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Large areas in the tropics and at mid-latitudes experience pronounced seasonality and inter-annual variability in rainfall and hence water availability. Despite the importance of these seasonally dry ecosystems (SDEs) for the global carbon cycling and in providing ecosystem services, a unifying ecohydrological framework to interpret the effects of climatic variability on SDEs is still lacking. A synthesis of existing data about plant functional adaptations in SDEs, covering some 400 species, shows that leaf phenological variations, rather than physiological traits, provide the dominant control on plant-water-carbon interactions. Motivated by this result, the combined implications of leaf phenology and climatic variability on plant water use strategies are here explored with a minimalist model of the coupled soil water and plant carbon balances. The analyses are extended to five locations with different hydroclimatic forcing, spanning seasonally dry tropical climates (without temperature seasonality) and Mediterranean climates (exhibiting out of phase seasonal patterns of rainfall and temperature). The most beneficial leaf phenology in terms of carbon uptake depends on the climatic regime: evergreen species are favoured by short dry seasons or access to persistent water stores, whereas high inter-annual variability of rainy season duration favours the coexistence of multiple drought-deciduous phenological strategies. We conclude that drought-deciduousness may provide a competitive advantage in face of predicted declines in rainfall totals, while reduced seasonality and access to deep water stores may favour evergreen species. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Elevated atmospheric CO 2 concentration is expected to increase leaf CO 2 assimilation rates, thus promoting plant growth and increasing leaf area. It also decreases stomatal conductance, allowing water savings, which have been hypothesized to drive large-scale greening, in particular in arid and semiarid climates. However, the increase in leaf area could reduce the benefits of elevated CO 2 concentration through soil water depletion. The net effect of elevated CO 2 on leaf- and canopy-level gas exchange remains uncertain. To address this question, we compare the outcomes of a heuristic model based on the Partitioning of Equilibrium Transpiration and Assimilation (PETA) hypothesis and three model variants based on stomatal optimization theory. Predicted relative changes in leaf- and canopy-level gas exchange rates are used as a metric of plant responses to changes in atmospheric CO 2 concentration. Both model approaches predict reductions in leaf-level transpiration rate due to decreased stomatal conductance under elevated CO 2 , but negligible (PETA) or no (optimization) changes in canopy-level transpiration due to the compensatory effect of increased leaf area. Leaf- and canopy-level CO 2 assimilation is predicted to increase, with an amplification of the CO 2 fertilization effect at the canopy level due to the enhanced leaf area. The expected increase in vapour pressure deficit (VPD) under warmer conditions is generally predicted to decrease the sensitivity of gas exchange to atmospheric CO 2 concentration in both models. The consistent predictions by different models that canopy-level transpiration varies little under elevated CO 2 due to combined stomatal conductance reduction and leaf area increase highlight the coordination of physiological and morphological characteristics in vegetation to maximize resource use (here water) under altered climatic conditions. [ABSTRACT FROM AUTHOR]

Soil drying and wetting cycles promote carbon (C) release through large heterotrophic respiration pulses at rewetting, known as the "Birch" effect. Empirical evidence shows that drier conditions before rewetting and larger changes in soil moisture at rewetting cause larger respiration pulses. Because soil moisture varies in response to rainfall, these respiration pulses also depend on the random timing and intensity of precipitation. In addition to rewetting pulses, heterotrophic respiration continues during soil drying, eventually ceasing when soils are too dry to sustain microbial activity. The importance of respiration pulses in contributing to the overall soil heterotrophic respiration flux has been demonstrated empirically, but no theoretical investigation has so far evaluated how the relative contribution of these pulses may change along climatic gradients or as precipitation regimes shift in a given location. To fill this gap, we start by assuming that heterotrophic respiration rates during soil drying and pulses at rewetting can be treated as random variables dependent on soil moisture fluctuations, and we develop a stochastic model for soil heterotrophic respiration rates that analytically links the statistical properties of respiration to those of precipitation. Model results show that both the mean rewetting pulse respiration and the mean respiration during drying increase with increasing mean precipitation. However, the contribution of respiration pulses to the total heterotrophic respiration increases with decreasing precipitation frequency and to a lesser degree with decreasing precipitation depth, leading to an overall higher contribution of respiration pulses under future more intermittent and intense precipitation. Specifically, higher rainfall intermittency at constant total rainfall can increase the contribution of respiration pulses up to ∼10 % or 20 % of the total heterotrophic respiration in mineral and organic soils, respectively. Moreover, the variability of both components of soil heterotrophic respiration is also predicted to increase under these conditions. Therefore, with future more intermittent precipitation, respiration pulses and the associated nutrient release will intensify and become more variable, contributing more to soil biogeochemical cycling. [ABSTRACT FROM AUTHOR]

Soil drying and wetting cycles promote carbon (C) release through large heterotrophic respiration pulses at rewetting, known as Birch effect. Empirical evidence shows that drier conditions before rewetting and larger changes in soil moisture at rewetting cause larger respiration pulses. Because soil moisture varies in response to rainfall, also these respiration pulses depend on the random timing and intensity of precipitation. In addition to rewetting pulses, heterotrophic respiration continues during soil drying, eventually ceasing when soils are too dry to sustain microbial activity. The importance of respiration pulses in contributing to the overall soil respiration flux has been demonstrated empirically, but no theoretical investigation has so far evaluated how the relative contribution of these pulses may change along climatic gradients or as precipitation regimes shift in a given location. To fill this gap, we start by assuming that rewetting pulses and respiration rates during soil drying can be treated as random variables dependent on soil moisture fluctuations, and develop a stochastic model for soil heterotrophic respiration rates that analytically links the statistical properties of respiration to those of precipitation. Model results show that both the mean rewetting pulse respiration and the mean respiration during drying increase with increasing mean precipitation. However, the contribution of respiration pulses to the total heterotrophic respiration increases with decreasing precipitation frequency and to a lesser degree with decreasing precipitation depth, leading to an overall higher contribution of respiration pulses under future more intermittent and intense precipitation. Moreover, the variability of both components of soil respiration is also predicted to increase under these conditions. Therefore, our results suggest that with future more intermittent precipitation, respiration pulses and the associated nutrient release will intensify and become more variable, contributing more to soil biogeochemical cycling. [ABSTRACT FROM AUTHOR]

Abstract: The sustainable management of unwanted vegetation in agricultural fields through integrated weed control strategies requires detailed knowledge about the maternal formation of primary seed dormancy, to support the prediction of seedling emergence dynamics. This knowledge is decisive for the timing of crop sowing and nonchemical weed control measures. Studies in controlled environments have already demonstrated that thermal conditions and, to some extent, water availability during seed set and maturation has an impact on the level of dormancy. However, it is still unclear if this applies also under field conditions, where environmental stressors and their timing are more variable. We address this question for Alopecurus myosuroides in south‐western Sweden. We quantified the effects of cumulated temperature and precipitation as well as soil water potential during the reproductive growth phase of A myosuroides on primary seed dormancy under field conditions. Empirical models differing in focal time intervals and, in case of soil water potential, focal soil depths were compared regarding their predictive power. The highest predictive power for the level of primary dormancy of A. myosuroides seeds was found for a two‐factorial linear model containing air temperature sum between 0 and 7 days before peak seed shedding as well as the number of days with soil water potential below field capacity between 7 and 35 days before peak seed shedding. For soil water potential, it was found that only the top 10 cm soil layer is of relevance, which is in line with the shallow root architecture of A. myosuroides. We conclude that for this species the level of dormancy depends on the magnitude and timing of temperature and water availability during the reproductive growth phase. Water availability appears to be more important during maternal environmental perception and temperature during zygotic environmental perception. [ABSTRACT FROM AUTHOR]

Quantification of stomatal responses to environmental variables, in particular to soil water status, is needed to model carbon and water exchange rates between plants and the atmosphere. Models based on stomatal optimality theory successfully describe leaf gas exchange under different environmental conditions, but the effects of water availability on the key optimization parameter [the marginal water use efficiency (WUE), λ = ∂ A/∂ E] has resisted complete theoretical treatment. Building on previous optimal leaf gas exchange models, we developed an analytical equation to estimate λ from gas exchange observations along gradients of soil water availability. This expression was then used in a meta-analysis of about 50 species to investigate patterns of variation in λ. Assuming that cuticular water losses are negligible λ increases under mild water stress but decreases when severe water stress limits photosynthesis. When cuticular conductance is considered, however, λ increases monotonically with increasing water stress, in agreement with previous theoretical predictions. Moreover, the shape of these response curves to soil water availability changes with plant functional type and climatic conditions. In general, λ is lower in species grown in dry climates, indicating lower marginal WUE. The proposed parameterization provides a framework to assess the responses of leaf gas exchange to changes in water availability. Moreover, the model can be extended to scale leaf-level fluxes to the canopy and ecosystem level. [ABSTRACT FROM AUTHOR]

Despite the observed impact of water stress on photosynthesis, some of the most used models of CO2 assimilation in C3 and C4 functional types do not directly account for it. We discuss an extension of these models, which explicitly includes the metabolic and diffusive limitations due to water stress on photosynthesis. Functional relationships describing the photosynthetic processes and CO2 diffusion inside leaves are modified to account for leaf water status on the basis of experimental results available in the literature. Extensive comparison with data shows that the model is suitable to describe the reduction in CO2 assimilation rate with decreasing leaf water potentials in various species. A simultaneous analysis of photosynthesis, transpiration and soil moisture dynamics is then carried out to explore the actual impact of drought on different photosynthesis processes and on the overall plant activity. The model well reproduces measured CO2 assimilation rate as a function of soil moisture and could be useful to formulate hypotheses for detailed experiments as well as to simulate in detail transpiration and photosynthesis dynamics under water stress. [ABSTRACT FROM AUTHOR]

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results. [ABSTRACT FROM AUTHOR]