Search Results (133)

Crop water use (ET_c) is typically estimated as the product of crop evapotranspiration (ET_o) and a crop coefficient (K_c). However, the estimation of ET_o requires various meteorological data, which are often unavailable or of poor quality, particularly in countries such as Guinea-Bissau, where the maintenance of weather stations is frequently inadequate. The present study aimed to assess alternative approaches, as outlined in the revised FAO56 guidelines, for estimating ET_o when only temperature data is available. These included the use of various predictors for the missing climatic variables, referred to as the Penman–Monteith temperature (PMT) approach. New approaches were developed, with a particular focus on optimizing the predictors at the cluster level. Furthermore, different gridded weather datasets (AgERA5 and MERRA-2 reanalysis) were evaluated for ET_o estimation to overcome the lack of ground-truth data and upscale ET_o estimates from point to regional and national levels, thereby supporting water management decision-making. The results demonstrate that the PMT is generally accurate, with RMSE not exceeding 26% of the average daily ET_o. With regard to shortwave radiation, using the temperature difference as a predictor in combination with cluster-focused multiple linear regression equations for estimating the radiation adjustment coefficient (k_Rs) yielded accurate results. ET_o estimates derived using raw (uncorrected) reanalysis data exhibit considerable bias and high RMSE (1.07–1.57 mm d⁻¹), indicating the need for bias correction. Various correction methods were tested, with the simple bias correction delivering the best overall performance, reducing RMSE to 0.99 mm d⁻¹ and 1.05 mm d⁻¹ for AgERA5 and MERRA-2, respectively, and achieving a normalized RMSE of about 22%. After implementing bias correction, the AgERA5 was found to be superior to the MERRA-2 for all the studied sites. Furthermore, the PMT outperformed the bias-corrected reanalysis in estimating ET_o. It was concluded that PMT-ET_o can be recommended for further application in countries with limited access to ground-truth meteorological data, as it requires only basic technical skills. It can also be used alongside reanalysis data, which demands more advanced expertise, particularly for data retrieval and processing. Full article

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

Accurate reference evapotranspiration (ET_o) prediction is important for water resource management, particularly in arid regions where water availability is highly variable. However, the nonlinear and non-stationary characteristics of ET_o time series pose challenges for conventional prediction models. Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ET_o forecasting. Using a 40-year dataset from a meteorological station, we employ the Penman-Monteith equation to calculate ET_o and systematically compare model performance. Results show that VMD-LSTM and EWT-LSTM achieve the highest accuracy in the testing set (R² = 0.983 and 0.992, respectively) but exhibit reduced robustness in the prediction phase due to excessive high-frequency components. In contrast, EMD-LSTM and ESMD-LSTM demonstrate superior predictive stability, with no significant differences from actual values (p > 0.05). These findings underscore the importance of selecting appropriate decomposition methods to balance high-frequency information and predictive accuracy, offering insights for improving ET_o forecasting in arid regions. Full article

Potential evapotranspiration (PET) is a critical component of the water cycle, driving plants’ growth and survival. This study focused on estimating the daily potential evapotranspiration (PET) in a forest site in Northern Greece and assessing the performance of nine empirical PET estimation methods. These methods, categorized into mass-transfer, temperature-based, and radiation-based models, were compared against the widely used FAO-56 Penman–Monteith benchmark. The results highlight significant seasonal and monthly variations in vegetation water requirements. Among the methods tested, radiation-based models, particularly the Makkink equation, outperformed the others, followed by the Turc and Priestley–Taylor models. Temperature-based methods showed moderate performance and could serve as viable alternatives in forests with limited data availability, though local calibration is advisable. 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

Reference evapotranspiration (ETo) refers to the combined processes of evaporation and transpiration, which are relevant for hydrology, climate change research, and irrigation system design. The ETo is considered for different climatological studies, agriculture-focused studies, drought indices and climate change as well. From the ETo, water needs can be obtained, and along with precipitation, it is important to determine water availability and drought indices like the Standardized Precipitation Evapotranspiration Index (SPEI). Currently, there are different methods to estimate the ETo based on various climatic variables, which have been proposed for different climates and applied in different regions worldwide. The method standardized by most studies for determining the ETo is the “modified Penman–Monteith” method by the Food and Agriculture Organization (FAO). This method is versatile as it considers different climatic conditions and global latitudes. Due to limited climate data in developing countries like Mexico, alternative methods are used. The present study analyzed 40 comparative methods for determining ETo and their influence on SPEI. The best methods for the study area were chosen, including Hansen, Hargreaves and Samani, and Trajkovic, as they are the best based on the available information in Mexico. Additionally, each equation was adjusted to reduce errors and achieve closer approximations to actual ETo values to obtain the most accurate values possible. The influence on SPEI calculation indicates overestimations in temperature-based methods and underestimations in radiation and mass-transfer-based methods. The SPEI calculation showed fewer errors when using the modified HANSEN equations. In the absence of information, Allen’s temperature-based method is recommended. 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 aim of this study is to compare crop evapotranspiration in the Konya Plain over a period of 10 years calculated by different crop evapotranspiration estimation methods using data collected from four meteorological stations. Accurate ET estimation is vital for sustainable water management in agriculture, especially in areas where there is a limited availability of water. This study highlights how the various estimation methods, particularly the radiation equation, support water-efficient agriculture when full weather data are available. To achieve this, it calculates the water requirements of five widely cultivated crops in the region: sugar beet, maize (grain), sunflower, dry bean, and wheat. The results show a significant difference between the FAO Penman–Monteith method and each of the other methods. It is also observed that the ETc values calculated according to the radiation equation are higher for most of the stations than the ETc values calculated using the other methods. At Akşehir, the ETc of dry bean obtained by using the radiation equation ranges from 501 mm to 679 mm; at Beyşehir, it ranges from 544 mm to 727 mm; at Cihanbeyli, from 679 mm to 738 mm; and at Ereğli, it ranges from 725 mm to 767 mm. The ASCE Penman–Monteith equation recorded the lowest ETc at all meteorological stations for the 10-year period. The radiation equation can be recommended for areas where there are not enough meteorological data to calculate the FAO Penman–Monteith equation, which is considered the standard approach for determining the water requirements of plants. 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

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature. Full article

Crop coefficient (K_c), the ratio of crop evapotranspiration (ET_c) to reference evapotranspiration (ET_o), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ET_c and growth-stage-specific K_c in flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of two temperature-based ET_o estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ET_c over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water in the 2021–2022 and 2022–2023 growing seasons, respectively. The ET_o over the growing season, computed using Penman–Monteith, Hargreaves–Samani, and Blaney–Criddle equations, were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021–2022, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022–2023, respectively. The result of global sensitivity analysis showed that the mean temperature is the main driving factor for estimated ET_o based on Blaney–Criddle and Hargreaves–Samani methods, but the sensitivity percentage for Blaney–Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves–Samani, given that Blaney–Criddle method is less accurate in ET_o estimation for this area, especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman–Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (K_c) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman–Monteith, 0.51, 1.0 and 0.46 based on Blaney–Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves–Samani. The results showed that the Hargreaves–Samani equation serves as an alternative tool to predict ET_o when fewer meteorological variables are available. The calculated local growth-stage-specific K_c can help improve irrigation water management in this region. Full article

The Tarim River Basin is one of the most ecologically fragile regions around the world in the arid areas of Northwest China. The study of natural vegetation ecological water requirement (EWR) is the basis for the promotion of regional ecological conservation and sustainable development of ecosystems when extreme environmental events occur frequently, which is of great significance for the formulation of scientific and rational ecological conservation strategies. In the study, we improved the vegetation EWR calculation method by introducing a dynamic soil moisture limitation coefficient (K_S) and a dynamic vegetation coefficient (K_C) that is coupled with a resistance correction factor (F_r) based on the Penman-Monteith method and analyzed its spatio-temporal variation characteristics. Additionally, this study utilized the latitude of ecosystem resilience (LER) to clarify the thresholds for vegetation EWR throughout the growing season in the study area and to analyze the water surplus and deficit (WSD) at different threshold levels. The results of the study show that: (1) Over the past 21 years, the EWR for vegetation has shown a downward trend, with the change in EWR for arbor-shrub forests being more significant than that for grasslands. The average EWR for arbor-shrub forests and grasslands is 36.76 × 10⁸ m³ and 459.59 × 10⁸ m³, respectively. (2) The minimum ecological water requirement (EWR_min) and optimal ecological water requirement (EWR_opt) for natural vegetation were 360.45 × 10⁸ m³ and 550.10 × 10⁸ m³, respectively. (3) In EWR_min conditions, the alpine plateau area as a whole showed a water surplus, and the plains area as a whole was in a state of water scarcity, but the precipitation in the study area as a whole could meet the basic survival needs of the vegetation. (4) In EWR_opt conditions, the plains and local alpine plateau areas are in a state of water scarcity, the area of water scarcity is gradually increasing, and the regional precipitation is unable to fully realize the objectives of ecological conservation and vegetation restoration. 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

We assessed and quantified future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region in the Southwestern U.S. and Mexico. The PET projections were calculated using the daily Penman–Monteith equation. The terrestrial meteorological variables needed for the equation (i.e., minimum and maximum daily temperature, specific humidity, wind speed, incoming shortwave radiation, and pressure) were obtained from the North American–CORDEX initiative. We used dynamically downscaled projections of three CMIP5 GCMs for RCP8.5 emission scenarios (i.e., HadGEM2-ES, MPI-ESM-LR, and GFDL-ESM2M), and each was dynamically downscaled to ~25 km by two RCMs (i.e., WRF and regCM4). All terrestrial annual PET projections showed a statistically significant increase when comparing the historical period (1986–2005) to future projections (2020–2039 and 2040–2059). The regional spatial average of the six GCM-RCM combinations projected an increase in the annual PET of about +4% and +8% for 2020–2039 and 2040–2059, respectively. The projected average 20-year annual changes over the study area range for the two projection periods were +1.4%–+8.7% and +3%–+14.2%, respectively. The projected annual PET increase trends are consistent across the entire region and for the six GCM-RCM combinations. Higher annual changes are projected in the northeast part of the region, while smaller changes are projected along the pacific coast. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit. The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area. Quantitative assessments of the projected PET changes provided by this study should be considered in upcoming studies to develop resilience plans and adaptation strategies for mitigating the projected future changes. Full article