Explore the vast range of titles available.

Creating a vital and lively urban environment is an inherent requirement of urban sustainable development, and understanding urban vibrancy is helpful for urban development policy making. The urban vibrancy theory needs more empirical supplementation and more evidence for the effect of the built environment on urban vibrancy. We use multisource urban spatial information data, including real-time population distribution (RPD) data and small catering business (SCB) data; quantitatively measure urban vibrancy; and build a comparative framework to explore the effect of the built environment on the urban vibrancy of a northwestern emerging city in China. The results demonstrate that the two urban vibrancy metrics present a spatial distribution pattern that is high in the south and low in the north areas of the city with significant spatial aggregation. Land-use intensity and diversity have strong positive effects on urban vibrancy but present a different pattern of effects on the two vibrancy measures. The influences on urban vibrancy of distance to the district center and distance to the nearest commercial complex are spatially complementary in the study area, and the effect of accessibility factors is weak. Our findings suggest that a somewhat cautious approach is required in the application of these classical planning theories to Urumqi.

Extreme heat events in the summer of 2022 were observed in Eurasia, North America and China. Glaciers are a unique indicator of climate change, and the European Alps experienced substantial glacier mass loss as a result of the conditions in 2022, which prompted a wide range of community concerns. However, relevant findings for glaciers in China have not been currently reported. Here, we document the response of Urumqi Glacier No. 1 in the eastern Tien Shan to the extreme heat observed in 2022 based on in situ measurements that span more than 60 years. In 2022, Urumqi Glacier No. 1 exhibited the second largest annual mass loss on record, and the summer mass balance was the most negative on record. The hottest summer on record and relatively lower solid precipitation ratio contributed to the exceptional mass losses at Urumqi Glacier No. 1 in 2022, demonstrating the significant influence of heatwaves on extreme glacier melt in China.

In this study, energy and mass balance is quantified using an energy balance model to represent the glacier melt of Urumqi Glacier No. 1, Chinese Tian Shan. Based on data from an Automatic Weather Station (4025 m a.s.l) and the mass balance field survey data nearby on the East Branch of the glacier, the “COupled Snowpack and Ice surface energy and Mass balance model” (COSIMA) was used to derive energy and mass balance simulations during the ablation season of 2018. Results show that the modeled cumulative mass balance (−0.67 ± 0.03 m w.e.) agrees well with the in-situ measurements (−0.64 ± 0.16 m w.e.) (r2 = 0.96) with the relative difference within 5% during the study period. The correlation coefficient between modeled and observed surface temperatures is 0.88 for daily means. The main source of melt energy at the glacier surface is net shortwave radiation (84%) and sensible heat flux (16%). The energy expenditures are from net longwave radiation (55%), heat flux for snow/ice melting (32%), latent heat flux of sublimation and evaporation (7%), and subsurface heat flux (6%). The sensitivity testing of mass balance shows that mass balance is more sensitive to temperature increase and precipitation decrease than temperature decrease and precipitation increase.

This study deploys RTK-GNSS in 2012, TLS in 2015 and UAV in 2018 to monitor the changes of Urumqi Glacier No. 1 (UG1), eastern Tien Shan, and analyzes the feasibility of three technologies in monitoring the mountain glaciers. DEM differencing shows that UG1 has experienced a pronounced thinning and mass loss for the period of 2012–18. The glacier surface elevation change of −0.83 ± 0.57 m w.e. a−1 has been recorded for 2012–15, whereas the changes of glacier tongue surface elevation in 2015–18 and 2012–18 were −2.03 ± 0.95 and −1.34 ± 0.88 m w.e. a−1, respectively. The glacier area shrunk by 0.07 ± 0.07 × 10−3 km2 and the terminus retreat rate was 6.28 ± 0.83 m a−1 during 2012–18. The good agreement between the glaciological and geodetic specific mass-balances is promising, showing the combination of the three technologies is suitable to monitor glacier mass change. We recommend application of the three technologies to assess each other in different locations of the glacier, e.g. RTK-GNSS base stations, ground control points, glacier tongue and terminus, in order to avoid the inherent limitations of each technology and to provide reliable data for the future studies of mountain glacier changes in western China.

Automatic determination of quantitative parameters describing the pattern of urban expansion is extremely important for urban planning, urban management and civic resource configuration. Though the widely adopted LEI (landscape expansion index) has exhibited the potential to capture the evolution of urban landscape patterns using multi-temporal remote sensing data, erroneous determination still exists, especially for patches with special shapes due to the limited consideration of spatial relationships among patches. In this paper, we improve the identification of urban landscape expansion patterns with an enhancement of the topological relationship. We propose MCI (Mean patch Compactness Index) and AWCI (Area-Weighted Compactness Index) in terms of the moment of inertia shape index. The effectiveness of the improved approach in identifying types of expansion patches is theoretically demonstrated with a series of designed experiments. Further, we apply the proposed approaches to the analysis of urban expansion features and dynamics of urban compactness over Urumqi at various 5-year stages using available SUBAD-China data from 1990–2015. The results achieved by the theoretical experiments and case application show our approach effectively suppressed the effects induced by shapes of patches and buffer or envelope box parameters for the accurate identification of patch type. Moreover, the modified MCI and AWCI exhibited an improved potential in capturing the landscape scale compactness of urban dynamics. The investigated 25-year urban expansion of Urumqi is dominated by edge-expansion patches and supplemented by outlying growth, with opposite trends of increasing and decreasing, with a gradual decrease in landscape fragmentation. Our examination using the proposed MCI and AWCI indicates Urumqi was growing more compact in latter 15-year period compared with the first 10 years studied, with the primary urban patches tending to be compacted earlier than the entire urban setting. The historical transformation trajectories based on remote sensing data show a significant construction land gain—from 1.06% in 1990 to 6.96% in 2015—due to 289.16 km2 of recently established construction, accompanied by fast expansion northward, less dynamic expansion southward, and earlier extension in the westward direction than eastward. This work provides a possible means to improve the identification of patch expansion type and further understand the compactness of urban dynamics.

To get a better overview of atmosphere-driven mass changes at Urumqi Glacier No.1, Chinese Tien Shan, the surface energy budget and mass balance is modeled by linking the COupled Snowpack and Ice surface energy and MAss balance model (COSIMA) with in-situ measured meteorological records for the ablation period in 2018. The COSIMA is calibrated by manual optimization and the modeled results agree well with the in-situ surface temperature, snow height and seasonal mass balance. Our results reveal that Urumqi Glacier No.1 experienced a significant mass loss, with an average value of − 0.77 m w.e. over the ablation period 2018. The surface energy budget components can be classified into two categories: radiation (shortwave and longwave) and turbulent fluxes. Surface melt and solid precipitation were dominated components of mass balance. The COSIMA can reproduce the glaciological mass balance compared with other models. Sensitivity analysis showed that the mass balance was more sensitive to the temperature than precipitation, and mass loss caused by temperature increase of 1 K needed to be compensated by at least 40% precipitation increase. Air temperature during the ablation period was more important than annual precipitation in controlling mass balance changes on Urumqi Glacier No. 1. These findings will enhance our understanding of the mechanisms underlying mass balance processes of ablation period and their contribution to the acceleration of glacier retreat in Tien Shan.

It is important to determine long-term changes in vegetation cover, and the associated driving forces, to better understand the natural and human-induced factors affecting vegetation growth. We calculated the fractional vegetation coverage (FVC) of the Urumqi River basin and selected seven natural factors (the clay and sand contents of surface soils, elevation, aspect, slope, precipitation and temperature) and one human factor (land use type). We then used the Sen–Man–Kendall method to calculate the changing trend of the FVC from 2000 to 2020. We used the optimal parameters-based geographical detector (OPGD) model to quantitatively analyze the influence of each factor on the change in vegetation coverage in the basin. The FVC of the Urumqi River basin fluctuated from 2000 to 2020, with average values between 0.22 and 0.33. The areas with no and low vegetation coverage accounted for two-thirds of the total area, whereas the areas with a medium, medium–high and high FVC accounted for one-third of the total area. The upper reaches of the river basin are glacial and forest areas with no vegetation coverage and a high FVC. The middle reaches are concentrated in areas of urban construction with a medium FVC. The lower reaches are in unstable farmland with a medium and high FVC and deserts with a low FVC and no vegetation. From the perspective of the change trend, the areas with an improved FVC accounted for 62.54% of the basin, stable areas accounted for 5.66% and degraded areas accounted for 31.8%. The FVC showed an increasing trend in the study area. The improvement was mainly in the areas of urban construction and desert. Degradation occurred in the high-elevation areas, whereas the transitional zone was unchanged. The analysis of driving forces showed that the human factor explained more of the changes in the FVC than the natural factors in the order: land use type (0.244) > temperature (0.216) > elevation (0.205) > soil clay content (0.172) > precipitation (0.163) > soil sand content (0.138) > slope (0.059) > aspect (0.014). Apart from aspect, the explanatory power (Q value) of the interaction of each factor was higher than that of the single factor. Risk detection showed that each factor had an interval in which the change in the FVC was inhibited or promoted. The optimum elevation interval of the study area was 1300–2700 m and the greatest inhibition of the FVC was seen above 3540 m. Too much or too little precipitation inhibited vegetation coverage.

Originating in the Tian Shan mountains, Urumqi River plays a key role in terms of water supply to downstream areas. In its headwaters, Urumqi Glacier No. 1 (UG1) is the largest glacier contributing to water discharge. Assessing its response to the changing climatic conditions in the area is of major importance to quantify future water availability. We here apply COSIPY, a COupled Snowpack and Ice surface energy and mass balance model in PYthon, to UG1, implementing a new albedo parameterization which integrates site-specific bare-ice albedo values on a pixel-by-pixel basis observed by remote sensing. We assess model performance threefold: quantitatively based on long-term measurement data of (1) surface mass balance (SMB) and (2) water discharge as well as qualitatively (3) comparing simulated snow line altitudes to such imated on the basis of time-lapse photography. Comparison of the modeled SMB with annually-averaged data from ablation stakes reveals that COSIPY including the new albedo parameterization accounts for 57.6% of the variance observed in the measurements. The original albedo parameterization performs only slightly inferior (57.1%). Glacier-wide comparison between modeled and glaciological SMB shows high agreement. In terms of discharge prediction, COSIPY reproduces onset and duration of the discharge season well. Estimated discharge from the whole catchment shows shortcomings in exactly matching the measured times series, but interannual variability is captured.

Climate change can modify regional wind power generation ability, as it may affect wind speed. Here, we developed a multivariate copula downscaling (MvCD) approach to statistically downscale the near-surface wind speed of CMIP5 global climate models (GCMs) to the scale of wind farms in Urumqi, China. The low computational cost and high random analysis capability of this approach allowed the rapid assessment of projected changes and randomness from nine GCMs, spanning a range of potential futures under four scenarios. Simulation data from multiple GCMs and historical data of the study area were incorporated into the MvCD to generate a high dimensional multivariate copula. Thereafter, the high dimensional multivariate copula was further used to identify future wind speed patterns based on multiple GCMs under different CO2 emission scenarios. The estimated amount of wind power generation was obtained using future wind speed data. Results revealed the regional characteristics and periodicity of wind speed for Urumqi in the future. Wind power generation results revealed the impacts of climate changes on regional wind power generation and indicated that high wind speeds would occur from June to September and low wind speeds would occur from December to March in future scenarios. Wind speed would be more extreme under each scenario in the future than before. The highest and lowest wind speeds will increase and decrease, respectively. Sustained high winds would increase the potential of wind power generation in the future. Wind instability based on CO2 emission increases will lead to wind power being curtailed and low wind-power generation.