Bright Sunshine Research Articles

Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China

Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.

Impact of environmental factors on infestation of cashew leaves by leaf miner, Acrocercops syngramma Meyrick in Gujarat

Studies were conducted in a cashew plantation at Waghai, Gujarat, India during 2017 – 19 on cashew variety vengurla- 4 throughout two consecutive years to determine the pest status of leaf miner, Acrocercops syngramma Meyrick and their relation with environmental factor. Damage to cashew leaf by leaf miner was prevailed only from July to December with maximum damage (2.19%) in the month of November 45th SMW in hilly area of the Dangs. Further, pest infestation on leaves found to have significant positive correlation with maximum temperature, mean temperature, bright sunshine and evaporation, while negative with morning relative humidity, evening relative humidity, mean relative humidity and wind speed.

English

Navsari district of rainfall was shows highest increasing rainfall trend obtained September and negative January, July, October, November and December. The regression slope of the yearly time series is about 12.35 mm/36 years. Maximum temperature shows the highest increasing trend in month October, followed by December and August. The month highest decreasing trend was noticed that January, followed by February and July. The regression slope of the yearly time series is about 0.025°C/36 years. Minimum temperature highest values of the slope (0.109°C/36 year) with high value of regression Slope of determination (0.111°C), the annual Kendall’s tau statistic (0.492°C/36 year), the Kendall Score (310). All the month January to December shows increasing trend. The highest increasing trend found that November, followed by March and July, respectively. This finding shows that all the month shows increasing trend with the range between 0.308°C to 0.390°C. In case of RH-I the highest increasing trend shows September, followed by April and June. Similarly decreasing trend was found that January, followed by February and October, respectively. Relative humidity-II increasing trend was found only at the September month 0.084%, the increasing trend was detected in January to August and October to December, respectively. The strongest trend in the Bright sunshine hour’s decline of all month’s average daily sunshine hours was for the Navsari district. No significant trends were detected in all months and seasons for all weather elements. A similar trend was found in Sen’s slope and regression slope all the months for all the weather elements.

The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.

Impact of weather parameters on alternaria blight of Indian mustard [(Brassica juncea (L.) Czern. & Coss.)

Progression of Alternaria blight disease was measured on two susceptible Indian mustard varieties viz., RH 30 and RH 0749 sown at three different dates. The maximum increase in disease severity was recorded between first weeks of February and last week of February. During this period, the maximum and minimum temperature, relative humidity at morning and evening, average vapour pressure of morning and evening, maximum and bright sunshine hours and wind speed were higher, which resulted in congenial conditions for severe infection by the pathogen. The disease severity was positively correlated with maximum and minimum temperature, average vapour pressure, wind speed, sunshine hours and evaporation, while relative humidity and rainfall negatively correlated with Alternaria blight on both the varieties. A maximum value of area under disease progress curve was observed on cultivar RH 30 (651.1 cm2) as compared to RH 0749 (578.9 cm2), when crop was sown on 9th November.

Analysis of ANN-based daily global horizontal irradiance prediction models with different meteorological parameters: a case study of mountainous region of India

ABSTRACT Solar resource availability of a location depends on the local meteorological parameters. Therefore, the selection of suitable meteorological variable is highly desirable to develop the accurate solar irradiance prediction models. In present study, global horizontal irradiance (GHI) prediction models are developed using artificial neural network (ANN) with different combinations of meteorological parameters. A four-year station measured dataset consists of minimum temperature (), maximum temperature (), temperature difference (), GHI, extraterrestrial radiation (), and bright sunshine hours () have been employed to establish ANN models. Five types of ANN models (ANN-1 to ANN-5) are developed with 32 possible input combinations to determine the best input combinations to predict the daily GHI accurately. The achieved maximum correlation coefficient for ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5 models are 0.9197, 0.9681, 0.9688, 0.9515, and 0.9457, respectively. The results revealed that ANN-2 and ANN-3 has shown best performance with the input combinations of [] and respectively. The proposed methodology is also used to assess the solar potential of the mountainous state of Uttarakhand, India, situated in foots of Himalayas, using the best ANN model. The obtained results suggest that Uttarakhand has good solar potential with annual GHI varies from 16.96 to 19.54 , which is sufficient to implement a broad range of solar applications in the region. The methodology proposed in this work can be utilized to develop solar irradiance prediction models for different locations where monitoring stations are not available.

The Never-Chosen: Samson Occom, the New Divinity, and Indigenous Self-Determination

The Never-Chosen: Samson Occom, the New Divinity, and Indigenous Self-Determination

Impact and reliability of weather factors on occurrence of sapota seed borer, Trymalitis margarias Meyrick

An investigation on association of seed borer, Trymalitis margarias Meyrick infestation with ecological parameters was carried out in sapota between 2016 to 2019. Seed borer incidence commenced from September (1.00–1200%) and the peak activity was reported during December (8.50 to 9.25%) at initial fruiting phase. The fruit damage slightly decline in January and again attained maximum in March (10.75%) and April (10.50%) at advance fruiting stage and negligible in July and August. The linear coefficient correlation data revealed that seed borer caused higher damage due to raise of bright sunshine hrs and evaporation as well as by lesser of minimum temperature, morning-evening relative humidity along with absence of rainfall and rainy days equally under preceding and current circumstances. The fruit damage persuades with higher number days comprising 6 h bright sunshine light along with lesser number days having >80% relative humidity. The regression equations showed variability between 72 to 93% under eight weather parameters with significant impact of minimum temperature, morning and evening relative humidity, evaporation and rainfall on alternate years and had impact on seed borer succession. There was no major deviation in observed and predicted incidence except at early and ending fruiting phase.

Study of PAR interception, energy balance studies and microclimatic profiles in potato (Solanum tuberosum) cultivars under varying planting dates

The crop experiments were carried out during Rabi season 2016-17 & 2017-18. The experiment conducted at Research farm adjacent to agromet observatory (Latitude: 29° 10’ N; Longitude: 75° 46’ E & Altitude: 215 m), Department of Agricultural Meteorology, CCS HAU, Hisar, Haryana. The main aim and objectives of this work were to be accesses the microclimatic condition of potato under varying planting dates and varieties. Results revealed that micrometeorological parameters observed more favourable in 3rd week of October (D2) planted crop as compared to other planting dates. The absorbed PAR was recorded higher in 3rd week of October (D2) sown crop and Kufri Pushkar then other treatments at phenophases, respectively higher in during both seasons. PAR transmitted (watt/m2) at bottom, Soil heat flux pattern was observed in decreasing trend from advanced vegetative stage to tuber bulking stage may be due to more cover of ground and absorption percentage of PAR. The consumption of PAR, bright sun shine and assimilation rate were more in second planting dates. Among planting dates, the net radiation (Rn) was higher at early vegetative stage in 3rd week of October (D2) and 1st week of November (D3) during both crop seasons. The major portion of Rn (Net radiation) was utilized by latent heat of vaporization (LE). The 2nd week of October (D1) was received higher values of soil heat flux (G) during both the crop seasons. The minimum value of G was observed at tuber bulking stage in 1st week of November (D3) during both crop seasons. LE was higher at all phenophases in 3rd week of October (D2) at early vegetative stage during both the crop seasons. G was highest at initiation of tuber and early vegetative stage in 3rd week of November (D4) respectively, during both season. Temperature profiles were inverse throughout the day within the canopy at all phenophases. Over the top of the crop canopy the temperature profile was lapse. The relative humidity profiles were lapse inside the crop canopy throughout the day at 9:00 hours where it was iso-humic. Further concluded that the microclimate profile condition pattern was optimum in second sown crops and produced higher tuber yield.

Impact of recent weather variability on rice yield in Ludhiana district of Punjab

Rice is one of the chief food grains and is staple food of about 50 percent of world's population. Rice crop is highly weather dependent and any change in weather parameters like temperature, rainfall can infuence its productivity. Rice is a predominant crop of north-west India including Punjab. In Punjab, during recent two years, i.e. 2018 and 2019 there has been a drop in its productivity as compared to 2017 though the crop management conditions were same. So the analysis of effect of weather variables on rice crop at Ludhiana was conducted for three years viz. 2017, 2018 and 2019. Rice productivity showed variability during these years as in 2017 rice yield was 48.15q/ha as compared to 2018 with yield of 47.43 q/ha and 2019 with further lower yield of 44.53 q/ha. One of the major reason for fall in rice yield during 2018 and 2019 was occurrence of above normal (+42%) rainfall especially at the ripening stage of rice in the month of September (above normal by +146% in 2018 and +160% in 2019). This rainfall resulted in sudden lowering of maximum temperature (3rd week September to 2nd week October) by 3.0 to 9.0oC which was not favourable for ripening of rice grains. There was reduction in bright sunshine hours (BSS) by 12.3% in 2018 and by 11.0% in 2019 as compared to actual BSS of 988.4 hour in 2017 which is detrimental for photosynthetic activity in rice. During 2019 the yield was reduced from that of 2017 and 2018 as the maximum temperature was consistently higher by >3–4oC during transplanting and tillering stage and the minimum temperature was higher by >3–4oC during fowering stage of rice crop. So all these weather conditions resulted in decrease in rice yield during 2018 and 2019 as compared to 2017 though the crop management practices (cultivars, fertilizers, biotic stress management) during these three consecutive years were similar.

Assessing the impact of global climatic changes on the regional hydrological ecosystems and resources

Among the number of factors affecting the watershed ecosystem, climate is unique having significant links with systems and water resources. Precipitation, temperature, wind speed, humidity, cloud cover and bright sunshine are the major components affecting climate change. Their impacts on local hydroclimatic variables are very crucial and cannot be studied at the regional level. Global climate models are the most sophisticated and numerical based coupled method used for future projections of hydroclimatic change using different emission scenarios. The objective of this paper is to examine the hydroclimatic variables to analyse the past, present and future behaviours and their change using the statistical downscaling method. The statistical downscaling model, multiple linear regression and vector autoregression are utilised to calibrate and validate future local scale temperature, rainfall and river flow hydrology. The predicted values of temperature, rainfall and river flow from these approaches show significant results with both calibration and validation phases. Statistical investigations found that the vector autoregression method produces relatively better results. Hence, this may be the best option for the future generation of climate and hydrological conditions of the Pakistan region.

Age of sediments on Danube terraces of the Pest Plain (Hungary) based on optically stimulated luminescence dating of quartz and feldspar

Abstract The numerical ages available for the sediments on the Danube terraces in the Pest Plain are scarce. In this study, we present quartz OSL and K feldspar post-IR IRSL290 ages for the sandy fluvial, aeolian and slope sediments collected from Danube terraces IIb, III and V. The feldspar post-IR IRSL290 ages without residual dose subtraction are older than the quartz OSL ages, except for one sample, but the two sets of ages are overlapping within one or two sigma errors. In the bleaching experiment under natural sunlight during summer, an unbleachable component ranging from 2.5±0.7 Gy to 5.2±0.3 Gy after 30 h exposure to bright sunshine is observed and it corresponds to 3−8% of the measured K feldspar post-IR IRSL290 equivalent doses. These facts indicate that residual dose subtraction would be necessary before age calculation, in most cases. The saturated fluvial gravelly sand of terrace V of the Danube is older than ~ 296 ka based on feldspar post-IR IRSL290 measurements. This age does not contradict the traditional terrace chronology and the earlier published age data of this terrace. The other studied sediments on the surface of the terraces V, III and IIb deposited much later than the formation of these terraces. They infer aeolian activity and fluvial sedimentation of small streams during the MIS 3 and MIS 2 periods. The age of the dated dune sands with coeval aeolian sediments in Hungary indicate the cold and dry periods with strong wind activity of the Late Weichselian.

Influence of abiotic factors on incidence of apple and nut borer, Thylocoptila panrosema Meyrick of cashew

A research was carried out on cashew variety Vengurla-4 at farmer’s field in hilly area of the Dangs district, Gujarat, India during 2018 and 2019 to study the association between the incidence of apple and nut borer of cashew and abiotic factors. Regular weekly observations were recorded on the per cent damage of the pest. Apple and nut borer incidence was confined during January to May with maximum damage during April and was negatively influenced by evening and mean relative humidity as well as bright sunshine hours.

Influence of ecological parameters on the incidence aphid population in different potato (Solanum tuberosum L.) cultivars

In a field trial, thirteen potato cultivars were evaluated for the incidence of aphids, Myzus persicae (Sulzer) and Aphis gossypii Glover (Aphididae: Hemiptera) during rabi seasons from 2015-2016 to 2017-2018 at District Seed Farm, Department of Agriculture, Burdwan, West Bengal. Results revealed that initiation of aphid population was noticed in the crop field on or before 51st meteorological standard week (3rd week of December) with the peak at 7th MSW (3rd week of February). However, the level of aphid population was not similar in all the potato cultivars. Though the aphid population in all the cultivars decreased gradually after the peak, the attainment of a higher population differs over the duration. In general, a positive significant correlation of aphid populations was recorded with maximum, minimum temperature, and bright sunshine hours while maximum and minimum humidity had a negative correlation. Stepwise multiple regression analysis revealed that maximum temperature had a significant influence on population fluctuation of aphid. Besides, collective influence (61.8% to 76.5%) of all the abiotic factors on the aphid population was also recorded from adjusted R2 (0.618 to 0.765) in different potatocultivars.

Effect of micronutrients foliar spray on thermal Indices, phenology and yield of Lentil in new Alluvial Zone of West Bengal

A field experiment was conducted at BCKV during Rabi, 2018-19 and 2019-20 to study the effect of Zn, Fe and B foliar spray on phenology and thermal indices of popular lentil variety, Moitree (WBL 77) under different sowing condition [1st week of November (normal) and 1st week of December (late)]. Delay in sowing of crop from November to December reduced the crop duration by 11.4 days (113.4 days vs 101.7 days). The variation in mean daily temperature and bright sunshine hour among two sowing dates resulted in varied accumulated Growing Degree Days (GDD), Helio Thermal Units (HTU) as well as Photo Thermal Unit (PTU). It was found that different dates of sowing have a larger impact on change in phenology of the crop and thus affect various thermal indices. Foliar spraying of micronutrients viz., B + Fe @ 0.5% produced significantly higher seed and stover yield of 1438 kg ha-1 and 3981 kg ha-1 respectively, which were 86.26% and 27.0% more than that obtained from the control treatment (772 and 3134 kg ha-1). The study conducted revealed that along with optimum date of sowing, spraying micronutrients especially a mixture of B+Fe@0.5 % and B+Zn+Fe@0.5 % could also bring in change of phenology to some extent. This may help in better accumulation of photosynthate hence, improving the yield responses of lentil.

Influence of Weather Parameters and Thermal Time Approach on Green Gram at Coimbatore, Tamil Nadu

An investigation was carried out at the Agro Climate Research Centre, Tamil Nadu Agricultural University, on the effect of weather parameters on the green gram yield sown at various sowing dates during the rabi season of 2019. At various sowing dates, two green gram cultivars, VBN 4 and ADT 3, were sown. For both cultivars, the phonological crop length decreased with delays in sowing dates beyond October 23rd. The yield of green gram sown on 23rd October was significantly higher than the crops sown on 30th October and 6th November. The weather parameters Maximum Temperature (Tmax), Diurnal Range (Trange), Bright Sunshine Hours (BSS), Relative Humidity (RH I), Wind Speed (WS) were found to be negatively correlated with seed yield whereas Minimum Temperature (Tmin), Relative Humidity (RH II), Vapour Pressure (VP) were found to be positively correlated with the yield of green gram. The accurate prediction of green gram yield could be done with the maximum temperature, bright sunshine hours, wind speed and with thermal indices especially hygrothermal unit II with 82 percent, accuracy level.

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India.

Globally, since the end of December 2019, coronavirus disease (COVID-19) has been recognized as a severe infectious disease. Therefore, this study has been attempted to examine the linkage between climatic variables and COVID-19 particularly in National Capital Territory of Delhi (NCT of Delhi), India. For this, daily data of COVID-19 has been used for the period March 14 to June 11, 2020, (90 days). Eight climatic variables such as maximum, minimum and mean temperature (°C), relative humidity (%), bright sunshine hours, wind speed (km/h), evaporation (mm), and rainfall (mm) have been analyzed in relation to COVID-19. To study the relationship among different climatic variables and COVID-19 spread, Karl Pearson’s correlation analysis has been performed. The Mann–Kendall method and Sen’s slope estimator have been used to detect the direction and magnitude of COVID-19 trends, respectively. The results have shown that out of eight selected climatic variables, six variables, viz. maximum temperature, minimum temperature, mean temperature, relative humidity, evaporation, and wind speed are positively associated with coronavirus disease cases (statistically significant at 95 and 99% confidence levels). No association of coronavirus disease has been found with bright sunshine hours and rainfall. Besides, COVID-19 cases and deaths have shown increasing trends, significant at 99% confidence level. The results of this study suggest that climatic conditions in NCT of Delhi are favorable for COVID-19 and the disease may spread further with the increasing temperature, relative humidity, evaporation and wind speed. This is the only study which has presented the analysis of COVID-19 spread in relation to several climatic variables for the most densely populated and rapidly growing city of India. Thus, considering the results obtained, effective policies and actions are necessary especially by identifying the areas where the spread rate is increasing rapidly in this megacity. The prevention and protection measures should be adopted aiming at to reduce the further transmission of disease in the city.

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

<p>The objective of this paper is to build an artificial neural network model to predict Global Solar Radiation (GSR) with improved accuracy using less number of best input parameters selected using sensitivity analysis. In this work, the input parameters used for training the artificial neural network (ANN) models are bright sunshine duration, maximum and minimum temperature, day length, months, extra terrestrial radiation (<em>H<sub>0</sub></em>), relative humidity and geographical parameters of the locations namely the latitude and longitude. Sensitivity analysis is used to discover how the output data are influenced by the changeability of the input data.Three ANN models namely T-ANN, S-ANN and TS-ANN are proposed with most suitable input parameters selected using sensitivity analysis. The principle of this feature selection using sensitivity analysis is to improve the prediction accuracy of solar radiation models with less number of inputs. The proposed ANN model is also tested under noisy data and proved that ANN is able to perform reasonably good in GSR prediction on practical applications where the data is affected by noise caused by errors on measuring, fault of data acquisition system, recording problems, and so on.</p>

Determining the radiation use efficiency of potato using sunshine hour data: a simple and costless approach

Aim of study: Radiation parameters and photoperiod influence potato biomass and tuber yield significantly. Lack of instrument facilities in developing countries is the main hindrance to estimate global solar radiation (GSR) and radiation use efficiency (RUE). Considering these facts, an experiment was conducted to estimate light extinction coefficient (K) and RUE using a simple but indirect approach that can be implied in any location lacking sophisticated instruments.Area of study: Field experiments were conducted in Kalyani, West Bengal, representing the Indo-Gangetic Plains.Material and methods: Angstrom-Prescott (A-P) equation was used to calculate GSR. The experiment was laid out in a split-plot design with three dates of planting (DOP), 15th Nov, 29th Nov and 13th Dec, as main plot treatment and three potato cultivars (ˈKufri Suryaˈ, ˈKufri Chandramukhiˈ and ˈKufri Jyotiˈ) as sub-plot treatment. Leaf area indices and K values were used to determine intercepted PAR (IPAR) as well as RUE.Main results: The cumulative IPAR from emergence to harvest ranged 246-429 MJ m-2 depending on planting time and varieties. Irrespective of DOPs, the highest mean RUE (4.19 g MJ-1) was calculated in ˈKufri Chandramukhiˈ, showing that it used the radiation more efficiently that the other two cultivars (ˈKufri Suryaˈ= 3.75 g MJ-1 and ˈKufri Jyotiˈ= 3.14 g MJ-1).Research highlights: Statistical indices confirmed that the A-P model can be reliably used in the study region for estimation of GSR. This simple way to estimating RUE using bright sunshine hours data can be used in developing countries, where costly radiation instruments are not available.

Qualitative analysis of random forests for evaporation prediction in Indian Regions

The performance of logistic regression, discriminant analysis, and random forest has been compared for the prediction of evaporation of different regions of India during 2019 at ICAR-IARI, New Delhi . The present experiment was performed at Raipur (Chhattisgarh), Karnal (Haryana), Pattambi (Kerala) and Anantpur (Andhra Pradesh). Evaporation and other weather parameters are collected from the year 1985-2012, 1973-2005, 1991-2005 and 1958-2010 respectively. The performance of the techniques is compared using classification, misclassification, and sensitivity of the model along with the Receiver Operating Characteristics (ROC) curve and Area Under Curve (AUC) value. The combinations of variables as independent variables are used in two sets. In the first set, maximum & minimum temperature, relative humidity morning & evening, wind speed, rainfall, and bright sunshine hours are used. In the second set mean temperature, mean relative humidity, bright sunshine hours, and wind speed is used to see the effect on evaporation. It is found that more accuracy is obtained using the second set as predictors. The model validation accuracy is checked via running developed model on out of sample data, i.e. testing data (last three years). The study demonstrates that the random forest approach predict evaporation in a much better way than logistic regression, discriminant analysis. The random forest model can provide timely information for the decision-makers to make crucial decisions impacting due to evaporation conditions in India.