During recent years, severe air-pollution problems have garnered worldwide attention due to their effects on human health and the environment. Air pollution in Bishkek, Kyrgyz Republic, is an ever-increasing problem with little research conducted on the impact of air pollutants on public health. We evaluate the performance of several machine learning algorithms applied to air quality and meteorology datasets and compare prediction accuracies of Bishkek air quality given its significant public importance. Data on 16 synoptic atmospheric process were collected by Kyrgyzhydromet from 2016 to 2018 and used to train and build a forecasting model. The model was then tested using data collected in 2020. Climate change in Bishkek and the impact on air pollution was assessed via the frequency of days characterized by daytime temperature inversions and air stagnation. Atmospheric stability increased from 2015 to 2020 with ongoing climate change leading to more temperature inversions. About 80%-90% of days with temperature inversions are associated with winter heating seasons and these numbers increased two-fold during the past 5 years. The impact of lockdown during COVID-19 (22 March - 11 May 2020) on air quality in Bishkek is also shown. During the lockdown period, CO, NO, NO2, SO2, and PM2.5 decreased by 64%, 1.5%, 75%, 24%, and 54%, respectively, compared to concentrations of these pollutants in 2019. Where identified, emissions from vehicles make up a significant part of the air pollution.

Drought is a perilous agrometeorological phenomenon that often causes crop damage in arid and semi-arid regions vulnerable to climate variability. However, accurate drought monitoring remains deficient in many countries, including Kyrgyzstan, and the interconnections between several types of drought and contributions to crop yield are still unclear. Hence, we aimed to determine the propagation time in three types of drought (meteorological drought, soil drought and vegetation drought) for understanding interconnections of them. Moreover, we focused on comprehensively evaluation the performance of multiple drought indices for each type over the complex terrain of Kyrgyzstan, especially for drought index of synergistic land surface temperature and vegetation conditions information. The results demonstrated that Standard Precipitation Index (SPI) effectively detected meteorological drought, while the vegetation health index (VHI) coupled with temperature data was optimal for vegetation drought monitoring in Kyrgyzstan. Furthermore, our findings indicated a one-month response time for soil drought at a 10 cm depth to SPI, and a four-month response time at a 40 cm depth to meteorological drought (SPI). The response time of VHI to soil drought condition index (SMCI) was approximately one month, regardless of whether the soil drought occurred at a depth of 10 cm or 40 cm. In general, the response time of VHI to SPI was three months. Finally, by analyzing the correlation between crop yield productivity and drought indices, we discovered that the crop yield predictions by the three types of drought were differential and complex, but VHI was the most effective index. At the same time, VHIacc(May-Sep.), SMCIr(0-40cm)_May-Sep. and SPI5_Aug. have different contributions to crop yield variations, and these are also differences in their impacts on different crops and provinces.The synergistic effect of the three types of drought may significantly improve crop yield prediction in Kyrgyzstan in future studies. These findings may significantly contribute to drought prevention and mitigation in drought-prone Central Asian countries.

Climate change is one of the greatest challenges in Kyrgyzstan. There have been negative spillover effects in agriculture. This study aims to assess the climate change impacts on cropland suitability in Kyrgyzstan. We used the random forest algorithm to develop a model that captures the effects of multiple climate and environment factors at a spatial resolution of 1 km². The model was then applied in the scenario analysis for an understanding of how climate change affects cropland distribution. The potential high-quality cropland was found to be included in existing croplands, while the remaining were distributed around the Chu-Talas valley, the Issyk-kul area, and the Fergana valley. These potential high-quality croplands comprise grasslands (47.1%) and croplands (43.7%). In the future, the potential high-quality cropland exhibited inland trends at the periphery of original cropland category, with grassland and cropland as the primary land components. Due to climate change, potential high-quality cropland is expected to gradually reduce from the 2050s to the 2070s, exhibiting the largest reduction in potential high-quality areas for the Representative Concentration Pathway 8.5 scenario. Therefore, the short- and long-term adaptation strategies are needed for prioritizing the croplands to ensure food security and agricultural resilience.

Mountain regions of Central Asia are experiencing strong influences from climate change, with significant reductions in snow cover and glacial reserves. A comprehensive assessment of the potential consequences under the worst-case climate scenario is vital for adaptation measures throughout the region. Water balance analysis in the Naryn River basin was conducted for the baseline period of 1981-2000 including potential changes under the worst-case SSP5-8.5 scenario for 2077-2096 by combining high-resolution (5 km) regional climate projections with fully distributed glacio-hydrological (1 km) modeling. Results showed that with the complete degradation of glaciers and increase in evapotranspiration, the overall runoff will decrease by 16%, and in the upper basins, the reduction will exceed 40%. The maximum snow water equivalent (SWE) is projected to decrease by 17%, and the seasonal peak of SWE will occur one month earlier. The transition from snow to rain will significantly affect lower regions, increasing extremes in peak runoff and causing 10-year recurrence interval events to occur every 3-4 years. Moreover, extreme runoff in high mountainous areas will increase due to intensified snowmelt and increased rainfall extremes. Additionally, a gradient of surface soil temperature change of 0.1 °C per 100 m elevation gain was observed, suggesting a potential snow-albedo feedback effect that could further amplify the warming, especially at higher altitudes. This study provides a robust analytical framework to assess the complex responses of mountain ecosystems to the impacts of climate change, with the potential of widespread application for addressing the challenges facing these critical regions.

Climate change poses various challenges for agriculture and water management practices in Central Asia (CA). Central to these challenges are cryosphere dynamics, fragile mountain ecosystems, and ongoing natural hazards that highlight the need for robust projections of regional climate change. For the first time, dynamic downscaling was conducted in Central Asia at a spatial resolution of 5 km. This produced a regional dataset that incorporated periods between 1980 and 2000 and 2076 to 2096. Results show that dynamic downscaling significantly improves the simulation of temperature and precipitation across CA compared to General Circulation Models (GCMs) and other Regional Climate Models (RCMs) due to better representation of topography and related meteorological fields. Our analysis shows that there will be a significant warming trend in Central Asia with a projected increase of 6°C under the Shared Socioeconomic Pathway (SSP) scenario SSP5-8.5 from 2076 to 2096. Pronounced warming is detected over mountainous areas of CA from autumn to spring, which can be explained by the snow-albedo feedback. Precipitation increases are projected from winter to spring and decreases are projected from summer to autumn.

Tree-ring-width chronologies for 33 samples of Picea abies (L.) Karst. were developed, and a relationship between tree growth and hydrometeorological features was established and analyzed. Precipitation, temperature, and discharge records were extrapolated to understand past climate trends to evaluate the accuracy of global climate models (GCMs). Using Machine Learning (ML) approaches, hydrometeorological records were reconstructed/extrapolated back to 1886. An increase in the mean annual temperature (Tmean_a) increased the mean annual discharge (Dmean_a) via glacier melting; however, no temporal trends in annual precipitation were detected. For these reconstructed climate data, root-mean-square error (RMSE), Taylor diagrams, and Kling–Gupta efficiency (KGE) were used to evaluate and assess the robustness of GCMs. The CORDEX REMO models indicated the best performance for simulating precipitation and temperature over northern Tien Shan; these models replicated historical Tmen_a and P_a quite well (KGE = 0.24 and KGE = 0.24, respectively). Moreover, the multi-model ensembles with selected GCMs and bias correction can significantly increase the performance of climate models, especially for mountains region where small-scale orographic effects abound.