The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Pete Smith - One of the best experts on this subject based on the ideXlab platform.
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an explicit and computationally efficient method to initialise first order based soil organic matter models the Geometric Series solution gss
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
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An explicit and computationally efficient method to initialise first-order-based soil organic matter models—The Geometric Series Solution (GSS)
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
Hon-man Wong - One of the best experts on this subject based on the ideXlab platform.
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an explicit and computationally efficient method to initialise first order based soil organic matter models the Geometric Series solution gss
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
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An explicit and computationally efficient method to initialise first-order-based soil organic matter models—The Geometric Series Solution (GSS)
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
J. Hillier - One of the best experts on this subject based on the ideXlab platform.
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an explicit and computationally efficient method to initialise first order based soil organic matter models the Geometric Series solution gss
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
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An explicit and computationally efficient method to initialise first-order-based soil organic matter models—The Geometric Series Solution (GSS)
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
Douglas B. Clark - One of the best experts on this subject based on the ideXlab platform.
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an explicit and computationally efficient method to initialise first order based soil organic matter models the Geometric Series solution gss
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
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An explicit and computationally efficient method to initialise first-order-based soil organic matter models—The Geometric Series Solution (GSS)
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
Jo Smith - One of the best experts on this subject based on the ideXlab platform.
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an explicit and computationally efficient method to initialise first order based soil organic matter models the Geometric Series solution gss
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
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An explicit and computationally efficient method to initialise first-order-based soil organic matter models—The Geometric Series Solution (GSS)
Ecological Modelling, 2013Co-Authors: Hon-man Wong, J. Hillier, Douglas B. Clark, Jo Smith, Pete SmithAbstract:This paper derives an algebraic solution (the Geometric Series Solution; GSS) to replace iterative runs of soil organic matter (SOM) models for initialisation of SOM pools. The method requires steady-state/long-term-average Series of plant input and soil climate driving data. It calculates the values of SOM pools as if SOM models are iterated for a large number of cycles. The method has a high computational efficiency because it is an explicit solution to the calculations used to initialise the model and so requires a single iteration of the SOM model. Under the premise that the iterative pool inputs can be derived analytically, the GSS equations are applicable for other first-order-based SOM models. To illustrate applicability the method is applied to the coupled JULES-ECOSSE model.
