No abstract

The Mara River Basin of Africa has a world-famous ecosystem with vast vegetation, which is home to many wild animals. However, the basin is experiencing vegetation degradation and bad climate change, which has caused conflicts between people and wild animals, especially in dry seasons. This paper studied the vegetation greenness (VG), vegetation greenness trends (VGT), and their responses to climate change in dry seasons in the Mara River Basin, Africa. Firstly, based on Google Earth Engine (GEE) platform and Sentinel-2 images, the vegetation distribution map of the Mara River Basin was drawn. Then dry seasons MODIS NDVI data (January to February and June to September) were used to analyze the VGT. Finally, a random forest regression algorithm was used to evaluate the response of VG and VGT to temperature and precipitation derived from ERA5 from 2000 to 2019 at a resolution of 250 m. The results showed that the VGT was fluctuating in dry seasons, and the spatial differentiation was obvious. The greenness increasing trends both upstream and downstream were significantly larger than that of in the midstream. The responses of VG to precipitation were almost twice larger than temperature, and the responses of VGT to temperature were about 1.5 times larger than precipitation. The climate change trend of rising temperature and falling precipitation will lead to the degradation of vegetation and the reduction of crop production. There will be a vegetation degradation crisis in dry seasons in the Mara River Basin in the future. Identifying the spatiotemporal changes of VGT in dry seasons will be helpful to understand the response of VG and VGT to climate change and could also provide technical support to cope with climate-change-related issues for the basin.

Global climate extremes are increasingly frequent and intense, especially in Africa, which is most vulnerable to climate change (de Sherbinin, 2013). However, the vulnerability of the landscapes composed of diverse ecosystems to climate extremes is far from being clearly understood. This study constructed a set of index systems based on the “exposure-sensitivity-adaptive capacity” framework to assess landscape vulnerability driven by abnormal drought and precipitation in sub-Saharan Africa. In addition, correlation analysis was used to discover factors affecting landscape vulnerability. The results showed that a high level of landscape vulnerability was determined by high exposure and high sensitivity, as adaptive capacity exhibited little difference. The drought and wet events occurred in 80.9% and 51.3% of the climate change-dominated areas during 2001-2020, respectively. In areas where drought anomalies occur, about 8% of the landscapes, primarily formed by sparse vegetation and grasslands, were susceptible to drought. Moreover, in areas with abnormal precipitation, high vulnerability occurred only in about 0.6% of landscapes mostly covered by grasslands and shrubs. In addition, the intensity of landscape vulnerability driven by drought was higher than that driven by precipitation anomalies in the areas that experienced both dry and wet anomalies. Furthermore, the greater the deviation of landscape richness, diversity, and evenness from the normal climate state, the stronger the landscape vulnerability. The results add new evidence for landscape instabilities - an obvious contrast driven by drought and wetness - from the perspective of landscape vulnerability. The methodology of assessing landscape vulnerability established in this study can provide a new way to guide the regulation of landscape composition in response to frequent climate extremes on a macro level.

No abstract