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variations in model parameters. As is shown by Krapivin (1996), a survivability
function J(t)re
ecting the dynamics of the total biomass of living elements enables
one to estimate this sensitivity. In this instance
fl
P i¼1 R ðu;kÞ2X R H ðu;kÞ
B i ð t
; u; k;
z Þ d
u
d
k
dz
0
J ð t Þ ¼
P i¼1 R ðu;kÞ2X
R H ðu;kÞ
0
B i ð t 0 ; u; k;
z Þ d
u
d
k
dz
The index J(t) provides an estimation of the uncertainty associated with the
SSMAE parameters. Although a complete investigation of the in
uence of the
SSMAE parameter variations on model results is an independent task, various esti-
mations are given here. Preliminary simulation results show that the SSMAE permits
variations of the initial data in the interval
fl
70 to 150 %. In this case the model
forgets
these variations during
40 days. Also a large uncertainty (
±
50 %) is
permitted in the value of such parameters as
ˁ 1 , k ij . The correlation
between variations of these parameters and the model results is linear. However, high
model sensitivity is observed under variations of
ʼ A ,
ʴ n , V i , T c , T opt ,
ʲ A ,
ʱ A , E 0 , T 0 . In general terms, the
acceptable variation of these parameters is
20 %. Moreover the deviation in the
model results due to variations of these parameters is nonlinear. For example,
±
fl
uc-
±
tuations of the surface temperature T 0 within
5 K turn out to be not hazardous to the
±
±
system, causing small variations of J(t)by
10%, but
fl
fluctuations of T 0 by
7 K cause
±
much larger variations in the value of J(t)by
30 %. Under this the temporal
dependence of the system dynamics to variations in the parameters is diverse.
The SSMAE structure and its realization do not completely describe the pro-
cesses taking place in Arctic Basin. The optimal extension of the SSMAE functions
is possible by the use of environmental monitoring data to control parametric and
functional model inputs. In this framework the prognosis of the Arctic aquageo-
system state is realized on the basis of the SSMAE and by processing of the
observed data.
6.5 The Angara-Yenisey River System Simulation Model
6.5.1 Introduction
The Arctic region is a mosaic of freshwater, terrestrial and marine ecosystems, inti-
mately interactive with the factors of the Nature/Society system. Interactions include
many components such as Ocean/Atmosphere/Ice, Land/Atmosphere/Ice and Land/
Ocean/Freshwater. An interpretation and prediction of correlations between the
processes occurring in the Arctic environment, is possible only in the framework of
the complex approach to the study of these processes. This approach is based on the
GIMS
technology (Krapivin and Shutko 2012). The interaction between the
atmosphere, land and sea ecosystems under the arctic climate is characterized by a
series of spatial-temporal scales. An understanding of the interior correlations at
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