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SENSITIVITY ANALYSIS OF BIOME-BGC MODEL FOR DRY TROPICAL FORESTS OF VINDHYAN HIGHLANDS, INDIA
Manoj Kumar
a
and A. S. Raghubanshi
b a
Ecosystem Analysis Laboratory, Department of Botany, Banaras Hindu University, Varanasi- 221005, India.
b
Institute of Environment Sustainable Development, Banaras Hindu University, Varanasi- 221005, India. E-mail: bhu.manojgmail.com
KEYWORDS: Biome-BGC model, Dry deciduous forest, Net primary productivity, Sensitivity index, Vindhyan highlands. ABSRACT:
A process-based model BIOME-BGC was run for sensitivity analysis to see the effect of ecophysiological parameters on net primary production NPP of dry tropical forest of India. The sensitivity test reveals that the forest NPP was highly sensitive to the following eco-
physiological parameters: Canopy light extinction coefficient k, Canopy average specific leaf area SLA, New stem C : New leaf C SC:LC, Maximum stomatal conductance g
s,max
, C:N of fine roots C:N
fr
, All-sided to projected leaf area ratio and Canopy water interception coefficient W
int
. Therefore, these parameters need more precision and attention during estimation and observation in the field studies.
1. INTRODUCTION
There are various types of models that may be used in ecosystem analysis. Ruimy et al. 1994, categorized the NPP model into
three groups: 1 statistical models Lieth, 1975, 2 parametric models Potter et al., 1993 and 3 process models Foley, 1994.
Traditional types of models are regression models, which are based on empirically derived statistical relationships. Such
models may be used for predicting stand development under stable conditions and in regions where the built-in relationships
were derived. Naturally, such models are not so useful for incorporating changing growth conditions and for spatial
extrapolation. Moreover, such models remain descriptive and do not offer much explanatory power for ecosystem analysis. For
this, so-called process-based models must be deployed. These models simulate ecosystem development as a result of
ecophysiological processes described mechanistically and usually incorporate the effect of environmental change on ecosystem
functioning and are able to quantify effects of, e.g., change in climate, elevated CO
2
, nitrogen deposition and land use scenarios Cienciala and Tatarinov, 2006.
Sensitivity analysis is the study of how the variation uncertainty in the output of a model can be apportioned, qualitatively or
quantitatively, to different sources of variation in the input of the model Xu et al., 2004. It can be used to examine which
variablesparameters have the largest effect on the model output. Sensitivity analysis determines what level of accuracy is
necessary for a parameter to make the model sufficiently useful and valid. If the sensitivity tests reveal that the model is
insensitive to a particular factor, then it may be possible to use an estimate rather than a value with greater.
For ecological analysis Biogeochemical BGC model is most preferred. BIOME-BGC Running and Coughlan, 1988;
Thornton, 1998 is such model which employs a simplified biochemical model of photosynthesis, environmentally regulated
stomatal conductance, and explicit calculations of respiration for various plant pools to calculate NPP. It is a process-based model
requiring a considerable number of ecophysiological and site parameters. Therefore, application of this model requires
parameterization, including sensitivity analysis of model output to the input data.
Parameters Symbol
Allocation current growth proportion cg
Allocation new coarse root C : new stem C CRC:SC
Allocation New fine root C : New leaf C FRC:LC
All-sided to projected leaf area ratio LAI
all:pro
Annual fire mortality fraction m
f
Annual leaf and fine root turnover fraction m
l
m
fr
Annual live wood turnover fraction m
w
Annual whole-plant mortality fraction m
t
autotrophic respiration Ra
Boundary layer conductance projected area basis g
bl
C:N of dead wood C:N
dw
C:N of fine roots C:N
fr
C:N of leaf litter, after retranslocation C:N
lit
C:N of leaves C:N
leaf
C:N of live wood C:N
lw
Canopy average specific leaf area projected area basis Canopy light extinction coefficient
SLA k
Canopy water interception coefficient W
int
Carbon C
Cuticular conductance projected area basis g
cut
Vapor pressure deficit VPD
Fraction of leaf N in Rubisco N
R
Gross primary productivity GPP
Leaf Area Index Leaf water potential: complete conductance reduction
LAI LWP
f
Leaf water potential: start of conductance reduction LWP
i
Litter fall as fraction of growing season T
lf
Maximum stomatal conductance projected area basis g
s,max
New live wood C : new total wood C LWC:T
WC New stem C : New leaf C
SC:LC Nitrogen
N Ratio of shaded SLA:sunlit SLA
SLA
shd:su
Transfer growth period T
t
Vapor pressure deficit: Complete conductance reduction VPD
f
Vapor pressure deficit: Start of conductance reduction VPD
i
Table 1: List of abbreviations International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVIII-8W20, 2011
ISPRS Bhopal 2011 Workshop, 8 November 2011, Bhopal, India
ISPRS Archives XXXVIII- 8W20; Workshop Proceedings: Earth Observation for Terrestrial Ecosystems
130 Previously, sensitivity analysis for BIOME-BGC
BioGeochemical Cycles model has been conducted by White et al. 2000 and Tatarinov and Cienciala 2006 for temperate
biomes. Reassessment of model sensitivity for dry tropical forest of India is needed because effect of parameters for different
combinations of site and eco-physiological parameters may differ. Therefore, the aim of the present study is i to collect
ecophysiological and site parameters for dry tropical forest of India from available literature and observations and ii to reveal
the effect of ecophysiological parameters on NPP and also identify critically sensitive input parameters.
2. MATERIAL AND METHODS
