Search Results (11)

The assessment of the hydrological functions of landscapes and the landscape–hydrological background is an important instrument for minimizing damage from rivers and preventing water conflicts under conditions of data scarcity for hydrological modeling. To assess the climate–hydrological background of the East Kazakhstan region, the Selyaninov Hydro-thermal Coefficient and the Vysotsky–Ivanov Moisture Coefficient were used. The East Kazakhstan region is a typical continental arid and semi-arid region. The presence of mountain ranges, such as the Altai, makes the climate and environment in the region highly varied. A dataset from 30 weather stations for the period 1961–2023 was used for calculations. Three interpolation methods and landscape extrapolation were used to construct maps of the coefficients. Over the observation period, the values of the moisture indices at the weather stations in the region fluctuated within a wide range. Both coefficients are in the range from extra arid to extra humid climates. Full article

In this study, the emphasis is on assessing how satellite-derived vegetation indices respond to drought stress characterized by meteorological observations. This study aimed to understand the dynamics of grassland vegetation and assess the impact of drought in the Wielkopolskie (PL41) and Podlaskie (PL84) regions of Poland. Spatial and temporal characteristics of grassland dynamics regarding drought occurrences from 2020 to 2023 were examined. Pearson correlation coefficients with standard errors were used to analyze vegetation indices, including NDVI, NDII, NDWI, and NDDI, in response to drought, characterized by the meteorological parameter the Hydrothermal Coefficient of Selyaninov (HTC), along with ground-based soil moisture measurements (SM). Among the vegetation indices studied, NDDI showed the strongest correlations with HTC at r = −0.75, R² = 0.56, RMSE = 1.58, and SM at r = −0.82, R² = 0.67, and RMSE = 16.33. The results indicated drought severity in 2023 within grassland fields in Wielkopolskie. Spatial–temporal analysis of NDDI revealed that approximately 50% of fields were at risk of drought during the initial decades of the growing season in 2023. Drought conditions intensified, notably in western Poland, while grasslands in northeastern Poland showed resilience to drought. These findings provide valuable insights for individual farmers through web and mobile applications, assisting in the development of strategies to mitigate the adverse effects of drought on grasslands and thereby reduce associated losses. Full article

The process of climate warming significantly affects agroclimatic resources and agricultural production. We study the agroclimatic resources and their variability on the territory of the Republic of Bashkortostan (Russia). The Bashkortostan has a high agricultural potential and holds a leading position in the country in the production of grain crops, potatoes, milk, and honey. Currently, no detailed studies have been conducted for this area to assess the effects of global climate change on agro-climatic resources. World experience shows such research becomes strategically important for regions with powerful agricultural production. We used the sums of average daily air temperatures above 0 and 10 °C, the G.T. Selyaninov hydrothermal coefficient, and the Ped aridity (humidification) index as agroclimatic indicators. We used data of long-term meteorological observations of 30 meteorological stations for the period of 1961–2020. We revealed the long-term dynamics of the agroclimatic indicators and the spatial and temporal regularities in their distribution on the territory of Bashkortostan. There is a steady increase in the sums of average daily air temperatures above 0 and 10 °C. Against this background, aridity increases, which is especially manifested in the southern parts of the Republic of Bashkortostan. We assessed the impact of agroclimatic indicators on the main types of agricultural crops in the republic. We revealed that the greatest positive impact on the yield of oilseeds, cereals, and industrial crops is made by precipitation at the beginning (r = 0.50, r = 0.44, and r = 0.52, respectively) and in the middle of the growing season (r = 0.55, r = 0.76, and r = 0.51, respectively). Temperature and precipitation during the growing season have a complex effect on cereals. This is proven by correlations with HCS and the Ped index (r = 0.45 and r = −0.56, respectively). Aridity at the beginning of the growing season affects the yield of oilseeds and potatoes. This is confirmed by correlations with the Ped index (r = −0.49 and r = −0.52, respectively). In general, the aridity of the growing season has a significant impact on the yield of cereals (r = −0.57). Negative relationships have been found between the air temperature growing season and the yield of potatoes (r = −0.50) and cereals (r = −0.53). The results of the study were compared with data from the Copernicus Climate Change Service database. We identified climate trends under RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. These scenarios should be taken into account when developing plans for the adaptation of agriculture in the Republic of Bashkortostan to changes in the regional climate. Maximum decrease in precipitation is established for the RCP 6.0 scenario. This can have an extremely negative impact on crop yields. This problem is especially relevant for the southern part of the Republic of Bashkortostan. The information presented in the study will allow for a more effective adaptation of the agricultural sector to current and future climate changes. Full article

Field peas are one of the most common crops and are grown in various climatic zones. However, the productivity of this crop can be largely limited by climatic factors. This study investigated the influence of climatic factors on pea grain yield in the semi-arid conditions of the Rostov region of Russia in 2008–2020. To quantify climatic factors, agro-climatic variables were used, such as total temperatures below the minimum temperature, the number of days with temperatures below the minimum temperature, total temperatures above the critical temperature, the number of days with temperatures above the critical temperature, and the Selyaninov hydrothermal coefficient. Agro-climatic variables were calculated using daily climatic variables, such as maximum and minimum temperatures, relative air humidity, and precipitation during pea growing season (April–June). The yield of the pea varied from 90 to 250 kg/ha. In general, the productivity of peas is negatively affected by high temperatures and low humidification level. The yield is negatively correlated with accumulative temperatures above the critical temperature and the number of days with temperatures above the critical temperature and positively correlated with the Selyaninov hydrothermal coefficient and the precipitation in all analyzed areas. The influence of the accumulative temperatures above the critical temperature is the most significant. It explains between 6.6% and 78.9% of the interannual variability of the pea yield. The increase in accumulative temperatures above the critical threshold by every 1 °C will contribute to a decrease in pea grain yield by an average of 0.150 kg/ha. The maximum temperatures in May and June (the period of flowering–grain filling) have the most negative impact on the yield. A 1 °C increase in the average maximum temperature during this period will contribute to a decrease in pea yield by an average of 19.175 kg/ha. The influence of total precipitation during the growing season explains between 12.3% and 50.0% of the variability. The 1 mm decrease in the total precipitation for the growing season will lead to a decrease in pea yields by an average of 0.736 kg/ha. The results of this study can be applied to regional yield forecasting, as well as predicting the impact of climate variability on the grain yield of pea crops in arid areas. Full article

The pattern of annual radial growth is influenced by various factors: the local growth conditions, the age structure, and the ecotypes or provenances of trees. A more in-depth approach to the study of specific growth patterns of tree forms is needed to predict the further genesis of forests. This research was carried out on healthy English oak trees of early (EF) and late (LF) phenological forms in Shipov Forest, Voronezh Region. The dendroclimatic analysis was performed on permanent sample plots in wet, dry, and very dry oak stands grown on different soil types. The effect of precipitation on annual ring width was assessed using a one-way ANOVA. The LF showed higher radial growth rates on wet sites than the EF did on dry ones. Their annual radial growth was less stable and more variable compared with the LF. For both phenoforms, the most important radial growth factors are the composite indicators reflecting the ratio of temperature and moisture (Selyaninov’s hydrothermal coefficient and Lang’s rain factor). Generally, the radial growth minima coincided in time on dry and wet sites, and the periods of maximum growth were associated with high-water years. Full article

An analysis of spatiotemporal variability of air temperature and precipitation in the Volga Federal District (European Russia) between 1966 and 2021 was carried out. Based on data from 20 meteorological stations, relatively evenly located on the territory under consideration, the spatial distribution of average monthly and average annual air temperatures and monthly and annual precipitation was assessed; some indicators of the temporal variability of these variables in the period under consideration were calculated and analyzed. It was revealed that throughout the Volga Federal District, there was a tendency of climate warming in all months, and a slight increase in annual precipitation, except for the southeast of the district, where the precipitation trend was negative. It is noted that in the period 1955–1998, the number of negative air temperature anomalies was approximately equal to the number of positive ones; however, in the later period 1999–2021, the number of positive anomalies significantly exceeded the number of negative ones. Based on reanalysis data, climatic maps of vaporization and runoff in the Volga Federal District during 1966–2021 were created. The dependence of air temperature fluctuations on the nature of atmospheric circulation was revealed using the NAO, AO, and SCAND indices. On the example of the central part of the district (Republic of Tatarstan), some increase in summer aridity of the climate was revealed by using Budyko’s dryness index, Selyaninov’s hydrothermal coefficient, and Sapozhnikov’s humidification coefficient. The indicators of runoff and evaporation were also calculated using the methods of Schreiber and Ivanov. Against the background of the positive trend in vaporization rates, favorable conditions for a decrease in runoff were noted. Full article

This paper shows the relevance of the problem of fire hazard in the forests of the Volga Federal District (VFD) of European Russia. The Nesterov index and the Selyaninov hydrothermal coefficient (HTC) are considered as indicators of fire hazard. The changes in climatic conditions in the VFD during 1955–2018 are shown; a trend towards warming and an increase in aridity in the study region were revealed. The repeatability of various fire hazard classes from May to September was calculated using the Nesterov method. It is shown that in July, the most dangerous situation was in the south of the VFD, where the repeatability of class IV fire hazard reached 27%. Using the HTC index, the degree of aridity of the district in the summer period was estimated. The frequency of the most arid conditions (HTC < 0.5) increases from the north to the south of the district, from 6% (Kirov Region) to 47% (Orenburg Region). Using the TT index, the potential thunderstorm danger in the VFD was assessed. With the help of the constructed maps, the hotspots of the most probable occurrence of thunderstorms were detected. The use of Rosstat data on the number of forest fires from 1992 to 2020 made it possible to consider the spatiotemporal distribution of forest fires in 14 administrative regions of the VFD. The distribution of the number of fires by the regions is shown depending on their forest cover and season. The peak of the number of fires was revealed in 2010, when the entire territory of the study region was covered by a severe drought, as a result of which the area of forests covered by fire increased many times over. In recent years (since 2017), there has been an increase in the area of burned forest due to the active phase of climate warming. Full article

We studied moisture content/aridity conditions in Buryatia (Russia) in summertime for the period of 1979–2019. Selyaninov’s hydrothermal coefficient (HTC) was used as the aridity criterion. The HTC was calculated on the basis of precipitation and 2 m temperature data from two datasets: meteorological stations and the ECMWF ERA5 project. A comparison of the HTC calculations for these two datasets was performed. The ERA5 data showed underestimated HTC values compared to the observations. The inconsistencies found are mainly related to the underestimation of precipitation in the ERA5 project compared to the observational data. The air temperature obtained from the two datasets agrees well for most stations, both in value and in long-term dynamics. It has been shown that at the stations in central and southern Buryatia, the increase in aridity (and decreased HTC) in 1979–2019 is mainly due to the increase in air temperature. Full article

Genotype and weather conditions play crucial roles in determining the volume and stability of a soybean yield. The aim of this study was to identify the key meteorological factors affecting the harvest date (model M_HARV) and yield of the soybean variety Augusta (model M_YIELD) using a neural network sensitivity analysis. The dates of the start of flowering and maturity, the yield data, the average daily temperatures and precipitation were collected, and the Selyaninov hydrothermal coefficients were calculated during a fifteen-year study (2005–2020 growing seasons). During the experiment, highly variable weather conditions occurred, strongly modifying the course of phenological phases in soybean and the achieved seed yield of Augusta cultivar. The harvesting of mature soybean seeds took place between 131 and 156 days after sowing, while the harvested yield ranged from 0.6 t·ha⁻¹ to 2.6 t·ha⁻¹. The sensitivity analysis of the MLP neural network made it possible to identify the factors which had the greatest impact on the tested dependent variables among all the analyzed factors. It was revealed that the variables assigned ranks 1 and 2 in the sensitivity analysis of the neural network forming the M_HARV model were total rainfall in the first decade of June and the first decade of August. The variables with the highest impact on the Augusta soybean seed yield (model M_YIELD) were the mean daily air temperature in the second decade of May and the Seljaninov coefficient values calculated for the sowing–flowering date period. Full article

We used 5 years of data from multi-environmental trials conducted in Poland to assess average winter wheat yield based on selected environmental factors to recommend cultivars depending on their performance in environments of different productivity. Average expected yields in particular environments were calculated using a model based on analysis of covariance (ANCOVA), which describes the relationship between winter wheat yield and environmental factors of soil suitability and pH, drought length and Selyaninov’s Hydrothermal Coefficient (HTC) in 10-day periods. The cultivar performance was evaluated using linear regression. The cultivar yield estimated by the mixed model was considered the dependent variable, whereas the environmental mean yields, estimated by ANCOVA, were considered independent variables. The cultivars were ranked according to the estimated yield in environments of determined average wheat productivity. Higher yielding cultivars were divided into two groups: widely and narrowly adapted cultivars, which were then recommended. The novelty of this study stems from the consideration of the environmental productivity in the recommendation process, the indication of widely adapted cultivars to be grown in a broad range of productivity sites and the selection of cultivars with narrow adaptation, which may outperform cultivars of wide adaptation in homogeneous fields. This study confirmed the importance of soil suitability and HTC for winter wheat yield. Direct application of our results is possible in Poland and in other countries with similar conditions. Full article

Climate change is affecting human health worldwide. In particular, changes to local and global climate parameters influence vector and water-borne diseases like malaria, dengue fever, and tick-borne encephalitis. The Republic of Sakha in northern Russia is no exception. Long-term trends of increasing annual temperatures and thawing permafrost have corresponded with the northward range expansion of tick-species in the Republic. Indigenous communities living in these remote areas may be severely affected by human and livestock diseases introduced by disease vectors like ticks. To better understand the risk of vector-borne diseases in Sakha, we aimed to describe the increase and spatial spread of tick-bite cases in the Republic. Between 2000 and 2018, the frequency of tick bite cases increased 40-fold. At the start of the period, only isolated cases were reported in southern districts, but by 2018, tick bites had been reported in 21 districts in the Republic. This trend coincides with a noticeable increase in the average annual temperature in the region since the 2000s by an average of 1 °C. Maps illustrate the northward spread of tick-bite cases. A negative binomial regression model was used to correlate the increase in cases with a number of climate parameters. Tick bite case frequency per district was significantly explained by average annual temperature, average temperature in the coldest month of the year, the observation year, as well as Selyaninov’s hydrothermal coefficient. These findings contribute to the growing literature that describe the relationship between tick abundance and spread in Northern Latitudes and changes in temperatures and moisture. Future studies might use these and similar results to map and identify areas at risk of infestation by ticks, as climates continue to change in Sakha. Full article