No abstract

Abstract. Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr −1 release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the landatmosphere carbon flux for the 20th century.

No abstract

[1] The Sahel region has been identified as a ''hot spot'' of global environmental change, but understanding of the roles of different climatic and anthropogenic forcing factors driving change in the region is incomplete. We show that a process-based ecosystem model driven by climatic and atmospheric CO 2 data alone closely reproduces the satelliteobserved greening trend of the Sahel vegetation and its interannual variability between 1982 and 1998. Changes in precipitation were identified as the primary driver of the aggregated simulated vegetation changes. According to the model, the increasing carbon uptake through vegetation was associated with an increasing relative carbon sink; but integrated over the whole period, the Sahel was predicted to be a net source of carbon.

31 32Although satellite-based sensors have made vegetation data series available for several decades, 33 the detection of vegetation trend and change is not yet straightforward. This is mainly due to the 34 quality of the available change detection algorithms, which seldom meet users' main need for 35 identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy 36 or computational speed. We propose a user-friendly program for analysing vegetation time 37 series, with two main application domains: generalising vegetation trends to main features, and 38 characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating 39 Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into 40 linear segments using one of three user-defined parameters: a generalisation-threshold parameter 41 δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. 42The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and 43 estimates for the characteristics (time and magnitude) of the change. DBEST was tested and 44 evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, 45 which included different types of changes. Evaluation results demonstrate that DBEST quickly 46 3 and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time 47 and magnitude. 48 49DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies 50 (GIMMS) NDVI image time series for Iraq for the period 1982-2006, and was able to detect and 51 quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend 53 detection, and is applicable to global change studies using time series of remotely sensed data 54 sets. 55 56 57