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Based on gridded temperature data from the China Meteorological Administration (CMA), two types of methods, i.e., absolute methods and relative methods, respectively, were used to identify heat waves in Mainland China. Four statistical indicators, including the occurrence frequency, duration days, earliest occurrence date, and latest extinction date, were constructed to analyze the spatial-temporal characteristics of heat waves, especially on the annual and decadal change trends. Firstly, we found that both the frequency and the duration of heat waves decreased in the period from 1960 to 1989 but increased in the 1990s and increased significantly from the early 2000s to the 2010s. Spatially, the frequency and the duration obtained by each type of method are significantly different among different regions when considering different facts, such as different regions that have different degrees of tolerance to heat waves. Secondly, the decadal distribution characteristics of the earliest occurrence date and the latest extinction date of heat waves well capture the hot summer, the stronger sensitivity of winter to warming than other seasons, and the gradually increasing intensity of heat waves. It provides a multidimensional reference for the cause analysis and prediction of extreme heat waves in China. [ABSTRACT FROM AUTHOR]

Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme climate events on soybean, which brings large uncertainties. This study proposed an approach of hybrid models to constrain such uncertainties by coupling the GEPIC model and extreme climate indicators using machine learning. Subsequently, the key extreme climate indicators for the globe and main soybean producing countries are explored, and future soybean yield changes and variability are analyzed using the proposed hybrid model. The results show the coupled GEPIC and Random Forest (GEPIC+RF) model (R: 0.812, RMSD: 0.716 t/ha and rRMSD: 36.62%) significantly eliminated uncertainties and underestimation of climate extremes from the GEPIC model (R: 0.138, RMSD: 1.401 t/ha and rRMSD: 71.57%) compared to the other five hybrid models (R: 0.365–0.612, RMSD: 0.928–1.021 and rRMSD: 47.48–52.24%) during the historical period. For global soybean yield and those in Brazil and Argentina, low-temperature-related indices are the main restriction factors, whereas drought is the constraining factor in the USA and China, and combined drought–heat disaster in India. The GEPIC model would overestimate soybean yields by 13.40–27.23%. The GEPIC+RF model reduced uncertainty by 28.45–41.83% for the period of 2040–2099. Our results imply that extreme climate events will possibly cause more losses in soybean in the future than we have expected, which would help policymakers prepare for future agriculture risk and food security under climate change. [ABSTRACT FROM AUTHOR]

Exploring the determinant between climate change and ecological restoration for streamflow dynamics is a significant issue in global change research and is essential for restoration policy assessment and climate change adaptation. In this paper, the combination of elasticity method and Budyko framework was applied with the meteorological and hydrological data from 1980 to 2014. The variability and variation of streamflow were explained by individual elasticity coefficient and contribution of climate change and ecological restoration between 1980-1999 and 2000-2014. Results showed that streamflow was more sensitive to climate change in the Yellow River source (YER) than in the Yangtze River source (YZR). Ecological restoration was positively correlated with the variability of streamflow in YER with 7.38% relative change in elasticity coefficients, while it was opposite trend in YZR with a relative change of − 7.41%. However, the impacts of climate change and ecological restoration on the variation of streamflow were not consistent with the variability. In YER, ecological restoration dominated the streamflow reduction with a contribution of 82.43%, whereas, in YZR, climate change mainly contributed to the streamflow increase which could explain 123.72% and the precipitation was the major contributor. By analysis, the difference in two catchments might result from the spatial heterogeneity of precipitation conditions and land use/cover change, especially the conversion of grassland. The results suggest that the implementation of ecological recovery should consider the difference of streamflow change in response to ecological restoration and climate change for the sustainability of water resources. [ABSTRACT FROM AUTHOR]

This study uses hourly surface wind direction and wind speed observations from 53 meteorological stations on the Tibetan Plateau (TP) (70–105° E, 25–45° N) between 1995 and 2017 to investigate diurnal variations in the surface wind. The results show large diurnal variations in surface wind on the TP. The minimum wind speed occurs in the morning and the maximum in the afternoon. In all four seasons, the prevailing meridional wind is a southerly, and this is typically evident for more than two-thirds of each day. However, in the mornings during December–February and September–November, this southerly wind is replaced by a northerly, but remains southerly in the afternoon. The TP shows remarkable regional characteristics with respect to diurnal variations in wind speed. In the eastern region, the minimum and maximum daily wind speeds occur about 1 h later than in the west. Among the 53 meteorological stations, 79% observed that it took less time for the minimum speed to rise to the maximum speed than for the maximum to drop to the minimum. The blocking effect of the high surrounding terrain causes the diurnal variations seen in the surface winds at the three stations in the Qaidam Basin to differ significantly from those observed at the other stations elsewhere on the plateau. These Qaidam Basin stations recorded their maximum wind speeds around noon, with the minimum at dusk, which is around 1900 LST. The EOF1 (EOF = empirical orthogonal function) of the hourly wind speed on the TP indicates the key daily circulation feature of the region; i.e., the wind speed is high in the afternoon and low in the morning. The EOF2 reflects the regional differences in the diurnal variations of wind speed on the TP; i.e., the eastern region reaches the daily maximum and minimum wind speeds slightly later than the western region. [ABSTRACT FROM AUTHOR]

The asymmetric unit of the title compound, C(12)H(10)N(4)O(2)Se(2)·2C(3)H(7)NO, contains two solvent mol-ecules and two half mol-ecules of the dinicotinamide, each of which sits on a center of symmetry passing through the middle of the Se-Se bond. In each mol-ecule, the two pyridyl groups and diseleno group are approximately coplanar (r.m.s. deviations from planarity for all non-H atoms = 0.011 and 0.008 Å in the two mol-ecules). Inter-molecular N-H⋯O hydrogen bonds stablilize the crystal packing.