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Reference crop evapotranspiration (ETo) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall‐runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g., the Penman‐Monteith benchmark model, are time‐consuming and require weather input data that are not routinely monitored. This paper has used the Kohonen self‐organizing map (KSOM), unsupervised artificial neural networks, to predict the ETo. based on observed daily weather data at two climatically diverse basins: a small experimental catchment in temperate Edinburgh, UK and a semiarid lake basin in Udaipur, India. This was achieved by using the powerful clustering capability of the KSOM to analyze the multidimensional data array comprising the estimated ETo (based on the Food and Agricultural Organization (FAO) Penman‐Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the KSOM‐based ETo estimates even with fewer input variables were in good agreement with those obtained using the conventional FAO Penman‐Monteith formulation employing the full complement of weather data at the two locations. More crucially, the KSOM‐based estimates were also found to be significantly superior to those estimated using currently recommended empirical ETo methods for data scarce situations such as those in developing countries. Key Points KSOM is feasible for ETo KSOM much better than existing incomplete ETo methods KSOM ETo models work in incomplete input‐data situations

This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman - Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R 2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R 2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture).

This study focuses on enhancing real-time irrigation decisions and stream flow forecasts using short-term daily forecasts of reference evapotranspiration (ETo). While conventional approaches rely on historical observations for daily forecasting, developed countries have transitioned to issuing ETo forecasts derived from Numerical Weather Prediction or General Circulation Models (GCM) outputs. In this study, a similar approach was applied to predict short-term ETo forecasts for Pakistan using GCM output, combining data from the Pakistan Meteorological Department in-situ observation data and GCM forecast data from the Copernicus data source. ETo is calculated using the Hargreaves Samani (HS) equation, calibrated, and parameterized for the newly defined agro-climatic region by K -means clustering of ETo and soil moisture. Results indicate that the modified HS performs well in all climate regions except arid and humid regions, where errors are attributed to temperature forecast issues at high altitudes and the HS model neglecting wind speed and relative humidity effects. The integration of temperature data in the modified HS generates temperature-ETo correlation coefficients exceeding 0.92 in all agro-climatic regions. The method demonstrated accurate daily ETo forecasts for real-time irrigation predictions, particularly valuable in regions with sparse meteorological networks. The HS equation was calibrated for different agro-climatic regions using methods like fuzzy logic, pressure ratio, and curve fitting. The modified HS equation, integrated with numerical weather forecasts and a geographic information system, provides weekly forecasts and 10-day lead time ETo forecasts for district level and defined agro-climatic regions in Pakistan, with acceptable error ranges. Furthermore, the study determines a robust Pearson correlation (0.79) and a root-mean-square error of 0.54 with a 95% significant level, contributing significantly to predictive capabilities in the field of evapotranspiration forecasting.

Reference evapotranspiration is a significant agrometeorological parameter used for estimation of crop water requirement and irrigation scheduling. The present study was undertaken to determine the reference evapotranspiration and crop water requirement for apple cultivation in the Kashmir valley. Reference evapotranspiration (ET0) was determined for seven major apple producing districts of Kashmir valley, viz. Srinagar, Budgam, Kupwara, Pulwama, Baramulla, Anantnag and Shopian. The average ET0 for apples cultivation in Kashmir Valley was 912 mm. The mean water requirement (ETc) was minimum during the initial stage being 69 mm and maximum during the mid-season stage being 668 mm. The mean water requirement during the late-season stage was 175 mm. The minimum annual ETc was observed at Baramulla (846 mm) and the maximum annual ETc at Srinagar (953 mm). Different stations showed variations in water requirement due to differences in altitude and local weather conditions.

Agricultural expansion, population growth, rapid urbanization, and climate change have all significantly impacted global water supply and demand and have led to a number of negative consequences including ecological degradation and decreases in biodiversity, especially in arid and semi-arid areas. The agricultural sector consumes the most water globally; crop irrigation alone uses up more than 80% of available agricultural water. Thus, to maintain sustainable development of the global economy and ecosystems, it is crucial to effectively manage crop irrigation water. We focus on the arid and semi-arid Heihe River Basin (HRB), China, as a case study in this paper, extracting spatiotemporal information on the distribution of crop planting using multi-temporal Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) remote sensing (RS) images. We estimate the spatiotemporal crop irrigation water requirements (IWRc) using the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method and reveal variations in IWRc. We also analyze the impact of changes in crop planting structure on IWRc and discuss strategies for the rational allocation of irrigation water as well as policies to alleviate imbalance between water supply and demand. The results of this study show that effective rainfall (ER) decreases upstream-to-downstream within the HRB, while crop evapotranspiration under standard conditions (ETc) increases, leading to increasing spatial variation in IWRc from zero up to 150 mm and between 300 and 450 mm. Data show that between 2007 and 2012, annual mean ER decreased from 139.49 to 106.29 mm, while annual mean ETc increased from 483.87 to 500.38 mm, and annual mean IWRc increased from 339.95 to 370.11 mm. Data show that monthly mean IWRc initially increased before decreasing in concert with crop growth. The largest values for this index were recorded during the month of June; results show that IWRc for May and June decreased by 8.14 and 11.67 mm, respectively, while values for July increased by 5.75 mm between 2007 and 2012. These variations have helped to ease the temporal imbalance between water supply and demand. Mean IWRc values for oilseed rape, corn, barley, and other crops all increased over the study period, from 208.43, 349.35, 229.26, and 352.85 mm, respectively, in 2007, to 241.81, 393.10, 251.17, and 378.86 mm, respectively, in 2012. At the same time, the mean IWRc of wheat decreased from 281.53 mm in 2007 to 266.69 mm in 2012. Mainly because of changes in planting structure, the total IWRc for the HRB in 2012 reached 2692.58 × 106 m3, an increase of 332.16 × 106 m3 (14.07%) compared to 2007. Data show that 23.11% (76.77 × 106 m3) of this increase is due to crop transfers, while the remaining 76.89% (255.39 × 106 m3) is the result of the rapid expansion of cultivated land. Thus, to maintain both the sustainable development and ecological security of the HRB, it is crucial to efficiently manage and utilize agricultural water in light of spatiotemporal patterns in IWRc changes as well as IWRc variations between different crops. The cultivation of water-demanding crops and the further expansion of agricultural land should also be avoided.

FAO Penman-Monteith (FAO-PM) is deemed as a sole standard method for estimating reference evapotranspiration (ET ). However, limited availability of meteorological data at spatial and temporal o scales restricts the application of this method. To address this issue, the FAO 56 experts suggested three methods when only maximum and minimum temperature data are available: (i) Temperature-based Penman-Monteith (PMT-1) method wherein T ≈ T (ii) PMT-2 wherein T ≈ T -2.5, and (iii) dew min dew min Hargreaves method. These ET methods were assessed for a semi-arid basin of Western India which lacks adequate climatic data. The performances of the ET methods were evaluated against the standard FAO-PM method using salient statistical and graphical indicators, together with the sensitivity analysis. The results of the three temperature-based methods had a tendency of over-predication of ET in the study area. The PMT-1 method, however, provided superior ET estimates compared to PMT-2 and Hargreaves methods. For estimating monthly ET , the FAO-PM method was most sensitive to temperature. Further, ET of the monsoon season over the study area increased from 5 to 12% during 'drought' years compared to 'normal' years. It was concluded that PMT-1 method is the most suitable temperature-based method for estimating ET in semi-arid regions under limited climatic condition.

Evapotranspiration (ET) is an important component of the surface energy balance and water cycle, especially in arid and semiarid regions. The characteristics of the actual evapotranspiration (ETa), which was calculated using the eddy covariance method, and the reference evapotranspiration (ET0), which was estimated using the Food and Agriculture Organisation (FAO) Penman–Monteith method, were analysed. This work focussed on the seasonal variations in evapotranspiration and crop coefficient (Kc) above the heterogeneous canopy of an arid oasis ecosystem in a cornfield of the Zhangye oasis in northwestern China. The results showed that in 2008, the total net radiation (Rn) was 2457.73 MJ∙m−2 and that the rainfall was 117 mm. The average wind velocity, air temperature, and specific humidity, which were observed 2 m above the ground surface, were 1.23 m∙s−1, 7.07 °C, and 3.66 g∙kg−1, respectively. The total ETa and ET0 were 654.69 mm and 1039.92 mm, respectively; thus, the ET0 was higher than the ETa. The difference between the ET0 and ETa was high in summer and autumn, and low in winter and spring. The ETa was greatly influenced by irrigation events, whereas the ET0 was not influenced by irrigation. The ETa and ET0 were both greatly influenced by meteorological elements. The Kc values were less than 0.5 outside of the maize-growing stage and greater than 0.5 during the entire maize-growing stage (from 20 April to 22 September 2008). The Kc values were 0.63, 0.75, 0.78, 0.76, 0.61 and 0.71 at the seedling, shooting, heading, filling, and maturity stages and the entire growth stage, respectively.

The reference evapotranspiration (ETo) is an important agrometeorological parameter which has been used in a number of applications. In present study daily ETo was determined for 16 stations of Gujarat having long period (10-20 years) weather data following P-M approach. The Kc values for maize and wheat as given in FAO-56 was used in which Kcmid and Kcend were corrected for climatic conditions of stations. The corrected Kc values were used to calculate the daily crop water requirement (ETc) for wheat and rabi maize crops grown at different locations of Gujarat. The results revealed that during winter season (Nov. 15 to March 13) the mean daily (ETo) varies from 4.2 to 7.6 mm day-1. However the large variation in ETo across the locations (2.9 to 9.8 mm day-1) was observed, the lowest being at Khedbrahma and highest being at Targadia. The correction applied in Kcmid and Kcend suggested that at most of the stations Kcmid and Kcend for wheat crop were higher than that of FAO values while the corrected Kc values for maize were found to be less than that given by FAO. The mean water requirement (ETc) of wheat during its initial stage was found to be lower and almost constant and it increased continuously during developmental stage (from 1.9 to 5.2 mm day-1) and during the mid season stage (from 5.6 to 7.5 mm day-1) and decreased during the late-season stage (from 7.3 to 3.6 mm day-1). The seasonal water requirement across the locations varies between 400.5 mm (Khedbrahma) to 684.0 mm (Arnej). The mean water requirement of maize during initial stage is 1.3 mm day-1, during developmental stage 1.4 to 5.0 mm day-1, during the mid season stage ETc varies between 5.0 to 6.6 mm day-1 and during lateseason stage it decreases from 6.4 to 2.5 mm day-1 . The seasonal water requirement of rabi maize varies between 330.7 mm (Khedbrahma) to 520.5 mm (Bharuch). 

Reference evapotranspiration is a significant agrometeorological parameter used for estimation of crop water requirement and irrigation scheduling. The present study was undertaken to determine the reference evapotranspiration and crop water requirement for apple cultivation in the Kashmir valley. Reference evapotranspiration (ET0) was determined for seven major apple producing districts of Kashmir valley, viz. Srinagar, Budgam, Kupwara, Pulwama, Baramulla, Anantnag and Shopian. The average ET0 for apples cultivation in Kashmir Valley was 912 mm. The mean water requirement (ETc) was minimum during the initial stage being 69 mm and maximum during the mid-season stage being 668 mm. The mean water requirement during the late-season stage was 175 mm. The minimum annual ETc was observed at Baramulla (846 mm) and the maximum annual ETc at Srinagar (953 mm). Different stations showed variations in water requirement due to differences in altitude and local weather conditions.

Reference evapotranspiration (ET 0 ) is required to determine crop water requirements and irrigation scheduling. Many equations have been presented for determining ET 0 using meteorological data, but in most of these equations weather stations are located in arid lands far away from agricultural areas, and therefore, the data are not valid for estimating ET 0 . Satellite images obtain data from vast agricultural areas. In this study, the FAO-56 Penman–Monteith equation was changed to a simple linear equation with three components, and for each component, a linear regression equation was fitted to NOAA satellite data. To establish regression models and their validity, 297 NOAA satellite images over 10 years (1999 to 2008) were used. The study area was Amir Kabir Agro-Industry Irrigation Network in Khuzestan province, Iran. Results showed that the simplified model proposed in this study, estimates ET 0 with a determination coefficient of 0.92 and relative root mean square error of 8 %.