FAO-56 PM Research Articles

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.

Daily reference evapotranspiration estimation utilizing deep learning models with varied combinations of weather data

Effective irrigation planning pivots on the meticulous monitoring of ETo (the reference evapotranspiration), a fundamental variable in diverse studies. The go-to method for approximate ETo, the FAO-56 Penman-Monteith (FAO-56 PM) equation, demands an array of weather data, encompassing relative humidity, temperature, solar radiation, and wind speed. However, this data-intensive requirement presents challenges in situations where such information is limited, and artificial intelligence is being used to address this challenge, come into play to estimate ET0 with a streamlined set of parameters. The study begins with a comprehensive analysis, comparing the performance of Penman-Monteith (FAO-56 PM) and (ASCE_PM) with deep learning models such as artificial neural networks (ANN) and one-dimensional convolutional neural networks (CNN 1d).The principal aim is to estimate daily reference evapotranspiration (ETo) in the region of Morocco, specifically Meknes, employing a minimal set of meteorological variables across various combinations of measured data on the fundamental variables that constitute ETo. These combinations encompass scenarios involving all four variables, different combinations of three, two, and each variable in isolation. Two implementation scenarios are considered: (i) cross-validation across all datasets and (ii) training with one station and validating with another. Across these varied techniques, commendable results emerge, portraying a favourable comparison against empirical models reliant on minimal meteorological data.

Improvement the estimation of reference evapotranspiration by combining different types of meteorological data Using machine learning models

Irrigation and the strategic planning thereof play a pivotal role in diverse hydrological inquiries, with reference evapotranspiration (ETo) standing as a paramount variable within this domain. While the equation (FAO-56 PM)is extensively employed for (ETo)estimation, its dependence on numerous weather datas such as solar radiation, temperature, relative humidity, extraterrestrial radiation and wind speed, introduces inherent constraints, the remote computation necessitates a substantial array of sensors, thereby incurring considerable expenses. To surmount this challenge, artificial intelligence methodologies, encompassing various machine learning (ML) models, are harnessed for ETo estimation, requiring only minimal parameters.This investigation scrutinizes the effectiveness of alternative equations (Hargreaves-Samani, Romannenko, Jensen-Haise, ASCE_PM) vis-à-vis (ML) models such as Xgboost,Support Vector Machine (SVM), and Random Forest (RF) in the estimation of ETo across the Meknes region, utilizing diverse permutations of the four measured variables. The study employs an extensive array of hyperparameters in two distinct scenarios: (i) randomization of all data, and (ii) training on one station while validating on another. All methodologies employed in this study yield satisfactory outcomes when juxtaposed against empirical models reliant on minimal meteorological data.

Ensemble learning-based estimation of reference evapotranspiration (ET[formula omitted

Reference Evapotranspiration (ETo) is crucial and influential in irrigation water management. Precise ETo rate estimation is vital for successful agriculture water management. There are numerous techniques for ETo rate simulation, but machine learning (ML) and deep learning (DL) approaches are currently popular. This study proposes an ensemble learning-based model for ETo rate estimation. The proposed model leverages minimum meteorological parameters, i.e., minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), and mean wind speed (WS) as input features. The proposed model employs Random Forest Bagging and Gradient Boosting models as bagging and boosting ensemble techniques for the accurate ETo rate estimation. The 10-fold cross-validation method is leveraged for the evaluation of the proposed model. The performance results of the proposed model are compared with the baseline model of ETo estimation, i.e., the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) and off-the-shelf deep learning models. The performance results indicate that Random Forest Bagging is significant as it yields Gradient Boosting and baseline models with 93.15% f-measure and reduces Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) by 17% and 10%, respectively.

Development of machine learning-based reference evapotranspiration model for the semi-arid region of Punjab, India

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances. Due to the unavailability and heterogeneity of broad parameters of the FAO PM method, it becomes a major constraint for accurately estimating ET0. To overcome the complexity of calculation, the present study was focused on developing a Random Forest-based (RF) ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the easily available data at the farm level. For comparative study existing models like Hargreaves–Samani, Modified Penman and modified Hargreaves–Samani were used to estimate the ET0. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (1990–2010) and nine years (2011–2019), respectively, and the FAO PM model was taken as a standard. The mean absolute error (MAE) and root-mean-square error (RMSE) were found to be least as 0.95 mm/d and 1.32 mm/d, respectively for the developed RF model, with an r2 value of 0.92. The seasonal ET0 estimated by modified Hargreaves–Samani (MHS) and RF were found as 498.3, 482.1 mm in rabi season and 755, 744.8 mm in kharif season respectively, whereas the annual ET0 was 1380.2 and 1355.7 mm respectively. The predicted ET0 values by RF-based model were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The outcome of the field trial also demonstrates that there was no appreciable yield drop in the crop when compared to irrigation scheduling by the FAO PM model, demonstrating the applicability of the developed model for irrigation in the semiarid region of the Punjab in India.

Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models

ABSTRACT This paper aimed to estimate the reference evapotranspiration (ET0) due to some limitations of the Food and Agriculture Organization-56 Penman-Monteith (FAO 56-PM) approach by using five alternative machine learning models. The study makes an important contribution to the ET0 estimation success for of the ET0 of 12 stations with variable climate characteristics in the Central Anatolian Region (CAR). The performances of the models were compared with the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) metrics that are frequently cited in the literature, and also with the performance index (PI). Long short-term memory (LSTM), artificial neural networks (ANN), and multivariate adaptive regression splines (MARS) models provided the best performance in eight, three, and one stations, respectively. The R2, MAE, RMSE, and PI values of the selected models from each station vary in the range of 0.987-0.999, 1.948-4.567, 2.671-6.659, and 1.544-4.018, respectively.

Intelligent optimization of Reference Evapotranspiration (ETo) for precision irrigation

Reference Evapotranspiration (ETo) is the basis for the conservation of irrigation water for sustainable development in agriculture. The FAO-56 Penman-Monteith (FAO-56 PM) is the standard method of ETo determination that is complex due to the involvement of many parameters. Many machine learning approaches were proposed to simplify ETo determination with limited parameters. The existing machine learning-based solutions rely on a fixed number of limited parameters for the determination of ETo. The study proposed a solution to determine the raw ETo with only temperature and optional optimization of the raw ETo according to the availability of Relative Humidity (RH), Wind Speed (WS), and sunshine duration ratio (n/N). The study proposed an ensembled Artificial Neural Network (ANN) model (Model-1) to determine ETo according to available minimal environmental parameters. The environmental conditions from the year 2001–2021 of Pakistan are used to develop a dataset with the FAO-56 PM method. The unique attribute of the proposed solutions is the flexibility in the use of environmental parameters for ETo determination according to the FAO-56 PM method. The performance of the proposed ensembled model (Model-1) is compared against the individual ANN model with all parameters (Model-2) and with the individual ANN model with only temperature (Model-3). The predictions by the proposed solution from Model-1 are also compared against the FAO-56 PM method. It is observed that the proposed Model-1 is more accurate with 91.44% accuracy, as compared to Model-2 and Model-3. The predictions by the proposed Model-1 are more correlated with the ETo by FAO-56 PM with a Pearson correlation of 0.996 as compared to the ETo predictions by Model-2 and Model-3.

Evaluating the Performance of Calibrated Temperature-based Equations as Compared to Standard FAO-56 Penman Monteith Equation in Humid Climatic Condition of Dehradun (India)

The present study was undertaken to calibrate, validate, and evaluate the performance of temperature-based evapotranspiration equations in comparison to the standard FAO-56 Penman Monteith (FAO-56 PM) model in humid climatic condition of Dehradun district of Uttarakhand using 31-years (1989-2019) daily meteorological dataset. The quality control of dataset was ensured by omitting days with missing data and outliers. The performance of 12 calibrated temperature-based ETo equations namely, Allen (1993) [AL93], Baier and Robertson (1965) [BR65], Bogawski and Bednorz (2014) [BB14], Droogers and Allen (2002) [DA02], Dorji et al. (2016) [DO16], Hargreaves (1994) [HA94], Heydari and Heydari (2014) [HH14], Kharrufa (1985) [KA85], Ravazzani et al. (2012) [RA12], Samani (2004) [SA04], Schendel (1967) [SC67], and Trajkovic (2007) [TR07] were evaluated in comparison to standard FAO-56 PM model in terms of daily ETo estimates. The analysis showed that calibrated temperature-based equations performed well with higher value of agreement index (0.85-0.98) and reduced errors. The values of Root mean square error (RMSE), Mean bias error (MBE), Maximum absolute error (MAXE), Percent error of estimate (PE), and Standard error of estimate (SEE) for calibrated equations ranged from 0.29 to 1.15 mm.day-1, (-)0.39 to 0.53 mm.day-1, 0.64 to 3.95 mm.day-1, 4.71 to 19.11%, and 0.17 to 1.00 mm.day-1, respectively; whereas for original equations they varied in the range from 0.47 to 3.64 mm.day-1, (-)0.32 to 2.92 mm.day-1, 0.95 to 10.65 mm.day-1, 11.74 to 106.15%, and 0.18 to 1.72 mm.day-1, respectively, indicating improved performance of calibrated equations. The ranking of calibrated ETo equations on the basis of Global Performance Indicator (GPI) values confirmed that calibrated Dorji et al. (2016) equation produced best result, while Samani (2004) equation with its lowest value performed poorly. Based on GPI values, calibrated equations can be ranked (best to worst performing) as DO16> AL93> TR07> RA12> DA02> HA94> SC67> KH85> BR65> HH14>BB14> SA04. Thus, calibrated Dorji et al. (2016) equation can be used as substitute for FAO-56 PM model in the absence of large number of meteorological parameters for accurate estimation of ETo values in the study area.

Pan evaporation is increased by submerged macrophytes

Abstract. The topic of evaporation estimates is fundamental to land-surface hydrology. In this study, FAO-56 Penman–Monteith equation (FAO56–PM), multiple stepwise regression (MLR), and Kohonen self-organising map (K–SOM) techniques were used for the estimation of daily pan evaporation (Ep) in three treatments, where C was the standard class A pan with top water, S was a pan with sediment covered bottom, and SM was class A pan containing submerged macrophytes (Myriophyllum spicatum, Potamogeton perfoliatus, and Najas marina), at Keszthely, Hungary, in a six-season experiment, between 2015 and 2020. The modelling approach included six measured meteorological variables. Average Ep varied from 0.6 to 6.9 mm d−1 for C, 0.7 to 7.9 mm d−1 for S, and from 0.9 to 8.2 mm d−1 for SM during the growing seasons studied. Correlation analysis and K–SOM visual representation revealed that air temperature and global radiation had positive correlation, while relative humidity had a negative correlation with the Ep of C, S, and SM. The results showed that the MLR method provided close compliance (R2=0.58–0.62) with the observed pan evaporation values, but the K–SOM method (R2=0.97–0.98) yielded by far the closest match to observed evaporation estimates for all three pans. To our best knowledge, no similar work has been published previously using the three modelling methods for seeded pan evaporation estimation. The current study differs from previous evaporation estimates by using neural networks even with those pans containing sediments and submerged macrophytes. Their evaporation will be treated directly by K–SOM, in which the modelling is more than the simple Ep of a class A pan filled with clean tap water.

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

The aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed for satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. The available independent variables were sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net radiation (Rn), wind speed (u2), vapour pressure deficit (es − ea), and altitude (Z). Sixteen MLR models were tested and compared to the corresponding ETo estimates computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, via statistical indices of error and agreement. The MLR5 model with five input variables outperformed the other models (RMSE = 0.28 mm d−1, adj. R2 = 98.1%). Half of the tested models (two to six inputs) exhibited very satisfactory predictions. Models of one input (e.g., N, Rn) were also promising. However, the MLR with u2 as the sole input variable presented the worst performance, probably because its relationship with ETo cannot be linearly described. The results indicate that MLR has the potential to produce very good predictive models of ETo for the Peloponnese, based on the literature standards.

Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using fewer inputs could lead to satisfactory predictions. Datasets from sixty-two meteorological stations were employed. The available inputs were mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapour pressure deficit (es-ea), wind speed (u2) and altitude (Z). Nineteen Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were tested and compared against the corresponding FAO-56 Penman Monteith (FAO PM) estimates of a previous study, via statistical indices. The MLP1 7-2 model with all the variables as inputs outperformed the rest of the models (RMSE = 0.290 mm d−1, R2 = 98%). The results indicate that even ANNs with simple architecture can be very good predictive models of ETo for the Peloponnese, based on the literature standards. The MLP1 model determined Tmean, followed by u2, as the two most influential factors for ETo. Moreover, when one input was used (Tmean, Rn), RBFs slightly outperformed MLPs (RMSE < 0.385 mm d−1, R2 ≥ 96%), which means that even a sole-input ANN resulted in satisfactory predictions of ETo.

The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China.

The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China.

Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China

The accurate estimation of reference crop evapotranspiration (ETO) plays an important role in guiding regional water resource management and crop water content research. In order to improve the accuracy of ETO prediction in regions with missing data, this study used the partial correlation analysis method to select factors that have a large impact on ETO as input combinations to construct ETO estimation models for typical stations in semi-arid regions of China. A biological heuristic optimization algorithm (Golden Eagle optimization algorithm (GEO) and Sparrow optimization algorithm (SSA)) and Extreme Learning Machine model (ELM) were combined to improve the estimation accuracy. The results showed that Ra was the primary factor affecting the ETO model, with an importance range of 0.187–0.566. Compared with the independent ELM model, the hybrid model has higher accuracy and stability. The estimated value of the SSA-ELM model under five-factor input condition (Ra, RH, Tmax, Tmin, U2) is closest to the standard value calculated by FAO56 PM: RMSE = 0.067–0.085, R2 = 0.998–0.999, MAE = 0.050–0.066 and NSE = 0.998–0.999. In general, the combination of a partial correlation analysis algorithm and a hybrid model can be used to estimate ETO with high accuracy under the condition of reducing input factors. Use of the first five factors extracted from the partial correlation analysis algorithm as input to build an ETO estimation model based on SSA-ELM in China’s semi-arid regions is recommended, which can also provide a reference for ETO estimation in similar regions.

Comparison of Fourteen Reference Evapotranspiration Models With Lysimeter Measurements at a Site in the Humid Alpine Meadow, Northeastern Qinghai-Tibetan Plateau.

Evapotranspiration is a key component in the terrestrial water cycle, and accurate evapotranspiration estimates are critical for water irrigation management. Although many applicable evapotranspiration models have been developed, they are largely focused on low-altitude regions, with less attention given to alpine ecosystems. In this study, we evaluated the performance of fourteen reference evapotranspiration (ET0) models by comparison with large weight lysimeter measurements. Specifically, we used the Bowen ratio energy balance method (BREB), three combination models, seven radiation-based models, and three temperature-based models based on data from June 2017 to December 2018 in a humid alpine meadow in the northeastern Qinghai–Tibetan Plateau. The daily actual evapotranspiration (ETa) data were obtained using large weighing lysimeters located in an alpine Kobresia meadow. We found that the performance of the fourteen ET0 models, ranked on the basis of their root mean square error (RMSE), decreased in the following order: BREB > Priestley-Taylor (PT) > DeBruin-Keijman (DK) > 1963 Penman > FAO-24 Penman > FAO-56 Penman–Monteith > IRMAK1 > Makkink (1957) > Makkink (1967) > Makkink > IRMAK2 > Hargreaves (HAR) > Hargreaves1 (HAR1) > Hargreaves2 (HAR2). For the combination models, the FAO-24 Penman model yielded the highest correlation (0.77), followed by 1963 Penman (0.75) and FAO-56 PM (0.76). For radiation-based models, PT and DK obtained the highest correlation (0.80), followed by Makkink (1967) (0.69), Makkink (1957) (0.69), IRMAK1 (0.66), and IRMAK2 (0.62). For temperature-based models, the HAR model yielded the highest correlation (0.62), HAR1, and HAR2 obtained the same correlation (0.59). Overall, the BREB performed best, with RMSEs of 0.98, followed by combination models (ranging from 1.19 to 1.27 mm day−1 and averaging 1.22 mm day−1), radiation-based models (ranging from 1.02 to 1.42 mm day−1 and averaging 1.27 mm day−1), and temperature-based models (ranging from 1.47 to 1.48 mm day−1 and averaging 1.47 mm day−1). Furthermore, all models tended to underestimate the measured ETa during periods of high evaporative demand (i.e., growing season) and overestimated measured ETa during low evaporative demand (i.e., nongrowing season). Our results provide new insights into the accurate assessment of evapotranspiration in humid alpine meadows in the northeastern Qinghai–Tibetan Plateau.

Differences in Spatiotemporal Variability of Potential and Reference Crop Evapotranspirations

Potential evapotranspiration (ETp) and reference crop evapotranspiration (ETo) are two key parameters in hydrology, meteorology, and agronomy. ETp and ETo are related to each other but have different meanings and applications. In this study, the ETp and ETo were distinguished and calculated with the Penman and FAO56 PM equations using the weather data of 551 stations in China from 1961 to 2018. The differences in their spatiotemporal variations were examined with an MMK test, an R/S test, and wavelet analysis. The monthly ETp and ETo were close but the ETp was always larger than the ETo, with values ranging from 1 to 356 mm and 2 to 323 mm, respectively. Their differences varied in different months and sub-regions. The maximum monthly difference transferred from south to north and then back to the south in a yearly cycle, showing spatiotemporal heterogeneity. The annual values of the ETp and ETo were also close, but the ETp was significantly higher than the ETo. The increasing future trends of ETp but decreasing trends of ETo were tested at most sites in China. Although the primary periods were almost the same, their spatial distribution was slightly different. In conclusion, ETp is different from ETo and they should be applied carefully. This study performs a thorough comparison and reveals the underlying basis of and discrepancy between ETp and ETo.

Modeling Reference Crop Evapotranspiration Using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) in Region IV-A, Philippines

The need for accurate estimates of reference crop evapotranspiration (ETo) is important in irrigation planning and design, irrigation scheduling, reservoir management among other applications. ETo can be accurately determined using the internationally accepted FAO Penman–Monteith (FAO-56 PM) equation. However, this requires numerous observed data, including solar radiation, air temperature, relative humidity, and wind speed, which in most cases are unavailable, particularly in developing countries such as the Philippines. This study developed models based on Support Vector Machines (SVMs) and Extreme Learning Machines (ELMs) for the estimation of daily ETo using different input combinations of meteorological data in Region IV-A, Philippines. The performance of machine learning models was compared with the different established alternative empirical models for ETo. The results show that the SVM and ELM models, with at least Tmax, Tmin, and Rs as inputs, provide the best daily ETo estimates. The accuracy of machine learning models was also found to be superior compared to the empirical models given with same input requirements. In general, SVM and ELM models showed similar modeling performance, although the former showed lower run time than the latter.

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.

Spatio-temporal calibration of Hargreaves\u2013Samani model in the Northern Region of Nigeria

One of the significant components of the hydrological cycle is evapotranspiration. Monthly meteorological parameters of 35 years from 19 meteorological stations across the Northern Region of Nigeria (NRN) were obtained and utilized for the calibration of Hargreaves–Samani (HS) model by comparing between potential evapotranspiration (ETo) values estimated from the original HS and the Penman–Monteith (FAO-56 PM) models. The calibrated HS equation was assessed using trend patterns and some statistical indices. The average value of root mean square error (RMSE) and the mean absolute error (MAE) decreased by 37.1 and 40%, respectively, after the calibration of the model. Also, the correlation coefficients (R) of stations that had values > 0.8 increased from 6 to 11 and the minimum R value increased by 12% above that of the original HS equation. The trend and spatial map of the statistical tests conducted also indicate better performance in most climatic regions after calibration. The precision of the HS equation improved significantly after calibration for semi-arid, humid, and sub-humid regions. However, few stations in the semi-arid, humid, and sub-humid regions did not show drastic improvement due to the peculiarity of the location and high variations in the wind speed and relative humidity parameters.

Comparative Evaluation of Simplified Surface Energy Balance Index-Based Actual ET against Lysimeter Data in a Tropical River Basin

In the past decades, multispectral and multitemporal remote sensing has been popularly used for estimating actual evapotranspiration (ETc) across the globe. It has been proven to be a cost-effective tool for understanding agricultural practices in a region. Today, because of the availability of different onboard sensors on an increasing number of different satellites, land surface activity can be captured at fine spatial and time scales. In the present study, three multi-date satellite imageries were used for the evaluation of remote sensing-based estimation of actual evapotranspiration in paddy in the command area of the tropical Kangsabati river basin. A surface energy balance model, the Simplified-Surface Energy Balance Index (S-SEBI), was applied for all three dates of the Rabi season (2014–2015) for the estimation of actual evapotranspiration. The crop coefficient was calculated using the exhaustive survey data collected from the command area and adjusted to local conditions. The ETc estimated using the S-SEBI-based model was compared with the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) method multiplied by the adjusted local crop coefficient and lysimeter data in the command area. The coefficient of determination (r2) was applied to examine the accuracy of the S-SEBI model with respect to lysimeter data and the FAO-56 PM-based ETc. The results showed that the S-SEBI model performed well with the lysimeter (r2 = 0.90) in comparison with FAO-56 PM, with an r2 of 0.65. In addition to this, the S-SEBI-based ET estimates correlated well with the FAO-56 PM, with r and RMSE values of 0.06 and 1.13 mm/day (initial stage), 0.85 and 0.48 mm/day (development stage), and 0.77 and 0.52 (maturity stage) for paddy, respectively. The S-SEBI-based ETc estimate varied with different stages of crop growth and successfully captured the spatial heterogeneity within the command area. In general, this study showed that the S-SEBI method has the potential to calculate spatial evapotranspiration and provide useful information for efficient water management. The results revealed the applicability and accuracy of remote sensing-based ET for managing water resources in a command area with scarce data.

Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d−1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d−1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics.