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Estimating Potential Evapotranspiration
Abstract
Increasing population and needs for an augmented food supply give greater importance to improved procedures for estimating agricultural water requirements both for irrigation and for rain- fed agriculture. Four methods for estimating potential evapotranspiration are compared and evaluated. These are the Class A evaporation pan located in an irrigated pasture area, the Hargreaves equation, the Jensen-Haise equation, and the Blaney-Criddle method. -from ASCE Publications Abstracts Dept of Agric & Irrig Eng, Utah State Univ, Logan, UT 84322, USA.
... where is the Julian day number, in fact, December of a year = 365 or 366 for a leap year. When only maximum and minimum temperature data are available, the values from equation 2 can also be calculated using the relation presented by Hargreaves and Samani [25] and Allen [26] as: ...
... Meteorological parameters involved in any given solar radiation model are the main features that distinguish it from another model. Our new conventional model combined the results of Allen [26], Hargreaves and Samani [25] and is simplified as it depends on extraterrestrial solar radiation, maximum and minimum air temperature, altitude of the terrain, and some constants. All these parameters can be calculated except for air temperature data that may be required. ...
... Similar but more complex models may have to take into account relative humidity, atmospheric pressure, transmission coefficient based on water vapor ozone, and albedo among others. Our derived models differ from that of [19][20], [15,29] and have similar approaches to that of Allen [26], Hailu et al. [15], Hargreaves and Samani [25].To test our model performance our results were compared with the well-known linear modified Angstrom-type empirical model. ...
... Hargreaves and Samani provides minimum climatological data to estimate evapotranspiration (G. H. Hargreaves & Samani, 1982). It uses temperatures data only and it also can be applied in tropical country (Srivastava, Sahoo, Raghuwanshi, & Chatterjee, 2018). ...
... Both variation of climatology and urbanization will impact on evapotranspiration in MAS. Therefore, the purpose of this paper are: a). to analyze traditional point-scale method of evapotranspiration in MAS applying Hargreaves and Samani (1982), b). to analyze the primary study of the condition of potential evapotranspiration in MAS, c). to analyze variation of maximum temperature with potential evapotranspiration as indication climate change impact on potential evapotranspiration. ...
... advance research. Potential evapotranspiration is estimated for 15 years periods from 1 January 2005 to 31 December 2016. It is because one year data sets on 2017 are missing for all stations owned by BBWS Opak Oyo. Hargreaves and Samani is applied to estimate potential evapotranspiration as express in Equation 1 to 3 (G. Hargreaves & Samani, 1985;G. H. Hargreaves & Samani, 1982;Srivastava et al., 2018). ...
Temperature variation due to climate change can give greatest impact on potential evapotranspiration. Increase of temperature in Yogyakarta Special Region can increase potential evapotranspiration. It is necessary to study potential evapotranspiration as one of indication impact of climate change to its potential evapotranspiration. Potential evapotranspiration (PET) can be estimated applying Hargreaves and Samani (1982) that requires limited meteorological parameter. Therefore, this research aims are: a) to analyze daily PET in 3 gauge station of Merapi Aquifer System, b) to analyze variation PET in the 3 gauge stations of Merapi Aquifer System. The largest values of PET is in Barongan station, while the lowest is in Adisucipto station. It is probably because Adisucipto station is located in urban and crowded areas. PET values are mostly 1.98 mm/day in rainy season, while 1.67 mm/day in dry season. Mostly, the correlation coefficient is low in dry season for Pluyon, Barongan and Adisucipto stations suggesting that drier and warmer temperature due to climate change do not have large impact to PET. The 5-month moving average trend also confirm the relatively stable line of PET variation from January 2005 to December 2016 reflecting that climate change do not greatly impact on PET in Merapi Aquifer System.
... Junliang Fan et al. [29] compared fourteen existing temperature-based models developed in Refs. [30][31][32][33][34][35][36][37][38][39][40][41][42][43] with six newly proposed temperature-based models for solar irradiation prediction at 20 sites in China. Among existing models, eight models only use temperature, while six use other meteorological parameters in addition to temperature. ...
... Keith De Souza [47] compared the performance of five existing models developed in Refs. [30,34,[48][49][50], one modified form of [30], and a newly developed model for solar irradiation prediction in Trinidad and Tobago. The new model uses only monthly mean temperatures for solar irradiation. ...
... Keith De Souza [47] compared the performance of five existing models developed in Refs. [30,34,[48][49][50], one modified form of [30], and a newly developed model for solar irradiation prediction in Trinidad and Tobago. The new model uses only monthly mean temperatures for solar irradiation. ...
Solar irradiation data is essential for the feasibility of solar energy projects. Notably, the intermittent nature of solar irradiation influences solar energy use in all forms, whether energy or agriculture. Accurate solar irradiation prediction is the only solution to effectively use solar energy in different forms. The estimation of solar irradiation is the most critical factor for site selection and sizing of solar energy projects and for selecting a suitable crop selection for the area. But the physical measurement of solar irradiation, due to the cost and technology involved, is not possible for all locations across the globe. Numerous techniques have been implemented to predict solar irradiation for this purpose. The two types of approaches that are most frequently employed are empirical techniques and artificial intelligence (AI). Both approaches have demonstrated good accuracy in various places of the world. To find out the best method, a thorough review of research articles discussing solar irradiation prediction has been done to compare different methods for solar irradiation prediction. In this paper, articles predicting solar irradiation using AI and empirical published from 2017 to 2022 have been reviewed, and both methods have been compared. The review showed that AI methods are more accurate than empirical methods. In empirical models, modified sunshine-based models (MSSM) have the highest accuracy, followed by sunshine-based (SSM) and non-sunshine-based models (NSM). The NSM has a little lower accuracy than MSSM and SSM, but the NSM can give good results in sunshine data unavailability. Also, the literature review confirmed that simple empirical models could predict accurately, and increasing the empirical model's polynomial order cannot improve results. Artificial neural networks (ANN) and Hybrid models have the highest accuracy among AI methods, followed by support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS). The increase in efficiency by hybrid models is minimal, but the complexity of models requires very sophisticated programming knowledge. ANN's most important input factors are maximum and minimum temperatures, temperature differential, relative humidity, clearness index and precipitation.
... Over the last few decades, researchers from many countries have developed numerous empirical models based on the correlation between GSR and other meteorological variables, namely sunshine durationbased (SDB) models (Prescott, 1940), temperature-based (TB) models (Hargreaves and Samani, 1982), cloud-based (CB) models (Black, 1956), and other models based on meteorological variables (Yorukoglu and Celik, 2006;Zhang et al., 2017). As a result, the most frequently employed empirical models are those based on SDB and TB, while SDB models were significantly more accurate than TB models. ...
... Thus, many TB models also received widespread recognition. Hargreaves developed an approach based on simple inputs using the relationship between GSR and minimum and maximum temperature (Hargreaves and Samani, 1982). The Bristow-Campbell (BC) model, which might account for 70-90% of GSR variability in America, was created by Bristow and Campbell (1984), after they evaluated an exponential relationship between GSR and temperature variations (ΔT). ...
Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).
... The specific formulas of these representative ET 0 models are shown in Table 1. (Hargreaves and Samani, 1982;Priestley and Taylor, 1972;Valipour, 2014). ...
... where a i anda * i represent the Lagrange multiplier. Then, for the linear regression problem, the SVM regression function can be expressed as follows: (Priestley and Taylor, 1972) RH-based MBR (Hargreaves and Samani, 1982) Note: T max , T min , and T a are maximum, minimum, and average air temperature, • C; n is the sunshine duration, h; R a is the extraterrestrial radiation, MJ.m − 2 d − 1 ; R n is the net solar radiation, MJ.m − 2 d − 1 . ...
... This is so because the rain occurs during the hottest period of the year which indicate what may be termed humid subtropical climate existence at Nadi. The only constant of correlation a, is estimated to be [31]. This difference in site specific value and the Hargreaves recommended value further buttress the need for the constant to be site specific and moreover not all climate can be classified into the group proposed by Hargreaves and Samani [31]. ...
... The only constant of correlation a, is estimated to be [31]. This difference in site specific value and the Hargreaves recommended value further buttress the need for the constant to be site specific and moreover not all climate can be classified into the group proposed by Hargreaves and Samani [31]. ...
Detailed knowledge of solar radiation distribution is highly important for many activities on earth such as agricultural practices, renewable energy installation, climate control and many more. Climatic diversity out of other diversities on earth has made it difficult to use knowledge of solar radiation distribution within a fragmental part of the earth in generalizing the solar radiation distribution on the earth surface. In view of this, this work tested and calibrated seven highly used empirical correlations for global solar radiation on horizontal surface on the earth of Nadi, Fiji. The result confirms that solar radiation is site specific as different correlation coefficients are obtained for this study site. Similarly, the result shows that five models that are based on relative sunshine hour, temperature, and precipitation are good models, while models based on relative humidity are poor models for predicting global solar radiation at Nadi, Fiji. Specifically, based on Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the Angstrom-page sunshine hour based correlation is the best, while relative humidity based correlation is poor. However, based on correlation coefficient, temperature difference correlation proves to be the best, while relative humidity based correlation proves still the poorest. In the light of the accuracy of the other models except relative humidity based correlation, choices can be made depending on the availability of data, quality of data, ease of computation and many other factors, in the estimation of monthly global solar radiation with satisfactory result. Summarily, Nadi, Fiji is endowed with abundant solar radiation as the entire clearness indexes are within partly overcast and also very close to clear sky in some months.
... Based on the above analysis, it is obvious that the deviations between precipitation data and forecasts along with their seasonal distribution, do not support the use of precipitation forecasts instead of measurements. Considering the case of reference evapotranspiration, since the forecast data missed information concerning solar radiation or any other applicable information, we implemented the Hargreaves-Samani equation [36]. It estimates the reference evapotranspiration, ETo (mm d -1 ) at daily scale using only air temperature data (°C) as: Based on the above analysis, it is obvious that the deviations between precipitation data and forecasts along with their seasonal distribution, do not support the use of precipitation forecasts instead of measurements. ...
... Considering the case of reference evapotranspiration, since the forecast data missed information concerning solar radiation or any other applicable information, we implemented the Hargreaves-Samani equation [36]. It estimates the reference evapotranspiration, ET o (mm d −1 ) at daily scale using only air temperature data ( • C) as: ...
The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined.
... Most of the met-stations in the world do not directly calculate PET therefore numerous techniques have been developed to calculate it indirectly from accessible meteorological factors [53]. Some example of the famous methodologies for PET calculation are Penman Monteith, the Hargreaves approach [54] and the Thornthwaite method [55]. In this study, the Thornthwaite technique was used to compute PET. ...
Droughts and prevailing arid conditions have a significant impacts on the natural environment, agriculture, and human life. To analyze the regional characteristics of drought in Baluchistan province, the aridity index (AI) and standardized potential evapotranspiration index (SPEI) were used in. The study analyzed the rainfall, temperature, and potential evapotranspiration (PET) data and the same were used for the calculation of AI as well as SPEI to find out the drought spells during the study period. The linear regression and Mann-Kendall test were applied to calculate the trend in AI as well as in SPEI results. The AI results revealed that most of the meteorological stations are arid and semi-arid, where the highest increasing aridity is noted at Kalat (0.0065/year). The results of the SPEI at 1 and 6-months identified the extreme to severe drought spell during 1998–2002 in all meteorological stations of Baluchistan province. The distinct drought spells identified from the SPEI results were in the years 1998–2003, 2006–2010, 2015–2016 and 2019. The drought frequency results showed highest frequency percentage at Lasbella (46%) of extreme to severe drought. The Mann-Kendall trend results showed negative trend in monthly AI and 1-month SPEI results and most significant trend was observed in April and October months, this shows that aridity and drought in the region are decreasing to some extent except Dalbandin and Lasbella observed increasing trend in winter season (November to January months) and Kalat met-station observed increasing trend in June. Prior investigation and planning of drought situations can help in controlling the far-reaching consequences on environment and human society.
... SPEI is calculated by fitting a three-parameter log-logistic distribution to the difference between precipitation and potential evapotranspiration (PET) and then transforming it to the standard Gaussian distribution. There are a variety of approaches to determine PET (Xiang et al., 2020), and the most commonly used methods include the Thornthwaite method (Thornthwaite, 1948), the Prestley-Taylor method (Priestley and Taylor, 1972), the Hargreaves-Samani method (Hargreaves and Samani, 1982), and the Penman-Monteith method (Allen et al., 1998). Among them, the GCM simulations based on Penman-Monteith method show good agreement with observations and the uncertainty in the future projections is small (Aadhar and Mishra, 2020;Liu et al., 2020;Song et al., 2022). ...
... Potential evapotranspiration is computed based on the energy balance methods such as a Penman-Monteith equation (Allen et al., 1998;Monteith and Unsworth, 1990) or alternative methods such as Priestley-Taylor (Priestley and Taylor, 1972) and Hargreaves equations (Hargreaves and Samani, 1982). Potential evapotranspiration can be partitioned into potential evaporation and transpiration in several ways (Kool et al., 2014). ...
As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings.
... Since it considers both temperature and precipitation, it is suitable for climate change analysis. PET is estimated using the Hargreaves-Samani (Hargreaves & Samani 1982) method. Although the SPEI can be calculated at different timescales, a 12-month timescale is more relevant for climate change studies (Ahmadalipour et al. 2017;Lee et al. 2019) so that short-term rainfall variabilities can be avoided. ...
Projecting flood and drought characteristics under climate change is important to management plans and enhancement of the resiliency of the society. However, studies that provide the integration of flood–drought hazard events is scarce. This study assessed the flood and drought hazards for future climate in the Mun River basin. A non-modelling approach is used to assess the flood hazard, while a multi-variate approach is used for the drought hazard. The results suggest that areas under ‘high’ and ‘very high’ drought hazard levels will increase from 27 and 4% during the baseline period to 43 and 37%, during the near-future period. Similarly, an increase in the ‘high’ and ‘very high’ flood hazard levels from 11 and 22% during the baseline period to 16 and 24% during the near-future period is projected. When both hazards are considered together, the total hazard is projected to increase by 155% in the near-future period. 76% of the catchment during the near-future period will have a combined hazard level from ‘medium’ to ‘very high’ compared to the 30% during the baseline period. The research presents a grim outlook on future floods and droughts in the basin, with the areas of Nakhon Ratchasima, Rio Et and Si Sa Ket provinces particularly at risk from both hydro-meteorological hazards.
... Although the Thornthwaite method (Thornthwaite, 1948) can be used in PET calculation in the study of Vicente-Serrano et al. (2010), Beguería et al. (2014) reported that the equation used to evaluate PET in the index values assessed with SPEI may have a significant effect in some parts of the world. In the same study, they suggested that in the PET calculation, the Penman-Monteith method (Penman, 1948) should be used first in cases of sufficient data, the Hargreaves-Samani method (Hargreaves and Samani, 1982) as a second alternative if there is not enough data, and the Thornthwaite method as a last alternative if only average temperature data is available. In addition, Ortiz-Gómez et al. (2022) used eight different PET equations to determine the method that gives the closest results to the Penman-Monteith equation, using different performance criteria. ...
Drought indices are one of the most widely used methods for drought monitoring because they are easy to apply and interpret. The two most widely used drought index methods are the Standardized Precipitation Index (SPI) method, using the precipitation parameter as input, and the Standardized Precipitation Evapotranspiration Index (SPEI) method, which uses the Potential Evapotranspiration (PET) values addition to precipitation. The primary goal of this study is to compare the two methods for determining drought characteristics in different climatic and geographical features in Turkey. The drought characteristics are calculated using meteorological data between 1970 and 2021 for 199 synoptic observation stations in seven geographical regions of Turkey with SPI and SPEI methods for the 3-, 6-, and 12-month time scales. The study's findings indicate that while the correlation coefficient (CC) between the two indices is generally high (ranging from 0.81 to 0.93, depending on the region and time scale), it is lower in regions with a low Aridity Index (AI) value, particularly for the 3-month time scale. For the 6- and 12-month time scales, no relationship is observed between AI and CC. Additionally, SPEI detected a higher occurrence percentage of moderate and severe droughts across all geographical regions (with an increase of up to 15 % compared to SPI), while SPI observed higher percentages of extreme droughts. The study results conclude that SPI and SPEI methods demonstrate significant differences in detecting certain drought characteristics after the 1990s, underscoring the necessity to consider temperature variations in drought monitoring, particularly in the context of ongoing climate change.
... Therefore, various temperature-based empirical models have been established to predict daily H from daily maximum/ minimum temperature for being the most available weather parameters at any stations [30]. Hargreaves and Samani [28] proposed the first temperature-based model for estimating H by assuming that the difference between maximum and minimum temperatures was largely related to the ratio of global and extraterrestrial solar radiation. Following this work, various modified forms of Hargreaves and Samani model have been developed to improve H estimates from the diurnal temperature range, e.g. ...
The knowledge of global solar radiation (H) is a prerequisite for the use of renewable solar energy, but H measurements are always not available due to high costs and technical complexities. The present study proposes two machine learning algorithms, i.e. Support Vector Machine (SVM) and a novel simple tree-based ensemble method named Extreme Gradient Boosting (XGBoost), for accurate prediction of daily H using limited meteorological data. Daily H, maximum and minimum air temperatures (T max and T min), transformed precipitation (P t , 1 for rainfall > 0 and 0 for rainfall = 0) and extra-terrestrial solar radiation (H 0) during 1966-2000 and 2001-2015 from three radiation stations in humid subtropical China were used to train and test the models, respectively. Two combinations of input parameters, i.e. (i) only T max , T min and R a , and (ii) complete data were considered for simulations. The proposed machine learning models were also compared with four well-known empirical models to evaluate their performances. The results suggest that the SVM and XGBoost models out-performed the selected empirical models. The performance of the machine learning models was improved by 5.9-12.2% for training phase and by 8.0-11.5% for testing phase in terms of RMSE when information of precipitation was further included. Compared with the SVM model, the XGBoost model generally showed better performance for training phase, and slightly weaker but comparable performance for testing phase in terms of accuracy. However, the XGBoost model was more stable with average increase of 6.3% in RMSE, compared to 10.5% for the SVM algorithm. Also, the XGBoost model (3.02 s and 0.05 s for training and testing phase, respectively) showed much higher computation speed than the SVM model (27.48 s and 4.13 s for training and testing phase, respectively). By jointly considering the prediction accuracy, model stability and computational efficiency, the XGBoost model is highly recommended to estimate daily H using commonly available temperature and precipitation data with excellent performance in humid subtropical climates.
... We computed daytime averages for Rg and Ta from the Senda Darwin meteorological station between 1999 and 2021 to derive annual variations in potential evapotranspiration (PET) using the Hargreaves equation (Hargreaves and Samani, 1982). 105 ...
The variability and drivers of carbon and water fluxes and their relationship to ecosystem water use efficiency (WUE) in natural ecosystems of southern South America are still poorly understood. For eight years (2015–2022), we measured water and carbon fluxes using eddy covariance towers in a temperate rainforest and a peatland in southern Chile. Different expressions for ecosystem WUE were derived from estimates of gross primary productivity (GPP) and evapotranspiration (ET), which was further partitioned into evaporation (E) and transpiration (T). We then used the correlation between detrended time series and structural equation modeling to identify the main environmental drivers of WUE, GPP, ET, E and T. The results showed that WUE is low in both ecosystems, and likely explained by the high annual precipitation in this region (∼2100 mm). Only expressions of WUE that included atmospheric water demand showed seasonal variation. Variations in WUE were related more to changes in ET than to changes in GPP, while T remained relatively stable accounting for around 47 % of ET for most of the study period. For both ecosystems, E increased with higher global radiation, higher surface conductance and when the water table was closer to the surface. Higher values for E were also found with increased wind speeds in the forest and higher air temperatures in the peatland. The absence of a close relationship between ET and GPP is likely related to the dominance of plant species that either do not have stomata (i.e., mosses in the peatland or epiphytes in the forest) or have poor stomatal control (i.e., anisohydric tree species in the forest). The observed increase in potential ET in the last two decades and the projected drought in this region suggests that WUE could increase in these ecosystems, particularly in the forest, where stomatal control may be more significant.
... Furthermore, we use this data to calculate effective rainfall for the site. Effective rainfall equals the difference between precipitation and modelled evapotranspiration, based on air temperature, incoming solar radiation and the global position of the site, along with an estimate of the influence of local vegetation according to Hargreaves and Samani [35] and Samani [36]. Consequently, effective rainfall can be positive during periods of increased precipitation with low evapotranspiration or negative when evapotranspiration exceeds input from precipitation. ...
... Global radiation has been calculated using the Hargreaves method [8]: ...
... Correspondingly, many researchers have formulated linear, power, exponential, and polynomial equations to establish relationships between the minimum and maximum, or average air temperatures, as well as the differences of air temperature and the clear sky index (e.g., Hargreaves GH et al. [19,20], Liu M − F et al., 2013 [21], Almorox J et al., 2013 [22]). The power function model developed by Hargreaves, which incorporates both the daily temperature variations and extraterrestrial solar radiation, is considered as one of the classic models [19]. The aforementioned models are highly appropriate and accurate for computing GSR daily value. ...
Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.
... Bahel et al. (1987) developed an empirical R s model (Bahel model) based on the unary cubic equation of n/N. Hargreaves and Samani (1982) proposed the most commonly used T-based model (H-S) based on the relationship of R s and diurnal air temperature variation (ΔT = T max -T min ). Bristow and Campbell (1984) established the Bristow-Campbell model (B-C) based on the exponential function that correlates R s with the diurnal temperature variation is another widely used T-based model. ...
... In the temperature-based models, it is assumed that the fraction of extra-terrestrial radiation that falls on the ground is directly related to the difference in maximum and minimum temperature [13]. Most of the available temperature-based models stem from the Hargreaves and Samani model [31] presented in Eq. (8). Various derivates of the Hargreaves and Samani model have been proposed in the literature; categorized as One-parameter, two-parameter, or three-parameter models, some of these are presented in Table 2. ...
... In addition to the hydrological data, datasets used also include land cover data (USDA-NASS 2021) and precipitation data (from the North American Land Data Assimilation System, NLDAS-2) (Xia et al 2012). Potential evapotranspiration (PET) was estimated using the temperature-based Hargreaves method (Hargreaves and Samani 1982), for which daily maximum and minimum temperature data were again derived from NLDAS-2. ...
This study ascertains the factors affecting streamflow and irrigation water demand under different land use/cover (LULC) changes and future climate scenarios in the Flint River Basin, Georgia, United States, using the seemingly unrelated regression (SUR) panel model. An advantage of using the SUR model is that it accounts for cross-hydrological correlation, which is important due to the cross-sectional dependence between streamflow and pumpages. A set of streamflow, ground/surface water withdrawal, climatic, and LULC data used in this study was gathered from publicly available data sources and state agencies. The results show that a 10% increase in corn acreage in the watershed could lead to a significant rise in surface water and groundwater pumpings demands, respectively at 124% and 168%. Furthermore, this study identifies potential evapotranspiration (PET) threshold, which may lead to a water deficit in the region. For various LULC scenarios involving corn and urban area expansion, the probability of facing water scarcity at least once from 2025 to 2060 is estimated to range from 0.2% to 3.8% and 0.7% to 2.6% under RCP 4.5 and RCP 8.5 scenarios, respectively. These findings underscore the trade-off between water scarcity and food security in the context of changing climate, highlighting a need to design appropriate incentives to enhance water-use efficiency and adopt climate-smart strategies. The study’s significance extends to other similar watersheds worldwide that face similar challenges arising from changing land use and climate, which impact the sustainability of water resources, particularly groundwater resources, over time.
... Similarly, Rahimikhoob [33] conducted a study on predicting global solar radiation based on temperature data using ANN in a semi-arid environment in Iran. The study also included a comparison with an empirical model, namely the Hargreaves and Samani model [34]. The results demonstrated that the developed ANN model outperformed the empirical model in accurately modeling daily global solar radiation. ...
Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R2 > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons (R2 > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well (R2 > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications.
... Many such models have since been developed and tested by researchers all over the world. Several of these models have been reported for their accuracy in predicting global solar radiation in various locations around the world [2][3]. ...
Data on monthly average daily global solar radiation are critical in the design and analysis of solar energy systems. Using data from the Lawra Solar Plant in Ghana's Upper West Region, a multiple regression model was developed to estimate the monthly average daily global solar radiation using the Angstrom-Garcia model. The parameters used were sunshine duration ratio, average maximum possible daily hours of sunshine and the difference between maximum and minimum temperature values. The model equation is given as 𝐻̅𝐻̅0⁄=0.15+0.426𝑛̅𝑁̅⁄+0.1392Δ𝑇𝑁̅⁄, which was found to be reliable; thus the equation can be employed for estimating global solar radiation of locations that have similar climate, latitude and altitude as Lawra.
... Therefore, procedures to estimate ET o with missing climate data are here proposed, as for example when only mean and/or maximum and minimum temperature are available. A methodology with low data requirement is the Hargreaves method (Hargreaves and Samani, 1982;1985). Garcia et al. (2004) found that the temperature-based Hargreaves-Samani formula is able to estimate ET o at the northern part of the Bolivian altiplano, but not in the southern part which is less humid. ...
Mountain regions play an important role in the global water cycle. Mountainous hydrology and hydraulics, however, are still not fully understood. The Amazon Basin, the largest drainage basin in the world, covers about 40 percent of South America and 66 percent of Bolivia (716500 km2). The sources of the principal tributaries of the Amazon River are located in the Andes mountain range. There-fore, understanding the water cycle in the upstream regions can lead to an im-portant contribution to the comprehension of the low-lands regions (which fre-quently suffer from floods). The Piraí river basin (2705 km2) located in Santa Cruz – Bolivia was selected as case study. This basin begins in a mountainous area and has important low-land areas. Huge variation in topography and lack of complete sets of meteorological data increase the research challenge.
Water sources have to be modelled in an integrated way taking into account the physical and natural sub-systems. The approach for such integrated modelling was adapted from the hydrological and hydraulic flood modelling experiences obtained in a more detailed studied case of the Dender basin in Belgium. Investi-gation was made on how the Belgian research outcomes can be transferred, how they can be of use, and how they need to be adjusted to the different conditions of the Bolivian study case.
Firstly, data pre-processing was conducted. This involved estimation of potential evapotranspiration taking into account the limited hydro-meteorological data available, testing of methodologies for gap filling of the rainfall records (33 sta-tions with daily series and 14 stations with hourly records) and disaggregation from monthly to daily and from daily to hourly rainfall values. With the com-plete(d) potential evapotranspiration and rainfall series a lumped conceptual rain-fall-runoff model was calibrated and validated for hourly and daily time steps (the study area had 5 gauging stations with hourly discharge data). The implementa-tion and calibration of the rainfall-runoff model was done based on the step-wise process already applied and tested in the Dender case. The hourly disaggregation techniques were tested based on the runoff results. Model efficiencies of around 0.6 for the small sub basins and higher than 0.65 for the large sub basins were obtained. For the daily runoff simulation, the use of 33 stations instead of 14 in-creased the model efficiency. After use of the 33 stations, better results were ob-tained in the peak flow estimations, but underestimations with respect to the ob-servations persisted. A long-term simulation was carried out with the calibrated rainfall-runoff model.
The hydrodynamic river processes and related model implementation were stud-ied by means of one-dimensional or quasi two-dimensional (2D) models. For the Dender case, a deep understanding of the quasi-2D implementation and flood-plain modelling were obtained. For the Piraí case, given that the river is influ-enced by morphological changes, the flood modelling methodology had to be ex-tended to account for these changes. This was done through a simplified concep-tual approach.
Based on the coupled modelling system (rainfall-runoff and river hydrodynamic models), the rainfall-runoff long-term simulation results and the river flow obser-vations, statistical extreme value analysis was conducted, and synthetic rainfall-runoff hydrographs constructed. These were used for estimation of river and floodplain conditions of given return periods.
To reduce model computational times, a conceptual model was identified and calibrated to the results of the more detailed 1D or quasi-2D hydrodynamic mod-el. To support the model structure identification and calibration, a semi-automatic methodology has been developed. The identification and calibration were done based on simulation results with the more detailed hydrodynamic model, includ-ing extreme synthetic and historical events. In this study, MATLAB® environ-ment was selected for implementing the conceptual model. A Conceptual Model Developer tool (CMD) has been developed and tested for the rivers Dender and Piraí with good results.
... The standard precipitation index (SPI) transforms the probability density function of precipitation to a standardised normal distribution, such that a value of −1 represents one standard deviation below (drier than) the mean 46 . To calculate the Standard Precipitation-Evapotranspiration Index (SPEI), potential evapotranspiration is estimated using the Hargreaves equation 69 , estimating the extra-terrestrial solar radiation from the latitude and day of year (calculated using the python software package Pyeto). The values of precipitation minus potential evapotranspiration are then transformed to a standardized normal distribution. ...
Runoff from glacierised Andean river basins is essential for sustaining the livelihoods of millions of people. By running a high-resolution climate model over the two most glacierised regions of Peru we unravel past climatic trends in precipitation and temperature. Future changes are determined from an ensemble of statistically downscaled global climate models. Projections under the high emissions scenario suggest substantial increases in temperature of 3.6 °C and 4.1 °C in the two regions, accompanied by a 12% precipitation increase by the late 21st century. Crucially, significant increases in precipitation extremes (around 75% for total precipitation on very wet days) occur together with an intensification of meteorological droughts caused by increased evapotranspiration. Despite higher precipitation, glacier mass losses are enhanced under both the highest emission and stabilization emission scenarios. Our modelling provides a new projection of combined and contrasting risks, in a region already experiencing rapid environmental change.
... External drift kriging (EDK; Ahmed and De Marsily, 1987) was used to interpolate the daily observed minimum, mean, and maximum temperature for all cells at a resolution similar to that of the precipitation with resampled elevation from the Shuttle Radar Topography Mission (SRTM; Farr et al., 2007) dataset as the drift. For potential evapotranspiration (PET), the Hargreaves-Samani (Hargreaves and Samani, 1982) equation was used with the interpolated temperature data at each cell as input. It was assumed that temperature and potential evapotranspiration are much more continuous in space as compared to precipitation, which behaves as a semi-Markov process in space-time that has a much larger effect on the hydrograph in the short term. ...
In this paper, the question of how the interpolation of precipitation in space by using various spatial gauge densities affects the rainfall–runoff model discharge if all other input variables are kept constant is investigated. The main focus was on the peak flows. This was done by using a physically based model as the reference with a reconstructed spatially variable precipitation model and a conceptual model calibrated to match the reference model's output as closely as possible. Both models were run with distributed and lumped inputs. Results showed that all considered interpolation methods resulted in the underestimation of the total precipitation volume and that the underestimation was directly proportional to the precipitation amount. More importantly, the underestimation of peaks was very severe for low observation densities and disappeared only for very high-density precipitation observation networks. This result was confirmed by using observed precipitation with different observation densities. Model runoffs showed worse performance for their highest discharges. Using lumped inputs for the models showed deteriorating performance for peak flows as well, even when using simulated precipitation.
... These transplanted seedlings were subjected to daily drip irrigation, and the water depth was calculated based on the reference evapotranspiration (ETo), estimated using data obtained by the IFTM weather station. In order to determine the water depth, daily temperature values were collected and reference evapotranspiration was calculated using the Hargreaves equation [25]: ...
Organomineral fertilizers (OFs) can provide the macro- and micronutrients contained in organic matter slowly and gradually throughout the crop cycle. However, the residual effect of this slow release is still unclear and needs to be better evaluated. In this context, the objective of this study was to evaluate the use of different doses of OF in the cultivation of vegetables and to quantify the residual effects of P, B, and Zn in the soil. A randomized block design was applied, using different doses of fertilizer as a P source, with four replications. In the randomized block design, different doses of OF were evaluated as a source of P (all with four repetitions): T1 = no P supplied (zero dose); T2 = 200 mg dm−3 of P2O5; T3 = 400 mg dm−3 of P2O5; T4 = 800 mg dm−3 of P2O5; and T5 = 1200 mg dm−3 of P2O5 plus an additional treatment with mineral fertilizer (MF) (200 mg dm−3 of P2O5). The fresh weight (FW) and dry weight (DW) and the nutritional status of the lettuce and cabbage were determined through leaf analysis at harvest. Soil analysis was also conducted before planting and immediately after harvest in order to assess soil P, B, and Zn content. The FW and DW cabbage production was higher when fertilization was used for the crop (either OF or MF). No differences were observed in the effects of the OF and MF doses in cabbage production, which ranged from 281.2 g plant−1 to 341.8 g plant−1, while lettuce production was highest in MF (45.1 g plant−1), followed by OF doses of 800 mg dm−3 (37.1 g plant−1) and 1200 mg dm−3 (36.8 g plant−1) of P2O5. OF fertilization had a beneficial residual effect on lettuce production, the FW and DW production of which increased as the OF doses increased (from 18.8 g plant−1 to 36.8 g plant−1 for FW and from 2.4 g plant−1 to 4.0 g plant−1 for DW). The highest doses of OF increased the availability of P and Zn in the soil and facilitated the absorption of nutrients by the cabbage and lettuce crops. In the cultivation of cabbage and lettuce, the residual effects of the P, B and Zn in the soil were higher under the highest doses of OF. An antagonistic effect between the P and Zn in the soil was evidenced in this study, and this needs to be confirmed in other subsequent studies.
... The required weather data depends on the selected potential evapotranspiration (PET) method. This study used the Hargreaves method to estimate PET (Hargreaves and Samani, 1982). Therefore, only daily precipitation and temperature data obtained from the Spanish National Meteorological Agency (AEMET) gridded dataset were used to execute the SWAT + The soil database is one of the most important inputs in SWAT + because it contains basic information about physico-chemical and hydraulic soil properties. ...
This research paper addresses the ongoing challenge of developing fine-resolution global digital soil property maps for hydrological modelling applications. Hydrological models are essential for understanding watershed dynamics and the impact of human activities on water resources. Soil data, which plays a crucial role in the hydrological cycle, is a requisite model input. Global digital soil property maps usually have coarse spatial resolutions, adding considerable uncertainty to hydrological models despite calibration efforts. To address this issue, a new global digital soil property map with 250 m spatial resolution, known as Digital Soil Open Land Map (DSOLMap), was developed and evaluated in this study. The DSOLMap has a finer spatial resolution than existing global soil maps and a more detailed soil profile divided into six soil horizons. This new high-resolution global digital soil property map was tailored to the SWAT + model format. SWAT + is the latest released version of the Soil and Water Assessment Tool (SWAT), one of the most comprehensive hydrological models, and is widely used worldwide. A hydrological evaluation was conducted with the DSOLMap and its results were compared to two other global soil databases using the SWAT + model in a basin located in the north of Spain. The findings showed that using more detailed, finer-resolution soil data, such as those that the DSOLMap offers, improved the hydrological performance of the SWAT + model on a daily scale before and after calibration and validation procedures. The DSOLMap represents a global step forward in hydrological modelling, notably for regions with scarce or unavailable soil information. This new digital soil property map can help decision-makers address global challenges related to water resources and environmental issues through hydrological modelling.
... CWB = P-PET), and so comes closer to identifying hydrological drought. The PET data (for calculating CWB) were obtained from the maximum and minimum temperature data using the Hargreaves method (Hargreaves and Samani 1982). Here we apply a 12 month accumulation period ending at the end of October in order to capture the annual time scale rainfall deficits. ...
Angola has been characterized as one of the most vulnerable regions to climate change. Climate change compounded by existing poverty, a legacy of conflict and other risk factors, currently impede development and are expected to become worse as climate change impacts increase. In this study we analyse the signal of climate change on temperature and rainfall variables for two time periods, 2020-2040 and 2040-2060. The analysis is based on multi-model ensemble of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6) and the Coordinated Regional Downscaling Experiments (CORDEX). Our findings from the observed dataset indicate that the mean annual temperature over Angola has risen by an average of 1.4 C since 1951, with a warming rate of approximately 0.2 [0.14 – 0.25] °C per decade. However, the rainfall pattern appears to be primarily influenced by natural variability. Projections of extreme temperature show an increase with the coldest nights projected to become warmer and the hottest days hotter. Rainfall projections suggest a change in the nature of the rainy season with increases in heavy precipitation events in the future. We investigated how droughts might change in all river basins of Angola, and we found an increased uncertainty about drought in the future. The changes in climate and increased variability demonstrate the need for adaptation measures that focuses on reducing risks in key sectors with a particular focus on adaptation of cities in Angola given a potential increase in mobility towards urban areas.
... In the Semi-arid region, the ANN model based on air temperature [54] performed well compared to the Hargreaves and Samani equation [94]. In India, 91 national parks and wildlife sanctuaries are located in semi-arid regions, which make up 37% of the nation's total land area (970,530 km 2 ). ...
The United Nations has set an ambitious goal to achieve net zero carbon emissions by 2050. This objective requires shifting towards green and renewable energy sources instead of conventional fossil fuels to address the global energy crisis without emitting greenhouse gases. While the energy radiated by the sun is one of the most abundant sources of energy available, its efficient and optimal use remains a significant challenge. To facilitate solar-energy-based applications, estimating the amount of solar energy available is crucial. Empirical and soft computing is the most-used method to estimate solar energy. This paper aims to analyze the existing techniques used in various models for estimating and predicting the quantity and quality of solar radiation using readily available data. Additionally, the study aims to identify the most appropriate techniques for developing prediction models using available explanatory variables. To fully harness the potential of solar energy, it is necessary to limit the terrestrial loss of solar radiation by minimizing the harmful effects of anthropogenic factors that reduce the quantity and quality of solar radiation in the area. This paper provides valuable insights to identify opportunities to maximize the potential of solar energy in different locations.
... The meteorological inputs were based on daily E-OBS data (Cornes et al., 2018) of precipitation in addition to minimum, maximum and average temperature. The potential evapotranspiration was derived using the method of (Hargreaves and Samani, 1982). The spatial resolution of the model grid 135 corresponds to 0.125 • . ...
The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia take place during spring, while median spatial patterns stem from summer changes in the water cycle.
... ET 0 can be calculated using microweather methods based on energy balance and vapor/mass flux transfer approaches or indirectly using empirical techniques (Penman 1948;Thornthwaite 1948;Blaney & Criddle 1950;Monteith 1965;Priestley & Taylor 1972;Hargreaves & Samani 1982). However, direct methods are time and cost effective when compared with indirect approaches, which require only site-specific micrometeorological data. ...
A study was carried out to develop and evaluate the performance of different machine learning (ML) models for predicting reference evapotranspiration (ET0). The models included multiple linear regression (MLR), least square-support vector machine (LS-SVM), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). The daily meteorological data for 50 years (1970–2019) were used to estimate ET0 using FAO-ET calculator. The FAO-ET calculator was compared with ML models to investigate the best-fit ML model for predicting ET. Thereafter, ET predicted by the best-fit ML model was compared with satellite (Moderate Resolution Imaging Spectroradiometer – MODIS) ET, which was finally mapped to a larger landscape (over entire Punjab and Haryana). Modeling of ET0 was best performed through LS-SVM followed by ANN2, ANN1, ANFIS10, ANFIS2, MLR and ANFIS9 models. Among developed models, coefficient of determination (R2) value varied from 0.800 to 0.998, being highest (0.998) under LS-SVM model. MODIS overestimated ET when compared with LS-SVM having R2 and root mean square error (RMSE) values of 0.73 and 3.95 mm, respectively. After applying the bias correction factor, R2 and RMSE were 0.74 and 1.19 mm, respectively. The ML and satellite-based ET estimation would be useful for timely water budgeting to manage the water scarcity problems from local to regional levels.
HIGHLIGHTS
The study region is experiencing an acute decline in groundwater resources, so it becomes imperative to manage water resources based on evapotranspiration.;
The traditional methods are not time-efficient, but machine learning and remote sensing techniques may solve this problem.;
The applications of machine learning models for estimating ET and use of satellite-derived ET are limited in the present study region.;
... This study used the Hargreaves method to estimate the potential evapotranspiration (Hargreaves and Samani, 1982). This method can be used to calculate values of the reference evapotranspiration, which is as follows: ...
The two major factors influencing river flow change in basins are anthropogenic activities and climate change (AACC), and separating the attribution of each can be crucial for managing water resources as well as economic, political, and social activities. In this study, a hydrologic model simulation was used to separate AACC attributions to river flow change in the southwest of Iran's Karkheh basin. The trend of annual potential evapotranspiration, precipitation, river flow, and air temperature was determined by the Mann-Kendall (MK) test. Multivariate dependence analysis was performed for precipitation changes, river flow changes, attributions of AACC to river flow change by different copula functions. The finding showed that the point of change in the annual river flow series by the double cumulative curve (DCC) and Pettitt test occurred in 1999. Thus, the pre-and post-change periods are before and after 1999, respectively. When compared to the pre-change period, the average annual river flow has decreased by 42.3%. The results of the hydrologic model simulation showed that anthropogenic activities and climate change have reduced river flow by 63.1% and 36.9%, respectively. The results showed that the attributions of AACC to river flow each year could be obtained based on the dependence analysis between precipitation changes, river flow changes, and the attributions of AACC to river flow change with copula functions. The study results can be a reference for developing, operating, and managing water resources and environmental conservation.
... Two pressure-compensating drippers (model GA 10 Grapa) were installed in each plant, each at a distance of 15 cm from the stem. From 30 days after transplantation the plants were irrigated daily, in the morning, with public-supply water, according to the irrigation interval adopted, and the reference evapotranspiration was estimated based on the method of Hargreaves and Samani (1982) and Bernardo et al. (2013), obtained by Equation 1: ...
The objective of this study was to evaluate the physiology and production of sugar-apple as a function of irrigation intervals and foliar application of proline under the conditions of Paraíba’s semi-arid region. A randomized block design was laid out in a 4 × 2 factorial scheme, with treatments resulting from the combination of four irrigation intervals (1, 4, 8 and 12 days) and two concentrations of proline (0 and 10 mM), with four replicates, and the plot consisted of four usable plants. Increase in irrigation intervals reduced the gas exchange of sugar-apple plants at 298 days after transplanting. Exogenous application of proline at concentration of 10 mM increased contents of chlorophyll a, chlorophyll b, total chlorophyll and carotenoids and fruit fresh mass in plants grown under 12-day irrigation intervals.
As the theoretical upper bound of evapotranspiration (ET) or water use by ecosystems, potential ET (PET) has always been widely used as a variable linking a variety of disciplines, such as climatology, ecology, hydrology, and agronomy. However, substantial uncertainties exist in the current PET methods (e.g., empiric models and single-layer models) and datasets because of unrealistic configurations of land surface and unreasonable parameterizations. Therefore, this study comprehensively considered interspecific differences in various vegetation-related parameters (e.g., plant stomatal resistance and CO 2 effects on stomatal resistance) to calibrate and parametrize the Shuttleworth-Wallace (SW) model for forests, shrubland, grassland, and cropland. We derived the parameters using identified daily ET observations with no water stress (i.e., PET) at 96 eddy covariance (EC) sites across the globe. Model validations suggest that the calibrated model could be transfer-able from known observations to any location. Based on four popular meteorological datasets, relatively realistic canopy height, time-varying land use or land cover, and the leaf area index, we generated a global 5 km ensemble mean monthly PET dataset that includes two components of potential transpiration (PT) and soil evaporation (PE) for the 1982-2015 time period. Using this new dataset, the climatological characteristics of PET partitioning and the spatiotemporal changes in PET, PE, and PT were investigated. The global mean annual PET was 1198.96 mm with PT/PET of 41 % and PE/PET of 59 %, controlled moreover by PT and PE of over 41 % and 59 % of the globe, respectively. Globally, the annual PET and PT significantly (p<0.05) increase by 1.26 and 1.27 mm yr −1 over the last 34 years, followed by a slight decrease in the annual PE. Overall, the annual PET changes over 53 % of the globe could be attributed to PT, and the rest to PE. The new PET dataset may be Published by Copernicus Publications. 4850 S. Sun et al.: A global 5 km monthly potential evapotranspiration dataset used by academic communities and various agencies to conduct climatological analyses, hydrological model-ing, drought studies, agricultural water management, and biodiversity conservation. The dataset is available at https://doi.org/10.11888/Terre.tpdc.300193 (Sun et al., 2023).
Solar radiation estimation is one of the integral part of solar power system design. Optimal harvesting of solar radiation is possible if it is estimated well in advance. Radiation measuring devices are found installed at Meteorological Stations. But these stations may not be our region of interest. Due to this limitation, solar radiation estimation models are developed. These models normally accept meteorological parameters like wind speed, ambient temperature, relative humidity, day temperature etc. and geographical entities like latitude, longitude and altitude as input and provide Global Solar Radiation(GSR). These models are statistically tested based on error calculation like MBE, MAE, Root RMSE etc. In this paper brief review of different solar radiation estimation models with methodology used is made. Soft computing based models are reviewed here. From this paper reader will come to know about various techniques used in solar radiation estimation. Study reveals that ANN based models have good performance in comparison to others.
21 st century is for the exploration of optimum utilization of Renewable Energy due to its great advantages over Non-renewable Energy. Among all other renewable energy like wind, tidal, etc. solar energy is preferred due to its abundancy and potential. Efficient utilization of Solar Energy is a great area of interest of the researchers. Optimum utilization of solar energy is directly related with amount of solar radiation received. Radiation data recorded by the measuring devices available at meteorological stations are the accurate one. But, due to its high cost and unavailability of meteorological stations everywhere, solar radiation is estimated based on geographical and meteorological parameters by the solar radiation estimation models. Modelling is executed mathematically or soft computing based techniques. Some of them are reviewed in this paper. They are compared and their results are analysed. Among Empirical Models, ANN Models, Fuzzy Models, ANN based Models are found to be most suitable one, due to better correlation and lesser statistical error rate like MBE or RMSE. Although, computational requirements are increased in ANN Models. Also, challenges behind solar radiation modelling and statistical evaluation methodologies are addressed.
Forecasting evaporation, an important variable in the hydrological cycle, is crucial for managing water resources and taking precautions against severe phenomena, such as droughts and floods. In this study, the prediction of daily pan evaporation was carried out in the Euphrates sub-basin, Turkey, which has different climate characteristics and is a critical region for Turkey or neighbouring countries. In this regard, two empirical models, namely the Griffith model and calibrated Hargreaves-Samani, and four ensemble empirical mode decomposition (EEMD) based data-driven models, namely EEMD-Random Forests (EEMD-RF), EEMD-Artificial Neural Network (EEMD-ANN), EEMD-Gradient Boosting Machines (EEMD-GBM), and EEMD-Regression Tree (EEMD-RT) were used for evaporation forecasting. The EEMD and Recursive Feature Elimination (RFE) were implemented as a signal decomposition technique and determination of the importance of the EEMD components, respectively. Although the empirical models yielded satisfactory performance, they predicted low and high evaporation values poorly, in general. The EEMD-RF, EEMD-ANN, and EEMD-GBM models performed better than the EEMD-RT model. The data-driven models, except EEMD-RT, outperformed the empirical models, especially regarding predicting extreme evaporation values. The sensitivity analysis indicated that wind speed, humidity, and maximum temperature could influence evaporation forecasting. This study shows that using data-driven models benefitting from EEMD and RFE can be a good alternative to empirical models for predicting evaporation.
Potential evapotranspiration (PET) is a particularly important parameter for understanding water interactions and balance in ecosystems, while it is also crucial for assessing vegetation water requirements. The accurate estimation of PET is typically data demanding, while specific climatic, geographical and local factors may further complicate this task. Especially in city environments, where built-up structures may highly influence the micrometeorological conditions and urban green sites may occupy limited spaces, the selection of proper PET estimation approaches is critical, considering also data availability issues. In this study, a wide variety of empirical PET methods were evaluated against the FAO56 Penman–Monteith benchmark method in the environment of two Mediterranean urban green sites in Greece, aiming to investigate their accuracy and suitability under specific local conditions. The methods under evaluation cover all the range of empirical
PET estimations: namely, mass transfer-based, temperature-based, radiation based, and combination approaches, including 112 methods. Furthermore, 15 locally calibrated and adjusted models have been developed based on the general forms of the mass transfer, temperature, and radiation equations, improving the performance of the original models for local application. Among the 127 (112 original and 15 adjusted) evaluated methods, the radiation-based methods and adjusted models
performed overall better than the temperature-based and the mass transfer methods, whereas the data-demanding combination methods received the highest ranking scores. The adjusted models seem to give accurate PET estimates for local use, while they might be applied in sites with similar conditions after proper validation.
The Yautepec river basin, Morelos, México is undergoing a strong process of urbanization and degradation of its natural resources. The objective of this study was to calibrate and validate the SWAT (Soil and Water Assessment Tool) model to predict monthly runoff production using the runoff curve number method. The data used for the calibration and validation were those measured in the Ticumán hydrometric station corresponding to the monthly series from 2007 to 2011 and from 1980 to 1985. For the monthly calibration of runoff from 2002 to 2009, an NSE of 0.79 and R2 of 0.81 were obtained, and for the 2011 validation period an NSE of 0.89 and R2 of 0.89 were yielded. When implemented in the period 1976 to 1985, an NSE for monthly runoff of 0.71 and R2 of 0.72 were obtained. The SWAT model turned out to be a good model for the estimation of runoff in the study basin.
Estimation of reference crop evapotranspiration (ETo) is very important in planning and scheduling irrigation water. An estimation of ETo by other simple methods is necessary with the loss of one or more meteorological factors required for estimation by the Penmen-Monteith equation. So, the aim of the study was to manage irrigation water in the Al-Baha region of Saudi Arabia using a simple equation to estimate ETo based on air temperature and an effective alternative to the Penmen-Monteith equation (PM). Four simple temperature dependent equations, Thornthwaite, Blaney-Criddle, Hamon and Linacre were selected. The results showed that the Linacre method is the best equation for estimating ETo and although the ETo rate was overestimated based on the Linacre equation compared to PM, it had the lowest error percentage (9.55%) in addition to the highest R ² (0.97). After deducing a new and accurate equation based on the Linacre equation and its high ability to estimate the ETo rate and give it values closest to the results obtained using PM in the Al-Baha region in Saudi Arabia, it can be relied upon to estimate the irrigation needs in the Al-Baha region with irrigation systems used in this region.
Assessing changes in future aridity requires an understanding of variations in the atmospheric demand for water. Such assessments are often driven by estimations of potential evapotranspiration (ETP) and/or reference evapotranspiration (ET0), yet no comprehensive and validated estimate of these climate metrics exists to date from the Coupled Model Intercomparison Project 6 (CMIP6). Here we describe the development and validation of a published dataset of global monthly estimates of the Penman-Monteith derived ET0, its advective and radiation components, Priestley-Taylor derived ETP, and vapor pressure deficit from 16 CMIP6 projections and four emissions scenarios. Historical validation of the ensemble of CMIP6 evaporative demand shows general agreement with observationally-derived baselines of ET0 and ETP from the Climate Research Unit (CRU) and ERA5-Land reanalysis products, with GCM biases driven primarily by regional differences in modeled humidity and advective contributions to ET0. Overall, evaporative demand is projected to increase across all emissions scenarios, with the largest increases over polar regions, and with a larger contribution from advection particularly for regions with higher baseline ET0.
The horizon attained by any transmitted radio signal is affected by the refractivity over the area, and which is in turn affected by other parameters of the environment or atmosphere such as temperature, pressure, relative humidity. The aim of this work is to examine the relationship between global solar radiation and radio refractivity of Jos, Plateau state using the atmospheric data of average monthly maximum and minimum temperature, pressure, and relative humidity (July1, 1983-June 30, 2005) obtained from earthdata.nasa.gov. The results showed that there was high correlation between global solar radiation and radio refractivity of the study area, with the correlation coefficient of-0.808 (80.8%). It was then concluded that global solar radiation of the area is inversely proportional to the radio refractivity.
Floods are the primary natural hazard in the French Mediterranean area causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. For a decade, a new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-Permitting regional climate Models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with Regional Climate Models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d’Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H), and a processed-based and distributed model (CREST), successively forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models, but differ between the two forcing climate models. With the CNRM-ALADIN RCM, all floods are projected to increase, whereas a threshold effect is found for simulations driven by the CNRM-AROME CPM, where the magnitude of the largest floods is expected to increase while the moderate floods are expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.
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