Abstract
Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. Grasslands are indeed very responsive to variations in precipitation. This study evaluates the sensitivity of the grassland ecosystem to precipitation variability in space and time, by identifying the factors controlling this response, based on monthly precipitation data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) data from the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) datasets, used as proxy for productivity, at 60 grassland sites in South Africa. Our results show that MISR-HR products adequately capture the spatial and temporal variability in productivity at scales that are relevant to this study, and they are therefore a good tool to study climate change impacts on ecosystem at small spatial scales over large spatial and temporal domains. We show that combining several determinants and accounting for legacies improves our ability to understand patterns, identify areas of vulnerability, and predict the future of grassland productivity. Mean annual precipitation is a good predictor of mean grassland productivity. The grasslands with a mean annual rainfall above about 530 mm have a different functional response to those receiving less than that amount of rain, on average. On the more arid and less fertile soils, large inter-annual variability reduces productivity. Our study suggests that grasslands on the more marginal soils are the most vulnerable to climate change.
Highlights
Grasslands, which are defined as ground covered with graminaceous plants with less than 10% tree and shrub cover [1], representing 40.5% of the global terrestrial area, excluding Greenland and Antarctica [2]
Precipitation data were extracted from the Climate Hazards Group InfraRed Precipitation with Station data, CHIRPS v2.0 [29], which covers the latitude band 50◦S–50◦N at all longitudes, from 1981 to the near-present, at a 0.05◦ spatial resolution. This dataset incorporates monthly precipitation climatology from the Climate Hazards Group’s Precipitation Climatology version 1 (CHPClim), infrared satellite observations from the Climate Prediction Center (CPC) and the National Climate Data Center (NCDC), the Tropical Rainfall Measuring Mission (TRMM) product, atmospheric model rainfall fields from the Coupled Forecast System version 2 (CFSv2), and in situ precipitation observations obtained from a variety of sources including national and regional meteorological services
The dataset contains soil characteristics—including soil organic carbon, pH, fraction of sand, silt and clay, bulk density, and cation-exchange capacity (CEC, cmol +/kg), in addition to estimates of depth to the bedrock, the probability of occurrence of R horizon or bedrock within 20 m, and the distribution of soil classes based on the World Reference Base (WRB) and the United States Department of Agriculture (USDA) classification systems—for the whole African continent at 25 spatial resolution at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm)
Summary
Grasslands, which are defined as ground covered with graminaceous plants with less than 10% tree and shrub cover [1], representing 40.5% of the global terrestrial area, excluding Greenland and Antarctica [2]. Degradation is considered to be human-induced, either directly or indirectly; for instance, through climate change In grasslands, it is the result of a combination of processes such as soil erosion, the deterioration of desirable soil properties, loss of natural vegetation cover, a switch to a plant species community (either indigenous or alien) that is less desirable than the native community, or an ecohydrological change resulting in poorer infiltration of rainfall [9]. In the absence of a benchmark, a decline in NPP can be inferred from a long production time series, provided that the degradation occurs in the observed period. These two conditions are seldom met; an accurate model of what the expected productivity would be under given circumstances would provide a useful reference level
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