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| Titel |
Sensitivity Analysis of the Land Surface Model NOAH-MP for Different Model Fluxes |
| VerfasserIn |
Juliane Mai, Stephan Thober, Luis Samaniego, Oliver Branch, Volker Wulfmeyer, Martyn Clark, Sabine Attinger, Rohini Kumar, Matthias Cuntz |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250106857
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| Publikation (Nr.) |
 EGU/EGU2015-6536.pdf |
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| Zusammenfassung |
| Land Surface Models (LSMs) use a plenitude of process descriptions to represent the
carbon, energy and water cycles. They are highly complex and computationally
expensive. Practitioners, however, are often only interested in specific outputs of the
model such as latent heat or surface runoff. In model applications like parameter
estimation, the most important parameters are then chosen by experience or expert
knowledge. Hydrologists interested in surface runoff therefore chose mostly soil
parameters while biogeochemists interested in carbon fluxes focus on vegetation
parameters. However, this might lead to the omission of parameters that are important, for
example, through strong interactions with the parameters chosen. It also happens
during model development that some process descriptions contain fixed values,
which are supposedly unimportant parameters. However, these hidden parameters
remain normally undetected although they might be highly relevant during model
calibration.
Sensitivity analyses are used to identify informative model parameters for a specific
model output. Standard methods for sensitivity analysis such as Sobol indexes require large
amounts of model evaluations, specifically in case of many model parameters. We hence
propose to first use a recently developed inexpensive sequential screening method based on
Elementary Effects that has proven to identify the relevant informative parameters. This
reduces the number parameters and therefore model evaluations for subsequent analyses such
as sensitivity analysis or model calibration.
In this study, we quantify parametric sensitivities of the land surface model NOAH-MP
that is a state-of-the-art LSM and used at regional scale as the land surface scheme of the
atmospheric Weather Research and Forecasting Model (WRF). NOAH-MP contains multiple
process parameterizations yielding a considerable amount of parameters (≈ 100).
Sensitivities for the three model outputs (a) surface runoff, (b) soil drainage and (c) latent
heat are calculated on twelve Model Parameter Estimation Experiment (MOPEX) catchments
ranging in size from 1020 to 4421 km2. This allows investigation of parametric
sensitivities for distinct hydro-climatic characteristics, emphasizing different land-surface
processes.
The sequential screening identifies the most informative parameters of NOAH-MP for
different model output variables. The number of parameters is reduced substantially for all
of the three model outputs to approximately 25. The subsequent Sobol method
quantifies the sensitivities of these informative parameters. The study demonstrates
the existence of sensitive, important parameters in almost all parts of the model
irrespective of the considered output. Soil parameters, e.g., are informative for all three
output variables whereas plant parameters are not only informative for latent heat but
also for soil drainage because soil drainage is strongly coupled to transpiration
through the soil water balance. These results contrast to the choice of only soil
parameters in hydrological studies and only plant parameters in biogeochemical
ones. The sequential screening identified several important hidden parameters that
carry large sensitivities and have hence to be included during model calibration. |
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