Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang

Accurate estimation of reference evapotranspiration is a key step in irrigation and water resources planning. The Penman Monteith (FAO56-PM) formula recommended by FAO56-PM is the standard for calculating the reference evapotranspiration. However, the FAO56-PM model is limited in the observation of meteorological variables, so it is necessary to choose an alternative ET0 model which requires less meteorological data. Based on the daily climate data of eight meteorological stations in northern Xinjiang from 2000 to 2020, seven empirical models (Hargreaves, Berti, Dorji, Dalton, Meyer, WMO, Albrecht) and four optimization algorithms (RF model, LS-SVR model, Bi-LSTM model and GA-BP model) combined with seven different parameters were evaluated comprehensively. The results show that the accurate of the empirical model based on temperature is obviously better than the empirical model based on air mass transport. The annual and multi-year alternative ET0 models of different input parameter combinations are: LS-SVR1, RF2, LS-SVR3, LS-SVR4, GA-BP5, LS-SVR6, GA-BP7. It can be used as a substitute for the reference evapotranspiration model without relevant meteorological data. Only the LS-SVR6 model and GA-BP7 model are recommended as the best alternative models for northern Xinjiang reference evapotranspiration at daily, monthly and seasonal scales.

Integrating Sentinel-1/2 Data and Machine Learning to Map Cotton Fields in Northern Xinjiang, China

Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting.

Moisture Transport and Sources of the Extreme Precipitation Over Northern and Southern Xinjiang in the Summer Half-Year During 1979–2018

Based on the output data from the Lagrangian flexible particle dispersion model (FLEXPART), we analyze the pathways of moisture to identify the moisture source areas for extreme precipitation in the summer half-year (April–September) over northern and southern Xinjiang, respectively. For both northern and southern Xinjiang, the local evaporation plays a decisive role for extreme precipitation in the summer half-year, of which contribution ratio accounts for 24.5% to northern Xinjiang and 30.2% to southern Xinjiang of all identified source areas. In addition, central Asia and northwestern Asia are the major moisture source areas as well and contribute similarly to extreme precipitation relative to local evaporation. For northern Xinjiang, central Asia surpasses northwestern Asia, and each of them contributes 24.1 and 18.8%, whereas northwestern Asia is somewhat more crucial than central Asia for southern Xinjiang, accounting 22.1 and 19.1%, respectively. Note that the three top-ranked moisture source areas make up a large proportion of total sources. Regarding the remaining source areas that also provide moisture, the contributions are entirely different for southern and northern Xinjiang. Originating from the North Atlantic Ocean, Europe, and the Mediterranean Sea, some water vapor enters northern Xinjiang and converge to precipitate, while this process is barely detectable for extreme precipitation over southern Xinjiang, which is affected by the westerly flow. On the contrary, the Arabian Sea, the Arabian Peninsula, and the Indian Peninsula contribute, even though slightly, to extreme precipitation over southern Xinjiang, which indicates that the meridional transport pathways from the Arabian Sea can carry moisture to this inland region.

Moisture Changes in the Northern Xinjiang Basin Over the Past 2400 years as Documented in Pollen Records of Jili Lake

Regional humidity is important for terrestrial ecosystem development, while it differs from region to region in inland Asia, knowledge of past moisture changes in the lower basin of northern Xinjiang remainly largely unclear. Based on a pollen record from Jili Lake, the Artemisia/(Amaranthaceae + Ephedra) (Ar/(Am + E)) ratio, as an index of regional humidity, has recorded four relatively dry phases: 1) 400 BCE to 1 CE, 2) the Roman Warm Period (RWP; c. 1–400 CE), 3) the Medieval Warm Period (MWP; c. 850–1200 CE) and 4) the Current Warm Period (CWP; since 1850 CE). In contrast, the Dark Age Cold Period (DACP; c. 400–850 CE) and the Little Ice Age (LIA; c. 1200–1850 CE) were relatively wet. Lower lake levels in a relatively humid climate background indicated by higher aquatic pollen (Typha and Sparganium) after c. 1700 CE are likely the result of intensified irrigation for agriculture in the catchment as documented in historical records. The pollen Ar/(Am + E) ratio also recorded a millennial-scale wetting trend from 1 CE to 1550 CE which is concomitant with a long-term cooling recorded in the Northern Hemisphere.

Improving Forecast Accuracy with an Auto Machine Learning Post-Correction Technique in Northern Xinjiang

Reliable meteorological forecasts of temperature and relative humidity are critically important to take necessary measures to avoid potential damage and losses. An operational meteorological forecast model based on the Weather Research and Forecast (WRF) model has been built in Xinjiang. Numerical forecasts usually have significant uncertainties and errors due to imperfections in models themselves. In this study, a straightforward automated machine learning (AutoML) approach has been developed to post-process the raw forecasts of the WRF model. The method was implemented and evaluated to post-process forecasts from 13 stations in northern Xinjiang. The post-processed temperature forecasts were significantly improved from the raw forecasts, with average RMSE values in the 13 stations decreasing from 3.24 °C to 2.34 °C by a large margin of 28%. As for relative humidity, the mean RMSE at 13 stations decreased from 19.54% to 11.54%, or it showed a percentage decrease of 41%. Meanwhile, biases were also significantly decreased, with average ME values being reduced from around 2 °C to ~0.33 °C for temperature and improved from −15.6% to ~0% for relative humidity. Moreover, forecast performance values after post-correction became much closer to each other than raw forecast performance values, improving forecast applicability at regional scales.

Spatio-temporal changes of AOD in Xinjiang of China from 2000 to 2019: Which factor is more influential, natural factor or human factor?

Aerosol optical depth (AOD), which represents the optical attenuation, poses a major threat to the production activity, air quality, human health and regional sustainable development of arid and semi-arid areas. To some degree, AOD shows areal air pollution level and possesses obvious spatio-temporal characteristics. However, long-time sequences and detailed AOD information can not be provided due to currently limited monitoring technology. In this paper, a daily AOD product, MODIS-based Multi-angle Implementation of Atmospheric Correction (MAIAC), is deployed to analyze the spatio-temporal characteristics in Xinjiang Uygur Autonomous Region from 2000 to 2019. In addition, the importance of influencing factors for AOD is calculated through Random Forest (RF) Model and the propagation trajectories of pollutants are simulated through Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. Spatio distribution of AOD presents a tendency that AOD value in northern Xinjiang is low while the value in southern Xinjiang is high. Regions with high AOD values are mainly concentrated in Tarim Basin. AOD in southern Xinjiang is the highest, followed by that in eastern Xinjiang and AOD value in northern Xinjiang is the lowest. Seasonal variation of AOD is significant: Spring (0.309) > summer (0.200) > autumn (0.161) > winter (0.158). Average AOD value in Xinjiang is 0.196. AOD appears wavy from 2000 to 2014 with its low inflection point (0.157) appearing in 2005, and then increases, reaching its peak in 2014 (0.223). The obvious downward tendency after 2014 shows that the use of coal to natural gas (NG) conversion project improves the conditions of local environment. According to RF Model, NG contributes most to AOD. HYSPLIT Model reveals that aerosol in southern Xinjiang is related to the short-distant carriage of dust aerosol from the Taklimakan Desert. Aerosol there can affect Inner Mongolia through long-distant transport. Blocked by the Tianshan Mountains, fine dust particles can not cross the Tianshan Mountains to become a factor contributing to AOD in northern Xinjiang.

Prevalence and Characteristics of Staphylococcus aureus Isolated From Retail Raw Milk in Northern Xinjiang, China

Staphylococcus aureus is one of the main pathogens causing mastitis in dairy animals worldwide. It is an important opportunistic pathogen of raw milk, and the enterotoxin causes significant food poisoning. Monitoring the antibiotic resistance of S. aureus in raw milk is helpful for a risk assessment of S. aureus. In this study, 62 strains (43.1%) of S. aureus were isolated from 144 retail raw milk samples of different varieties from four regions in northern Xinjiang, China. Among them, the isolation rates at Shihezi, Hami, Altay, and Tacheng were 58.1% (54/93), 12.9% (4/31), 18.2% (2/11), and 22.2% (2/9), respectively. The isolation rate of positive strains in cow milk samples was the highest (61.7%, 37/60), followed by camel milk (35.9%, 23/64), and horse milk (10.0%, 2/20). The results of the classical virulence genes test showed that 12.9% (8/62) of the isolates carried at least one virulence gene. The main genotype was see (6.5%, 4/62), followed by sea+sec (3.2%, 2/62), sea (1.6%, 1/62), and sec (1.6%, 1/62). The analysis of 13 resistance genes and the susceptibility to 12 different antibiotics of 62 isolates showed that 80.6% (50/62) of the strains were resistant to at least one antibiotic, and 46.8% (29/62) were resistant to three or more antibiotics. The isolated strains had the highest resistance rate to penicillin (72.6%, 45/62), and 25.8% (16/62) of the isolates carried the blaZ resistance gene. In addition, 32 strains (51.6%, 32/62) of methicillin-resistant S. aureus were detected. All isolates had the ability to form biofilms. The pulsed-field gel electrophoresis results showed that the 47 isolates revealed 13 major pulsotypes (P1–P13) and 26 subtypes with 80% similarity, indicating the overall genetic diversity in the distribution area and sources of the samples. These findings indicate that S. aureus causes serious pollution of raw milk in northern Xinjiang, which has a negative effect on public health. Therefore, control measures and continuous monitoring should be undertaken to ensure the quality and safety of raw milk.