Estimation Of Evapotranspiration Research Articles

Estimation of Actual Evapotranspiration and Crop Coefficient to Improve Water Use Efficiency of Sesamum (Sesamum indicum L.) in Kharif Season Using Digital Weighing Lysimeter

The study was conducted on Estimation of Actual Evapotranspiration and Crop Coefficient to Improve Water Use Efficiency of Sesamum (Sesamum indicum L.) in Kharif Season Using Digital Weighing Lysimeter,” was conducted during the 2023-24 Kharif season with the aim of enhancing sesamum water use efficiency. For estimation of actual evapotranspiration and crop coefficient following are the objectives were to calculate and compare reference evapotranspiration (ETr) using various methods, measure crop evapotranspiration (ETc) using a digital weighing lysimeter, estimate crop coefficient (Kc) value. ETr was calculated using six different methods: Penman-Monteith, Modified Penman-Monteith, FAO Radiation, Priestly-Taylor, Hargreaves-Samani, and Pan Evaporation. The result obtained from that is the seasonal ETr was found 328.53, 395.47, 369.37, 430.26, 416.04, 372.90 mm respectively. While calculating or estimating reference evapotranspiration it is observed that The Modified Penman Monteith method had the highest correlation (R²=0.98) with the standard Penman-Monteith method, while the FAO Radiation and Pan Evaporation methods also performed well. crop Evapotranspiration (ETc) of Sesamum crop, measured using a digital weighing lysimeter, ranged from 0.61 to 5.65 mm/day, with a total of 244.94 mm for the season and the Crop coefficients (Kc) values were calculated by ratio of crop Evapotranspiration (ETc) with Reference Evapotranspiration (ETr), from that polynomial regression models were developed to estimate daily Kc values based on the crop growth ratio (t/T). The daily Kc values determined by using the best fit polynomial equations were averaged over initial, development, mid-season and end-season stage, the Kc values developed by using different methods i.e (Penman-Monteith method) are 0.58, 0.77, 1.00, and 0.36; Kc values (Modified Penman Monteith method) were 0.48, 0.64, 0.84 and 0.32; Kc values (FAO Radiation method) were 0.61, 0.73, 0.91 and 0.33; Kc values (Priestly Taylor method) were 0.26, 0.61, 0.98 and 0.33; Kc values (Hargreaves Samani method) were 0.36, 0.58, 0.92 and 0.28; and Kc values (Pan Evaporation method) were 0.48, 0.65, 0.93 and 0.32, respectively. On an average Kc determined by Penman Monteith method, Modified Penman Monteith method, FAO Radiation method and Pan Evaporation method overestimated the Kc by 26.17, 7.00, 21.92 and 9.38 per cent, respectively compared to FAO-56 values. However, Priestly-Taylor method and Hargraves-Samani method underestimated Kc by 5.55 and 5.78 per cent, respectively.

Estimation of daily reference evapotranspiration based on an improved Slime Mould Algorithm (SMA) for the Hetao irrigation district in northwest China

Estimation of daily reference evapotranspiration based on an improved Slime Mould Algorithm (SMA) for the Hetao irrigation district in northwest China

Evaluation of Micrometeorological Models for Estimating Crop Evapotranspiration Using a Smart Field Weighing Lysimeter

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions.

Utility of single-source surface energy balance models in estimation of daily actual evapotranspiration in arid regions

Single-source surface energy balance (SS-SEB) models estimate daily actual evapotranspiration (ETa eb) in arid and semi-arid regions. A meta-analysis explored the impact of model types, upscaling methods, satellite data selection, topography, and validation methods on ETa eb accuracy. SEBAL outperformed other SS-SEB models, especially in arid areas. Clear-sky models were more precise than continuous ones. For SEBAL, the Constant Reference Evaporative Fraction (ETrF) method was best for upscaling, while Constant Evaporative Fraction (EF) was optimal for METRIC, SEBS, and S-SEBI. Multi-source remote sensing data enhanced SS-SEB model performance compared to single-source data, particularly with higher spatial resolution. Study area size didn’t affect accuracy, but SEBAL struggled in higher altitudes and complex terrains. Lysimeter or FAO-56 validation yielded the best results. Recommendations include developing continuous SS-SEB models using appropriate methods and considering SSEB/SSEBop as alternatives to SEBAL and METRIC in data-scarce regions.

A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning

Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.

Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation

AbstractGolf courses are increasingly affected by water scarcity and climate change. An understanding of spatial variability of actual evapotranspiration (ETa) and turfgrass quality (TQ) site‐specific management zones (SSMZ) is important for the implementation of precision turfgrass management. Therefore, the main objectives of this study were to quantify the relationship between remotely sensed TQ and ETa estimates and to evaluate the spatial variations of TQ and ETa at a golf course in Utah. Ground‐based normalized difference vegetation index was collected using a TCM‐500 sensor, and aerial multispectral and thermal imagery data were acquired from unpiloted aircraft systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ‐random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ETa estimates. The TQ and ETa SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results demonstrated that TQ‐RF model was accurate within a root mean square error of 0.05. The correlation between TQ‐RF and ETa was stronger for fairways (R2 = 0.74), tees (R2 = 0.66), and roughs (R2 = 0.75) as compared to greens (R2 = 0.25) and the driving range (R2 = 0.36) on July 20, 2022. Actual evapotranspiration SSMZ, in combination with TQ‐RF SSMZ, is useful for irrigation scheduling, addressing the question of how much and where to irrigate. This study demonstrates the ability of TQ‐RF and ETa SSMZ to identify spatial variation for the purpose of landscape irrigation management in semi‐arid areas.

Observed Interannual Variability and Projected Scenarios of Drought using Drought Indicators

The observation-based analysis of drought development in the Uttar Pradesh region in India showed that, despite the area being relatively large, agricultural drought exhibits high spatial variability. However, the lack of net radiation data hinders the capacity to provide reliable estimates of evapotranspiration (ET), affecting the assessment of drought occurrence since its propagation across the hydrological system becomes very sensitive to the estimation of ET. The most prominent precipitation deficits occur during the monsoon season (June to October), showing that changes in the large-scale circulation are responsible for the impact of severe drought. El Niño-Southern Oscillation (ENSO) modulates the variability of drought with a warm phase favoring drought development with the strongest influence between August and October. The climate change projections under RCP4.5 and RCP8.5 scenarios suggest the intensification of drought events in the Uttar Pradesh region in the mid-century, with the Chambal River of the Ganges River basin being the most affected area in terms of precipitation and temperature. The projected scenarios correspond to an increase of 1.7°C for mean temperature, and 3.5°C for minimum and maximum temperature in the 2050 horizon, and a decrease of 400 to 800 mm for annual precipitation was projected under both RCPs.

Penman–Monteith Reference Evapotranspiration Estimation Models, Using Latitude–Temperature Data, in the State of Sinaloa, Mexico

Penman–Monteith Reference Evapotranspiration Estimation Models, Using Latitude–Temperature Data, in the State of Sinaloa, Mexico

Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm.

Performance and recovery of turfgrasses irrigated with varying crop coefficients

AbstractDeficit irrigation is a water conserving practice that involves watering below an estimated evapotranspiration (ET) replacement level. Research is limited to comparing cool‐season (CS) and warm‐season (WS) turfgrass varieties grown in arid regions under varying deficit irrigation replacement levels. This study investigated the effects of five levels of reference evapotranspiration for short grass (ETOS) replacement (55%, 70%, 85%, 100%, and 115%) on the performance and fall recovery of several turfgrasses in the southwestern United States. Three years of field research evaluated green cover and visual quality of three CS Kentucky bluegrass (Poa pratensis L.) (four cultivars), tall fescue [Schedonorus arundinaceus (Schreb.)] (three cultivars), and perennial ryegrass (Lolium perenne L.) (three cultivars), and two WS turfgrasses bermudagrass (Cynodon dactylon L.) (three cultivars) and buffalograss Buchloe dactyloides (two cultivars). CS grasses required higher ETOS replacement than WS grasses to maintain acceptable quality (1–9, ≥6 = minimum acceptable) and coverage. Among CS grasses, Barserati Kentucky bluegrass maintained the best quality and green cover under deficit irrigation and demonstrated the most consistent ability to recover. Notably, bermudagrass performed well under deficit irrigation, maintaining acceptable visual quality and better green cover than CS species like Kentucky bluegrass and tall fescue at lower irrigation levels. Overall, there were significant differences among cultivars, demonstrating the importance of the selection process in drought tolerance. These findings support the promotion of drought‐resistant WS grasses to conserve water in arid regions without compromising turfgrass functionality. Future research should focus on variable and seasonal ETOS for irrigation of turfgrasses and estimating irrigation requirements.

Assessment of irrigation water management using FAO-CROPWAT 8 in the Paliganj distributary of the Sone irrigation scheme in Bihar: a case study

ABSTRACT Around 15% of India's GDP comes from agriculture, which is the foundation of the country's economy. India's economic development and efforts to combat poverty have already benefited greatly from its agricultural sector. Sustainable management of water resources can increase agricultural productivity. The study performed in the Paliganj distributary is a part of the Sone canal system in south Bihar, India. The Paliganj distributary command area lies wholly in the Gangetic plain. The estimation of crop evapotranspiration is carried out using the modified Penman–Monteith equation for the period 1980–2015. The crop coefficient of the crops has been estimated by using a single crop coefficient and a dual crop coefficient. The crop water requirement, irrigation water requirement, and irrigation scheduling of crops grown in the study area are determined using CROPWAT 8.0 software. The results conclude that the maximum, minimum, and average values of ET0 were found to be 7.65, 1.31, and 3.86 mm/day, respectively. The crop water requirement and irrigation requirement of crops grown in kharif, rabi, hot weather, and annual seasons in the study area were lower by using dual crop coefficient.

COMPARATIVE EVALUATION OF THREE PENMAN-MONTEITH VARIANT MODELS FOR GROUNDWATER LEVEL ESTIMATION IN JEJU ISLAND

Accurate estimation of groundwater levels is crucial for effective water resource management, particularly in regions like Jeju Island, where groundwater is the primary water source. This study emphasizes the importance of preprocessing meteorological observations to address the temporal disconnect between rapid meteorological fluctuations and the slower responses of groundwater systems. Key factors such as cumulative effects, delayed reactions, and seasonal variations were considered during preprocessing to improve the reliability of evapotranspiration estimates. Three Penman-Monteith evapotranspiration models (PM, FAO-24, FAO-56) were evaluated using pre-processed data, including accumulated precipitation (100-day), temperature (20-day), and other meteorological parameters. Validation against 2023 groundwater level data demonstrated that the FAO-56 model preprocessing achieved the best performance, with the highest correlation (r = 0.89), lowest mean squared error (MSE = 0.10), and smallest error range (overestimation: +1.1m, underestimation: -1.4m). These results highlight that accurate groundwater level estimation relies on proper preprocessing of observations rather than solely optimizing model operations. The findings provide valuable insights for enhancing groundwater monitoring and sustainable water management in areas with complex geological and hydrological conditions.

A review of evapotranspiration estimation methods for climate-smart agriculture tools under a changing climate: vulnerabilities, consequences, and implications

ABSTRACT Climate change (CC) seriously threatens global water resources, exacerbating extreme water scarcity issues, especially in agriculture. Evapotranspiration (ET) is one of the essential components of the water cycle and is particularly sensitive to CC. Thus, this study presents an overview of the importance of ET estimation as climate-smart agriculture (C-SA) and its relevance in addressing CC's challenges. We adopted a systematic review methodology to select the relevant literature based on predefined inclusion and exclusion criteria. Based on the analysis, we found that CC has significantly affected the yield of various crops and changed the ET over time. Besides, C-SA tools are vital for addressing the challenges of CC. Its adoption in traditional agriculture can build strong resilience against threats posed by CC. In addition, significant development has been attained in the precision monitoring of the ET from agriculture fields, ranging from direct and indirect to more sophisticated energy balance and modern techniques. However, the accuracy of each method mainly depended on the specific regional climate conditions. In the absence of actual field measurements, empirical or modern models are helpful to estimate ET using routine meteorological variables. Yet, these models require local calibration for the best accuracy.

Time-series analysis of evapotranspiration using normalized difference vegetation index over north-eastern and north-western agro-climatic zones of Tamil Nadu

Estimation of evapotranspiration spatially is mandatory for water budgeting and water resource monitoring based on crop water demand. The amount of evapotranspiration can be estimated spatially from reference evapotranspiration and fractional vegetation cover over the region. The incorporation of specific characteristics like spatial variability over the land cover produced highly reliable results. The AquaCrop model gives reference evapotranspiration based on climatic parameters like temperature, rainfall, solar radiation, relative humidity and elevation. Normalized Difference Vegetation Index (NDVI) depicts the health of vegetation spatially ranging from -1 to +1. The dimidiate pixel model converts NDVI to Fractional Vegetation Cover (FVC) which was then used as a substitute for crop coefficient value. The products of these two parameters produce actual evapotranspiration spatially over the region with high spatial resolution. The average amount of actual evapotranspiration varies for each Land Use Land Cover (LULC) type over different agroclimatic zones as the climatic parameters and water usage patterns vary for each land cover type.

Improving time upscaling of instantaneous evapotranspiration based on machine learning models

ABSTRACT Evapotranspiration (ET) plays a crucial role in the global water and energy cycle. Upscaling instantaneous ET ( E T i ) to daily ET ( E T d ) is vital for thermal-based ET estimation. Conventional methods – such as the constant evaporative fraction method (ConEF), radiation-based method, and evaporative ratio method – often overlook environmental factors, leading to biased estimates of E T d from E T i . To resolve this issue, this study aimed to assess four machine learning (ML) algorithms—XGBoost, LightGBM, AdaBoost, and Random Forest—to integrate meteorological and remote sensing data for upscaling E T i across 88 global flux sites. Each ML model was tested with eight different variable combinations. Results indicated that XGBoost exhibited the best performance, with a root mean square error (RMSE) generally below 13 W m − 2 in estimating E T d from E T i . The best variable combination simultaneously considers evaporative fraction, available energy, meteorology factors, remote sensing albedo, normalized vegetation index, and leaf area index. Using this combination, the XGBoost model achieved an R 2 = 0.88 and an RMSE = 12.33 W m − 2 , outperforming the ConEF method ( R 2 = 0.71 and RMSE = 18.86 W m − 2 ) and its expansions. These findings support the application of ML models in ET upscaling, enabling ET estimation across large spatiotemporal scales.

Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia

Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia

Methodology for Obtaining ETo Data for Climate Change Studies: Quality Analysis and Calibration of the Hargreaves–Samani Equation

Reference evapotranspiration (ETo) is an important part of the water cycle, essential for climate studies, water resource management, and agricultural planning. However, accurate estimation of ETo is challenging when meteorological data are insufficient or of low quality. Furthermore, in climate change studies where large amounts of data need to be managed, it is important to minimize the complexity of the ETo calculation. This study presents a comprehensive approach that integrates data quality analysis with two calibration methods—annual and cluster-based—to improve ETo estimates based solely on temperature data from a set of weather stations (WS). First, the quality and integrity of meteorological data from several WS were analyzed to reduce uncertainty. Second, the Hargreaves–Samani equation (HS) is site calibrated using two approaches: (a) annual calibration, where the radiation coefficient (kRs) is adjusted using a data set covering the entire year; (b) cluster-based calibration, where independent radiation coefficients are adjusted for clusters of years and months. The methodology was evaluated for the Alentejo region in Southern Portugal, using data from 1996 to 2023. When using the original HS equation with a kRs = 0.17 °C−0.5, ETo was estimated with errors from 14.9% to 22.9% with bias ranging from −9.0% to 8.8%. The annual calibration resulted in kRs values between 0.157 and 0.165 °C−0.5 with estimation errors between 13.3% and 20.6% and bias ranging from −1.5% to 1.0% across the different weather stations. Calibration based on clusters of months and years produced unclear results. Dry season months showed better results using cluster-based calibration, while wet season months performed poorly regardless of the calibration approach. The results highlight the importance of meteorological data quality and site-specific calibration for refining temperature-based ETo estimation methods, and for the region studied, the gains do not justify the increased complexity of the cluster-based approach.

Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model

Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model

Assessment of reference evapotranspiration forecasting in the Mediterranean climate of Central Chile using the ASCE standardized Penman-Monteith equation, the Hargreaves-Samani equation, and weather predictions from the Global Forecast System model

Accurate estimation of crop evapotranspiration (ETc) is essential for effective agricultural water resource planning. To determine ETc it is common multiplying the reference evapotranspiration (ETref) by an appropriate crop coefficient (Kc). Forecasting ETref could be particularly beneficial for irrigation scheduling. In this work, daily and cumulative (7 days) ETref predictions were estimated with the Penman-Monteith (PM) and the Hargreaves-Samani (HS) models using environmental variables obtained from the Global Forecast System (GFS). Results were compared with ETref calculated with the ASCE (American Society of Civil Engineers) PM equation from measured daily values of solar radiation, air temperature, relative humidity and wind speed. Four weather stations located in Central Chile during 2020 to 2022 were used. The best results were obtained using the HS equation, for all weather stations, the statistical indicators for daily ETref predictions fall within the following ranges: Accuracy (defined as the percentage of daily ETref predictions with an absolute error within 1mmd−1) varies between 72.8% and 91.8%; the root mean square error (RMSE) ranges from 0.59mmd−1 to 0.98mmd−1; the normalized root mean square error (NRMSE) is between 16.1% and 24.1%; the mean bias error (MBE) is between 0.10mmd−1 and 0.36mmd−1; and the normalized mean bias error (NMBE) varies from 2.5% to 8.3%. On the other hand, the indicators for the 7-day cumulative ETref forecasts were: RMSE from 2.86mm to 5.08mm; NRMSE from 11.0% to 16.3%; MBE from 0.66mm to 2.57mm; and NMBE from 2.4% to 8.3%. These results suggest that HS ETref values predicted from GFS temperature forecasts could be effective for foreseeing near-future water demand and enhancing the accuracy of irrigation scheduling and management in Central Chile.

Applying Budyko framework to explore the projected hydrologic changes in southern Taiwan

This study combines future rainfall and temperature projections with the Budyko framework to analyze future water resource changes in four key watersheds in southern Taiwan under climate change scenarios. Since only temperature and rainfall data are available in the future climate change projections, the future evapotranspiration projections are estimated by this study. This study establishes Budyko equations of each watershed, using historical evapotranspiration and temperature data along with potential evapotranspiration estimates, to project future evapotranspiration based on future temperature and rainfall data. The future rainfall is expected to gradually increase, with the most significant increase under the SSP5-8.5 scenario. However, the rainfall increase varies with the geographical location (latitude) of the watershed, with higher rainfall increases in northern watersheds, such as Zengwen, Jiaxian, and Nanhua. Moreover, the projected evapotranspiration differs in watersheds in southern Taiwan indicating the uncertainty and challenge of evapotranspiration projection under climate change. The future runoff projections indicate an increasing trend with a significant increase (13.4–20.3%) by the end of the century (2081–2100), due to projected increases in rainfall and minor changes in evapotranspiration. The projected increase in runoff implies an optimistic outlook in water resources planning and management, but may pose a threat in flood disasters due to increased rainfall in the wet season.