Maximum Humidity Research Articles

Characterization of drought propagation over the Tibetan Plateau

Study regionThis study was carried out on the Tibetan Plateau (TP), which contains multiple important ecosystems, is the source of many rivers in China, and is experiencing significant climate change. Study focusClarifying drought propagation characteristics is crucial for understanding the mechanism of drought development and benefiting drought mitigation and early warning schemes. However, there is currently a notable lack of research on droughts on the TP and drought propagation characteristics on the TP have yet to be investigated. In this study, we investigated drought propagation time, probability, threshold, and the climatic factors associated with drought development based on the copula probability model and correlation, Bayesian network, and attribution analyses, allowing us to fill current research gaps. New hydrological insights for the regionThe research framework proposed in this study effectively reflected the characteristics of drought propagation. Our results showed that seasonality and aridity controlled the propagation time from meteorological drought (MD) to agricultural drought (AD), i.e., the time was shortest in summer (2.311.0 months) and in the humid zone (2.37 months); the same pattern was found for the propagation of MD to hydrological drought (HD), with the propagation time being 2.33.0 months in summer and 2.36 months in humid zones. The propagation probability for both AD and HD generally increased synchronously with the severity of MD, and also exhibited seasonal patterns, with the highest probability values in summer (0.590.90 for AD and 0.510.82 for HD). The highest propagation thresholds were also found in summer (−1.68 to −1.09 for AD and −1.68 to −1.36 for HD). The trends of both propagation time and probability were significant, and the probability of HD exhibited significant downward trends (−0.074 to −0.030/decade) across the TP. Precipitation was the dominant factor controlling the development of drought in most cases; however, other climatic factors, such as maximum temperature, solar radiation, and specific humidity, contributed 14.0 %43.9 % of the variances of AD and HD.

Epidemiological Study of Downy Mildew Disease of Opium Poppy

The downy mildew caused by Peronosporaspp is a major disastrous disease of many plant species. The symptoms of downy mildew disease incidence on opium poppy (Papaver somniferum L.) were collected in three years from 2019-20 to 2021-22 for prediction of weather parameters on the progression of downy mildew disease. The downy mildew disease initiation was recorded in the 52nd SMW (10.0%DMI) when the maximum temperature was 19.430C. At 3rd SMW, the maximum temperature dropped 17.330C and DMI reached up to 20.00 percent depicting the progression and spread of downy mildew disease in opium poppy with the decrease of maximum temperature. The maximum disease incidence was recorded 92.53 percent in 10th SMW when maximum and minimum temperature were 29.030C and 13.530C. Regression analysis between dependent variable downy mildew disease incidence Vs. independent variables (viz., rainfall, maximum and minimum temperature and relative humidity) showed that all the weather parameters contributed more than 85 percent variation (R2 = 0.869, 0.957, 0.859) in the downy mildew incidence of opium poppy. One unit change of maximum temperature, minimum temperature (1.00c), maximum and minimum relative humidity (1.0%) might cause to change 0.128, 0.70, 0.117 0.130 units in downy mildew incidence, respectively.

Spatiotemporal Dynamics of Hot-Dry Winds in Northern China: A Multidimensional Analysis Using Gridded Datasets

Abstract Hot-dry winds (HDWs), characterized by simultaneous high temperature, low humidity, and strong wind speed, represent prevalent agrometeorological hazards in northern China. To date, our comprehension of HDWs has been constrained to regional scales due to the dependence upon in situ meteorological observations and the lack of a universal definition of HDWs. In this study, we leveraged a gridded dataset to elucidate the spatiotemporal dynamics of HDWs in northern China from 1961 to 2022. We also introduced a standardized HDW index (sHDWI) to investigate HDW severity. The results indicated more HDW occurrences in eastern Northwest China (NW) and western Inner Mongolia (IM). While there were declining trends in HDW frequency, extent, and severity on average in northern China from 1961 to 1993, these trends were reversed by an overall increase afterward. Intriguingly, regions spanning from NW to IM exhibited significant upward trends in HDW severity according to sHDWI. The spatial extents of light, moderate, and heavy HDWs consistently decreased in North China (NC), with notable expansions over IM, NW, and Northeast China (NE) from 2001 to 2022. Importantly, the increased frequency, expanded extent, and intensified severity of HDWs, particularly, the heavy ones, underscored the escalation of HDW events in recent decades over the arid IM and hyperarid NW, known for their fragile ecological environment. The findings from this research carry significant implications for agricultural production and water management in northern China influenced by the changing patterns of HDWs in the context of climate change. Significance Statement This study aims to enhance our understanding of the large-scale spatiotemporal dynamics of hot-dry winds (HDWs) in northern China using a 0.25° × 0.25° gridded dataset spanning the period from 1961 to 2022. The adoption of the gridded dataset enables a multidimensional exploration of HDWs, encompassing their frequency, extent, and severity which cannot be fully captured by in situ observations. Meanwhile, to quantify changes in HDW severity, we introduce a standardized HDW index that surpasses existing HDW definitions relying on absolute thresholds or relative percentiles of maximum air temperature, relative humidity, and wind speed. Our results reveal a consistent reversal in HDW frequency, extent, and severity around 1993 in northern China and highlight the intensification of heavy HDWs, particularly in the arid and hyperarid regions within the study area.

A study on meteorological characteristics and its consequences on human mortality in Rajkot City, Gujarat

Abstract Background: Climate change presents a crucial threat to human health. The health of human is affected by heat waves and extreme weather events. Hence, the present study was planned to explore weather conditions and their consequences on mortality of human being by accessing the meteorological and mortality data. Methodology: Meteorological data include maximum and minimum temperature, average wind speed, and total rainfall; relative humidity was obtained for January 1, 2017–December 31, 2021, from India Meteorological Department, Pune. Trend analysis of climatic variables was done. Human mortality data were obtained from January 1, 2017, to December 31, 2020, from Health Department, Rajkot Municipal Corporation, Rajkot. The impact of temperature and rainfall on mortality was observed. Results: Upward trend of maximum temperature, rainfall, and relative humidity while downward trend for minimum temperature and average wind speed was observed. Human mortality due to communicable and noncommunicable diseases was not affected by maximum temperature. The effect of rainfall on mortality due to vector-borne diseases was observed. Conclusion: The effect of rainfall on human mortality was observed. Upward and downward trend of climatic variables was not found to be statistically significant. Further study needs to be conducted by analyzing climate data over a longer duration and across a larger geographical area.

Separation layers and their influence on green roof water storage

ABSTRACT This study deals with how adding separation layers to flat roofs improves their design, construction, and sustainability. Better understanding and use of these layers leads to increased durability, lower maintenance, and simpler execution, with potential for material reuse and performance enhancement. Currently, the optional nature of separation layers hinders the overall sustainability of roofing systems, so this research seeks to guide building professionals in selecting suitable separation layers for flat roofs, considering factors like location, function, material composition, and mass. Particularly, it examines the impact of Water Storage layers on self-sustaining green roofs, aiming to enhance thermal performance, especially when water is scarce. Results indicate that installing these layers significantly increases water retention below the drainage layer, primarily in extensive green roofs, where values rise by 17%. Water Storage layers also extend water availability after rainfall, raising maximum daily humidity levels inside the roof by up to 10% on the third day post-rainfall compared to green roofs without such a layer. Highlights The optional character related to separation layers lends a voluntary aspect to sustainability. Proper selection of separation layers’ characteristics ensures optimal roof functionality. The Water Storage separation layer increases humidity on the green roof's inner layers. The Water Storage separation layer extends the time for water usage on green roofs.

Mathematical Modeling of Sorptive Extraction of Lithium Chloride from Lithium-containing Brine of the Aral Sea Region

Introduction The article presents the results of chemical and physicochemical analysis of initial lithium-containing hydromineral raw materials and sorbent based on bentonite clay with titanium oxide addition. Methods It is established that the mineral is montmorillonite group and kaolinite according to the results of the analysis of the microstructure and elemental composition of bentonitic clay of the Darbazinsky deposit. Brine is characterized by the change of lithium chloride content in the range of 403- 1001 mg/l. Physical and mechanical characteristics of the obtained sorbents based on bentonite clay and titanium oxide additive are characterized by a high mechanical strength of 5.31 MPa, density of up to 2,6 g/cm3, and specific surface area of 1572 cm2/g. The technological parameters of sorption extraction of lithium chloride from lithium-containing hydromineral raw materials were optimized using the system analysis “STATISTICA” developed by StatSoft company. It is established that to increase the rate of lithium chloride extraction from brine, it is necessary to maintain the rate of brine flow within 8 l/min at maximum humidity of solution output up to 43g/m3 on the basis of the obtained volumetric graphical dependencies. Results The microstructure of the used sorbents with titanium oxide addition up to 20% is characterized by the predominance of lithium chlorides, which are represented by heap-shaped, prismatic crystals. Conclusion The maximum amount of lithium chloride in the form of clusters of light-white crystals of tabular shape on the surface of the sorbent is observed at increasing the content of titanium oxide up to 40%.

Mapping dengue in Brazil: an epidemiological, geospatial and climate correlation

Brazil has been suffering for many years from an extensive dengue epidemic. The objective of this study was to map dengue cases in Brazil between 2007 and 2022, correlating their epidemiology, geospatial distribution, and relationship to climatic and social data. An epidemiological survey was conducted based on data available in the Notifiable Diseases Information System, calculating incidence rates (IR), mortality rates (MR), case fatality rates (CFR), and the distribution of epidemiological characteristics, allowing the geolocation of cases and the creation of maps for each year and Brazilian states. Finally, the ecological determinants were correlated with IR and MR. We observed that IR and MR remained high throughout the analyzed period, with a slight decrease in IR in 2017 and 2018. Cases occurring in female individuals were higher, however, with a similar percentage of deaths. A decrease in the average mortality was observed, during the evaluation period, in children and adolescents; however, an increase in the average mortality among elderly was correlated with the presence of comorbidities. The states located in the Midwest region were the most affected, followed by the states in the Northern region. In addition, we observed a significant, but weak, correlation between ecological determinants (maximum temperature and humidity) and IR, GDP per capita and HDI (Human Development Index) with MR. Due to the hyperendemic scenario, it is necessary to take efficient control and constant epidemiological surveillance.

Weather Influence on Fall Armyworm Infestation in Maize: A Study in Perambalur District, Tamil Nadu, India

Maize, Zea mays L (Family: Poaceae), is a main cereal crop cultivated across diverse agro-climatic regions worldwide. The Fall Armyworm (FAW), Spodopterafrugiperda (J. E. Smith) (Lepidoptera: Noctuidae), migratory insect is notorious for its economic impact, inflicting severe damage on various crops including maize, rice, sorghum, millet, sugarcane, vegetables, and cotton, and has been documented to affect over 80 other plant species. A roving survey was carried out in four major maize growing blocks of Perambalur district viz., Perambalur, Veppanthattai, Alathur and Veppur during Kharif and Rabi season of 2023-2024. The experiment consisted of sixty standard weeks from 3rd July 2023 to 4th August 2024. The results indicated a positive correlation between the number of fall armyworm larvae/plant with Tmax, relative humidity (Evening), and the negative correlation was observed between the number of fall armyworm larvae/plant with Tmin, relative humidity (Morning), Sunshine and rainfall. The data concluded that the maximum temperature significantly influenced the emergence of the fall armyworm and its infestation on maize. The incidence of FAW started in 46th mean standard week MSW (13 larvae/plant) Alathur, (17.25 larvae/plant) Perambalur, (17.25 larvae/plant) Veppanthattai, (18 larvae/plant) Veppur and the population increased gradually and reached the peak in the 47th mean standard week MSW (26 larvae/plant) Alathur, (32.75 larvae/plant) Perambalur, (33 larvae/plant) Veppanthattai, (42 larvae/plant). Correlation analysis revealed that larval population showed a significant positive correlation with the maximum temperature and relative humidity. At the same time, minimum temperature, sunshine, and rainfall exhibited a negative association with larval incidence of S. frugiperda. Propose adaptation strategies for maize farmers in Perambalur to manage fall armyworm infestations in the context of changing weather patterns. This could include adjustments in planting dates, pest monitoring techniques, or integrated pest management approaches.

Seasonal Incidence of Insect Pests on Blackgram [Vigna mungo (L.) Hepper] in Malwa Region of Madhya Pradesh, India

The study investigated the prevalence of major insect pests and their natural enemies in Blackgram crops, focusing on leaf hoppers, whiteflies, Bihar hairy caterpillars, spotted pod borers, stem flies, coccinellids and spiders. Throughout the crop period, the population dynamics of these pests were monitored, revealing varying levels of infestation. The high population of 5.60 leafhoppers per plant and 3.50 larvae per plant of Bihar hairy caterpillar were recorded during 35th week of the crop cycle. The high population of 6.90 whiteflies per plant, 2.60 spotted pod borer larvae per plant and 60 per cent of pod fly infestation were observed during 36th, 37th and 38th week of the crop cycle, respectively. The highest populations of coccinellids 2.40 beetles per plant in 35th week and 2.00 spiders per plant in 36th week recorded duringthe crop cycle. The maximum temperature and relative humidity showed significant positive correlation and minimum temperature and rainfall showed negative non-significant correlation with the population of leaf hopper, Bihar hairy caterpillar, whitefly, stem fly, spotted pod borer and coccinellids. Relative humidity showed positive significant correlation and maximum temperature, minimum temperature and rainfall showed negative non-significant correlation with population of spider.

Statistical and machine learning models for predicting spalling in CRCP

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP’s functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.

Machine learning models for daily net radiation prediction across different climatic zones of China

Net radiation (Rn), a critical component in land surface energy cycling, is calculated as the difference between net shortwave radiation and longwave radiation at the Earth’s surface and holds significant importance in crop models for precision agriculture management. In this study, we examined the performance of four machine learning models, including extreme learning machine (ELM), hybrid artificial neural networks with genetic algorithm models (GANN), generalized regression neural networks (GRNN), and random forests (RF), in estimating daily Rn at four representative sites across different climatic zones of China. The input variables included common meteorological factors such as minimum and maximum temperature, relative humidity, sunshine duration, and shortwave solar radiation. Model performance was assessed and compared using statistical parameters such as the correlation coefficient (R2), root mean square errors (RMSE), mean absolute errors (MAE), and Nash–Sutcliffe coefficient (NS). The results indicated that all models slightly underestimated actual Rn, with linear regression slopes ranging from 0.810 to 0.870 across different zones. The estimated Rn was found to be comparable to observed values in terms of data distribution characteristics. Among the models, the ELM and GANN demonstrated higher consistency with observed values, exhibiting R2 values ranging from 0.838 to 0.963 and 0.836 to 0.963, respectively, across varying climatic zones. These values surpassed those of the RF (0.809–0.959) and GRNN (0.812–0.949) models. Additionally, the ELM and GANN models showed smaller simulation errors in terms of RMSE, MAE, and NS across the four climatic zones compared to the RF and GRNN models. Overall, the ELM and GANN models outperformed the RF and GRNN models. Notably, the ELM model's faster computational speed makes it a strong recommendation for Rn estimates across different climatic zones of China.

Observation of tapered multicore fibers based on Nb2CTx for humidity sensing: Experiment and DFT simulation investigations

Recent advancements in the area of human healthcare monitoring and medical diagnosis have sparked a revived interest in humidity sensors. Nb2CTx, characterized by its multilayered structure, large specific surface area, and plentiful hydrophilic groups, actively absorbs a sufficient quantity of water molecules. In this work, the sensing capability and mechanism of water molecules are investigated under different proportions of functional group distribution on the surface of Nb2CTx MXene employing plane-wave based density functional theory calculations, and the provided models can offer guiding principles for the experimental preparation of the highly sensitive humidity sensor based on Nb2CTx. To verify the effectiveness of the model, Nb2CTx material is coated on a Tapered Three-Core Fiber (TTCF), and the high evanescent field characteristics of the sensor are utilized to further improve its performance. As the relative humidity (RH) increased from 35 % to 75 %, the transmitted spectra exhibited a redshift with a sensitivity of 22pm/% RH. Within the relative humidity range of 75 % to 95 %, the Nb2CTx coating heightened water molecule absorption, resulting in a more pronounced redshift in the transmitted spectra. This phase manifests a maximum humidity sensitivity of 299pm/% RH. Noteworthy advantages of this sensor include its uncomplicated structure, cost-effectiveness, and robust stability, rendering it highly suitable a wide variety of potential applications in fields such as biology, chemical and health processing.

Wild-caught adult black flies (Diptera: Simuliidae) from various ecological landscapes in Malaysia

Most studies on black flies focus on the taxonomy and ecology of their aquatic stages. Despite posing a public health threat, the adults remain poorly studied in many countries, including Malaysia. The present study represents the first investigation of the distribution of wild-caught black flies from various ecological landscapes and climatic conditions in Malaysia. CO2-baited Malaise traps were set randomly at 41 sampling sites across Peninsular Malaysia from 2020 to 2023. In total, 532 black flies belonging to 14 species of four subgenera were captured. To ensure taxonomic rigor, specimens were identified to species morphologically and molecularly. The subgenus Gomphostilbia was the most abundant (71.43 %), followed by Simulium (14.28 %) and Davieselleum and Nevermannia each representing 7.14 % of the total captures. These species represented 14.74 % of the total species recorded from Malaysia. The most frequently collected species were Simulium roslihashimi (24.39 %), followed by S. aureohirtum, S. vanluni, and S. (Gomphostilbia) sp. 1 with 7.32 % each. The highest relative abundance was found for S. vanluni (86.09 %) and S. roslihashimi (7.14 %). Most species were found at elevation below 300 m (78.57 %); fewer were at elevation higher than 1,000 m (21.43 %). Two principal components accounted for 85.3 % of the total intersite variance. Simulium roslihashimi was found at almost every site, with a maximum relative humidity of 90 %. Simulium aureohirtum and S. vanluni were found at sites with relative humidity up to 73 %, but S. aureohirtum was found at higher temperatures (31 °C) compared with S. roslihashimi (28 °C) and S. vanluni (29 °C). The present study establishes the groundwork for further studies of wild adults in Malaysia and identifies the need to use more traps over the range of seasons and environmental conditions, particularly near breeding sites.

Influence of Temperature and Relative Humidity on Spore Germination and Early Blight Disease Development of Tomato Caused by Alternaria solani

Early blight of tomato caused by Alternaria solani is one of the most important and devastating disease of tomato which causing extensive yield losses to the crop. Environmental factors play an important role in the development of disease. To investigate the congenial conditions to develop the disease and germination of fungal spores an experiment was conducted in the year 2019 and 2020 at vegetable pathology laboratory of Dr Yashwant Singh Parmar University of Horticulture and Forestry Nauni Solan, Himachal Pradesh, India. Under investigations effect of temperature and relative humidity (RH) on conidial germination and early blight diseases development of tomato caused by Alternaria solani was studied under in vitro and in vivo conditions. Under in vitro conditions, maximum germination of conidia (75.54%) was recorded at 30°C after 24 hours and minimum (23.24%) was recorded at 15°C. length of germ tube (129.55 µm) was maximum at 25°C, whereas, minimum (67.09 µm) was observed at 15°C. In case of relative humidity maximum conidial germination (95.18%) and germ tube length of 238.95 µm was observed at 100% level. Under in vivo conditions disease severity was maximum (81.48%) at 30°C followed by 25°C (78.21%), whereas, minimum (18.88%) was recorded at 40°C. Similarly, the maximum (88.09%) disease severity was observed at 100% RH level while, it was minimum at 38% level of humidity. Overall, the relative humidity of more than 90% and temperature range of 25–30°C was found most congenial for the development of disease.

Experimental study on the performance of various porous water-absorbing fiber materials for indoor humidification

To increase the relative humidity in heating rooms in winter, a new humidification method is proposed. The capillary water absorption performance of porous fiber materials is utilized to compose a bottom water-absorbing component, and the spray device of the traditional direct evaporative cooling system is eliminated, which can be used for miniaturized heating humidification devices. To compare the heating and humidification performance of various fiber materials, an experimental system was designed and constructed with fiber materials arranged in parallel as humidification components. The experimental materials include nonwoven fabric (Sample A), polyester fiber (Sample B) and fiber cotton (Sample C). The experimentally obtained temperature drop is lower than the conventional evaporative cooling system, and the relative humidity enhancement is between 6 % and 15 %. Sample B had a maximum temperature drop of 6.83 °C, a maximum relative humidity increment of 14.07 %, a maximum humidification capacity of 272.4 g/h, and a maximum humidification capacity per unit area of material of 972.86 g/(m2·h). The maximum humidification capacity of Sample B was 13.8 % higher than that of Sample A and 18.5 % higher than that of Sample C. The cooling energy density of the three samples was only within 330 J/m3 under all working conditions. Applying sample B to the fan coil unit increased the relative humidity by 33.8 % with only 0.3 °C temperature drop, providing humidification while heating the indoor air. This paper provides a theoretical basis for a fiber material humidification, which to some extent can meet the heating and humidification integration in winter.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection

BackgroundThe government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space–time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.MethodsSpatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.ResultsMalaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010–2011, and with P. vivax incidence in the north and central regions during 2012–2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.ConclusionsMalaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.

LABORATORY DETERMINATION OF THE MAXIMUM DENSITY AND OPTIMAL HUMIDITY OF LARGE-BLOCK SOILS

he purpose of our field research was to develop and select a method of work on laying and compacting the material of large-block soils, ensuring the receipt and determination of maximum density and optimal soil moisture. On the basis of these studies, technical conditions for the construction of thrust prisms are further drawn up. This article presents the results of the work performed with a brief methodology of field and laboratory research. The article provides data characterizing the material intended for installation in the body of the dam. The currently existing methods for determining the control parameters of laying large-block soils are contradictory and require experimental work. A number of parameters are taken into account: grain composition, humidity, density. Therefore, the article considers a laboratory method for determining the maximum density and optimal humidity of stone material when laying in the body of the dam. The results of the research carried out on the development of our quarry area, the thickness of the uncompacted layer was determined, the choice of a sealing mechanism, the number of passes along one track, the determination of the density of dry soil in experimental field sites intended for use in filling the body of the dam, graphs of the required densities for full-scale grain composition are presented. The strength properties of the stone material were determined experimentally for the average grain composition and for the upper curve of the accepted “fish”

Decametric-scale buffering of climate extremes in forest understory within a riparian microrefugia: the key role of microtopography.

Riparian corridors often act as low-land climate refugia for temperate tree species in their southern distribution range. A plausible mechanism is the buffering of regional climate extremes by local physiographic and biotic factors. We tested this idea using a 3-year-long microclimate dataset collected along the Ciron river, a refugia for European beech (Fagus sylvatica) in southwestern France. Across the whole network, canopy gap fraction was the main predictor for spatial microclimatic variations, together with two other landscape features (elevation above the river and woodland fraction within a 300m radius). However, within the riparian forest only (canopy gap fraction < 25%, distance to the river < 150m), variations of up to -4°C and + 15% in summertime daily maximum air temperature and minimum relative humidity, respectively, were still found from the plateau to the cooler, moister river banks, only ~ 5-10m below. Elevation above the river was then identified as the main predictor, and explained the marked variations from the plateau to the banks much better than canopy gap fraction. The microclimate measured near the river is as cool but moister than the macroclimate encountered at 700-1000m asl further east in F. sylvatica's main distribution range. Indeed, at all locations, we found that air relative humidity was higher than expected from a temperature-only effect, suggesting that extra moisture is brought by the river. Our results explain well why beech trees in this climate refugium are restricted to the river gorges where microtopographic variations are the strongest and canopy gaps are rare.