Search

This paper explores changes in climatic variables, including solar radiation, rainfall, fraction of diffuse radiation (FDR) and temperature, during wheat season (October to May) and maize season (June to September) from 1961 to 2003 at four sites in the North China Plain (NCP), and then evaluates the effects of these changes on crop growth processes, productivity and water demand by using the Agricultural Production Systems Simulator. A significant decline in radiation and rainfall was detected during the 43 years, while both temperature and FDR exhibit an increasing trend in both wheat and maize seasons. The average trend of each climatic variable for each crop season from the four sites is that radiation decreased by 13.2 and 6.2 MJ m−2 a−1, precipitation decreased by 0.1 and 1.8 mm a−1, minimum temperature increased by 0.05 and 0.02°C a−1, maximum temperature increased by 0.03 and 0.01°C a−1, FDR increased by 0.21 and 0.38% a−1 during wheat and maize season, respectively. Simulated crop water demand and potential yield was significantly decreased because of the declining trend in solar radiation. On average, crop water demand was decreased by 2.3 mm a−1 for wheat and 1.8 mm a−1 for maize if changes in crop variety were not considered. Simulated potential crop yields under fully irrigated condition declined about 45.3 kg ha−1 a−1 for wheat and 51.4 kg ha−1 a−1 for maize at the northern sites, Beijing and Tianjin. They had no significant changes in the southern sites, Jinan and Zhengzhou. Irrigation, fertilization development and crop variety improvement are main factors to contribute to the increase in actual crop yield for the wheat–maize double cropping system, contrasted to the decline in the potential crop yield. Further research on how the improvement in crop varieties and management practices can counteract the impact of climatic change may provide insight into the future sustainability of wheat–maize double crop rotations in the NCP. [ABSTRACT FROM AUTHOR]

Landslides result in a significant number of casualties every year in China. The frequency and intensity of extreme precipitation are expected to increase due to climate change, leading to a change in landslide occurrence. This study focuses on climate change impacts on event rainfall characteristics that are commonly linked to landslide occurrence in China. A modelling framework was proposed to quantitatively assess the spatiotemporal change in event rainfall characteristics influencing landslide occurrences in China under future scenarios. First, an algorithm was used to extract the rainfall events from observed precipitation data and the 21 Global Circulation Models dataset. Then, the cumulative event rainfall-rainfall duration (E-D) threshold was identified and used as a proxy of landslide occurrence. Finally, the historical (1971–2000) and future (2031–2060 and 2066–2095) data of 21 GCMs were then applied to determine the E-D threshold in areas highly susceptible to landslides in China to assess the impact of climate change. Landslide occurrence is projected to increase potentially under all GCMs, by amounts ranging from 19.9% to 33.2% in the late 21st century compared to the historical period under the RCP4.5 and RCP85 scenarios, respectively. There are regional differences in the impact of climate change. Future landslide increases in the Northwest region and the Qinghai-Tibet region are the most significant, with consistency among multiple GCMs. However, there is only a slight increase in the South China region with high uncertainty. The monthly variations in landslides are bimodal, with the largest increases in spring and autumn. The results indicate that using a single GCM to assess climate change impacts may have biases, and consideration of median trends and variations among multiple GCMs is suggested. However, the study is a first hint on how climate change may affect landslide occurrence in the future, as the assessment of the effect of climate change on landslides is not straightforward based on only the precipitation-related proxy. The influence on air temperature and soil moisture and the selection of projection datasets and proxies should be carefully considered when applying the presented methods for climate change impacts on landslide studies. [ABSTRACT FROM AUTHOR]