Surface albedo is a quantitative indicator for land surface processes and climate modeling, and plays an important role in surface radiation balance and climate change. In this study, by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS), we analyzed the spatiotemporal variation, persistence status, land cover type differences, and annual and seasonal differences of surface albedo, as well as the relationship between surface albedo and various influencing factors (including Normalized Difference Snow Index (NDSI), precipitation, Normalized Difference Vegetation Index (NDVI), land surface temperature, soil moisture, air temperature, and digital elevation model (DEM)) in the north of Xinjiang Uygur Autonomous Region (northern Xinjiang) of Northwest China from 2010 to 2020 based on the unary linear regression, Hurst index, and Pearson's correlation coefficient analyses. Combined with the random forest (RF) model and geographical detector (Geodetector), the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated. The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer. The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south, showing a weak decreasing trend and a small and stable overall variation. Land cover types had a significant impact on the variation of surface albedo. The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation, and negatively correlated with NDVI, land surface temperature, soil moisture, and air temperature. In addition, the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons. To be specific, NDSI had the largest influence on surface albedo, followed by precipitation, land surface temperature, and soil moisture; whereas NDVI, air temperature, and DEM showed relatively weak influences. However, the interactions of any two influencing factors on surface albedo were enhanced, especially the interaction of air temperature and DEM. NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation, with an explanatory power greater than 92.00%. This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

To understand the characteristics of particulate matter (PM) and other air pollutants in Xinjiang, a region with one of the largest sand-shifting deserts in the world and significant natural dust emissions, the concentrations of six air pollutants monitored in 16 cities were analyzed for the period January 2013-June 2019. The annual mean PM2.5, PM10, SO2, NO2, CO, and O-3 concentrations ranged from 51.44 to 59.54 mu g m(-3), 128.43-155.28 mu g m(-3), 10.99-17.99 mu g m(-3), 26.27-31.71 mu g m(-3), 1.04-1.32 mg m(-3), and 55.27-65.26 mu g m(-3), respectively. The highest PM concentrations were recorded in cities surrounding the Taklimakan Desert during the spring season and caused by higher amounts of wind-blown dust from the desert. Coarse PM (PM10-2.5) was predominant, particularly during the spring and summer seasons. The highest PM2.5/PM10 ratio was recorded in most cities during the winter months, indicating the influence of anthropogenic emissions in winters. The annual mean PM2.5 (PM10) concentrations in the study area exceeded the annual mean guidelines recommended by the World Health Organization (WHO) by a factor of ca. similar to 5-6 (similar to 7-8). Very high ambient PM concentrations were recorded during March 19-22, 2019, that gradually influenced the air quality across four different cities, with daily mean PM2.5 (PM10) concentrations similar to 8-54 (similar to 26-115) times higher than the WHO guidelines for daily mean concentrations, and the daily mean coarse PM concentration reaching 4.4 mg m(-3). Such high PM2.5 and concentrations pose a significant risk to public health. These findings call for the formulation of various policies and action plans, including controlling the land degradation and desertification and reducing the concentrations of PM and other air pollutants in the region. (C) 2020 Elsevier Ltd. All rights reserved.

Drifting snow is a significant factor in snow redistribution and cascading snow incidents. However, field observations of drifting snow are relatively difficult due to limitations in observation technology, and drifting snow observation data are scarce. The FlowCapt sensor is a relatively stable sensor that has been widely used in recent years to obtain drifting snow observations. This study presents the results from two FlowCapt sensors that were employed to obtain field observations of drifting snow during the 2017-2018 snow season in the southern Altai Mountains, Central Asia, where the snow cover is widely distributed. The results demonstrate that the FlowCapt sensor can successfully acquire stable field observations of drifting snow. Drifting snow occurs mainly within the height range of 80-cm zone above the snow surface, which accounts for 97.73% of the total snow mass transport. There were three typical snowdrift events during the 2017-2018 observation period, and the total snowdrift flux caused during these key events accounted for 87.5% of the total snow mass transport. Wind speed controls the occurrence of drifting snow, and the threshold wind speed (friction velocity) for drifting snow is approximately 3.0 m/s (0.15 m/s); the potential for drifting snow increases rapidly above 3.0 m/s, with drifting snow essentially being inevitable for wind speeds above 7.0 m/s. Similarly, the snowdrift flux is also controlled by wind speed. The observed maximum snowdrift flux reaches 192.00 g/(m(2)center dot s) and the total snow transport is 584.9 kg/m during the snow season. Although drifting snow will lead to a redistribution of the snow mass, any accumulation or loss of the snow mass is also affected synergistically by other factors, such as topography and snow properties. This study provides a paradigm for establishing a field observation network for drifting snow monitoring in the southern Altai Mountains and bridges the gaps toward elucidating the mechanisms of drifting snow in the Altai Mountains of Central Asia. A broader network of drifting snow observations will provide key data for the prevention and control of drifting snow incidents, such as the design height of windbreak fences installed on both sides of highways.

Black carbon (BC), which consists of the strongest light-absorbing particles (LAPs) in snow, has been regarded as a potential factor accelerating regional climate change and the melting of snow cover globally. In this study, we used remote sensing (Moderate-resolution Imaging Spectroradiometer, MODIS) observations combined with a snow albedo model (Snow, Ice, and Aerosol Radiation, SNICAR) and a radiative transfer model (Santa Barbara DISORT Atmospheric Radiative Transfer, SBDART) to retrieve the radiative forcing (RF) by BC in snow (R-MODIS(BC)) across Xinjiang, China, for the first time. The observations in January-February show that the concentrations of BC (equivalent BC) in snow ranged from 44.08 to 1949.9 ng g(-1), with an average of 536.71 ng g(-1). The lowest concentrations of BC were on the border of the Altay region (AR), with a median concentration in snow of 98.5 ng g(-1). South of this area in the industrial region (Tianshan Mountain North Slope Economic Development Belt, TMNSEDB), the median concentration of BC in snow was 913.2 ng g(-1) R-MODIS(BC) presents distinct spatial variability, with the minimum (3.01W m(-2)) in the AR and the maximum (40.2W m(-2)) near industrial areas in TMNSEDB. The regional mean R-MODIS(BC) was 20.43 +/- 7.3 W m(-2) in Xinjiang, and the average values of the impurity index (I-LAPs) and SGS in the region were 0.273 and 241.38 mu m, respectively. Moreover, based on the multiple linear regressions, the BC emission intensity values were significantly correlated with I-LAPs and RF, and the correlation reached 0.681 and 0.661, respectively; thus, the BC emission could explain above 75% of the spatial variance of BC contents in TMNSEDB, confirming the reasonability of the spatial patterns of retrieved RFMODISBC in Xinjiang. Additionally, we found that the distribution of R-MODIS(BC) in northern Xinjiang is dominated by I-LAPs and BC emissions. We validated R-MODIS(BC) using in situ RF estimates (R-site(estimate)), and the error was 24.05 W m(-2); furthermore, the biases in R-MODIS(BC) were negatively correlated with the BC concentrations and ranged from 24.3% to 326% in Xinjiang.

Light-absorbing impurities (LAIs, e.g. black carbon (BC), organic carbon (OC), mineral dust (MD)) deposited on snow cover reduce albedo and accelerate its melting. Northern Xinjiang (NX) is an arid and semi-arid inland region, where snowmelt leads to frequent floods that have been a serious threat to local ecological security. There is still a lack of quantitative assessments of the effects of LAIs on snowmelt in the region. This study investigates spatial variations of LAIs in snow and its effect on snow albedo, radiative forcing (RF) and snowmelt across NX. Results showed that concentrations of BC, OC (only water-insoluble OC), MD ranged from 32 to 8841 ng g(-1), 77 to 8568 ng g(-1) and 0.46 to 236 mu g g(-1) respectively. Weather Research and Forecasting Chemistry model suggested that residential emission was the largest source of BC. Snow, Ice, and Aerosol Radiative modelling showed that the average contribution of BC and MD to snow albedo reduction was 17 and 3%, respectively. RF caused by BC significantly exceeded RF caused by MD. In different scenarios, changes in snow cover duration (SCD) caused by BC and MD decreased by 1.36 +/- 0.61 to 6.12 +/- 3.38 d. Compared with MD, BC was the main dominant factor in reducing snow albedo and SCD across NX.

Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e.,Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Nino-southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April-October) and the water year, which is from October of the previous year to September of the current year for a period from 1955-2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin. (C) 2013 American Society of Civil Engineers.