Explore the vast range of titles available.

Summary The interactions between hydrological and ecological processes are key issues to improve our predictions of ecosystem responses to increasing droughts. However, predicting the dynamics and the impacts of vegetation water stress remains challenging because of complex ecohydrological feedbacks. The ecohydrological optimality approach proposes that functional adjustments within plant communities may buffer the increase in vegetation water stress despite local water shortage. This study aimed to test whether vegetation water stress may be invariant across contrasting plant communities, reflecting possible optimality processes. We addressed the following question: does a lower soil water storage capacity under the same climate generate greater vegetation water stress over time? We hypothesized that vegetation water stress would be buffered around a low and constant level through the adjustment of vegetation biomass productivity net primary productivity (NPP), evapotranspiration (ET) and/or water‐use efficiency (WUE) in relation with local soil water storage capacity. We monitored 12 native plant communities distributed along a gradient of soil water storage capacity (ranging from 20 mm to 120 mm) during five successive years. Net primary productivity, ET, WUE as well as soil water dynamics were assessed and modelled for each plant community throughout the 5 years of study. Vegetation water stress was determined for each plant community as the deviation of between actual ET and their maximum ETm rate achieved under non‐limiting conditions. We found that NPP and ET were together proportionally related to local soil water storage capacity across the 5 years of study while WUE did not differ between plant communities. Vegetation water stress was found quite similar for all plant communities whatever the soil water storage capacity. These results suggested that vegetation water stress was strongly buffered by the community‐level plant growth rates and total water use along the soil gradient, but not by WUE. Our results suggest that stressful environments rarely exist for plant communities. A dynamic scaling relationship between NPP and ET may underpin the control of vegetation water stress over seasonal and pluriannual time‐scales. Such results could contribute to better understanding processes associated with ecohydrological optimality and improve the predictions of vegetation dynamics under increasing droughts. A lay summary is available for this article. Lay Summary

During ecological restoration, it is necessary to comprehensively consider the state of vegetation in climate–soil–vegetation systems. The theory of ecohydrological optimality assumes that this state tends to reach long-term dynamic equilibrium between the available water supply of the system and the water demand of vegetation, which is driven by the maximization of productivity. This study aimed to understand the factors that affect the spatial distribution of vegetation and simulate the ideal vegetation coverage (M0) that a specific climate and soil can maintain under an equilibrium state. The ecohydrological optimality model was applied based on meteorological, soil, and vegetation data during the 2000–2018 growing seasons, and the sensitivity of the simulated results to input data under distinct vegetation and soil conditions was also considered in the Yongding River watershed, China. The results revealed that the average observed vegetation coverage (M) was affected by precipitation characteristic factors, followed by wind speed and relative humidity. The M, as a whole, exhibited horizontal zonal changes from a spatial perspective, with an average value of 0.502, whereas the average M0 was 0.475. The ecohydrological optimality theory ignores the drought resistance measures evolved by vegetation in high vegetation coverage areas and is applicable to simulate the long-term average vegetation coverage that minimizes water stress and maximizes productivity. The differences between M and M0 increased from the northwest to the southeast of this area, with a maximum value exceeding 0.3. Meteorological factors were the most sensitive factors of this model, and the M0 of the steppe was most sensitive to the stem fraction, mean storm depth, and air temperature. Whether soil factors are sensitive depends on soil texture. Overall, the study of the carrying capacity of vegetation in the natural environment contributes to providing new insights into vegetation restoration and the conservation of water resources.

PurposeSoil moisture is a key ecohydrological variable in the soil–plant–atmosphere systems; understanding soil moisture–plant interactions is at the core of ecohydrology research. Here we review the current state of knowledge regarding soil moisture–plant interactions and the ecohydrological effects of soil moisture dynamics. Approaches for investigating soil moisture–plant interactions are also reviewed, with emphasis on their ability to predict plant/ecosystem responses to soil moisture variations under environment change.ResultsThe status and distribution of soil moisture affect ecohydrological processes such as runoff, infiltration and evaporation and plant morphology and function (e.g. transpiration and photosynthetic rate). Plants also affect soil moisture dynamics through its involvement in the water cycle. Soil moisture, evapotranspiration and atmospheric factors (e.g. vapour pressure deficit) are closely linked in transitional soil moisture regimes (ranging from dry to wet soil conditions), the identification of which is critical for quantifying these relationships under different soil moisture conditions. Clarifying the mechanisms of soil moisture–plant interactions can aid in the development of soil moisture models, especially those comprising detailed process representation and feedback.Future perspectives and conclusionsLong-term controlled experiments examining soil moisture dynamics and a meta-analysis of the results are useful for elucidating and quantifying the soil moisture–plant interactions. Soil moisture models are important tools for predicting changes in soil moisture–plant interactions. Simplifying descriptions of each process in models is important; moreover, optimality-based models can provide novel insights that would allow prediction of plant responses to changes in soil moisture dynamics due to environment fluctuations.

Optimality principles have been applied in ecohydrological modeling to derive optimal vegetation properties and describe co-evolution states of vegetation and water cycle. Unfortunately, most existing optimality-based models only consider vertical vegetation-soil-water interactions on plot scale, without considering the lateral hydrological processes. This work aims to extend the field-scale Vegetation Optimality Model (VOM) to the watershed scale. Lateral flow is incorporated to VOM through a hierarchical strategy, establishing the Distributed Vegetation Optimality Model (DisVOM). The model is tested with long-tem flux measurements in the Walnut Gulch watershed, a United States Agricultural Research Service (US-ARS) experimental watershed in southern Arizona. The results indicate the model performance is acceptable for most of years, especially for the growing season. The seasonal dynamic of ET, soil water, and GPP demonstrate good consistency with observations. The model provides reasonable spatial distribution of ET and GPP, suggesting the model can discriminate the effect of lateral flow on water redistribution, and consequently on root water uptake, as well as carbon assimilation. The model could be a useful tool assessing the impact of climate change and human activities on vegetation and water cycle.

Evapotranspiration constitutes more than 80% of the long-term water balance in Northern China. In this area, crop transpiration due to large areas of agriculture and irrigation is responsible for the majority of evapotranspiration. A model for crop transpiration is therefore essential for estimating the agricultural water consumption and understanding its feedback to the environment. However, most existing hydrological models usually calculate transpiration by relying on parameter calibration against local observations, and do not take into account crop feedback to the ambient environment. This study presents an optimality-based ecohydrology model that couples an ecological hypothesis, the photosynthetic process, stomatal movement, water balance, root water uptake and crop senescence, with the aim of predicting crop characteristics, CO2 assimilation and water balance based only on given meteorological data. Field experiments were conducted in the Weishan Irrigation District of Northern China to evaluate performance of the model. Agreement between simulation and measurement was achieved for CO2 assimilation, evapotranspiration and soil moisture content. The vegetation optimality was proven valid for crops and the model was applicable for both C3 and C4 plants. Due to the simple scheme of the optimality-based approach as well as its capability for modeling dynamic interactions between crops and the water cycle without prior vegetation information, this methodology is potentially useful to couple with the distributed hydrological model for application at the watershed scale.