Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China.

In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Effects of drought and climate factors on vegetation dynamics in Central Asia from 1982 to 2020.

Ecological security and ecosystem stability in Central Asia depend heavily on the local vegetation. Vegetation dynamics and the response and hysteresis relationships to climate factors and drought on multiple scales over long time series in the region still need to be further explored. Using the net primary productivity (NPP) values as the vegetation change index of interest, in this study, we analyzed vegetation dynamics in Central Asia from 1982 to 2020 and assessed the responses and time lags of vegetation to climate factors and drought. The results showed that NPP gradually decreased from north to south and from east to west. Vegetation was distributed along both sides of the mountains. The temperatures rose from northeast to southwest, while precipitation gradually increased from southwest to northeast. The proportion of dry and wet years was as follows: normal (56.41%) > slightly dry (28.2%) > slightly humid (15.39%). Precipitation and drought conditions were positively correlated with NPP during the growing season, while temperature was negatively correlated with NPP. Increased spring temperature, precipitation, and drought conditions positively affected vegetation, while sustained summer temperature resulted in suppressed vegetation growth. Autumn vegetation was positively affected by temperature and drought, and precipitation was negatively correlated with autumn vegetation. Increasing winter temperatures promoted vegetation growth. The time lag between NPP and temperature gradually increased from northeast to southwest, and the time lag between NPP and precipitation gradually increased from south to north. Spring temperatures had the greatest beneficial impact on forestlands; summer climatic factors and drought had little effect on shrublands; the autumn climate exhibited small differences in its influence of each plant type; and winter temperatures had the greatest positive effect on grasslands. No time lag effect was found between any of the four vegetation types and precipitation. A one-month lag was found between cultivated lands and temperature; a two-month lag was found between forestlands and temperature; and a one-month lag was found between forestlands and drought and between shrublands and drought. The results can provide a scientific foundation for the sustainable development and management of ecosystems.

Predictions Based on Different Climate Change Scenarios: The Habitat of Typical Locust Species Is Shrinking in Kazakhstan and Xinjiang, China.

Climate change, especially climate extremes, can increase the uncertainty of locust outbreaks. The Italian locust (Calliptamus italicus (Linnaeus, 1758)), Asian migratory locust (Locusta migratoria migratoria Linnaeus, 1758), and Siberian locust (Gomphocerus sibiricus (Linnaeus, 1767)) are common pests widely distributed in the semidesert grasslands of Central Asia and its surrounding regions. Predicting the geographic distribution changes and future habitats of locusts in the context of climate warming is essential to effectively prevent large and sudden locust outbreaks. In this study, the optimized maximum entropy (MaxEnt) model, employing a combination of climatic, soil, and topographic factors, was used to predict the potential fitness areas of typical locusts in the 2030s and 2050s, assuming four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. Modeling results showed that the mean area under the curve (AUC) and true statistical skill (TSS) of the MaxEnt model reached 0.933 and 0.7651, respectively, indicating that the model exhibited good prediction performance. Our results showed that soil surface sand content, slope, mean precipitation during the hottest season, and precipitation seasonality were the key environmental variables affecting locust distribution in the region. The three locust species were mainly distributed in the upstream region of the Irtysh River, the Alatao Mountain region, the northern slopes of the Tianshan Mountains, around Sayram Lake, the eastern part of the Alakol Lake region, the Tekes River region, the western part of Ulungur Lake, the Ili River, and the upstream region of the Tarim River. According to several climate projections, the area of potential habitat for the three most common locust species will decrease by 3.9 × 104-4.6 × 104 km2 by the 2030s and by 6.4 × 104-10.6 × 104 km2 by the 2050s. As the climate becomes more extreme, the suitable area will shrink, but the highly suitable area will expand; thus, the risk of infestation should be taken seriously. Our study present a timely investigation to add to extensive literature currently appearing regarding the myriad ways climate change may affect species. While this naturally details a limited range of taxa, methods and potential impacts may be more broadly applicable to other locust species.

The potential habitat of desert locusts is contracting: predictions under climate change scenarios.

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 106 km2 and 1.55 × 106 km2, respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

Trend prediction of COVID-19 based on ARIMA model in mainland of China

The ongoing pandemic of COVID-19 has aroused widespread concern around the world and poses a severe threat to public health worldwide. In this paper, the autoregressive integrated moving average (ARIMA) model was used to predict the epidemic trend of COVID-19 in mainland of China. We collected the cumulative cases, cumulative deaths, and cumulative recovery in mainland of China from January 20 to June 30, 2020, and divided the data into experimental group and test group. The ARIMA model was fitted with the experimental group data, and the optimal model was selected for prediction analysis. The predicted data were compared with the test group. The average relative errors of actual cumulative cases, deaths, recovery and predicted values in each province are between -22.32%-22.66%, -9.52%-0.08%, -8.84%-1.16, the results of the comprehensive experimental group and test group show The error of fitting and prediction is small, the degree of fitting is good, the model supports and is suitable for the prediction of the epidemic situation, which has practical guiding significance for the prevention and control of the epidemic situation. HighlightsO_LIWe predicted future COVID-19 occurrences in mainland of China based on ARIMA model. C_LIO_LIWe validated the model based on the previous outbreak data with actual data for June, 2020. C_LIO_LIThe measures taken by the government have contained spread of the epidemic C_LIO_LIThe combination of multiple models may improve the robustness of the model C_LI