10.1 Modeling Approaches in the Uplands Program
10.1.1 Applications and Approaches
In the context of this chapter the term integrated modeling embraces: (a) the spatial
interaction between higher and lower elevation positions in a watershed, linked by
material flows of water and soil (Sect. 10.2), (b) up-scaling from the plot through
the catchment (up to 50 km
2 Sect. 10.2) and on to the regional or national scales
(Sect. 10.3) (c) combinations of models covering different disciplines; mainly
human-environment interactions (Sects. 10.5, 10.6, 10.7, and 10.8) and (d) the
inclusion of innovative elements in the modeling cycle, particularly scenario
building and calibration/validation (Sect. 10.4).
All case studies presented deal with land cover and land use change and their
impacts on natural resources as this has been a main focus of the Uplands Program
(Nikolic et al. 2008; Saint-Macary et al. 2010; Lippe et al. 2011) and many of its
partners in the region (e.g., Ziegler et al. 2007; Lusiana et al. 2011; Pansak et al.
2010) and because assessing such effects in a spatially explicit manner requires
modeling. In small mountainous catchments spatially distributed modeling
approaches were chosen to represent erosion and nutrient translocation, often
triggered by the introduction of mono-cropped continuous maize cultivation and
other intensified cropping systems. For regional/national level decision support,
GIS-coupled plot models were used that were fed with data from large-scale data
bases, e.g., of soils and weather. Participatory methods were used mainly in order to
cross-check and improve plausibility of model calibration, but also to adjust the
modelers’ concepts and perceptions and to aid the identification and formulation of
scenarios. Combining models of different scientific domains can serve different
purposes, representing topics that are not covered by one model alone being
probably the most common reason to integrate models. If two models overlap in
their domains, useful comparisons can be made between the outputs of both, and
may improve the level of understanding of the processes, the sensitivity to certain
parameters or the trends observed.
10.1.2 Complexity
Models with higher predictive capacity, better accuracy or a more mechanistic
representation of processes may be preferred over simpler ones, as long as sufficient
data are available. In addition, more process-based complex models may be used to
obtain simplified empiric transfer functions for certain processes, which can then be
used on a wider scale or in places of low data density, e.g., landscape modeling.
This approach is also useful for more comprehensive models, which are usually less
specific. Where models from various disciplines are combined, the detailed representation of processes needs to be simplified, as complexity shifts from the process
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10.1.1 Applications and Approaches
In the context of this chapter the term integrated modeling embraces: (a) the spatial
interaction between higher and lower elevation positions in a watershed, linked by
material flows of water and soil (Sect. 10.2), (b) up-scaling from the plot through
the catchment (up to 50 km
2 Sect. 10.2) and on to the regional or national scales
(Sect. 10.3) (c) combinations of models covering different disciplines; mainly
human-environment interactions (Sects. 10.5, 10.6, 10.7, and 10.8) and (d) the
inclusion of innovative elements in the modeling cycle, particularly scenario
building and calibration/validation (Sect. 10.4).
All case studies presented deal with land cover and land use change and their
impacts on natural resources as this has been a main focus of the Uplands Program
(Nikolic et al. 2008; Saint-Macary et al. 2010; Lippe et al. 2011) and many of its
partners in the region (e.g., Ziegler et al. 2007; Lusiana et al. 2011; Pansak et al.
2010) and because assessing such effects in a spatially explicit manner requires
modeling. In small mountainous catchments spatially distributed modeling
approaches were chosen to represent erosion and nutrient translocation, often
triggered by the introduction of mono-cropped continuous maize cultivation and
other intensified cropping systems. For regional/national level decision support,
GIS-coupled plot models were used that were fed with data from large-scale data
bases, e.g., of soils and weather. Participatory methods were used mainly in order to
cross-check and improve plausibility of model calibration, but also to adjust the
modelers’ concepts and perceptions and to aid the identification and formulation of
scenarios. Combining models of different scientific domains can serve different
purposes, representing topics that are not covered by one model alone being
probably the most common reason to integrate models. If two models overlap in
their domains, useful comparisons can be made between the outputs of both, and
may improve the level of understanding of the processes, the sensitivity to certain
parameters or the trends observed.
10.1.2 Complexity
Models with higher predictive capacity, better accuracy or a more mechanistic
representation of processes may be preferred over simpler ones, as long as sufficient
data are available. In addition, more process-based complex models may be used to
obtain simplified empiric transfer functions for certain processes, which can then be
used on a wider scale or in places of low data density, e.g., landscape modeling.
This approach is also useful for more comprehensive models, which are usually less
specific. Where models from various disciplines are combined, the detailed representation of processes needs to be simplified, as complexity shifts from the process
10 Integrated Modeling of Agricultural Systems in Mountainous Areas
369