Abstract
Abstract. West African Sahelian and Sudanian ecosystems provide essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing, and uncertainty remains with respect to how these changes will affect the potential of these regions to provide food and fodder resources or how they will affect the biosphere–atmosphere exchange of CO2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. In order to improve the simulation of phenology, we introduced soil-water availability as a common driver of foliage development and productivity for all of these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations as well as leaf area index data from satellite images (MODIS, MODerate resolution Imaging Spectroradiometer). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed good performance regarding biomass of crops, grass, or trees, yielding correlation coefficients of 0.82, 0.94, and 0.77 and root-mean-square errors of 0.15, 0.22, and 0.12 kg m−2, respectively. The simulations indicate aboveground carbon stocks of up to 0.17, 0.33, and 0.54 kg C ha−1 m−2 for agricultural, savanna grasslands, and savanna mixed tree–grassland sites, respectively. Carbon stocks and exchange rates were particularly correlated with the abundance of trees, and grass biomass and crop yields were higher under more humid climatic conditions. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semiarid West Africa under a wide range of conditions; thus, the model could be used to assess the impact of land-use and climate change on the regional biomass productivity.
Highlights
Land-cover and land-use changes significantly affect water, carbon (C), and energy exchange processes between the biosphere and the atmosphere and, climate change (Massad et al, 2019; Pielke et al, 2011)
A possible reason for this may have been the occurrence of weeds, which may have prevented peanut and millet from growing to their full potential, or that the fetch of the eddy-covariance tower extended beyond the investigation area where less productive plants or bare land resulted in a reduction in the average data from measurements (Quansah et al, 2015)
Biogeochemical models can be used to determine terrestrial carbon pool developments and fluxes at temporal resolutions and regional scales that cannot be covered by field measurements
Summary
Land-cover and land-use changes significantly affect water, carbon (C), and energy exchange processes between the biosphere and the atmosphere and, climate change (Massad et al, 2019; Pielke et al, 2011). The role of West African savanna systems in global C cycling has attracted increasing attention over the last decade (Quenum et al, 2019; Bocksberger et al, 2016; Sjöström et al, 2011) due to considerable changes in climate and owing to considerable changes in land cover, such as the extension of agriculture and the intensification of forest logging (Odekunle et al, 2008) These changes may already have and will further affect the C exchange rates between semiarid West African savanna ecosystems and the atmosphere, which might affect biomass production but may threaten biodiversity as well as the livelihood of people (Dimobe et al, 2018; Hartley et al, 2016; Dayamba et al, 2016). This can be done by using existing knowledge to set up ecosystem models and to test if these models are able to (a) realistically represent the sensitive responses of semiarid ecosystems to climatic variation and land-use management, and (b) accurately represent C exchange processes as well as (c) the distribution of above- and belowground C pools
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