ALBERT

Description: It is widely accepted that soil water repellency (SWR) is mainly caused by plant-derived hydrophobic organic compounds in soils; such hydrophobic compounds are defined as SWR-markers. However, the detailed influence of SWR-markers on SWR is yet unclear and the knowledge of their original sources is still limited. The aims of this study are to select important SWR-markers to predict SWR based on their correlation with SWR and to determine their origin. In our study, sandy soils with different SWR were collected, along with their covering vegetation, i.e. plant leaves/needles and roots. A sequential extraction procedure was applied to the soils to obtain three organic fractions: DCM / MeOH soluble fraction (D), DCM / MeOH insoluble fraction of IPA / NH3 extract (AI) and DCM / MeOH soluble fraction of IPA / NH3 extract (AS), which were subdivided into ten dominant SWR-marker groups: (D) fatty acid, (D) alcohol, (D) alkane, (AI) fatty acid, (AI) alcohol, (AI) ω-hydroxy fatty acid, (AI) α, ω-dicarboxylic acid, (AS) fatty acid, (AS) alcohol and (AS) ω-hydroxy fatty acid. Waxes and biopolyesters of the vegetation were also sequentially extracted from plants. In short, the soils with higher SWR have significantly higher relative concentrations of (AS) alcohols. A number of indications suggest that (AS) alcohols are mainly derived from roots and most likely produced by microbial hydrolysis of biopolyesters/suberins. In addition, the strong correlation between the biomarkers of plant tissues and SWR-markers in soils suggests that it is more accurate to predict SWR of topsoils using ester-bound alcohols from roots, and to predict SWR of subsoils using root-derived ω-hydroxy fatty acids and α, ω-dicarboxylic acids. Our analysis indicates that plant roots have a primary role influencing SWR relative to plant leaves.

Description: It is widely accepted that soil water repellency (SWR) is mainly caused by plant-derived hydrophobic organic compounds in soils; such hydrophobic compounds are defined as SWR markers. However, the detailed influence of SWR markers on SWR is yet unclear and the knowledge of their original sources is still limited. The aims of this study are to select important SWR markers to predict SWR based on their correlation with SWR and to determine their origin. In our study, sandy soils with different SWR were collected, along with their covering vegetation, i.e. plant leaves/needles and roots. A sequential extraction procedure was applied to the soils to obtain three organic fractions: dichloromethane (DCM)/MeOH soluble fraction (D), DCM/MeOH insoluble fraction of isopropanol/ammonia solution (IPA/NH3) extract (AI) and DCM/MeOH soluble fraction of IPA/NH3 extract (AS), which were subdivided into 10 dominant SWR marker groups: D fatty acid, D alcohol, D alkane, AI fatty acid, AI alcohol, AI ω-hydroxy fatty acid, AI α,ω-dicarboxylic acid, AS fatty acid, AS alcohol and AS ω-hydroxy fatty acid. Waxes and biopolyesters of the vegetation were also sequentially extracted from plants. The soils with higher SWR have significantly higher relative concentrations of AS alcohols. A number of indications suggest that AS alcohols are mainly derived from roots and most likely produced by microbial hydrolysis of biopolyesters (mainly suberins). In addition, the strong correlation between the biomarkers of plant tissues and SWR markers in soils suggests that it is more accurate to predict SWR of topsoils using ester-bound alcohols from roots, and to predict SWR of subsoils using root-derived ω-hydroxy fatty acids and α,ω-dicarboxylic acids. Considering the sandy soils studied here, the relationships we obtained need to be tested for other types of soils. Our analysis indicates that plant roots have a primary role influencing SWR relative to plant leaves.

Description: Traditionally, long term predictions of river discharges and their extremes include constant relationships between landscape properties and model parameters. However, due to co-evolution of many of landscape properties more sophisticated methods to quantify future landscape-hydrological model relationships are likely necessary. As a first step towards such an approach we use the Brutsaert and Nieber (1977) analysis method to characterize streamflow recession behaviour of ≈ 200 Swedish catchments within the context of global change and landscape co-evolution. Results suggest that the Brutsaert–Nieber parameters are strongly linked to the climate, soil, land-use and their interdependencies. Many catchments show a trend towards more non-linear behaviour, meaning faster initial recession, but also slower recession towards baseflow. This trend has been found to be independent from climate change. Instead, we suggest that land cover change, both natural (restoration of natural soil profiles in forested areas) and anthropogenic (reforestation and optimized water management), is probably responsible. Both change types are characterised by system adaptation and change, towards more optimal ecohydrological conditions, suggesting landscape co-evolution is at play. Given the observed magnitudes of recession changes during the past 50 years, predictions of future river discharge critically need to include effects of landscape co-evolution. The interconnections between the controls of land cover and climate on river recession behaviour, as we have quantified in this paper, provide first-order handles to do so.

Description: Observed bimodal distributions of woody cover in West Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. Understanding the explicit climate conditions where the woody cover bimodality can exist is important to predict crucial transitions of ecosystems due to climate change. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above ground biomass. Through conditional histogram analysis, we find that the bimodality of woody cover can only exist under low mean annual shortwave radiation and low above ground biomass. Based on a land cover map, in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water-radiation coupling are analyzed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not a sufficient predictor of a potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators can only demonstrate the potential occurrence of bimodality but cannot exclude the probability of bimodal vegetation distributions. A new indicator: the normalized difference of precipitation, successfully expresses the stability of the precipitation regime and can improve the accuracy of predictions of forest states. We evaluate the land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are displayed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility to significantly decrease. An increase in the area covered by savanna and grass is possible, which coincides with an observed re-greening of the Sahara.

Description: 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.