Global Solar Radiation Data Research Articles

Forecasting of global solar radiation: A statistical approach using simulated annealing algorithm

In recent years, the share of renewable energy sources in current energy production has been increasing due to the depletion of fossil fuel resources, increasing energy needs and environmental concerns. Solar energy, one of the most important renewable energy sources depending on being clean, sustainable and environmentally friendly energy source. Understanding the solar radiation value is crucial for maximizing the potential of solar energy and ensuring the efficient operation of solar energy systems. In this paper, it is aimed to develop a new global solar radiation (GSR) prediction model by using simulated annealing algorithm (SAA). Modeling solar radiation data with significant variability is a challenging task that necessitates the use of nonlinear approaches. Although the SAA is widely used in engineering and natural sciences, it has not previously been used to develop a GSR prediction model. While developing the SAA forecast model for the Adana region, long-term sunshine duration and solar radiation data obtained from the Turkish General Directorate of State Meteorology were used, as well as geographical features such as latitude and longitude of the selected region. The performance and applicability of the proposed model was examined by comparing it with different GSR estimation methods developed in the literature. The primary contribution of this study lies in the introduction of an inventive model, which has been constructed for the first time in the assessment of GSR, to the current collection of literature. The produced results were statistically compared with the observed data using six separate performance and error measures. The results of all years showed that the relative percentage error (RPE) less than 11.66 %, the mean percentage error (MPE) does not exceed 12.55 %, the correlation coefficient (R2) greater than 0.98, the mean absolute percentage error (MAPE) test results appear to vary close to the value 10, the sum of squared relative error (SSRE) test results seem to approaching 0 and the t-statistic test less than 4.06 for the annual GSR. It has been observed that the developed SAA GSR forecasting model has a successful performance when compared to different forecasting models popularly used in the literature. According to the statistical error results, the estimated GSR data from the novel SAA-GSR forecast model aligns well with the measured meteorological values. Given the efficacy of the established SAA model in GSR estimation, this study is anticipated to offer valuable insights and contributions to the application of the SAA approach in other energy applications.

Empirical models and artificial intelligence for estimating hourly diffuse solar radiation in the state of Alagoas, Northeastern Brazil

Diffuse solar irradiation (HD) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, HD observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (HDh) and global solar irradiation (HGh) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating KDh(=HDh/HGh) (r2 > 0.726, modified Willmott – dm > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating KDh for hourly atmospheric transmittance (KTh) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, dm > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, dm > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating KDh superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for HD estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation.

Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye

Today’s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Tilted global solar irradiance in Bahrain: an experimental study

Abstract In the present work, tilted global solar irradiance data are presented and analysed, measured for a period of 1 year on the campus of Bahrain Polytechnic, Kingdom of Bahrain, from both a fixed photovoltaic panel and a moving one via a two-axis solar tracker. The fixed panel faces south with an angle of 26° with respect to the horizontal, coinciding with the local geographical latitude. The second panel is moved by two motors, controlled by a global positioning system and suitable software so that the Sun’s rays are perpendicular to the panel surface. A pyranometer is installed on each panel, recording the tilted global solar irradiance, stored by using a data logger. The analysis of the data obtained shows a 33% solar energy gain on an annual basis for the moving panel (2780 versus 2088 kWh/m2 on the fixed panel). More importantly, in June, when the energy demand in Bahrain is elevated due to the increased residential cooling loads, the solar energy received by the moving panel is 54.7% higher compared with that of the fixed panel. On a percentage basis, the increase in solar energy from the moving panel is profound in the early morning and late evening hours. Moreover, the reduction in the solar energy received by the fixed panel from May to June does not appear in the moving panel because of the adjustable orientation of the latter. Throughout the year, the mean daily solar power varies between 0.37 and 0.56 kW/m2 for the fixed panel, and 0.45 and 0.70 kW/m2 for the moving panel. In winter, solar energy fluctuations are elevated due to erratic weather conditions that present a peak standard deviation of 28% of the corresponding mean. The data presented are useful for potential solar investments in Gulf countries.

Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

AbstractRecently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ($$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m−2 day−1), from 0.6430 to 1.7060 (MJ m−2 day−1), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.

Investigation of the Earth’s Albedo Using Meteorological Parameters over Maiduguri, Nigeria

The present study estimate and investigate the variation of albedo for Maiduguri situated in the Sahelian region of Nigeria, using meteorological data of global solar radiation obtained from the National Aeronautics and Space Administration (NASA) extending for a period between 1984 to 2021. Investigation was carried out on the variation of albedo with surface temperature, maximum wavelength, clearness index, global solar radiation, relative humidity and mean temperature. The study found that the estimated surface albedo exhibited a direct opposite relationship with the clearness index, an inverse relationship with the emitting Earth’s surface temperature and a direct relationship with the wavelength for the location. The highest value of 0.5125 and lowest value of 0.3344 were found in August and November respectively. The emitting Earth surface temperature ranged between 232.8674 K in August and 251.7177 K in November. This is in agreement with the standard emitting Earth surface temperature (255.0000 K). The values of the maximum emitting wavelength were found to be &gt; 4 indicating longwave radiation which is within the infrared region of the electromagnetic spectrum. The results from this study will be useful for the design of solar energy collectors and researches on atmospheric radiative transfer.

An hourly solar radiation prediction model using eXtreme gradient boosting algorithm with the effect of fog-haze

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover.

Sunshine and Temperature Based Models for Estimating Global Solar Radiation in Maiduguri, Nigeria

Nine existing sunshine based models and three existing temperature based models were evaluated and compared to ascertain the most suitable models for estimating global solar radiation in Maiduguri; the most suitable sunshine based and most suitable temperature based models were also compared. The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one years (1980 – 2010) was utilized and the evaluated models were tested statistically using validation indicators of coefficient of determination, Mean Bias Error, Root Mean Square Error, Mean Percentage Error, t – test, Nash – Sutcliffe Equation and Index of Agreement. The results indicated that the linear exponential sunshine based model and the logarithmic temperature based model were found more accurate for global solar radiation estimation in Maiduguri as compared to other evaluated models. Furthermore, the logarithmic temperature based model was found more accurate for estimating global solar radiation as compared to the linear exponential sunshine based model, and this was testified from the figure showing the comparison between the recommended sunshine based and temperature based models in which the recommended temperature based model depicts the best fitting with the measured global solar radiation data.

Development criterion of estimating hourly global solar radiation for all sky conditions in China

The high quality hourly global solar radiation data becomes more and more important under the trend of low building energy consumption and carbon emission. The models based on daily global solar radiation and hourly meteorological parameters are two kinds of efficient methods to compensate for measured data deficiency. However, the applicable climate and sky conditions of two models are needed to be clarified. Based on 45 stations in China, the coupling correlations between hourly global solar radiation and meteorological parameters are obtained by correlation analysis. The eight decomposition models and four models based on the main hourly meteorological parameters are developed and evaluated for climates and sky conditions using the mean absolute bias error, relative root mean squared error, and Nash-Sutcliffe efficiency coefficient. Considering the advantages of two kinds of models, the criterion of estimating hourly global solar radiation data is developed. The results show that hourly sunshine duration and hourly surface temperature are the main factors reflecting the change of hourly global solar radiation. In humid and sub-humid climates, the hourly relative humidity can further characterize the fluctuation of hourly global solar radiation. CPR model performs best among decomposition models, especially in sunny sky conditions. The meteorological parameters models own higher accuracy in cloudy conditions. In regions with hourly sunshine duration measurement, CPR model is recommended when daily clearness index is higher than 0.7 and lower than 0.2. When daily clearness index varies from 0.2 to 0.7, the model should be established by selecting hourly sunshine duration and hourly surface temperature in arid and semi-arid climates and hourly sunshine duration, hourly surface temperature and hourly relative humidity in humid and subhumid climates, respectively. In regions without hourly sunshine duration measurement, CPR model should be used regardless of weather conditions and climate types.

Generation of a Typical Meteorological Year for Global Solar Radiation in Taiwan

Solar energy technology is now a mature and environmentally friendly solution. Long-term and credible solar radiation data are required for energy assessments of solar applications. Due to the lack of a typical year and accurate, long-term global solar radiation data for Taiwan, data for the typical meteorological year (TMY) of global solar radiation from 30 weather stations across Taiwan of the Central Weather Bureau were gathered for this study. The database for solar radiation contains data for the 15 years between 2004 and 2018, except for one (Chigu) weather station which provides data for the 12 years between 2004 and 2015, which possesses credible data quality and meets the requirements of the TMY method. The minimum and maximum TMY global radiation observed from the 30 weather stations are 3421.8 and 5479.9 MJ/m2 at the Zhuzihu (Station 2) and Tainan (Station 16) weather stations, respectively. The effects of topography, geography, and latitude on the global radiation distribution in Taiwan are discussed. A trend of increasing annual global radiation from the northeast to the southwest in the Taiwanese mainland, which is attributed to the combined effects of topography and latitude, is observed. This credible, long-term database for global solar radiation is a prerequisite reference for solar information for use in determining the performance of solar energy applications in Taiwan.

The spatial distribution of China's solar energy resources and the optimum tilt angle and power generation potential of PV systems

This study aims at filling the gaps and completing the missing global solar radiation and diffuse solar radiation data considering the limited radiation data of 85 meteorological observation stations and the routine meteorological data of 2001 meteorological observation stations. Employing the Inverse Distance Weighing model, the annual and seasonal spatial distributions of global solar radiation and diffuse solar radiation are obtained. A calculation method of the optimum tilt angle of photovoltaic systems for optimal utilization is established using the Collares-Pereira and Rabl hourly solar radiation model and the solar radiation on sloped surfaces model. The spatial distribution of the optimum tilt angle is obtained using the proposed method. The simplified calculation method of the optimum tilt angle based on latitude and altitude is established by introducing two factors of latitude and altitude. In addition, the annual and seasonal photovoltaic power of China is calculated, and the spatial distribution of China's solar resource utilization potential is obtained using the calculated optimum tilt angle, solar radiation data on sloped surfaces, and the photovoltaic power model. The results show that the annual global solar radiation in China is in the range of 3097–7311 MJm−2, and the annual diffuse solar radiation value ranges from 495 to 3036 MJm−2. Moreover, the optimum tilt angle value in different regions of China is found to range between 14.5°and 49.1°, and the theoretical optimum tilt angle value generally aligns with the law of angle increasing with latitude. The annual photovoltaic power generation is between 117 kWhm−2 and 483 kWhm−2. Compared with the solar energy utilization potential of a PV placed on the horizontal surface, the annual average power generation of a PV panel placed at the optimum tilt angle can increase by up to 144.76 kWhm−2, with an average increase of 10.41%.

Empirical model for the estimation of global solar radiation in the Aceh Besar Regency, Aceh

Solar energy plays an important role in the temperature distribution on the Earth's surface and essential energy that sustains life for human. In the calculation of solar energy, the limitation of solar radiation data is a major obstacle. Solar radiation data is very limited at some observation stations due to the costly procurement of measuring instruments and complicated techniques. Estimation of global solar radiation using empirical models is one way to overcome the limitations of global solar radiation data at various locations. This study built the Angstrom-Prescott model to estimate global solar radiation based on the sunshine duration’s parameter in Aceh Besar Regency. Two years (2019-2020) global solar radiation and sunshine duration data from Aceh Climatological Station were used to estimate global solar radiation in 2021. Data for 2021 was using to evaluated the equation H/H0=0.28+0.22 (n/N), the results showed good model accuracy with an RMSE value of 0.052 and a MAPE of 11% (good forecasting). The model equation has been reliable to calculate solar radiation in four locations in Aceh Besar Regency.

Comparative Study of Solar Radiation Models for the Estimation of Solar Radiation Using Short-Term Meteorological Data in Lawra, Ghana

Monthly average daily global solar radiation data are essential for the design and study of solar energy systems. The performance and accuracy of eleven models for the estimation of monthly average global solar radiation were compared in this study. Nineteen months (Nov 2020 – May 2022) ground measurement data consisting of monthly mean daily sunshine duration, relative humidity, minimum and maximum temperatures, and global solar radiation collected from the Lawra Solar Plant were used. The models were compared using statistical indices. According to the indices, most of the models were in reasonably good agreement with the measured data. Two model equations, however, were found to have the highest accuracy and can thus be used to estimate monthly average global solar radiation in Lawra and other places with similar climatic conditions where radiation data is unavailable.

Potentiality of Solar Photovoltaic Energy in Paraiba, Brazil, and the Relation with Sustainable Development

The use of non-renewable energy has been a major environmental concern and, therefore, there is a need to look for other renewable energy sources, especially photovoltaic’s. In view of this, an attempt was made to quantify the potential of solar irradiance in the State of Paraiba, as an alternative source for conversion and use in electrical energy, these determinations being the main objectives. Global solar irradiance and solar photovoltaic data were extracted from scientific publications and/or made available on the websites of the National Institute of Meteorology (INMET), the Ministry of Mines and Energy and the National Electric Energy Agency, among others. For the case study, semi-structured questionnaires were applied in different business establishments in Campina Grande, with questions related to socioeconomic aspects and photovoltaic technology. Data were analyzed using descriptive statistics criteria and using an Excel spreadsheet. The main results indicated that the Brazilian energy matrix is predominantly from renewable sources. The Northeast is the second region with the highest production of photovoltaic solar energy and the State of Paraiba occupies its fourth position in the generation of this type of energy. The option of photovoltaic technology is a promising alternative, especially for rural areas, where there is not always a conventional electricity grid. The high availability of solar energy in northeastern Brazil, in almost all months of the year, especially in the state of Paraiba, demonstrates the existence of a high potential to generate electricity from photovoltaic systems. This technology contributes to local sustainable development, as it is an activity that generates employment and income, without degrading the environment.

Extracting Statistical Properties of Solar and Photovoltaic Power Production for the Scope of Building a Sophisticated Forecasting Framework

Building a sophisticated forecasting framework for solar and photovoltaic power production in geographic zones with severe meteorological conditions is very challenging. This difficulty is linked to the high variability of the global solar radiation on which the energy production depends. A suitable forecasting framework might take into account this high variability and could be able to adjust/re-adjust model parameters to reduce sensitivity to estimation errors. The framework should also be able to re-adapt the model parameters whenever the atmospheric conditions change drastically or suddenly—this changes according to microscopic variations. This work presents a new methodology to analyze carefully the meaningful features of global solar radiation variability and extract some relevant information about the probabilistic laws which governs its dynamic evolution. The work establishes a framework able to identify the macroscopic variations from the solar irradiance. The different categories of variability correspond to different levels of meteorological conditions and events and can occur in different time intervals. Thereafter, the tool will be able to extract the abrupt changes, corresponding to microscopic variations, inside each level of variability. The methodology is based on a combination of probability and possibility theory. An unsupervised clustering technique based on a Gaussian mixture model is proposed to identify, first, the categories of variability and, using a hidden Markov model, we study the temporal dependency of the process to identify the dynamic evolution of the solar irradiance as different temporal states. Finally, by means of some transformations of probabilities to possibilities, we identify the abrupt changes in the solar radiation. The study is performed in Guadeloupe, where we have a long record of global solar radiation data recorded at 1 Hertz.

Potential assessment of the TVF-EMD algorithm in forecasting hourly global solar radiation: Review and case studies

Accurate and effective forecasting of short-term global solar radiation is critical for the development of photovoltaic systems, particularly for integration into existing grid systems. However, its non-stationary characteristics caused by climatic factors make its estimation extremely challenging. In this regard, a newly designed learning technique for multi-hour global solar radiation forecasting is proposed based on a time-varying filter-empirical mode decomposition (TVF-EMD), feature selection, and extreme learning machine (ELM) as an essence regression. The proposed hybridization strategy consists of three main steps for understanding the fundamental behavioral aspects of hourly global solar radiation data. The first phase employs the TVF-EMD algorithm to deal with the variability of global solar radiation data by separating it into a series of more stable and constant subseries. Then, the feature selection step is employed to evaluate and identify distinctive features set from the whole decomposed subseries by means of the RReliefF algorithm. The selected feature sets are used to train and optimize our forecasting extreme learning machine model, then the tuned ELM model is used to assess the forecasting accuracy. The proposed TVF-EMD-RF-ELM model is evaluated and validated in different regions in Algeria with various climate conditions. The forecasting findings of the TVF-EMD algorithm demonstrate high accuracy compared to the recent version of empirical mode decomposition CEEMDAN. Overall forecasting periods, the TVF-EMD-RF-ELM model produces an error less than 8.3% in terms of normalized root mean square error nRMSE in all studied regions.

Optimal Parameters for Spatial Distribution Modeling of Global Horizontal Solar Radiation in Iraq

Solar radiation plays an essential role in all interior physical processes and the radiative budget of the earth-atmosphere system. However, information about the spatial distribution of global solar radiation or its components is limited and exclusive at some scientific centers that specialized in solar radiation physics. Based on daily data of global solar radiation from (1984) to (2004) available at (16) locations in Iraq retrieved from NASA e-archive, this study aims to determine the optimal parameters of the experimental model to estimate the amount of global solar radiation on a horizontal surface as a function of latitude only. The results showed that the suggested model which is mathematically expressed by the sum of sine of latitude is highly appropriate for estimation of global solar radiation in clear sky conditions after adjusting the parameters according to geographical location. Through comparison between modeled output and an independent measured, globally used, and satellite available data and based on overall results besides some validity metrics: MBE, RMSE, pRMSD, NSE, R2, and r; It can be concluded the proposed model is achievable with high accurate practically performance to calculate and estimate the horizontal global solar radiation (GH) in Iraq region.

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

The accurate calculation of reference evapotranspiration (ET0) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (Rs) data, the machine learning methods obtain better results in their estimation of ET0. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U2) data, using RHCLD and U2CLD from the CLDAS to estimate ET0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

Climate change on Eucalyptus plantations and adaptive measures for sustainable forestry development across Brazil

The main challenges facing the Eucalyptus plantation industry are employing sustainable projects in suitable areas, and using safer forestry strategies, given climate variability and climate change. Brazil is the world’s largest Eucalyptus producer, and more areas have been allocated for Eucalyptus plantations, even in areas that are considered persistent climate change hotspots. Researchers can combine adequate agroclimatic zoning methods with Earth System Model (ESMs) outputs to analyze changes to climate variables, to map suitable areas, and to outline adaptive measures for Eucalyptus plantations. Despite these facts, studies on the Eucalyptus species are still scarce. This study analyzed the impacts of climate change to suitable areas for planting Eucalyptus in Brazil, using proper Agroclimatic Zoning (AZ) method, and outlining effective adaptive measures for sustainable Eucalyptus cultivation under future climate scenarios. Daily precipitation, air temperature (mean and maximum), and global solar radiation data were used from a set of simulations and projections of nine ESMs, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), in two Representative Concentration Pathway scenarios (RCP 4.5 and 8.5). The AZ method was simulated for the current climate (1980–2005), and projected for the near (2021–2050), and far future (2071–2100) climates, in two RCPs. Given intense and persistent changes to precipitation, air temperature, evapotranspiration, and water deficits, i.e., prolonged and continued changes to climate variables resulting from climate change, Eucalyptus plantations will be restricted and economically unfeasible for industrial purposes for about 15 % (RCP 4.5), to 47 % (RCP 8.5), of Brazil in the far future, covering a large part of the North, the north of the Center-West, and the northwestern part of the Northeast, including the new Brazilian agroforestry frontier (MATOPIBA). Only ∼12 % of Brazil, encompassing small areas of the South, the South-Southeast, and a small portion in the Northwest region of the North, will be suitable for Eucalyptus plantations, since these regions are less vulnerable to climate change. As for the rest of Brazil (between ∼42 % and ∼80 %), regular suitability is projected, and the future of Eucalyptus plantations in these places will depend on adopting ‘effective adaptive measures’ and making ‘safe silvicultural decisions’ in order to deal with projected temperature increases and water deficits, including genotypes selection, improvements in hybridization techniques or breeding programs, planting in wider spacings (or lower stockings), and thinning and pruning at shorter intervals.