A Benchmark Test for Ecohydrological Models of Interannual Variability of NDVI in Semi-arid Tropical Grasslands

P Choler, William Sea, Ray Leuning

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    Pulses of aboveground net primary productivity (ANPP) in response to discrete precipitation events are an integral feature of ecosystem functioning in arid and semi-arid lands. Yet, the usefulness of nonlinear, ecohydrological pulse response functions to predict regional-scale patterns of annual ANPP at decadal scales remains unclear. Here, we assessed how different pulse response (PR) models compete with simple linear statistical models to capture variability in yearly integrated values of Normalized Difference Vegetation Index (NDVIint), a remotely sensed proxy of annual ANPP. We examined 24-year-long time series of NDVIint calculated from Advanced Very High Resolution Radiometer (AVHRR) NDVI for 350,000 km2 of tropical grasslands in northern Australia. Based on goodness-of-fit statistics, PR models clearly outperformed statistical models when parameters were optimized for each site but all models showed the same error magnitude when all sites were combined in ensemble simulations or when the models were evaluated outside the calibration period. PR models were less biased and their performance did not deteriorate in the driest areas compared to linear models. Increasing the complexity of PR models to provide a better representation of soil water balance and its feedback with plant growth did not improve model performance in ensemble simulations. When error magnitude, bias, and sensitivity to parameter uncertainty were all considered, we concluded that a low-dimensional PR model was the most robust to capture NDVIint variability. This study shows the potential of long time series of AVHRR NDVI to benchmark process-oriented models of interannual variability of NDVIint in water-controlled ecosystems. This opens new avenues to examine at the global scale and over several decades the causal relationships between climate and leaf dynamics in the grassland biome.
    Original languageEnglish
    Pages (from-to)183-197
    Number of pages15
    JournalEcosystems
    Volume14
    Issue number2
    DOIs
    Publication statusPublished - 2011

    Fingerprint

    tropical grasslands
    NDVI
    grassland
    testing
    primary productivity
    radiometers
    Advanced very high resolution radiometers (AVHRR)
    Productivity
    statistical models
    ecosystems
    time series analysis
    Ecosystems
    Time series
    linear models
    AVHRR
    productivity
    test
    parameter uncertainty
    soil water balance
    time series

    Cite this

    Choler, P ; Sea, William ; Leuning, Ray. / A Benchmark Test for Ecohydrological Models of Interannual Variability of NDVI in Semi-arid Tropical Grasslands. In: Ecosystems. 2011 ; Vol. 14, No. 2. pp. 183-197.
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    abstract = "Pulses of aboveground net primary productivity (ANPP) in response to discrete precipitation events are an integral feature of ecosystem functioning in arid and semi-arid lands. Yet, the usefulness of nonlinear, ecohydrological pulse response functions to predict regional-scale patterns of annual ANPP at decadal scales remains unclear. Here, we assessed how different pulse response (PR) models compete with simple linear statistical models to capture variability in yearly integrated values of Normalized Difference Vegetation Index (NDVIint), a remotely sensed proxy of annual ANPP. We examined 24-year-long time series of NDVIint calculated from Advanced Very High Resolution Radiometer (AVHRR) NDVI for 350,000 km2 of tropical grasslands in northern Australia. Based on goodness-of-fit statistics, PR models clearly outperformed statistical models when parameters were optimized for each site but all models showed the same error magnitude when all sites were combined in ensemble simulations or when the models were evaluated outside the calibration period. PR models were less biased and their performance did not deteriorate in the driest areas compared to linear models. Increasing the complexity of PR models to provide a better representation of soil water balance and its feedback with plant growth did not improve model performance in ensemble simulations. When error magnitude, bias, and sensitivity to parameter uncertainty were all considered, we concluded that a low-dimensional PR model was the most robust to capture NDVIint variability. This study shows the potential of long time series of AVHRR NDVI to benchmark process-oriented models of interannual variability of NDVIint in water-controlled ecosystems. This opens new avenues to examine at the global scale and over several decades the causal relationships between climate and leaf dynamics in the grassland biome.",
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    A Benchmark Test for Ecohydrological Models of Interannual Variability of NDVI in Semi-arid Tropical Grasslands. / Choler, P; Sea, William; Leuning, Ray.

    In: Ecosystems, Vol. 14, No. 2, 2011, p. 183-197.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - A Benchmark Test for Ecohydrological Models of Interannual Variability of NDVI in Semi-arid Tropical Grasslands

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    AU - Sea, William

    AU - Leuning, Ray

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    N2 - Pulses of aboveground net primary productivity (ANPP) in response to discrete precipitation events are an integral feature of ecosystem functioning in arid and semi-arid lands. Yet, the usefulness of nonlinear, ecohydrological pulse response functions to predict regional-scale patterns of annual ANPP at decadal scales remains unclear. Here, we assessed how different pulse response (PR) models compete with simple linear statistical models to capture variability in yearly integrated values of Normalized Difference Vegetation Index (NDVIint), a remotely sensed proxy of annual ANPP. We examined 24-year-long time series of NDVIint calculated from Advanced Very High Resolution Radiometer (AVHRR) NDVI for 350,000 km2 of tropical grasslands in northern Australia. Based on goodness-of-fit statistics, PR models clearly outperformed statistical models when parameters were optimized for each site but all models showed the same error magnitude when all sites were combined in ensemble simulations or when the models were evaluated outside the calibration period. PR models were less biased and their performance did not deteriorate in the driest areas compared to linear models. Increasing the complexity of PR models to provide a better representation of soil water balance and its feedback with plant growth did not improve model performance in ensemble simulations. When error magnitude, bias, and sensitivity to parameter uncertainty were all considered, we concluded that a low-dimensional PR model was the most robust to capture NDVIint variability. This study shows the potential of long time series of AVHRR NDVI to benchmark process-oriented models of interannual variability of NDVIint in water-controlled ecosystems. This opens new avenues to examine at the global scale and over several decades the causal relationships between climate and leaf dynamics in the grassland biome.

    AB - Pulses of aboveground net primary productivity (ANPP) in response to discrete precipitation events are an integral feature of ecosystem functioning in arid and semi-arid lands. Yet, the usefulness of nonlinear, ecohydrological pulse response functions to predict regional-scale patterns of annual ANPP at decadal scales remains unclear. Here, we assessed how different pulse response (PR) models compete with simple linear statistical models to capture variability in yearly integrated values of Normalized Difference Vegetation Index (NDVIint), a remotely sensed proxy of annual ANPP. We examined 24-year-long time series of NDVIint calculated from Advanced Very High Resolution Radiometer (AVHRR) NDVI for 350,000 km2 of tropical grasslands in northern Australia. Based on goodness-of-fit statistics, PR models clearly outperformed statistical models when parameters were optimized for each site but all models showed the same error magnitude when all sites were combined in ensemble simulations or when the models were evaluated outside the calibration period. PR models were less biased and their performance did not deteriorate in the driest areas compared to linear models. Increasing the complexity of PR models to provide a better representation of soil water balance and its feedback with plant growth did not improve model performance in ensemble simulations. When error magnitude, bias, and sensitivity to parameter uncertainty were all considered, we concluded that a low-dimensional PR model was the most robust to capture NDVIint variability. This study shows the potential of long time series of AVHRR NDVI to benchmark process-oriented models of interannual variability of NDVIint in water-controlled ecosystems. This opens new avenues to examine at the global scale and over several decades the causal relationships between climate and leaf dynamics in the grassland biome.

    KW - C4 grasslands â¿¿ ecohydrology â¿¿ phenology â¿¿ precipitation pulses â¿¿ semi-arid ecosystems

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