Hotspots of Yield Loss for Four Crops of the Belt and Road Terrestrial Countries under 1.5 °C Global Warming

The Fifth Assessment Report of the Intergovernmental Panel on Climate change (IPCC) shows that climate change poses severe risks to the Belt and Road region and could cut future crop production. Identifying the positions and features of hotspots, which refer to regions with severe yield loss at 1.5 °C global warming, is the key to developing proper mitigation and adaptation policies to ensure regional food security. This study examined yield loss hotspots of four crops (maize, rice, soybean and wheat) at 1.5 °C global warming under RCP8.5. Yield data were derived from simulations of multiple climate-crop model ensembles from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). Hotspots were identified by setting a threshold of the 10th percentile of crop yields during the reference period (1986–2005). To quantify the likelihood of crop yield loss hotspots within multi-model ensembles, the agreement of model combinations for hotspots was calculated for each crop at the grid scale with 0.5° × 0.5° spatial resolution. Results revealed spatial heterogeneity of cultivation structure and hotspot likelihood for four crops. The four crops’ production of SA (South Asia) and SEA (Southeast Asia) accounts for more than 40% of the total production in the Belt and Road region, roughly four times the amount produced in CEE (Central and Eastern Europe) and NEA (Northeast Asia). Besides, the hotspots likelihood of maize, rice and soybean is generally larger in SA/SEA than that in CEE/NEA which means the risk of yield reduction is higher in the current main agricultural area. According to IPCC’s classification rules for likelihood, four crops’ hotspot patterns were displayed under the 1.5 °C global warming. As the highest-yielding crop, maize shows the largest proportion of “likely” hotspots (hotspot likelihood > 66%), which is about 6.48%, accounting for more than four times that of the other three crops. In addition, four crops’ hotspots are mainly distributed in SEA and SA. Overall, SEA and SA are vulnerable subregions and maize is the vulnerable crop of the Belt and Road region. Our results could provide information on target areas where mitigation or adaptations are needed to reduce the adverse influence of climate change in the agricultural system

Modeling the Relative Contributions of Land Use Change and Harvest to Forest Landscape Change in the Taihe County, China

Forests are under pressure from land use change due to anthropogenic activities. Land use change and harvest are the main disturbances of forest landscape changes. Few studies have focused on the relative contributions of different disturbances. In this study, we used the CA-Markov model, a land-use change model, coupled with a forest landscape model, LANDIS-II, to simulate dynamic change in Taihe County, China, from 2010 to 2050. Scenarios analysis was conducted to quantify the relative contributions of land use change and harvest. Our results show that forestland and arable land will remain the primary land-use types in 2050, whereas the built-up land will sprawl drastically. Land use change and harvest may result in the significant loss of forest area and changes in landscape structure. The simulated forest area will increase by 16.2% under the no disturbance scenario. However, under harvest, forest conversion, and integrated scenario, the area will be reduced by 5.2%, 16.5%, and 34.9%, respectively. The effect of harvest is gradually enhanced. The land use change will account for 60% and harvest will account for 40% of forest landscape change in 2050, respectively. Our results may benefit from the integration of regional forest management and land-use policy-making, and help to achieve a trade-off between economy and ecological environment

Statistical relations between geomorphic parameters - A case study of the Yunnan reach of the Lancangjiang River in southwestern China

The properties of rivers and their catchments can be expressed by statistical relationships between geomorphic parameters. These statistical relationships may reveal some inherent differences in geomorphic evolution for different reaches or different order tributaries of a river basin. A case study was undertaken of the Yunnan reach of the Langcangjiang River. The catchment area, channel length and gradient of the first-, second-and third-order tributaries all with catchment areas larger than 100 km² in the Yunnan reach were the main geomorphic parameters evaluated. The correlation between catchment area and channel length as well as between catchment area and channel gradient with respect to the total tributaries, different reach tributaries, and different order tributaries were revealed using statistical methods. In general, the channel length as a function of catchment area, was best expressed by a quadratic function where channel length increases with increasing catchment area (half parabola), while the channel gradient as a function of catchment area is best expressed by an exponential decay function. Comparison of the best-fit formulas revealed the following phenomena: the lower Yunnan reach tributaries and the first-order tributaries have a dominant effect on geomorphic parameters of the total tributaries. In addition, the statistical relationships indicate that the river geomorphic system in the upper and lower Yunnan reaches evolved differently. This study method used to differentiate river characteristics by determining statistical relationships between geomorphic parameters may be extended to other rivers and their catchments

Interannual Variations in Growing-Season NDVI and Its Correlation with Climate Variables in the Southwestern Karst Region of China

In this study, the updated Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for growing season (April to October), which can better reflect the vegetation vigor, was used to investigate the interannual variations in NDVI and its relationship with climatic factors, in order to preliminarily understand the climate impact on vegetation and provide theoretical basis for the response of ecosystem to climate change. Multivariate linear regression models, including the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), were adopted to analyze the correlation between NDVI and climatic factors (temperature and precipitation) together. Average growing-season NDVI significantly increased at a rate of 0.0015/year from 1982 to 2013, larger than several regions in China. On the whole, its relationship with temperature is positive and also stronger than precipitation, which indicated that temperature may be a limiting factor for the vegetation growth in the Karst region. Moreover, the correlation coefficients between grassland NDVI and climatic factors are the largest. Under the background of NDVI increasing trend from 1982 to 2013, the period of 2009–2012 was chosen to investigate the influencing factors of a sharp decline in NDVI. It can be found that the reduced temperature and solar radiation, caused by the increase in cloud cover and precipitation, may play important roles in the vegetation cover change. All in all, the systematic research on the interannual variations of growing-season NDVI and its relationship with climate revealed the heterogeneity and variability in the complicated climate change in the Karst ecosystem for the study area. It is the Karst characteristics that hinder obtaining more representative conclusions and tendencies in this region. Hence, more attention should be paid to promoting Karst research in the future

Quantitative Assessment of Regional Debris-Flow Risk: A Case Study in Southwest China

This paper uses a comprehensive risk assessment method to investigate the population risk of debris flows in Southwest China. The methodology integrates models from hazard, vulnerability literature and some empirical equations. The main steps include debris-flow disaster-hazard zoning, estimation of the frequency of the disaster, factor identification of population vulnerability, and calculation of the fragility rate. The results demonstrate that the most hazardous regions in Southwest China are primarily observed in the mountains around the Sichuan Basin, the border area between Sichuan and Yunnan Provinces, the eastern and southern regions of Yunnan Province, and the eastern area of Guizhou Province. The extremely high vulnerability zones are characterized by a fragility rate of 3.89 persons per 10,000 people. The comprehensive risk gradually increases from the southeast of the study area to the central region, reaching its highest value (more than 100 persons/year) on the Jiangyou–Zhaotong–Baoshan Line and decreasing thereafter to its lowest in the northwestern region. Extremely large-scale disasters are the major factor of casualties. Appropriate risk management and mitigation solutions should be comprehensively determined based on the combination of debris-hazard levels and fragility rates in the hazardous regions