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Some essential features of the terrestrial hydrologic cycle and ecosystem response are singled out by confronting empirical observations of the soil water balance of different ecosystems with the results of a stochastic model of soil moisture dynamics. The simplified framework analytically describes how hydroclimatic variability (especially the frequency and amount of rainfall events) concurs with soil and plant characteristics in producing the soil moisture dynamics that in turn impact vegetation conditions. The results of the model extend and help interpret the classical curve of Budyko, which relates evapotranspiration losses to a dryness index, describing the partitioning of precipitation into evapotranspiration, runoff, and deep infiltration. They also provide a general classification of soil water balance of the world ecosystems based on two governing dimensionless groups summarizing the climate, soil, and vegetation conditions. The subsequent analysis of the links among soil moisture dynamics, plant water stress, and carbon assimilation offers an interpretation of recent manipulative field experiments on ecosystem response to shifts in the rainfall regime, showing that plant carbon assimilation crucially depends not only on the total rainfall during the growing season but also on the intermittency and magnitude of the rainfall events.

Aim: Vegetation is sensitive to mean annual precipitation (MAP), but the sensitivity of vegetation to precipitation variability (PV) is less clear. Tropical ecosystems are likely to experience increased PV in the future. Here we assessed the importance, magnitude and mechanism of PV effects on tree cover in the context of covarying environmental drivers such as fire, temperature and soil properties. Location: Tropical land. Time period: 2000–2010. Major taxa studied: Trees. Methods: We compiled climate, soil and remotely-sensed tree cover data over tropical land. We then comprehensively assessed the contribution of PV at different time-scales to tropical tree cover variations and estimated the sensitivity of tree cover to PV changes by conducting rolling-window regression and variance decomposition analyses. We further adopted a mechanistic modelling approach to test whether water competition between trees and grasses can explain the observed effect of PV. Results: We find that PV contributes 33–56% to the total explained spatial variation (65–79%) in tree cover. The contribution of PV depends on MAP and is highest under intermediate MAP (500–1,500 mm). Tree cover generally increases with rainy day frequency and wet season length but shows mixed responses to inter-annual PV. Based on the estimated sensitivity, tropical tree cover can decrease by 3–5% overall and by up to 20% in Amazonia under a 20% decrease in rainy days. Mechanistic modelling analysis reproduced the continental differences in tree cover along an MAP gradient. Main conclusions: Under intermediate rainfall regimes (500–1,500 mm), PV can be a more important determinant of tropical tree cover than conventionally proposed drivers such as MAP and fire. The effect of PV likely results from the sensitivity of tree–grass competition to the temporal distribution of water resources. These results show that climate variability can strongly shape the biosphere.

Climate change is altering the seasonal distribution, interannual variability and overall magnitude of precipitation. A new global measure of precipitation seasonality is proposed, and application of this method to observations from the tropics shows that increases in variability were accompanied by shifts in seasonal magnitude, timing and duration. Climate change has altered not only the overall magnitude of rainfall but also its seasonal distribution and interannual variability worldwide 1 , 2 , 3 . Such changes in the rainfall regimes will be most keenly felt in arid and semiarid regions 4 , where water availability and timing are key factors controlling biogeochemical cycles 5 , primary productivity 6 , 7 , and the phenology of growth and reproduction 8 , 9 , 10 , while also regulating agricultural production 11 . Nevertheless, a comprehensive framework to understand the complex seasonal rainfall regimes across multiple timescales is still lacking. Here, we formulate a global measure of seasonality, which captures the effects of both magnitude and concentration of the rainy season, and use it to identify regions across the tropics with highly seasonal rainfall regimes. By further decomposing rainfall seasonality into its magnitude, timing and duration components, we find increases in the interannual variability of seasonality over many parts of the dry tropics, implying increasing uncertainty in the intensity, arrival and duration of seasonal rainfall over the past century. We show that such increases in rainfall variability were accompanied by shifts in its seasonal magnitude, timing and duration, thus underscoring the importance of analysing seasonal rainfall regimes in a context that is most relevant to local ecological and social processes.

Tree abundance in tropical savannas exhibits large and unexplained spatial variability. Here, we propose that differentiated tree and grass water use strategies can explain the observed negative relation between maximum tree abundance and rainfall intensity (defined as the characteristic rainfall depth on rainy days), and we present a biophysical tree–grass competition model to test this idea. The model is founded on a premise that has been well established in empirical studies, namely, that the relative growth rate of grasses is much higher compared with trees in wet conditions but that grasses are more susceptible to water stress and lose biomass more quickly in dry conditions. The model is coupled with a stochastic rainfall generator and then calibrated and tested using field observations from several African savanna sites. We show that the observed negative relation between maximum tree abundance and rainfall intensity can be explained only when differentiated water use strategies are accounted for. Numerical experiments reveal that this effect is more significant than the effect of root niche separation. Our results emphasize the importance of vegetation physiology in determining the responses of tree abundance to climate variations in tropical savannas and suggest that projected increases in rainfall intensity may lead to an increase in grass in this biome.

We run a comparative study of ecohydrological models of streamflow probability distributions (pdfs), p(Q), derived by Botter et al. (2007a, 2009), against field data gathered in different hydrological contexts. Streamflows measured in several catchments across various climatic regions of northeastern Italy and the United States are employed. The relevance of the work stems from the implied analytical predictive ability of hydrologic variability, whose role on stream and riparian ecological processes and large‐scale management schemes is fundamental. The tools employed are analytical models of p(Q) (and of the related flow duration curve, D(Q)) derived by coupling suitable storage‐discharge relations with a stochastic description of streamflow production through soil moisture dynamics, and are expressed as a function of few macroscopic rainfall, soil, vegetation and geomorphological parameters. In this work we compare the performances of a recent version of the model (which includes the effects of nonlinear subsurface storage‐discharge relations) to those provided by the linear version through the application of the models to 13 test catchments belonging to various climatic and geomorphic contexts. A general agreement between predicted and observed daily streamflows pdfs is shown, though differences emerge between the linear and the nonlinear approaches. In particular, by including the effects of a nonlinear storage‐discharge relation the model accuracy is shown to increase with respect to the linear scheme in most examined cases. We show that this is not simply attributable to the added parameter but corresponds to a proper likelihood increase. The nonlinear model is shown to exhibit three basic forms for p(Q) (monotonically decreasing with an atom of probability in Q = 0, bell‐shaped with the mode close to zero, bell‐shaped with the mode close to the mean), corresponding to different hydrologic regimes which are clearly detectable in field data. Inferences on the nonlinear character of the relation between subsurface storage and discharge from observed p(Q) are finally drawn.

In this paper, we develop a simple analysis method to infer some properties of the watershed processes from daily streamflow data. The method is built on a simple streamflow model with a link to rainfall stochasticity, which characterizes the streamflow as a series of overlapping gamma distribution‐shaped pulses. The key premise of the method is that the complex streamflow processes can be effectively captured by simply dividing streamflow into two regimes. Specifically in this method, the gamma pulse model is applied separately to low‐ and high‐flow regimes. We demonstrate the application of the method to five watersheds and show that it is capable of capturing at least two important statistical properties of streamflow, namely the probability density function and the autocorrelation function for wide ranges of values (i.e., from low to large flows and time lags, respectively).

The analysis of soil water partitioning in seasonally dry climates necessarily requires careful consideration of the periodic climatic forcing at the intra-annual timescale in addition to daily scale variabilities. Here, we introduce three new extensions to a stochastic soil moisture model which yields seasonal evolution of soil moisture and relevant hydrological fluxes. These approximations allow seasonal climatic forcings (e.g. rainfall and potential evapotranspiration) to be fully resolved, extending the analysis of soil water partitioning to account explicitly for the seasonal amplitude and the phase difference between the climatic forcings. The results provide accurate descriptions of probabilistic soil moisture dynamics under seasonal climates without requiring extensive numerical simulations. We also find that the transfer of soil moisture between the wet to the dry season is responsible for hysteresis in the hydrological response, showing asymmetrical trajectories in the mean soil moisture and in the transient Budyko's curves during the ‘dry-down‘ versus the ‘rewetting‘ phases of the year. Furthermore, in some dry climates where rainfall and potential evapotranspiration are in-phase, annual evapotranspiration can be shown to increase because of inter-seasonal soil moisture transfer, highlighting the importance of soil water storage in the seasonal context.

Global freshwater resources are under increasing pressure from economic development, population growth, and climate change. The international trade of water-intensive products (e.g., agricultural commodities) or virtual water trade has been suggested as a way to save water globally. We focus on the virtual water trade network associated with international food trade built with annual trade data and annual modeled virtual water content. The evolution of this network from 1986 to 2007 is analyzed and linked to trade policies, socioeconomic circumstances, and agricultural efficiency. We find that the number of trade connections and the volume of water associated with global food trade more than doubled in 22 years. Despite this growth, constant organizational features were observed in the network. However, both regional and national virtual water trade patterns significantly changed. Indeed, Asia increased its virtual water imports by more than 170%, switching from North America to South America as its main partner, whereas North America oriented to a growing intraregional trade. A dramatic rise in China's virtual water imports is associated with its increased soy imports after a domestic policy shift in 2000. Significantly, this shift has led the global soy market to save water on a global scale, but it also relies on expanding soy production in Brazil, which contributes to deforestation in the Amazon. We find that the international food trade has led to enhanced savings in global water resources over time, indicating its growing efficiency in terms of global water use.

Agriculture will need to significantly intensify in the next decades to continue providing essential nutritive food to a growing global population. However, it can have harmful environmental impacts, due to the use of natural and synthetic resources and the emission of greenhouse gases, which alter the water, carbon and nitrogen cycles, and threaten the fertility, health and biodiversity of landscapes. Because of the spatial heterogeneity of resource productivity, farming practices, climate, and land and water availability, the environmental impact of producing food is highly dependent on its origin. For this reason, food trade can either increase or reduce the overall environmental impacts of agriculture, depending on whether or not the impact is greater in the exporting region. Here, we review current scientific understanding of the environmental impacts of food trade, focusing on water and land use, pollution and greenhouse gas emissions. In the case of water, these impacts are mainly beneficial. However, in the cases of pollution and greenhouse gas emissions, this conclusion is not as clear. Overall, there is an urgent need for a more comprehensive, integrated approach to estimate the global impacts of food trade on the environment. Second, research is needed to improve the evaluation of some key aspects of the relative value of each resource depending on the local and regional biophysical and socio-economic context. Finally, to enhance the impact of such evaluations and their applicability in decision-making, scenario analyses and accounting of key issues like deforestation and groundwater exhaustion will be required.

REST cure for stem cells The neuronal repressor protein known as REST or NRSF has a variety of effects — including both oncogenic and tumour suppressor activity — depending on cellular context. It is expressed at high levels in mouse embryonic stem cells but its function has not been understood. Now Singh et al . show that REST/NRSF is an element in the transcriptional network maintaining stem cell 'stemness'. It maintains self-renewal and pluripotency by blocking the expression of a microRNA that inhibits stem cell self-renewal and prompts them to differentiate into specific cell types. This paper demonstrates that a simple, neutral metacommunity model successfully predicts large-scale patterns of fish diversity in the Mississippi–Missouri Basin River System. River networks, seen as ecological corridors featuring connected and hierarchical dendritic landscapes for animals and plants, present unique challenges and opportunities for testing biogeographical theories and macroecological laws 1 . Although local and basin-scale differences in riverine fish diversity have been analysed as functions of energy availability and habitat heterogeneity 2 , scale-dependent environmental conditions 3 and river discharge 4 , 5 , a model that predicts a comprehensive set of system-wide diversity patterns has been hard to find. Here we show that fish diversity patterns throughout the Mississippi–Missouri River System are well described by a neutral metacommunity model coupled with an appropriate habitat capacity distribution and dispersal kernel. River network structure acts as an effective template for characterizing spatial attributes of fish biodiversity. We show that estimates of average dispersal behaviour and habitat capacities, objectively calculated from average runoff production, yield reliable predictions of large-scale spatial biodiversity patterns in riverine systems. The success of the neutral theory in two-dimensional forest ecosystems 6 , 7 , 8 and here in dendritic riverine ecosystems suggests the possible application of neutral metacommunity models in a diverse suite of ecosystems. This framework offers direct linkage from large-scale forcing, such as global climate change, to biodiversity patterns.