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et al. 2003 ); the Catchment LSM (Koster et al. 2000 ); the TOPMODEL-based Land
Atmosphere Transfer Scheme (TOPLATS) model (Famiglietti and Wood 1994 ); the
Hydrology-Tiled European Centre for Medium-range Weather Forecasts (ECMWF)
Scheme for Surface Exchange over Land (H-TESSEL) model (Balsamo et al. 2009 ); the
SURFEX model (Le Moigne 2009 ); the Interaction between Soil Biosphere and Atmo-
sphere (ISBA) model (Noilhan and Mahfouf 1996 ); and the Joint UK Land Environment
Simulator (JULES) model (Best et al. 2011 ; Clark et al. 2011a ). An example of an inte-
grated system is the NASA Land Information System (LIS), which offers the capability to
simulate with different models, observations and data assimilation techniques (Kumar et al.
2008 ).
An LSM has several elements, including a soil moisture scheme,asnow scheme,a
rainfall-run-off scheme and a routing/hydraulic scheme. The soil moisture scheme can take
several forms, such as explicit numerical solutions of Richards' equations over multiple
discretized layers (e.g., in CLM), or using a force-restore method (e.g., Deardorff 1977 ,
used in SURFEX), or other more non-traditional approaches, such as a soil moisture
calculation as a deviation from the equilibrium soil moisture profile between the surface
and the water table (Catchment LSM). The different profile structures involve different
state variables, for example, describing soil moisture at the surface (superficial volumetric
water content) or describing soil moisture over the root zone (mean volumetric content of
the root zone). The coupling strength between the surface and deeper soil layers is a
sensitive point for successful propagation of surface observations to deeper layers (Kumar
et al. 2009 ).
The presence of snow covering the ground and vegetation can greatly influence the
energy and mass transfers between the land surface and the atmosphere. Notably, the snow
layer modifies the radiative balance by increasing the albedo. Furthermore, the amount of
water stored in the snowpack has an important impact on water availability in the spring
time. The prognostic variables in most snow schemes include variables related to snow
water equivalent (SWE), including snow depth and density, and the snow heat content.
These variables most often determine the diagnostics such as snow area extent and albedo.
The snow scheme can have one layer, or several layers. In a one-layer scheme, the
evolution of the snow water equivalent of the snow reservoir depends on the precipitation
of snow (a source) and the snow sublimation from the snow surface (a sink). Multi-layer
schemes are often designed to have intermediate complexity, having simplified physical
parametrizations based on those of highly detailed internal-process snow models, while
having computational requirements resembling those of single-layer schemes (Loth et al.
1993 ; Lynch-Stieglitz 1994 ; Sun et al. 1999 ).
A number of approaches have been implemented for rainfall-run-off schemes. Water
that cannot be stored in the soil profile either runs off over land (Horton run-off, e.g.,
Decharme and Douville 2006 ) or gravitationally drains out of the profile (Mahfouf and
Noilhan 1996 ; Boone 1999 ). The TOPMODEL run-off approach combines key distributed
effects of channel network topology and dynamic contributing areas for run-off generation
(Beven and Kirkby 1979 ; Silvapalan et al. 1987 ). This formalism takes explicit account of
topographic heterogeneities (Decharme et al. 2006 ; Decharme and Douville 2006 , 2007 ).
Run-off and drainage exiting from hydrological models can be used as a boundary to
hydraulic models that predict river flow and potential flooding (Matgen et al. 2010 ).
A hydraulic flood routing scheme uses numerical methods to solve simultaneously the
equations of continuity and momentum for a fluid (see, e.g., Guo 2006 ). It is often applied
to a river network, typically in a hierarchy including hillslope routing, sub-network routing
and main channel routing. An example of a routing scheme is the river transport model
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