Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Daily global solar irradiation (R _s) is one of the main inputs in environmental modeling. Because of the lack of its measuring facilities, high-quality and long-term data are limited. In this research, R _s values were estimated based on measured sunshine duration and cloud cover of our synoptic meteorological stations in central and southern Iran during 2008, 2009, and 2011. Clear sky solar irradiation was estimated from linear regression using extraterrestrial solar irradiation as the independent variable with normalized root mean square error (NRMSE) of 4.69 %. Daily R _s was calibrated using measured sunshine duration and cloud cover data under different sky conditions during 2008 and 2009. The 2011 data were used for model validation. According to the results, in the presence of clouds, the R _s model using sunshine duration data was more accurate when compared with the model using cloud cover data (NRMSE = 11. 69 %). In both models, with increasing sky cloudiness, the accuracy decreased. In the study region, more than 92 % of sunshine durations were clear or partly cloudy, which received close to 95 % of total solar irradiation. Hence, it was possible to estimate solar irradiation with a good accuracy in most days with the measurements of sunshine duration.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

Error assessment of climate variables for FAO-56 reference evapotranspiration

Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ET_o) using the Food and Agriculture Organization Penman?CMonteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ET_o estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ET_o showed small differences [i.e., mean bias error (MBE) varying ?0.22 to 0.25?mm?day^?1 and root-mean-square-error (RMSE) varying 0.06?C0.50?mm?day^?1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ET_o (i.e., MBE of ?0.50?mm?day^?1 and RMSE of 0.60?C0.73?mm?day^?1). Estimated ET_o showed different sensitivity to variations of the meteorological parameters??in order of vapor pressure?>?wind speed?>?radiation. It is clear that the FAO56-PM method is applicable for reasonable ET_o estimation at a daily time scale especially in data-limited regions in Korea.     

What Ångström—Prescott equation tells us about the cloud and clear-sky climatologies?

Theoretical and Applied Climatology - The Ångström–Prescott equation defines generically the relationship between solar energy available at ground level and sunshine duration. From...     

Trend analysis of rainfall time series for Sindh river basin in India

The main goal of this paper is to estimate a set of optimal seasonal, daily, and hourly values of atmospheric turbidity and surface radiative parameters Ångström’s turbidity coefficient (β), Ångström’s wavelength exponent (α), aerosol single scattering albedo (ω_o), forward scatterance (F_c) and average surface albedo (ρ_g), using the Brute Force multidimensional minimization method to minimize the difference between measured and simulated solar irradiance components, expressed as cost functions. In order to simulate the components of short-wave solar irradiance (direct, diffuse and global) for clear sky conditions, incidents on a horizontal surface in the Metropolitan Area of Rio de Janeiro (MARJ), Brazil (22° 51′ 27″ S, 43° 13′ 58″ W), we use two parameterized broadband solar irradiance models, called CPCR2 and Iqbal C, based on synoptic information. The meteorological variables such as precipitable water (u_w) and ozone concentration (u_o) required by the broadband solar models were obtained from moderate-resolution imaging spectroradiometer (MODIS) sensor on Terra and Aqua NASA platforms. For the implementation and validation processes, we use global and diffuse solar irradiance data measured by the radiometric platform of LabMiM, located in the north area of the MARJ. The data were measured between the years 2010 and 2012 at 1-min intervals. The performance of solar irradiance models using optimal parameters was evaluated with several quantitative statistical indicators and a subset of measured solar irradiance data. Some daily results for Ångström’s wavelength exponent α were compared with Ångström’s parameter (440–870 nm) values obtained by aerosol robotic network (AERONET) for 11 days, showing an acceptable level of agreement. Results for Ångström’s turbidity coefficient β, associated with the amount of aerosols in the atmosphere, show a seasonal pattern according with increased precipitation during summer months (December–February) in the MARJ.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

Spatiotemporal variations of potential evapotranspiration and aridity index in relation to influencing factors over Southwest China during 1960–2013

This study investigated the spatial–temporal patterns and trends of potential evapotranspiration (ET₀) and aridity index (AI) over Southwest China during 1960–2013 based on daily temperature, precipitation, wind speed, sunshine duration, total solar radiation, and relative humidity data from 108 meteorological stations. The Penman–Monteith model, Mann–Kendall (M–K) test, moving t test, and Morlet wavelet method were used. The results indicated that ET₀ and AI across the region displayed decreasing trends, but the former was significant. After 2000, regionally average trends in ET₀ and AI increased rapidly, indicating that droughts increased over Southwest China in recent years. Spatially, the changes of ET₀ and AI were dissimilar and not clustered, either. Temporally, both ET₀ and AI displayed obvious abrupt change points over different timescales and that of AI was during the winter monsoon period. Significant periodic variations with periods of 27, 13, and 5 years were found in ET₀, but only of 13 and 5 years existed in AI. Correlation analysis revealed that the sunshine duration and wind speed were the dominant factors affecting ET₀ and that AI showed strong negative correlation with precipitation. The findings of this study enhance the understanding of the relationship between climate change and drought in Southwest China, while the mechanism controlling the variation in drought requires further study.     

Retrieving daily global solar radiation from routine climate variables

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m² day^?1 and 2.25 MJ m² day^?1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.     

Analysis of photosynthetically active radiation and applied parameterization model for estimating of PAR in the North China Plain

As photosynthetically active radiation (PAR) variability and PAR estimating methods play an important role in climate change and ecological process research, PAR variation trends and broadband global solar radiation (R _s ) ratios (PAR/R _s ) in the North China Plain (NCP) are examined using in situ PAR and R _s observed data for 2005 to 2011. The annual average PAR value found in the NCP is 22.9 mol m^?2 d^?1. The highest and lowest values were recorded at Changwu and Luancheng sites, respectively. The highest PAR/R _s value was found in Jiaozhouwan due to large water vapor volumes present in this area. PAR/R _s levels have increased in the NCP due to a decrease in fine aerosols and increase in water vapor concentration. From these analysis results, a parameterization model that can be applied to all sky conditions was checked. Empirical estimation model comparisons for obtaining PAR values indicate that model was least accurate when R _s was used independently. When the model included R _s, the clearness index (K _s) and the solar zenith angle, the model estimated PAR values with acceptable accuracy. A parameterization model was constructed by considering K _s and attenuation factors of PAR under clear weather conditions (ρ _clear). The improved parameterization model more accurately predicts values for local sites and for various observation sites.     

Seasonal inhomogeneity of soot particles over the central Indo-Gangetic Plains,India: Influence of meteorology

Black carbon (BC) particles play a unique and important role in earth’s climate system. BC was measured (in-situ) in the central part of the Indo-Gangetic Plains (IGP) at Varanasi, which is a highly populated and polluted region due to its topography and extensive emission sources. The annual mean BC mass concentration was 8.92 ± 7.0 µg m ^-3, with 34% of samples exceeding the average value. Seasonally, BC was highest during the post-monsoon and winter periods (approximately 18 µg m ^-3) and lower in the premonsoon/ monsoon seasons (approximately 6 µg m ^-3). The highest frequency (approximately 46%) observed for BC mass was in the interval from 5 to 10 µg m ^-3. However, during the post-monsoon season, the most common values (approximately 23%) were between 20 and 25 µg m ^-3. The nighttime concentrations of BC were approximately twice as much as the daytime values because of lower boundary layer heights at nighttime. The Ångström exponent was significantly positively correlated (0.55) with ground-level BC concentrations, indicating the impact of BC on the columnar aerosol properties. The estimated mean absorption Ångström exponent was 1.02 ± 0.08 µg m ^-3, indicating that the major source of BC was from fossil fuel combustion. Significant negative correlations between BC mass and meteorological parameters indicate a pronounced effect of atmospheric dynamics on the BC mass in this region. The highest mean BC mass concentration (18.1 ± 6.9 µg m ^-3) as a function of wind speed was under calm wind conditions (38% of the time).     

Precise estimation of hourly global solar radiation for micrometeorological analysis by using data classification and hourly sunshine

We have developed a method for estimating hourly global solar radiation (GSR) from hourly sunshine duration data. This procedure requires only hourly sunshine duration as the input data and utilizes hourly precipitation and daily snow cover as auxiliary data to classify time intervals into six cases according to weather conditions. To obtain hourly GSR using a simple algebraic form, a quadratic function of the solar elevation angle and the sunshine duration ratio is used. Daily GSR is given by a sum of hourly GSRs. We evaluated the performance of the newly developed method using data obtained at 67 meteorological stations and found that the estimated GSR is highly consistent with that observed. Hourly and daily root-mean-square misfits are approximately 0.2 MJ/m²/h (~55 W/m²) and 1.4 to 1.5 MJ/m²/day (~16 to 17 W/m²), respectively. Our classification of weather conditions is effective for reducing estimation errors, especially under cloudy skies. Since the sunshine duration is observed at more meteorological stations than GSR, the proposed new method is a powerful tool for obtaining solar radiation with hourly resolution and a dense geographical distribution. One of the proposed methods, GSRgrn, can be applicable to hourly GSR estimations at different observation sites by setting local parameters (the precipitable water, surface albedo, and atmospheric turbidity) suitable to the sites. The hourly GSR can be applied for various micrometeorological studies, such as the heat budget of crop fields.     

Modeling daily reference evapotranspiration (ET0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): a comparative study

Estimation of reference evapotranspiration (ET₀) is needed to support irrigation design and scheduling, and watershed hydrology studies. There are many available methods to estimate evapotranspiration from a water surface, comprising both direct and indirect methods. In the first part of this study, the generalized regression neural networks model (GRNN) and radial basis function neural network (RBFNN) are developed and compared in order to estimate the reference ET₀ for the first time in Algeria. Various daily climatic data, that is, daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature, and wind speed from Dar El Beida, Algiers, Algeria, are used as inputs to the GRNN and RBFNN models to estimate the ET₀ obtained using the FAO-56 Penman-Monteith equation (PM56). The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. In the second part of the study, the empirical Hargreaves-Samani (HG) and Priestley-Taylor (PT) equations are also considered for the comparison. Based on the comparisons, the GRNN was found to perform better than the RBFNN, Priestley-Taylor and Hargreaves-Samani models. The RBFNN model is ranked as the second best model.     

The climatological characteristics of photosynthetically active radiation in arid and semi-arid regions of China

Photosynthetically active radiation (Q _p ) is a key variable in models of net primary productivity and carbon cycle modelling. The relationship between broadband global solar radiation (R _s) and Q _p is investigated using 6?years of radiation data collected at 9 sites in arid and semi-arid regions of China. The dependence of Q _p /R _S on aerosol optical depth (AOD) and water vapour content are also discussed. A simple and efficient all-weather empirically derived model is developed to estimate Q _p from R _s. The annual average daily Q _p in arid and semi-arid areas is 29.9?±?11.7 and 27.3?±?10.1?mol?m^-2 d^-1, respectively. The highest value (31.9?±?11.3?mol?m^-2 d^-1) appears at Linze in the arid area. The lowest value (24.3?±?9.7?mol?m^-2 d^-1) appears at Ansai in the semi-arid area. The results show that the monthly variation of the Q _p /R _s ratio ranges from 1.69?±?0.19?mol?MJ^-1 in Aksu to 1.91?±?0.08?mol?MJ^-1 in Fukang. There is a small decreasing trend of the ratio of Q _p to R _s (PAR fraction) in arid and semi-arid regions because of the recent increase in fine aerosols. A simple and efficient empirically model suit for all-weather condition was developed to estimate Q _p from R _s. The slope a and intercept b of the regression line between estimated and measured values is close to 1 and zero, respectively. The application of the model to data collected from different locations also results in reasonable estimates of Q _p .     

Single and dual crop coefficients and crop evapotranspiration for wheat and maize in a semi-arid region

In this study, weighing lysimeters were used to investigate the daily crop coefficient and evapotranspiration of wheat and maize in the Fars province, Iran. The locally calibrated Food and Agriculture Organization (FAO) Penman–Monteith equation was used to calculate the reference crop evapotranspiration (ET_o). Micro-lysimetry was used to measure soil evaporation (E). Transpiration (T) was estimated by the difference between crop evapotranspiration (ET_c) and E. The single crop coefficient (K _c) was calculated by the ratio of ET_c to ET_o. Furthermore, the dual crop coefficient is composed of the soil evaporation coefficient (K _e) and the basal crop coefficients (K _cb) calculated from the ratio of E and T to ET_o, respectively. The maximum measured evapotranspiration rate for wheat was 9.9 mm?day^?1 and for maize was 10 mm?day^?1. The total evaporation from the soil surface was about 30 % of the total wheat ET_c and 29.8 % of total maize ET_c. The single crop coefficient (K _c) values for the initial, mid-, and end-season growth stages of maize were 0.48, 1.40, and 0.31 and those of wheat were 0.77, 1.35, and 0.26, respectively. The measured K _c values for the initial and mid-season stages were different from the FAO recommended values. Therefore, the FAO standard equation for K _c-_mid was calibrated locally for wheat and maize. The K _cb values for the initial, mid-, and end-season growth stages were 0.23, 1.14, and 0.13 for wheat and 0.10, 1.07, and 0.06 for maize, respectively. Furthermore, the FAO procedure for single crop coefficient showed better predictions on a daily basis, although the dual crop coefficient method was more accurate on seasonal scale.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

Egyptian cotton (Gossypium barbadense) flower and boll production as affected by climatic factors and soil moisture status

The information on impact of climatic factors on cotton production is not generally available, or at least not available in the required form. Understanding this impact may help physiologists determine a possible control of the flowering mechanism in the cotton plant. Two field trials were conducted to investigate the relationships between climatic factors, soil moisture status, and flower and boll production of Gossypium barbadense. The climatic factors considered were daily maximum air temperature (°C), minimum air temperature (°C), maximum–minimum temperature (diurnal temperature range) (°C), sunshine duration (h day^?1), maximum relative humidity (%), minimum relative humidity (%), and wind speed (m s^?1). Minimum relative humidity and sunshine duration were the most significant climatic factors affecting flower and boll retention and production. Temperature appeared to be less important in the reproduction growth stage of cotton in Egypt than minimum relative humidity and sunshine duration. The soil moisture status showed low and insignificant correlation to flower and boll production. High minimum relative humidity, short period of sunshine duration, and low temperatures enhanced flower and boll formation.