1. General Model Information
Name: Savanna - Landscape and Regional Ecosystem Model
Acronym: SAVANNA
Main medium: terrestrial
Main subject: biogeochemistry, agriculture
Organization level: ecosystem
Type of model: difference equations (2D)
Main application: research
Keywords: vegetation, grassland, shrubland, savanna, soil water, arid system, pastoralism, forage, primary production, herbivore, spatially explicit, process-based
Contact:
Coughenour, Michael B.
Swift, David M.
Galvin, Kathleen A.
Ellis, James E.
Natural Resource Ecology Laboratory
Natural and Environmental Sciences Building,
Colorado State University
Fort Collins, Colorado 80523-1499
Author(s):
Coughenour, Michael B.
Swift, David M.
Galvin, Kathleen A.
Ellis, James E.
Abstract:
Savanna is a spatially explicit, process-oriented model of grassland,
shrubland, savanna and forested ecosystems. The Savanna model simulates
processes at landscape through regional spatial scales over annual to decadal
time scales. The model is composed of hydrologic, plant biomass production,
plant population dynamics, ungulate herbivory, ungulate spatial distribution,
ungulate energy balance, ungulate population dynamics and wolf predation
submodels . Since Savanna is process-oriented rather than empirical or
rule-based, it aims toward realistic, general and explanatory representations
of ecological change as opposed to descriptions of ecological states or
prescribed responses.
Every simulation model has a certain level of tradeoff between mechanistic
detail and model simplicity. While highly aggregated or simplified models are
easier to use, they are less realistic, less generalizable, and less
explanatory. On the other hand, highly mechanistic models are more difficult
to implement and the marginal costs of added complexity are high.
Highly resolved models are more computationally demanding, which may
prohibit their implementation at large spatial or long temporal scales.
Therefore, Savanna treats ecological processes at an intermediate level of
resolution. The time step of the Savanna model is a week, which allows
simulations over longer time scales and larger spatial scales. This time step
allows Savanna to simulate landscapes composed of 100-1000 grid cells
over 5-50 year time spans in a reasonable amount of time on the current
generation of microprocessors.
Source: Coughenour M.B. (1994). Savanna - Landscape and Regional Ecosystem Model
(Model Description)
MS-WORD
Wordperfect
Overall structure of Savanna, a spatially explicit, dynamic ecosystem
model that was originally developed for studies of African pastoralism. It has
also been applied to western U.S. and Canadian national parks as an ecosystem
management model. Ungulates and their interactions with plants are a principal
concern. Savanna is unique in representing linkages between plant production and
population processes as well as between ungulate energy balance and their
population processes. Primary production is tied directly to the soil water
budget. The model uses geographic information systems for both data input and
output.
II. Technical Information
II.1 Executables:
Operating System(s): Operating Systems: DOS
II.2 Source-code:
Programming Language(s):
II.3 Manuals:
II.4 Data:
III. Mathematical Information
III.1 Mathematics
Model equations
III.2 Quantities
III.2.1 Input
III.2.2 Output
IV. References
Coughenour, M. B. 1991. Dwarf shrub and graminoid responses to clipping, nitrogen, and water: simplified simulations of biomass and nitrogen dynamics.
Ecological Modelling 54:81-110.
Coughenour, M. B. 1992. Spatial modeling and landscape characterization of an African pastoral ecosystem: a prototype model and its potential use for monitoring drought.
pp. 787-810 In: D.H. McKenzie , D.E. Hyatt and V.J. McDonald (eds.). Ecological Indicators, Vol. I. Elsevier Applied Science, London and New York.
Ellis, J.E., J.A. Weins, D.F. Rodell and J.C. Anway. 1976. A conceptual model of diet selection as an ecosystem process.
J.. Theor. Biol. 60:93-108.
Jenkins, K.T., P.J. Happe, and R.G. Wright. 1990.Evaluating above-snow browse availability using nonlinear regression.
Wildl. Soc. Bull. 18:49-55.
Hobbs, N.T. 1989. Linking energy balance to survival in mule deer: development and test of a simulation model.
Wildl. Monogr. No. 101.
Minson, J. 1981. Nutritional differences between tropical and temperate pastures.
pp. 143-157 In: F,H.W. Morley (ed.), Grazing Animals, World Animal Science. B.1 (A. Neimann-Sorensen and D.E. Tribe (eds.). Elsevier, Amsterdam.
Parton, W.J., J.M.O. Scurlock, D.S. Ojima, T.G. Gilmanov, R.J. Scholes, D.S.Schimel, T. Kirchner, J-C. Menaut, T. Seastedt, E. Garcia Moya, Apinan Kamnalrut and J.I. Kinyamario. 1993. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide.
Global Biogeochem. Cycles 7:875-809.
Priestly, C.H.B. and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters.
Mon. Weather Rev. 100:81-92.
Ritchie, J.T. 1972. A model for predicting evaporation from a row crop with incomplete cover.
Water Resources Res. 8:1204-1213.
Shugart, H.H. 1984. A theory of forest dynamics.: the ecological implications of forest succession models.
Springer-Verlag, New York. 278pp.
Wight, J.R. and J.W. Skiles (eds.) 1987. SPUR: Simulation of production and utilization of rangelands. Documentation and user guide.
U.S. Dept. of Agric., Agric. Res. Serv., ARS 63. 372pp.
V. Further information in the World-Wide-Web
VI. Additional remarks
Last review of this document by: T. Gabele: Jan 27 1998
Status of the document:
last modified by
Tobias Gabele Wed Aug 21 21:44:49 CEST 2002