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Chapter 5 Modeling Grazing Effects on Coupled Water and Heat Fluxes in Inner Mongolia Grassland
six unknown parameters (θr, θs, α, n, Ks, and L). Hence, our three-layer soil
profile (second and third depths were combined in one layer) may have as many
as 18 unknown variables. To improve the optimization efficiency, the parameters
θs, α, n, and Ks were simultaneously optimized in all layers with other
parameters (i.e., θr and L) constant using time series of observations of θw for
each depth. For a preliminary sensitive analysis, we also mixed the simulation 1
and simulation 2, i.e., combined θr, θs, α, and n from LDP but Ks from NN
(LDP-NN; simulation 4) and combined Ks from LDP but θr, θs, α, and n from NN
(LDP+NN; simulation 5), respectively. The model was calibrated in 2004 and
validated in 2005 and 2006. Optimizing efficiency, as well as model efficiency
was evaluated by the root mean square errors (RMSE):
RMSE
=
N
1 2
N ∑ Pi
− Oi
)
i=
1
( [8]
where N is the number of observations and Pi and Oi are the simulated and
measured values, respectively.
Table 5.1. Summary of simulation approaches used in model.
Simulation
approach
Abbreviation
Description
Simulation 1 LDP
Laboratory-derived hydraulic parameter from soil core,
fitted by RETC software
Simulation 2 NN
Neutral network values based on soil texture
and bulk density analysis, predicted by ROSETTA
Simulation 3 Inverse Simulation with inverse model
Simulation 4 LDP-NN Simulation with Ks from NN and measured θr, θs, α, n
Simulation 5 LDP+NN Simulation with θr, θs, α, n from NN and measured Ks
RESULTS
Soil Texture, Bulk Density and Carbon Content
At all four sites soil texture is coarser in the subsoil (18-22 cm) than in the
topmost soil layer (4-8 cm) (Table 2). Also bulk density increases from the first to
the second depth, while total C-content generally decreases (except at the
second layer of UG 99) with increasing soil depth. Sand content is the highest in
HG and the lowest in WG. Compared to UG 99 and WG, a higher sand content in
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