Impacts of Spatial Partitioning in Hydroecological Models: Predicting Grassland Productivity with RHESSys

Environmental models constructed with a spatial domain require choices about the representation of space. Decisions in the adaptation of a spatial data model can have significant consequences on the ability to predict environmental function as a result of changes to levels of aggregation of input parameters and scaling issues in the processes being modelled. In some cases, it is possible to construct a systematic framework to evaluate the uncertainty in predictions using different spatial models; in other cases, the realm of possibilities plus the complexity of the environmental model in question may inhibit numeric uncertainty estimates. We demonstrate a range of potential spatial data models to parameterize a landscape-level hydroecological model (RHESSys). The effects of data model choice are illustrated, both in terms of input parameter distributions and resulting ecophysiological predictions. Predicted productivity varied widely, as a function of both the number of modelling units, and of arbitrary decisions such as the origin of a raster grid. It is therefore important to use as much information about the modelled environment as possible. Combinations of adaptive methods to evaluate distributions of input data, plus knowledge of dominant controls of ecosystem processes, can help evaluate potential representations. In this case, variance-based delineation of vegetation patches is shown to improve the ability to intelligently choose a patch distribution that minimizes the number of patches, while maintaining a degree of aggregation that does not overly bias the predictions.

[1]  Steven W. Running,et al.  Testing scale dependent assumptions in regional ecosystem simulations , 1994 .

[2]  Vemap Participants.  Vegetation/ecosystem modeling and analysis project: Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling , 1995 .

[3]  J. Aber,et al.  A strategy for the regional analysis of the effects of physical and chemical climate change on biogeochemical cycles in northeastern (U.S.) forests , 1993 .

[4]  I. Burke,et al.  PRODUCTIVITY PATTERNS OF C3 AND C4 FUNCTIONAL TYPES IN THE U.S. GREAT PLAINS , 1997 .

[5]  Meine van Noordwijk,et al.  Scaling trade-offs between crop productivity, carbon stocks and biodiversity in shifting cultivation landscape mosaics: the FALLOW model , 2002 .

[6]  Scott W Mitchell,et al.  Advantages of open-source GIS to improve spatial environmental modelling , 2002 .

[7]  M. R. Carter,et al.  Terrestrial Ecosystems , 2018, Critical Transitions in Nature and Society.

[8]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[9]  L. Band,et al.  The effect of different terrain representations and resolution on simulated watershed processes , 1995 .

[10]  J. Vose,et al.  Scaling Predicted Pine Forest Hydrology and Productivity Across the Southern United States , 2003 .

[11]  James W. Jones,et al.  Short survey Scaling-up crop models for climate variability applications $ , 2000 .

[12]  Vincent B. Robinson,et al.  Classification of higher order topographic objects on digital terrain data , 1992 .

[13]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[14]  J. Chen.  Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[15]  Richard A. Fleming,et al.  Scaling-up an autoregressive time-series model (of spruce budworm population dynamics) changes its qualitative behavior , 2002 .

[16]  L. D. Danny Harvey,et al.  Upscaling in Global Change Research , 2000 .

[17]  Ferenc Csillag,et al.  Assessing the stability and uncertainty of predicted vegetation growth under climatic variability: northern mixed grass prairie , 2001 .

[18]  Vincent B. Robinson,et al.  Semantic Modeling for the Integration of Geographic Information and Regional Hydroecological Simulat , 1995 .

[19]  Mark A. Friedl,et al.  Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE , 1995 .

[20]  A. Musy,et al.  Digital terrain analysis of the Haute-Mentue catchment an scale effect for hydrological modelling with TOPMODEL , 2000 .

[21]  W. Parton,et al.  Global grassland ecosystem modelling: development and test of ecosystem models for grassland systems , 1996 .

[22]  Landscape Distribution of Organisms and the Scaling of Soil Resources , 1994, The American Naturalist.

[23]  Lawrence E. Band,et al.  Forest ecosystem processes at the watershed scale: hydrological and ecological controls of nitrogen export , 2001 .

[24]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[25]  Christina L. Tague,et al.  RHESSys: Regional Hydro-Ecologic Simulation System—An Object- Oriented Approach to Spatially Distributed Modeling of Carbon, Water, and Nutrient Cycling , 2004 .

[26]  Vincent B. Robinson,et al.  A multiple criteria decision support system for testing integrated environmental models , 2000, Fuzzy Sets Syst..

[27]  M. Kirkby.  TOPMODEL: A personal view , 1997 .

[28]  Roger Bivand,et al.  Using the R statistical data analysis language on GRASS 5 , 2000 .

[29]  Jing M. Chen,et al.  Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications , 1999 .

[30]  K. G. McNaughton,et al.  Stomatal Control of Transpiration: Scaling Up from Leaf to Region , 1986 .

[31]  Keith Beven,et al.  TOPMODEL : a critique. , 1997 .

[32]  Richard B. Lammers,et al.  Extending hydroecological simulation models from local to regional scales , 1998 .

[33]  Anthony W King,et al.  Aggregating Fine-Scale Ecological Knowledge to Model Coarser-Scale Attributes of Ecosystems. , 1992, Ecological applications : a publication of the Ecological Society of America.

[34]  Lawrence E. Band,et al.  Effect of land surface representation on forest water and carbon budgets , 1993 .

[35]  Ferenc Csillag,et al.  Ecoregionalization assessment: Spatio-temporal analysis of net primary production across Ontario , 2002 .

[36]  L. D. Danny Harvey,et al.  Upscaling in Global Change Research , 2000 .

[37]  D. Pury,et al.  Simple scaling of photosynthesis from leaves to canopies without the errors of big‐leaf models , 1997 .

[38]  A. Allen Bradley,et al.  Effect of temporal sampling of precipitation on hydrologic model calibration , 1999 .

[39]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[40]  Kai W. Wirtz.  Second order up-scaling : theory and an exercise with a complex photosynthesis model , 2000 .

[41]  Ferenc Csillag,et al.  Wavelets, boundaries, and the spatial analysis of landscape pattern , 2002 .

[42]  S. Fuhlendorf,et al.  Spatial scale influence on longterm temporal patterns of a semi-arid grassland , 1996, Landscape Ecology.

[43]  David S. Schimel,et al.  Texture, climate, and cultivation effects on soil organic matter content in U.S. grassland soils , 1989 .

[44]  Lawrence E. Band,et al.  Forest ecosystem processes at the watershed scale: dynamic coupling of distributed hydrology and canopy growth , 1997 .

[45]  Miklós Kertész,et al.  Optimal tiling of heterogeneous images , 1995 .

[46]  W. Parton,et al.  Impact of climate and atmospheric carbon dioxide changes on grasslands of the world , 1996 .

[47]  Keith Beven,et al.  Functional similarity in landscape scale SVAT modelling. , 1999 .

[48]  A-Xing Zhu,et al.  Effects of spatial detail of soil information on watershed modeling , 2001 .

[49]  L. Band,et al.  Export of nitrogen from catchments within a temperate forest: Evidence for a unifying mechanism regulated by variable source area dynamics , 1998 .