Trends In Solar Radiation Research Articles

Can satellite products monitor solar brightening in Europe?

Satellite products provide the best way to monitor the solar radiation reaching the Earth’s surface on a global scale. However, their capability to monitor solar radiation trends needs to be constantly evaluated. This depends on their temporal stability and the accurate representation of all processes driving solar radiation. This study evaluates these aspects by comparing and cross-comparing different solar radiation products (ERA5, CAMS-RAD 4.6, SARAH-3, CLARA-A3, CERES-EBAF 4.2) against in-situ measurements over Europe.All products show a moderate positive bias over Europe but strong differences in their root mean squared deviation (RMSD) related to their different cloud transmittance models. Geostationary-based products (SARAH-3, CAMS-RAD 4.6) provide the smallest RMSD closely followed by CLARA-A3, whereas ERA5 shows a large RMSD due to random errors in cloud transmittance.All products show an increase in surface solar radiation, or brightening, over the last 40 years over Europe, but the magnitude of the trends and their spatiotemporal variability differ between products. Despite finding temporal inhomogeneities in some products, the different trends are mostly due to different aerosol modeling approaches implemented by each product. Both SARAH-3 (+2.3 W/m2/decade, 2001–22) and CERES-EBAF 4.2 (+2.2 W/m2/decade, 2001–22) provide the most consistent trends compared to in-situ data, showing that after stabilizing in the late 2000s, brightening is particularly recovering in Western Europe. In-situ measurements show a reduction of aerosol optical depth from 2001 to 2022 that has been accentuated in the last 10 years, particularly in Western Europe. This would be consistent with the hypothesis that brightening recovery is driven by an aerosol reduction, though other analyses suggest that clouds also play a role in this recovery. More work is needed to understand the contribution of aerosols to solar radiation trends and the exact aerosol effects represented by each solar radiation product.

Trend of surface solar radiation over China in relation to changing synoptic patterns

A comprehensive understanding of the relationship between atmospheric circulations and variations in surface solar radiation (SSR) is important for effectively utilizing solar resources and assessing climate change impacts. Here, we identify four synoptic patterns by T-mode principal component analysis method during 1980–2020 associated with the spatio-temporal variations of SSR in China, including geopotential height anomalies of west-high-east-low (Type 1), north-high-south-low (Type 2), east-low-west-high (Type 3), and north-low-south-high (Type 4). For the whole summer, there are more days of Type 1 and Type 2, accounting for 28.74 % and 28.58 % respectively. In particular, Type 1 experiences positive anomalies in cloud cover, water vapor and significantly reduced SSR reaching the Earth’s surface. Type 2 pattern is characterized with low (high) anomalies of SSR over the Yangtze River Basin (Northeast China). And the north of the Yangtze River Basin is controlled by a northerly dry and cold airflow, leading to low cloudiness and precipitation with increased SSR reaching the ground in Type 3. Type 4 pattern exhibits a negative (positive) SSR anomalies in the Yellow River Basin (the Yangtze River Basin and Northeast China). Notably, the reduction of summer SSR in China since the 1980s is largely explained by the long-term changes of Type 2 and Type 4 synoptic patterns. These findings improve the understanding of the relationship between atmospheric circulations and SSR, and provide a scientific basis for policy planning of solar energy and dual-carbon targets in China.

Solar energy feasibility for power generation in lower shire districts: A case study of Chikwawa District

Background: Electricity availability is an essential part of both economic and non-economic activities in any country. Malawi being one of the developing nations experiences a lot of challenges both in its economic and non-economic activities due to unlimited levels of electric supply in its country. This inadequacy of limited supply has remained a challenge for a longer period of time to the effect that the country Malawi for a longer period of time has an overwhelming record of blackouts and load shedding. Consequently, this has affected in a negative way the economic activities of this nation such as low productivity in the production of goods and services making livelihood a difficult. Similarly, noneconomic activities such social interactions seem to be challenging due to lack of electricity. In this regard an investigation in determining new innovations towards electricity production remained one of the most important aspects in this county of Malawi. Method: This study was conducted in Chikwawa district, Malawi, to assess the feasibility of solar energy production. The methods used include temporal variability, correlation, trend, and spatial analysis to assess solar radiation, sunshine hours, and their distribution in the area. Result: As a result, the study was established with the purpose to assess feasible regions within the country of Malawi to innovate solar energy production to boost high levels of solar energy production. This study was conducted in the lower shire district and to be specific in Chikwawa district. The objectives of the feasibility in Chikwawa district included; assessing solar radiation and sunshine hours towards solar energy production, the spatial distribution of solar radiation and sunshine hours towards solar energy production, the trend of sunshine and solar radiation towards solar production and at the end to map the areas in Chikwawa District where solar energy production could be harnessed. Through the use of temporal variability, correlation analysis, trend analysis, and spatial analysis, the findings of this investigation established that harnessing solar energy is much more possible in areas of the lower shire which is the among others include Chikwawa district of Malawi as most areas currently in this region has less places with solar energy production but with high availability of solar radiation. Conclusion: For the fact that the findings reveal more feasible places to establish solar energy production, this study recommends commitment by government and private sector to greatly establish solar energy production units in this area whilst focusing on identifying other potential areas where solar energy production can be harnessed in Malawi. In this regard, this exposition has achieved its purpose of fostering renewable energy production through innovating and investing in solar energy production. Novelty/Originality of this study: This detailed spatial and temporal analysis study has identified optimal locations for solar energy production, providing a new approach to renewable energy planning in developing countries.

Trend Analysis of Climatic Variables in the Cross River Basin, Nigeria

There have been several incidences of flood recently, which are believed to be aggravated by increased climatic variables as a result of perceived changes in climatic conditions (due to climate change) in the Cross River Basin. The basin is the most extensively developed and used river basin in the management of the water resources of the Cross River and Akwa Ibom States in Nigeria. In this paper, 30 years (from 1992 to 2021) of hydro-meteorological data (annual average rainfall, maximum and minimum temperatures, hu midity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure) from four stations in the Cross River Basin were obtained from the Nigerian Meteorological Agency (NIMET), Abuja and subjected to trend detection analysis using the Mann–Kendall test to determine the trend in climatic parameters. The results indicate that there is a significant upward trend in annual rainfall in Ogoja but a downward trend in Calabar. The evaporation trend is significantly downward in Eket, whereas in Calabar, there is an upward trend in solar radiation. Generally, there is a significant rise in annual maximum temperature across the basin. Serial correlation and segmented regression analyses were performed to measure the impact of fluctuations in monthly and long-term Tahiti and Darwin’s Sea level pressures on the climatic variables at the Cross River Basin catchment. These analyses were necessary to determine the extent of the influence of the El Nino Southern Oscillation climatic cycle. The analyses show no significant association between the El Niño Southern Oscillation (ENSO) and rainfall or between the ENSO and runoff in the catchment. This implies that the impact of the ENSO on rainfall and runoff in the Cross River Basin catchment is not considerable. The intercepts derived from the segmented regression in Eket and Ogoja show significant positive trends in both areas for rainfall and runoff. The trends in intercepts suggest that there are external factors influencing rainfall and runoff other than ENSO events, thus strengthening the assertion of climate change. Results from this study will facilitate the understanding of the variability in climatic parameters by stakeholders in the basin, researchers, policymakers, and water resource managers.

A statistical model of solar radiation absorption in the United States

The transitivity of solar radiation in the atmosphere varies greatly depending on location, time of day, earth-to-sun distance, angle of incidence, and other variables. Solar radiation has an impact on climate change and can be used as energy. So, its modelling will help plan and design policies for climate change and the sustainable use of energy. This study aimed to investigate solar energy patterns and trends on the Earth’s surface via solar radiation absorption by cloud cover. Data on solar radiation absorption from 133 stations between the years 1998 and 2020 across the United States were downloaded from the National Solar Radiation Database (NSRDB) website. A linear regression model was used to model solar absorption by cloud and factor analysis was used to group the regions by reducing the spatial correlation of solar radiation absorption. After that, a multivariate regression model was utilized to investigate average changes. There were seven regions obtained from factor analysis. All regions showed a seasonal pattern, with the peak in December to January and the lowest level in June to July. The north, north-east, or south-east of the country experienced an increase in solar radiation absorption, while the north-west, central, and south of the country experienced a decrease. The overall average absorption increased by 0.015%. The patterns and trends of solar radiation by location and time help climate scientists make better decisions. It is also useful to manage renewable energy sources, which will lead policymakers to make better policies.

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.

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

Experimental and simulation study on the thermoelectric performance of semi-transparent crystalline silicon photovoltaic curtain walls

This study aims to evaluate and optimize the thermoelectric performance of semi-transparent crystalline silicon photovoltaic (PV) curtain walls. An integrated thermoelectric performance coupling calculation model was developed, combining heat transfer and electricity generation calculations as a novel approach. Simulations and experiments were conducted to compare the performance of PV curtain walls with conventional curtain walls under various weather conditions, and were validated by experimental data. The results demonstrate that PV curtain walls enhance the thermal environment inside buildings and promote efficient power generation, with the arrangement of PV cells notably affecting performance. Under sunny conditions, the temperatures of the PV cell backplate and glass part backsheet in semi-transparent PV curtain walls exceeded those in ordinary glass curtain walls, yet the indoor air temperature was lower. Furthermore, when the working temperature of PV cells reaches to a certain level, it slightly deviates the electricity generation trend from the real-time solar radiation trend. Under cloudy conditions, the backsheet temperatures of semi-transparent PV curtain walls and standard glass curtain walls align with outdoor temperatures. Different PV module forms demonstrated that striped and square PV module forms offer better lighting effects than whole forms. The working temperatures among the three were close, with an annual average temperature difference of less than 1 °C and a peak temperature difference of approximately 3 °C, impacting the monthly total power generation by less than 1 %. This study presents a more precise and thorough approach for evaluating semi-transparent PV curtain walls' performance, providing insights for future sustainable architectural design.

Causes for Decadal Trends in Surface Solar Radiation in the Alpine Region in the 1981–2020 Period

AbstractExtending across seven countries, the Alps represent an important element for climate and atmospheric circulation in Central Europe. Its complex topography affects processes on different scales within the atmospheric system. This is of major relevance for the decadal trends in surface solar radiation (SSR), also known as periods of global dimming and brightening (GDB). In this study we analyzed data from 38 stations in and around the Swiss and Austrian Alps, over a period ranging from the 1980s up to the 2020s, with the aim of characterizing the spatio‐temporal variations of the GDB and understanding the causes for such trends in this region. Our results show a different behavior in the SSR decadal trends in the western part of the Alps in comparison to the eastern part. Our results also suggest a remarkable difference between the causes of such trends at the stations at low altitudes in comparison to the stations at higher altitudes. Significant contributions from changes in cloud optical depth and surface albedo to the SSR decadal trends at high elevation sites were also found, in contrast to a substantial clear‐sky forcing that strongly dominates at low elevations. Results from previous literature and available data suggest that cloud optical depth changes at high altitudes and clear‐sky forcing at low altitudes could be associated with the indirect and direct aerosol effects, respectively, due to differing pollution levels at low and high elevation sites.

Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data.

Understanding the climatology and long-term trends in solar radiation using ground based in-situ observations in India

Understanding the variations of solar power potential over the country is essential for the optimum utilisation of solar energy in power generation, which demands accurate information of solar radiation and its variations. In the present study, we investigate the climatology and trends of global radiation (GR), diffuse radiation (DR), bright sunshine hours (BHS) and technical potential of solar power (Solar Photovoltaic potential; SPV potential) using in-situ data procured from India Meteorological Department for the period 1985-2019. GR is high (low) over the northwest and inland areas of peninsular (extreme north and northeast) India, whereas DR is high (low) over the coastal stations (extreme northern parts of the country). BHS is more (less) over northwest (north, northeast and southern peninsular) India. The country has SPV potential in the range of 1800-3400 Wm-2 with substantial regional variations. High (low) SPV potential is observed in the northwest regions (north, northeast and southern peninsular India). The GR and BHS (DR) have (has) a significant decreasing (increasing) trend in most parts of the country. However, the rate of decreasing (increasing) of GR (DR) has been weakened (strengthened) in the recent decade. The technical potential of solar power has a significant decreasing trend in most of the selected stations which is alarming. It necessitates the wide use of solar panels with better efficiency to meet the energy requirements from solar resources.

Changes in free amino acid and protein polymerization in wheat caryopsis and endosperm during filling after shading.

Over the past several decades, a decreasing trend in solar radiation has been observed during the wheat growing season. The effects of shade stress on grain yield formation have been extensively studied. However, little information on shade stress's effects on protein formation warrants further investigation. Two wheat cultivars were grown under three treatments, no shade as the control group (CK), shading from the joint to the anthesis stage (S1), and shading from the joint to the mature stage (S2), to investigate the effects of shade stress on the free amino acids of the caryopsis and endosperm and protein accumulation during grain filling. The dry mass of caryopsis and endosperm was significantly decreased under shade stress, whereas Glu, Ser, Ala, and Asp and protein relative content increased during grain filling. The observed increases in total protein in S1 and S2 were attributed to the increases in the SDS-isoluble and SDS-soluble protein extracts, respectively. S1 improved polymer protein formation, but S2 delayed the conversion of albumins and globulins into monomeric and polymeric proteins. Moreover, shade stress increased the proportion of SDS-unextractable polymeric protein, which represented an increase in the degree of protein polymerization. The polymerization of protein interrelations between protein components and accumulation in caryopsis and endosperm provided novel insights into wheat quality formation under shade stress.

Analyses of Climate Variability and Trends in the Oil Palm Belt of Ghana

The current production of oil palm in Africa has been severely limited by long periods of drought and perennial floods due to the increased rates of climate change. The study was aimed at assessing the climatic variability trends in 7 major Oil Palm growing areas (Benso, Mpohor, Bogoso, Twifo Praso, Kade, Juabeng and Brewaniese) located in the oil palm belt in Ghana spanning the semideciduous and the wet evergreen rainforest zones of Ghana using 30 years of historical weather data (1993-2022). The data was analysed to evaluate the extent of seasonal changes in weather over the 30-year period using the Statistical Package for Social Sciences (SPSS 20.0 version), using descriptive statistics and time series analysis. Trend analysis was conducted using the non-parametric Mann-Kendall (MK) and Sen’s slope estimator tests. A seasonal trend in rainfall was observed at all sites with maximum levels observed between May and July as well as a minor rainy season in September to Mid-November. The Mann-Kendall trend analysis showed a generally increasing trend at most stations in rainfall, temperature and solar radiation. A generally increasing trend in annual rainfall was observed from 1993-2022 with significant trends at Mpohor and Juabeng. The trend was higher in the moist evergreen rainforest zone than in the semi-deciduous rainforest zone. An average increase of 6.7 mm/yr in rainfall trend was observed for the period. An increasing trend in monthly maximum, minimum and mean temperatures were also observed. Significant increases in mean temperatures were observed at Bogoso (0.022°C/yr), Benso (0.018°C/yr), Juabeng (0.011°C/yr) and Mpohor (0.017°C/yr). The trend in mean annual solar radiation was generally not significant except in Benso where the mean solar radiation increased at the rate of 0.044 MJ/day. Higher drought levels in the first half year combined with very heavy rains later in the year are features of the ongoing climate change menace.

Changes in cloudiness contribute to changing seasonality in the Baltic Sea region

In the Baltic Sea region, a significant increase in solar radiation has been detected during the past half-century. Changes in shortwave irradiance are associated with atmospheric transparency and cloudiness parameters like cloud fraction and albedo. One of the most important reasons for day-to-day changes in cloudiness is the synoptic-scale atmospheric circulation; thus, we look for reasons for solar radiation trends due to changes in atmospheric circulation. We analysed regional time series and trends from satellite-based cloud climate data record CLARA-A2 for the Baltic Sea region in 1982–2018. As the rise in solar radiation depends on the seasonally averaged values of total fractional cloud cover (CFC), surface incoming shortwave radiation (SIS) and occurrences of circulation types were analysed. We show that the shift in seasonality connected to the earlier accumulated sums of SIS is at least partly explained by the changes in synoptic-scale atmospheric circulation.

Changes in maize traits and yield under the cultivar, environment and management interactions across China’s Maize Belt in the past two decades

It is important to investigate the response and adaptation mechanisms of crop traits and yield to ongoing climate change under genotype (G), environment (E), and management (M) interactions in order to develop climate-resilient cultivars and agricultural systems. Here, using long-term trial data at six comprehensive agricultural experiment stations across China’s Maize Belt, we investigated the changes in maize traits and yield during 1998–2019 under the interactions of G×E × M. The first-order difference, multi-collinearity detection, Pearson correlation, stepwise linear and a machine learning method were employed to construct the best statistical regression models between maize traits and climate variables and disentangle the relative contributions of climate, cultivar and management to grain yield changes. The results showed a warming trend but a decreasing trend in solar radiation at all the investigated stations. Climate variations affected maize traits and subsequently grain yield, and there were trade-offs among maize traits. With local agricultural management practices, about 20.5–73.8%, 1.2–42.7% and 2.3–37.0% of the yield changes were associated with climate change, cultivar shifts and management, respectively. The stations with higher latitudes exhibited greater variations in yield due to climate variations, compared to the stations with lower latitudes. Wind speed, radiation, and rainfall was found to affect maize yield in Northeast China by affecting evapotranspiration, soil moisture and drought occurrence. The ideal cultivars were characterized by a relatively long grain-filling stage, large hundred-grain weight and number of rows. This study gains new insights into the response and adaptation mechanisms of maize traits and yield to ongoing climate change, providing evidence for designing cultivar ideotypes and optimizing agricultural management in different environments.

Multi-decadal change in summer mean water temperature in Lake Konnevesi, Finland (1984–2021)

Depth-resolved water temperature data on the thermal environment of lakes are often hindered by sparse temporal frequency, limited depth resolution, or short duration that create many challenges for long-term analysis. Where high frequency and depth-resolved data exist, they can provide a wealth of knowledge about how lakes are responding to a changing climate. In this study, we analyzed around 950 profiles of summer mean water temperature (July to September), which includes about 30,600 unique observations, from a subarctic lake (Lake Konnevesi, Finland) to understand the changes in lake surface water temperature (LSWT), lake deepwater temperature (LDWT), and lake volumetrically weighted mean temperature (LVWMT) from 1984 to 2021. Statistical analysis of this dataset revealed a substantial warming of LSWT (0.41 °C decade−1) and LVWMT (0.32 °C decade−1), whilst LDWT remained unchanged (0.00 °C decade−1). Our analysis using a generalized additive model suggested the inter-annual variability in LSWT and LVWMT correlated significantly with the upward trends of summer mean air temperature and solar radiation, but suggested no significant effect of observed changes in ice departure dates and near-surface wind speed. None of the investigated predictors correlated with the change in the LDWT. Due to the variable response of lake surface and bottom water temperature to climate change in this subarctic lake, our data suggest a substantial increase in lake thermal stability. Our study supports the growing literature on lake thermal responses to climate change, and illustrates the unique contrast of climate change impacts at the surface and at depth in lake ecosystems, with deep waters acting as a potential thermal refuge to aquatic organisms within a warming world.

The Effects of Light Treatments on Growth and Flowering Characteristics of Oncidesa Gower Ramsey ‘Honey Angel’ at Different Growth Stages

In our previous work, we observed that Oncidesa Gower Ramsey ‘Honey Angel’ (HA) plants became stunned on hot summer afternoons, and the seasonal trend in solar radiation affected its production schedule by limiting flower yield and quality. The objective of this work was to study the growth and flowering characteristics of HA pseudobulbs at three stages of growth (G2–G4) in response to three types of light-emitting diode (LED) lighting treatments, including full spectrum (FS), deep red/white-medium blue (DR/W-MB), and deep red/white-low blue (DR/W-LB), for two additional time intervals. The supplementary LED lighting time intervals (S) applied daily were carried out for 1 h (4:00~5:00 a.m., as S-1) or 2 h (4:00~6:00 a.m., as S-2) from March to September, 2022. Natural light without supplemental lighting was the control. The length of pseudobulb (PL), width of pseudobulb (PW), thickness of pseudobulb (PT), length of inflorescence (FL), number of branches (FB), number of florets (FN), and days to flowering (FD) per plant were recorded andcalculated when 80% of florets became mature. Light treatments significantly affected all pseudobulb growth and flower quality traits at different Gs, especially pseudobulb length (PL) and flower number (FN) under different LED types and lighting time intervals. MB-1 treatment promoted PT at both G3 and G4, whereas MB-2 treatment increased PW at both G2 and G4. Both MB-1 and LB-1 treatments had augmented effects on PL, respectively, at G2 and G3. The PW, FL, FB, and FN increased with additional light time and reached maxima under MB-2 treatment at G4 compared to other treatments and controls. Early flowering and an increased number of flowers at G4 were observed in plants grown under MB-2 treatment. Controlling light quality and supplementary light time intervals enables the production of HA plants with the desired growth and flowering quality characteristics of the pseudobulbs.

Unraveling the dynamics of climate: empirical evidence from the Indian state of West Bengal

Abstract Understanding climate variability and trends is crucial for managing a host of sectors. Everything from water availability to agricultural productivity is affected by variability and trends in temperature, rainfall, evapotranspiration, and solar radiation. Nevertheless, their dynamics have seldom been explored together, especially in India. To address this gap, the present study investigates the variability, trend, and magnitude of those parameters individually and concurrently using fractal dimension and non-parametric statistics over the Indian state of West Bengal from 1951 to 2020. The results show a south–north gradient in overall climate variability. The Gangetic West Bengal (GWB) is experiencing higher variability, along with a rising minimum temperature (≥0.008 °C year−1) and declining rainfall (≥− 1 mm year−1). Though the Sub-Himalayan West Bengal as a whole shows less variability, its foothills reveal modest variation coupled with increasing maximum temperature (≥0.005 °C year−1), reference evapotranspiration (≥0.4 mm year−1), and decreasing rainfall in the post-monsoon and winter seasons. Based on the results, we identified the western GWB, the Sundarbans, and the sub-Himalayan foothills as the most vulnerable areas and recommended proactive crop and water management strategies. Finally, we underline the need to analyze climate dynamics holistically to manage climate-sensitive sectors efficiently and sustainably.

Analysis of Trend in Meteorological and Hydrological Time-series using Mann-Kendall and Sen’s Slope Estimator Statistical Test in Akwa Ibom State, Nigeria

The monthly, seasonal, annual, and decadal trends of seven hydro-meteorological variables were analysed for stations in Akwa Ibom State, Nigeria, controlled by the Nigerian Meteorological Agency (NiMet) and the Cross River Basin Development Authority (CRBDA) from 1972 to 2021. At the 5% statistical significance level, the non-parametric Mann-Kendall and Sen's slope estimator techniques were used to detect if there was a positive or negative trend and the magnitude of the trend in hydro-meteorological data. In the present study, there was a significant statistically increasing (positive) trend in mean seasonal and annual rainfall, maximum temperature, minimum temperature, and runoff. However, there was a significant statistically decreasing (negative) trend in average annual relative humidity, solar radiation, and potential evapotranspiration. The magnitudes of the trends were 19.39mm/year, 0.0314oC/year, 0.013oC/year, -0.104%/year, -8.78MJ/m2/year, -1.440mm/year, and 0.028m3/s/year for annual rainfall, maximum temperature, minimum temperature, relative humidity, solar radiation, PET, and runoff, respectively. The rising trends in precipitation, temperature, and runoff in this research area show that this region is subject to climatic variability. The results of the Mann-Kendall and Sen's slope estimator statistical tests revealed the consistency of performance in the detection of the trend for the hydro-meteorological variables.

Revisiting vegetation activity of Mongolian Plateau using multiple remote sensing datasets

In-depth understanding of the changes and characteristics of vegetation activity on the Mongolian Plateau (MP) is essential to address climate change in temperate regions and even globally, but the seasonal dynamics and spatial patterns of vegetation activity have not been fully investigated. To this end, we used different types of satellite vegetation datasets from 2001 to 2020, namely, gross primary production (GPP), solar-induced chlorophyll fluorescence (SIF), and normalized difference vegetation index (NDVI), in conjunction with environmental factor datasets, then we investigated the interaction between vegetation activity of the MP and environmental factors. The spatial pattern of vegetation activity in JJA exhibited obvious spatial heterogeneity, which mainly manifested that strong vegetation activity occurred mostly in the northeast and north MP, while weak vegetation activity occurred in the southeast MP, and this pattern was closely related to hydrothermal conditions. Vegetation activity mainly exhibited an increasing or stable trend on the MP in JJA. By exploring the changes in JJA vegetation activity and its trends over the last 20 years, we found that vegetation activity in the study area is predominantly positively correlated with temperature (Tem) and negatively correlated with vapor pressure deficit (VPD) and deep soil moisture (SM). Additionally, the trends of wind speed, solar radiation, and deep SM made significant contributions to the trend in vegetation activity, suggesting that water availability has an important influence on vegetation change. Clarifying the effects of environmental factors on vegetation activity is fundamental to understanding the impact of climate change on vegetation.