity and Sustainability

Accurate estimation of the blue and green water requirements of the various crops cultivated in a water basin is essential for planning specific plant/crop irrigation schedules. In this study, the crop water requirements of the major crops grown in the Wami/Ruvu basin were determined using the FAO-CROPWAT model based on meteorological parameters: monthly maximum and minimum temperature, wind speed, mean relative humidity, sunshine hours, rainfall data, and effective rainfall for 23 hydro-meteorological stations distributed in the study area. The studied crops are rice, maize, beans, tomatoes, and sugarcane. The crop reference evapotranspiration (ETo) and actual evapotranspiration (ETc) for each crop were determined using crop coefficients (Kc) of various growth stages of crops. The results indicated that the total annual reference evapotranspiration (ETo) was 1604 mm, whereas an average ETo per month was 134 mm. The highest total irrigation water requirements were recorded in sugarcane, followed by rice, while the lowest water requirements were observed in beans and tomatoes. The results presented in this study may facilitate plant-specific water irrigation implementation and maximize crop production while conserving the environment.

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration (ET0) is an important factor in determining DI. ET0 forecasting predicts field water consumption and enables proactive irrigation decisions, offering guidance for water resource management. However, implementation of ET0 forecasting faces challenges due to complex calculations and extensive meteorological data requirements. This project aims to develop a machine learning system for ET0 forecasting. The project involves studying ET0 methods and identifying required meteorological parameters. Historical meteorological data and weather forecasts were obtained from meteorological websites and analyzed for accuracy after preprocessing. A machine learning-based model was created to forecast reference crop evapotranspiration. The model’s input parameters were selected through path analysis before it was optimized using Bayesian optimization to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather forecasts, and one that corrects the weather forecasts. All three models achieved good accuracy, with root mean square errors ranging from 0.52 to 0.81 mm/day. Among them, the model based on weather forecast had the highest accuracy; the RMSE six days before the forecast period was between 0.52 and 0.75 mm/day, and the RMSE on the seventh day of the forecast period was 1.12 mm/day. In summary, this project has established a mathematical model of ET0 prediction based on machine learning, which can achieve more accurate predictions for within a few days.

Comparison of potential evapotranspiration methods in Ethiopia

The Food and Agricultural Organization method for estimating reference crop evapotranspiration (ETo), FAO56, is the standard method across the globe. Daily ETo estimated using seventeen potential evapotranspiration (PET) methods that has less data requirments were compared with FAO56 at five sites in Ethiopia (Bahir Dar, Bale Robe, Hawassa, Metehara, and Nazareth). The average rank of five model validation metrics namely standard deviation, RMSE, MAPE, NSE, and correlation were used to compare the methods. By taking the areal average ETo and PET, ten methods, Wen, Ant, Pen, Mak, PT, Ha5, Ha4, Tho, Rad, and Enk, having correlation > 0.57 and MAPE < 28% were ranked 1st to 10th, respectively. Also, at each site, the above ten PET methods were compared. By taking the average ranks of the five sites, Wen, Pen, Ant, PT, and Mak & Ha5 ranked 1st to 5th, respectively. Wen & Ant methods which are applied in Ethiopia for the first time had shown the best performance.

Environmental and biological controls on the interannual variations of evapotranspiration in a natural oak forest

Investigating the interannual variation (IAV) of evapotranspiration (ET) is important for understanding the water cycle in forest ecosystems. However, there has been little research on interannual variability of ET in warm-temperate forests under a changing environment. In this study, 6-year eddy covariance data (2017–2022) in a warm-temperate natural oak forest in central China were used to examine the IAV of ET and its driving factors. The mean and standard deviation of annual ET from 2017 to 2022 were 610 and 122.8 mm (with a coefficient of variation (CV) of 20 %), respectively. The annual ET did not exhibit an obvious trend over the six years with a larger IAV. Environmental factors had a greater impact on the IAV of annual ET compared to the biological factors, with reference crop evapotranspiration (ET0) exerting the most significant influence on ET. The IAV of monthly ET was primarily controlled by precipitation during the early growing season, but it changed to be controlled by radiation and temperature (i.e., atmospheric evaporation demand) during the mid-growing season. We concluded that atmospheric evaporation demand rather than water supply dominated the IAV of annual ET in the warm-temperate natural oak forest in central China, and occasional seasonal drought only affected the ET in a short time-scale (no more than three years). There is a need to measure three components of ET (transpiration, canopy interception evaporation and soil evaporation) in order to better understand the IAV of ET and the underlying biophysical control mechanisms.

SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation

Irrigation is the most significant consumer of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production. The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, its calculation requires measurements from multiple sensors over an extended reference grass surface. The cost of land, sensors, maintenance, and water to keep the grass surface green impedes having a dense network of ETO stations. To solve this challenge, this research aims to develop an input-limited ETO estimation approach based on historical weather data and machine learning (ML) algorithms to relax the need for a reference grass surface. This approach, called "SolarET," takes solar radiation (RS) data as its sole input. RS is the only meteorological driving factor of ETO that does not rely on the measuring surface. To test the generalizability of SolarET, we test its performance over unseen arbitrary locations across California. California is chosen as the case study since it is one of the world's most hydrologically altered and agriculturally productive regions. In total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Our findings point to the superiority of decision tree-based algorithms versus neural networks. SolarET works best in irrigation-oriented regions of California (e.g., Central Valley) and is less accurate in coastal and desert zones. Our results demonstrate the higher accuracy of SolarET using hourly (RMSE = 0.93 mm/day) and daily (RMSE = 0.97 mm/day) RS data in comparison to well-known empirical alternatives like Priestley-Taylor (PT) (RMSE = 1.35 mm/day) and Hargreaves-Samani (HS) (RMSE = 2.69 mm/day).

Cotton yield responses to climate change and adaptability of sowing date simulated by AquaCrop model

The feedback mechanism of cotton yield to climate change is important knowledge for cotton planting planning and stable production. However, the impact of sowing date on cotton yield in China's main cotton producing areas under future climate change is still unclear. To deal with it, this study first evaluated the applicability of the AquaCrop model using actual observed cotton growth and yield data. Then, based on the 27 Global Climate Models (GCMs) multi-model ensemble data in the Sixth Coupled Model Intercomparison Project (CMIP6), the future climate changes in China 's main cotton producing areas from 2021 and 2060 (2040 s) and 2061–2100 (2080 s) under two typical Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5–8.5 were predicted. Finally, the effects of different sowing dates on cotton yield under SSP2–4.5 and SSP5–8.5 scenarios were simulated. The results showed that: (1) AquaCrop model had good applicability and it could accurately simulate cotton growth and yield. (2) The temperature under the SSP2–4.5 and SSP5–8.5 scenarios increased significantly compared with the baseline period (1985–2020). Precipitation during the cotton growth period mainly showed an increasing trend, but the spatial and temporal variability was large. The reference crop evapotranspiration (ETo) during the cotton growth period mainly showed a decreasing trend under the SSP2–4.5 (2040 s) scenario, while it mainly showed an increasing trend in other future periods. (3) The cotton yield under the SSP2–4.5 and SSP5–8.5 scenarios increased by an average of 10.51% and 13.67%, 14.76% and 28.04% in 2040 s and 2080 s compared to the baseline period (1981–2015), respectively. (4) Early sowing was beneficial to increase cotton yield, cotton yield sown on March 31 increased by an average of 5.32% compared to May 10. In conclusion, future climate change will have positive impacts on cotton production in China, and early sowing is an effective adaptation measure to cope with climate change.

Evaluation of daily crop reference evapotranspiration and sensitivity analysis of FAO Penman-Monteith equation using ERA5-Land reanalysis database in Sicily, Italy

Crop evapotranspiration (ET) is one of the most important components in many hydrological processes. The crop reference evapotranspiration (ETo) represents the atmospheric water demand in each crop type, development stage, and management practices. The Penman-Monteith equation in the version suggested by the Food and Agriculture Organization (FAO56-PM), is one of the most used methods to estimate ETo. In several regions of the world, meteorological observations are not always available. The most recent reanalysis database ERA5-Land, released in 2019, can be useful to overcome this limit. The database provides, with a spatial grid of 0.1° latitude and 0.1° longitude, several hourly climate data such as air temperature, dew point temperature, solar radiation, and wind speed components all at 2.0 m above the soil surface, except wind speed components at 10 m, useful to apply the FAO56-PM equation. The objective of this research is to assess the quality of ERA5-Land climate variables data to estimate daily ETo in Sicily, Italy. The effect of the weather station’s elevation associated with the statistical indicators was also evaluated to verify how the morphology affects the measurements. Finally, the sensitivity analysis of the FAO56-PM equation was carried out to identify which climate variables have the most influence on the ETo estimation. For the period 2006–2015, the comparison between air temperature, global solar radiation, wind speed, and relative air humidity, measured from 39 ground weather stations in Sicily, and ERA5-Land was carried out and then, through FAO56-PM equation daily ETo values were estimated using both databases. The statistical indicators Root Mean Square Error (RMSE) and Mean Bias Error (MBE) confirm the possibility of considering the ERA5-Land a suitable solution to estimate ETo. The sensitivity analysis showed that good ETo estimation depends mainly on the accuracy of the relative air humidity and air temperature data.

Determination of Evapotranspiration and Crop Coefficients of Irrigated Legumes on Different Soil Textures Using the FAO56 Approach

Climates and locations specific crop coefficients (Kc) plays pivotal roles in effective water management and crop yield optimization. Research on Kc for many staple crops remains limited in many regions across Africa. This study examined the crop coefficients of two varieties of cowpea (Sampea 14 and Sampea 17) and a variety of soybean (TGX 1465-1D) across different soil textures for optimized water management and crop yield. This study ascertained the Kc of Sampea14 (V1), Sampea17 (V2), and TGX 1465-1D (V3) cultivated on sandy, sandy clay loam, and sandy loam soils, within Port Harcourt, Southern Nigeria. This formed a 3 by 3 factorial experiment in three replicates. Changes in soil water content and crops’ agronomic parameters were monitored regularly. Meteorological data were obtained from the National Aeronautic and Space Administration website and reference evapotranspiration were estimated from the meteorological data using the FAO Penman Monteith’s method. Kc were estimated from the actual crop water use and reference crop evapotranspiration using the FAO56 method. During the initial-growth stage, the average Kc for V1, V2, and V3 were computed as 0.55, 0.72, and 0.81, respectively. At the mid-growth stage, these coefficients increased to 1.48, 1.30, and 1.60 and at the late season, the average Kc were 0.80, 0.69, and 0.86, for the respective varieties. The findings from this study hold the potential to significantly contribute to proper water management practices in the studied area. The understanding of Kc for these specific legume varieties across different soil textures offers valuable insights for optimizing irrigation scheduling and ultimately enhancing crop yield and water productivity.

Increasing Water Use Efficiency of Irrigated Rice through Water Saving Techniques in Yobe Basin, North East, Nigeria

Increasing world population and dwindling water resources is exerting pressure to develop strategies for producing more food using less water.&#x0D; Aims: To investigate the effect of different irrigation schedules on grain yield and water use efficiency (WUE) of FARO 44 rice variety.&#x0D; Study Design: The research was laid out in a split plot arrangement. Twelve treatments replicated three time comprising of 3 irrigation intervals as main plot and 4 irrigation depth as sub plot.&#x0D; Place and Duration of Study: Gashua, Yobe State on the floodplains of river Yobe, Nigeria between March 2023 and July 2023.&#x0D; Methodology: Comprised of 3, 5 and 7 days irrigation intervals as main plot, with irrigation depth (amount) at 100% of crop water requirement (ETc), 85% of ETc, 70% of ETc and the farmers flooding practice as the sub plot treatments. Some growth, yield and components of the rice were determined. Reference evapotranspiration (ETo) and Crop evapotranspiration (ETc) were computed using Cropwat model. Crop and irrigation water use efficiencies were also calculated.&#x0D; Results: The 3 days irrigation interval produced the maximum growth parameters of rice along with both application of 100% of crop water requirement and the traditional flooding type of irrigation. The interaction of 3 days irrigation interval and 100%ETc and 85% of ETc significantly produced the highest grain yield (6484.85 kg ha-1.) 3 and 5 days irrigation interval with 100% and 85% of ETc irrigation depths significantly produced the highest water use efficiencies. A well fitted linear relationship (R2 = 0.834) existed between the irrigation scenarios and the rice grain yield.&#x0D; Conclusion: It can be concluded that irrigation frequency and amount can be reduced to arrive at highly acceptable yield in the study area. High rice yield can be maintained with 3 days irrigation interval and 85% of the crop water requirement and also improve its water use efficiency.

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.

Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

AbstractAccurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements.

Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, crop productivity, and irrigation systems. As the standard ET0 estimation method, the Penman-Monteith equation has been widely recommended worldwide. However, its application is still restricted to comprehensive meteorological data deficiency, making the exploration of alternative simpler models for acceptable ET0 estimation highly meaningful. Concerning the aforementioned requirement, this study developed the novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, and regression component, to estimate ET0 based on radiation-based (Rn-based), humidity-based (RH-based), and temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal and external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), and empirical models, the result indicated that MA-CNN-BiLSTM achieved superior precision, with values of Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 mm d−1 for internal strategy and 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 mm d−1, 0.294–0.699 mm d−1 for external strategy. Specifically, Rn-based MA-CNN-BiLSTM excelled in the temperate continental zone (TCZ) and mountain plateau zone (MPZ), while RH-based MA-CNN-BiLSTM yielded best precision in others. Furthermore, the internal strategy was superior to external strategy by 2.74–106.04% for R2, 1.11–120.49% for NSE, 1.41–40.27% for RRMSE, 1.68–45.53% for RMSE, and 1.21–38.87% for MAE, respectively. In summary, the main contribution of the present study is the proposal of a novel LSTM-type ET0 model (MA-CNN-BiLSTM) to cope with various data-missing scenarios throughout China, which can provide effective support for decision-making in regional agriculture water management.

Productivity of olive oil (Coratina variety) in response to irrigation treatments in sandy soil

AbstractA 2‐year field experiment was conducted on drip‐irrigated olive oil orchards (Coratina var.) on a private farm in the newly reclaimed sandy soil of the West Nile Delta region, Egypt, during the 2020 and 2021 seasons to evaluate the effect of five irrigation treatments (120%, 100%, 80% and 60% reference crop evapotranspiration [ETo] and farmer practice) on applied irrigation water (AIW), water consumptive use, olive fruit and oil yields and some fruit quality parameters, water use efficiency, water productivity, electric energy consumed, farm income and benefit–cost ratio and to develop local crop coefficients (Kc) and yield response factors (Ky). The results indicated that the 2‐year average AIW values were 9953 (17.9 m3/tree/year), 8484 (15.3 m3/tree/year), 6971 (12.5 m3/tree/year), 5480 (9.9 m3/tree/year) and 17,488 m3/ha (31.5 m3/tree/year) for the tested irrigation treatments. Olive fruit and oil yields and quality parameters were significantly affected by the tested treatments. The highest fruit and oil yields were recorded for the 120% ETo treatment. A seasonal Kc of 0.75 and a seasonal Ky of 0.83 were obtained. It can be concluded that producing oil from olive trees (Coratina var.) in sandy soils can be achieved by applying amounts of water equal to 80% or 60% ETo.

Analysis of spatial-temporal trends and causes of vapor pressure deficit in China from 1961 to 2020

Understanding the spatial-temporal trends and variability of vapor pressure deficit (VPD) is important for promoting climate protection and water management. This study was based on long-term series data of maximum/minimum air temperature (Tmax/Tmin) and relative humidity (RH) from 1740 meteorological stations in China from 1961 to 2020 to jointly analyze the spatial-temporal variation characteristics of VPD at different time scales, climate zones and extreme events and their influencing factor. The results showed an overall highly significant increasing trend in VPD, Tmax, and Tmin, with Sen's slope (SS) values of 0.0178 kPa/decade, 0.22 °C/decade, and 0.32 °C/decade, respectively. The opposite was true for RH (SS = −0.41%/decade). Moreover, the trends all changed abruptly around the year 2000. The VPD exhibited a highly significant upward trend in spring as well as at the junction of climatic zones and along the southeastern coast. This was particularly evident in the semi-arid steppe of Inner Mongolia (IM), the humid tropical south China (SC), and the semi-humid warm-temperate north China (NC) regions. For the frequency distribution of VPD extremes in each climate zone, the arid desert of northwest China (NWC)> > IM > NC. The frequency range was 758–910 days/decade, 249–410 days/decade, and 170–300 days/decade, respectively. Moreover, their average frequency of extreme events was 88 times/decade, 55 times/decade, and 38 times/decade respectively. The main meteorological factors affecting VPD taken together were Tmax, RH, Tmin, sunshine duration (N), and wind speed (U2) with mean importance values of 0.612, 0.351, 0.020, 0.016, and 0.001 respectively. Meanwhile, we found that VPD and reference crop evapotranspiration (ET0) were positively correlated and negatively correlated with soil water content and altitude. The maximum growth rate of VPD was observed in Cropland and Impervious environments. This research reveals the spatial-temporal characteristics of meteorological elements in China and provides a reference for scholars to formulate countermeasures against climate change.

Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data

Water resource management and crop growth control require the calculation of reference evapotranspiration (ET0), but meteorological data can often be incomplete, necessitating models with minimal inputs. This study was conducted in Iran’s arid synoptic stations of Sirjan and Kerman, where data scarcity is severe. Penman–Monteith FAO-56 was selected as the target data for modeling artificial neural network (ANN), fuzzy neural adaptive inference system (ANFIS), and ANN-gray wolf optimization (ANN-GWO). The performance of these models was evaluated using an input dataset consisting of the current station’s minimum and maximum temperatures, ET0, and the wind speed of the nearby station (external data) in three different combinations. The models’ accuracy was assessed using two widely used criteria: root mean square error (RMSE) and coefficient of determination (R2), as well as the empirical Hargreaves equation. In the absence of climatic data, the ANFIS, ANN, and ANN-GWO methods using minimum and maximum temperatures, which are relatively easier to estimate, outperformed the empirical Hargreaves equation method in both stations. The results demonstrate that the ANFIS method performed better than ANN and ANN-GWO in all three input combinations. All three methods showed improvement when external data (wind speed and ET0 of the adjacent station) were used. Ultimately, the ANFIS method using minimum and maximum temperatures and the adjacent station’s ET0 in Kerman and Sirjan yielded the best results, with an RMSE of 0.33 and 0.36, respectively.