Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation

Kristi R. Arsenault Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, and Science Applications International Corporation, McLean, Virginia

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Grey S. Nearing Department of Geological Sciences, University of Alabama, Tuscaloosa, Alabama

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Shugong Wang Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, and Science Applications International Corporation, McLean, Virginia

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Soni Yatheendradas Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, and Earth Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Christa D. Peters-Lidard Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0205.s1.

Corresponding author: Kristi R. Arsenault, kristi.r.arsenault@nasa.gov

Abstract

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0205.s1.

Corresponding author: Kristi R. Arsenault, kristi.r.arsenault@nasa.gov

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  • Zhu, Z., and Coauthors, 2013: Global data sets of vegetation leaf area index (LAI) 3g and Fraction of Photosynthetically Active Radiation (FPAR) 3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011. Remote Sens., 5, 927948, https://doi.org/10.3390/rs5020927.

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