1. Introduction
Soil erosion is an important environmental issue that has adverse effects on agricultural output, water quality, and the holistic well-being of ecosystems. Soil erosion occurs when particles of soil become dislodged and are subsequently transported from their original position to another location by the effect of natural factors such as wind, water, or other similar conditions. The issue of soil erosion has been a matter of concern since ancient times. However, due to the growth of agricultural practices and urban development, overexploitation, land abandonment, and agricultural intensification, it has evolved into a substantial worldwide problem [
1,
2,
3,
4,
5]. Globally, estimated annual soil erosion of around 75 billion tons occurs, surpassing the natural erosion rate by a factor of 13–40 [
6,
7]. The susceptibility of Kazakhstan to soil erosion is significantly influenced by its geographical positioning and the prevailing arid to semi-arid climatic conditions [
8]. Kazakhstan has an arid to semi-arid climate across various regions, with a notable concentration in the southern and central areas of the country [
9]. These regions often have minimal annual precipitation levels, resulting in not enough soil moisture availability [
10]. The concurrent occurrence of high temperatures and less precipitation may result in soil desiccation, hence making the soil more vulnerable to erosion [
11]. Arid and semi-arid regions often have sparse vegetation cover due to water scarcity [
12]. The lack of vegetation exposes the soil to the erosive forces of wind and water. Strong winds can lift and transport dry, loose soil particles over long distances. Kazakhstan’s open landscapes, especially in the steppe regions, provide ample opportunity for wind erosion to occur, leading to the loss of topsoil and degradation of agricultural land [
13]. However, this research exclusively focuses on the modeling and discussion of water erosion, namely rainfall erosion. Rainfall-runoff erosivity refers to the ability of rainfall to cause soil erosion and contribute to runoff [
14]. It is a crucial factor in soil erosion studies and is often assessed based on the characteristics of rainfall events [
15]. In semi-arid regions, when rainfall does occur, it can sometimes be intense and sporadic. This can result in significant water erosion as runoff carries away soil particles. Additionally, when soil is already desiccated due to arid conditions, it becomes less capable of absorbing and retaining water, making it more prone to erosion during heavy rainfall events [
16].
The dynamics of rainfall patterns are being modified by climate change [
17,
18,
19]. The elevation in mean air temperature and alterations in weather patterns contribute to heightened intensity and irregular nature of rainfall episodes, hence augmenting the erosive capacity of precipitation. The impact of climate change on rainfall characteristics, including both strength and frequency, necessitates the integration of these alterations into soil erosion models [
20,
21]. Models are crucial tools for studying soil erosion at various scales, particularly when it comes to large geographic areas such as watersheds, regions, or even entire countries or continents [
22]. The direct measurement and monitoring of soil erosion at these scales can be logistically challenging and expensive. Erosion models function as predictive tools for assessing soil loss, developing conservation strategies, conducting erosion surveys, and crafting project plans. Additionally, these models can serve as instruments to comprehend erosion processes and their implications [
23]. Gaining a grasp of the principles behind constructing erosion models and the various types available can be beneficial for assessing model performance in diverse settings with different parameters. Among the commonly employed empirical erosion models are the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) [
24,
25]. The RUSLE model, in particular, holds a central position in the natural resources field due to its extensive use and widespread acceptance, largely attributed to its user-friendly features [
26]. The choice of the RUSLE model in this study is grounded in its simplicity of application, utilization of readily available data, and consistently accurate outcomes [
27,
28].
Climate change is one of the most pressing challenges of our time, with far-reaching implications for the environment, ecosystems, economies, and human societies. General circulation models (GCMs), also known as global climate models, play a crucial role in comprehending, simulating, and forecasting alterations in the Earth’s climate system [
28,
29]. GCMs are invaluable in this context, serving as powerful instruments for studying and projecting the effects of climate change on erosion and helping us develop strategies to mitigate its adverse impacts. Understanding these interactions is vital for our efforts to ensure a sustainable and resilient future. In order to assist climate change studies that necessitate more precise analysis, NASA established the NASA Earth Exchange Global Daily Downscaled Projections 6 (NEX-GDDP-CMIP6) initiative, which aims to furnish downscaled historical and prospective projections spanning from 1950 to 2100. These projections are derived from CMIP6 models. A comprehensive understanding of these interconnections is vital in our efforts to ensure a resilient and sustainable future [
30,
31,
32].
Multiple studies have investigated climate patterns in central Asia at different scales, employing various climate models. Gao et al. [
33] assessed precipitation outputs from 30 global circulation models (GCMs) under the Coupled Model Inter-comparison Project Phase 6 (CMIP6) from 1951 to 2014, focusing on six climate zones in arid central Asia. Duulatov et al. [
11] used GCMs from CMIP5 to assess rainfall changes for radiative concentration pathways (RCPs) 2.6 and 8.5. Ta et al. [
34] employed CMIP5 to evaluate the ability of 37 GCMs to simulate historical precipitation in central Asia. Gulakhmadov [
35] et al. used five GCMs from CMIP5 to project precipitation (Pr), maximum temperature (Tmx), and minimum temperature (Tmn) in the Vakhsh River Basin of CA for RCPs 4.5 and 8.5. Salehie et al. [
36] selected CMIP6 GCMs to project climate changes over the Amu Darya River Basin. Gafforov et al. [
37] utilized CMIP5 (RCPs 4.5 and 8.5) to evaluate the impact of climate change on rainfall-runoff erosivity in the Chirchik–Akhangaran Basin, Uzbekistan. Golian et al. [
30] used CMIP6 models to assess future climate change effects on mine sites in Kazakhstan, considering Shared Socioeconomic Pathways (SSPs) 245 and 585. Lei et al. [
38] evaluated CMIP6 models and a multi-model ensemble (MME) for extreme precipitation over arid central Asia. These studies collectively contribute to our understanding of climate variability and change in central Asia, utilizing a range of models, scenarios, and assessments focused on different aspects of the climate system. For the initial time in the Talas region, the study evaluates anticipated alterations in future precipitation using the recent CMIP6 models for the Revised Universal Soil Loss Equation (RUSLE). This approach aims to offer more comprehensive insights into potential future climate conditions in the Talas region. We utilize the latest CMIP6 models and including these updated models enhances the accuracy and relevance of our findings. By doing so, we seek to provide more targeted and region-specific information that can aid in informed decision making and adaptive strategies.
The purpose of this study is to assess the current state of soil erosion in the Talas region of Kazakhstan using the RUSLE model and a combination of global climate models under CMIP6. To the best of our knowledge, similar studies using a combination of the RUSLE model with CMIP6 have not yet been conducted in the Talas region of Kazakhstan in central Asia.
4. Discussion
The utilization of the Revised Universal Soil Loss Equation (RUSLE) in arid and semi-arid environments might provide distinct outcomes that hold significance in comprehending and effectively addressing soil erosion in such areas [
10,
11,
37]. Our examination of the existing state of soil erosion in the Talas area, employing the RUSLE model, has provided valuable insights into the magnitude and spatial distribution of erosional processes.
Central Asia is susceptible to the adverse effects of climate change, including heightened variability in precipitation, severe weather issues, and droughts [
8,
61]. Also, arid and semi-arid regions exhibit heightened susceptibility to soil erosion as a consequence of factors such as restricted plant coverage, irregular and strong precipitation patterns, and delicate soil characteristics [
62]. The alterations in the magnitude and volume of precipitation are mostly influenced by the intricate mechanisms of the hydrological cycle inside the Earth’s atmosphere [
17,
61]. These modifications may lead to alterations in precipitation patterns, such as an increase in the intensity of rainfall events in some areas and the occurrence of lengthy periods of drought in others [
18]. Precipitation patterns may also show seasonal and regional variations.
Areas with higher erosion risk can receive priority for re-vegetation or terracing, while areas with lower risk can be conserved as they are. The process of soil erosion modeling is inherently complex due to the geographical and temporal variability of soil loss, which is influenced by several elements and their intricate interrelationships. When making predictions about soil loss in unfamiliar areas, it is crucial to possess knowledge of both the estimations and the corresponding uncertainty.
Multiple studies have shown that erosion densities beyond a value of 1 result in a higher occurrence of precipitation erosion compared with precipitation activity [
59,
60]. Rainfall erosivity, the power of rainfall to cause soil erosion, is an essential factor in soil erosion studies [
63]. In the context of climate change, understanding and modeling the relationship between rainfall erosivity and shifting climate patterns is critical for effective soil conservation and land management. The findings of our research indicate that the subject region exhibits susceptibility to water erosion. The mean annual R coefficient demonstrated comparable values across various global climate models (GCMs). Consequently, the observed data values in the southern regions consistently rose. Various global studies consistently demonstrate an increase in rainfall throughout different time periods and geographical areas [
64]. These fluctuations are probably associated with changes in the frequency and intensity of precipitation, rising temperatures, and changes in land use. This susceptibility to water erosion in the studied region underscores the importance of addressing the evolving climate patterns for sustainable soil conservation and land management practices. As climate change continues to exert its influence, understanding the intricate dynamics of rainfall erosivity becomes a cornerstone in developing effective strategies to mitigate soil erosion. The mean annual R coefficient, exhibiting comparable values across various global climate models (GCMs), adds a level of robustness to our findings. This consistency suggests that, despite variations in model outputs, there is a coherent trend in the susceptibility of the region to water erosion. Numerous studies have characterized and evaluated soil erosion in central Asia and our results coincide with the results of the above studies, specifically those by Duulatov et al. [
11], Mukanov et al. [
10], and Gafforov et al. [
37], who conducted research at the central Asian scale.
The incorporation of climate projections from the CMIP6 GCM enhances the predictive capacity of our study. By integrating climatic data into the RUSLE model, we can simulate and anticipate potential alterations in soil erosion patterns under varying climatic scenarios. In our future research, hydrological models can be used to assess soil erosion under different land use types.
5. Conclusions
This study utilized observational data spanning 35 years and the RUSLE model with a combination of several GCMs to estimate soil erosion and evaluate the impact of various factors, including precipitation, soil characteristics, topography, land use, and conservation efforts, on erosion susceptibility in the Talas district. The findings indicate that the mean annual soil erosion rate observed over the study period ranges from 0 to 127 (t y−1). Approximately 56.29% of the study area exhibits a low susceptibility to soil erosion, with an additional 33.56% classified as a moderate risk and 7.36% deemed at high risk of erosion. Additionally, the main general circulation models (GCMs) used for the SSPs 245 and 585 were evaluated, focusing on temporal spans for the near and far future—specifically, 2040 (2026–2050), 2060 (2051–2075), and 2080 (2076–2100). The assessment revealed a moderate increase in precipitation levels compared with the reference point, with predicted growth rates of 21.4%, 24.2%, and 26.4% for the years 2030, 2050, and 2070, respectively. Moreover, the research highlighted a positive correlation between soil erosion and precipitation, evidenced by a proportional increase in average erosion of 34%, 35.5%, and 38.9% during the respective time periods. The integration of the RUSLE and GCMs furnishes actionable insights, empowering scientists and stakeholders to make informed decisions regarding land management, conservation, and climate resilience.