Differing Responses to Rainfall Suggest More Than One Functional Type of Grassland in South Africa
| Autor: | Michel M. Verstraete, Robert J. Scholes, Catherine Van den Hoof |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
MISR-HR products 010504 meteorology & atmospheric sciences Science vulnerability precipitation variability Climate change non-linearity 010603 evolutionary biology 01 natural sciences consumption by fire and grazers Grassland Grassland productivity semi-arid regions legacy effects soil characteristics Ecosystem Precipitation 0105 earth and related environmental sciences geography geography.geographical_feature_category Arid Productivity (ecology) Photosynthetically active radiation Soil water General Earth and Planetary Sciences Environmental science Physical geography |
| Zdroj: | Remote Sensing, Vol 10, Iss 12, p 2055 (2018) Remote Sensing; Volume 10; Issue 12; Pages: 2055 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs10122055 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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