FAO Penman-Monteith Equation Research Articles

Development and evaluation of site-specific evapotranspiration models in Malawi through a comparative analysis of existing models

Locally based information on reference evapotranspiration pertinent to efficient irrigation water management is scarce in developing countries. This compromises the accuracy in determining irrigation crop water requirements since most existing reference evapotranspiration models are empirically based and less accurate unless calibrated to local conditions. The FAO Penman-Monteith equation has been proven to estimate reference evapotranspiration for different environments worldwide. However, its intensive data input requirements impede utilization of the model in developing countries due to inadequate climate data collection and management capacities. Therefore, a need existed to develop site-specific models that are less data intensive. A study was conducted aimed at developing site-specific evapotranspiration models using comparative analysis of the Kharrufa, Linacre, Hargreaves Original and Hargreaves Modified empirical models during the wet and dry season. Performance of each model was compared with the FAO Penman-Monteith model, regarded as the standard model. The models whose Root Mean Square Error (RMSE), Mean Bias Error (MBE) and coefficient of determination (R2) were satisfactory were selected. The selected models were developed statistically through regression analysis. Performance of the Kharrufa model was satisfactory comparatively as observed from its RMSEs of 1.02 mm/day and MBE -0.34 to 0.8 for the dry season. However, the model less accurately estimated reference evapotranspiration during the wet season (RMSE 2.18 mm/day and MBE 1.03 to 2.13). The study recommends the use of the Kharrufa model in the study locations in dry seasons while utilization in wet season may require further studies to ascertain the model's useability and reliability.

Impact of native vegetation and soil moisture dynamics on evapotranspiration in green roof systems

Evapotranspiration (ET) is an important water budget term for understanding the recovery of stormwater retention in green roof systems (GRs). However, ET evaluations, particularly in full-scale GRs, remain challenging. This study investigated ET dynamics within a GR in the City of Pittsburgh, USA, using a water balance based on continuously monitored soil moisture from moisture sensors over 15 months. Results suggest under well-watered soil conditions, daily moisture loss correlated with solar radiation, temperature, and humidity, in decreasing order of correlation strength, while wind speed had limited effects. Compared to sensor-informed moisture loss (using moisture-based water balance), the Hargreaves and FAO-56 Penman-Monteith equations predicted cumulative ET that was 1.8 and 2.1 times higher, respectively. When soil moisture declined and approached the temporary wilting points, a noticeable reduction in daily moisture loss was observed. This suggests the necessity of using a water stress coefficient alongside a crop coefficient to represent actual ET based on FAO-56 Penman–Monteith estimates. Seasonal crop coefficients from dominant native plant species present at our monitored location, eastern bluestar (Amsonia tabernaemontana) and creeping woodsorrel (Oxalis corniculata), had mean values of 0.48, 0.62, and 0.65 for fall, spring, and summer, respectively. The impact of water stress on ET could be characterized by a linear relationship with moisture content. Our results highlight the importance of soil moisture in regulating ET processes and demonstrate the utility of soil moisture data for evaluating ET in GRs and informing irrigation practices.

Determination of Water Requirements and Irrigation Scheduling for Major Cereal Crops Grown in Welmera District, Central Highland of Ethiopia

Determination of crop water requirements and appropriate irrigation scheduling is important to prevent over or under-irrigation. The study was conducted to determine the crop water requirement and irrigation scheduling of the selected cereal crops grown under irrigated conditions at Holeta, Central Highland of Ethiopia. The crops include wheat, maize, and barley. By using the 30-years climatic data, the crop evapotranspiration (ETc), reference crop evapotranspiration (ETo), and irrigation water requirement for each crop were determined by using the CROPWAT model which is based on FAO-Penman Monteith equation. The results indicated that, the CWR for the early January sown wheat, maize, and barley was 380.2mm, 433.2mm, and 399.2mm respectively. The seasonal gross irrigation requirement was estimated to be 633.67 mm, 722 mm, and 665.33 mm for wheat, maize, and barley respectively. for 1<sup>st</sup> January sown wheat, maize, and barley, irrigation should be given nine times for wheat (1-Jan, 10-Jan, 21-Jan,5-Feb, 16-Feb, 26-Feb, 11-Mar, 25-Mar, and last irrigation on 10-Apr), with GIR application of 50.3mm, 28.1mm, 34.6mm, 48.2mm,56.6mm,67.2mm,69.5mm, 66.2mm, and 72.3mm depth respectively. Seven times for maize (1-Jan, 19-Jan, 5-Feb, 19-Feb, 6-Mar, 21-Mar, and last irrigation on 10-Apr) with GIR of 54.1mm, 51.2mm, 73mm, 89.8mm,96.9mm, 97.7mm, and 100.8mm depth respectively and eight times for barley (1-Jan, 11-Jan, 25-Jan, 6-Feb, 16-Feb, 1-Mar, 15-Mar and last irrigation on 30-Mar) with GIR amount of 42.5mm, 29mm, 39.6mm, 54.1mm, 60mm, 61.6mm, 63.1mm, and 64.1mm depth at each irrigation date respectively. This study might be useful in preventing over or under-irrigation and planning water management strategies in the district for the selected crops.

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

This study was conducted with the aim of evaluating the performance of artificial neural networks (ANNs) to estimate the reference evapotranspiration using limited climate data in Shahat region in Libya, compared to using the FAO Penman-Monteith equation (FPM), which requires temperature, wind speed, relative humidity and number of sunshine hours, which are rarely available in most meteorological stations in developing countries. In this study, we used the average temperature (Tmean) and the average relative humidity (RHmean) obtained from Shahat meteorological station for the period from 1963 to 1999, and the extraterrestrial radiation (Ra), which can be calculated given the location and time of the day. These data are divided into two groups, from 1963 to 1988 and from 1989 to 1999 for the training and validation phases of the neural networks, respectively. This study concluded that using (Tmean), (RHmean) and (Ra) gave the best agreement with the results calculated with the FAO Penman-Monteith equation, where the values of R2 and RMSE are equal to 0.98 and 0.26, respectively.

Assessing irrigation water demand and pumping operations for rice farming in the Bengawan Solo River, Indonesia

Owing to population growth, the rice demand in Indonesia has been increasing, which has led to an increase in rice consumption. One way to boost rice production is to enhance pump irrigation in rainfed fields. The aim of this study is to evaluate irrigation water usage and water pumping practices in the Bengawan Solo River, focusing on enhancing rice production. Data were sourced from governmental entities, which include the Indonesian Bureau of Meteorology, Climatology, and Geophysics and the Ministry of Public Works and Housing. Water requirement was calculated using the FAO Penman–Monteith equation. The study highlights that throughout the three distinct growing seasons (GS), the water requirements for irrigating rainfed rice fields vary, with the most substantial demand observed during the first growing season (GS I), followed by the third growing season (GS III), and the second growing season (GS II). In dry years, a consistent pattern of low water balances occurs, which persists below 500 mm across all months. Compared with the other two scenarios, the dry year shows higher variability in rainfall, as evidenced by its higher coefficient of variation of 0.620 compared with 0.347 and 0.416 for the wet and normal years, respectively. The electricity cost rate peaks in GS I, trailed by GS II and GS III, with rates of IDR 2,400, 1,180, and 1,028 per kilowatt-hour, respectively. The findings play a pivotal role in shaping regional planning decisions regarding the utilization and necessity of river water resources and the development of cropping calendars.

Case Study of Impact Evaluation of Agrivoltaic Structure Sizing on Water Availability for Wheat

Agrivoltaic (AV) Systems are a new solution for cropping conditions improvement by mitigating extreme weather conditions. Indeed, AV Systems affect microclimate, notably Air Temperature, Irradiance or Evapotranspiration that determines Soil Water Availability. To evaluate crop water stress protection and ensure optimized AV Systems sizing, a methodology was developed using a microclimate simulation tool. This paper presents a case study of Wheat focused on Water Availability, from a project located near Orléans, Center France. The methodology uses Irradiance Simulations at crop level by AGRISOLEO software, which has been parameterized with the structures sizing under study and a panel steering algorithm adapted to wheat phenology. The results are used for evapotranspiration modelling following the FAO-56 Penman-Monteith equation. For this case study, results showed that AV Systems under test reduced irradiance up to 40%. This effect may be reduced up to 17% by controlling the panels rotation angle to maximize irradiance during crop’s key development stages. Furthermore, AV Systems reduced Water Stress up to 48%. Microclimate simulation tool demonstrated possibility to assess AV Systems sizing impact on irradiance received by crop and Water Stress protection. Moreover, controlling the solar panels at key development stages of the crop is the central lever in the synergy of dynamic AV Systems. The methodology presented here applies not only to Wheat but to a wider range of crops and climate conditions, hence opening promising perspectives to optimize AV systems sizing and agronomic benefits.

Evaluation of daily crop reference evapotranspiration and sensitivity analysis of FAO Penman-Monteith equation using ERA5-Land reanalysis database in Sicily, Italy

Crop evapotranspiration (ET) is one of the most important components in many hydrological processes. The crop reference evapotranspiration (ETo) represents the atmospheric water demand in each crop type, development stage, and management practices. The Penman-Monteith equation in the version suggested by the Food and Agriculture Organization (FAO56-PM), is one of the most used methods to estimate ETo. In several regions of the world, meteorological observations are not always available. The most recent reanalysis database ERA5-Land, released in 2019, can be useful to overcome this limit. The database provides, with a spatial grid of 0.1° latitude and 0.1° longitude, several hourly climate data such as air temperature, dew point temperature, solar radiation, and wind speed components all at 2.0 m above the soil surface, except wind speed components at 10 m, useful to apply the FAO56-PM equation. The objective of this research is to assess the quality of ERA5-Land climate variables data to estimate daily ETo in Sicily, Italy. The effect of the weather station’s elevation associated with the statistical indicators was also evaluated to verify how the morphology affects the measurements. Finally, the sensitivity analysis of the FAO56-PM equation was carried out to identify which climate variables have the most influence on the ETo estimation. For the period 2006–2015, the comparison between air temperature, global solar radiation, wind speed, and relative air humidity, measured from 39 ground weather stations in Sicily, and ERA5-Land was carried out and then, through FAO56-PM equation daily ETo values were estimated using both databases. The statistical indicators Root Mean Square Error (RMSE) and Mean Bias Error (MBE) confirm the possibility of considering the ERA5-Land a suitable solution to estimate ETo. The sensitivity analysis showed that good ETo estimation depends mainly on the accuracy of the relative air humidity and air temperature data.

Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions

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.

Estimation of reference evapotranspiration using some class-A pan evaporimeter pan coefficient estimation models in Mediterranean-Southeastern Anatolian transitional zone conditions of Turkey.

Reference evapotranspiration (ETo), which is used as the basic data in many studies within the scope of hydrology, meteorology, irrigation and soil sciences, can be estimated by using the evaporation (Epan) measured from the class-A pan evaporimeter. However, this method requires reliable pan coefficients (Kp). Many empirical models are used to estimate Kp coefficients. The reliability of these models varies depending on climatic and environmental conditions. Therefore, they need to be tested in the local conditions where they will be used. In this study, conducted in Kahramanmaraş, which has a semi-arid Mediterranean climate in Turkey during the July-October periods of 2020 and 2021, aimed to determine the usability levels of six Kp models in estimating daily and monthly average ETo. The Kp coefficients estimated by the models were multiplied with the daily Epan values, and the daily average ETo values were estimated on the basis of the model. The daily Epan values were measured using an ultrasonic sensor sensitive to the water surface placed on the class-A pan evaporimeter. The ultrasonic sensor was managed by a programmable logic controller (PLC). To enable the sensor to be managed by PLC, a software was prepared using the CODESYS programming language and uploaded to the PLC. The daily average ETo values determined by the FAO-56 Penman-Monteith equation were accepted as actual values. The ETo values estimated by the Kp models were compared with the actual ETo values using the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2) statistical approaches. The Wahed & Snyder outperformed the other models in estimating daily (MAE = 0.78 mm day-1, MAPE = 14.40%, RMSE = 0.97 mm day-1, R2 = 0.82) and monthly (MAE = 0.32 mm day-1, MAPE = 5.88%, RMSE = 0.32 mm day-1, R2 = 0.99) average ETo. FAO-56 showed the nearest performance to Wahed & Snyder. The Snyder model presented the worst performance in estimating daily (MAE = 2.09 mm day-1, MAPE = 37.53%, RMSE = 2.36 mm day-1, R2 = 0.82) and monthly (MAE = 1.83 mm day-1, MAPE = 31.82%, RMSE = 1.87 mm day-1, R2 = 0.99) average ETo. It has been concluded that none of the six Kp models can be used to estimate the daily ETo in Kahramanmaraş located in the Mediterranean-Southeastern Anatolian transitional zone, and only Wahed & Snyder and FAO-56 can be used to estimate the monthly ETo without calibration.

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.

Performance of the Copernicus European Regional Reanalysis (CERRA) dataset as proxy of ground-based agrometeorological data

The continuous advances in numerical modeling of the atmosphere, computing power and data assimilation techniques entail frequent updates of numerical weather prediction (NWP) models that show improved forecast skill. This circumstance leads to the recurrent delivery of revised reanalysis databases that provide weather estimates for several decades back in time by combining the latest NWP models with observations. Since climate studies and agriculture water management applications require the availability of accurate and reliable weather data, assessing the performance of reanalysis products contributes to informed choices of potential weather proxy of ground-based agrometeorological data. CERRA (Copernicus European Regional ReAnalysis) dataset is the latest regional reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF), in August 2022. CERRA is forced by the global ERA5 reanalysis, and it provides weather data with resolution of 5.5 km for the pan-European territory from 1984. For the first time in literature, this study explores the performance of CERRA data at 38 ground-based weather stations located in Sicily, an Italian region with Mediterranean climate, during the irrigation seasons 2003–2022. The objective of the study lies in evaluating CERRA performance with respect to air temperature, actual vapor pressure, wind speed and solar radiation that are input variables for assessing reference evapotranspiration, ETO, which is a key variable for quantifying irrigation volumes needed for water resources studies. The accuracy of ETO estimates depends on those input variables through the equation provided by the Food and Agriculture Organization of the United Nations (FAO), i.e., the FAO Penman-Monteith equation. Here, it is also evaluated the performance of ETO estimates by using CERRA weather inputs in the FAO Penman-Monteith equation. The results show that the performances of CERRA weather data are excellent, especially for air temperature, and this determines that CERRA ETO estimates present high accuracy and reliability with a mean PBIAS and NRMSE equal to 5.6% and 13%, respectively, over the region. Those outcomes lead to the conclusion that CERRA dataset represents a valid alternative to ground-based agrometeorological measurements and their spatial interpolation for water resource regional studies.

Mapping crop evapotranspiration with high-resolution imagery and meteorological data: insights into sustainable agriculture in Prince Edward Island

Soil moisture variability caused by soil erosion, weather extremes, and spatial variations in soil health is a limiting factor for crop growth and productivity. Crop evapotranspiration (ET) is significant for irrigation water management systems. The variability in crop water requirements at various growth stages is a common concern at a global level. In Canada’s Prince Edward Island (PEI), where agriculture is particularly prominent, this concern is predominantly evident. The island’s most prominent business, agriculture, finds it challenging to predict agricultural water needs due to shifting climate extremes, weather patterns, and precipitation patterns. Thus, accurate estimations for irrigation water requirements are essential for water conservation and precision farming. This work used a satellite-based normalized difference vegetation index (NDVI) technique to simulate the crop coefficient (Kc) and crop evapotranspiration (ETc) for field-scale potato cultivation at various crop growth stages for the growing seasons of 2021 and 2022. The standard FAO Penman–Monteith equation was used to estimate the reference evapotranspiration (ETr) using weather data from the nearest weather stations. The findings showed a statistically significant (p < 0.05) positive association between NDVI and tabulated Kc values extracted from all three satellites (Landsat 8, Sentinel-2A, and Planet) for the 2021 season. However, the correlation weakened in the subsequent year, particularly for Sentinel-2A and Planet data, while the association with Landsat 8 data became statistically insignificant (p > 0.05). Sentinel-2A outperformed Landsat 8 and Planet overall. The Kc values peaked at the halfway stage, fell before the maturity period, and were at their lowest at the start of the season. A similar pattern was observed for ETc (mm/day), which peaked at midseason and decreased with each developmental stage of the potato crop. Similar trends were observed for ETc (mm/day), which peaked at the mid-stage with mean values of 4.0 (2021) and 3.7 (2022), was the lowest in the initial phase with mean values of 1.8 (2021) and 1.5 (2022), and grew with each developmental stage of the potato crop. The study’s ET maps show how agricultural water use varies throughout a growing season. Farmers in Prince Edward Island may find the applied technique helpful in creating sustainable growth plans at different phases of crop development. Integrating high-resolution imagery with soil health, yield mapping, and crop growth parameters can help develop a decision support system to tailor sustainable management practices to improve profit margins, crop yield, and quality.

Fog water harvesting potential and its use in supplementary irrigation of rainfed crops (winter wheat) in Abi-beyglu, Ardabil (Iran)

Abstract In arid and semi-arid areas where available water resources are very limited, the application of unconventional sources of water like the fog is of paramount importance. In this paper, the feasibility of using a standard fog collector (SFC) to collect fog water for complementary irrigation of rainfed wheat in the Abi-beyglu area was investigated. For this purpose, collected water volume was measured on a daily basis during fog time in 2021. The water demand of the winter wheat was estimated by the FAO Penman–Monteith equation under dry and normal conditions. Then the contribution of the collected water to supply the water demand of the wheat and the resultant increase in the yield under two different scenarios, namely complementary irrigation with 30 and 60 mm of collected water, was estimated using the AquaCrop model. Results showed that it is feasible to obtain an average water production of 3.6 L/m2/day over the studied period. Upon irrigation with 30 and 60 mm of collected water under dry and normal conditions, 26 and 34% of the water deficiency for wheat farming was supplied, leading to increased crop yields by 0.6 and 1.7 ton/ha, respectively.

Assessment of the Correction of the Reference Evapotranspiration at Nonirrigated Weather Stations Affected by Aridity and Delimitation of the Meteorological Conditions that Limit its Implementation

As most reference crop evapotranspiration (ETref) estimates are computed from weather stations located outside irrigated plots, the site aridity can produce ETref overestimation. To obtain more reliable ETref estimates, the potential of using the methodology that corrects the observed air and dew point temperatures was first analyzed in this study, and the meteorological conditions that limit this methodology implementation were subsequently assessed. A statistical analysis was conducted of pairwise comparisons between a station under reference conditions (Montañana) and four nearby stations affected by aridity in Spain for 2020. The daily reference evapotranspiration was calculated with the FAO56 Penman–Monteith equation (ETo PM), and afterward, the corrected daily reference evapotranspiration (ETo PMcor) was obtained using the previously mentioned correction methodology. The statistical analysis showed a greater connection between ETref and ETo PMcor in all pairwise comparisons, with the strongest connection reached between ETref Montañana station and ETo PMcor Pastríz station (coefficient of determination r2 = 0.98 and root mean square error RMSE = 0.34 mm/day.). This ETo PM correction methodology was established considering a difference between the minimum temperature (Tmin) and the dew point temperature (Tdew) greater than 2 °C, as this value is considered the benchmark of aridity, but this research found that high aridity (P/ETo < 0.5) inland locations and high aridity coastal locations with high relative humidity (RH), markedly as of 61.5% and 68%, respectively, could exhibit days where Tmin-Tdew < 2 °C does not reflect well-watered soil and healthy grass but rather high RH levels (r2 = 0.77 and 0.57, respectively). As this correction methodology was not established considering Tmin-Tdew values smaller than 2 °C, the application scope of this methodology is limited under the above scenarios.

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

Estimation of daily evapotranspiration in gully area scrub ecosystems on Loess Plateau of China based on multisource observation data

Evapotranspiration is an important process in the hydrologic cycle. In this study, different types of daily evapotranspiration were accurately estimated and comparatively analyzed for 2020–2021 in the gully area of Loess Plateau, China with multisource observation data. The daily actual evapotranspiration was directly observed by the eddy covariance system and then compared with the daily evapotranspiration simulated by HYDRUS-1D, and they showed reasonable agreement and similar variability characteristics it with a correlation coefficient of 0.71. The FAO-56 Penman-Monteith equation was used to calculate the reference evapotranspiration and compared with the pan evaporation in the study area. The reference evapotranspiration has a single-peak pattern and seasonal variation, similar to the actual evapotranspiration process during the annual period. The crop coefficient had seasonal-variation characteristics and remained 0.8 in the mid-growth period. The correlation analysis between evapotranspiration and environmental factors at different time scales suggested that energy was the dominant factor affecting evapotranspiration in the study area. The validation results of the complementary relationship of evaporation indicated a significant asymmetric complementary relationship in the study area. Both generalized nonlinear complementary relationship functions have good applicability, but the performance of the sigmoid function was slightly better than that of the polynomial function. The results provided a reference for local water resource management and agricultural production.

A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives

Reference Evapotranspiration (ETo) is a complex, dynamic and non-linear hydrological process. Accurate estimation of ETo has long been an eminent topic of interest in the research community for its importance in effective planning and sustainable water resource management. Although the FAO-56 Penman-Monteith (PM) equation has been accepted as a standard equation for ETo measurement, the primary concern that inhibits the applicability of this equation is the requirement for all the climatological variables, which might not be available at a given location. Owning to the remarkable success and accuracy achieved by Artificial Intelligence (AI) in almost every sphere, scientists have proposed the usage AI models for ETo prediction as an alternate to the conventional methods. This comprehensive review will serve to raise awareness regarding the various state-of-the-art standalone AI frameworks, along with capturing the intriguing developments in the advanced AI space such as the hybrid and ensemble models, evolutionary models and a range of optimization techniques. The results from the publications published over the last 15 years (2007–2022) for ETo prediction using AI under varied agro-climatic scenarios have been analysed and synthesized. The advantages and disadvantages of the established AI techniques have been discussed in each subsection. Some of the derived insights and major findings are discussed along with the future research recommendations. This review will not only provide a research vision for the novice researchers in the applicability of the aforementioned techniques, in context of ETo prediction, but also be helpful as a compilation of the AI modelling studies for ETo prediction for the established water resource engineers and hydrologists.

Investigating the roles of different extracted parameters from satellite images in improving the accuracy of daily reference evapotranspiration estimation

Agricultural water management, crop modeling, and irrigation scheduling are all dependent on the accurate estimation of reference evapotranspiration (ET0). A satellite image can also compensate for the lack of reliable weather information. So, in this study, stochastic gradient descent (SGD) has been implemented for optimizing multilayer perceptron (MLP) and developing SGD-MLP to estimate daily ET0 in Tabriz (semi-arid climate) and Babolsar (humid climate) stations, Iran, using extracted data from satellite images. The estimated ET0 values were compared to the determined ET0 based on the FAO-Penman–Monteith equation. Based on satellite image data collected from 2003 to 2021, the database was constructed. During the development of the abovementioned models, data from 2003 to 2016 (70%) were used for training purposes, and residual data (30%) were used for testing purposes. Additionally, the input variables, including land surface temperature (LST) day and night, normalized difference vegetation index (NDVI), leaf area index (LAI), and a fraction of photosynthetically active radiation (FPAR) from MODIS sensor, were utilized to estimate the daily ET0. Thus, there are three studied models; first is based on the LST, second on the vegetation indices, and third on the combination of the LST and the vegetation indices. Additionally, four performance indexes, including the coefficient of determination (R2), the root-mean-square error (RMSE), Willmott’s index of agreement (WI), and Nash–Sutcliffe efficiency, were utilized in order to measure the implemented model’s accuracy. According to the obtained results, the SGD-MLP-3 with input parameters of LSTday&night, LSTmean, LAI, NDVI, and FPAR gave the most accurate results with RMSE and WI values of as 0.417 mm/day, 0.973, for Tabriz and 0.754 mm/day, 0.922 for Babolsar stations, respectively. Conclusively, LST of daytime, nighttime, and average may be suggested as the most influential parameter for ET0 estimation.

Improvement of reference crop evapotranspiration estimates using limited data for the Brazilian Cerrado

Evapotranspiration (ET) is a key component of the hydrological cycle. Therefore, adequately estimating it is crucial to improving water resource planning and management.One of the most affordable methods of estimating ET is first to estimate reference crop evapotranspiration (ETo) and later associate it to crop and soil coefficients. The FAO Kc-ETo approach can be used only when ETo is computed with the FAO Penman-Monteith equation. However, low data availability may restict the equations used to estimate ETo. In this study, we assess and calibrate common methods used to estimate ETo under such conditions of limited data availability. Based on the annual calibration, the Makkink (NSE = 0.85) outperformed the Priestley-Taylor (NSE = 0.73), Hargreaves-Samani (NSE = 0.56), and Penman-Monteith temperature approach (NSE = 0.58). The seasonal calibration of parameters showed no significant improvement to the methods assessed (ΔNSE ≤ 0.01), except for the Priestley-Taylor (ΔNSE = 0.06). The performance of temperature-based equations was particularly limited due to the performance of the equation adopted to estimate global solar radiation. Thus, improving the representation of global solar radiation for limited data availability can also play a key role in improving ETo prediction.

Estimation of Actual Evapotranspiration and Crop Coefficient of Transplanted Puddled Rice Using a Modified Non-Weighing Paddy Lysimeter

Lysimetric and eddy covariance techniques are commonly used to directly estimate actual crop evapotranspiration (ETa). However, these technologies are costly, laborious, and require skills which make in situ ET estimation difficult, particularly in developing countries. With this in mind, an attempt was made to determine ETa and stagewise crop coefficient (Kc) values of transplanted puddled rice using a modified non-weighing paddy lysimeter. The results were compared to indirect methods, viz., FAO Penman–Monteith and pan evaporation. Daily ETa ranged from 1.9 to 8.2 mmday−1, with a mean of 4.02 ± 1.35 mmday−1, and their comparison showed that the FAO Penman–Monteith equation performed well for the coefficient of determination (R2 of 0.63), root mean squared error (RMSE = 0.80), and mean absolute percentage error (MAPE = 13.6 %), and was highly correlated with ETa throughout the crop season. However, the pan evaporation approach was underestimated (R2 of 0.24; RMSE = 0.98; MAPE = 22.13%) due to a consistent pan coefficient value (0.71), vegetation role and measurement errors. In addition, actual Kc values were obtained as 1.13 ± 0.13, 1.27 ± 0.2, 1.23 ± 0.16, and 0.93 ± 0.18 for the initial, crop development, mid-season, and end-season stages, respectively. These estimated crop coefficient values were higher than FAO Kc values. Statistical analysis results revealed that the overall stagewise-derived average Kc values were in line with FAO values, but different from the derived pan Kc values, although found insignificant at a 5% significance level. In addition, water productivity and agro-meteorological indices were derived to evaluate the cultivar performance in this experiment. Therefore, such a methodology may be used in the absence of weighing lysimeter-derived Kc values. The derived regional Kc values can be applied to improve irrigation scheduling under similar agro-climatic conditions.