Penman-Monteith Method Research Articles

Study Evapotranspiration in Iraq Using the Penniman-Monteith Equation for the Period (2008-2020)

Evapotranspiration is considered an essential element for the stability of the ecosystem due to its importance in many aspects, including the water cycle, which is an important source of fresh water for humans, animals and plants. It also influences climate and temperatures and regulates weather. It also enters into the design of crop irrigation networks, knowing their water needs to prevent drought and desertification. The Penman-Monteith method is effective and accurate for calculating daily reference evapotranspiration and has been adopted by the Food and Agriculture Organization (FAO) as a standard method. This study aims to calculate the variation in evapotranspiration rates in Iraq using the Penman-Monteith method over the past thirteen years (2008-2020) in five equal periods (2008, 2011, 2014, 2017, and 2022). This study showed that the total rates of evapotranspiration are decreasing, and these values ​​peaked in 2008 and then decreased significantly in subsequent years. The reason for this decrease is the decrease in wind speed and the increase in relative humidity. It was found that evapotranspiration rates in the central and southern governorates are higher than in the northern governorates. However, there are deviations in the rates for some stations, such as in 2020, which witnessed a general decrease in evapotranspiration rates, except for some stations that recorded an increase. For example, Khanaqin station recorded the highest increase in evapotranspiration rates, which amounted to 23.6%. The reason for this increase is the increase in temperatures by 5.2% and the decrease in relative humidity by -6.5%.

Leveraging transformer models for enhanced temperature forecasting: a comparative analysis in the Beni Mellal region

The remarkable impact of transformers in artificial intelligence, exemplified by applications like GPT-3 in language processing, has sparked interest in their potential for time series analysis. This study aims to explore whether transformers, specifically temporal fusion transformers (TFT), can outperform conventional methods in this domain. The research question is whether TFT exhibits superior performance compared to conventional recurrent neural network (RNN) methods, specifically gated recurrent unit (GRU), and traditional machine learning approaches, notably autoregressive integrated moving average (ARIMA), in the context of time series analysis and temperature prediction. A comparative analysis is conducted among three models: ARIMA, GRU, and TFT. The study utilizes time series data spanning from 1984 to the end of 2022. The models’ performances are evaluated using multiple metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The TFT model achieves the lowest MAE, indicating high accuracy in its predictions. It outperforms both the RNN and traditional machine learning in temperature prediction tasks. Integrating the TFT model with the FAO penman-monteith method could improve irrigation scheduling due to more accurate temperature predictions, potentially enhancing water efficiency and crop yields.

Impact Assessment of Climate Change on Water Requirements for Summer Paddy Cultivation in Raipur District, Chhattisgarh

Water is a crucial and finite resource, primarily utilized for irrigation, necessitating effective distribution for long-term sustainability. This study quantifies the water requirements for summer paddy cultivation in Raipur district, Chhattisgarh, and assesses its implications under changing climatic conditions. The FAO-CROPWAT 8.0 model was applied to assess irrigation parameters using historical meteorological data from 2000 to 2020 and projected meteorological data from 2040 to 2100. Reference evapotranspiration (ET₀) was estimated using Penman-Monteith method, incorporating crop coefficient (Kc) values to determine crop evapotranspiration (ETc). Irrigation water requirements were estimated by accounting for various losses and efficiencies of crop and field. Comprehensive water balance models were developed across the century, focusing on a 20-year average time span, including the historical period as 2000-2020 and the predicted time span from 2040 to 2100 with two socio-economic scenarios: SSP2-4.5 and SSP5-8.5, along with soil, meteorological, and phenological factors. Over the baseline period, the crop water requirement (CWR) for summer paddy was calculated as 751.4 mm, with net irrigation water requirement (NIWR) and gross irrigation water requirement (GIWR) as 1094.7 mm and 1563.8 mm, respectively. Under SSP2-4.5 scenario, the minimum and maximum temperatures are projected to rise by 1.3 °C and 1 °C respectively with rainfall increasing by 378.5 mm. This scenario anticipates a CWR increase of 32.6 mm by century's end, resulting in final values of CWR as 784 mm, NIWR as 1164.7 mm, and GIWR as 1663.9 mm. In the SSP5-8.5 scenario, temperatures are expected to rise by 4.4 °C and 3.1 °C as minimum and maximum temperature, leading to a CWR of 800.4 mm, with NIWR and GIWR as 1176.5 mm and 1680.7 mm, respectively. Overall, most parameters indicate an upward trend throughout the century. This study provides critical insights for efficient irrigation management in summer paddy cultivation and highlights the implications of future climate change on irrigation needs, promoting sustainable agricultural practices.

Regional calibration of the Hargreaves model for estimation of reference evapotranspiration

Abstract Estimation of reference evapotranspiration values is crucial in climatological and hydrological research, agricultural engineering, and irrigation design. The Penman-Monteith method, endorsed by the Food and Agriculture Organization (FAO) of the United Nations and numerous research studies, is widely regarded as the gold standard. However, its extensive data requirements limit its applicability in regions with sparse meteorological networks or limited measurement capabilities. The Hargreaves method, which requires only basic air temperature inputs, offers an alternative solution. The aims of this study were to calibrate the Hargreaves model for Central European climate conditions, considering altitudinal dependence, and to evaluate the temporal stability of the model parameters. In the first part of the research, we regionalized the Hargreaves coefficients using a curve-fitting method to ensure the best accuracy across 60 climatological stations in Slovakia. The regionalization of the Hargreaves coefficient improved accuracy by 10.1%, reducing the weighted absolute percentage error (WAPE) to 17.9%. However, our results showed that the accuracy of the modified Hargreaves model decreased with the increasing altitude of a climatological station. Incorporating altitude into the Hargreaves equation significantly improved model accuracy in stations at higher altitudes, providing a consistent level of accuracy across all climatological stations, regardless of their location and altitude. The results also indicated that the optimal model coefficient values change over time, showing a decreasing trend of –0.5 for the B coefficient and –0.1 for the C coefficient between the periods 1981–2000 and 2001–2020. Although regionalizing the Hargreaves model coefficients for local conditions can achieve good model performance, the model’s accuracy is not stable over time. Thus, periodic validation of the model is necessary for short-term applications.

Sentinel-2A imaging in mapping greenhouse rose production and rainwater harvesting for agricultural irrigation use

Abstract Agricultural production within controlled environments, such as rose cultivation, has traditionally followed the principles of a linear economy focused on maximizing crop yields through increased evapotranspiration. This approach significantly elevates water consumption throughout the year, leading to water scarcity and pollution in various regions. In this context, this study aims to assess the expansion of areas designated for production and the volume of rainwater captured by the roofs of floral greenhouses. The study was conducted in the Pisque River basin in Ecuador from 2016 to 2023. Using remote sensing techniques, the increase in greenhouse areas was quantified, water capture was measured in two types of greenhouses through rainfall simulations, and water requirements were calculated using the Penman-Monteith method. Results indicate a 70% increase in flower production, particularly in the upper and middle basin communities, with a notable rise following the COVID-19 pandemic. A total of 5,473.54 hectares of greenhouses were identified, which can capture rainwater with 93% efficiency, thereby meeting 35% of the annual water needs for crop irrigation. The study concludes that capturing, storing, and utilizing rainwater in rose production is viable and facilitates the transition from a linear to a circular economy.

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.

Urban trees left high and dry – Modelling urban trees water supply and evapotranspiration under drought

Abstract Long-lasting extreme weather conditions are expected to occur more frequently in the future owing to climate change, as demonstrated by the recent heat waves. In particular, the decrease in precipitation during the summer months had a significant impact on urban tree water availability. Therefore, it is imperative to develop methodologies for determining the available water supply and evapotranspiration rates for urban trees. We mapped data from 49 urban small-leaved linden trees with varying characteristics including groundwater levels, shading situations, tree pit sizes, pavement materials, and sealing ratios. By combining these data with an adapted Penman-Monteith method to calculate evapotranspiration, we simulated the soil water storage and evapotranspiration rates of these trees during the very dry year of 2018 as an example. Model validations were performed using lysimeter and sap-flow studies on Tilia cordata trees in 2022. During the growing season, most trees experienced water stress on > 85% of the days because of weak precipitation events that failed to refill soil water storage. In contrast, trees with additional water supply through capillary rise reached water stress approximately 45 days later. The model results suggest that many trees will require additional water supply during predicted droughts in the future, which could have significant implications for urban forestry management. This model approach can be used to test and refine future water supply management strategies, making it a useful planning tool for improving the water efficiency of trees in urban areas and blue-green infrastructure.

The Impact of Climate Change on Cropping Patterns in the Jurang Sate Hulu Irrigation Area

For many years, the active management of the Jurang Sate Hulu Irrigation Area has provided significant benefits to cropping patterns. However, the impacts of climate change on water availability and temperature variations present critical challenges to crop productivity. Therefore, an analysis of the effects of climate change is essential. The methods employed in the analysis of climate change include correlation and regression methods. This study uses nine variables of global climate change. The correlation method is applied to determine the extent of the influence of global climate variables, represented by Global Circulation Model (GCM) data, on rainfall and climate in the Jurang Sate Hulu Irrigation Area. The regression method is used to validate the prediction model for rainfall from 2024 to 2100. In the cropping pattern analysis, the Penman-Monteith method is used to calculate evapotranspiration, while the F.J. Mock method is used to calculate flow discharge. Based on the analysis, the global climate variables that most significantly influence rainfall in the Jurang Sate Hulu Irrigation Area are air temperature, wind speed, eastward wind, and surface temperature, with maximum correlation values of 0.551, 0.540, 0.589, and 0.625, respectively. In contrast, local climate variables have less influence, as they yield correlation values below 0.500. The regression analysis showed an average validation result for the selected model using two goodness-of-fit parameters: Root Mean Square Error (RMSE) of 124.371 and Nash-Sutcliffe Efficiency (NSE) of 0.403. The highest irrigation water requirement occurs during the third planting period, from 2051 to 2075, at 0.465 m³/s. The planned cropping pattern is paddy-paddy-second crops, with planting areas of 3,807 hectares for the period from 2024 to 2050, 3,595 hectares for the period from 2051 to 2075, and 3,383 hectares for the period from 2076 to 2100, with a total planting intensity of 300% during each period.

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.

Developing mathematical models to calculate annual evapotranspiration rates in Iraq using climate data.

Abstract Evapotranspiration is considered the fuel that controls the water cycle in the natural world. It is necessary for the stability of ecosystems and is involved in many important aspects in calculating groundwater quantities, determining optimal locations for building dams and reservoirs and knowing the water needs of crops. The work is divided into two main parts. The first includes the use of The Penman-Monteith method, which is considered the most standard method in the world according to the Food and Agriculture Organization (FAO), for calculating the annual rate of evapotranspiration in Iraq for the past fifteen years (2008-2022). The second part is a modification of a simple method for calculating evapotranspiration, which is the Thornthwaite method, to give results close to the accuracy of the Penman-Monteith method, as the modification was made using different statistical methods. The study showed the possibility of using the modified Thornthwaite method as an alternative to the Penman-Monteith method to calculate evapotranspiration in all regions of Iraq.

Comparative analysis of FAO dual Kc, Priestley‐Taylor and flux variance similarity methods in partitioning evapotranspiration from flood‐irrigated rice fields

AbstractAccurate quantification of evapotranspiration (ET) and its bifurcation into productive transpiration (T) and unproductive evaporation (E) are essential for the successful implementation of sustainable irrigation practices. This study compares the performances of three distinct methods for partitioning ET into E and T fluxes from flood‐irrigated rice fields. These methods include: i) the FAO dual Kc‐ETo model, which utilizes Penman–Monteith (PM) estimates of reference ET and crop‐specific scaling factors; ii) the Priestley‐Taylor (PT) method, which relies on radiation‐driven energy balance at the canopy surface; and iii) the flux variance similarity (FVS) method, which utilizes high‐frequency eddy covariance (EC) measurements of carbon and water vapour fluxes. Meteorological, phenological and hydrological parameters were monitored over two winter seasons in a paddy field, replicating site‐specific management strategies. The cumulative ETs obtained from the PM, PT and FVS methods were 445 mm, 300 mm and 356 mm, respectively, for season 1 and 428 mm, 321 mm and 375 mm, respectively, for season 2. The accuracy of the ET estimation and partition methods was assessed against independent measurements of ET and E using a lysimeter and a microlysimeter. The results demonstrated that the EC‐based FVS method offers superior accuracy in both quantifying (R2 = 0.67, RMSE = 0.70 mm) and partitioning (R2 = 0.51, RMSE = 0.76 mm) ET fluxes, followed by the PM‐based FAO dual Kc‐ETo and PT methods. Evaporation accounted for 29 ± 5% of consumptive water use, highlighting an opportunity for the implementation of water‐saving strategies, particularly during vegetative and transplantation stages. The findings of the path analysis indicate that vegetative and radiation factors influence ET variation, while meteorological and soil factors significantly impact T variation.

A dataset of monthly potential evapotranspiration at ecological stations in arid central Asia from 2005 to 2020

The potential evapotranspiration represents the atmospheric evaporation capacity, which is an important indicator to measure the regional evaporation capacity, and also a key parameter to evaluate drought degree, the balance between supply and demand of water resources, and the water consumption of vegetation. Based on the meteorological data collected by 12 ecological field stations of Central Asia Ecosystem Monitoring Network and 11 ecological field stations of China Ecosystem Research Network in the arid region of Northwest China, and by using Penman-Monteith method recommended by FAO, the potential evapotranspiration was calculated from 2005 to 2020. This dataset has a long time series and covers a variety of ecosystem types. It can be used as basic data, model input data, simulation result verification data for drought research in Central Asia, and also provide data support for the research on the rational development and utilization of water resources, ecological environmental protection and other aspects in this region.

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

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

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.

Evaluation of different methods for reference evapotranspiration assessment: A case study for Ukraine

The paper presents the results of the study devoted to the examination of the accuracy and reliability of the temperature-based approach of the ‘Evapotranspiration Calculator (Ukraine)’ application for the reference evapotranspiration assessment in Ukraine. The objective of the study was to determine the level of reliability and accuracy of modern alternative temperature-based algorithms for the reference evapotranspiration assessment in Ukraine compared to internationally recognized methods. The study was carried out for the territories of Ukraine, based on the meteorological data for the period 2021–2023. The basic methodology was compared to the standard Penman-Monteith method and the method of Hargreaves. The comparison was performed using the values of MAPE, RMSE, and correlation coefficient. Statistical analysis testified that there is a strong correlation and subtle difference between the Hargreaves method and the ‘Evapotranspiration Calculator (Ukraine)’ application, with an average MAPE of 30.3%, the correlation coefficient of 0.92, and RMSE of 1.46 mm. The difference between the Penman-Monteith method and the studied methodology was greater, as the MAPE averaged 41.0%, the correlation coefficient was 0.87, and the RMSE value was 2.05 mm. However, the high variation of the results by the regions of the country and the years of the study did not allow us to draw solid conclusions on whether the methodology embedded in the ‘Evapotranspiration Calculator (Ukraine)’ application is inferior to the method of Penman-Monteith. Further studies are required to clarify this issue through the improvement in the dataset, involvement of in-field reference evapotranspiration measurements, and application of adjustment guidelines for the ‘Evapotranspiration Calculator (Ukraine)’ application.

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.