Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models

Luo, Yiqi; Weng, Ensheng; Wu, Xiaowen; Gao, Chao; Zhou, Xuhui; Zhang, Li

doi:10.1890/08-0561.1

Cited by 125 publications

(102 citation statements)

References 69 publications

(71 reference statements)

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“…In order to enhance the model performance, further improvement in the calculation of LAI and TER are necessary. Another potential way to improve the performance of CO 2 and H 2 O budgets would be to assimilate remotely sensed LAI, soil moisture and/or soil temperature into the model (Viovy et al, 2001;Peylin et al, 2005;Luo et al, 2009). …”

Section: Discussionmentioning

confidence: 99%

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO2 and H2O fluxes at five European maize (Zea mays L.) sites

Vuichard

Viovy

et al. 2011

Biogeosciences

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Abstract. This paper is a modelling study of crop management impacts on carbon and water fluxes at a range of European sites. The model is a crop growth model (STICS) coupled with a process-based land surface model (ORCHIDEE). The data are online eddy-covariance observations of CO 2 and H 2 O fluxes at five European maize cultivation sites. The results show that the ORCHIDEE-STICS model explains up to 75 % of the observed daily net CO 2 ecosystem exchange (NEE) variance, and up to 79 % of the latent heat flux (LE) variance at five sites. The model is better able to reproduce gross primary production (GPP) variations than terrestrial ecosystem respiration (TER) variations. We conclude that structural deficiencies in the model parameterizations of leaf area index (LAI) and TER are the main sources of error in simulating CO 2 and H 2 O fluxes. A number of sensitivity tests, with variable crop variety, nitrogen fertilization, irrigation, and planting date, indicate that any of these management factors is able to change NEE by more than 15 %, but that the response of NEE to management parameters is highly site-dependent. Changes in management parametersCorrespondence to: L. Li (lilhchn@gmail.com) are found to impact not only the daily values of NEE and LE, but also the cumulative yearly values. In addition, LE is shown to be less sensitive to management parameters than NEE. Multi-site model evaluations, coupled with sensitivity analysis to management parameters, thus provide important information about model errors, which helps to improve the simulation of CO 2 and H 2 O fluxes across European croplands.

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Section: Discussionmentioning

confidence: 99%

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO2 and H2O fluxes at five European maize (Zea mays L.) sites

Vuichard

Viovy

et al. 2011

Biogeosciences

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“…In addition, benchmarks should be selected to reduce equifinality as much as possible. Although extensive data sets are available for benchmarking land models, equifinality remains a major issue in model evaluation (Tang and Zhuang, 2008;Luo et al, 2009). That is, the available data streams are insufficient to constrain model parameterization Wang et al, 2001;Carvalhais et al, 2010) or to distinguish between different modeling structures (Frank et al, 1998).…”

Section: Criteria Of Benchmarksmentioning

confidence: 99%

A framework for benchmarking land models

et al. 2012

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Abstract. Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of Published by Copernicus Publications on behalf of the European Geosciences Union. Y. Q. Luo et al.: A framework for benchmarking land modelsbenchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

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“…Ecosystem manipulation experiments provide a controlled environment in which data collected can be used to describe how forests acclimate and operate under altered environmental conditions (Medlyn et al, 2015) and can potentially allow for the optimization of model parameters associated with the altered environmental factor in the experiment. Furthermore, the assimilation of data from ecosystem manipulation experiments may increase parameter identifiability (reducing equifinality; Luo et al, 2009), where two parameters have compensating controls on the same processes, by isolating the response to a manipulated driver. Observations that span environmental gradients include measures of forest ecosystem stocks and fluxes across a range of climatic conditions, nutrient availabilities, and soil water dynamics.…”

Section: Introductionmentioning

confidence: 99%

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

et al. 2017

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Abstract. Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2 ) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2 , decreased precipPublished by Copernicus Publications on behalf of the European Geosciences Union. itation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.

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Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models

Cited by 125 publications

References 69 publications

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes at five European maize (<i>Zea mays</i> L.) sites

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes at five European maize (<i>Zea mays</i> L.) sites

A framework for benchmarking land models

Leveraging 35 years of <i>Pinus taeda</i> research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

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Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models

Cited by 125 publications

References 69 publications

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO&lt;sub&gt;2&lt;/sub&gt; and H&lt;sub&gt;2&lt;/sub&gt;O fluxes at five European maize (&lt;i&gt;Zea mays&lt;/i&gt; L.) sites

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO&lt;sub&gt;2&lt;/sub&gt; and H&lt;sub&gt;2&lt;/sub&gt;O fluxes at five European maize (&lt;i&gt;Zea mays&lt;/i&gt; L.) sites

A framework for benchmarking land models

Leveraging 35 years of &lt;i&gt;Pinus taeda&lt;/i&gt; research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

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Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes at five European maize (<i>Zea mays</i> L.) sites

Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes at five European maize (<i>Zea mays</i> L.) sites

Leveraging 35 years of <i>Pinus taeda</i> research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments