Average Maximum Air Temperature Research Articles

Dynamics of extreme climatic variables for viticulture in the main zones of ampelocenosis of the Krasnodar region

Modern climate changes affect all branches of agriculture. Everywhere there is an increase in air temperature, changes in precipitation, an increase in extreme weather events. Since the productive lifespan of a grape plant is 30-40 years, it is necessary to assess climatic changes in order to create a variety adapted to changes. The purpose of the research is to assess changes in extreme heat supply and relative humidity in the main viticultural areas of the Krasnodar region. The average values of extreme heat supply and relative humidity variables of two climatological periods of 1961–1990 and 1991–2020, their changes over time and the course of variable’s anomalies of 1991–2020 compared with the average values of 1961–1990 are calculated. An increase in the absolute maximum air temperature by 0.2–1.6 °C for the period 1991–2020 was noted compared to the previous period, with the exception of Novorossiysk (decrease by 1.4 °C); an increase in the average absolute maximum air temperature by 1.5–2.5 °C, an increase in the number of days with a maximum air temperature above +35 °C by 1.0–2.3 days; a decrease in the average relative humidity of April–October by 0.7–2.7 % and an increase in the number of days with a minimum relative humidity of less than 30 % over the summer by 0.8–5.4 days. The variability of these variables over time for the period 1991–2020 is consistent with the change in the average. An increase in the absolute maximum was established (by 0.65–0.9 °C/10 years), the number of days with a maximum air temperature above +35 °C (by 0.8–1.1 days/10 years), the number of days with a minimum relative humidity of less than 30% over the summer (by 1.2–7.2 days/10 years); decrease in the average relative humidity of April–October (by 0.5–6.5 %/10 years). These changes indicate an increase in climate extremes and the frequency of unfavorable conditions for grapes in the summer, which requires an adjustment of the assortment.

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

The impact of temperature factor on Dengue Hemorrhagic Fever in Khanh Hoa province during 2007 – 2021

A retrospective descriptive study was conducted to describe the impact of temperature (Average air temperature (AAT), average maximum air temperature (AMaxT), average minimum air temperature (AMinT)) on the number of dengue hemorrhagic fever (DHF) cases in Khanh Hoa province from January 2007 to December 2021. Monthly data were collected and analyzed using a negative binomial regression model with the average temperature over a lag period of 3 months. The results showed that there was a statistically significant positive correlation between AAT, or a statistically significant negative correlation between AMaxT, AMinT and the relative risk of DHF. With a 1oC increase in AAT, the number of DHF increased by 4791.09% (95% CI: 2479.03 ÷ 9175.86, p < 0.001). For 1oC increase in AMaxT, the number of DHF decreased by -36.24% (95% CI: -58.10 ÷ -2.95, p = 0.036). With a 1 oC increase in AMinT, the number of DHF decreased by -94.97% (95% CI: -96.86 ÷ -91.95, p < 0.001). The study found that the environmental temperatures, specifically AAT, AMaxT and AMinT, contributed to the impact on the number of DHF cases in Khanh Hoa province. Monthly temperature data can be valuable in the assessment and prevention of DHF.

Batrachochytrium dendrobatidis in the Arid and Thermally Extreme Sonoran Desert.

Batrachochytrium dendrobatidis (Bd), the causative agent of the devastating global amphibian disease chytridiomycosis, was not projected to threaten amphibians in hot and arid regions due to its sensitivity to heat and desiccation. However, Bd is being detected more frequently than ever in hot and arid regions of Australia and the USA, challenging our current understanding of the environmental tolerances of the pathogen under natural conditions. We surveyed for Bd in an extremely hot and arid portion of the Sonoran Desert, where the pathogen is not projected to occur, and related presence and prevalence of the pathogen to local environmental conditions. We collected eDNA samples from isolated desert water sites including six tinajas and 13 catchments in June and August of 2020 and swabbed a total of 281 anurans of three species (red-spotted toad Anaxyrus punctatus, Couch's spadefoot Scaphiopus couchii, and the Sonoran Desert toad Incillius alvarius) across five catchments and six tinajas from June to September of 2020. Overall, Bd occurred at 68.4% of sites, despite extreme heat and aridity routinely exceeding tolerances established in laboratory studies. Average summer maximum air and water temperatures were 40.7°C and 30.7°C, respectively, and sites received an average of just 16.9mm of precipitation throughout the summer monsoon season. Prevalence was low (5.7%) across species and life stage. Our results demonstrate that Bd is capable of persisting and infecting amphibians beyond its projected range, indicating a need to account for higher thermal tolerances when quantifying risk of Bd presence and infection.

Effects of Artificial Green Land on Land–Atmosphere Interactions in the Taklamakan Desert

Land–atmosphere interactions are influenced by the earth’s complex underlying subsurface, which in turn indirectly affects atmospheric motion and climate change. Human activities are increasingly exerting an influence on desert ecosystems, and artificial green land with clear functional orientation has been established in many desert areas. Consequently, the previously dominant, shifting, sand-covered, underlying surface in these desert regions is gradually transforming. This transformation has significant implications for the characteristics of land–atmosphere interactions, causing them to deviate from their original state. At present, existing studies still have not presented a systematic understanding of this change and have ignored the impact of human activities on land–atmosphere interactions in artificial green land. To address these research gaps, this study specifically targets artificial green land in the Tazhong region of Taklamakan Desert. We carried out observation experiments on land–atmosphere interactions in three different functional units from outside to inside: natural shifting sands, the shelter forest, and the living area. We also analyzed the differences and attribution of land–atmosphere interactions characteristics of different functional units. Compared with the natural shifting sands, the daily average maximum values of wind speed in the shelter forest decreased by 78%, and the daily average maximum air temperature and soil (0 cm) temperature decreased by 2.6 °C and 7 °C, respectively. Additionally, the soil moisture level was significantly increased throughout the green land due to the shelter forest. The surface albedo experienced a decrease, with an annual average of 0.21. Furthermore, the aerodynamic roughness and bulk transport coefficient increased by two orders of magnitude. The daily average maximum values of sensible heat flux and soil heat flux (G05) decreased by 18.7% and 75%, respectively, and the daily average maximum value of latent heat flux increased by 70.3%. This effectively improved the microclimate environment of the green land. The living area was greatly reduced by the shelter forest coverage and influenced by the buildings. Consequently, the environmental improvement was not as large as it was inside the shelter forest. However, it still provided a good shelter for production and living in the desert area. Throughout the year, a total of 4.60 × 105 t water was consumed through evapotranspiration in the artificial green land. The findings of this study have the potential to enhance our comprehension of land–atmosphere interactions in desert regions, thereby offering valuable insights for the establishment and effective management of artificial desert green lands.

Monitoring intertidal golden tides dominated by Ectocarpus siliculosus using Sentinel-2 imagery

Golden seaweed tides are a global environmental and social problem, that have been occurring along Ireland's eastern coastline since the 1990s. This study focused on analysing the spatiotemporal dynamics of golden seaweed (Ectocarpus siliculosus) coverage at Dollymount Strand in Dublin Bay (Ireland), between 2016 and 2022, and its relationship with meteorological conditions. Hyperspectral measurements in the field and Sentinel-2 imagery were utilized to monitor macroalgal blooms with a spatiotemporal resolution of 10 m and minimum costs. The normalized difference vegetation index (NDVI) values filtered between 0.1 and 1 were used to identify the coverage of golden seaweeds. The results showed that the golden seaweed coverage extended over 99% of the study area (54 ha) in June 2020 with an average NDVI of 0.25. A seasonal pattern of golden seaweed abundance was observed and modelled from May to October using a Generalized Additive Model. Approximately 28% of the coverage was correlated with daily average global radiance, and 38% of the mean NDVI was associated with daily average maximum air temperatures and wind direction by means of the Generalized Linear Models. A greater biomass of Ectocarpus spp. was accumulated on the beach when the wind direction was from the north-east and south-east. The results also suggested that freshwater nutrient inputs in winter from nearby estuaries may be contributing to golden tides on the shoreline. These findings are useful in developing strategies aimed at controlling and managing golden tides globally, as well as using them as bioindicators of ecological status in coastal environments.

The unprecedented 2022 extreme summer heatwaves increased harmful cyanobacteria blooms

Heatwaves are increasing and expected to intensify in coming decades with global warming. However, direct evidence and knowledge of the mechanisms of the effects of heatwaves on harmful cyanobacteria blooms are limited and unclear. In 2022, we measured chlorophyll-a (Chla) at 20-s intervals based on a novel ground-based proximal sensing system (GBPSs) in the shallow eutrophic Lake Taihu and combined in situ Chla measurements with meteorological data to explore the impacts of heatwaves on cyanobacterial blooms and the potential relevant mechanisms. We found that three unprecedented summer heatwaves (July 4–15, July 22–August 16, and August 18–23) lasting a total of 44 days were observed with average maximum air temperatures (MATs) of 38.1 ± 1.9 °C, 38.7 ± 1.9 °C, and 40.2 ± 2.1 °C, respectively, and that these heatwaves were characterized by high air temperature, strong PAR, low wind speed and rainfall. The daily Chla significantly increased with increasing MAT and photosynthetically active radiation (PAR) and decreasing wind speed, revealing a clear promotion effect on harmful cyanobacteria blooms from the heatwaves. Moreover, the combined effects of high temperature, strong PAR and low wind, enhanced the stability of the water column, the light availability and the phosphorus release from the sediment which ultimately boosted cyanobacteria blooms. The projected increase in heatwave occurrence under future climate change underscores the urgency of reducing nutrient input to eutrophic lakes to combat cyanobacteria growth and of improving early warning systems to ensure secure water management.

The Role of Crop Management Practices and Adaptation Options to Minimize the Impact of Climate Change on Maize (Zea mays L.) Production for Ethiopia

Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield.

Mapping and Analysis of Monthly Average Maximum and Minimum Air Temperature Distribution of Muğla Province in Geographic Information Systems (GIS) Environment with IDW Method

Muğla province, which is located in Southwest Anatolia and has a surface area of 13,338 km2, generally has a mountainous and rough terrain. In this study, it is aimed to map and analyze the distribution of monthly and annual maximum and minimum average temperature values of Muğla province by using the Inverse Distance Weighted (IDW) interpolation method. For this purpose, meteorological data of 13 stations of the General Directorate of Meteorology in Muğla and the stations of Gölhisar, Bozdoğan, Kaş, Elmalı, which are close to the borders of the province, were used. The average values of the annual and monthly maximum and minimum temperatures of these stations were transferred to the ArcGIS environment with the help of Geographic Information Systems (GIS) and their distribution was mapped with the IDW method. According to these maps, the highest average annual temperature in Muğla is in Milas and its surroundings, while the lowest is the provincial borders in the northeast of Muğla center and Seydikemer district. These two places are followed by Datça district, which is located on a long peninsula. The places with the highest annual minimum average temperatures are around Datça and Marmaris districts, while the lowest is the Burdur provincial border in the northeast of Kavaklıdere and Seydikemer. The maximum average temperature in the province is July (between 41.6-41.1°C in Milas and Köyceğiz), and the lowest is January (between 14-17°C in Muğla center and Kavaklıdere). The minimum average temperatures in the province are highest in June (between 37.7-34.6°C in Marmaris), and the lowest in January (between -7.9 and -6.6°C in Kavaklıdere). Elevation conditions, maritime influence and aspect conditions, and the intrusion of thermal pressure areas, especially in summer, have been effective in the distribution of maximum and minimum average temperature values in the province. These values may also vary depending on time during the year

Multi-Temporal Assessment of Remotely Sensed Autumn Grass Senescence across Climatic and Topographic Gradients

Climate and topography are influential variables in the autumn senescence of grassland ecosystems. For instance, extreme weather can lead to earlier or later senescence than normal, while higher altitudes often favor early grass senescence. However, to date, there is no comprehensive understanding of key remote-sensing-derived environmental variables that influence the occurrence of autumn grassland senescence, particularly in tropical and subtropical regions. Meanwhile, knowledge of the relationship between autumn grass senescence and environmental variables is required to aid the formulation of optimal rangeland management practices. Therefore, this study aimed to examine the spatial autocorrelations between remotely sensed autumn grass senescence vis-a-vis climatic and topographic variables in the subtropical grasslands. Sentinel 2′s Normalized Difference NIR/Rededge Normalized Difference Red-Edge (NDRE) and the Chlorophyll Red-Edge (Chlred-edge) indices were used as best proxies to explain the occurrence of autumn grassland senescence, while monthly (i.e., March to June) estimates of the remotely sensed autumn grass senescence were examined against their corresponding climatic and topographic factors using the Partial Least Square Regression (PLSR), the Multiple Linear Regression (MLR), the Classification and Regression Trees (CART), and the Random Forest Regression (RFR) models. The RFR model displayed a superior performance on both proxies (i.e., RMSEs of 0.017, 0.012, 0.056, and 0.013, as well as R2s of 0.69, 0.71, 0.56, and 0.71 for the NDRE, with RMSEs and R2s 0.023, 0.018, 0.014 and 0.056, as well as 0.59, 0.60, 0.69, and 0.72 for the Chlred-edge in March, April, May, and June, respectively). Next, the mean monthly values of the remotely sensed autumn grass senescence were separately tested for significance against the average monthly climatic (i.e., minimum (Tmin) and maximum (Tmax) air temperatures, rainfall, soil moisture, and solar radiation) and topographic (i.e., slope, aspect, and elevation) factors to define the environmental drivers of autumn grassland senescence. Overall, the results indicated that Tmax (p = 0.000 and 0.005 for the NDRE and the Chlred-edge, respectively), Tmin (p = 0.021 and 0.041 for the NDRE and the Chlred-edge, respectively), and the soil moisture (p = 0.031 and 0.040 for the NDRE and the Chlred-edge, respectively) were the most influential autumn grass senescence drivers. Overall, these results have shown the role of remote sensing techniques in assessing autumn grassland senescence along climatic and topographic gradients as well as in determining key environmental drivers of this senescence in the study area

Wildland Fires in the Subtropical Hill Forests of Southeastern Bangladesh

The first ever comprehensive study on wildland fires in Bangladesh is carried out to develop a fire prevention and prediction method. The major causes of huge wildland fires (88%) in the subtropical Chittagong Hill forest (43% of total) of southern Bangladesh are reported as shifting cultivation, grazing and unauthorized settlement. We used satellite hotspot (HS) data from 2003 to 2021 (a total of 54,669 HSs) to clarify the spatio-temporal structure of wildland fires. Fire weather conditions were analyzed using various weather data and synoptic-scale weather maps at different air levels. Fires concentrated from March through April or a transitional period from the dry season, caused by the Asian winter monsoon, to the wet season, due to the Asian summer monsoon. Fire occurrence depended on dry conditions and pre-monsoon showers and their timing. The difference in 925 hPa heights of high and low pressure systems may be attributed to the different types of the Asian winter and summer monsoons. The average maximum air temperature and incident shortwave solar energy in April were the highest and strongest, contributing fire-prone weather conditions. Based on the analysis, a fire prevention and prediction method will be developed, and this report may also facilitate the establishment of future CO2 reduction measures for Bangladesh.

The impact of changes in climatic factors of the Aktumsuk region on the formations of Medicageta sativa

The article presents a brief analysis of 20-year changes in climatic parameters of the Aktumsuk region of the Eastern Cliff of Ustyurt and their impact on the species composition of the Medicageta sativa formation. From the obtained analysis of the daily indicators of 2 sources (NASA POWER and the Aktumsuk meteorological station), long-term trends of changes in average, average maximum and average minimum air temperatures, as well as annual precipitation have been established. Data from 2000-2020 were taken for analysis. The warm and cold seasons of the year have been established, which affected the amplitude of the parameters. Basically, the decrease in minimum air temperature occurs in the cold half-year (November-December, January-March), which explains the increase in precipitation during these periods. The most significant warming in the territory of the Aktumsuk region was noted in warm seasons (April, June, July and August). The obtained data comparing the average annual air temperatures for the 2 above-mentioned sources of the 20-year period indicates that the climate of the Aktumsuk region is sharply continental, here we can observe sharp changes in the temperature regime in the direction of warming, which could lead to the transformation of the Medicageta sativa formation. The appearance of xerophytes in this type of vegetation in recent years provides information about the xerophytization of flora due to an increase in drought in the Eastern Cliff. Climate aridization, reduction in the number of mesophytes in the species composition, as well as their projective coverage, a sharp increase in xerophytes and halophytes in the species composition, communities may indicate significant changes in communities of this type of vegetation due to climate change.

Water Requirements in Bean Crops Using Cropwat 8.0 in Oenoni Village Kupang District, Indonesia

A study has been carried out to plan the provision of water in the cultivation of green beans based on Cropwat analysis. Climatology data was obtained from BMKG Kupang Class II Climatology Station which included rainfall, minimum temperature, maximum temperature, relative humidity, wind speed, and duration of sun exposure. The data needed is the result of observations for five (5) years, with the period 2014 to 2018 for the Regency of Kupang. Data processing in this study uses 4 main panels in the Cropwat 8.0 program, namely Climate / ETo (climate data) is input data in the form of monthly average maximum air temperature and minimum air temperature, the average month-long sun exposure, and monthly average humidity. The climate data input is used to estimate ETo and the magnitude of the value of solar radiation. Rainfall data to determine effective rainfall with the input of average monthly rainfall over the period 2014- 2018. Plant data are plant characteristics and the time of the start of the plant season. Data on plant characteristics referred to are Kc (Plant growth coefficient), length of day for each phase, root depth, plant height, and soil type. The results showed that crop evapotranspiration (ETc) using Cropwat 8.0 software was 270.8. with the highest actual crop evapotranspiration achieved in October of the third period of 4.44 mm and the lowest in October of the second decade of 2.6 mm. The highest irrigation water demand is in the second decade of September at 38.7 mm and the lowest in October the third period at 1.6 mm. These results are quite in accordance with the conditions in the field to plan the start of the growing season. However, daily irrigation operations need to be carried out based on observing actual conditions in the field to maintain groundwater levels in the appropriate range for plant growth.

Hot and dry conditions predict shorter nestling telomeres in an endangered songbird: Implications for population persistence

Climate warming is increasingly exposing wildlife to sublethal high temperatures, which may lead to chronic impacts and reduced fitness. Telomere length (TL) may link heat exposure to fitness, particularly at early-life stages, because developing organisms are especially vulnerable to adverse conditions, adversity can shorten telomeres, and TL predicts fitness. Here, we quantify how climatic and environmental conditions during early life are associated with TL in nestlings of wild purple-crowned fairy-wrens (Malurus coronatus), endangered songbirds of the monsoonal tropics. We found that higher average maximum air temperature (range 31 to 45 °C) during the nestling period was associated with shorter early-life TL. This effect was mitigated by water availability (i.e., during the wet season, with rainfall), but independent of other pertinent environmental conditions, implicating a direct effect of heat exposure. Models incorporating existing information that shorter early-life TL predicts shorter lifespan and reduced fitness showed that shorter TL under projected warming scenarios could lead to population decline across plausible future water availability scenarios. However, if TL is assumed to be an adaptive trait, population viability could be maintained through evolution. These results are concerning because the capacity to change breeding phenology to coincide with increased water availability appears limited, and the evolutionary potential of TL is unknown. Thus, sublethal climate warming effects early in life may have repercussions beyond individual fitness, extending to population persistence. Incorporating the delayed reproductive costs associated with sublethal heat exposure early in life is necessary for understanding future population dynamics with climate change.

Time after time: detecting annual patterns in stream bacterial biofilm communities.

SummaryTo quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36‐months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non‐cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.

УСЛОВИЯ ВЕГЕТАЦИИ И ПРОДОЛЖИТЕЛЬНОСТЬ МЕЖФАЗНЫХ ПЕРИОДОВ ВЕГЕТАЦИИ ЯРОВОЙ МЯГКОЙ ПШЕНИЦЫ В ЗАСУШЛИВЫХ УСЛОВИЯХ

The research was carried out on southern carbonate black soil in the central zone of Orenburg region. The aim of the study was to study the relation between the vegetation season duration and the interphase periods of spring wheat and the air temperature regime and productivity. Rise of climate aridity as well as rise of temperature stress on the plant due to lack of precipitation leads to changes in crop phenology towards reduction of the growing season duration. The dependence of wheat yield on average daily and maximum air temperature for interphase periods was studied, their adaptive parameters were specified: for average temperature for the period from germination to earing - 10.5 ° С and 19.7 ° С for the period from earing to full ripeness. The actual data on duration of the interphase vegetation periods of spring wheat in different years on favourableness are given. Reliable correlations between the yield and the duration of the interfacial periods of the growing season are established. The variety specificity in the studied variants of the experiment is shown. As a result of the study, it was found that the relative duration of the vegetation season for spring wheat in the Orenburg Cis-Urals zone is 91 days with a yield level of 27.4 dt/ ha. A sharp (by 15 days) reduction of the growing season duration leads to a yield decrease to 2.8 dt/ ha.

Heatwaves in Southern Armenia in the context of climate change

AbstractThe quantitative and qualitative evaluations of long‐term trends and spatial distribution of heatwaves (HW) are carried out in Southern Armenia (Syunik province). For this purpose, the daily average maximum air temperature data (𝑇max) from four meteorological stations (Sisian, Goris, Kapan, and Meghri) are studied for the 1936–2019 period. The HW catchment algorithm and a new concept of HW (which is called potential HWs [PHWs]) are developed. The presence of trends and their significance for different HW measures are assessed utilizing linear and exponential trends, Sen's slope, and Mann–Kendall (MK) trend test. The results show that HWs become increasingly frequent and intense. Moreover, they have extended duration, as they emerge earlier and fade later in Syunik province. Significant variation trends of HW are observed (e.g., the frequency of HWs increased up to five times), especially after the 1990s in southern and central parts of the province. Contrary to this, relatively weaker or no trends occurred in the northern parts. Our methodological approach successfully determines the HW measures' trends in the province based on the relief features and altitude differences. The outcomes highlight their notable impact on the progression of HW. Overall, this study provides significant insights into understanding HW's variations and their transformation in the mountainous areas.

Surface air temperature trends in Kathmandu Valley for 2011-2017

In this study, we reviewed the maximum and minimum temperature trends of the Kathmandu valley over the period of 2011-2017. In addition, the average monthly temperature trends were studied annually for the same period, with the data made available from the Department of Hydrology and Meteorology (DHM) of Nepal. The annual temperature trended in the same direction, with winter temperatures being lower and summer temperatures being higher. The annual average minimum and maximum air temperature trends were found to be slightly rising at 0.097˚C/year and 0.04˚C/year, respectively. The mean air temperature in Kathmandu valley is increasing at a rate of 0.06 degrees Celsius per year, with 2016 being the warmest year and 2012 being the least warm, with annual mean temperatures of 19.82˚C and 19.32˚C, respectively. The temperature difference is much smaller in the summer (less than ~12˚C) than in the winter. 
 BIBECHANA 18 (2) (2021) 95-104

Spatiotemporal variability of air temperatures in Central Serbia from 1949 to 2018

The paper presents trends for three categories of variables: average annual, average maximum and average minimum air temperatures. Data was provided by the meteorological yearbooks of the Republic Hydrometeorological Service of Serbia. The main goal of this paper is to detect possible temperature trends in Central Serbia. The trend equation, trend magnitude, and Mann-Kendall non-parametric test were used in the analysis of climate parameters. The used statistical methods were supplemented by GIS numerical analysis, which aimed to analyze the spatial distribution of isotherms from 1949 to 2018. The obtained results indicate that out of the 72 analyzed time series, an increase in air temperature is dominant in 61 time series, while 11 time series show no changes. The highest increase was recorded in the average maximum time series (4.2 °C), followed by an increase of 3.5°C in average maximum air temperatures. The highest increase in the average annual time-series was 3.0 °C. The lowest increases in air temperature were recorded in the average minimum time series (0.1 and 0.2 °C). In two average minimum time series a decrease in average air temperatures was identified (-0.6 and -0.4 °C. The application of GIS tools indicates the existence of interregional differences in the arrangement of isotherms, leaded by the orography of the terrain. In the spatial distribution of the analyzed variables, "poles of heat" and "poles of cold" stand out, and the influence of the urban heat island is evident (especially in the case of the urban agglomeration of Belgrade). The manifested spatial patterns of air temperature need to be further examined and the correlation with possible causes need to be determined. For these reasons, the paper provides a solid basis for studying the climate of this area in the future, as it provides insight into climate dynamics over the past decades.

Аномалії температури повітря на Чернігівщині

In the context of modern climate change, the air temperature rises throughout Ukraine, while it is in the north of the country that significant positive temperature anomalies are released, the repetitiveness and duration of periods of summer heat and droughts increase. The temperature regime on the territory of Chernihiv region is investigated according to the data of seven weather stations: Nizhyn, Oster, Pokoshichi, Pryluky, Semenivka, Snovsk (Schors), Chernihiv. The purpose of the study is to determine the distribution of temperature anomalies for the period 1991–2020. The objectives of the study are to establish changes in the indicators of absolute highs and minimum air temperature and anomalies of the average monthly and average annual temperature. During the study period, the absolute maximum air temperature was exceeded at 5 stations in 2010 with an extreme in Semenivka of 41.4°; for the entire period, the absolute minimum was higher than the climatic norm at the stations by 3.4...7.2°С. The average absolute maximum and minimum air temperature increased at all stations, on average in the region by 2.6 and 1.9°C, respectively. The study of changes in air temperature for decades indicates a gradual increase in temperature every next 10 years. This is especially indicated in the distribution of average absolute minimums; average absolute highs emit the warmest decade of 2001–2010, due to abnormally hot August 2010. Comparison of the thirty years (1961–1990 and 1991–2020) clearly demonstrates the tendency for the temperature to rise. Average monthly air temperature at all stations of the region highlights positive anomalies, the largest – in January–February and March (2–3°C), the smallest – in May and autumn. The average absolute maximum temperature in all months was higher than normal, and only in Chernihiv in September corresponded to the climatological norm, the highest anomalies of 2.4–3.1°C were noted in October. The average absolute minimum allocated the largest positive anomaly in January and March (⁓3°C), only in April in the region the average minimum temperature was below the norm by 0.1°C, and in October it corresponded to the norm. MS Chernigov and Semenovka show slightly higher above zero temperature anomalies.