• Energy Research

The dramatic climate change has far-reaching impacts on vegetation in drylands such as Central Asia. Recent attempts to assess vegetation stability to short-term climate variability often account solely for vegetation sensitivity or resilience but ignore the composite effects of these two indicators. Meanwhile, our understanding of the vegetation stability at the seasonal scale remains insufficient. In this study, considering the cumulative effects of vegetation response to three key climate factors, we assessed the stability of vegetation in Central Asia using normalized difference vegetation index (NDVI) and the meteorological data from 1982 to 2014 by integrating vegetation sensitivity and resilience, and further identified the critical regions and seasons of vegetation that experience high risks of pending change. The results show that the sensitivity of vegetation has a strong correlation (R2 = 0.83, p < 0.001) with the aridity index (AI), with the vegetation of drier areas having lower sensitivities to climate variability. At the temporal scale, the sensitivity of vegetation to climate variability varied among different seasons. The average vegetation sensitivity index (VSI) is 41.17, 33.32 and 28.63 in spring, summer and autumn, respectively. Spatially, a trade-off between vegetation sensitivity and resilience is found both for the growing season (R2 = 0.67) and seasonal scale (R2 = 0.71, 0.32 and 0.43 for spring, summer and autumn, respectively), regions with high vegetation sensitivity were always accompanied by strong resilience. Based on the relationship between vegetation sensitivity and resilience, we further identify the critical regions and periods of vegetation with high change risk in Central Asia. Results suggest that herbaceous plants in semi-arid areas present high instability, especially in summer. This study offers a comprehensive perspective to assess vegetation stability to climate variability and the results will facilitate the protection of ecosystems and the implementation of sustainable development goals in Central Asia.

The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

The effects of global climate change threaten the availability of water resources worldwide and modify their tempo-spatial pattern. Properly quantifying the possible effects of climate change on water resources under different hydrological models is a great challenge in ungauged alpine regions. By using remote sensing data to support established models, this study aimed to reveal the effects of climate change using two models of hydrological processes including total water resources, peak flows, evapotranspiration, snowmelt and snow accumulation in the ungauged Hotan River Basin under future representative concentration pathway (RCP) scenarios. The results revealed that stream flow was much more sensitive to temperature variation than precipitation change and increased by 0.9–10.0% according to MIKE SHE or 6.5–10.5% according to SWAT. Increased evapotranspiration was similar for both models with a range of 7.6–31.3%. The snow-covered area shrank from 32.5% to 11.9% between the elevations of 4200–6400 m, respectively, and snow accumulation increased when the elevation exceeded 6400 m above sea level (asl). The results also suggested that the fully distributed and semi-distributed structures of these two models strongly influenced the responses to climate change. The study proposes a practical approach to assess the climate change effect in ungauged regions.

Managing water resources in alpine, cold regions is increasingly complicated by climate change, which alters streamflow patterns and poses significant challenges to resource sustainability. This study addresses the critical issue of understanding how distinct hydrological watersheds respond to these climatic shifts. By examining four watersheds (BRW, the Burqin River Watershed; MRW, the Manas River Watershed; URW, the Urumqi River Watershed, and HRW, the Hotan River Watershed) with varying streamflow recharged characteristics, long-term streamflow was constructed by the SWAT model and Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate the key climatic drivers of streamflow characteristics. Over the past 60 years, streamflow in typical watersheds has generally increased, with more frequent alternation between wet and dry periods. Notably, abrupt changes predominantly occurred in the 1990s, and these changes have shown a progressive delay as one moves geographically from north to south. Cold-season precipitation provides crucial solid water resources for alpine watersheds, with glacier melt driven by minimum and summer temperatures, while precipitation dominates other recharge systems. Future scenarios predict a significant increase in URW streamflow (approximately 18.3 %-25.8 %), while other watersheds are expected to see more moderate increases (around 4.6 %-12.9 %). Anticipated seasonal shifts include an earlier onset of streamflow advancing from April to March in the BRW, and delayed streamflow shifting to June in the HRW. Under the SSP5-8.5 scenario, the BRW shows a decrease in late summer and early autumn streamflow, while the URW may become increasingly dependent on rainfall, indicating a potential temperature threshold for streamflow changes. The earlier onset of spring snowmelt, combined with increased streamflow and reduced summer precipitation, heightens the risk of spring floods and summer droughts. Building emergency storage and adopting dynamic seasonal water allocation can reduce the impacts of streamflow changes and boost climate resilience.

Due to an absence of an essential daily data set, changing characteristics, and cause of flow extremes in the Tianshan Mountains are rarely explored in depth. In this study, daily based long-term meteorological and hydrological observation data were collected in four typical watersheds in the middle Tianshan Mountains; Manne-Kendall trend analysis and Pettit’s test were used to detect the trends and alterations of extreme flow series; Generalized Extreme Value distribution (GEV) and General Pareto distribution (GDP) models were used to describe the probability distributions of annual maximum (AM) and peaks over threshold (POT) series based on daily discharge; and the relationship between extreme flow and climate indices, were also investigated. The findings indicated that, change of the AM series at five hydrological stations experienced positive trends; the POT series generally showed no significant trends, while the peaks over threshold number (POTN) present a positive trend at the five stations. Change points exist in the POT and occurrence time of maximum daily discharge in spring (AM-SPR) series at the Kensiwate (KSWT) station in Manas watershed; the mean extreme flow decreased after 1986, and the occurrence time the annual maximum daily flow in spring significant forward after 1978. The AM series can well fit the GEV distribution, while the POT series fit the GDP distribution better; the GEV model performed worse in estimating flood events with high return period than low return period events. Moreover, acceleration of glacier melting lead to the magnitude and frequency increments of flood in the north slope; intensifying and frequent precipitation extremes are dominate factors of extreme flow variations in south slope watersheds which without large amount of glacier coverage; and continually temperature rising in spring and increased precipitation in winter lead to the change on magnitude and timing of spring extreme floods.

A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.

In the 21st century, heavier rainfall events and warmer temperatures in mountainous regions have significant impacts on hydrological processes and the occurrence of flood/drought extremes. Long-term modeling and peak flow detection of streamflow series are crucial in understanding the behavior of flood and drought. This study was conducted to analyze the impacts of future climate change on extreme flows in the Kaidu River Basin, northwestern China. The soil water assessment tool (SWAT) was used for hydrological modeling. The projected future precipitation and temperature under Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) scenarios were downscaled and used to drive the validated SWAT model. A generalized extreme value (GEV) distribution was employed to assess the probability distribution of flood events. The modeling results showed that the simulated discharge well matched the observed ones both in the calibration and validation periods. Comparing with the historical period, the ensemble with 15 general circulation models (GCMs) showed that the annual precipitation will increase by 7.9–16.1% in the future, and extreme precipitation events will increase in winter months. Future temperature will increase from 0.42 °C/10 a to 0.70 °C/10 a. However, with respect to the hydrological response to climate change, annual mean runoff will decrease by 21.5–40.0% under the mean conditions of the four RCP scenarios. A reduction in streamflow will occur in winter, while significantly increased discharge will occur from April to May. In addition, designed floods for return periods of five, 10 and 20 years in the future, as predicted by the GEV distribution, will decrease by 3–20% over the entire Kaidu watershed compared to those in the historical period. The results will be used to help local water resource management with hazard warning and flood control.

The variations of climate and water resources in the Buqtyrma River Basin (BRB), which is located at the cross-section of the Altai Mountains, Eurasian Steppe and Tian Shan Mountains, have a great significance for agriculture and ecosystems in the region. Changing climatic conditions will change the hydrological cycle in the whole basin. In this study, we examined the historical trends and change points of the climate and hydrological variables, the contributions of climate change and human activities to runoff changes, and the relative changes in the runoff to the precipitation and potential evapotranspiration from 1950 to 2015 by using the Mann–Kendall trend test, Pettitt test, double cumulative curve and elasticities methods. In addition, a multi-model ensemble (MME) of the six general circulation models (GCMs) for two future periods (2036–2065 and 2071–2100) was assessed to estimate the spatio-temporal variations in precipitation and temperature under two representative concentration pathways (RCPs 4.5 and 8.5) scenarios. Our study detected that the runoff change-point occurred in 1982. The impacts induced by climate change on runoff change were as follows—70% in the upstream, 62.11% in the midstream and 15.34% in the downstream area. The impacts of human activity on runoff change were greater in the downstream area (84.66%) than in the upstream and midstream areas. A continuously increasing trend was indicated regarding average annual temperature under RCP 4.5 (from 0.37 to 0.33 °C/decade) and under RCP 8.5 (from 0.50 to 0.61 °C/decade) during two future periods. Additionally, an increasing trend in predicted precipitation was exhibited under RCP 4.5 (13.6% and 19.9%) and under RCP 8.5 (10.5% and 18.1%) during both future periods. The results of the relative runoff changes to the predicted precipitation and potential evapotranspiration were expected to increase during two future time periods under RCP 4.5 (18.53% and 25.40%) and under RCP 8.5 (8.91% and 13.38%) relative to the base period. The present work can provide a reference for the utilization and management of regional water resources and for ecological environment protection.