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International audienceThe diurnal temperature range (DTR) is an important indicator of climate change, and it has decreased worldwide since the 1950s, particularly over arid and semiarid regions. This study analyses the effect of meteorological and anthropogenic factors on DTR variation to investigate the possible causes of DTR decreases in semiarid climates. The study region is located in northeast China, and the study period is from 1957 to 2006. There are three main results. First, the rate of decrease in the DTR is −1.24 K per 50 years. This decrease is mainly attributed to the increasing daily minimum temperature rate (Tmin, 2.24 K per 50 years), which is greater than the change in the daily maximum temperature (Tmax, 1.00 K per 50 years). Second, sunshine duration (SD) appears to be the most significant meteorological factor that determines the DTR through downward shortwave radiation (Rsw,d) and surface soil moisture (SM). The effect of Rsw,d is larger for Tmax than for Tmin; therefore, the decrease in Rsw,d results in a smaller increase in Tmax than in Tmin. On the other hand, the increase in SM can strengthen daytime latent heat release, and the increase in Tmax is then slowed because of the cooling effect of evaporation. The precipitation values and the leaf area index show a negative correlation with the DTR, whereas the cloud amount and the relative humidity appear not to be main causes of the DTR decrease in this region. Finally, atmospheric aerosols can reduce the SD by 0.27 h year–1 by decreasing atmospheric transparency, as indicated by an analysis of the Total Ozone Mapping Spectrometer Aerosol Index from 1979 to 2005. The decrease in direct solar radiation is the main cause of decreases in Rsw,d. These findings will provide references for DTR variation studies in similar climates

The objective of this study was to investigate how storage temperatures influence the bacterial community of oat silage during the ensiling process via PacBio single molecule, real-time sequencing technology (SMRT). Forage oat was ensiled at four different temperatures (5 °C, 10 °C, 15 °C, and 25 °C) and ensiling days (7, 14, 30, and 60 days). With the rise in storage temperature, the lactic acid content showed an increased trend. Acetic acid production was observed highest in silage fermented at 5 °C compared with other treatments, and Enterococcus mundtii was also the dominant bacterial species. Lactiplantibacillus pentosus and Loigolactobacillus rennini were exclusively detected in silages at 10 °C, 15 °C, and 25 °C, and dominated the fermentation after 60 days of ensiling at 10 °C and 25 °C, respectively. In addition, L. pentosus, L. rennini, and E. mundtii may be related to changes in the fermentation products due to the differences in ensiling temperature. In conclusion, results of this study improve our understanding of the complicated microbial composition underlying silage fermentation at low temperatures, which might contribute to target-based regulation methods for enhancing silage quality and developing new inoculants.

Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow.