Measurement and analysis of refractive index structure constant Cn2 profile over Delhi on diurnal and seasonal basis

jsfM;ksa rjax ds lapj.k dks izHkkfor djus esa jsfM;ks viorZdrk ,d egRoiw.kZ dkjd dk dk;Z djrh gSA jsfM;ks viorZdrk] ok;qeaMy dh HkkSfrd voLFkkvksa tSls & rkieku] nkc vkSj vknzZrk ij fuHkZj djrh gSA jsMkj vR;Ur NksVh vk—fr ds viorZukad fHkUurkvksa tks jsMkj ds rjax nS/;Z dh vk/kh gksrh gS] ds izfr laosnh gksrs gSA i'p izdh.kZu 'kfDr viorZukad fu;rkad Cn2 dh vk—fr ds ifjek.k ij fuHkZj djrh gSA vr% ekSle jsMkj] fo’k"k :i foaM izksQkbyj jsMkj ds fM+tkbu ds fy, fdlh LFkku ds Cn2 ds eku mi;ksxh gksrs gSA bl 'kks/k i= esa fnYyh ds Åij ds mijhru ok;qeaMy esa ok;qeaMyh; viorZukad fu;rkad Cn2 dh :ijs[kk nSfud ,oa _rqvksa ds vk/kkj ij rS;kj djus dh dksf’k’k dh xbZ gSA The radio refractivity is an important factor which effects radio wave propagation. Radio refractivity depends upon the physical states of atmosphere, i.e., its temperature, pressure and humidity. Radars are sensitive to refractive index irregularities on scale size equal to half wavelength of Radar. Backscattered power is dependent on the magnitude of refractive index structure constant Cn2. Hence Cn2values of a place are useful for designing weather radar specially wind profiler radars. This paper is an attempt to map the profile of refractive index structure constant Cn2 of atmosphere in the upper atmosphere, over Delhi on diurnal and seasonal basis.

Impact of climate change on agriculture in eastern Uttar Pradesh and Bihar states (India)

Production of crops is greatly influenced by weather phenomena and therefore any change in climate will have major effects on crop yield and productivity. Using NYD analysis for prediction of crop yield on seasonal basis, it has been observed that maximum temperature may cause the reduction in yield of rice in Eastern Uttar Pradesh by 1.0 to 1.1% per ha by 2020. Similarly, minimum temperature may decrease the yield of rice by 1.5 to 1.9% per ha in Eastern Uttar Pradesh. From future scenario of rainfall it was observed that south-west monsoonal rainfall would be the major factor for controlling the yield of rice. The role of maximum temperature for wheat production in Bihar state is more significant as compared to Eastern Uttar Pradesh. The model predicts that wheat yield may decrease by 5-6% in Bihar state due to increase in maximum temperature by the end of 2080 whereas this decrement in Eastern Uttar Pradesh may be 1.5-2.0%.

PRECIS-model simulated changes in climatic parameters under various scenarios in different agro-climatic zones of Punjab

In this study,the future simulated climatic data (temperature and rainfall) for the 21st century were downscaled using the regional climate model, viz., PRECIS model (Providing Regional Climates for Impact Studies) for different agro-climatic zones, i.e., Zone II (Ballowal Saunkhri), Zone III (Ludhiana, Amritsar, Patiala and Jalandhar) and Zone V (Bathinda) of Punjab. The corrected simulated data were then analyzed on the annual and seasonal basis to quantify the changes in maximum and minimum temperature and rainfall. The study showed that the maximum and minimum temperature and rainfall by the end of 21st century are likely to increase by 2.0 to 2.2 °C, 3.3 to 5.4 °C and 33 to 66% respectively in agro-climatic zone II; by 0.4 to 5.8 °C, 2.5 to 7.4 °C and 3 to 62% respectively in agro-climatic zone III and by 0.5 to 4.0 °C, 4.7 to 7.7 °C and 58 to 69% respectively in agro-climatic zone V at different locations of Punjab state under various scenarios of climate change. The trend analysis of these parameters revealed there is positive linear increasing trend under different scenarios in the Punjab state.

Gene drive that results in addiction to a temperature-sensitive version of an essential gene triggers population collapse in Drosophila

One strategy for population suppression seeks to use gene drive to spread genes that confer conditional lethality or sterility, providing a way of combining population modification with suppression. Stimuli of potential interest could be introduced by humans, such as an otherwise benign virus or chemical, or occur naturally on a seasonal basis, such as a change in temperature. Cleave and Rescue (ClvR) selfish genetic elements use Cas9 and guide RNAs (gRNAs) to disrupt endogenous versions of an essential gene while also including a Rescue version of the essential gene resistant to disruption. ClvR spreads by creating loss-of-function alleles of the essential gene that select against those lacking it, resulting in populations in which the Rescue provides the only source of essential gene function. As a consequence, if function of the Rescue, a kind of Trojan horse now omnipresent in a population, is condition dependent, so too will be the survival of that population. To test this idea, we created a ClvR in Drosophila in which Rescue activity of an essential gene, dribble, requires splicing of a temperature-sensitive intein (TS-ClvRdbe). This element spreads to transgene fixation at 23 °C, but when populations now dependent on Ts-ClvRdbe are shifted to 29 °C, death and sterility result in a rapid population crash. These results show that conditional population elimination can be achieved. A similar logic, in which Rescue activity is conditional, could also be used in homing-based drive and to bring about suppression and/or killing of specific individuals in response to other stimuli.

Effect of Repeated Plant Debris Reutilization as Organic Amendment on Greenhouse Soil Fertility

Greenhouse agriculture typically generates large amounts of waste with plant residue (agricultural biomass) being the most abundant. This residue is generated on a seasonal basis, which complicates the external management of the material. Recently, the European Union (EU) has been implementing a policy based on sustainability through the circular economy that seeks to minimize waste generation. The effect of reusing 3.5 kg·m−2 tomato plants from the previous season as the only fertilizer versus no fertilization and inorganic fertilization in 215-day tomato cycles after transplanting was studied in this trial. The study was carried out during three seasons in greenhouse agriculture in Almeria (Spain) with the repeated use of the solarization technique. The plant debris had similar production results during two of the three seasons and fruit quality parameters were similar to inorganic fertilization. In addition, some physicochemical variables improved and the biological depressive effect of solarization was mitigated. The results suggest that the reuse of the tomato plant debris as the only fertilizer could be an alternative to conventional fertilization under the conditions tested.

Long-Term Patterns and Trends of Shortwave Global Irradiance over the Euro-Mediterranean Region

The spatiotemporal patterns and trends of shortwave global irradiance (SWGI) are a crucial factor affecting not only the climate but also sectors of the economy. In this work, the ERA5-Land reanalysis dataset is employed and evaluated against in situ measurements from a dense network of surface stations operated by the National Observatory of Athens over Greece, revealing a good agreement between the two datasets. Then, the spatiotemporal variability of SWGI is investigated over the Euro-Mediterranean region (10° W–42° E and 30° N–52° N) for a 40-year period (1981–2020). SWGI exhibits a smooth latitudinal variability from north to south of −5.4 W/m2/degree on an annual scale, while it varies significantly on a seasonal basis and is almost four times lower in the winter than in the summer. The SWGI trend during the analyzed period was found to be positive and statistically significant at the 95% confidence level. Spring and summer are the periods where positive and the strongest rates of SWGI trends are evident, while in the winter and autumn, negative or neutral trends were found. The increasing SWGI trend shows a slowdown during the beginning of the 2000s in all seasons, except autumn. The SWGI trend decreases by about −0.06 W/m2/decade every 100 m of elevation increase.

Long term rainfall variability and trend analysis in lower Shivaliks of Punjab, India

The trend analysis of historical rainfall data on monthly, annual and seasonal basis for three locations in lower Shivaliks of Punjab, viz., Patiala-ki-Rao (1982-2015), Ballowal Saunkhri (1987-2015) and Saleran (1984-2017) has been done in the present study using linear regression model, Mann Kendall test and Sen’s slope. Further, the data for annual and seasonal rainfall and rainy days has also been analyzed on quindecennial basis, i.e., for the period of 1986-2000 and 2001-2015. The analysis of data showed that annual rainfall in the region ranged from 1000 to 1150 mm. The trend analysis of the data shows that the monthly rainfall is decreasing at Patiala-ki-Rao and Saleran, however, the trend was significant for May at Patiala-ki-Rao; and in March and November at Saleran. At Ballowal Saunkhri, the decreasing trend is observed from May to October, however, the trend is significant only in August. The decrease in annual and monsoon rainfall is about 13 to 17 mm and 12 to 13 mm per year respectively at three locations in lower Shivaliks of Punjab. The highest annual (1600-2000 mm) and monsoon (1500-1800 mm) rainfall during the entire study period was recorded in the year 1988 at three locations. The decadal analysis of the data shows below normal rainfall during April to October. The analysis of the rainfall and rainy days on monthly, annual and seasonal averages of 15 year basis showed that both rainfall and rainy days have decreased during the 2001-2015 as compared to 1986-2000 during all the seasons of the year.

Sequenced crop evapotranspiration and water requirement in developing a multi-trigger rainfall index insurance and risk-contingent credit

Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intra-seasonal basis risk. To minimize intra-seasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have shown to be ineffective in some cases as they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agro-meteorology we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage, that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modelling to price an operational WII and WII-linked credit product. The risk modelling approach we develop includes incorporation of correlation between rainfall and evapotranspiration indexes that can minimise significant intertemporal basis risk in WII.

Simulating Rainfall Interception by Caatinga Vegetation Using the Gash Model Parametrized on Daily and Seasonal Bases

Rainfall partitioning by trees is an important hydrological process in the contexts of water resource management and climate change. It becomes even more complex where vegetation is sparse and in vulnerable natural systems, such as the Caatinga domain. Rainfall interception modelling allows extrapolating experimental results both in time and space, helping to better understand this hydrological process and contributing as a prediction tool for forest managers. In this work, the Gash model was applied in two ways of parameterization. One was the parameterization on a daily basis and another on a seasonal basis. They were validated, improving the description of rainfall partitioning by tree species of Caatinga dry tropical forest already reported in the scientific literature and allowing a detailed evaluation of the influence of rainfall depth and event intensity on rainfall partitioning associated with these species. Very small (0.0–5.0 mm) and low-intensity (0–2.5 mm h−1) events were significantly more frequent during the dry season. Both model approaches resulted in good predictions, with absence of constant and systematic errors during simulations. The sparse Gash model parametrized on a daily basis performed slightly better, reaching maximum cumulative mean error of 9.8%, while, for the seasonal parametrization, this value was 11.5%. Seasonal model predictions were also the most sensitive to canopy and climatic parameters.