Towards vulnerability minimization of grassland soil organic matter using metamodels

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Highlights

  • We propose a generic quantitative method for climate change vulnerability assessment.

  • We illustrate the method on grassland soil organic matter (SOM) simulated by PaSim.

  • We present a model of concepts related to climate change vulnerability.

  • We project that SOM content may increase in the future and its vulnerability decrease.

  • We show a reduction of SOM vulnerability in adapted grassland systems in the future.

Abstract

Vulnerability is the degree to which a human or environmental system is likely to experience harm due to a perturbation or a stress. This paper aims at proposing a generic quantitative method for climate change vulnerability assessment and to illustrate it on the particular case of the steady-state soil organic matter (SOM) of grassland thanks to PaSim, a mechanistic biochemical model. Based on literature review, we first present a model of concepts related to climate change vulnerability, and then we give our numerical method for vulnerability assessment. We documented all the different steps of our approach (from building of the initial design of experiments, to assessment of vulnerability with adaptation, through generating response surfaces and searching for vulnerability minima with different optimization methods). This study showed that steady-state SOM content will globally increase in future and that their vulnerability will decrease (due to higher increase of average values compared to the increased variability). Moreover, the analysis of the found vulnerability minima suggests both a reduction of vulnerability of SOM of adapted system and an increase of the gain by adaptation.

Introduction

Soil is the largest terrestrial reservoir of carbon (C), storing more than twice the amount of C than the atmosphere (Chapin et al., 2009), in the form of plant litter and decomposed residue (Cole et al., 1993). As grasslands cover approximately 40% of the earth land surfaces (Wang and Fang, 2009), they may have a high potential to store an appreciable fraction of atmospheric carbon dioxide (CO2) as stable C in the soil (Conant et al., 2005). Biogeochemical ecosystem models generally incorporate a mechanistic view of the soil organic matter (SOM) dynamics and inform us on the behaviour of SOM pools (Petersen et al., 2002). SOM turnover can be modelled using several approaches. The majority of biogeochemical models divide the SOM into a number of conceptual/functional pools, each with different residence times varying from months for labile products of microbial decomposition to thousands of years for organic substances with firm organic-mineral bonds (e.g. (Yadav and Malanson, 2007)). Residence times vary with biophysical conditions. There is a concern that SOM is vulnerable to future warming (IPCC 2001), and this emphasizes the need for improving our understanding of SOM dynamics thanks to biogeochemical models which are able to make long-term simulations (Pan et al., 2010).

One can in a generic manner define vulnerability as the degree to which a human or environmental system is likely to experience harm before being damaged (Turner et al., 2003). It has become in recent years a central focus of global change (including climate change). Climate change, seen as a durable modification of the climate, has become a major concern since the impacts of climate change are being observed (IPCC 2007). For example, the assessment of agro-ecosystem vulnerability to climate change is part of the priority of the French National Institute for Agricultural Research (INRA). Developing and implementing adaptation policy has become a priority (Hinkel 2011). As a matter of fact, policymakers often ask which countries, regions or sectors are the most vulnerable in order to prioritize efforts that need to be undertaken with the aim to minimize risks and mitigate possible consequences (e.g. Füssel and Klein (2006)).

In the climate change studies, the term of vulnerability is used in a variety of meanings, within the scientific community. Moreover, it is often not defined properly or even used without any definition (e.g. (Ionescu et al., 2009)). As a result, a considerable diversity of methods is applied for assessing vulnerability ((Eakin and Luers, 2006), (Füssel and Klein, 2006)). Through the history of vulnerability assessment, methods have grown in complexity with increasing numbers of subsystems, processes, drivers, feedbacks and types of impacts taken into account. The climate change literature contains many explanations of vulnerability, based on the notion of sensitivity and possibly using more complex ideas, from taking into account the system exposure up to the residual impacts of climate change after adaptation. Even if the scientific literature contains many methods, achieving a vulnerability analysis is still a difficult problem, due to the lack of agreement on the meaning of the term itself, but mainly because vulnerability is not an observable phenomenon (Downing et al., 2001). If some aspect, like resilience, can be experimentally observed, some other aspects necessarily call for the use of modelling. Due to the cascades of uncertainties in climate change impacts, and to the fact that vulnerability assessment is related to sensitivity analysis, a huge number of simulations are needed. In this context, in order to reduce user's waiting time, an appropriate design of experiment is needed, as well as the use of high performance computing.

The definition of the vulnerability used in the paper, is that conventionally used in the climate change context (IPCC 2001): “vulnerability is defined as the extent to which a natural or social system is susceptible to sustaining damage from climate change. Vulnerability is a function of the sensitivity of a system to changes in climate (the degree to which a system will respond to a given change in climate, including beneficial and harmful effects) and of the adaptive capacity”. In international literature, when a quantitative approach is used for vulnerability assessment, and when adaptation is considered (not only the impacts), we can distinguish mostly two different cases. Either, vulnerability assessment is done by considering on the one hand the adaptation capacity and on the other hand the exposure (e.g. (Nelson et al., 2010)) or the potential impacts (e.g. (Metzger et al., 2006) (Downing and Patwardhan, 2005)). Or, vulnerability assessment is done by a scenario approach to test the effects of adaptation measures (e.g. (Soora et al., 2013)).

This paper aims at proposing a generic quantitative method for climate change vulnerability assessment and to illustrate it on the particular case of the steady-state soil organic matter. This method is meant to be generic relatively to the type of agro-ecological model used and relatively to underlying assumptions of vulnerability analysis (such as the type of uncertainties taken into account). This paper is structured in six sections. In Section 2 we document the concepts behind ecosystem climate change vulnerability assessment, and present our numerical method for vulnerability assessment. Vulnerability assessment usually implies the intensive use of model-based simulations and design of experiments (to reduce the amount of needed simulation). In order to illustrate our approach we used a grassland model (PaSim, the Pasture Simulation model), which is briefly documented in Section 3. In this same part, we describe the designs of experiments and the methods for searching for vulnerability minima used in the study. In Section 4 Results, 5 Discussion, we present and discuss results of the vulnerability assessment with account of adaptations. The concluding section identifies key results and explores future research needs.

Section snippets

Vulnerability analysis methods

As preliminary step of this paper, we define vulnerability and the related concepts. Then, based on that, we propose a numerical method for vulnerability assessment, which is a two step method and the use of a set of quantitative indices.

Methods

This section presents the methods used in the many steps of the approach. Firstly a brief description of the PaSim model is given. Then the two designs of experiments used to build regression surfaces (step 1 and 5) and for local vulnerability assessment (step 7 and 9) are presented. The last part explains the three methods used for searching for vulnerability minimum.

Results

The results are presented in three parts. First, we evaluate our regression surfaces in order to check if they can be used as metamodels for the vulnerability minimum search step. At the same time, we use them to estimate vulnerability without adaptation for different climate periods. In a second step, results of vulnerability minimum search (i.e. managements and vulnerability indices for vulnerability minimum) are provided. In the third part, we show results of assessment of the minimum of

Discussion

Steady-state SOM level is a potential state, to which agro-ecological systems tend to converge (even if they are above or below that level). This means that an increase or a decrease of steady-state SOM vulnerability can represent the tendency of real SOM content. It follows that the analysis of steady-state SOM level performed in this study, as an insight about real SOM vulnerability, substantiates the importance of advanced assessment of vulnerabilities to interpret the influence of climate

Conclusion

In this paper, we presented our vulnerability assessment method with adaptation. We detailed the key concepts of ecosystem vulnerability to climate change, which includes adaptation and mitigation capacity, ecosystem stability (sensitivity, ecological resilience and elasticity), exposure and impacts. Then we proposed a two-step vulnerability assessment approach. This can be summarized as a first stage of sensitivity analysis to get vulnerability without adaptation and to build metamodels, and a

Acknowledgments

The research leading to these results has received funding from the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreements n° 244122 (GHG-Europe, http://www.ghg-europe.eu/) and n° 266018 (ANIMALCHANGE, http://www.animalchange.eu). Climate data was provided by the French ANR ‘CLIMATOR’ project and soil data by the French ANR ‘VALIDATE’ project. The authors thank Anne-Isabelle Graux and Raphaël Martin for their help in preliminary step of this work.

References (70)

  • Z. Pan et al.

    Uncertainty in future soil carbon trends at a central U.S. site under an ensemble of GCM scenario climates

    Ecol. Model.

    (2010)
  • B.,M. Petersen et al.

    A flexible tool for simulation of soilcarbon turnover

    Ecol. Model.

    (2002)
  • D.,W. Pribyl

    A critical review of the conventional SOC to SOM conversion factor

    Geoderma

    (2010)
  • R.,M. Rees et al.

    The role of plants and land management in sequestering soil carbon in temperate arable and grassland ecosystems

    Geoderma

    (2005)
  • M. Riedo et al.

    A Pasture Simulation model for dry matter production, and fluxes of carbon, nitrogen, water and energy

    Ecol. Model.

    (1998)
  • N.,A. Skondras et al.

    Application and assessment of the Environmental Vulnerability Index in Greece

    Ecol. Indic.

    (2011)
  • J.,-F. Soussana et al.

    The regulation of clover shoot growing points density and morphology during short-term clover decline in mixed swards

    Eur. J. Agron.

    (1995)
  • J.-F. Soussana et al.

    Full accounting of the greenhouse gas budget of nine European grassland sites

    Agric. Ecosyst. Environ.

    (2007)
  • F. Tardieu et al.

    Water deficit and growth. Co-ordinating processes without an orchestrator?

    Curr. Opin. Plant Biol.

    (2011)
  • W. Wang et al.

    Soil respiration and human effects on global grasslands

    Glob. Planet. Change

    (2009)
  • E. Aarts et al.

    Chapter 7 Simmulated Annealing

  • F. Amblard et al.

    MDA compliant design of SimExplorer a software tool to handle simulation experimental frameworks

  • A. Brunelle et al.

    Guide des pratiques de conservation en grandes cultures. Module 3: Gestion de la matière organique

    (2000)
  • F.,S. Chapin et al.

    The changing global carbon cycle: linking plant-soil carbon dynamics to global consequences

    J. Ecol.

    (2009)
  • P. Ciais et al.

    Europe-wide reduction in primary productivity caused by the heat and drought in 2003

    Nature

    (2005)
  • C.,V. Cole et al.

    Analysis of agroecosystem carbon pools

    Water Air Soil Pollut.

    (1993)
  • R.,T. Conant et al.

    Grassland management and conversion into grassland: effects on soil carbon

    Ecol. Appl.

    (2001)
  • R.,T. Conant et al.

    Nitrogen pools and fluxes in grassland soils sequestering carbon

    Nitrogen Cycl. Agroecosyst.

    (2005)
  • E. De Martonne

    Nouvelle carte mondiale de l'indice d'aridité

    Ann. de Géogr.

    (1942)
  • M. Déqué et al.

    An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections

    Clim. Change

    (2007)
  • T.,E. Downing et al.

    Assessing vulnerability for climate adaptation

  • T.,E. Downing et al.

    Climate Change Vulnerability: Linking Impacts and Adaptation

    (2001)
  • H. Eakin et al.

    Assessing the vulnerability of social-environmental systems

    Annu. Rev. Environ. Resour.

    (2006)
  • O. Faurié et al.

    Radiation interception, partitioning and use in grass-clover mixtures

    Ann. Bot.

    (1996)
  • J. Foster et al.

    A class of decomposable poverty measures

    Econometrica

    (1984)
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