Search Results (48)

Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, water resource management, and climate studies. Among various approaches that are employed for estimating ET, the Penman–Monteith equation is known as the widely accepted reference approach. However, the extensive data requirement of this method is a crucial challenge that limits its usage, particularly in data-scarce regions. Therefore, as an alternative approach, artificial intelligence (AI) models have gained prominence for estimating evapotranspiration because of their capacity to handle complicated relationships between meteorological variables and water loss processes. These models leverage large datasets and advanced algorithms to provide accurate and timely ET predictions. The current research aims to review previous studies addressing the application of the AI model in ET modeling under four main categories: neuron-based, tree-based, kernel-based, and hybrid models. The results of this study indicated that traditional models like the Penman–Monteith (PM) require extensive input data, while AI-based approaches offer promising alternatives due to their ability to model complex nonlinear relationships. Despite their potential, AI models face challenges such as overfitting, interpretability, inconsistent input variable selection, and lack of integration with physical ET processes, highlighting the need for standardized input configurations, better pre-processing techniques, and incorporation of hydrological and remote sensing data. Full article

The Penman-Monteith (PM) equation is considered the most accurate method for estimating reference evapotranspiration (ETo); however, its application requires a large amount of data that is not always available. This study aimed to regionalize the coefficients of the Hargreaves-Samani (HS) equation to estimate ETo in high-altitude areas, specifically the Peruvian Altiplano (PA). The methodology included (1) evaluation of the original HS equation, (2) calibration and validation of the empirical coefficient ($CH$) and empirical exponent ($EH$) at each weather station, and (3) regionalization of the calibrated coefficients using a multiple linear regression approach. The results showed that the original HS equation had NSE values ranging from −0.57 to 0.87, PBIAS from −18.60% to 12.70%, MAE from 0.16 to 0.65 mm/d, and RMSE from 0.20 to 0.67 mm/d. After calibrating $CH$ and $EH$, performance improved significantly, achieving validation values of NSE ranging from 0.67 to 0.94, PBIAS from −0.55% to 1.37%, MAE from 0.01 to 0.05 mm/d, and RMSE from 0.13 to 0.21 mm/d. Finally, the regionalization of 0.859 and 0.744, respectively. These results indicate that the HS equation, with calibrated and regionalized coefficients, is a viable alternative for estimating ETo in regions with limited meteorological data. Full article

An automatic, weight-based, small 20 cm diameter pan was used for real-time calculations of evaporation and precipitation in a semiarid environment. The water evaporated from the evaporimeter (E_P) was found to be a significant predictor of evapotranspiration (ET_O; r² = 0.84), which was calculated with the Penman–Monteith (P-M) equation by retrieving climatic data from a weather station. The results revealed seasonal variations of the pan coefficient (K_P; dimensionless), with a mean value estimated at 0.84 (±0.16). Validation of ET_O measurements using a calibrated regression model (ET_O = 0.831*E_P + 0.025), against the P-M equation indicated a high correlation coefficient (r² = 0.99, slope of the regression line of 0.9). The present paper evaluates and discusses the potential of using a reduced-size pan for real-time monitoring of water evaporation and precipitation, proposing an open-source irrigation decision support system. Full article

Accurately estimating reference crop evapotranspiration (ETo) improves agricultural water use efficiency. However, the accuracy of ETo estimation needs to be further improved in the Northeast region of China, the country’s main grain production area. In this research, meteorological data from 30 sites in Northeast China over the past 59 years (1961–2019) were selected to evaluate the simulation accuracy of 11 ETo estimation models. By using the least square method (LSM) and three population heuristic intelligent algorithms—a genetic algorithm (GA), a particle swarm optimization algorithm (PSO), and a differential evolution algorithm (DE)—the parameters of eleven kinds of models were optimized, respectively, and the ETo estimation model suitable for northeast China was selected. The results showed that the radiation-based Jensen and Haise (JH) model had the best simulation accuracy for ETo in Northeast China among the 11 empirical models, with R² of 0.92. The Hamon model had an acceptable estimation accuracy, while the combination model had low simulation accuracy in Northeast China, with R² ranges of 0.74–0.88. After LSM optimization, the simulation accuracy of all models had been significantly improved by 0.58–12.1%. The results of heuristic intelligent algorithms showed that Hamon and Door models optimized by GA and DE algorithms had higher simulation accuracy, with R² of 0.92. Although the JH model requires more meteorological factors than the Hamon and Door model, it shows better stability. Regardless of the original empirical formula or the optimization of various algorithms, JH has higher simulation accuracy, and R² is greater than 0.91. Therefore, when only temperature or radiation factors were available, it was recommended to use the Hamon or Door model optimized by GA to estimate ETo, respectively; both models underestimated ETo with an absolute error range of 0.01–0.02 mm d⁻¹ compared to the reference Penman–Monteith (P–M) equation. When more meteorological factors were available, the JH model optimized by LSM or GA could be used to estimate ETo in Northeast China, with an absolute error of less than 0.01 mm d⁻¹. This study provided a more accurate ETo estimation method within the regional scope with incomplete meteorological data. Full article

The accurate estimation of reference crop evapotranspiration (ET₀) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimization (TSO), and sparrow search algorithm (SSA)) were combined with an extreme learning machine (ELM) model to form three mixed models (AO-ELM, TSO-ELM, and SSA-ELM). The accuracy of the ET₀ estimates for five climate regions in China from 1970 to 2019 was evaluated using the FAO-56 Penman–Monteith (P-M) equation. The results showed that the predicted values of the three mixed models and the ELM model fitted the P-M calculated values well. R² and RMSE were 0.7654–0.9864 and 0.1271–0.7842 mm·d⁻¹, respectively, for which the prediction accuracy of the AO-ELM model was the highest. The performance of the AO-ELM combination5 (maximum temperature (T_max), minimum temperature (T_min), total solar radiation (R_s), sunshine duration (n)) was most significantly improved on the basis of the ELM model. The prediction accuracy for the stations in the plateau mountain climate (PMC) region was the best, while the prediction accuracy for the stations in the tropical monsoon climate region (TPMC) was the worst. In addition to the wind speed (U₂) in the temperate continental climate region (TCC)—which was the largest variable affecting ET₀—n, R_a, and total solar radiation (R_s) in the other climate regions were more important than relative humidity (RH) and wind speed (U₂) in predicting ET₀. Therefore, AO-ELM4 was selected for the TCC region (with T_max, T_min, R_s, and U₂ as inputs) and AO-ELM5 (with T_max, T_min, R_s, and n as inputs) was selected for the TMC, PMC, SMC, and TPMC regions when determining the best model for each climate region with limited meteorological data. Full article

Climate change significantly impacts agriculture and forage production, requiring the implementation of strategies toward increased water and energy use efficiency. So, this study investigated the yield of forage cactus (Opuntia stricta (Haw.) Haw) under different irrigation depths using brackish groundwater (1.7 dS m⁻¹), whose management was based on reference evapotranspiration (ETo) estimated by the Hargreave–Samani (HS) and Penman–Monteith (PM) equations. The research was conducted in Independência, Ceará, Brazil, under the tropical semi-arid climate. A randomized block design in a 2 × 5 factorial scheme was employed, varying the ET₀ estimation equations (HS and PM) and irrigation levels (0; 20; 40; 70; and 100% of total required irrigation—TRI). Growth, productivity, and water use efficiency variables were evaluated at 6, 12, and 18 months after treatment initiation. The economic analysis focused on added value, farmer income, and social reproduction level. The results showed no isolated effect of the equations or their interaction with irrigation depths on the analyzed variables, suggesting that irrigation management can be effectively performed using the simpler HS equation. Furthermore, there was no statistical difference between the means of 100% and 70% TRI as well as between 70% and 40% TRI for most variables. This indicates satisfactory crop yield under deficit irrigation. Dry matter productivity and farmer income at 12 months resulting from complementary irrigation with depths between 40% and 70% of TRI were significantly higher than under rainfed conditions. The 70% depth resulted in yields equivalent to those at 100% TRI, with the social reproduction level being achieved on 0.65 hectares in the second year. Full article

Potential evapotranspiration (PET) is a crucial parameter for forest development, having an important role in ecological, biometeorological, and hydrological assessments. Accurate estimations of PET using the FAO–56 Penman–Monteith (FAO–56 PM) benchmark method require a wide range of data parameters, which are not typically available at meteorological stations installed in forest environments. The aim of this study is to investigate the accuracy of various methods with low data requirements for assessing PET in a Mediterranean forest environment and propose appropriate alternatives for accurate PET estimation. Specifically, 50 temperature-based methods were evaluated against the FAO–56 PM method in a sub-humid forest in northern Greece, using high-quality daily meteorological data. The outcomes indicate that temperature-based methods offer a viable alternative for PET estimation when data availability is limited, with a considerable number of methods (22) presenting low deviations (up to 10%) compared to the benchmark method. Temperature-based models outperformed those incorporating water-related parameters (as relative humidity or precipitation) in Mediterranean forest environments. The top performing methods in the study site, based on several statistical indices, were the equations of Ravazzani et al., proposed in 2012, followed by Hargreaves–Samani in 1985 and Heydari and Heydari in 2014. Full article

The reference evapotranspiration (ET₀) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET₀ calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years of meteorological data from 18 stations in four geographic regions of Taiwan to evaluate the effectiveness of an artificial intelligence (AI) model for estimating PM-calculated ET₀ using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET₀ forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET₀ estimation. The artificial neural network using temperature, solar radiation, and relative humidity as input variables performed best, with the correlation coefficient (r) ranging from 0.992 to 0.998, mean absolute error (MAE) ranging from 0.07 to 0.16 mm/day, and root mean square error (RMSE) ranging from 0.12 to 0.25 mm/day. For short-term ET₀ forecasting, the long short-term memory model using temperature, solar radiation, and relative humidity as input variables was the best structure to forecast four-day-ahead ET₀, with the r ranging from 0.608 to 0.756, MAE ranging from 1.05 to 1.28 mm/day, and RMSE ranging from 1.35 to 1.62 mm/day. The percentage error of this structure was within $\pm$5% for most meteorological stations over the one-year test period, underscoring the potential of the proposed models to deliver daily ET₀ forecasts with acceptable accuracy. Finally, the proposed estimating and forecasting models were developed in regional and variable-limited scenarios, making them highly advantageous for practical applications. Full article

A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R²), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day⁻¹) and RMSE (0.326 mm day⁻¹) were also obtained. Full article

The hydrological cycle should be scrutinized and investigated under recent climate change scenarios to ensure global water management and to increase its utilization. Although the FAO proposed the use of the Penman–Monteith (PM) equation worldwide to predict evapotranspiration (ET), which is one of the most crucial components of the hydrological cycle, its complexity and time-consuming nature, have led researchers to examine alternative methods. In this study, the performances of numerous temperature-driven ET methods were examined relative to the PM using daily climatic parameters from central stations in 11 districts of the Kahramanmaras province. Owing to its geographical location and other influencing factors, the city has a degraded Mediterranean climate with varying elevation gradients, while its meteorological patterns (i.e., temperature and precipitation) deviate from those of the main Mediterranean climate. A separate evaluation was performed via ten different statistical metrics, and spatiotemporal ET variability was reported for the districts. This study revealed that factors such as altitude, terrain features, slope, aspect geography, solar radiation, and climatic conditions significantly impact capturing reference values, in addition to temperature. Moreover, an assessment was conducted in the region to evaluate the effect of modified ET formulae on simulations. It can be drawn as a general conclusion that the Hargreaves–Samani and modified Blaney–Criddle techniques can be utilized as alternatives to PM in estimating ET, while the Schendel method exhibited the lowest performance throughout Kahramanmaras. Full article

The decrease in arable land, water scarcity, and climate change increase the pressure on natural resources and agricultural production systems. In this context, agriculture must ensure food production for the rapidly growing and increasingly urban population of the world. Efforts must be made to obtain the highest yield from the unit area and promote the transition to more sustainable production systems Hydroponics is a modern growing technology mainly applied in greenhouses, which has developed rapidly over the past 30–40 years. Substrate-free hydroponic vertical crops (VC) can reduce the pressure conventional agriculture exerts on resources, saving water and nutrients, and increasing crop yields per unit area. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. On the basis of the Penman–Monteith equation, the PM estimates transpiration, while nutrient uptake was estimated using the Carmassi–Sonneveld submodel. The PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions during spring 2023. The irrigation technique was a closed-loop fertigation circuit. The experiment consisted of testing two densities (50 and 80 plants·m⁻²) and three plant positions (low, medium, and upper). ANOVA (p < 0.05) and R² were used to evaluate the PM performance and crop behavior. The low density and the upper position had significantly higher mass values. The results suggest a high degree of performance for the PM, as the R² ranged from 0.7 to 0.9 for water and nutrient uptake. Both densities had a yield 17–20 times higher than conventional lettuce production and significant savings in water, about 85–88%. In this sense, the PM has great potential to intelligently manage VC fertigation, saving water and nutrients, which represents an advance toward reaching SDG 6 and SDG 12 within the 2030 Agenda. Full article

Accurate estimation of reference evapotranspiration (ET_r) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ET_r across various timescales using ground weather station data. However, discrepancies persist between estimated ET_r and measured ET_r obtained from weighing lysimeters (ET_r-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ET_r estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R²). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ET_r estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ET_r across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning. Full article

Canopy resistance is a key parameter in the Penman–Monteith (P–M) equation for calculating evapotranspiration (ET). In this study, we compared a machine learning algorithm–support vector machine (SVM) and an analytical solution (Todorovic, 1999) for estimating canopy resistances. Then, these estimated canopy resistances were applied to the P–M equation for estimating ET; as a benchmark, a constant (fixed) canopy resistance was also adopted for ET estimations. ET data were measured using the eddy-covariance method above three sites: a grassland (south Ireland), Cypress forest (north Taiwan), and Cryptomeria forest (central Taiwan) were used to test the accuracy of the above two methods. The observed canopy resistance was derived from rearranging the P–M equation. From the measurements, the average canopy resistances for the grassland, Cypress forest, and Cryptomeria forest were 163, 346, and 321 (s/m), respectively. Our results show that both methods tend to reproduce canopy resistances within a certain range of intervals. In general, the SVM model performs better, and the analytical solution systematically underestimates the canopy resistances and leads to an overestimation of evapotranspiration. It is found that the analytical solution is only suitable for low canopy resistance (less than 100 s/m) conditions. Full article

Reference evapotranspiration (ET₀) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman–Monteith (PM) is the only standard method for defining and calculating ET₀. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET₀ calculated with the Blaney–Criddle (BC) and Hargreaves–Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods. The information collected corresponds to Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated the reference evapotranspiration (ET₀) by 5.5% compared to the PM method considering the total ET₀ of the study period (26 February to 9 August 2021) and yielded the best fit in the different evaluation periods (daily, 5-day mean, and 5-day cumulative); the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET₀ using the HS equation. This data source is a suitable alternative, particularly in semi-arid regions with limited climatological data from weather stations. Full article

Wind drift and evaporation loss (WDEL) of mid-elevation spray application (MESA) and low-elevation spray application (LESA) sprinklers on a center pivot and linear-move irrigation machines are measured and reported to be about 20% and 3%, respectively. It is important to estimate the fraction of WDEL that cools and humidifies the microclimate causing evapotranspiration (ET) suppression, mitigating the measured irrigation system losses. An experiment was conducted in 2018 and 2019 in a commercial spearmint field near Toppenish, Washington. The field was irrigated with an 8-span center pivot equipped with MESA but had three spans that were converted to LESA. All-in-one weather sensors (ATMOS-41) were installed just above the crop canopy in the middle of each MESA and LESA span and nearby but outside of the pivot field (control) to record meteorological parameters on 1 min intervals. The ASCE Penman–Monteith (ASCE-PM) standardized reference equations were used to calculate grass reference evapotranspiration (ET_o) from this data on a one-minute basis. A comparison was made for the three phases of before, during, and after the irrigation system passed the in-field ATMOS-41 sensors. In addition, a small unmanned aerial system (UAS) was used to capture 5-band multispectral (ground sampling distance [GSD]: 7 cm/pixel) and thermal infrared images (GSD: 13 cm/pixel) while the center pivot irrigation system was irrigating the field. This imagery data was used to estimate crop evapotranspiration (ET_c) using a UAS-METRIC energy balance model. The UAS-METRIC model showed that the estimated ET_c under MESA was suppressed by 0.16 mm/day compared to the LESA. Calculating the ET_o by the ASCE-PM method showed that the instantaneous ET_o rate under the MESA was suppressed between 8% and 18% compared to the LESA. However, as the time of the ET suppression was short, the total amount of the estimated suppressed ET of the MESA was less than 0.5% of the total applied water. Overall, the total reduction in the ET due to the microclimate modifications from wind drift and evaporation losses were small compared to the reported 17% average differences in the irrigation application efficiency between the MESA and the LESA. Therefore, the irrigation application efficiency differences between these two technologies were very large even if the ET suppression by wind drift and evaporation losses was accounted for. Full article