Global Radiation Research Articles

Effect of Major Dust Events on Ambient Temperature and Solar Irradiance Components over Saudi Arabia

The Saudi government targets building eight solar plants across the country by 2030, which are expected to produce more than 3600 MW, enough to power more than 500,000 homes. However, the vast desert environment in Saudi Arabia increases dust and aerosol loading in the atmosphere, which affect the performance of photovoltaic systems due to scattering and absorption of the solar radiation by dust particles. In this work, ground-based data from weather stations located in six Saudi cities, Dammam, Hafar Al Batin, Riyadh, Jeddah, Najran, and Arar, along with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to examine the effects of dust loading on aerosol optical parameters, air temperature, and solar irradiance. The effects of three major dust storms that blew over different regions in Saudi Arabia on 20 March 2017, 23 April 2018, and 15 April 2021 have been investigated. It is found that there is a strong correlation between dust loading and aerosol optical parameters. The maximum Aerosol Optical Depth (AOD) was recorded over Jeddah on 19 March 2017 (about 2), over Riyadh on 20 March 2017 (about 2.3), over Riyadh on 24 April 2018 (about 1.5), and over Najran on 15 April 2021 (about 0.9). Strong dust events are found to reduce air temperature by a few degrees in high dust loading regions. The study found that such large dust loading decreases the direct and global solar irradiance components, while it increases the diffuse component over the cities of Jeddah, Riyadh, and Najran. This could be an indication that scattering from dust particles can play a significant role in the solar irradiance intensity.

Value-Added Products Derived from 15 Years of High-Quality Surface Solar Radiation Measurements at Xianghe, a Suburban Site in the North China Plain

Surface solar radiation (SSR) is a key component of the energy budget of the Earth’s surface, and it varies at different spatial and temporal scales. Considerable knowledge of how and why SSR varies is crucial to a better understanding of climate change, which surely requires long-term measurements of high quality. The objective of this study is to introduce a value-added SSR dataset from Oct 2004 to Oct 2019 based on measurements taken at Xianghe, a suburban site in the North China Plain; two value-added products based on the 1-minute SSR measurements are developed. The first is clear sky detection by using a machine learning model. The second is cloud fraction estimation derived from an effective semi-empirical method. A “brightening” of global horizontal irradiance (GHI) was revealed and found to occur under both clear and cloudy conditions. This could likely be attributed to a reduction in aerosol loading and cloud fraction. This dataset could not only improve our knowledge of the variability and trend of SSR in the North China Plain, but also be beneficial for solar energy assessment and forecasting.

Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting

Accurate and reliable global horizontal irradiance forecasting is one of the solutions for the associated problems with grid-integrated PV plants. This study proposes a novel hybrid MEMD-PCA-GRU model for an hour ahead of GHI forecasting. The multivariate empirical mode decomposition (MEMD) breaks the multidimensional data into multivariate subseries termed intrinsic mode functions (IMFs). MEMD helps to remove the naturally produced non-stationary and nonlinear deficiencies within the target series and meteorological predictors. A large number of obtained IMFs necessitates the application of a dimensionality reduction technique. Principal component analysis (PCA) is used here to identify the most informative features from a large set of IMFs. Finally, the gated recurrent unit (GRU) is utilized to predict GHI at four places in India. The performance of the proposed model is tested against some hybrid and standalone models. Double decomposition techniques enhanced the GRU performance by a minimum % RMSE (% MAE) improvement of 48.38 (24.97). The proposed model reported an average nRMSE (RMSE) of 7.82% (36.85 W/m2) across four locations. The lowest error metrics of the proposed model reflect the relatively stable and good performance compared to studied single-stage and hybrid benchmark models under different climatic conditions.

Performance assessment of clear-sky solar irradiance predictions using state-of-the-art radiation models and input atmospheric data from reanalysis or ground measurements

In this work, the performance of clear-sky direct normal irradiance (DNI) and global horizontal irradiance (GHI) predictions generated with three state-of-the-art solar radiation models with different degrees of complexity is assessed by comparison with high-quality measured irradiance data at Évora, Portugal. The libRadtran, SMARTS, and REST2 radiation models are alternatively operated using input data from three different data sources: co-located AERONET ground-based measurements, and CAMS and MERRA-2 gridded reanalysis data. For these nine combinations (three models and three data sources), the results are assessed using five statistical indicators, namely mean bias error (MBE), root mean square error (RMSE), fractional bias (FB), fractional gross error (FGE), and coefficient of determination (R2). Overall, it is found that AERONET is the data source that provides the best DNI estimates. In general, libRadtran and SMARTS produced closer estimates to the ground-based DNI observations. For GHI, however, no firm conclusion can be drawn regarding the best data source. MERRA-2 produces better estimates in combination with libRadtran and SMARTS according to all statistical indicators except R2, whereas AERONET is to be preferred according to FB, FGE, and R2 when using REST2. Curiously, the latter generates better GHI estimates despite being the simplest model. Overall, it is concluded that the best combinations of model/data source to estimate DNI are either libRadtran/MERRA-2 (according to MBE and FB) and SMARTS/AERONET (according to RMSE, FGE, and R2). In the case of GHI, the best combinations are REST2/AERONET (according to FB, FGE, and R2) and REST2/MERRA-2 (according to MBE and RMSE).

Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed.

Intraday forecast of global horizontal irradiance using optical flow method and long short-term memory model

Short-term global horizontal irradiance (GHI) forecast methodologies are routinely employed to mitigate the instability of photovoltaic power and, more importantly, to secure early participation in the energy auction market. The intra-day forecast model substantially consists of the combination of a cloud motion vector (CMV) derivation and a GHI extraction model. This study utilized optical flow (OF) method to calculate CMV from two subsequent satellite images and predict cloud displacement up to 180 min ahead. Meanwhile, the conventional GHI extraction models (Hammer and the fast all-sky radiation model for solar applications (FARMS) models) were employed to extract future GHI. Moreover, to the best of our knowledge, only a few studies have been dedicated to combining OF method with deep learning model. Hence, this study proposed a novel combination based on OF method and long short-term memory model (LSTM) to enhance temporal horizon and accuracy. After all combinations of OF method and GHI models were simulated and assessed under all-sky conditions, the OF method that combined with LSTM outperformed the OF method combined with conventional models across all time horizons. For root mean square error (RMSE) and forecast skill (FS), the accuracy of OF and LSTM models for all time horizon ranges from 59.64 W/m2 to 120.63 W/m2 and 16.40 % to 54.42 %, respectively. The study findings are expected to encourage the inclusion of satellite data in GHI forecast as well as the optimization of energy management systems that integrate with a photovoltaic system.

Feasibility and performance analysis of bifacial module under extremely hot and dry climate

Solar panels with a bi-facial design provide energy from both sides; thus, it would be interesting to examine the viability of such modules in an extremely hot and dry city like Ahmedabad, Gujarat, India. This study assesses the temperature, efficiency, and bifacial gain of PV bifacial modules as a function of solar irradiation, ambient temperature, wind speed, and additional parameters. The global horizontal irradiance has been used in this study to calculate Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI) and Front and Rear Irradiance. The results show that an optimal tilt angle of 43.5° provides the best efficiency of 24% at ambient temperature. For a constant value of tilt angle, the bi-facial gain of the module increases linearly with albedo factor. It has also been discovered that continuing to raise the elevation beyond 1 meter will eventually diminish the bifacial gain because the reflected and diffuse irradiance will become less intense on the rear side of the bifacial module.

Algorithms for separating diffuse and beam irradiance from data over the East Asia-Pacific region: A multi-temporal-scale evaluation based on minute-level ground observations

Diffuse irradiance and direct normal irradiance are two components that are critical to both energy and ecological systems; however, these are rarely available in most ground observations, which only provide the global horizontal irradiance (GHI), leaving the separation of them from the GHI an inevitable process. Although many separation models have been developed to provide solutions, a large proportion of these has been constructed based on site-specific datasets at different time resolutions, limiting their possibilities to be adopted to other sites without previously being extensively evaluated. In this study, we evaluated a total of 80 separation algorithms with specific attention to their time resolutions, based on the finest minute-scale observations from 13 irradiation sites across six Köppen-Geiger-Photovoltaic climate classification zones in East Asia-Pacific. We found that there was a dramatic difference in the performance of hourly-scale models and that the Starke3 model gave the best performance of the minute-scale models. Further residual analyses revealed that the clearness index, solar zenith angle, and atmospheric parameters played important roles. Since data from the geostationary Himawari-8 satellite are available to provide minute-scale atmospheric parameters, there is the possibility of reducing the residuals by incorporating the information with high temporal resolutions into separation models. The results of this study will provide a more comprehensive understanding of the utilization and improvement of separation models.

Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

Solar radiation's intermittent and fluctuating nature poses severe limitations for most applications. Accurate prediction of solar radiation is an essential factor in predicting the output power of a photovoltaic power system. For this purpose, the potential of the 20 MW solar photovoltaic power plant in Zahedan city has been evaluated in this article. With the help of monthly data (1984–2021) and MLP, LSTM, GRU, CNN, and CNN-LSTM models, solar radiation and temperature are predicted for the next ten years. CNN exhibits the best performance compared to other models with four input parameters: global horizontal irradiance, temperature, surface pressure, relative humidity (RH), and two outputs of temperature and radiation. The root mean square error values for global horizontal irradiance and temperature were 12.68 W/m2 and 1.75 °C, respectively. Relative humidity exhibited more significant effect on the model in comparison with surface pressure. Finally, the average annual power output for ten years from 2022 to 2031 is calculated and predicted to be 50.37 GWh.

Performance Ratio and Loss Analysis for a Grid-Connected Solar Photovoltaic System: Case of the 7MW Plant in Malbaza, Niger

The use of solar energy in sunny countries is an efficient way to overcome the energy shortage. The interest of this energy is not only economic but also environmental, as it emits few greenhouse gases. Niger, a vast landlocked country in the Sahel, is characterized by an average sunshine duration of 8.5 hours per day and an estimated average level of sunshine of around 5 to 7 kW/m2 per day. However, the rate of access to electricity in Niger remains very low. To address this problem, a 7MW solar photovoltaic power plant has been built by the State of Niger in the town of Malbaza. It is composed of monocrystalline photovoltaic panels and injects its energy into the national grid. The objective of this study is to compare the performance ratio determined by measurements with that obtained by simulation under PVsyst in order to evaluate its efficiency. The study is based on annual energy production data, recorded at the site, from 1 January 2021 to 31 December 2021. The annual average values of the global irradiation of the site, the energy produced by the system and the energy injected into the grid are respectively 249.86kWh/m², 1225.25MWh and 972.62MWh; those of the ambient temperature and the performance ratio (PR) are respectively 29.78°C and 0.795. The global losses caused by meteorological phenomena and those linked to the technologies of the various components of the system are evaluated at 20.10%.

Typical meteorological year data analysis for optimal usage of energy systems at six selected locations in Nigeria

ABSTRACT In this study, the typical meteorological year (TMY) data for six locations representing the six geopolitical zones in Nigeria were generated and analyzed using the Sandia method. The analysis shows that seasonal variations exist in all the selected locations indicating two distinct seasons: the dry and wet seasons with varying lengths from north to south of the country. Due to its high global radiation levels (21–25 MJ/m2/d), the North is a desirable location for solar-thermal systems. In addition, the high monthly mean temperature variations (~18°C), low relative humidity (RHM) (15%) and constant wind speeds (4 m/s) experienced in the first 3 months of the year aid the installation of wind energy systems and the application of evaporative cooling techniques that reduce the thermal load and energy consumption of buildings. On the other side, the high RHM (80%) and mediocre radiation values derived almost throughout the year in the Southwest, Southeast and South–south regions discourages the extensive application of evaporative cooling and solar energy-based systems in such locations, but the moderate wind speeds (2.9 m/s) and monthly mean temperature variations associated with these regions between the first 3 months of the year allow for the application of natural ventilation and some passive cooling systems so as to reduce the thermal load of buildings in the regions. The information presented in this work can serve as a guide for design and selection of energy systems and application of energy-related projects in Nigeria.

Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

Reservoirs are aquatic environments with complex interactions between bio-chemical and physical components. Many of those parameters can be estimated from remote sensing tools, especially since increasing technological progress allows the analysis of those environments in high spatial and temporal resolutions. This work aimed to analyze the water quality of four reservoirs, with high energy productivity, located in the center-southern region of Brazil from bio-optical and thermal water parameters. Therefore, hundreds of Landsat 5 (TM), 7 (ETM+), and 8 (OLI) satellite images were processed by the Google Earth Engine cloud computing platform to estimate water surface temperature, chlorophyll-a density (chl-a), Secchi depth (SD), and trophic state index (TSI). To calibrate the equations, 11 in situ monitoring points were used in 27 scenes from the TM and ETM+ sensors and 30 from the OLI sensor, within 6 days interval between the monitoring date and the image acquisition. Additionally, records from two weather stations near the reservoirs were analyzed to evaluate meteorological influences on water parameters. SD has influenced TSI in three reservoirs, while chl-a has acted as a proxy in only one. Moderate correlations were observed between air temperature, rainfall, and global radiation and the bio-optical and thermal water parameters. Remote sensing has detected a clear spatio-temporal variation, which allowed regionalizing the reservoirs. Reservoir regionalization based on the mapped parameters may be able not only to visualize the spatial dynamics of the waters but also as a tool for the management of water bodies with multiple users and issues.

Downscaling of Hourly Climate Data for the Assessment of Building Energy Performance

In Italy, the calculation of the energy needs of buildings has been mainly based on quasi-steady state calculation procedures. Nowadays, the increasing interest in more detailed energy analysis for high-efficiency buildings requires more accurate calculation methods. In this work, starting from the hourly data of UNI 10349, the downscaling of a typical meteorological year was carried out by applying different mathematical and physical models for the main climate variables considered in the energy balance of a building to be used in dynamic simulation tools. All results were validated with one-minute measurements observed at the ENEA Research Centre in Rome, Italy. The results showed an MBE% of 0.008% and RMSE% of 0.114% using the interpolation spline method for the temperature, while, for the global horizontal irradiance, applying the novel sinusoidal–physical interpolation method showed an MBE% of −0.4% and an RMSE% of 31.8% for the 1 min observation data. In this paper, an easily implemented novel model for downscaling solar irradiance for all sky conditions that takes into account the physical aspects of atmospheric phenomena is presented.

Quantitative assessment of the impact of biomass burning episodes on surface solar radiation using machine learning technology: A case study of a pollution event in Beijing

As an important source of pollutants in Beijing, biomass burning (BB) aerosols emitted by BB have a strong absorption and scattering effect on surface solar radiation (SSR). However, the existence of meteorological factors has a powerful impact on the estimation of the BB aerosol radiation effect. In this paper, a deweathered technique based on machine learning is presented to quantitatively isolate the influence of weather conditions from the radiative effect of BB aerosols. Furthermore, the seasonal trend of SSR is constrained by removing the general seasonal changes by the detrending method. The BB event on October 7–11, 2014, in Beijing was used as a case to validate the effectiveness of the proposed method. Experimental results show that meteorological factors aggravate the influence of BB events on SSR. Specifically, during the BB event, meteorological factors led to an additional increase of 10.9% in diffuse radiation and an additional decrease of 17.5%, 13.1%, 181.3%, 9.7% and 86.9% in global radiation, net radiation, horizontal direct radiation, reflected radiation and vertical direct radiation, respectively. After isolating the impact of meteorological factors on SSR in the BB event, we found that the average effects of BB aerosols on global radiation, net radiation, diffuse radiation, horizontal direct solar radiation, reflected radiation and vertical direct solar radiation during the BB event were −35.6 Wm−2 (−3.1 MJm−2day−1), −14.1 Wm−2 (−1.2 MJm−2day−1), 20.7 Wm−2 (1.8 MJm−2day−1), −67.3 Wm−2 (−5.8 MJm−2day−1), −8.9 Wm−2 (−0.8MJm−2day−1), and −132.9 Wm−2 (−11.5 MJm−2day−1), respectively. In conclusion, the weather normalization and detrended methods in this study provide a new idea for the accurate estimation of the impact of aerosols on SSR. This provides a quantitative reference for further understanding the impacts of atmospheric pollution on SSR.

Experimental and Numerical Analysis of a Low-Cost Solar Still

The ability to treat saltwater to make it suitable for human consumption has long been sought by mankind. More than three-quarters of the earth’s surface is covered with saltwater. Although this water is important for some forms of transportation and fishing, it contains too much salt to sustain human life or agricultural activities. The current work consists of building a low-cost solar still and numerically modeling this device to predict the performance of the solar still without using any experimental measurements. The simulated results were compared with the best experimental values obtained from the water-covering temperatures and desalinated water yield under Brazilian climatic conditions (coordinates: 23°26′31.344″ S and 46°27′27.468″ W). The simulation results were in acceptable agreement with the experimental data. The main results obtained indicate that the solar still has greater efficiency when the volume of water is smaller inside the equipment owing to the lower height of the water and when the global radiation has greater intensity. In addition, numerical modeling allows the analysis of the behavior of the volume fraction over time for water and vapor and indicates better performance in water production after 30 min.

Impacts of the Aerosol Representation in WRF-Solar Clear-Sky Irradiance Forecasts over CONUS

Abstract Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) the default Tegen climatology, 2) imposing AOD forecasts from the GEOS-5 model, 3) imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model, and 4) the Thompson–Eidhammer aerosol-aware water/ice-friendly aerosol climatology. More than 8 months of these 15-min output forecasts are compared with high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected because of the lesser sensitivity of GHI to aerosol loading. Hourly average clear-sky DNI and GHI were also analyzed, and they were additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is. Significance Statement Particles in the atmosphere called aerosols, which can include dust, smoke, sea salt, sulfates, black carbon, and organic carbon, absorb and scatter incoming sunlight. Improving the representation of aerosols in numerical weather prediction models reduces forecast errors in solar irradiance at ground level, particularly direct normal irradiance, during cloud-free conditions. This in turn should result in improved accuracy of solar power forecasts, especially for concentrated solar power (CSP) plants. CSP plants tend to be built in more arid, less cloudy regions that are also prone to dust loading, so accurate aerosol forecasts are particularly relevant. Comparing four representations of aerosols in the WRF-Solar model over eight months of forecasts across the United States reveals substantial differences in clear-sky irradiance forecast skill.

Anthropogenic and biogenic tracers in fine aerosol based on seasonal distributions of dicarboxylic acids, sugars and related compounds at a rural background site in Central Europe

Water-soluble organic compounds in aerosols are considered as relevant indicators of atmospheric processes. Fine particulate matter (PM1) samples were collected at National Atmospheric Observatory Košetice (NAOK), a rural background site representative of Central Europe, from September 27, 2013 to August 9, 2014. The samples (n = 146) were analyzed for water-soluble dicarboxylic acids (hereafter referred to as diacids) and related compounds to identify their seasonal variations and origins. Based on the Positive Matrix Factorization (PMF) analysis, we identified 5 factors – 2 anthropogenic, 2 biogenic and 1 background factors.In winter, anthropogenic contributions dominated in total organic matter (OM). Typical tracers for the main winter anthropogenic factor 1, connected with biomass burning (BB), were anhydrosugars together with maleic (M), methylmaleic (mM) and methylsuccinic (iC5) acids. This BB factor accounted for 64.1 ± 14.3% of OM in winter (annual avg. 36.7 ± 27.4%). A secondary anthropogenic factor 2 was characterized by phthalic (Ph), terephthalic (tPh) and ketomalonic (kC3) acids, which we assigned to secondary combustion products. The contribution of anthropogenic factor 2 was at a similar level throughout the year (12.5 ± 10.1% in OM). Mainly in winter, although also in spring and autumn, was the characteristic formation of diacids by secondary aqueous phase reactions, typically accompanied by lower temperatures, global radiation and ozone (O3) concentrations, yet higher relative humidity (RH) and aerosol liquid water content (ALWC).In summer, contributions of biogenic origin dominated. Secondary organic aerosols (SOA) of biogenic origin were typically represented by malonic (C3), methylmalonic (iC4), 3-oxopropanoic (ωC3), 4-ketopimelic (kC7), 7-oxoheptanoic (ωC7), pimelic (C7) and suberic (C8) acids. The factor, named as Biogenic 1, was dominant in summer with a contribution of 40.3 ± 19.6% in OM, while in other seasons, its contribution was below 10%. This factor was mainly characterized by a relative summer increase in the concentrations of kC7 and ωC7 acids. The second biogenic factor 2, also significant in summer (36.8 ± 20.5%) and dominant in spring (34.9 ± 19.9%), was represented by primary sugars (fructose, galactose and sucrose), normal chain diacids (oxalic (C2) to azelaic (C9)) and their oxidative precursors (ωC3, 4-oxobutanoic (ωC4) and 5-oxopentanoic (ωC5) acids). The photochemical formation of SOA in the gas phase was characteristic mostly for the summer season, accompanied by higher temperatures, global radiation and O3 concentrations, and lower RH.Additionally, background factor was resolved, which represents compounds with no distinctive seasonal variation and can therefore be of both anthropogenic and biogenic origin and contained mainly less oxidized compounds (methylglyoxal (MeGly), glyoxal (Gly), glyoxylic acid (ωC2) and pyruvic acid (Pyr)).

Near constant groundwater recharge efficiency under global change in a central European catchment

AbstractThe fraction of precipitation that infiltrates soils and subsequently becomes recharge is one of the principle components of an unconfined aquifer's water budget (this fraction is here termed recharge efficiency). Here we tested how recharge efficiency will respond to climate change including a possible plant physiological response to climate change (e.g., stomatal closure; increasing leaf area) in a catchment used for drinking water production in western Germany. To this end we used a soil water model (HYDRUS‐1D) forced with climate data spanning the time period from 1971 to 2099. Three different vegetation types were considered: turf grass representing the primary infiltration sites within residential areas; maize representing the main crop on agriculturally used land; and beech representing the forested parts of the catchment. We found that, the positive effects of climate change on recharge efficiency (more rain during the main recharge season in winter, less crop water demand due to faster plant ripening in spring and summer, increased plant water use efficiency, reduced global radiation as cloud density increases) were not completely compensated by the negative effects (less precipitation, higher leaf area index and increasing vapour pressure deficit in summer season) at our study site. Because total annual precipitation increased slightly until the end of the 21th century, changes in the amount of total annual recharge were also positive, though moderate (up to +20% change in the period 2071–2099 as compared to 1970–2000). The results of this study will be helpful for water authorities managing water rights under the perspective of a changing climate. In the future, our study site is expected to receive sufficient recharge from precipitation to maintain current rates of groundwater withdrawal for public water supply and irrigation. Thus, the region's agriculture sector may become a ‘global warming winner,’ when cropping in other regions in Europe may increasingly suffer from drying conditions during the growing season.

Shertered horticulure adapted to different climate zones in Radhort Countries

Over the last decade, the total population of the sub-Saharan region of Africa has been increasing rapidly at a rate of more than 3% annually, with urbanization expected to be approximately 40% of the total population by 2050. Parallel growth has not been achieved in the agricultural sector in West Africa, with vegetable production and consumption being amongst the lowest in the world. This has aggravated the already food insecurity and malnutrition situation in the region. In this context, and within the framework of their agricultural development policies, 10 countries of West Africa (Burkina Faso; Cabo Verde; Côte d’Ivoire; Guinée; Guinée Bissau; Mali; Mauritanie; Niger; Sénégal; Chad), established the “African Network for Horticultural Development “RADHORT” (Réseau Africain pour le Développement de l’Horticulture), in order to cooperate for the diversification and intensification of horticulture in the region. The countries of RADHORT cover different climate zones ranging from the arid climate (desert), to the Sahelian zone (semi-arid), to the dry tropical zone (with long dry season and short rainy season), and to the wet tropical zone (humid zone with bimodal rainfall). Temperatures and global radiation are very suitable for vegetable production in tropical countries throughout the year, but open air cultivation can be severely hampered by high temperatures, winds, heavy rainfall, while being exposed to pest and disease infestation. Sheltered cultivation will help to moderate negative effects of climate factors on the crop, improve water productivity and the efficiency of eco-friendly pest and disease management. The paper analyses and discusses different technical options of sheltered cultivation to be tested in RADHORT countries, as a means to enhance horticulture crops productivity and quality for meeting the growing demand of an expanding rural and urban population.

Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models

In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (IUV), on the basis of data collected at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance through a comparison of supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was sufficient to determine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance (IGH), clearness index (kt), solar altitude angle (α), horizontal beam irradiance (IBH), diffuse fraction (D), temperature (T), sky clearness (ε), cloud cover (Cc), horizontal diffuse irradiance (IDH), and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models.