摘要：Land use/land cover (LULC) change and climate change are two major factors affecting the provision of ecosystem services which are closely related to human well-being. However, a clear understanding of the relationships between these two factors and ecosystem services in Central Asia is still lacking. This study aimed to comprehensively assess ecosystem services in Central Asia and analyze how they are impacted by changes in LULC and climate. The spatiotemporal patterns of three ecosystem services during the period of 2000–2015, namely the net primary productivity (NPP), water yield, and soil retention, were quantified and mapped by the Carnegie-Ames-Stanford Approach (CASA) model, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and Revised Universal Soil Loss Equation (RUSLE). Scenarios were used to determine the relative importance and combined effect of LULC change and climate change on ecosystem services. Then, the relationships between climate factors (precipitation and temperature) and ecosystem services, as well as between LULC change and ecosystem services, were further discussed. The results showed that the high values of ecosystem services appeared in the southeast of Central Asia. Among the six biomes (alpine forest region (AFR), alpine meadow region (AMR), typical steppe region (TSR), desert steppe region (DSR), desert region (DR), and lake region (LR)), the values of ecosystem services followed the order of AFR>AMR>TSR>DSR> DR>LR. In addition, the values of ecosystem services fluctuated during the period of 2000–2015, with the most significant decreases observed in the southeast mountainous area and northwest of Central Asia. LULC change had a greater impact on the NPP, while climate change had a stronger influence on the water yield and soil retention. The combined LULC change and climate change exhibited a significant synergistic effect on ecosystem services in most of Central Asia. Moreover, ecosystem services were more strongly and positively correlated with precipitation than with temperature. The greening of desert areas and forest land expansion could improve ecosystem services, but unreasonable development of cropland and urbanization have had an adverse impact on ecosystem services. According to the results, ecological stability in Central Asia can be achieved through the natural vegetation protection, reasonable urbanization, and ecological agriculture development.
Abstract: Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015–2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046–2075, but degenerates during 2015–2044 and 2076–2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.
摘要：Snow avalanches are a common natural hazard in many countries with seasonally snow-covered mountains. The avalanche hazard varies with snow avalanche type in different snow climate regions and at different times. The ability to understand the characteristics of avalanche activity and hazards of different snow avalanche types is a prerequisite for improving avalanche disaster management in the mid-altitude region of the Central Tianshan Mountains. In this study, we collected data related to avalanche, snowpack, and meteorology during four snow seasons (from 2015 to 2019), and analysed the characteristics and hazards of different types of avalanches. The snow climate of the mid-altitude region of the Central Tianshan Mountains was examined using a snow climate classification scheme, and the results showed that the mountain range has a continental snow climate. To quantify the hazards of different types of avalanches and describe their situation over time in the continental snow climate region, this study used the avalanche hazard degree to assess the hazards of four types of avalanches, i.e., full-depth dry snow avalanches, full-depth wet snow avalanches, surface-layer dry snow avalanches, and surface-layer wet snow avalanches. The results indicated that surface-layer dry snow avalanches were characterized by large sizes and high release frequencies, which made them having the highest avalanche hazard degree in the Central Tianshan Mountains with a continental snow climate. The overall avalanche hazard showed a single peak pattern over time during the snow season, and the greatest hazard occurred in the second half of February when the snowpack was deep and the temperature increased. This study can help the disaster and emergency management departments rationally arrange avalanche relief resources and develop avalanche prevention strategies.
摘要：Playing an important role in global warming and plant growth, relative humidity (RH) has profound impacts on production and living, and can be used as an integrated indicator for evaluating the wet-dry conditions in the arid and semi-arid area. However, information on the spatial-temporal variation and the influencing factors of RH in these regions is still limited. This study attempted to use daily meteorological data during 1966–2017 to reveal the spatial-temporal characteristics of RH in the arid region of Northwest China through rotated empirical orthogonal function and statistical analysis method, and the path analysis was used to clarify the impact of temperature (T), precipitation (P), actual evapotranspiration (ETa), wind speed (W) and sunshine duration (S) on RH. The results demonstrated that climatic conditions in North Xinjiang (NXJ) was more humid than those in Hexi Corridor (HXC) and South Xinjiang (SXJ). RH had a less significant downtrend in NXJ than that in HXC, but an increasingly rising trend was observed in SXJ during the last five decades, implying that HXC and NXJ were under the process of droughts, while SXJ was getting wetter. There was a turning point for the trend of RH in Xinjiang, which occurred in 2000. Path analysis indicated that RH was negatively correlated to T, ETa, W and S, but it increased with increase of P. S, T and W had the greatest direct effects on RH in HXC, NXJ and SXJ, respectively. ETa was the factor which had the greatest indirect effect on RH in HXC and NXJ, while T was the dominant factor in SXJ.
摘要：Short-term climate reconstruction, i.e., the reproduction of short-term (several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5 (CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit (CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region (TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature (MAT) and monthly accumulated precipitation (MAP) in the TMR using the delta, physical scaling (SP) and artiﬁcial neural network (ANN) methods. Performance and uncertainty during the calibration (1971–1999) and verification (1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis (RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty (positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty (negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850 (or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term (several decades) regional climate reconstruction and longer-term (100 a or more) assessments of regional climate change.
摘要： Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season (October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression (MLR) model (a conventional measuring method) and random forest (RF) model (a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R2 value (R2=0.74; R2, coefficient of determination) and a lower bias error (RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.