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19 août 2021
Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations
 Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations

Examples of surface soil moisture (SSM) temporal evolutions used to identify drydowns at (a) an arid site in the Sudan (SD-Dem) and (b) a temperate site in the United States (US-UMB). In each case, raw time series are shown (no bias correction), with rainfall shown in the bottom panel and the SSM time series for the in situ observations (gray) and modeled values (red) shown in (a). Shaded in orange are periods identified as having at least 5 days without significant rainfall (<0.001 mm shown by the dotted gray line). The periods retained are shown by a darker shade of orange. Values of τ (days), which determine the shape of each drydown [see Eq. (1)], can be found at the top of each drydown, and the exponential fit to each drydown shown by a dotted black curve. Each drydown is labeled with a letter to help cross referencing.

The rate at which land surface soils dry following rain events is an important feature of terrestrial models. It
determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges.
As such, surface soil moisture (SSM) ‘‘drydowns,’’ i.e., the SSM temporal dynamics following a significant rainfall event, are
of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized
by an exponential decay time scale t, we aim to improve the representation of SSM in the ORCHIDEE global LSM. We
consider t calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers,
covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare t from the modeled SSM time
series to values computed from in situ SSM measurements. We then assess the potential of using t observations to constrain
some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard
Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration
and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using
observation-based t estimates, when there is no need to match the absolute observed and modeled SSM values. When
evaluated using independent data, t-calibration parameters were able to improve drydowns for 73% of the sites.
Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the
potential of global satellite products to scale up the experiment to a global-scale optimization.

Authors: Nina Raoult, Catherine Ottlé, Philippe Peylin, Vladislav Bastrikov, and Pascal Maugis

Ref: Journal of Hydrometeorology, 22(4), 1025-1043.

#275 - Màj : 03/09/2021
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