FAO Penman-Monteith Method Research Articles

Seasonal and annual trends in reference evapotranspiration and prediction using machine learning models across seven climatic zones of Bangladesh

ABSTRACT The primary aim of this investigation is to examine the historical (1989–2020) and future trends and magnitude of Reference Evapotranspiration (ET0) in terms of spatiotemporal measures, considering its significance as a hydro-meteorological parameter influenced by changing climate. The FAO-56 Penman-Monteith method is employed to analyze ET0, while the Modified Mann Kendall test is utilized to assess trends and Sen’s Slope Estimator is used for magnitude analysis. The future prediction is conducted using Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models. Results show an increasing ET0 trend annually in the southeastern and northeastern zones, while decreased values are observed in other regions, particularly in the northwest (0.83 mm). Sen’s slope estimator reveals distinct fluctuations in ET0, with notable disparities in the Southeastern, Northeastern, Southwestern, and South-Central zones. Notably, Chattogram and Sitakunda experience an ET0 magnitude of 0.02 mm/Year, while other zones show a magnitude of 0.01 mm/Year. SVM outperformed other models, predicting rising ET0 in various seasons. These findings offer insights for optimizing irrigation systems and sustainable water management under changing climatic conditions.

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Estimation of Actual Evapotranspiration and Water Stress in Typical Irrigation Areas in Xinjiang, Northwest China

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas.

Geo-Spatial Investigation of Climate Change Impacts on Irrigation Demand of Dry Season Boro Rice in Northwest Bangladesh

The present study is an attempt to assess the irrigation demand of the dry season Boro rice in the northwest regions of Bangladesh using climatic, soil and crop data. The FAO-56 Penman–Monteith method was applied to calculate reference evapotranspiration and irrigation demand of Boro rice has been estimated using CROPWAT model. Both the observed and downscaled Representative Concentration Pathway (RCP) climatic data were used in the study. The result reveals that there is no significant change in irrigation demand of Boro rice from 1975 to present. However, the demand will increase significantly in future. The study projects that future irrigation demand of Boro rice in climate change situation for RCP 8.5 will be increased by 180-220 mm in 2075. It is expected that the findings will contribute in devising appropriate agricultural and irrigation plans for the region. The Chittagong Univ. J. B. Sci.,Vol. 10(1 &2):217-235, 2020

Development of temperature and mass transfer-based empirical models for estimating reference evapotranspiration in Nigeria

ABSTRACT The empirical models commonly employed as alternatives for estimating evapotranspiration provide constraints and yield inaccurate results when applied to Nigeria. This study aims to develop novel empirical models to enhance evapotranspiration (ET0) estimation accuracy in Nigeria. The coefficients of non-linear equations were optimised using the particle swarm optimisation (PSO) algorithm for the development of the two new ET0 models for Nigeria, Awhari1 (temperature-based) and Awhari2 (mass transfer-based). ERA5 reanalysis data with a 0.1° × 0.1° resolution was used. The models were rigorously assessed against the FAO-56 Penman–Monteith method, resulting in Kling–Gupta efficiency (KGE) and percentage bias (Pbias) values of 0.75, 6.49, and 0.92, 5.67, respectively. The spatial distribution analysis of performance metrics showed both equations exhibited superior accuracy in estimating ET0 across diverse climatic zones in Nigeria. The incorporation of PSO in model development, coupled with spatial analysis, highlights the study's multidimensional approach. The spatial performance of the models indicates that they can be valuable tools for water resource management, irrigation planning, and sustainable agriculture practices in Nigeria.

Analysis of factors affecting evapotranspiration zoning.

Reference evapotranspiration (ETo) has a significant role in water resource planning and management as well as analysis of crop production and other agricultural tasks. Methods for estimating ETo may require diurnal/monthly assessments to perceive the consequences of climatic changes on local regions. The spatial and temporal patterns of ETo were analyzed in the current work using data from 340 weather stations in Iran. The entropy theory was used to assess the uncertainty of the utilized variables and the modified Kendall test was applied for temporal trend analysis. The interpolation (e.g., kriging) and ordinary least squares (OLS) methods were used for spatio-temporal ETo classification/modeling. The spatial analysis demonstrated that the OLS method with a good fit measure (R2 = 0.985) successfully simulated the spatial relationships of ETo with climatic parameters. After examining error indices, the cokriging method with an exponential variogram was introduced as the best method of seasonal and annual ETo classification in Iran. Spatially and temporally calculated ETo patterns using modified Hargreaves (MHGR) and MODIS methods closely resembled the standard FAO Penman-Monteith (FPM-56) method, all indicating a gradual increase in ETo. MHGR and MODIS methods serve as suitable alternatives for estimating ETo in various climatic regions of Iran, provided data availability.

Vegetation Indices Based Spatial Water Demand of Rabi Onion Crop

There are numerous methods for estimating the water requirements of crops. However, the FAO Penman-Monteith Method has been adopted worldwide but it lacks spatial variability. The accuracy can be improved by adopting VI based method. Therefore, the present study was planned to investigate the use of vegetation indices (VIs) as a surrogate of crop coefficients in place of tabulated crop coefficients and find the best VI in the case of onion crops which require the amount of water. The study was conducted in 3 districts of Maharashtra i.e. Nashik, Dhule, and Jalgaon where onion is a major rabi crop grown on a large scale, for two consecutive rabi seasons of the year 2020-21 and 2021-22. Multitemporal vegetation indices such as NDVI, NDWI, SAVI, MSAVI2, and RVI were estimated using temporal images of Sentinel 2A satellite during the growth period of onion and week-wise values were obtained by using ERDAS Imagine. Linear regression was applied on VI values versus Kc recommended by MPKV, Rahuri which resulted in linear models. NDWI-Kc model showed the strongest relation with high R2 values. The spatial crop evapotranspiration (ETc) was then calculated by applying this model. For the 2020–21 growing season, the total crop evapotranspiration for onions in Dhule, Jalgaon and Nashik was calculated to be 486.5 mm, 482.7 mm, and 488.7 mm, respectively. Whereas during 2021–22, it was estimated to be 493.7 mm, 473.9 mm, and 477.2 mm, respectively in these districts. For getting water demand crop evapotranspiration values were multiplied by the acreage estimated by remote sensing. Total water demand for rabi onion for the districts Dhule, Jalgaon and Nashik was found to be 70.54 Mm3, 43.8 Mm3 and 693.2 Mm3, respectively and 128.1 Mm3, 33.9 Mm3, and 825.7Mm3 respectively for the years 2020-21 and 2021-22, respectively.

The different vegetation types responses to potential evapotranspiration and precipitation in China

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman–Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5–6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them.

Assessment of crop water requirement and irrigation scheduling for selected crops in the Kohima district, Nagaland, India

Abstract This study was done to examine agricultural water and irrigation schedules for key crops grown in Kohima area of Nagaland, India, which has a humid subtropical climate. Climatic data over 16 years (2006–2021) were used to estimate crop water requirements using CROPWAT 8.0 model. Rice, maize, soybean, potato, cabbage, dry bean, ginger, and naga chili are the most common crops. ET0 was calculated using the FAO Penman–Monteith method and ranged from 1.41 to 3.65 mm/day. The USDA SCS approach calculated effective rainfall. The average annual ETC for rice, maize, ginger, soybean, bean (dry), potato, cabbage, and naga chili were 537.1, 305.7, 342.7, 292.2, 288.1, 364.3, 190.6, and 141 mm, respectively. Rice, maize, ginger, soybean, bean (dry), potato, cabbage, and naga chili require an average of 251.7, 54.9, 26, 73.1, 21.3, 21.9, 121.9, and 14.5 mm of irrigation each year, respectively. The crop irrigation schedules were created with 70% efficiency. The study emphasizes that irrigation must be adjusted to each crop's individual needs, whether through strategic scheduling or modifying net and gross irrigation volumes, to enhance water management and maximize crop production in the region.

Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico

Accurate field-scale estimates of the actual evapotranspiration (ETact) based on readily available input data is indispensable to optimize irrigation in (semi-)arid regions. In this study, at an irrigated pecan orchard in Northwest Mexico we explored the potential for three different machine learning algorithms to improve upon the 30-min ETact estimates provided by the FAO Penman-Monteith method (FAO-PM) when trained and tested on multi-year eddy-covariance measurements. We found that all three machine learning algorithms performed well (R2>0.9), with the Random Forest (RF) giving the best overall performance. RF consistently outperformed FAO-PM on sub-daily scales in the growing season, showing its potential to further improve sub-daily ETact estimates. In addition, RF 1) shows a clear improvement (R2 increase up to 0.168) compared to a typical FAO-PM estimate based on the procedures described in FAO report 56, and 2) mostly performed similar as FAO-PM with a highly accurate locally fitted crop factor on the seasonal scale. The latter highlights its ability to infer the correct seasonal trend through input variables that reflect the underlying physical processes, giving potential for application at other similar fields in the region with limited local tuning. To assess whether incorporating existing (semi-)physical parameterizations could improve the performance, we additionally combined our machine learning models with FAO-PM both with and without locally fitted crop factor. Overall, we found this resulted in a modest improvement in performance in most cases (R2 increase up to 0.01). Notably, incorporating the locally calibrated crop factor and FAO-PM each reduced the errors made by the RF in the second half of the growing season. This implies that it is still useful to incorporate FAO-PM and a crop factor in the machine learning model, especially when the crop factor can be determined with high accuracy.

Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan

Abstract Remote sensing (RS) can efficiently support the quantification of crop water requirements and water productivity (WP) for evaluating the performance of agricultural production systems and provides relevant feedback for management. This research aimed to estimate winter wheat water consumption and WP in the central clay plain of Sudan by integrating remotely sensed images, climate data, and biophysical modelling. The wheat crop was cultivated under a centre-pivot irrigation system during the winter season of 2014/2015. The Landsat-8 satellite data were used to retrieve the required spectral data. The Surface Energy Balance Algorithm for Land (SEBAL) was supported with RS and climate data for estimating the Actual Evapotranspiration (ETa) and the WP for the wheat crop. The SEBAL outputs were validated using the FAO Penman–Monteith method coupled with field measurements and observation. The results showed that the seasonal ETa ranged from 400 to 600 mm. However, the WP was between 1.2 and 1.5 kg/m3 during the wheat cycle. The spatial ETa and WP maps produced by the SEBAL model and Landsat-8 images can improve water use efficiency at field scale environment and estimate the water balance over large agricultural areas.

Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state.

Assessment of Maize Crop Evapotranspiration Using Eddy Covariance Flux Tower Weather Data

The study conducted at the Maize Research Centre, Agriculture Research Institute, Professor Jayashankar Telangana State Agricultural University (PJTSAU), Rajendranagar, Hyderabad, from November, 2022 to March, 2023 aimed to compute the crop evapotranspiration (ETc) of maize crop under two different irrigation treatments using the weather data generated from the Eddy Covariance (EC) flux tower. The two treatments comprise of scheduling irrigation at certain Depletion of Available Soil Moisture (DASM) viz., T1: 20% DASM and T2: 40% DASM. The maize crop was sown and cultivated as per the recommended practices. The bio-physical parameters like plant height and LAI were recorded at 15 days interval. These parameters were statistically analysed by two sample T-test with equal variance. The plant height increased from 35 to 198 cm in 20% DASM and 36 to 180 cm in 40% DASM during the crop growth period (30 to 100 DAS). Similarly, LAI increased from 0.72 to 3.9 and 0.77 to 3.2 in 20 and 40 % DASM treatments till 90 DAS, respectively and later on decreased till harvest. The findings emphasize a positive influence of optimum moisture availability in the root zone on plant growth parameters. The amount of irrigation given was measured to compute ETc using Soil Water Balance (SWB) method and the crop parameters like plant height, LAI, stomatal resistance values etc., were used for computing the Penman-Monteith equation using the weather parameters generated from the EC flux tower. Seasonal ETc estimated from the Soil Water Balance (SWB) method (340 & 280 mm) and FAO Penman-Monteith method (350 & 295 mm) in 20 and 40% DASM treatments showed a deviation of +10 and +15 mm, respectively. Furthermore, the study concludes that FAO Penman-Monteith method can accurately estimate ET by using EC flux tower measured weather data, with minor deviations from the SWB method.

Calibration of alternative equations to estimate the reference evapotranspiration in Nova Venécia, Espírito Santo, Brazil

The estimation of the reference evapotranspiration is fundamental in defining irrigation projects. However, an estimation using the standard equation requires climate variables that are difficult to measure and are not very accessible. Thus, the objective of this study was to calibrate and validate alternative methods to estimate evapotranspiration that use simple variables and to compare performance with the standard Penman-Monteith method for the municipality of Nova Venécia, Espírito Santo, Brazil. For this, a 12-year time series (2008-2019) of meteorological data from the Instituto Nacional de Meteorología was used. The standard FAO-56 Penman–Monteith method was used to evaluate alternative methods: Hargreaves and Samani, Benevides and Lopes, Linacre, Hamon and Camargo. Method performance was analyzed by correlation coefficient, Willmott index, root mean square of normalized error, and performance index. Calibration improved the statistical indices, increasing the performance of the Hargreaves and Samani, Benevides and Lopes, and Linacre methods to “very good” in the rainy season and to “intermediate” in the dry season. They were superior to the Hamon and Camargo methods, which continued to show “tolerable” to “very poor” performance in both periods.

Quantification of rainfall, temperature, and reference evapotranspiration trend and their interrelationship in sub-climatic zones of Bangladesh

Rainfall, temperature, and reference evapotranspiration (ET0) have a significant influence on irrigation, aridity, flooding, and crop water requirements. The primary aims of this study were to analyze the trends in rainfall, temperature, and ET0 in seven sub-climatic zones of Bangladesh from 1989 to 2020, as well as examine their interrelationships. The Modified Mann-Kendall method was employed to assess trends, while linear regression was used for trend validation. ET0 was calculated using the FAO-56 Penman-Monteith method, and Sen’s slope was utilized to quantify the magnitude. Spatial analysis was conducted using Inverse Distance Weighting techniques. The findings revealed that annual rainfall increased only in the south-eastern zone, while the other zones experienced a decline. No significant changes were observed in annual maximum temperature, except in the south-eastern, north-eastern, and south-central zones, which showed variations ranging from 0.02 to 0.05 (°C/year). However, the yearly minimum temperature increased in all zones. Additionally, negative changes were observed in the annual magnitude of ET0 for all zones and seasons, except for the south-eastern and north-eastern zones, with a range of 0.01–0.02 mm/year. It was also noted that rainfall and ET0 displayed a strong decreasing relationship, except during the pre-monsoon season. Regarding regional variation, the northern regions exhibited a significant decreasing trend in both rainfall and ET0. The study identified key challenges, including water scarcity and irrigation difficulties due to declining rainfall and evapotranspiration, increased aridity, changing flood patterns, temperature-related impacts on crop growth, regional disparities in climate trends, and the need for effective climate change adaptation measures. Therefore, the study’s findings can contribute to knowledge in areas such as irrigation scheduling, promoting climate-smart agricultural practices, encouraging crop diversification to reduce dependence on water-intensive crops cultivation, and planning resilient water resource management to minimize the effects of environmental shifts, regulate human operations, and implement disaster remedial actions in Bangladesh.

Spatiotemporal patterns of water hyacinth dynamics as a response to seasonal climate variability in Lake Tana, Ethiopia

Lake Tana, which is the largest Lake in Ethiopia, has been invaded by water hyacinths since 2011. Although the government and the community have devoted considerable time and energy over a long period to removing the invasive weed mechanically and manually, the weed has been increasing significantly. Accurate, reliable, and timely information on the spatiotemporal distribution and extent of water hyacinth is crucial to determine its evolution, propagation, and potentially vulnerable areas of the Lake. Therefore, comprehensive information on the spatial distribution of water hyacinths and their annual and seasonal variability is essential for Lake Tana’s water resource planning, development, and management. This study aims to evaluate the spatiotemporal pattern of water hyacinth and its dynamics with seasonal climate variability and impact on evapotranspiration. Landsat 7 ETM+, Landsat 8 OLI, and Sentinel 2 and meteorological datasets were employed. Supervised and manual digitization image classification methods were applied to prepare Land-use/ Land-cover in the Lake. The Mann–Kendall trend test and Pearson correlation coefficient were used to evaluate the trend of water hyacinth and the impact of climate variability on water hyacinth distribution respectively. Besides, the evapotranspiration and water losses were estimated using the FAO-56 Penman–Monteith method. The surface extent of the water hyacinth in Lake Tana has increased by 96% in 2019 from 2011. However, the surface area of the Lake has declined. That means 1603 ha of water surface area has been changed to land surface from 2011 to 2019. The average volume of water loss in Lake Tana was 0.21% of the volume of the Lake from September 2016 to December 2018.

A multi-decadal national scale assessment of reference evapotranspiration methods in continental and temperate climate zones of South Korea

Agricultural water resource management and hydrological models rely heavily on reference evapotranspiration (ET0), which is typically calculated using FAO-56 Penman-Monteith (FPM) method. However, FPM is restricted in its applicability by comprehensive meteorological data requirements, motivating identification of alternative approaches for estimating ET0 that require fewer meteorological inputs. This study compares 30 empirical models for estimating ET0 over 50 years (1972–2021; at daily scale) at 30 stations spanning continental (22 stations) and temperate (8 stations) climate zones across South Korea. Candidate empirical models were categorized based on their composition (temperature-based (n = 5), radiation-based (n = 15), temperature-radiation-based (n = 2), and mass-transfer-based (n = 8)) and complexity with models requiring between one and four meteorological variables. Model performance did not vary significantly across mapped climate zones (i.e., continental vs. temperate), and more complex models (i.e., those requiring 4 meteorological inputs) did not necessarily yield better prediction. Temperature-radiation-based models were consistently effective, whereas the models based on mass-transfer were always poor. Given the lack of long-term spatially extensive meteorological data in most developing countries in continental and temperate climate zones, this study establishes best practices for estimating ET0 using empirical models, which will aid water resources management decision-making in regions with limited meteorological data.

Investigation of Valiantzas’ Simplified forms of FAO56 Penman-Monteith Reference Evapotranspiration Models in a semi-arid region

The performance of sixteen Valiantzas’ reference evapotranspiration models was investigated using the meteorological data obtained from Agricultural Engineering College and Research Institute, Kumulur, Lalgudi Taluk of Tiruchirapalli district, which is a semi-arid region located in Tamil Nadu, India. The Valiantzas’ reference evapotranspiration was compared with the globally used FAO56 Penman-Monteith method. The indexes used for comparison are coefficient of determination (R2), Standard Error Estimate (SEE) and long-term average ratio (RT). The Valiantzas’ models requiring complete dataset performed excellently in this station. The models not requiring wind speed data also performed equally well in this station and exhibited a fairly good correlation with FAO56-PM method. The other formulae accounting for local average wind conditions, reduced set formulae with temperature and relative humidity data alone and reduced set formulae with temperature and radiation data alone also performed well in this station. The investigation showed fair accuracy of Valiantzas’ Models and hence researchers can use these models in the absence of availability of the complete dataset.

Determination of Crop water Requirement and Irrigation Scheduling for Major Crops of Surendranagar, Gujarat Using CROPWAT 8.0

Abstract: Due to the growing need for water for uses such as hydropower, farming, and drinking purpose among others, water is becoming an endangered asset. Water is increasingly needed for a variety of reasons as the population grows. On the contrary, the accessibility of water resources is limited. To determine crop water requirement for any crop and evaluate irrigation scheduling for effective water management, crop coefficients and reference crop evapotranspiration must be calculated. The purpose of this research is to determine crop water requirement of Cotton, Wheat and Sesame for Surendranagar Region and Prepare Irrigation Scheduling using the FAO-56 Penman-Monteith Method using FAO CROPWAT 8.0 Software. Meteorological data for Surendranagar region such as maximum and minimum temperatures, mean relative humidity, sunshine hours, and wind speed, are obtained from Wadhwan taluka of Surendranagar used to calculate reference crop evapotranspiration. Crop water requirement for Cotton using CROPWAT 8.0 model is obtained 876.3 mm/dec Crop water Requirement for Wheat is obtained 543.9 mm/dec and Crop water Requirement for Sesame is obtained 529.6 mm/dec This finding may be helpful for agricultural planning and effective irrigation control in the growing of cotton, Wheat and Sesame.

Evaluation of SEBS and SEBAL algorithms for estimating wheat evapotranspiration (case study: central areas of Khuzestan province)

This study aimed to accurately estimate daily wheat evapotranspiration using two remote sensing algorithms, Surface Energy Balance System (SEBS) and Surface Energy Balance Algorithm for Land (SEBAL), in central Khuzestan province during 2019–2020. The results of two algorithms were compared with lysimeter (as a direct method), FAO-Penman–Monteith (FAO-PM), two temperature-based methods (Hargreaves-Samani and Blaney-Criddle), two radiation-based methods (Priestley–Taylor and Doorenbos–Pruitt), and two mass transfer-based methods (Mahringer and World Meteorology Organization) (as indirect methods). Coefficient of Determination (R2), Root-Mean-Square Error (RMSE), Percentage of Bias (PBIAS), Mean Bias Error, Mean Absolute Percentage Error, and Nash–Sutcliffe indicators used for comparing the results. According to the results, both SEBAL and SEBS algorithms showed the highest compatibility with lysimeter data (R2 = 0.92 and 0.96, RMSE = 2.15 and 1.53 mm/day, respectively). Comparing both algorithms with the FAO-PM method, resulted in RMSE and R2 of 2.42 mm/day and 0.87 for SEBS and 3.14 mm/day and 0.79 for SEBAL. The Hargreaves-Samani method (R2 = 0.72, RMSE = 16.4 mm/day) and (R2 = 0.8, RMSE = 10.4 mm/day) among temperature-based methods, Doorenbos–Pruitt (R2 = 0.71, RMSE = 3.33 mm/day) and (R2 = 0.79, RMSE = 2.63 mm/day) among radiation-based methods, and the Mahringer method (R2 = 0.6, RMSE = 6.8 mm/day mm/day) and (R2 = 0.68, RMSE = 5.51 mm/day) among mass transfer-based methods yielded better estimations than SEBAL and SEBS algorithms, respectively. Owing to the high accuracy of SEBAL and SEBS algorithms, in estimating the amount of evapotranspiration in the study area and close to the actual values in the field, using energy balance algorithms is recommended in Khuzestan province.