Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models
In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP)...
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| Published in: | Journal of advances in modeling earth systems Vol. 15; no. 8 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Washington
John Wiley & Sons, Inc
01.08.2023
American Geophysical Union (AGU) |
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| ISSN: | 1942-2466, 1942-2466 |
| Online Access: | Get full text |
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| Abstract | In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant‐functional‐type (PFT)‐dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition, and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT‐ and climate‐specific parameterizations, global and PFT‐based parameter optimization, site‐similarity, and regression approaches. All methods were assessed using Nash‐Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error, and contrasted with site‐specific calibrations. Ten‐fold cross‐validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. Taking site‐level calibrations as a benchmark (NSE = 0.95), SPIE performed with an NSE of 0.68, while all the other investigated approaches showed negative NSE. The Shapley value, layer‐wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs determine parameters. SPIE overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.
Plain Language Summary
Parameters can represent ecosystem properties and sensitivities of ecosystem processes to environmental changes, affecting model accuracy and output uncertainties. Therefore determining parameters is of great importance for applying models. Current ecosystem‐level models mostly determine parameters according to biomes. For example, light use efficiency (LUE) models, a typical tool to estimate gross primary productivity (GPP), use plant‐functional‐type (PFT)‐specific parameters. However, PFT‐specific parameters cannot represent the spatial variance of GPP sensitivities to environmental conditions within PFT and introduce significant estimation errors. To overcome these issues, we propose a Simultaneous Parameter Inversion and Extrapolation method (SPIE). Taking an LUE model as an example, we estimated model parameters using SPIE based on the input features representing vegetation, climate and soil properties at 196 observational sites. We compared SPIE with 11 other parameter extrapolating methods and all of these methods with site‐specific calibrations based on full‐time‐series observed GPP. The results were validated and showed that SPIE was the best method to extrapolate parameters across various temporal and spatial scales. According to the importance of input features, vegetation features, bioclimatic variables, soil properties and some PFTs are dominating spatial changes of parameters. Overall, SPIE is a robust method that overcomes strong limitations in many standard parameter extrapolating methods.
Key Points
We propose a machine‐learning‐based parameterization method to model gross primary productivity
The method overcomes significant biases in extrapolations using assumptions of biome‐dependent and globally fixed parameterizations
Vegetation, climate and soil properties explain most variability in model parameters, challenging current prescriptions of their patterns |
|---|---|
| AbstractList | In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant‐functional‐type (PFT)‐dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition, and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT‐ and climate‐specific parameterizations, global and PFT‐based parameter optimization, site‐similarity, and regression approaches. All methods were assessed using Nash‐Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error, and contrasted with site‐specific calibrations. Ten‐fold cross‐validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. Taking site‐level calibrations as a benchmark (NSE = 0.95), SPIE performed with an NSE of 0.68, while all the other investigated approaches showed negative NSE. The Shapley value, layer‐wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs determine parameters. SPIE overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.
Plain Language Summary
Parameters can represent ecosystem properties and sensitivities of ecosystem processes to environmental changes, affecting model accuracy and output uncertainties. Therefore determining parameters is of great importance for applying models. Current ecosystem‐level models mostly determine parameters according to biomes. For example, light use efficiency (LUE) models, a typical tool to estimate gross primary productivity (GPP), use plant‐functional‐type (PFT)‐specific parameters. However, PFT‐specific parameters cannot represent the spatial variance of GPP sensitivities to environmental conditions within PFT and introduce significant estimation errors. To overcome these issues, we propose a Simultaneous Parameter Inversion and Extrapolation method (SPIE). Taking an LUE model as an example, we estimated model parameters using SPIE based on the input features representing vegetation, climate and soil properties at 196 observational sites. We compared SPIE with 11 other parameter extrapolating methods and all of these methods with site‐specific calibrations based on full‐time‐series observed GPP. The results were validated and showed that SPIE was the best method to extrapolate parameters across various temporal and spatial scales. According to the importance of input features, vegetation features, bioclimatic variables, soil properties and some PFTs are dominating spatial changes of parameters. Overall, SPIE is a robust method that overcomes strong limitations in many standard parameter extrapolating methods.
Key Points
We propose a machine‐learning‐based parameterization method to model gross primary productivity
The method overcomes significant biases in extrapolations using assumptions of biome‐dependent and globally fixed parameterizations
Vegetation, climate and soil properties explain most variability in model parameters, challenging current prescriptions of their patterns In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant-functional-type (PFT)-dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition, and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression approaches. All methods were assessed using Nash-Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error, and contrasted with site-specific calibrations. Ten-fold cross-validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. Taking site-level calibrations as a benchmark (NSE = 0.95), SPIE performed with an NSE of 0.68, while all the other investigated approaches showed lower NSE. The Shapley value, layer-wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs determine parameters. SPIE overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models. In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant‐functional‐type (PFT)‐dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition, and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT‐ and climate‐specific parameterizations, global and PFT‐based parameter optimization, site‐similarity, and regression approaches. All methods were assessed using Nash‐Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error, and contrasted with site‐specific calibrations. Ten‐fold cross‐validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. Taking site‐level calibrations as a benchmark (NSE = 0.95), SPIE performed with an NSE of 0.68, while all the other investigated approaches showed lower NSE. The Shapley value, layer‐wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs determine parameters. SPIE overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models. Parameters can represent ecosystem properties and sensitivities of ecosystem processes to environmental changes, affecting model accuracy and output uncertainties. Therefore determining parameters is of great importance for applying models. Current ecosystem‐level models mostly determine parameters according to biomes. For example, light use efficiency (LUE) models, a typical tool to estimate gross primary productivity (GPP), use plant‐functional‐type (PFT)‐specific parameters. However, PFT‐specific parameters cannot represent the spatial variance of GPP sensitivities to environmental conditions within PFT and introduce significant estimation errors. To overcome these issues, we propose a Simultaneous Parameter Inversion and Extrapolation method (SPIE). Taking an LUE model as an example, we estimated model parameters using SPIE based on the input features representing vegetation, climate and soil properties at 196 observational sites. We compared SPIE with 11 other parameter extrapolating methods and all of these methods with site‐specific calibrations based on full‐time‐series observed GPP. The results were validated and showed that SPIE was the best method to extrapolate parameters across various temporal and spatial scales. According to the importance of input features, vegetation features, bioclimatic variables, soil properties and some PFTs are dominating spatial changes of parameters. Overall, SPIE is a robust method that overcomes strong limitations in many standard parameter extrapolating methods. We propose a machine‐learning‐based parameterization method to model gross primary productivity The method overcomes biases in extrapolations using assumptions of biome‐dependent and globally fixed parameterizations Vegetation, climate and soil properties explain most variability in model parameters, improving current prescriptions of their patterns Abstract In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant‐functional‐type (PFT)‐dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition, and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT‐ and climate‐specific parameterizations, global and PFT‐based parameter optimization, site‐similarity, and regression approaches. All methods were assessed using Nash‐Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error, and contrasted with site‐specific calibrations. Ten‐fold cross‐validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. Taking site‐level calibrations as a benchmark (NSE = 0.95), SPIE performed with an NSE of 0.68, while all the other investigated approaches showed lower NSE. The Shapley value, layer‐wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs determine parameters. SPIE overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models. |
| Author | Carvalhais, Nuno Alonso, Lazaro Gensheimer, Johannes Bao, Shanning Wang, Siyuan De, Ranit |
| Author_xml | – sequence: 1 givenname: Shanning orcidid: 0000-0002-0893-1833 surname: Bao fullname: Bao, Shanning email: baoshanning@nssc.ac.cn organization: Chinese Academy of Sciences – sequence: 2 givenname: Lazaro surname: Alonso fullname: Alonso, Lazaro organization: Max‐Planck‐Institute for Biogeochemistry – sequence: 3 givenname: Siyuan surname: Wang fullname: Wang, Siyuan organization: Max‐Planck‐Institute for Biogeochemistry – sequence: 4 givenname: Johannes surname: Gensheimer fullname: Gensheimer, Johannes organization: Max‐Planck‐Institute for Biogeochemistry – sequence: 5 givenname: Ranit surname: De fullname: De, Ranit organization: Max‐Planck‐Institute for Biogeochemistry – sequence: 6 givenname: Nuno orcidid: 0000-0003-0465-1436 surname: Carvalhais fullname: Carvalhais, Nuno email: ncarvalhais@bgc-jena.mpg.de organization: ELLIS Unit Jena |
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| CitedBy_id | crossref_primary_10_1111_gcb_70462 crossref_primary_10_1111_ele_70012 crossref_primary_10_1016_j_agrformet_2025_110701 crossref_primary_10_1029_2024MS004697 crossref_primary_10_1029_2023MS004099 crossref_primary_10_1016_j_agrformet_2025_110762 |
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| ContentType | Journal Article |
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| Title | Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models |
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