Penman-Monteith Method Research Articles

Evaluation and Modelling of Reference Evapotranspiration Using Different Machine Learning Techniques for a Brazilian Tropical Savanna

Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized.

Estimating evapotranspiration from soil moisture using the improved soil water balance method in cold mountainous areas

Evapotranspiration (ET) is critical for ecosystem protection and water services, especially in the mountainous areas of arid and semi-arid watersheds. The lysimeter and Eddy Covariance (EC) methods are widely used for directly measuring ET, but are difficult to install and apply in mountainous areas with complex topography. The commonly used indirect methods for estimating ET, such as the Penman-Monteith (PM) method, present significant challenges in mountainous areas with scarce data. The simple soil water balance (SWB) method, which estimates ET from soil moisture dynamics, is another reliable and simple method for estimating ET. However, a drawback of the original SWB method is that it assumes soil moisture depletion only occurs through ET, ignoring the process of deep percolation. This restriction limits the applicability of the SWB method. In this study, we improve the SWB method (ISWB) by incorporating a deep percolation module into the soil water balance equation. Subsequently, we compare the estimated ET obtained from the ISWB, the Food and Agriculture Organization (FAO)-56 PM, and the Hargreaves-Samani (HS) methods with the observed ET. Results show that the ISWB method for estimating ET performs better when using the soil moisture of the 0–25 cm and below layers, compared to the 0–20 cm and above layers. Meanwhile, there is no significant difference in performance between using the soil moisture of the 0–25 cm layer and the soil layers below 25 cm. In addition, ignoring interception evaporation has an obvious influence on ET estimation using the ISWB. Furthermore, the comparison indicated that the performance of the ISWB method is superior to that of the FAO-56 PM and HS methods in the study areas. Our study shows that the ISWB method has significant potential for ET estimation in data-scarce and topographic-complex mountainous areas.

CALIBRATION OF TWO MODELS FOR ESTIMATING REFERENCE EVAPOTRANSPIRATION BY USING FAO-56 PENMAN-MONTEITH MODEL UNDER ARID CONDITIONS

The Penman-Monteith method (P-M) to estimate the reference evapotranspiration (ETo) is the most reliable method and recommended by the FAO as the standard to verify other empi- rical methods. However, the Thornthwaite (Th) and Hargreaves-Samani (H-S) models are widely used because they are based on measurements of air temperature, frequently recorded in any meteorological stations. In this study, the daily meteorological parameters of air temperature, relative humidity, wind velocity, were available at six stations (Riyadh), (Ha’il), (Tabuk), (Turayf), (Makkah) and (Jazan). The net radiation was computed using a mathematical model based on a serie of related equations. Therefore, the application of Penman-Monteith became possible to calibrate the Thornthwaite and Hargreaves-Samani models. The local calibration of the both models (Th and H-S) in arid conditions is based on modifying the original coefficients of the named models using the ratio for estimated ET (Th and H-S mpdels) and the reference ETo of (P-M model). In the comparison, the indices of concordance (D), confidence (C), correlation coefficient (r) were analyzed, together with the root mean square error (RMSE) and Nash-Stucliff Efficiency (NSE). So, the ET of H-S model without adjustment were greater than the ETo of P-M during all the months at the total of the studied stations. Contrary, the use of non-adjusted Th ET show a smaller values of the monthly average in a total of the selected stations. After adjustment of the original coefficients of (0.0023) for H-S model and (1.6) for the Th model, we can obtain the new equations of estimating the monthly average of ET fitting better with the P-M Eto model.

Performance evaluation of simpler reference crop evapotranspiration estimation equations with and without local calibration

The present study was taken up to demonstrate an overall methodology for local calibration of simpler reference crop evapotranspiration (ET0) estimation equations relative to the physically-based, albeit more data-intensive Penman-Monteith (PM) method. Climate records for the historical period 2006-2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used for this purpose. The numerical coefficients in the original forms of the simpler equations namely, Blaney-Criddle (BC), Priestley-Taylor (PT), Radiation (RAD), and Turc (TC) were treated as unknown parameters and optimized for each station using PM ET0 estimates as reference. Results of local calibration indicated significant improvement in prediction accuracies and bias reduction for all the equations with the calibrated PT equation exhibiting the best performance. Spatial maps depicting the variations in the optimal values of parameters were developed to aid the accurate estimation of monthly ET0 values in the study area using only minimal climate inputs.

Modeling medium resolution evapotranspiration using downscaling techniques in north-western part of India

The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.

Investigation of Valiantzas’ Simplified forms of FAO56 Penman-Monteith Reference Evapotranspiration Models in a semi-arid region

The performance of sixteen Valiantzas’ reference evapotranspiration models was investigated using the meteorological data obtained from Agricultural Engineering College and Research Institute, Kumulur, Lalgudi Taluk of Tiruchirapalli district, which is a semi-arid region located in Tamil Nadu, India. The Valiantzas’ reference evapotranspiration was compared with the globally used FAO56 Penman-Monteith method. The indexes used for comparison are coefficient of determination (R2), Standard Error Estimate (SEE) and long-term average ratio (RT). The Valiantzas’ models requiring complete dataset performed excellently in this station. The models not requiring wind speed data also performed equally well in this station and exhibited a fairly good correlation with FAO56-PM method. The other formulae accounting for local average wind conditions, reduced set formulae with temperature and relative humidity data alone and reduced set formulae with temperature and radiation data alone also performed well in this station. The investigation showed fair accuracy of Valiantzas’ Models and hence researchers can use these models in the absence of availability of the complete dataset.

Evaluation of machine learning models for prediction of daily reference evapotranspiration in semi-arid India

Reference evapotranspiration (ET ) is controlled by climatic factors; hence, its estimation provides an idea 0 about the atmospheric demand of water. Machine learning techniques like elastic net (ELNET), K-nearest neighbours (KNN), multivariate adaptive regression splines (MARS), partial least squares regression (PSLR), random forest (RF), support vector regression (SVR), XGBoost and cubist were employed to predict daily reference evapotranspiration based on daily weather parameters of twenty years. Penman-Monteith method was used as the reference method for ET estimation. All models performed well during calibration showing 0 higher coefficient of determination (R2) which ranged from 0.97 (for PLSR) to 1 (for cubist models). Mean absolute error during calibration ranges from 0.027 mm d-1 for cubist to 0.607 mm d-1 for ELNET. Cubist model (R2 = 1, MAE = 0.017 mm d-1, RMSE = 0.027 mm d-1) outperformed other models during the calibration. During validation, the coefficient of determination (R2) for the machine learning models varied from 0.819 to 1, RMSE varied from 0.06 to 0.60 mm d-1 and MAE varied from 0.031 to 0.38 mm d-1. Based on statistical parameters, best performance was observed for cubist model (R2 = 1, RMSE = 0.06 mm d-1, MAE = 0.031 mm d-1) among the studied machine learning models for the prediction of reference evapotranspiration. Hence, the cubist model may be used to estimate daily reference evapotranspiration for the studied region.

Investigation of Multilayer Perceptron Regression-based Models to Forecast Reference Evapotranspiration (ETo)

Reference evapotranspiration (ETo) is a valuable factor in the hydrological process and its estimation is a sophisticated and nonlinear problem. In this study, the utility of multilayer perceptron regression is investigated to estimate ETo of Jodhpur city, India which has a hot arid climate. Four different multilayer perceptron regression-based models are created and compared in this study. Multilayer perceptron regression is a popular tool used to predict the results of sophisticated problems. Each created model has a different architecture, in which the size (neurons) of the input and hidden layers is decided by the maximal correlation relationship between meteorological attributes and observed ETo using the Food Agriculture Organization Penman-Monteith method (FAO-PM56). This study found that model with meteorology inputs (namely both high and low temperatures, solar radiation, wind speed at 2 m, and humidity) and nine neurons at the hidden layer achieved high predictive accuracy with mean absolute error (MAE) of 0.08, mean squared error (MSE) of 0.01, root mean squared error (RMSE) of 0.10, Pearson correlation (r) of 0.99, and coefficient of determination (r2) of 0.99. The finding of this study is that the multilayer perceptron regression-based models with at least three meteorological inputs (temperature, solar radiation, and wind speed) can effectively utilize to estimate ETo and may receive attention from agriculturists, engineers, and researchers for irrigation scheduling, water resource handling, crop production enhancement, draught area prediction, etc.

Hydrological regime of Sahelian small waterbodies from combined Sentinel-2 MSI and Sentinel-3 Synthetic Aperture Radar Altimeter data

Abstract. In the Sahelian semi-arid region, water resources, especially small waterbodies such as ponds, small lakes, and reservoirs in rural areas are of vital importance. However, because of their high number and the scarce in situ monitoring networks, these resources and their spatiotemporal variability are not well known at the regional scale. This study investigates the hydrological regime of 37 small waterbodies, located in Mali, Niger, and Burkina Faso in central Sahel. We propose a method based on remote sensing data only, which consists of combining water height data from Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) with water area data obtained with the Sentinel-2 MultiSpectral Instrument (MSI) to create a dense water height time series. Water height variations are then compared to the evaporation estimated by the Penman–Monteith method, using ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) to infer water regimes during the dry season. Three main regimes stand out, namely a net water loss, mainly resulting from anthropogenic withdrawals, a net water supply occurring after the end of the rainy season through river network or water table exchange, and a balanced behaviour, where water losses during the dry season closely correspond to evaporation rates. Spatial patterns have been identified; in central Burkina Faso, most of the reservoirs show a net dry season water loss, which is explained by frequent irrigation, while reservoirs in northern Burkina Faso, generally show little water loss, indicating that water withdrawal is not significant in this area. Lakes located in the Inner Niger Delta in Mali and connected to the Niger River network generally show an important water supply, particularly at the beginning of the dry season. Lakes in Niger tend to show a weak signal toward water inflow that could be explained by exchange processes with the groundwater. These results show that satellite data are effective at estimating hydrological regimes and the anthropogenic impact on water resources at the large scale, including resources found in small waterbodies.

Determination of Crop water Requirement and Irrigation Scheduling for Major Crops of Surendranagar, Gujarat Using CROPWAT 8.0

Abstract: Due to the growing need for water for uses such as hydropower, farming, and drinking purpose among others, water is becoming an endangered asset. Water is increasingly needed for a variety of reasons as the population grows. On the contrary, the accessibility of water resources is limited. To determine crop water requirement for any crop and evaluate irrigation scheduling for effective water management, crop coefficients and reference crop evapotranspiration must be calculated. The purpose of this research is to determine crop water requirement of Cotton, Wheat and Sesame for Surendranagar Region and Prepare Irrigation Scheduling using the FAO-56 Penman-Monteith Method using FAO CROPWAT 8.0 Software. Meteorological data for Surendranagar region such as maximum and minimum temperatures, mean relative humidity, sunshine hours, and wind speed, are obtained from Wadhwan taluka of Surendranagar used to calculate reference crop evapotranspiration. Crop water requirement for Cotton using CROPWAT 8.0 model is obtained 876.3 mm/dec Crop water Requirement for Wheat is obtained 543.9 mm/dec and Crop water Requirement for Sesame is obtained 529.6 mm/dec This finding may be helpful for agricultural planning and effective irrigation control in the growing of cotton, Wheat and Sesame.

Spatiotemporal Variations of Reference Evapotranspiration and Its Climatic Driving Factors in Guangdong, a Humid Subtropical Province of South China

It is of great importance to study the changes in reference evapotranspiration (ET0) and the factors that influence it to ensure sustainable and efficient water resource utilization. Daily ET0 data calculated using the Penman–Monteith method from 37 meteorological stations located within Guangdong Province in the humid zone of southern China from 1960 to 2020 were analyzed. The trend analysis and Mann–Kendall test were used to analyze the time series changes in ET0 and major climatic factors (air temperature (T), relative humidity (RH), sunshine duration (SD), and wind speed (u2)) for over 61 years. Sensitivity and contribution analyses were used to evaluate the driving factors of ET0. The main findings of the study are as follows: (1) the trend in average annual ET0 time series in Guangdong slightly increased at a trend rate of 1.61 mm/10a over the past 61 years, with most stations experiencing an increase in ET0. During the same period, air temperature significantly increased, while RH and SD decreased; u2 also decreased. (2) Sensitivity analysis showed that ET0 was more sensitive to RH and T than SD and u2, with ET0 being most sensitive to RH in spring and winter and T in summer and autumn. (3) The contribution analysis showed that T was the dominant factor for ET0 variation in Guangdong, followed by SD. SD was found to be the dominant factor in ET0 changes in areas where the “evaporation paradox” occurred, as well as in spring and summer. The study concludes that the climate in Guangdong became warmer and drier over the past 61 years, and if the current global warming trend continues, it will lead to higher evapotranspiration and drought occurrence in the future.

Using artificial neural networks to predict the reference evapotranspiration

Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature ( Tmax and Tmin), dew point temperature ( Tdw), wind speed ( u), and precipitation (P), how well artificial neural networks could predict ETo values. A feed- forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error ( RMSE) of 0.1295 mm∙day –1 and the correlation coefficient ( r) of 0.996 are estimated by artificial neural network ETo models. When fewer inputs are used, ETo values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day –1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day –1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ETo values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman–Monteith method to estimate the missing variables by using the ETo calculator the normalised root mean squared error ( NRMSE) reached about 30% to predict ETo if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ETo using variables of temperature only.

Spatio-Temporal Trend Analysis of Reference Evapotranspiration in Central Luzon, Philippines

Understanding trends in reference evapotranspiration (ETₒ) and their influencing factors is crucial for calculations of irrigation requirements and water management. Meteorological data in Central Luzon (1985–2019) were used to estimate ETₒ using the FAO Penman-Monteith method. Spatial and temporal ETₒ trends were analyzed using the Mann-Kendall test and Sen’s slope estimator. Correlation and sensitivity analyses were conducted to analyze the impact of weather variables on ETₒ. Positive correlations were observed for maximum temperature, solar radiation, and wind speed whereas negative correlations were observed for relative humidity and minimum temperature. In general, ETₒ was statistically dependent and most sensitive to solar radiation, maximum temperature, and relative humidity. ETₒ in the stations surrounded by mountains was consistently lower than the stations in the agricultural areas by 197 and 207 mm for the dry season and annual basis, respectively. The increasing trends with a magnitude of 3.98 mm/yr in annual ETₒ were caused by the rising trend in solar radiation and maximum temperature and a decreasing trend in relative humidity. The decreasing ETₒ trend (–2.6 to –4.63 mm/yr) predominant in the study area was mainly attributed to the decreasing trend of solar radiation and wind speed. Areas with increasing ETₒ trends indicate the need for supplemental irrigation. Decreasing ETₒ trends may indicate climate change, land-use change, or human-related factors.

Crop Water Requirement for Cotton Crop in Unchadi Section Command Area of Shetrunji Right Bank Main Canal using WEAP Model

Abstract: Crop coefficients and reference crop evapotranspiration must be determined in order to estimate irrigation water requirements for any crop and improve irrigation scheduling and water management. The objective of this study is to evaluate the crop water requirements of cotton using the FAO-56 Penman-Monteith Method with dual crop coefficients. Meteorological data for the Unchadi Section Command Area of Shetrunji Right Bank Main Canal, such as maximum and minimum temperatures, mean relative humidity, sunlight hours, and wind speed, are used to calculate reference crop evapotranspiration. The MABIA method in WEAP (Water Evaluation And Planning) software uses the dual-Kc approach to calculate crop evapotranspiration or crop water requirement, which is described in FAO Irrigation and Drainage Paper No. 56, whereby the Kc value is divided into a ‘basal’ crop coefficient (Kcb) and a separate component (Ke) representing evaporation from the soil surface. The basal crop coefficient represents actual ET conditions when the soil surface is dry but sufficient root zone moisture is present to support full transpiration. Cotton crop water requirement (ETc) using is found about 940.93 mm during the growing season in region. This result can be useful for agricultural planning and efficient irrigation management for cotton cultivation in Unchadi Section Command Area of Shetrunji Right Bank Main Canal.

Changes in drought characteristics over China during 1961–2019

Drought is extremely complex and can be influenced by various factors. This study uses Penman-Monteith (PM) method to calculate the potential evapotranspiration and get the monthly drought metric of Standardized Precipitation-Evapotranspiration Index (SPEI) across China during the period of 1961–2019. Results show that the regions of the west of Northwest China (WNWC), Northeast China (NEC), and South China (SC) are reported to experience a wetting trend, while the droughts in the regions of the east of Northwest China (ENWC), North China (NC), and Southwest China (SWC) deteriorated over the past decades. Seasonally, the significant wetting trends mainly occurred in summer, autumn, and winter for WNWC, while mainly in winter for the region of NEC. Additionally, the droughts in China show an obvious interdecadal variability, with more severe droughts for the periods of 1961–1979 and 2000–2019, but with relatively fewer events in the period of 1980–1999. During the 1960 s and 1970 s, droughts in China are observed to mainly happen in summer and winter, while the droughts mainly occurred in autumn and winter during the 1980 s and 1990 s. Since the early 21st century, both the frequency and intensity of spring droughts have increased obviously and played a dominant role in seasonal droughts. Further studies indicate that the drought changes in southern China are mainly affected by the precipitation anomalies, while that in northern China are mainly influenced by the temperature and wind speed anomalies. Hence, our analyses provide theoretical basis for further understanding the drought changes and the prevention control of drought disasters in China.

Diminishing evapotranspiration paradox and its cause in the Middle East and North Africa

Global warming has significantly affected different climate variables and potential evapotranspiration (PET), influencing irrigation needs and water balance. However, the influence of climate change on PET trends is inconsistent in different regions due to the PET paradox. The climate change influence on PET can also vary with time due to the changing pattern of climatic variables and their interactions. This study applies sensitivity analysis of PET to various climate factors and PET trends to reveal the dynamics of PET in the Middle East and North Africa (MENA) and decipher its cause. Penman-Monteith (PM) method was used to estimate PET using ERA-5 data for three 30-year periods, 1951–1980, 1971–2000 and 1991–2020. The results showed a decrease in PET over a large part of MENA during 1951–1980, while an increase over most of MENA during 1971–2000 and 1991–2020, indicating demising evapotranspiration paradox in the region over time. Sensitivity analysis showed wind speed (70–80%) as the most influential factor in determining PET in MENA, followed by solar radiation (10–20%) and daily maximum temperature (5–10%) for all periods. However, wind speed and solar radiation have not changed significantly over periods in most areas. In contrast, a large increase in temperature (0.1 to 0.8 °C/decade) over most of the region caused a diminishing PET paradox and increased PET over most of the MENA. Overall, a 0.1 to 0.2 mm/day increase in PET per decade was observed. The study indicates the rising temperature would cause an increasing PET which can have severe implications in this global water stress hotspot.

Estimation of reference evapotranspiration by three methods, for the region of Ilhéus/BA

Knowledge of evapotranspiration at various stages of plant development allows irrigation management to be more efficiently carried out according to the actual crop demand. There are a variety of methods that can be used to determine reference evapotranspiration, and due to the high variability of parameters influencing this phenomenon, it is common to have studies that compare different methods to evaluate which one has the greatest applicability to the study location. Monthly climatological data from the National Institute of Meteorology - INMET were collected for the municipality of Ilhéus/BA. Reference evapotranspiration was estimated using the Thornthwaite, Camargo, and Priestley & Taylor methods, and their results were compared with the standard Penman-Monteith method (FAO-56) using statistical indices, the Wilmott agreement index, Pearson correlation coefficient, and confidence index. The Priestley-Taylor method was classified as "Excellent" (c > 0.85). The Thornthwaite and Camargo methodologies were classified as "Fair" (0.61 < c < 0.65) and "Good" (0.66 < c < 0.75), respectively. The Priestley-Taylor method overestimated the values of the PMFAO method, but obtained a higher coefficient of determination with values close to those found by the standard method, and can be used as an alternative to the Penman-Monteith (FAO-56) reference method for the Ilhéus-BA region. The Thornthwaite and Camargo methods had low coefficients of determination and obtained the worst statistical performance, and therefore should be used with caution.

An Assessment of Trends of Potential Evapotranspiration at Multiple Timescales and Locations in Sicily from 2002 to 2022

Climate change and the related temperature rise can cause an increase in evapotranspiration. Thus, the assessment of potential evapotranspiration (PET) trends is important to identify possible ongoing signals of climate change, in order to develop adaptation measures for water resource management and improve irrigation efficiency. In this study, we capitalize on the data available from a network of 46 complete meteorological stations in Sicily that cover a period of about 21 years (2002–2022) to estimate PET by the Food and Agriculture Organization (FAO) using the Penman–Monteith method at the daily time scale in Sicily (southern Italy). We then analyse the trends of PET and assess their significance by Sen’s Slope and the Mann–Kendall test at multiple temporal scales (monthly, seasonal, and annual). Most of the locations do not show significant trends. For instance, at the annual timescale, only five locations have a significantly increasing trend. However, there are many locations where the monthly trend is statistically significant. The number of locations where monthly trend is significant is maximum for August, where 18 out of these 46 stations have an increasing trend. In contrast, in March, there are no locations with a significant trend. The location with the highest increasing trend of PET indicates trend slopes of 1.73, 3.42, and 10.68 mm/year at monthly (August), seasonal (summer), and annual timescales, respectively. In contrast, decreasing PET trends are present only at the monthly and seasonal scales, with a maximum of, respectively, −1.82 (July) and −3.28 (summer) mm/year. Overall, the findings of this study are useful for climate change adaptation strategies to be pursued in the region.

Sensitive analysis of meteorological data and selecting appropriate machine learning model for estimation of reference evapotranspiration

This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman-Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture).

Spatio-temporal analysis of water requirement of maize (Zea mays L.) in Haryana state of India

Climate change have a considerable impact on crop water demand (ETc). The present study analyzed the spatio-temporal variation of the crop water requirement (ETc), crop water surplus deficit index (CWSDI), and the coupling degree of ETc and effective precipitation (Pe) for maize (Zea mays L.) crop grown in Haryana State of India using ArcMap 10.8 software. ETc was calculated using the Penman-Monteith method and crop coefficient (Kc) approach for 34 years (1985-2018) climatic data. Two statistical models, namely Mann–Kendall test and Sen’s slope estimator, were applied to understand the trend, while Pettitt's test was used to identify abrupt change points by using XLSTAT 2021 software.The result showed that for maize ETc ranged from 571 to 766 mm, CWSDI ranged from -29 to -74% and the degree of coupling of ETc and Pe ranged from 0.26 to 0.71 during 1985-2018. The decadal temporal variation of these indices indicated that ETc during 2007-2018 was relatively higher than it was from 1985-1995 and 1996-2006. The spatio-temporal values of CWSDI indicated that almost all the districts were under water deficit conditions for growing maize crop.Trend analysis showed significantly increasing trend of CWR for most of the districts except Jind, Mewat, Palwal, Panchkula, Rewari, Rohtak and Yamunanagar.The abrupt change point detection analysis of CWR indicates toward different change points, with maximum change points between the years 2004–2008.Such information will help to the farmers in the effective management of water for sustainable production under climate change in the near future.