Influenza as a severe infectious disease has caused catastrophes throughout human history, and every pandemic of influenza has produced a great social burden. We compiled monthly data of influenza incidence from all provinces and autonomous regions in mainland China from January 2004 to December 2011, comprehensively evaluated and classified these data, and then randomly selected 4 provinces with higher incidence (Hebei, Gansu, Guizhou, and Hunan), 2 provinces with median incidence (Tianjin and Henan), 1 province with lower incidence (Shandong), using time series analysis to construct an ARIMA model, which is based on the monthly incidence from 2004 to 2011 as the training set. We exerted the X-12-ARIMA procedure for modeling due to the seasonality these data implied. Autocorrelation function (ACF), partial autocorrelation function (PACF), and automatic model selection were to determine the order of the model parameters. The optimal model was decided by a nonseasonal and seasonal moving average test. Finally, we applied this model to predict the monthly incidence of influenza in 2012 as the test set, and the simulated incidence was compared with the observed incidence to evaluate the model's validity by the criterion of both percentage variability in regression analyses (R2) and root mean square error (RMSE). It is conceivable that SARIMA (0,1,1)(0,1,1)12 could simultaneously forecast the influenza incidence of the Hebei Province, Guizhou Province, Henan Province, and Shandong Province; SARIMA (1,0,0)(0,1,1)12 could forecast the influenza incidence in Gansu Province; SARIMA (3,1,1)(0,1,1)12 could forecast the influenza incidence in Tianjin City; and SARIMA (0,1,1)(0,0,1)12 could forecast the influenza incidence in Hunan Province. Time series analysis is a good tool for prediction of disease incidence.

Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km2), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 105 km2 and 6.81 × 105 km2, respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.

The diurnal temperature range (DTR) is considered a signature of observed climate change, which is defined as the difference between the maximum (Tmax) and minimum temperatures (Tmin). It is well known that the warming rate of mean temperature is larger at high elevations than at low elevations in northeast China. However, it is still uncertain whether DTR trend is greater at high elevations. This study examined the spatiotemporal variation in DTR and its relationship with elevation in northeast China based on data from 68 meteorological stations from 1961 to 2015. The results show that there was a significant declining trend (0.252 °C/decade) in DTR from 1961 to 2015 due to the fact that Tmin increased at a faster rate than Tmax. Seasonally, DTR in northeast China showed a decreasing trend with the largest decrease rate in spring (−0.3167 °C/decade) and the smallest decrease rate in summer (−0.1725 °C/decade). The results of correlation analysis show that there was a significant positive correlation between the annual DTR trend and elevation in northeast China. This is due to the fact that increasing elevation has a significant warming effect on Tmax. Seasonally, there were significant positive correlations between the DTR trend and elevation in all seasons. The elevation gradient of DTR trend was the greatest in winter (0.392 °C/decade/km) and the lowest in autumn (0.209 °C/decade/km). In spring, summer, and autumn, increasing elevation has a significant warming effect on Tmax, leading to a significant increase of the DTR trend with increasing elevation. However, in winter, increasing elevation has a significant cooling effect on Tmin, resulting in a significant increase of the DTR trend with increasing elevation.