Ecohydrological Concepts of Water-Vegetation Interaction in the Drylands of Africa
Summary and Keywords
Drylands cover around 40% of the land surface on Earth and are inhabited by more than 2 billion people, who are directly dependent on these lands. Drylands are characterized by a highly variable rainfall regime and inherent vegetation-climate feedbacks that can enhance the resilience of the system, but also can amplify disturbances. In that way, the system may get locked into two alternate stable states: one relatively wet and vegetated, and the other dry and barren. The resilience of dryland ecosystems derives from a number of adaptive mechanisms by which the vegetation copes with prolonged water stress, such as hydraulic redistribution. The stochastic nature of both the vegetation dynamics and the rainfall regime is a key characteristic of these systems and affects its management in relation to the feedbacks. How the ecohydrology of the African drylands will change in the future depends on further changes in climate, human disturbances, land use, and the socioeconomic system.
Drylands cover around 40% of the land surface on Earth and are inhabited by more than 2 billion people, who are directly dependent on dryland ecosystems for their livelihood (EMG, 2011). Drylands respond strongly to changes in precipitation and surface air temperatures, which potentially trigger important feedback mechanisms to the global climate and carbon cycle (Charney, 1975, Huang, Zhang, & Prospero, 2009). Furthermore, dryland ecosystems are prone to degradation and rapid desertification as a result of overgrazing, tillage, and fuelwood collection (UNCCD, 2009). All dryland climate zones share the characteristic of large variability in rainfall, both in total amount annually and in partition through the rainy season. These characteristics have important impacts on the resilience of ecosystems and humans that are in these areas.
The most widely used definitions of drylands come from the Food and Agriculture Organization of the United Nations (FAO) and the United Nations Convention to Combat Desertification (UNCCD, 2009). The FAO defines drylands as areas experiencing a growing period of 1–179 days (FAO, 2011); this includes many regions classified climatologically as arid, semiarid, and dry subhumid. The UNCCD classification employs the ratio of annual precipitation to potential evapotranspiration (P/PET), thereby taking into account the amount of water available for vegetation growth. Following the UNCCD classification, drylands are characterized by a P/PET ratio of 0.05 to 0.65. Besides a low annual precipitation, drylands are often characterized by a strong seasonality in rainfall, consisting of a dry season, with virtually no rainfall, and a wet season, with intermittent storms.
Approximately half the land area on the African continent is classified as dryland following this definition. It is estimated that 45% of the African population (or around 325 million people) are currently living in dryland environments (EMG, 2011). Figure 1 provides an overview of the distribution of drylands in Africa and the variety of natural ecosystems and agroecosystems that are found in these dryland environments. Three climatological dryland subregions can be defined based on the P/PET ratio indicated in Figure 1, with different land cover and land use. Arid areas (P/PET = 0.05–0.25) receive the lowest amount of rainfall, around 200–500 mm yr−1. Soils are generally poor, with low organic matter content (Freschet, Masse, Hien, Sal, & Chotte, 2008). The arid zone is characterized by open shrubland or wooded steppe vegetation with widely spaced, drought-tolerant trees, typically of the genus Acacia and Commiphora (Aubréville et al., 1958). In Africa, the arid zone makes up 20% of the land surface area (6.2 million km2). Semiarid areas (P/PET = 0.25–0.45) receive around 500–1000 mm yr−1 and experience a wet growing season of 3–6 months. These areas are covered by relatively dry woodland and savannah where pastoralists and agropastoralists make their livelihood via a combination of crop cultivation and livestock rearing. Semiarid areas cover around 4.3 million km2, or another 14% of the continent. Finally, the subhumid areas (P/PET = 0.45–0.65) are covered by relatively moist woodland and savannah vegetation, making up 18% of the continent (5.6 million km2). The subhumid zone receives around 1,000–1,300 mm yr−1 and has a growing season of 6–9 months. The dominant livelihood in the subhumid zone is smallholder, rain-fed, arable farming.
Changing climate and changing decadal weather patterns have made the dryland vegetation always stand out in its sensitivity and response. The Sahel, for example, was subject to severe drought events from the late 1960s up to the 1980s, which triggered large-scale famine and political unrest. The causes and effects of the Sahel droughts have been subject to debate, whether the observed desertification and land degradation was in fact anthropogenic in origin (e.g., Mainguet, 2012) or caused by changes in precipitation patterns (Nicholson, Tucker, & Ba, 1998). Since the 1980s, the Sahel shows a consistent vegetation greening trend that is generally attributed to increased precipitation over the area. However, the greening trend is not spatially uniform, which leaves open the possibility of a human footprint on the recent greening trend that is superimposed on the climatic trend (Herrmann, Anyamba, & Tucker, 2005).
The human population of sub-Saharan Africa numbered a mere 221 million people in 1960. By 2015, this number had exploded to 962 million, or just under 1 billion people. In 2050, the human population of sub-Saharan Africa is expected to top 2 billion and continue to increase to 4 billion by 2100 (UN, 2015). This dramatic population growth will increase the already high demands on food and other resources. African dryland vegetation is rapidly being converted to agricultural land, a trend that is expected to continue (Andela & van der Werf, 2014, FAO, 2011). While food production is increasing in both absolute numbers and efficiency, feeding such an immense population will require massive investment in crop enhancement, irrigation and soil management, and fertilization techniques (UN, 2011).
Remote sensing studies recently reported an increase of woody vegetation cover in the arid, semiarid, and subhumid zones of Africa, including the Sahel (Andela, Liu, van Dijk, de Jeu, & McVicar 2013; Horion, Fensholt, Tagesson, & Ehammer, 2014) and the more humid woodlands and savannah north and south of the equator (Andela et al., 2013; Mitchard & Flintrop, 2013). The consistency of the observed trend of woody encroachment (i.e., the replacement of herbaceous and graminoid vegetation by shrubs and trees) is still controversial and could be attributed to recovery after extreme droughts and improved land management (Herrmann et al., 2005). Changing fire regimes are also likely to be an important driver of the observed changes in woody vegetation cover (Mitchard & Flintrop, 2013). Large areas in Africa are experiencing a decrease in area burned annually (Andela & van der Werf, 2014), which could facilitate the establishment of woody vegetation at the expense of grasses and herbs (e.g., Sankaran et al., 2005). Another less obvious driver of woody encroachment could be the increase in atmospheric CO2 concentration as the adaptive advantage of most grasses that use the C4 photosynthetic pathway over C3 trees and shrubs is lost (Lloyd & Farquhar, 2008; Buitenwerf, Bond, & Stevens, 2012; Bond & Midgley, 2012). The rapid woody encroachment of Africa’s drylands can have major implications for the water and carbon cycle in these ecosystems. Woody plant encroachment is linked to increased gross primary production (GPP) and carbon accumulation in the ecosystem, as well as an increase in the ratio of plant transpiration to total evapotranspiration as a result of increased leaf area, increased depth of the rooting system, and increased physiological activity (Huxman et al., 2005). It remains unclear whether the driving mechanisms behind the observed woody encroachment are of human origin or the results of a changing climate.
In this article, the focus is primarily on the ecohydrological adaptions of dryland vegetation and the interaction of the water and carbon cycle, in order to answer the overarching question of how climate and human land use are affecting the ecohydrology and stability of drylands. The following three questions are addressed:
• What are the key ecohydrological mechanisms in dryland ecosystems?
• What feedbacks are important for dryland ecohydrology and are likely to play a major role in dryland stability?
• How will drylands respond to expected climatic changes?
Ecohydrological Mechanisms on the Local to Regional Scale
Traditionally, precipitation is acknowledged as the key determinant of dryland vegetation variability, and many studies have been devoted to investigating this effect (e.g., Dolman et al., 1997). Charney (1975) was the first to postulate the hypothesis that the vegetation could affect rainfall patterns in the Sahel through its interaction with the radiation balance (see also Xue & Shukla, 1993). However, human land use and management are also considered to play an increasingly important role in the hydrological functioning of dryland ecosystems (Wang et al., 2012).
The stochastic nature of the rainfall makes the dryland system particularly amenable to a general analysis using stochastic models. Porporato, D’Odorico, Laio, Ridolfi, and Rodriguez-Iturbe (2002) use such a model to formulate the water balance of a dryland vegetation as
with n as soil porosity, Zr as soil depth, s as relative soil moisture content, I(s,t) as infiltration rate, E(s) as evaporation rate, and L(s) as rate of leakage or deep drainage (i.e., the loss of water downward or laterally through the soil). This stochastic differential equation can be solved by developing relations that describe the loss terms in Equation (1) as a function of soil moisture, and assume that precipitation can be described stochastically by a Poisson function with an average storm frequency rate and random rainfall with an exponential distribution around the mean amount (Porporato et al., 2002). Their result showed that the model was characterized by a pronounced bimodality. Bimodality can be defined for soil moisture as the phenomenon that the probability distribution of soil moisture shows two distinct modes (a wet one and a dry one), rather than a single, Gaussian-shaped form.
Note that a strongly seasonal rainfall distribution also can generate bimodality (e.g., Teuling, Uijlenhoet, & Troch, 2005). This bimodality is important to appreciate, as it is a key characteristic of the dryland ecohydrological system, particularly African drylands. Figure 2 shows a bimodality plot for Africa and, for a point in the Sahel, the precise bimodal distribution as derived from satellite observations of surface soil moisture. The bimodality estimated from satellites corresponds well to the dryland areas depicted in Figure 1, particularly the Sahelian zone, while it is a little less and more scattered in southeast Africa.
On a larger scale, the stability of the wet and dry states is maintained by a positive feedback mechanism as soil moisture enhances rainfall frequency via higher evapotranspiration. Increased rainfall in turn replenishes the soil moisture content. Locally, this picture can be modified by other effects, such as the more likely occurrence of rainfall over dry areas (Taylor et al., 2011). The precise interplay between these large-scale constraints of moisture supply and smaller-scale effects of convective onset still needs to be resolved (Guillod, Orlowsky, Miralles, Teuling, & Seneviratne, 2015). Figure 2 shows not only that land-surface atmosphere feedbacks may be able to sustain and enhance the effect of initial moisture anomalies occurring at the beginning of the rainy season, but also that semiarid soil moisture dynamics may evolve toward either a stable dry or a stable wet state for prolonged periods of time. Such bimodality appears to be typical for arid to semiarid regions; however, with larger spatial scales, other mechanisms, such as fire (Higgins & Scheiter, 2012), seasonal rainfall caused by monsoonal flow patterns, or movements of the Intertropical Convergence Zone may also play a role (e.g., Taylor et al., 2002). Whatever the precise cause, however, the bimodality of precipitation and soil moisture in the drylands shown in Figure 2 almost certainly depict the existence of two alternate stable ecosystem states, one wet and one dry.
The recurring, highly variable periods of drought typical for arid and semiarid areas require dryland vegetation to cope with and adapt to prolonged periods of severe water limitation. There are various ways in which plant species in dry areas can adapt to the temporal and spatial inhomogeneity of rainfall (van der Molen et al., 2011). Eagleson (2002) found that crown shape, leaf inclination, and crown height are among other important morphological traits that dryland plant species have adjusted in order to reduce radiative overheating and prevent further water loss. The use of crassulacean acid metabolism (CAM) and C4 photosynthetic pathways is another adaptation in which CO2 is more efficiently fixed in terms of water use in hot and dry conditions compared to the C3 photosynthetic pathway (Schulze, 2005). Apart from these adaptive mechanisms that reduce plant water loss while maintaining productivity, dryland systems are characterized by specific adaptations that allow the ecosystem as a whole to cope with severe water stress.
One of the more specific and interesting ecohydrological adaptions is the phenomenon of hydraulic rearrangement (e.g., Lubczynski, 2009; Schulze et al., 1998). Hydraulic rearrangement occurs primarily in woody species with deep rooting systems. Deep-rooting trees have the specific ability to redistribute water passively through their roots, which act as conduits and transfer water from moist to dry soil. This redistribution can occur when a water potential gradient exists across several soil layers, where roots are in direct hydraulic contact with the soil. The vertical gradient can take an upward or downward direction. Hydraulic ascent (or hydraulic lift) follows the gradient from the soil to the canopy, while hydraulic descent moves water from the canopy to the soil. Hydraulic ascent, the “classical” way to transport water in plants, is driven by low atmospheric water potential in dry periods and takes place when the tree lifts water from the water-saturated subsurface to the dry surface. During the daytime, water ascends via the taproots and shallow lateral roots and is transported through the stem toward the canopy, where it leaves the plant through leaf stomata (Figure 3a). At night, however, water still ascends in the taproot, but it is redistributed by shallow lateral roots away from the trunk because there is no transport to the canopy, as the stomata close at night (Figure 3b). As a result, the excess water irrigates the shallow soil layer surrounding the tree. Estimates of the quantity of water that is exported via hydraulic rearrangement vary, with a maximum of 0.34 mm day−1, or 60% of the water, available for understory transpiration (Yu & D’Odorico, 2014).
In the wet season, there is no shortage of water at the surface and shallow subsurface; thus, a large water potential exists. The deeper soil layers are dried out, having lower water potential. This generates an inverse water potential gradient, which can result in hydraulic descent (Figure 3c) moisture captured by lateral roots and transported by root conduits down through tap roots in order to store water in the deep subsurface for use during upcoming periods of water stress (Figure 3c). Note that via this mechanism, the roots, not the soil capillaries, act as conduits for water transport. Therefore, the process of hydraulic rearrangement by plant roots is fundamentally different from regular infiltration and groundwater recharge.
The downward direction of the water flow may change during the day to an upward direction in response to solar irradiance and increased photosynthetic activity (Lubczynski, 2009). Vertical hydraulic rearrangement thus is an important process that alters the ecosystem water balance and enables a sustained ecosystem net primary productivity (NPP), thereby increasing the potential ecosystem carbon gain. Over longer time scales, this process can in fact determine the community species composition and affect nutrient cycling (Horton & Hart, 1998).
Another adaptive ecohydrological mechanism is more vegetation dependent and results in different spatial rearrangements of vegetation. Using a modified version of Equation (1), coupled to a differential equation for infiltration and biomass, Rietkerk et al. (2002) showed that the vegetation in semiarid regions may have several stable states (Figure 2). This is visible in the spatial aggregation of biomass alternated with bare soil and depends on rainfall regime, grazing pressure, and slope. Interestingly, they were able to predict the occurrence of so-called brousse tigre patterns, which are typical of some parts of the Sahel, by introducing a small slope that generated surface runoff in their model. This creates dense areas of vegetation with strips of bare soil in between. The water that runs from the bare soils is harvested by the vegetation, which doubles the amount of available water to the plant. Adaptation not only occurs in this pattern, but also the soil has developed an almost impermeable crust, thus accelerating surface runoff to the vegetated areas. By increasing the rainfall in their model, they obtained the first three patterns shown in Figure 4, with an increasing biomass density.
When these stable states are plotted in a so-called bifurcation diagram (Figure 5), several interesting phenomena occur. If the vegetation developed only in a spatially homogeneous fashion, and linearly with increasing rainfall (as shown by the blue line), rainfall amounts below 1.25 mm day−1 would lead to only bare soil. However, if the possibility of spatial aggregation of the vegetation is taken into account, a much higher plant density can be achieved with the same amount of rainfall (shown by the red line). With decreasing rainfall, the homogeneous plant equilibrium (blue line) decreases linearly until plants disappear entirely for rainfall levels less than 1 mm day−1. Close to this threshold, the homogeneous plant equilibrium is unstable for small disturbances, such as those that may occur through human use of vegetation for wood or grazing. At these so-called Turing instability points (T1, T2), unstable and nonhomogeneous equilibria (broken red lines) originate, which match up to a stable, nonhomogeneous equilibrium (LP1, LP2).
Therefore, an unstable situation can occur on the dashed line. However, the stable, nonhomogeneous equilibria, where vegetation can grow sustainably, exist for a much lower range of rainfall levels, at which in a homogeneous and linear system, plants would already have disappeared (rainfall ≤ 1.0 mm day−1). In general, the pattern formation (such as the one depicted in Figure 4) leads to a higher average plant productivity compared to the spatially homogeneous situation (compare the green and blue lines in Figure 5). This seems to be the case for the abovementioned brousse tigre vegetation, which has very a high biomass density in the vegetated areas.
Note that for a small rainfall range on the right side of the Turing instability, the opposite is true. In this case, a local gap in the plant cover is further amplified, leading to below-average plant productivity compared with the homogeneous situation (i.e., the green line dives under the blue). Rietkerk et al. (2002) also found that the rainfall range for which spatial patterns are predicted widens when plant mortality increases. The occurrence of alternate stable states in these dryland ecosystems has implications for management practices and human use. Increased tree mortality, such as that caused by increased anthropogenic pressure, may lead to a shift from a vegetated and high biomass state to a nonvegetated state (plant density < LP1). The hysteresis, implied by the nonstable range in Figure 5, then puts a limit on the recovery: A much larger amount of rainfall is now required to shift the system back to the vegetated stable state (rainfall = LP2). While the precise model formulation matters, of course, it is important to emphasize the positive feedback mechanism on ecosystem resilience: Degradation can occur quickly, while recovery may take a much longer time due to self-amplifying mechanisms (e.g., Rietkerk, Dekker, de Ruiter, & van de Koppel, 2004).
Sankaran et al. (2005) describe the occurrence of savannah vegetation, a mixture of grasses and widely spaced trees, as a result of competition between trees and grasses for available resources such as water and nutrients, as well as processes that limit woody vegetation cover such as browsing by herbivores and fire. They showed that the maximum woody cover in savannahs at rainfall levels of 150 to 650 mm yr−1 was independent of fire frequency. The maximum woody cover in these arid and semiarid savannahs increases linearly with mean annual precipitation, while nutrient limitations and browsing herbivores reduce woody cover locally below its climatological maximum. Savannahs receiving less than 650 mm yr−1 are considered stable ecosystems in which canopy closure is never possible as a result of water limitation. Above 650 mm yr−1, the amount of rainfall is sufficient to enable canopy closure, which would cause a shift from savannah to closed woodland or dry forest. At higher rainfall levels, disturbances such as fire, herbivory, and fuelwood collection are therefore required to reduce the woody cover and thereby permit grasses to coexist next to the woody vegetation and maintain the savannah system. Changing the analysis from maximum possible cover to observed cover does not change this view dramatically (Sankaran, Ratnam, & Hanan, 2008). Ecohydrology is thus the underlying determinant for the existence of savannah at low precipitation amounts, while at higher amounts, disturbances such as browsing and fire kick in.
While humans ignite almost all fires in Africa, the factors driving the fire spread, and those that determine the extent of area burned are often not directly controlled by humans (Archibald, Roy, van Wilgen, & Scholes, 2009). The fuel load and moisture content, both dependent on rainfall amount and seasonality, are considered to be the factors driving fire spread and area burned in the African savannah system (Archibald et al., 2009; Andela & van der Werf, 2014). In the more arid areas of Africa, the fuel load is often limiting fire because biomass growth is limited by water availability. Contrarily, precipitation limits fires in the more humid areas, as the fuel moisture content and air humidity are increased. These complex and contrasting ecohydrological interactions between climate, vegetation, and human factors ultimately determine the fire regime of an area, and thus the annual flux of carbon leaving the system (Archibald et al., 2009).
Ecohydrological Mechanisms at the Continental Scale
A combination of recently assembled global satellite data sets, distributed networks of terrestrial eddy covariance flux observations, and advancements in interpretative and predictive modeling has enabled the exploration of large-scale changes in vegetation cover and the impact of vegetation changes on the hydrological and carbon cycle (Dolman et al., 2014). The implications of these advancements for our understanding of the coupled carbon and water cycles in drylands are important.
Ciais, Piao, Cadule, Friedlingstein, and Chédin (2009) performed a simulation using the highly complex vegetation model ORCHIDEE and identified five important characteristics of the carbon cycle on the African continent. The first is a strong correlation, at least in sign, between interannual variability in GPP and terrestrial ecosystem respiration (TER). The authors suggest that this correlation is related to severe drought events that reduce GPP, which in turn reduces assimilates available for respiration (e.g., Zscheischler et al., 2014). Furthermore, as soil moisture is reduced during droughts, the decomposition of litter and soil organic matter is partly inhibited and heterotrophic respiration declines. Ciais et al. (2009) also noted that interannual variability in net biome productivity (the net carbon balance over many years) of the African savannah was driven largely by variations in GPP that resulted from precipitation anomalies rather than temperature. This is in line with local-scale studies. Precipitation and water availability, therefore, play a key role in the large-scale carbon balance of these savannah ecosystems.
Fires form another integral part of the African savannah ecosystem and contribute significantly to the carbon emissions of the African continent. El Niño–La Niña phase transitions have, via precipitation variability, a pronounced effect on the spatial distribution of GPP and carbon emissions from fires in Africa. Andela and van der Werf (2014) found that the area burned on the African continent was significantly higher in the dry El Niño year compared to the wetter La Niña year. In Africa, the dry-season length gradually increases when moving away from the equator. The highest annual burned area has been observed in savannah with intermediate levels of precipitation and productivity and distinct wet and dry seasons (Figure 6). Most of the fire emissions originate from these savannah ecosystems (Andela & van der Werf, 2014). When analyzing the variability in fires in Africa, Andela and van der Werf (2014) found that precipitation, as forced by the El Niño Southern Oscillation, is an important driver of fire in the zone from 400–1,500 mm yr−1. Importantly, they found that over 2001–2012, the trend in annually burned area showed a decrease in northern Africa. The decrease in area burned was attributed partly to an increase in rainfall and partly to land use change to arable farming. Savannahs are increasingly being converted into cropland, which reduces the number of fires significantly in these areas. This may also explain why the relative spatial distribution of fires on the African continent is strongly dependent on El Niño conditions, while the actual number of fires and amount of area burned is dependent on other factors (Ciais et al., 2009).
Chen, Werf, Jeu, Wang, and Dolman (2013) performed a global analysis of the effect of drought on NPP and found that for the arid regions of Africa, a good correlation existed between soil moisture, as estimated by the Standardized Precipitation Evapotranspiration Index (Vicente-Serrano et al., 2010), and NPP, as calculated from the Carnegie-Ames-Stanford Approach (CASA) model using satellite data of the normalized difference vegetation index (NDVI). They found, in fact, similar patterns for other arid regions in the world and those with pronounced dry seasons. This suggests that in these areas, precipitation mainly affects the growth of vegetation.
How this relation interacts with fire, by adding fuel load in wet years, is a key area of further research. Poulter et al. (2014) used a new data set of microwave-based biomass remote sensing to show that the quantity of biomass changed in the savannahs and how this affected the amount of CO2 that is released into the atmosphere by fires in savannahs. Their analysis was focused on Australia, but the conclusion is relevant here because until now, tropical forests have always been seen as the most important factor in regulating the amount of CO₂, and these results may indicate that drylands are taking over this role.
Discussion and Conclusions
Africa’s drylands face unprecedented changes in the coming century. Rapid population increase, urbanization, and land use change toward agriculture will increase the pressure on dryland ecosystems (UN, 2015; FAO, 2011). Furthermore, projected climatic changes, including surface temperature increase and regional drying, will have tremendous impacts on food security, water resources, and human health and well-being. Therefore, it is most relevant to advance our knowledge and to be able to monitor and predict future changes that will occur in these dryland ecosystems. Insights into ecohydrological adaptations of dryland vegetation, monitoring fire and vegetation changes and identifying feedback mechanisms, are important components that are required to solve this complex puzzle. In Figure 7, the main processes driving the water and carbon cycle in dryland ecosystems are shown.
African dryland ecosystems have developed a remarkable set of adaptations in response to high rainfall variability. Besides physiological adaptations that are required to photosynthesize and survive in extreme water-limited conditions, some adaptations have direct impacts on the plant’s surroundings and the ecosystem as a whole. Hydraulic rearrangement drives water availability for the vegetation surrounding deep rooting trees and, in this sense, contributes to a positive feedback of the vegetation on moisture availability. A similar mechanism is the spatial aggregation of the vegetation and biomass, which results in higher intercepted water from runoff and thus again a positive feedback of the vegetation on moisture availability. These adaptations have increased the resilience of the dryland vegetation to climate variability. However, the existence of large-scale feedbacks may also amplify initial disturbances, such as those caused by humans, to the extent that changes become irreversible after the system has shifted to a stable, dry state (Rietkerk et al., 2004). Returning to the more favorable wet state is then likely to be impossible due to hysteresis in the rainfall-vegetation relationship.
Due to the dynamic nature of dryland ecosystems, it is important to accurately monitor changes in vegetation cover and ecohydrological properties that are occurring. Many dryland regions in sub-Saharan Africa are currently showing a greening trend, which is sometimes attributed to woody encroachment of the savannah (Hermann et al., 2005; Andela et al., 2013; Mitchard & Flintrop, 2013). However, this trend is not spatially uniform, with human disturbances potentially limiting the recovery or development of the woody vegetation in dry environments (Hermann et al., 2005). The increase of woody cover in the savannah will likely trigger numerous ecohydrological feedbacks to the regional climate system, such as an increase in transpiration due to increased rooting depth, increased leaf area, and changes in the ratio of transpiration to total evapotranspiration (Huxman et al., 2005). However, this increase in ecosystem transpiration could be counteracted by a reduced transpiration per unit leaf area caused by elevated atmospheric CO2 concentrations. The increased availability of CO2 for photosynthesis could limit stomatal conductance and increase water use efficiency, especially in C3 plants (Bond, 2008). How the ecohydrology of the African drylands will change in the future with expected climatic changes, and how this will affect or will be affected by the vegetation, are still largely unresolved questions. Uncertainties related to changes in human disturbances, land-use changes, and other socioeconomic mechanisms add to the already complex nature of dryland ecosystems.
Africa in general, and especially its drylands, remain a severely undersampled region direly in need of improved observations. Both in terms of atmospheric observations and ground observations, the African area is poorly sampled. Until this situation improves, satellite remote sensing is our only tool to get estimates of ecohydrological processes. Using satellites, it is possible to derive meaningful estimates of evaporation (Miralles et al., 2011; Mueller et al., 2013, and GPP, e.g., Chen et al., 2013; Poulter et al., 2014) to get more insight into water vegetation-carbon relations. However, ground-based observations using eddy covariance measurements and other procedures are badly needed to validate these satellite observations and process models. With the increasing importance of woody biomass in Africa, the significance of drylands in the global GPP (and maybe NPP as well) will increase. Understanding this trajectory is of obvious importance in predicting future climate impacts on the ecohydrological cycle using coupled climate-biogeochemical models.
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