Plant biomass and soil moisture dynamics: analytical results

Schaffer, Benjamin E.; Nordbotten, Jan Martin; Rodrı́guez-Iturbe, Ignacio

doi:10.1098/rspa.2015.0179

Cited by 12 publications

(30 citation statements)

References 25 publications

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“…The modeling approach described above is used to examine the biomass response to an alternating regime of wet and dry seasons, as found at the study site. Schaffer et al (3) showed that for typical dryland parameter values, the end-of-season biomass would be significantly different from that of the constantparameter steady state (a similar calculation is performed in ref.…”

Section: Discussionmentioning

confidence: 99%

“…The stochasticity of this infiltration process, with arrival rate λ and a rescaled mean infiltration depth 1/θ (again, expressed as a fraction of the root zone water storage volume), induces a probability density function (pdf) on the biomass-soil moisture state space. Schaffer et al (3) showed that for constant rainfall parameters this system allowed for an exact, closed-form steady-state pdf, but also, in recognition of the fact that convergence to this state might take longer than the length of a typical (constant parameter) season, derived an approximation of the system's transient behavior. This approximation exploits the fact that biomass varies on a much slower timescale than soil moisture, so that changes in biomass on an interval of interest (such as a season) involve an integration that tends to average out the soil moisture fluctuations in that interval, allowing for a simple estimation of the marginal pdf of biomass.…”

Section: [2]mentioning

confidence: 99%

“…In our previous work (3,7), B has denoted various plant tissue types, but here we are modeling only the leaf component of the vegetation. (Details on the derivation of these dynamics, especially the resolution of and interaction between different tissue types, are given in Theoretical Model.)…”

Section: Significancementioning

confidence: 99%

“…Characterizing the vegetation in water-limited ecosystems, with regard to quantity, species composition, and stability, is a long-standing problem in restoration ecology (3). Field surveys and different types of measurements have been taken for decades (4), but they have mostly yielded only descriptive results [e.g., links between soil moisture and accompanying biomass (5)].…”

mentioning

confidence: 99%

“…Schaffer et al (3) recently developed an analytical description of the transient joint behavior of plant biomass and soil moisture induced by stochastic rainfall dynamics. These analytical results allow for predictions of ecosystem behavior under changing climate conditions and also illuminate the sensitivities of the dynamics to plant physiology, as well as to climate and soil characteristics that govern the system.…”

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confidence: 99%

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Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China

Wang

Schaffer

Yang

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend-for each season-on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons.ecohydrology | stochastic dynamics | vegetation modeling | climate change impacts | soil moisture I n arid and semiarid ecosystems, successful use of limited water resources is of central importance in determining the evolutionary trends of vegetation. Soil moisture there is the principal limiting factor for vegetation restoration and plays a key role in controlling the spatiotemporal patterns of vegetation regulating the complex dynamics of the climate-soil-vegetation system (1, 2).Characterizing the vegetation in water-limited ecosystems, with regard to quantity, species composition, and stability, is a long-standing problem in restoration ecology (3). Field surveys and different types of measurements have been taken for decades (4), but they have mostly yielded only descriptive results [e.g., links between soil moisture and accompanying biomass (5)].Schaffer et al. (3) recently developed an analytical description of the transient joint behavior of plant biomass and soil moisture induced by stochastic rainfall dynamics. These analytical results allow for predictions of ecosystem behavior under changing climate conditions and also illuminate the sensitivities of the dynamics to plant physiology, as well as to climate and soil characteristics that govern the system. The objective of this study is first to test the accuracy of the analytical model under current conditions by comparing its predicted distribution for the biomass density in both the wet and dry seasons with the statistics observed in a long-term field experiment in northwestern (NW) China. Subsequently, using the climate change forecast of the field site, predictions will be made for the seasonal mean biomass and its variability in the future.Ecosystem Characteristics: Climate, Soil, and Vegetation Long-term detailed measurements of vegetation dynamics were carried out at the plant level in four plots located at the Shapotou Desert Research and Experi...

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Section: Discussionmentioning

confidence: 99%

Section: [2]mentioning

confidence: 99%

Section: Significancementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China

Wang

Schaffer

Yang

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Ecohydrology

Yin

Porporato

D’Odorico

et al. 2019

Encyclopedia of Water

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An interdisciplinary and fast‐growing science, ecohydrology aims at understanding the interactions among soil, water, vegetation, microbial biomass, atmosphere, and human societies. We provide a synthetic overview of these complex interactions, by reviewing the developments in ecohydrology following a stochastic modeling approach from the plot to the continental scales. We begin with the soil water balance at a point, which is primarily controlled by the pulsing and intermittent behaviors of rainfall input. It provides the mathematical basis for the analysis of plant water stress, plant biomass, and soil nutrient dynamics. We then review more general ecohydrological feedbacks, including groundwater feedbacks, land–atmosphere interactions at the continental scale, and irrigation in agroecosystem. To analyze the general behaviors of these interactions including the effects of stochastic external forcing, a “macroscopic approach” dealing with reduced order models of the main mean variables is also briefly discussed as a promising tool for future ecohydrological research.

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Impact of temporal precipitation variability on ecosystem productivity

Liu

Duan

et al. 2020

WIREs Water

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Projected increases in temporal precipitation variability, including intra‐annual and interannual variability, will likely have important impacts on terrestrial ecosystem productivity. The direction and magnitude of these impacts and how they vary across biomes, however, remain largely uncertain. Here, we review published literature that investigated the effect of different characteristics of temporal precipitation variability on vegetation productivity. We first reviewed commonly used methods, including manipulation experiments, process‐based modeling, and data‐driven analysis, and further discussed their strengths and limitations. Then, we summarized state‐of‐the‐art research on this topic by categorizing the results based on the characteristics of temporal precipitation variability. Given the same amount of growing season precipitation, a more extreme precipitation regime, characterized as fewer but larger precipitation events, tends to have a negative impact on vegetation productivity of most ecosystems except xeric grasslands and wet‐cold forests. Precipitation in the early growing season was found to be particularly important to vegetation productivity. Greater interannual precipitation variability tends to decrease vegetation productivity, but the reported patterns are complex, as both concave‐up and concave‐down precipitation‐productivity relations were found. Despite the progress made so far, critical challenges and knowledge gaps remain, such as the global‐scale impacts across different biomes, the role of biological adaption, and the contribution of individual precipitation events. Future research needs to combine manipulation experiments across a broad spectrum of ecosystem types and environmental gradients with model‐data integration strategies to disentangle the interactions between abiotic and biotic factors controlling vegetation responses to precipitation variability. This article is categorized under: Science of Water > Hydrological Processes Water and Life > Nature of Freshwater Ecosystems

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Plant biomass and soil moisture dynamics: analytical results

Cited by 12 publications

References 25 publications

Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China

Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China

Ecohydrology

Impact of temporal precipitation variability on ecosystem productivity

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