Bioclimatic Conditions of the Kapshagay Reservoir Under Climate Change Conditions

Abstract

The assessment of bioclimatic conditions and meteorological parameters—such as air temperature and precipitation—helps identify optimal periods for various activities, considering regional and individual factors. Climatic and bioclimatic conditions are major factors influencing human health and daily activity. These factors are instrumental in determining the quality of life, the state of health, and the overall well-being of individuals. The analysis of meteorological parameters, including air temperature, humidity, and precipitation, facilitates the evaluation of climatic comfort across diverse regions. Bioclimatic studies are instrumental in identifying zones with favorable or unfavorable living conditions, a matter of particular importance in the planning of urban development and the formulation of landscaping and gardening measures. The study aims to assess the bioclimatic conditions prevailing in the Almaty region. It focuses on the Kapshagay Reservoir during the period 1990–2020, applying commonly used biometeorological indices. The software product ClimPACT2, which was developed for the analysis of extreme phenomena and weather changes, was utilized for the calculations. The primary meteorological indicators, specifically temperature and precipitation, were selected for the calculation of climatic indices. The observed spatial and temporal trends of climate change in the study area were analyzed. The findings indicated a substantial increase in the frequency of warm days and nights, concurrently accompanied by a decline in the occurrence of cold days and nights. The identified trends indicate a marked warming of the climate, which may have serious consequences for ecosystems and human activities. The analysis also revealed a significant increase in total annual precipitation in coastal zones.

1. Introduction

Recently, many researchers have paid special attention to the study of modern weather and climate changes and their impact on human life and health.

The interaction between humans and climatic conditions is a highly active area of research in bioclimatology. It has given rise to numerous works in various disciplines, including physical geography, ecology, climatology, biometeorology, environmental climatology, and medical geography [1].

Biometeorology is an interdisciplinary science that considers the interactions between atmospheric processes and living organisms (plants, animals and humans) [2].

Bioclimatology as an interdisciplinary science has undergone significant development in recent decades due to the growing impact of climate change on human health, ecosystems, and economic activity. Contemporary research focuses not only on general climate trends, but also on local bioclimatic characteristics that determine the level of comfort, morbidity, and adaptive capacity of the population [3,4,5].

A key area of bioclimatic research is the analysis of extreme climate indices and their dynamics, which reflects the degree of climate risk for individual territories. To this end, specialized methodological approaches are widely used, such as the ClimPACT2 v1.2 software package, which is designed to calculate extreme temperature and precipitation indices [6], as well as BIOCLIM models used in paleoclimatic and regional reconstructions [7]. In addition, modern research actively uses the results of global climate models (e.g., CMIP6) in combination with downscaling methods, which allows for analysis at a small scale relevant to specific regions.

For Kazakhstan and Central Asia as a whole, the issue of local bioclimatic assessment remains relevant. Despite the existence of studies on climate dynamics at the macro-regional level, studies devoted to the local bioclimatic conditions of individual territories are very limited [8,9,10,11,12]. This is especially true for the Kapshagay Reservoir and the surrounding areas of the Almaty Region, where anthropogenic and natural factors together form specific microclimatic regimes.

Thus, the need for systematic analysis of bioclimatic conditions at the local level is driven both by scientific interest in regional climate dynamics and by practical tasks, ranging from planning recreational areas to assessing risks to public health.

In most cases, the climatic-ecological state of the environment exerts a significant influence on various aspects of human activity. Consequently, the mounting demand for quantitative and qualitative assessments of environmental components signifies the importance of examining the spatial and temporal distribution of bioclimatic manifestations within a specified territory.

The bioclimate of the territory constitutes a significant natural resource, the quality of which is contingent upon the comfort of human feelings and well-being, working capacity, labor productivity, and the health of the organism as a whole. By determining the phenomenon of changes in meteorological conditions on adaptation mechanisms, it is possible to solve the problem of preserving human health in conditions of habitat deterioration. These studies are particularly important. The task of these studies is to assess bioclimatic conditions and differentiate bioclimatic conditions at the regional level.

A bioclimatic assessment is a scientific method that analyzes the positive and negative effects of various climatic factors and their complexes on organisms. This analysis reveals the medico-climatic potential of a specific territory. In other words, it determines the area’s capacity for the rational use of landscape and climatic conditions in public health and recreation [13]. The assessment of biometeorological conditions for recreational purposes is typically predicated on fundamental meteorological data, climate indices, and tourism indices or biometeorological indices [14].

A study of the impact of meteorological factors on humans is conducted through the utilization of various temperature scales and indices. These factors are investigated through calculation and analysis. Bioclimatic indices are determined by the physical characteristics of the environment and serve as indirect indicators of the thermal state. They are defined by the heat of the environment and the characteristics of the human environment.

The most prevalent of these are complex bioclimatic indices, which are formalized complexes of meteorological factors. An analysis of climatic comfort is imperative for regions comprising the industrial–agricultural complex, housing and communal services enterprises [15].

In this study, the following stations in the Almaty region were selected for the calculation of indices in the study area: Almaty, Aidarly, Shelek, Kapshagay, Yesik, Bakanas, Kegen, Matay, Sarkand, Saryozek, and Ushtobe. The calculation was executed by employing meteorological data (temperature and precipitation) for the period 1990–2020.

The aim of the study is to assess the bioclimatic conditions of the Almaty region, with a focus on the Kapshagay Reservoir, for the period 1990–2020, using biometeorological indices and ClimPACT2 software to analyze extreme climate events. To achieve this goal, the following tasks were set: to analyze the current climate regime of the region based on meteorological observation data; to analyze extreme climate indices using the WMO methodology; to assess the bioclimatic features of the Kapshagay Reservoir area.

2. Materials and Methods

The Almaty region, situated in the south-eastern part of Kazakhstan, is distinguished by its diverse range of climatic conditions, which are influenced by its geographical location and topographical features. The prevailing climatic patterns in this region are classified as continental, though notable variations emerge in response to variations in topography and proximity to mountain ranges. The northern part of the Almaty region is characterized by a plain relief, with the presence of ridges and barchan sand dunes [16].

The table presents a compendium of data pertaining to the geographical location of meteorological stations within the designated study area. The geographical location of the selected weather stations is shown in Figure 1.

The research methods were based on statistical analysis of time series, including assessment of the reliability of the results obtained. Data from the Republican Hydrometeorological Fund of the RSE “Kazhydromet” for the period from 1990 to 2020 (daily air temperature and precipitation values) were used [17,18].

To identify climate trends, daily meteorological data were aggregated into monthly and annual values. Spatial and temporal variability in air temperature and precipitation was assessed based on long-term observations conducted at 11 meteorological stations. It should be noted that the number of stations used is limited by the existing observation network.

The analysis used homogeneous meteorological data, the homogenization of which is preliminarily verified by the National Hydrometeorological Service of Kazakhstan [19]. The database for open access to the results of observations by the meteorological stations of the Kazhydromet RSE was formed in accordance with the Rules for the Provision of Information by the National Hydrometeorological Service, approved by Order No. 267 of the Minister of Ecology, Geology and Natural Resources of the Republic of Kazakhstan No. 267 dated 23 July 2021 (registered with the Ministry of Justice of the Republic of Kazakhstan on 27 July 2021 under No. 23716).

These weather stations were picked because they are in key areas for the study and have complete and verified time series. We preferred direct observation data because it provides greater accuracy for analyzing local conditions compared to approximate data obtained from global databases such as WorldCLIM or CHELSA. To identify climate trends, daily meteorological data was aggregated into monthly and annual values.

It is important to note that climate and its state play a decisive role, which is why several methods for assessing the recreational conditions of a territory are currently available. In particular, a number of bioclimatic indicators (indices) developed on the basis of parallel physiological and meteorological observations were used. All of them differ in the set of input parameters, the complexity of calculations, and physical validity.

Based on the results of an analysis of publications devoted to the study of the bioclimatic regime, we selected bioclimatic indices that characterize the relationship between human thermal perception and climate: effective temperature (ET), normal equivalent effective temperature (NEET), biologically active temperature (BAT), REET (radiation equivalent effective temperature index).

It is important to note that climate and its condition play a decisive role, which is why several methods for assessing the recreational conditions of a territory are currently available. In particular, a number of bioclimatic indicators (indices) developed on the basis of parallel physiological and meteorological observations were used. All of them differ in terms of the set of input parameters, the complexity of calculations, and physical validity. To monitor extreme values of climatic parameters that are most significant for specific sectors of the economy and social sphere, a set of specialized climate indices recommended by the WMO Commission for Climatology (WCP and WCSP, 2016) was used. The ClimPACT2 software product) was used to calculate these indices, which allows for the analysis of extreme climate events using weather or climate data, including an assessment of change and intensity. The indices used allow for a comprehensive analysis of air temperature and precipitation patterns, as well as the identification and analysis of their extreme characteristics.

Use of a set of climate indices recommended by the WMO and the ClimPACT2 software product for analyzing extreme climate events. ClimPACT2 is a software tool designed to analyze climate data and calculate climate indices. It is designed to assess weather and climate extremes and their impact on various areas such as agriculture, ecology, and risk management.

Current climate change trends in the study area. In recent decades, many domestic and foreign scientists have been studying climate change trends in the Republic of Kazakhstan, taking into account various economic sectors [20,21,22,23,24,25,26]. Studies show that an increase in the average annual air temperature is observed throughout Kazakhstan and in the region under study. According to [27], the average increase in the average annual air temperature in Kazakhstan is 0.32 °C every 10 years.

Climate change assessment. The Mann–Kendall nonparametric test was used to assess climate change. In recent years, most authors have used nonparametric statistical methods when studying climate change trends. A review of the studies cited shows that the most common methods for determining trends in climate indicators are nonparametric methods, in particular the Mann–Kendall test and the Sen slope assessment.

In connection with the above, when determining climate change trends, the Mann–Kendall nonparametric statistical method with a significance level of p-value 95% was used to assess the general trend in air temperature and atmospheric precipitation. The calculations were performed in the R program in the mk.test application. The test detects any upward or downward trend in time series data. If the p-value is less than the significance level α (alpha) = 0.05, this indicates the presence of a trend in the time series, i.e., the result is statistically significant; if the p-value is greater than the significance level, this indicates that no trend has been detected.

Calculations made between 1990 and 2020 showed that the air temperature in the study area increased uniformly. Therefore, it is important to use the Mann–Kendall test to understand the significance of these changes. The results of the Mann–Kendall test showed that the trend toward increasing air temperatures at all stations throughout the year was significant. The most significant warming was characteristic of the winter and summer months of the year and showed that it was unevenly distributed in the region under study, while in the spring and autumn months, warming was uneven at meteorological stations. The trend in precipitation change is not significant for all stations, as its value changed several times lower than the temperature each year.

In this study, as well as for the analysis of climate change trends, its key characteristics were considered: air temperature and precipitation. The assessment was carried out using the nonparametric Mann–Kendall statistical method, implemented in the R program using the mk.test function [28]. This test allows us to identify trends toward an increase or decrease in values in time series.

To monitor extreme values of climatic parameters that are most significant for specific sectors of the economy and social sphere, a set of specialized climate indices recommended by the WMO Commission for Climatology (WCP and WCSP, 2016) was used. The ClimPACT2 software product (https://climpact-sci.org/assets/climpact2-user-guide.pdf (accessed on 5 October 2025)) was used to calculate these indices, which allows for the analysis of extreme climate events using weather or climate data, including an assessment of change and intensity [29]. The indices used allow for a comprehensive analysis of air temperature and precipitation patterns, identifying and analyzing the characteristics of their extremes. This approach allows for a comparison of the results of the analysis of precipitation and temperature extremes at the global and regional levels.

Table 1. Temperature and Precipitation indexes with their definitions and units used in this study.

ID	Index Name	Definitions	Units	Plain Language Description	Sectors
FD	Frost day	Number of days when TN < 0 °C	Days	Days when minimum temperature is below 0 °C	Health, Agriculture and food security, Disaster Risk Reduction
TNltm20	TN below −20 °C	Number of days when TN < −20 °C	Days	Days when minimum temperature is below −20 °C
SU	Summer days	Number of days when TX > 25 °C	Days	Days when maximum temperature exceeds 25 °C	Health, Disaster Risk Reduction
WSDI	Warm spell duration Indicator	Annual number of days contributing to events where 6 or more consecutive days experience TX > 90th percentil	Number of days contributing to a warm period (where the period has to be at least 6 days long	Days	Health, Agriculture and food security, Disaster Risk Reduction
CSDI	Cold spell duration Indicator	Annual number of days contributing to events where 6 or more consecutive days experience TN < 10th percentile	Number of days contributing to a cold period (where the period has to be at least 6 days long)	Days	Health, Agriculture and food security, Disaster Risk Reduction
CDD	Consecutive Dry Days	Maximum number of consecutive dry days (when PR < 1.0 mm)	Longest dry spell	Days	Health, Agriculture and food security, Disaster Risk Reduction
R20mm	Number of very heavy rain days	Number of days when PR ≥ 20 Mm	Days when rainfall is at least 20 mm	Days	AFS, WRH
TX10p	Amount of cool days	Percentage of days when TX < 10th percentile	Fraction of days with cool day time temperatures	%	Energy
TX90p	Amount of hot days	Percentage of days when TX > 90th percentile	Fraction of days with hot day time temperatures	%	Energy
CWD	Consecutive Wet Days	Maximum annual number of consecutive wet days (when PR ≥ 1.0 mm)	The longest wet spell	Days	Health, Agriculture and food security, Disaster Risk Reduction
R10mm	Number of heavy rain Days	Number of days when PR ≥ 10 Mm	Days when rainfall is at least 10 mm	Days	Agriculture and food security
DTR	Daily Temperature Range	Mean difference between daily TX and daily TN	Average range of maximum and minimum temperature	°C	Agriculture and food security
Tx35ge	the number of hot days (Tx35GE, T max ≥ 35 °C)
Tx30ge	the number of hot days (Tx30GE, T max ≥ 30 °C)

Note: The first 30 years of each time series were used as the percentile-based measures.

presents the definitions and measurement units of the temperature and precipitation indexes applied in this study.

3. Results and Discussion

The results of the analysis showed that there is a statistically significant increase in the average annual temperature in the study area with a confidence level of 90%. A significant increase in air temperature was recorded in spring, especially in March and April, at all weather stations considered. In summer, statistically significant increases in temperature were recorded at the Aidarly, Shelek, Almaty, and Kegen. At the same time, a slight decrease in temperatures was observed at most weather stations in the autumn.

These changes were confirmed by the Mann–Kendall test, which gave a positive Z value, indicating an upward trend for this climate indicator.

Data analysis also indicates an overall decrease in atmospheric precipitation, but these changes are not statistically significant and remain within the range of natural variability.

Thus, contemporary climate change in the study region is characterized by a significant increase in average annual and seasonal surface air temperatures, while the trend toward reduced precipitation is not statistically significant.

Bioclimatic characteristics of the study region.

Among modern approaches to bioclimatic classification, the Rivas-Martínez system [30] occupies a special place. Unlike simple thermohydrological indices, this model is based on a comprehensive system of indicators, including the continentality index, ombrotypology, and thermotypology, which allows for more accurate differentiation between macro-, meso-, and microclimatic zones. This system is widely used in Europe and the Mediterranean countries for geobotanical zoning, analysis of floristic diversity, and assessment of the adaptive potential of ecosystems [31].

A key advantage of the model is its ability not only to describe climatic conditions, but also to link them to vegetation cover and biodiversity. This makes the Rivas-Martinez approach an important tool for sustainable land use, environmental protection, and adaptation to climate change.

Internationally, the system is used to reconstruct the paleoclimate, identify bioclimatic zones, and model the potential range of species under conditions of global warming [31,32]. For Kazakhstan and Central Asia, such studies are still few in number, but the application of this methodology would enrich regional bioclimatic assessments and allow them to be compared with global results.

In Kazakhstan and the CIS countries, bioclimatic indices (ET, EET, RET, NEET) are widely used to assess climatic comfort and its impact on public health [33,34,35,36,37,38,39].

In studies of the southern and southeastern regions of Kazakhstan [8,9,10] show that these indices allow for an objective determination of the seasonal dynamics of comfortable and uncomfortable conditions. Urban studies [40] have revealed significant differences between Almaty and Astana in terms of EET indicators. The works of Shkurinsky [41] and Nysanbayeva, Abdirazak [10] supplemented the cartographic assessment of climatic comfort for individual regions and cities. In a broader context, the studies are based on the fundamental works of Budyko [42] and Eisenstadt [43], who laid the foundations of bioclimatology in the USSR.

Based on the results of the analysis of publications devoted to the study of the bioclimatic regime, we selected bioclimatic indices that characterize the relationship between human thermal perception and climate: effective temperature (ET), normal equivalent effective temperature (NEET), biologically active temperature (BAT), REET (radiation equivalent effective temperature index). The bioclimatic indices used in this study are presented in

Table 2. Bioclimatic indices.

Bioclimatic Indices	Warm Period	Cold Period	Comments
ET (effective temperature)	18–23 °C—comfort; >25 °C—warm; <17 °C—cold	17–21 °C—comfort; <15 °C—cold	takes into account tempreture and humidity
EET (equivalent effective temperature)	20–22 °C—optimal; 23–25 °C—permissible	18–20 °C—optimal; 16–18 °C—permissible	the effect of wind speed is added
NEET (normal equivalent effective temperature)	19–21 °C—comfort; >23 °C—overheating	17–19 °C—comfort; <15 °C—hypothermia	more accurate index compared to EET
BAT (biologically active temperature)	conditionally favorable with EET= 20–25 °C	conditionally favorable with EET = 17–20 °C	Characterizes the overall impact of climate on organisms
REET (radiation equivalent effective temperature index)	18–24 °C—comfort zone; 25–27 °C—stress; >27 °C—discomfort	16–20 °C—comfort zone; 12–15 °C—moderate cooling; <12 °C—strong cooling	the most «sensitive» indicator

Effective temperature (ET). To assess the degree of comfort in this study, ET was calculated, which is a characteristic of the sensation of heat or cold by the human body and is an empirical function of temperature used to assess climatic comfort. Table 3 illustrates the distribution patterns of ET during the period 1990–2020.

.

Table 3. Distribution of ET in 1990–2020.

Station	1	2	3	4	5	6	7	8	9	10	11	12
Aidarly	−6.0	−3.7	4.2	11.9	16.7	20.9	22.3	20.8	16.2	10.6	3.1	−3.5
Almaty	−3.0	−1.9	−3.0	11.4	15.7	19.5	21.0	20.2	16.2	10.5	3.5	−1.6
Bakanas	−8.1	−6.1	2.2	11.9	16.6	21.1	22.4	20.7	15.7	9.7	1.3	−4.7
Yesik	−3.5	−2.5	4.0	10.9	14.8	18.7	20.2	19.3	15.4	9.9	3.5	−1.8
Kapshagay	−10.6	−7.7	−0.4	7.8	13.2	16.5	17.7	13.7	10.4	8.8	3.6	−7.1
Matai	−10.9	−8.3	4.5	11.1	16.3	20.7	22.0	20.6	15.5	9.2	0.6	−7.1
Sarkand	−5.7	−4.3	1.7	10.4	14.9	18.8	20.2	19.0	14.5	9.0	1.8	−3.8
Saryozek	−7.1	−5.0	2.4	9.9	14.4	18.4	19.8	18.7	14.4	8.7	1.9	−4.8
Shelek	−3.9	−1.4	6.4	12.4	17.1	20.5	21.8	20.7	16.8	10.9	4.2	−1.6
Ushtobe	−8.8	−6.4	2.1	11.3	16.2	20.2	21.4	19.8	15.0	9.2	1.2	−5.9

Table 3. Distribution of ET in 1990–2020.

Station	1	2	3	4	5	6	7	8	9	10	11	12
Aidarly	−6.0	−3.7	4.2	11.9	16.7	20.9	22.3	20.8	16.2	10.6	3.1	−3.5
Almaty	−3.0	−1.9	−3.0	11.4	15.7	19.5	21.0	20.2	16.2	10.5	3.5	−1.6
Bakanas	−8.1	−6.1	2.2	11.9	16.6	21.1	22.4	20.7	15.7	9.7	1.3	−4.7
Yesik	−3.5	−2.5	4.0	10.9	14.8	18.7	20.2	19.3	15.4	9.9	3.5	−1.8
Kapshagay	−10.6	−7.7	−0.4	7.8	13.2	16.5	17.7	13.7	10.4	8.8	3.6	−7.1
Matai	−10.9	−8.3	4.5	11.1	16.3	20.7	22.0	20.6	15.5	9.2	0.6	−7.1
Sarkand	−5.7	−4.3	1.7	10.4	14.9	18.8	20.2	19.0	14.5	9.0	1.8	−3.8
Saryozek	−7.1	−5.0	2.4	9.9	14.4	18.4	19.8	18.7	14.4	8.7	1.9	−4.8
Shelek	−3.9	−1.4	6.4	12.4	17.1	20.5	21.8	20.7	16.8	10.9	4.2	−1.6
Ushtobe	−8.8	−6.4	2.1	11.3	16.2	20.2	21.4	19.8	15.0	9.2	1.2	−5.9

Analysis of data for 1990–2020 showed clear seasonal differences: in winter, ET values are negative, indicating cold stress, while in summer they reach +20 °C and above, creating a risk of thermal discomfort. During the winter months (November–March), ET values are negative, indicating unfavorable conditions for human life. From April onwards, there is a transition to more comfortable values, with the most favorable conditions observed in May and September, when ET ranges between 10 and 16 °C. The summer months (July–August) are characterized by high ET values (above 20 °C), which can lead to heat stress and is also considered an uncomfortable period. Thus, the optimal conditions for humans occur during the spring-autumn transition season, while winter and mid-summer are considered unfavorable. The distribution of EET during the period 1990–2020 is presented in

Table 4. Distribution of EET in 1990–2020.

Station	1	2	3	4	5	6	7	8	9	10	11	12
Aidarly	−17.7	−15.0	−5.0	5.3	12.1	17.9	19.8	18.2	12.3	4.3	−6.2	−15.1
Almaty	−6.4	−5.8	−6.4	7.9	12.9	17.5	19.3	18.4	14.1	7.9	0.0	−5.2
Bakanas	−16.4	−14.4	−5.4	6.5	12.9	19.1	20.6	18.6	12.6	5.8	−5.2	−11.7
Yesik	−9.3	−8.5	−2.1	5.3	10.3	15.5	17.6	16.8	12.2	5.9	−1.5	−7.2
Kapshagay	−21.6	−19.4	−6.2	−4.3	13.2	18.5	23.3	19.7	8.7	7.7	−5.6	−18.8
Matai	−20.2	−17.6	0.3	5.3	12.4	18.2	19.9	18.3	11.9	3.9	−6.9	−15.3
Sarkand	−10.2	−9.0	−3.7	5.8	11.5	16.5	18.2	17.0	12.1	6.0	−1.9	−8.2
Saryozek	−12.9	−11.4	−4.2	4.1	10.0	15.3	17.1	15.8	10.7	4.2	−3.7	−10.5
Shelek	−13.1	−9.7	0.3	7.5	14.2	18.8	20.5	19.4	14.5	6.8	−2.0	−10.2
Ushtobe	−18.2	−16.1	−7.4	4.8	11.8	17.6	19.4	17.3	11.2	4.5	−6.1	−14.3

.

Analysis of the equivalent effective temperature (EET) showed pronounced seasonality. The winter months (December–February) are characterized by extremely low EET values (down to –20 °C and below), indicating unfavorable conditions and severe cold stress. In spring, conditions gradually improve: in April and especially in May–June, the indicators are within the range of 10–15 °C, creating the most comfortable conditions for humans. The summer months (July–August) are marked by maximum EET values (19–23 °C), which leads to thermal discomfort and is also considered an unfavorable period. In autumn, in September, comfortable values (12–14 °C) remain, but in October there is a sharp decline and a transition to cold conditions. Thus, the most favorable periods for life are May–June and September, while winter and mid-summer are uncomfortable seasons.

Table 5. Distribution of NEET in 1990–2020.

Station	1	2	3	4	5	6	7	8	9	10	11	12
Aidarly	−7.1	−5.0	3.0	11.2	16.7	21.3	22.9	21.5	16.8	10.4	2.1	−5.1
Almaty	1.8	2.4	1.8	13.3	17.3	21.0	22.4	21.8	18.3	13.3	7.0	2.8
Bakanas	−6.1	−4.5	2.6	12.2	17.3	22.2	23.5	21.9	17.1	11.7	2.9	−2.3
Yesik	−0.4	0.2	5.3	11.2	15.2	19.4	21.1	20.4	16.8	11.7	5.8	1.3
Kapshagay	−6.2	−3.5	−2.1	10.4	17.5	23.5	24.2	22.8	13.9	11.3	−1.8	−2.8
Matai	−9.2	−7.0	7.2	11.2	16.9	21.5	22.9	21.7	16.5	10.1	1.5	−5.2
Sarkand	−1.1	−0.2	4.0	11.7	16.2	20.2	21.5	20.6	16.7	11.8	5.5	0.4
Saryozek	−3.3	−2.1	3.6	10.2	15.0	19.2	20.7	19.6	15.5	10.4	4.0	−1.4
Shelek	−3.5	−0.8	7.2	13.0	18.3	22.1	23.4	22.5	18.6	12.5	5.4	−1.1
Ushtobe	−7.6	−5.9	1.0	10.8	16.5	21.1	22.5	20.9	16.0	10.6	2.1	−4.5

shows the temporal distribution of NEET from 1990 to 2020.

The distribution of NEET values also shows pronounced seasonal dynamics. In the winter months (December–February), the indicators at most stations are negative (down to −9 °C), which indicates unfavorable conditions and cold stress. In spring, starting in March, the values gradually increase, with the most comfortable conditions observed in April–June (10–17 °C). In summer, especially in July–August, NEET reaches its maximum values (22–24 °C), causing thermal discomfort. In September, favorable conditions persist (16–18 °C), but in October, a noticeable decline begins. Thus, May–June and September are considered optimal for life, while winter and mid-summer are unfavorable seasons. The distribution of BAT for the period 1990–2020 is summarized in

Table 6. Distribution of BAT in 1990–2020.

Station	1	2	3	4	5	6	7	8	9	10	11	12
Aidarly	3.3	5.0	11.4	18.0	22.3	26.0	27.3	26.2	22.5	17.3	10.6	4.9
Almaty	10.5	10.9	10.5	19.7	22.9	25.8	27.0	26.4	23.6	19.7	14.6	11.2
Bakanas	4.1	5.4	11.1	18.7	22.9	26.8	27.8	26.5	22.7	18.3	11.3	7.1
Yesik	8.7	9.1	13.2	18.0	21.2	24.5	25.9	25.3	22.4	18.4	13.6	10.0
Kapshagay	9.8	9.2	7.5	17.3	23.4	27.7	28.2	22.1	20.3	18.5	12.6	9.6
Matai	1.7	3.4	14.8	18.0	22.5	26.2	27.3	26.3	22.2	17.1	10.2	4.8
Sarkand	8.1	8.8	12.2	18.3	21.9	25.2	26.2	25.5	22.3	18.5	13.4	9.3
Saryozek	6.3	7.3	11.9	17.2	21.0	24.4	25.5	24.7	21.4	17.3	12.2	7.9
Shelek	6.2	8.4	14.8	19.4	23.7	26.7	27.7	27.0	23.9	19.0	13.3	8.1
Ushtobe	2.9	4.3	9.8	17.7	22.2	25.9	27.0	25.7	21.8	17.5	10.7	5.4

.

The BAT is unevenly distributed throughout the year. The most favorable months begin in May and continue until September, when the indicators reach their maximum values. At this time, good conditions are created for the growth and development of agricultural crops. From October, the temperature begins to drop, and conditions gradually deteriorate. From November to March, the BAT remains low, making this period unfavorable. Thus, the warm half of the year is considered favorable, and the cold half is considered unfavorable. The distribution of REET during the period 1990–2020 is presented in

Table 7. Distribution of REET in 1990–2020.

Station	4	5	6	7	8	9
Aidarly	16.2	21.7	26.3	27.9	26.5	21.8
Almaty	18.3	22.3	26.0	27.4	26.8	23.3
Bakanas	17.2	22.3	27.2	28.5	26.9	22.1
Yesik	16.2	20.2	24.4	26.1	25.4	21.8
Kapshagay	16.6	24.2	27.5	20.2	26.3	22.3
Matai	16.2	21.9	26.5	27.9	26.7	21.5
Sarkand	16.7	21.2	25.2	26.5	25.6	21.7
Saryozek	15.2	20.0	24.2	25.7	24.6	20.5
Shelek	18.0	23.3	27.1	28.4	27.5	23.6
Ushtobe	15.8	21.5	26.1	27.5	25.9	21.0

.

According to REET data, indicators begin to grow rapidly in April and peak in July. The period from May to August is the most favorable, as values remain high and create good conditions for agricultural development. The most optimal indicators are observed in June and July. In September, values begin to decline, and from October onwards, conditions become less suitable. The winter months are considered an unfavorable period.

Bioclimatic analysis based on various indices revealed clear seasonal dynamics. The most favorable conditions for life and agricultural activity in Kazakhstan occur in May–June and September. The winter months are characterized by severe cold stress, while mid-summer is marked by overheating and thermal discomfort. Thus, the transitional seasons (spring and autumn) are optimal in terms of bioclimatic comfort.

Analysis of specialized climate indices. In the context of global climate change, it was interesting to study the extreme values of climate parameters.

The utilization of bioclimatic and tourism-climatic indices in the examination of human–climate and tourism–climate relationships is imperative, as well as a potential avenue for further exploration within the scientific community. Throughout the 20th century, a plethora of indices—exceeding one hundred—were utilized exclusively for the evaluation of bioclimatic conditions [44]. The notion of employing a composite meteorological parameter approach to evaluate human climate impacts was put forth nearly a century ago. Bioclimatic indices are defined as metrics that evaluate the relationship between environmental factors and human comfort or discomfort, stress levels, and the onset of pathologies. These indices are particularly relevant in contexts where caloric intake or expenditure plays a significant role in the human body’s physiological responses. Given that the majority of the global population inhabits urban areas, the majority of bioclimatic studies are conducted in built-up regions [45].

To obtain a comprehensive understanding of the alterations in the air temperature field, the analysis encompassed climatic indices that characterize extreme values. These indices are of paramount importance and are instrumental in evaluating bioclimatic changes.

Temperature indices Tx10p and Tx90p. The presented maps show the results of analysis of changes in climatic indices Tx10p and Tx90p for different meteorological stations of Almaty region. These indices characterize the number of cool (Tx10p) and hot (Tx90p) days during the year, expressed as a percentage of the total number of days.

The Tx10p index measures the percentage of days in a year when the maximum temperature remains below the 10th percentile. This index is useful for estimating the frequency of cool days and helps to understand the climatic features of different regions.

In the above Figure 2, it can be seen that in the northern and eastern regions (MS Sarkand, MS Kegen) of the studied territory, high index values were recorded (16%). This indicates the presence of a significant number of cool days, which is associated with more severe climatic conditions and proximity to mountain ranges.

Low Tx10p values (less than 3%) are found in southern and western areas such as Shelek and Kapshagay. This indicates a warmer and drier climate with few cool days. Such conditions may be favorable for certain types of recreation (extending the swimming season of the Kapshagay Reservoir), but may also lead to problems associated with water shortages and increased temperatures in the summer months.

Central areas such as Almaty and Yesik show average values (around 5–8%). This indicates a moderate climate with less pronounced temperature extremes. Such conditions may reduce the risks associated with extreme temperatures.

Analysis of the Tx10p index shows that climate conditions vary significantly across regions. High values in northern and eastern regions indicate harsh climates, while southern and western regions are warm and dry. These differences are important for developing adaptation and resource management strategies that take into account the unique climate characteristics of each region.

The second map shows the Tx90p index, which measures the percentage of days with maximum temperatures above the 90th percentile. This indicator is important for understanding changing climate conditions and the increase in hot days in different regions. The highest Tx90p values are observed in Shelek and Kegen, where more than 14% of days per year are characterized by hot weather. This indicates significant climate stress for local populations and the ecosystem. Rising temperatures may lead to increased water consumption, worsening agricultural conditions, and an increase in heat-related diseases.

At MS Sarkand and Ushtobe, where Tx90p values are around 6%, the climate is more moderate. The reasons for the low values are related to natural geographical conditions, such as the presence of mountain ranges, which affect the temperature.

Moderate values (8–11%) in Almaty and Yesik indicate a transitional climate. This may mean that the region is on the verge of change, with the possibility of an increase in hot days in the future, which will require careful monitoring and adaptation strategies.

The analysis of the presented data shows a significant territorial diversity of climatic conditions in the Almaty region. The northern and eastern regions are characterized by harsher climatic conditions with a high percentage of cool days and a low percentage of hot days. In contrast, the southern and central regions are subject to a warmer climate with fewer cool days and more hot days.

CSDI and WSDI temperature indices. These indices are used to plan climate change adaptation measures, manage resources, and develop strategies to improve the bioclimate and public health.

The CSDI index shows the number of days in a year when the temperature was significantly below the average minimum temperature. Positive CSDI values (marked in green and blue) indicate a significant number of cold days. The spatial distribution of the cold wave duration trend (CSDI) for the period 1990–2020 is shown in Figure 3.

Negative CSDI values (marked in orange and red) indicate the absence of significant cold periods, which is typical for lower-lying and desert areas such as Matai and Kapshagay.

In Figure 4, the distribution of WSDI is shown, which reflects the number of days in a year when the temperature was significantly higher than the average maximum temperature. Positive WSDI values (marked in yellow and orange) are mainly found in the southern regions, indicating longer warm periods. This is characteristic of areas with more moderate and continental climates. Negative WSDI values (marked in blue) are almost absent on the map, which may indicate a general increase in temperatures and a reduction in the number of cold periods in the region.

Figure 4 shows the distribution of WSDI, which reflects the number of days in a year when the temperature was significantly higher than the average maximum temperature. Positive WSDI values (marked in yellow and orange) occur mainly in southern regions, indicating longer warm periods. This is typical for areas with a more temperate and continental climate. Negative WSDI values (marked in blue) are almost absent from the map, which may indicate a general increase in temperatures and a decrease in the number of cold periods in the region.

Differences in CSDI and WSDI indices across different parts of the region highlight the influence of local topography and elevation on climatic conditions. Mountainous areas are more prone to extreme temperatures during both cold and warm periods of the year. The observed index values may also reflect broader climate change trends, including global warming, which leads to fewer extremely cold days and longer warm periods. The observed index values may also reflect broader climate change trends, including global warming, which is leading to fewer extremely cold days and longer warm periods.

The Daily Temperature Range (DTR) index represents the difference between the daily maximum and nightly minimum temperatures. It is an important indicator for understanding climate conditions and their impact on various sectors, including public health.

Figure 5 shows the DTR values for the studied weather stations in the Almaty region. In areas with positive DTR values, marked by shades of blue and green, the daily temperature amplitude is most significant. For example, weather stations in Sarkand (0.013) and Usharal (0.01) show significant daily temperature fluctuations.

Weather stations with moderate DTR values, marked in yellow, show average daily temperature fluctuations. Such values are observed in Kegen (0.006) and Kapshagay (0.001).

Areas with negative DTR values, marked with shades of orange and red, show the smallest daily temperature fluctuations. For example, weather stations in Almaty (−0.03) and Shelek (−0.04) indicate smaller daily temperature fluctuations.

High DTR values are often associated with mountainous and foothill areas, where relief and elevation play a significant role in the formation of diurnal temperature fluctuations. Differences in DTR values throughout the Almaty region reflect the diversity of climatic conditions in the region. High amplitudes indicate more extreme conditions, while low values indicate more stable climatic conditions.

The FD (Frost Days) and Su (Summer Days) indices provide insights into the climate of a region, its weather conditions, and potential consequences for the bioclimate and other sectors.

The Frost Days index measures the number of days in a year when the minimum temperature drops below 0 °C, i.e., the number of frost days. The spatial distribution of the temperature indices FD (Frost Days) and Su (Summer Days) is shown in Figure 6.

Almaty FD index is −0.18, which corresponds to a moderate number of frosty days (blue). This indicates that in Almaty in winter there are several frosty days, but, in general, the climate is not characterized by prolonged cold weather. Aidarly, Sarkand, Shelek, Kegen—the index varies from −0.43 to −0.51 (green), which indicates a low number of frosty days. These regions have milder winters with occasional frost.

MS Bakanas, Kapshagay, Yesik, Matai—have an index from −0.29 to −0.37 (yellow), which means a moderate number of frosty days. Winters in these places are colder than in the aforementioned regions, but are not characterized by severe frosts.

Ushtobe—FD −0.20 (orange color) also indicates a moderate number of frosty days, but closer to a low value within this range.

The regions represented in the list vary in the number of frost days depending on their geographical location and climatic features. Most settlements are characterized by a moderate or low number of frosty days, which indicates relatively mild winters, but for some regions (for example, for Ushtobe) frosty days can still be quite common.

The Su (Summer Days) index shows the number of days in a year when the maximum temperature exceeds 25 °C, indicating hot days. The Su index (summer days) measures the number of days in a year when the maximum temperature is 25 °C, that is, hot days. This index is used to assess the climatic conditions of the region, borders with summer temperatures, as well as to assess the impact of heat on the ecosystem, the state of the economy and human activity.

In the regions listed in this analysis, there is a change in climatic conditions, especially during the summer period. Almaty and Aidarly, with their high percentage of hot days, have hot summers, while other regions such as Bakanas and Sarkand live in moderate warmth. The least number of hot days is observed in Kapshagay and Kegen, which makes these places more comfortable in the summer. This diversity of climatic conditions is important to take into account when planning agricultural, construction and economic projects, as well as issues.

Precipitation indices. The distribution of precipitation over the study area and trends in total annual and extreme precipitation are also considered using the specialized indices mentioned above. In Figure 7, the spatial distribution of days with daily rainfall ≥ 10 mm (R10mm) and ≥20 mm (R20mm) (days/year) is presented.

The R10mm index shows the change in the number of days per year with precipitation exceeding 10 mm. This index is important for understanding intense precipitation that can affect human life. The highest index values are observed in the Yesik (0.13) and Almaty (0.12) areas, indicating an increase in the number of days with intense precipitation in these regions. Areas such as Saryozek and Kapshagay show moderate changes (0.063 and 0.02, respectively), indicating a smaller increase in intense precipitation.

Some areas, including Bakanas and Matay, show negative values (−0.04 and −0.12), which may indicate a decrease in the number of days with intense precipitation. The R20mm index measures the change in the number of days per year with precipitation greater than 20 mm. This index is an indicator of even more intense precipitation events, which have a significant impact on ecosystems and human activities. Ushtobe and Yesik show the largest increase in the number of days with precipitation exceeding 20 mm (0.19 and 0.03), highlighting a significant increase in very intense precipitation in these areas. Areas such as Kapshagay and Almaty show a more moderate increase (0.02 and 0.01), which still indicates an increase in intense precipitation, but to a lesser extent. Saryozek and Matay show negative values (−0.012 and −0.003), which may indicate a decrease in the number of days with very intense precipitation. Regions with an increase in dry and wet days. Almaty and Ushtobe show an increase.

Figure 8 presents the spatial variability of Tx30ge, indicating the number of days when the daily maximum temperature reached or exceeded 30 °C.

The Tx30ge index shows the number of days per year when the maximum temperature reaches or exceeds 30 °C.

The highest values were observed at MS Almaty (0.68) and Aidarly (0.64), indicating a significant number of hot days. At MS Yesik (0.57) and Kapshagay (0.47), a moderate number of hot days is observed. MS Sarkand (0.07) and Matay (0.09) demonstrate low values, suggesting a reduced number of hot days. The spatial distribution of extremely hot days (Tx35ge) with maximum temperature ≥ 35 °C is shown in Figure 9.

The Tx35ge index is a metric that quantifies the number of days per year when the maximum temperature reaches or exceeds 35 °C. This index serves as an indicator of days characterized by extremely high temperatures. In addition, the Tx30ge Index at MS Almaty (0.61) and Aidarly (0.61) exhibited elevated values, suggesting a substantial number of exceedingly hot days. The lowest values of the considered index are observed on MS Sarkand (0.11) and MS Kegen (0.11).

The increase in the number of hot and very hot days in a number of areas necessitates the implementation of adaptation and mitigation measures. Such measures may include improving water management, optimizing practices affecting bioclimate, and ensuring public health.

The spatial distribution of extremely cold days (Tntlm20), when the minimum temperature drops below −20 °C, is shown in Figure 10.

The Tntlm20 index is a metric that quantifies the number of days per year when the minimum temperature falls below −20 °C, a threshold that is critical for evaluating the risks posed by extreme cold. The figure indicates that MS Matay (0.12) and Kegen (0.11) exhibit a substantial number of extreme cold days. Almaty (−0.26) and Kapshagay (−0.08) demonstrate low values, suggesting a reduced frequency of extreme cold days.

These maps underscore the significance of monitoring and analyzing temperature indices to comprehend climate change and its ramifications on diverse aspects of human and natural life. The increase in the number of hot and very hot days in some areas necessitates the implementation of adaptation and mitigation measures. Such measures may include improving water management, optimizing agricultural practices, and ensuring public health.

To assess the bioclimatic conditions of the Kapshagay Reservoir under climate change conditions, a point-based assessment of the study area was carried out using a point scale developed by the authors.

The scoring system for bioclimatic conditions proposed by the authors has analogies in a number of international studies. For example, in the works of Blazejczyk [44,46], integral indicators with gradation into comfortable and uncomfortable periods were also used to assess the tourist and recreational potential of the climate. A similar approach was used in the studies by Toy, Yilmaz [13], where a three-point scale (unfavorable—favorable—optimal conditions) was used to assess bioclimatic comfort in urban conditions.

The use of a simplified scoring system allows for a clear representation of the complex impact of climatic factors and a comparison of different indices. In recent years, this practice has also been used in regional studies [9,47,48], where a set of climate indices is aggregated into a single comfort assessment system.

The use of ClimPACT2 indices, recommended by the WMO for the analysis of extreme climate events [6], deserves special attention. In a number of studies [49,50,51,52], the authors also grouped climate indicators into several categories to identify areas of climate risk and compare them.

A total of 13 indices were used for the analysis, calculated with the ClimPACT2 package following WMO recommendations (Tx30ge, Tx35ge, FD, SU, R10mm, R20mm, CSDI, WSDI). Each index value was converted into a category using a three-level scale: 0 points—unfavorable conditions, 1 point—favorable conditions, 2 points—very favorable conditions.

Threshold values were taken from Blazejczyk [8], Toy et al. [12] and adapted to regional conditions [9]. After categorizing all 13 indices, their values were summed, and the total score was classified as follows (

Table 8. Integral assessment of bioclimatic indices.

Bioclimatic Assessment	Point Scale	Characteristic
very discomfort	9–11	Additional protective measures will be required to ensure a comfortable life.
Discomfort	11.1–13	indicates significant adverse climatic conditions, although not a significant negative impact
Subcomfort	13.1–15	a transitional range between discomfort and comfort conditions.
Comfort	15.1–17	comfortable, some indices have a negative impact, but has a weak effect
very favorable conditions	17.1–26	comfortable, that is, favorable conditions

).

This approach made it possible to comprehensively integrate climatic and bioclimatic characteristics and obtain an overall integral assessment of the regional conditions. On the basis of the developed ball scale 13 climatic indices were analyzed and bioclimatic assessment of stations near the Kapshagay reservoir was carried out (Figure 11).

The most bioclimatically comfortable zones of Almaty region can be considered the neighborhoods near Kapshagay meteorological station. Aidarly and Shelek meteorological stations are the least comfortable. Other regions are in favorable bioclimatic conditions.

4. Conclusions

Based on the analysis of bioclimatic conditions in the Almaty region for the period from 1990 to 2020, as well as the analysis of various climate indices and their spatial distribution, the following conclusions can be drawn:

1.

A statistically significant increase in the average annual temperature has been observed in the study area with a confidence level of 90%. A significant increase in air temperature was recorded in spring, especially in March and April, at all weather stations under consideration. In summer, statistically significant increases in temperature were recorded at the Aidarly, Shelek, Almaty, and Kegen stations. There has been an overall decrease in precipitation, but these changes are not statistically significant and remain within the range of natural variability.

2.

A study of ET, EET, NEET, BAT, and REET for 1990–2020 showed pronounced seasonality. The most favorable conditions are recorded in May–June and September, when the index values are within the climatic comfort range. In winter, extremely low values are observed, causing cold stress, and in summer, in July–August, excessively high values are observed, leading to thermal discomfort. Thus, the spring-autumn transition period can be considered optimal, while winter and mid-summer are unfavorable seasons.

3.

The least comfortable are the Aidarly and Shelek microclimates. The rest of the regions have favorable bioclimatic conditions.

The results of the study show that the climate of the Almaty region has undergone significant changes over the past three decades, manifested in an increase in air temperature and seasonal contrasts in bioclimatic conditions. The most comfortable periods are May–June and September, while the winter months and mid-summer place extreme stress on the human body. The data obtained can be used to assess climate risks, develop adaptation measures, and plan the region’s socio-economic activities.

The study revealed statistically significant warming trends in the Almaty region during 1990–2020, while precipitation changes remained within natural variability. Bioclimatic indices (ET, EET, NEET, BAT, REET) demonstrated clear seasonality, with May–June and September identified as the most favorable periods, and winter and mid-summer as unfavorable seasons. The most comfortable bioclimatic conditions were observed near Kapshagay, whereas Aidarly and Shelek showed less favorable conditions. These findings are important for urban planning, agriculture, tourism development, and public health adaptation strategies. Future research should integrate regional climate projections (e.g., CMIP6) and satellite data to refine risk assessment and support sustainable adaptation policies.