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- Abdi, A. M., et al.
(författare)
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First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
- 2019
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 78, s. 249-260
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Tidskriftsartikel (refereegranskat)abstract
- The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO 2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO 2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness (T-G) model, the greenness and radiation (GöR) model and a light use efficiency model (MOD17). The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3–65%). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation (R 2 ), root-mean-square error (RMSE), and Bayesian information criterion (BIC). The GöR model produced R 2 = 0.73, RMSE = 1.45 g C m −2 d −1 , and BIC = 678; the T-G model produced R 2 = 0.68, RMSE = 1.57 g C m −2 d −1 , and BIC = 707; the MOD17 model produced R 2 = 0.49, RMSE = 1.98 g C m −2 d −1 , and BIC = 800. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models (R 2 = 0.77, RMSE = 1.32 g C m −2 d −1 , and BIC = 631). These results show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.
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- Baudena, M., et al.
(författare)
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Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models
- 2015
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 12:6, s. 1833-1848
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Tidskriftsartikel (refereegranskat)abstract
- The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.
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- Nicholas, Kimberly A., et al.
(författare)
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A harmonized and spatially explicit dataset from 16 million payments from the European Union's Common Agricultural Policy for 2015
- 2021
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Ingår i: Patterns. - : Elsevier BV. - 2666-3899. ; 2:4
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Tidskriftsartikel (refereegranskat)abstract
- The Common Agricultural Policy (CAP) is the largest budget item in the European Union, but varied data reporting hampers holistic analysis. Here we have assembled the first dataset to our knowledge to report individual CAP payments by standardized CAP funding measures and geolocation. We created this dataset by translating, geolocating to the county or province (NUTS3) level, and consistently harmonizing payment measures for over 16 million payments from 2015, originally reported by EU member states and compiled by the Open Knowledge Foundation Germany. This dataset and code allow in-depth analysis of over €60 billion in public spending by purpose and location for the first time, which enables both individual payment tracing and analysis by aggregation. These data are representative of the distribution of annual CAP payments from 2014 to 2020 and are of interest to researchers, policy makers, non-governmental organizations, and journalists for evaluating the distribution and impacts of CAP spending.
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