Search

Over the last few decades, the African Sahel has become the focus of many studies regarding vegetation dynamics and their relationships with climate and people. This is because rainfall limits the production of biomass in the region, a resource on which people are directly dependent for their livelihoods. In this study, we utilized a remote-sensing approach to answering the following two questions: (1) how does the dynamic relationship between soil moisture and plant growth vary across hydrological regimes, and (2) are vegetation-type-dependent responses to soil moisture availability detectable from satellite imagery? In order to answer these questions, we studied the relationship between monthly modelled soil moisture as an indicator for water availability and the remotely sensed normalized difference vegetation index (NDVI) as a proxy for vegetation growth between a “recovery rainfall period” (1982 to 1997) and a “stable rainfall period” (1998 to 2013), at different time lags across the Sahel region. Using windowed cross-correlation, we find a strong significant positive relationship between NDVI and soil moisture at a concurrent time and at NDVI lagging behind soil moisture by 1 month for grassland, cropland, and deciduous shrubland vegetation – the dominant vegetation classes in the Sahel. South of the Sahel (the Sudanian and Guinean areas), we find longer optimal lags (soil moisture lagged by 1–3 months) in association with mixed forest and deciduous shrubland. We find no major significant change in optimal lag between the recovery and stable periods in the Sahelian region; however, in the Sudanian and Guinean areas, we observe a trend towards shorter time lags. This change in optimal lag suggests a vegetation change, which may be a response to a climatic shift or land-use change. This approach of identifying spatiotemporal trends in optimal lag correlations between modelled soil moisture and NDVI could prove to be a useful tool for mapping vegetation change and ecosystem behaviour, in turn helping inform climate change mitigation approaches and agricultural planning. [ABSTRACT FROM PUBLISHER]

In the 21st century, climate change in combination with increasing demand, mainly from population growth, will exert greater pressure on the ecosystems of the Sahel to supply food and feed resources. The balance between supply and demand (annual biomass required for human consumption) serves as a key metric for quantifying basic resource shortfalls over broad regions. Here we apply an exploratory modelling framework to analyze the variations in the timing and geography of different NPP (net primary production) supply-demand scenarios (with distinct assumptions determining supply and demand) for the 21st century Sahel. We achieve this by coupling a simple NPP supply model (forced with projections from four representative concentration pathways) with a global, reduced-complexity demand model (driven by socio-economic data and assumptions derived from five shared socio-economic pathways). For the scenario that deviates least from current socio-economic and climate trends, we find that per capita NPP outstrips its supply in the 2070s, while by 2050, half the countries in the Sahel experience NPP shortfalls. We also find that despite variations in the timing of the onset of NPP shortfalls, demand cannot consistently be met across the majority of scenarios. Moreover, large between-country variations are shown across the scenarios where by the year 2050, some countries consistently experience shortage, others surplus while yet others shift from surplus to shortage. At the local level (i.e. grid cell) hotspots of total NPP shortfall consistently occur in the same locations across all scenarios, but vary in size and magnitude. These hotspots are linked to population density and high demand. For all scenarios, total simulated NPP supply doubles by 2050 but is outpaced by increasing demand due to a combination of population growth and adoption of a diets rich in animal products. Finally, variations in the timing of onset and end of supply shortfalls stem from the assumptions that underpin the shared socio-economic pathways rather than the representative concentration pathways. Our results suggest that the UN sustainable development goals for eradicating hunger are at high risk for failure. This emphasizes the importance of policy interventions such as the implementation of sustainable and healthy diets, family planning, reducing yield gaps, and encouraging transfer of resources to impoverished areas via trade relations. [ABSTRACT FROM AUTHOR]

Net primary production (NPP) is the difference in gross photosynthetic assimilation of carbon and carbon loss due to autotrophic respiration, and is an important ecosystem variable that facilitates understanding of climate change impacts on terrestrial ecosystem productivity and ecosystem services. The aim of this study is to rapidly estimate the NPP of European potential natural vegetation for current and future climate and carbon-dioxide scenarios (CO 2 ). A NPP meta-model was developed and evaluated based on the dynamic global vegetation model LPJ-GUESS. LPJ-GUESS was used to simulate NPP under current and future climate change as well as CO 2 scenarios. The NPP dataset produced from these simulations was used to determine the empirical relationships between NPP and driving climate variables (maximum temperature, minimum temperature, summer precipitation, winter precipitation) along with CO 2 concentration. The climate variables’ relationships were combined in a synergistic function including CO 2 relationships to estimate NPP. The meta-model was compared with randomly chosen NPP data originated from LPJ-GUESS. Furthermore, the meta-model's performance was evaluated on the European level with LPJ-GUESS simulations. The meta-model performed reasonably well with regard to estimating total NPP while performances for species-specific NPP were poor. For total NPP, the meta-model generated an agreement of R 2 = 0.68 and RMSE = 0.06 at CO 2 = 350 ppm in comparison to LPJ-GUESS simulations. The consideration of all CO 2 concentration scenarios yielded R 2 = 0.62 and RMSE = 0.08. A rapid synergistic approach is suggested that enables interactions between climate variables and their intra-annual variability to estimate NPP. This is a useful alternative to traditional empirical models that control NPP with the most limiting climate variable. The meta-model performed reasonably well for estimating total NPP for future climate change and CO 2 scenarios. However, species-specific NPP estimates were unsatisfactory, implying that the synergistic approach cannot account for species specific dynamics. Comparison between the meta-model and LPJ-GUESS at the European scale showed that additional environmental variables (e.g. solar radiation) would be necessary to improve the meta-model. [ABSTRACT FROM AUTHOR]

Abstract: Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8days to 16days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~13 to 20days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to −5days) than the SOS estimates from NDVI composites that use the midpoint (−2 to −27days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates. [Copyright &y& Elsevier]

Abstract: Semi-arid areas, defined as those areas of the world where water is an important limitation for plant growth, have become the subject of increased interest due to the impacts of current global changes and sustainability of human lifestyles. While many ground-based reports of declining vegetation productivity have been published over the last decades, a number of recent publications have shown a nuanced and, for some regions, positive picture. With this background, the paper provides an analysis of trends in vegetation greenness of semi-arid areas using AVHRR GIMMS from 1981 to 2007. The vegetation index dataset is used as a proxy for vegetation productivity and trends are analyzed for characterization of changes in semi-arid vegetation greenness. Calculated vegetation trends are analyzed with gridded data on potential climatic constraints to plant growth to explore possible causes of the observed changes. An analysis of changes in the seasonal variation of vegetation greenness and climatic drivers is conducted for selected regions to further understand the causes of observed inter-annual vegetation changes in semi-arid areas across the globe. It is concluded that semi-arid areas, across the globe, on average experience an increase in greenness (0.015 NDVI units over the period of analysis). Further it is observed that increases in greenness are found both in semi-arid areas where precipitation is the dominating limiting factor for plant production (0.019 NDVI units) and in semi-arid areas where air temperature is the primarily growth constraint (0.013 NDVI units). Finally, in the analysis of changes in the intra-annual variation of greenness it is found that seemingly similar increases in greenness over the study period may have widely different explanations. This implies that current generalizations, claiming that land degradation is ongoing in semi-arid areas worldwide, are not supported by the satellite based analysis of vegetation greenness. [Copyright &y& Elsevier]