Samani Model Research Articles

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.

An evolutionary parsimonious approach to estimate daily reference evapotranspiration

The reference evapotranspiration (ETo) is an essential component in hydrological and ecological processes. The objective of this research is to develop an explicit model to estimate ETo only using commonly measurable meteorological parameters such as relative humidity, air temperature, and wind speed, where the measurements corresponding to solar radiation are omitted. The model was generated using Genetic Programming (GP), evaluated, and validated with reference data ETo using FAO56-PM. This reference data was obtained from different climates (warm-temperate and arid-warm) and latitudes, acquired from CIMIS stations in the state of California, United States, and the El Porvenir station in the state of Coahuila, located in north-central Mexico. After applying the proposed methodology, a total of 3754 results were generated, demonstrating a significant improvement in the estimation of ETo compared to the Hargreaves–Samani model. A particularly noteworthy result revealed that our approach outperformed the Hargreaves–Samani model in the training phase by 27%, and in the testing phase by 16%, on average. In order to achieve a generalized model, a dataset encompassing meteorological stations in two different climates (warm-temperate and arid-warm) and various latitudes was utilized. The obtained outcome unveiled a highly effective model for estimating ETo in diverse climatic contexts, eliminating the need for local adjustments. This model significantly surpassed the Hargreaves–Samani model, exhibiting superior performance by 17% during the training phase and 18% during the testing phase. These results conclusively underscore the capability of our approach to provide more accurate and reliable ETo estimates. These results conclusively underscore the capability of our approach to provide more accurate and reliable ETo estimates. Finally, to validate the model, four different datasets with climates similar to those used for model creation (warm-temperate, warm-arid) and different latitudes were employed. The validation stage results clearly indicate the superiority of our reference evapotranspiration ETo11 model over the Hargreaves–Samani model by 51% in warm-temperate climates. For the dataset with arid-warm climate, our model continued to show satisfactory results, surpassing the Hargreaves–Samani model by 8%. GP emerges as an innovative and effective alternative for simplified model development. This approach introduces a novel paradigm that facilitates the efficient development of models, standing out for its simplicity and effectiveness in generating solutions.

Temperature extremities-dependent global solar radiation models for country-wide applications in India

Calibration of models has been performed for estimating global solar radiation with input as daily temperature extremes. Measured data for 22 locations were obtained from the Indian Meteorological Department (India) for 15 years and ten global solar radiation model forms were selected. Data were then bifurcated, where sixteen locations were used for training and the remaining six for validation. Models were compared using the statistical tools-based error analysis. The ranking of models was introduced using a Global Performance Indicator. Period-based testing of models declared Chen et al. model as the superior one. Location-based validation of the models was further performed which indicated the best suitability of the Bristow and Campbell model for semi-arid climate, Hargreaves et al. model for Arid climate and montane climate, Samani model for Tropical wet climate, Goodin et al. model for Humid subtropical climate and wet-dry climate, with GPI values of 5.3965, 0.5325, 0.5174, 0.5182, 0.5333 and 0.5862, respectively.

Evaluation of artificial intelligence models for daily prediction of reference evapotranspiration using temperature, rainfall and relative humidity in a warm sub-humid environment

Accurate estimation of reference evapotranspiration is essential for agricultural management and water resources engineering applications. In the present study, the ability and precision of three artificial intelligence (AI) models (i.e., Support Vector Machines (SVMs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Categorical Boosting (CatBoost)) were assessed for estimating daily reference evapotranspiration (ET0) using limited weather data from five locations in a warm sub-humid climate in Mexico. The Penman–Monteith FAO-56 equation was used as a reference target for ET0 values. Three different input combinations were investigated, namely: temperature-based (minimum and maximum air temperature), rainfall-based (minimum air temperature, maximum air temperature and rainfall), and relative humidity-based (minimum air temperature, maximum air temperature and relative humidity). Extraterrestrial radiation values were used in all combinations. The temperature-based AI models were compared with the conventional Hargreaves–Samani (HS) model commonly used to estimate ET0 when only temperature records are available. The goodness of fit for all models was assessed in terms of the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results showed that among the AI models evaluated, the SVM models outperformed ANFIS and CatBoost for modeling ET0. Further, the influence of relative humidity and rainfall on the performance of the models was investigated. The analysis indicated that relative humidity significantly improved the accuracy of the models. Finally, the results showed a better response of the temperature-based AI models over the HS method. AI models can be an adequate alternative to conventional models for ET0 modeling.

Evaluating the Influence of Deficit Irrigation on Fruit Yield and Quality Indices of Tomatoes Grown in Sandy Loam and Silty Loam Soils

The most important biotic stress factor impacting tomato crop biophysical, biochemical, physiological, and morphological features is water stress. A pot experiment was undertaken in a greenhouse to study the drought responsiveness of tomato (Solanum lycopersicum) yield and quality indices in sandy loam and silty loam soils. For both sandy loam and silty loam soils, the water supply levels were 70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC of ETo (crop evapotranspiration) from the vegetative stage to the fruit ripening stage, calculated using the Hargreaves–Samani (HS) model compared to the time-domain reflectometer (TDR) values calibrated using volumetric water content (VWC). The experiment was conducted as a 2 × 4 factorial experiment, arranged in a completely randomized block design, with four treatments replicated four times. In this study, we examined how sandy loam and silty loam soils at different % FC affect the total marketable yield and quality components of tomatoes, concentrating on total soluble solids (Brix), fruit firmness, dry fruit mass, pH, titratable acid (TA), ascorbic acid (Vit. C), and carotenoid composition. Lycopene and β-Carotene were estimated using the UV spectroscopy method, with absorption spectra bands centered at 451 nm, 472 nm, 485 nm, and 502 nm. The results revealed that even though there were some limitations, TDR-based soil moisture content values had a strong positive correlation with HS-based evapotranspiration, with R2 = 0.8, indicating an improvement whereby TDR can solely be used to estimate soil water content. Tomato plants subjected to 40–50% FC (ETo) water stress in both sandy loam and silty loam soils recorded the highest total soluble solids, titratable acidity, ascorbic acid content, and β-carotene content at an absorption peak of 482 nm, and lycopene content at an absorption peak of 472 nm, with lower fruit firmness, fruit juice content, and fruit juice pH, and a reduced marketable yield. Similarly, tomato plants subjected to 60–70% FC throughout the growing season achieved good fruit firmness, percent juice content, total soluble solids, titratable acidity, ascorbic acid content, and chlorophyll content (SPAD), with minimum fruit juice pH and high marketable yield in both soil textural types. It is concluded that subjecting tomato plants to 60–70% FC (ETo) has a constructive impact on the marketable yield quality indices of tomatoes.

Estimation of the Global Solar Radiation in Anyigba Kogi State Using Hargreaver And Samani

ABSTRACT: This work estimates the global solar radiation in Anyigba with latitude 7.498 using Hargreaves and Samani model. The average monthly global radiation for Anyigba was found to be 32 MJ/m2 /day with a MBE of 15.6333 MJ/m2 /day and RMSE OF 5.985 MJ/m2 /day.

Modification of Hargreaves–Samani Model for Estimation of Reference Evapotranspiration in Semi-Arid Region of Punjab, India

Modification of Hargreaves–Samani Model for Estimation of Reference Evapotranspiration in Semi-Arid Region of Punjab, India

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

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

A Review of Evapotranspiration Measurement Models, Techniques and Methods for Open and Closed Agricultural Field Applications

Detailed knowledge of energy and mass fluxes between land and the atmosphere are necessary to monitor the climate of the land and effectively exploit it in growing agricultural commodities. One of the important surface land fluxes is evapotranspiration, which combines the process of evaporation from the soil and that of transpiration from plants, describing the movement of water vapour from the land to the atmosphere. Accurately estimating evapotranspiration in agricultural systems is of high importance for efficient use of water resources and precise irrigation scheduling operations that will lead to improved water use efficiency. This paper reviews the major mechanistic and empirical models for estimating evapotranspiration including the Penman–Monteith, Stanghellini, Priestly–Taylor, and Hargreaves and Samani models. Moreover, the major differences between the models and their underlined assumptions are discussed. The application of these models is also reviewed for both open and closed field mediums and limitations of each model are highlighted. The main parameters affecting evapotranspiration rates in greenhouse settings including aerodynamic resistance, stomatal resistance and intercepted radiation are thoroughly discussed for accurate measurement and consideration in evapotranspiration models. Moreover, this review discusses direct evapotranspiration measurements systems such as eddy covariance and gas exchange systems. Other direct measurements appertaining to specific parameters such as leaf area index and surface leaf temperature and indirect measurements such as remote sensing are also presented, which can be integrated into evapotranspiration models for adaptation depending on climate and physiological characteristics of the growing medium. This review offers important directions for the estimation of evapotranspiration rates depending on the agricultural setting and the available climatological and physiological data, in addition to experimentally based adaptation processes for ET models. It also discusses how accurate evapotranspiration measurements can optimise the energy, water and food nexus.

COMPARATIVE STUDY ON EVAPORATING DEMAND OF THE ATMOSPHERE USING ENERGY BALANCE METHODS

In this study, two models are computed which are modified penman's monteith and Hargreaves – Samani model. The essence is to provide qualitative information related to the antagonistic effect of climate change on sustainable crop production through qualitative understanding of evaporation and transpiration processes in simple term evapotranspiration (ETo). This is computed using climatic parameters obtained from Abubakar Tafawa Balewa University; Agro weather station, Bauchi for the period of three years. This describes the two processes of water loss on plants, at first, through transpiration and on another note, on the soil surface by evaporation. The study deduced a comparative analysis on aforementioned Methods to determine the evaporating power of the atmosphere in improving crops yield and production through estimating the amount of water needed at the root zone of the plant and also, the seasonal variation during the study. The result of this study shows a little deviation in the two models. The model based on Modified Penman's Monteith displays optimal evapotranspiration. This makes the model satisfy its creation for estimation of reference evapotranspiration. In May, June, September, and October for 2013-2015, high trends are recorded. While In July and August low trend was recorded between climatic parameter and the estimated evapotranspiration. The statistical analyses also show that there is a linear relationship between the two estimated models. In the above months, it shows that application of water is needed for the healthy growth of crops and improved crops yield

Performance of potential evapotranspiration models in Peninsular Malaysia

Abstract Potential evapotranspiration (PET) is an important parameter for the operation of irrigation projects and water resources management. The globally recognized PET estimation model, the FAO-56 Penman–Monteith (FAO-56 PM) model, had been criticized for its requirement of many detailed meteorological variables, but nevertheless has been accepted as the baseline model in many worldwide studies. The performances of different PET models can be found to be excellent for a specific location but may not be representative in other regions. The aim of this study is to select the most suitable PET model to estimate PET in Malaysia. Three radiation-based models and four temperature-based models were compared with the FAO-56 PM model at seven selected meteorological stations in Peninsular Malaysia. The mean bias error, relative error (Re) and normalized root-mean-square error (NRMSE) and coefficient of determination (R2) were used to evaluate the performances of the PET models. The Re values of Turc models were below 0.2 at all stations, while Priestly–Taylor, Thornthwaite, Thornthwaite-corrected and Blaney–Criddle models were above 0.2. The Makkink and Hargreaves–Samani models were below 0.2 at most of the stations. Thus, the Turc model was recommended as the best model to estimate PET in Peninsular Malaysia.

Estimasi Dampak Urban Heat Island terhadap Laju Evapotranspirasi: Studi Kasus di Kota Palembang

A review of air temperature in the Palembang city by reviewing data from the National Agency for Meteorology, Climatology, and Geophysics/BMKG (Kenten Climatology Station and the SMB II Meteorological Station) shows a difference in air temperature can indicate the occurrence of Urban Heat Island (UHI). The difference in air temperature affects the evapotranspiration rate (ET) because air temperature very influencing water evaporation. ET rate estimation with air temperature data is the first step to prove this hypothesis. Hargreaves and Samani, Blaney and Criddle, Linacre, and Kharuffa models is the ET model that using air temperature as the variable was used to estimate the ET rate. Air temperature data used in the period 2011-2020 by reviewing data from the Kenten Climatology Station and the SMB II Meteorological Station. The results of this study of air temperature data from the Kenten Climatology Station and the SMB II Meteorology Station showed a difference in air temperature with the minimum ∆T of 0.42 oC, the maximum of 0.43 oC, and the daily average of 0.41 oC. This difference in air temperature has an impact on the difference in the ET rate with the average ∆ET of the Hargreaves and Samani model of 0.05 mm/day, the Blaney and Criddle model of 0.05 mm/day, the Linacre model of 0.06 mm/day, and the Kharuffa model of 0.14 mm/day. The results of this study predicted that an increase in air temperature causes an increase in the ET rate of ± 10-30%.

Evaluation of long-term climate change impact on the growing season and water use of mature pecan in Lower Rio Grande Valley

Pecan is a major crop in the Lower Rio Grande Valley (LRGV), New Mexico. New Mexico is currently the largest pecan producer in the United States. Pecan is also considered a major water user which can consume as much as 1200–1300 mm of water during the growing season. Climate change can have significant potential impact on the water requirement of pecan as well as its productivity and cost of production. This study evaluates the climate change effects on the duration of growing season (DGS) and water requirement of pecan in LRGV over the period of 1901–2019. Temperature data for the study period was used to identify the DGS. Hargreaves–Samani model was used to calculate reference evapotranspiration (ET0). Daily crop coefficient (Kc) over the study period was calculated using a developed regression model relating growing season Kc to cumulative growing degree days (GDD). The Mann-Kendall and Sen’s methods were applied to determine if there was a significant positive or negative trend in the DGS, ET0, and actual evapotranspiration (ETa). Statistical results showed a decreasing trend in the DGS over 1901–1950 whereas an increasing trend in the DGS was detected for the period of 1950–2019. Like the DGS variations, decreasing and increasing trends for both seasonal ET0 and ETa were observed for the first 50 years and the rest of the period with 99% confidence level, respectively. Results showed an increase of 124 mm in seasonal ETa between 1950 and 2019. The study showed that climate change effects in LRGV have been occurring for the study period, continuous increases in both DGS and ETa may bring changes in crop water requirement, and economics of pecan production as well as other horticultural challenges such as insufficient chill period for flowering, increased vivipary, and delayed or prolonged harvest.

Possibilities to Estimate Daily Solar Radiation on 2-Axis Tracking Plane Using a Model Based on Temperature Amplitude

Significant errors may occur when estimating daily solar radiation in central Europe using empirical models based on air temperature (especially in the winter months). The first goal of this article is to investigate to what extent it is possible to increase the accuracy of the Hargreaves and Samani model, by using the calibration dedicated for each month. We also corrected the temperature amplitudes by narrowing the daily intervals from which the minimum and maximum values were taken. The second goal of this article is to compare the precision of the daily solar radiation estimation on the horizontal plane and on the 2-axis tracking plane. The database comprises the series of parallel measurements on both planes over a period of 10 years. We considered two procedures, direct and indirect, for the 2-axis tracking plane. The second procedure, dubbed “the first estimate horizontal than calculate tracking” is based on the strong relationship between daily solar radiations on both planes. The direct procedure allows for a slightly more accurate estimation. The estimation of daily radiation on the 2-axis tracking plane reflects the measured values worse than estimation on the horizontal plane. We discovered that the increase of estimation errors on the 2-axis tracking plane, compared to the horizontal one, is proportional to the increase in the coefficient of variability of the daily solar radiations.

Evaluation and Calibration of Alternative Methods for Estimating Reference Evapotranspiration in the Senegal River Basin

Reference evapotranspiration (ET0) is a key element of the water cycle in tropical areas for the planning and management of water resources, hydrological modeling, and irrigation management. The objective of this research is to assess twenty methods in computing ET0 in the Senegal River Basin and to calibrate and validate the best methods that integrate fewer climate variables. The performance of alternative methods compared to the Penman Monteith (FAO56-PM) model is evaluated using the coefficient of determination (R2), normalized root mean square error (NRMSE), percentage of bias (PBIAS), and the Kling–Gupta Efficiency (KGE). The most robust methods integrating fewer climate variables were calibrated and validated and the results show that Trabert, Valiantzas 2, Valiantzas 3, and Hargreaves and Samani models are, respectively, the most robust for ET0 estimation. The calibration improves the estimates of reference evapotranspiration compared to original models. It improved the performance of these models with an increase in KGE values of 45%, 32%, 29%, and 19% for Trabert, Valiantzas 2, Valiantzas 3, and Hargreaves and Samani models, respectively. From a spatial point of view, the calibrated models of Trabert and Valiantzas 2 are robust in all the climatic zones of the Senegal River Basin, whereas, those of Valiantzas 3 and Hargreaves and Samani are more efficient in the Guinean zone. This study provides information on the choice of a model for estimating evapotranspiration in the Senegal River Basin.

Estimation of evapotranspiration by the Food and Agricultural Organization of the United Nations (FAO) Penman–Monteith temperature (PMT) and Hargreaves–Samani (HS) models under temporal and spatial criteria – a case study in Duero basin (Spain)

Abstract. The evapotranspiration-based scheduling method is the most common method for irrigation programming in agriculture. There is no doubt that the estimation of the reference evapotranspiration (ETo) is a key factor in irrigated agriculture. However, the high cost and maintenance of agrometeorological stations and high number of sensors required to estimate it make it non-plausible, especially in rural areas. For this reason, the estimation of ETo using air temperature, in places where wind speed, solar radiation and air humidity data are not readily available, is particularly attractive. A daily data record of 49 stations distributed over Duero basin (Spain), for the period 2000–2018, was used for estimation of ETo based on seven models against Penman–Monteith (PM) FAO 56 (FAO – Food and Agricultural Organization of the United Nations) from a temporal (annual or seasonal) and spatial perspective. Two Hargreaves–Samani (HS) models, with and without calibration, and five Penman–Monteith temperature (PMT) models were used in this study. The results show that the models' performance changes considerably, depending on whether the scale is annual or seasonal. The performance of the seven models was acceptable from an annual perspective (R2>0.91, NSE > 0.88, MAE < 0.52 and RMSE < 0.69 mm d−1; NSE – Nash–Sutcliffe model efficiency; MAE – mean absolute error; RMSE – root-mean-square error). For winter, no model showed good performance. In the rest of the seasons, the models with the best performance were the following three models: PMTCUH (Penman–Monteith temperature with calibration of Hargreaves empirical coefficient – kRS, average monthly value of wind speed, and average monthly value of maximum and minimum relative humidity), HSC (Hargreaves–Samani with calibration of kRS) and PMTOUH (Penman–Monteith temperature without calibration of kRS, average monthly value of wind speed and average monthly value of maximum and minimum relative humidity). The HSC model presents a calibration of the Hargreaves empirical coefficient (kRS). In the PMTCUH model, kRS was calibrated and average monthly values were used for wind speed and maximum and minimum relative humidity. Finally, the PMTOUH model is like the PMTCUH model except that kRS was not calibrated. These results are very useful for adopting appropriate measures for efficient water management, especially in the intensive agriculture in semi-arid zones, under the limitation of agrometeorological data.

Estimation of Global Solar Radiation on Horizontal Surface in Kano, Nigeria Using Air Temperature Amplitude

The need for a renewable source of energy is inevitable due to globally uneven distribution of the fossil fuel as well as its associated greenhouse effect. For effective implementation of renewable energy resources in a particular location, its potential availability must be investigated. Solar energy studies of a given geographical location require inputs such as solar radiation profile for the performance estimation and development of various solar technologies. Advance solar radiation measuring equipment such as pyranometer and pyrheliometer are normally installed at the selected locations to collect, measure and output data. However, many developing nations could not afford the cost and maintenance of such equipment. The objective of this work is to calibrate, validate and evaluate and compare four empirical air temperature-based models and choose the most appropriate for estimating the global solar radiation at Kano Airport, Nigeria. The methodology used involved the application of four different empirical models namely the Hargreavees, Allen, Bristow-Campbell and the Samani models by adopting monthly average maximum and minimum air temperatures obtained from meteorological stations as the input parameters. The simulations and analysis were performed in R programing software applications. The analysis shows that average global solar radiation can be precisely estimated using air temperature as the only input parameter to the empirical models at locations where solar radiation data are not obtainable. Using statistical performance analysis to compare the estimated global solar radiation values with the measure solar radiation data. The Samani model with RMSE value of 3.5443, coefficient of determination (R2) value of 0.3508, mean bias error (MBE) of value -3.1506, coefficient residual mass (CRM) of value 0.1448 and mean percentage error (MPE) of value -14.0538 was chosen as the most accurate among the four models. The negative signs in both MBE and MPE indicate non-correlation between the models estimated and observed radiation data. From the simulation and analysis results, simple and non-complex empirical models can be utilized to predict solar radiation with easily obtainable air temperature where sophisticated solar radiation instrument are not readily available. Meanwhile, the accuracy of the estimated solar radiation values of the empirical models using the air temperature can be further improved by using auto- calibration of the models in future works.

Evaluating seasonal and regional calibration of temperature-based methods for estimating potential evaporation in Ontario

Development of appropriate water management strategies in the context of projected air temperature increases under a changing climate necessitates assessing how increases in atmospheric demand for water might constrain future water availability. This is most easily assessed using estimates of potential evaporation (PE). Most temperature-based methods for estimating PE have been calibrated in different landscapes in more southern locations using a singular calibration coefficient. This study assessed whether regionally calibrated temperature-based models improve estimates of PE in Ontario and whether further improvements could be made by adjusting coefficients monthly to address a marked seasonality in Ontario or spatially to address the province’s size and varied landscape. Monthly calibration coefficients were calculated for four temperature-based methods (Thornthwaite, Blaney–Criddle, Hamon and Hargreaves–Samani) using climate data and evaporation estimates from 18 pan evaporation stations in Ontario. Calibrated models were then evaluated against an additional 19 pan evaporation stations by comparing estimates of PE calculated using the adjusted and literature-derived coefficients with pan evaporation estimates. Finally, PE estimated using adjusted coefficients was compared to evaporation (E) estimated using a water balance approach for 64 watersheds across Ontario. As anticipated, the regional calibration process increased the accuracy of the PE estimates relative to those calculated using published model coefficients. Use of monthly model coefficients addressed the marked seasonality of evaporation in Ontario and resulted in improved PE estimates. Adjusted model coefficients varied spatially but the paucity of measurement stations precluded further regionalization. PE estimates improved using adjusted coefficients, varied less between models, and were highly correlated with water balance estimates of E. The Hargreaves–Samani model produced the best estimates of PE using unadjusted and adjusted coefficients and was most highly correlated with long-term water balance estimates of E.

Estimation of crop evapotranspiration through spatial distributed crop coefficient in a semi-arid environment

Accurate and reliable estimation of reference crop evapotranspiration (ETo) is important due to its critical role in determining crop water requirement in irrigated agriculture. The aim of the paper was to estimate the evapotranspiration (ETc) using spatially distributed crop coefficient (Kc) and open access earth observing datasets in a semi-arid environment. We have estimated the actual evapotranspiration (ETc) based on spatially distributed crop coefficient (Kc) using following models namely Surface Energy Balance Algorithm for Land (SEBAL), Makkink model, Hargreaves and Samani model, Camargo method and Jensen-Haise model. Further, these models estimates were compared with Penman-Monteith estimate.Comparative evaluation of models was performed through statistical tests, and it was found that the Makkink model performed satisfactory compared with standard Penman-Monteith model estimate (R2 = 0.88). Leaf area index (LAI) and the Kc was in strong (second order polynomial) relationship (R2 = 0.98). The analysis also shows distributed actual evapotranspiration based on Makkink model has good agreement with actual evapotranspiration from Penman-Monteith with R2 of 0.84, respectively, for the study area. The finding suggests that the Makkink model estimates ETo are very close to the universally accepted Penman-Monteith model based ETo.

Discussion of “Modification of the Hargreaves–Samani Model for Estimating Solar Radiation from Temperature and Humidity Data” by John D. Valiantzas

Discussion of “Modification of the Hargreaves–Samani Model for Estimating Solar Radiation from Temperature and Humidity Data” by John D. Valiantzas