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

Abstract

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.

Spatial scaling of evapotranspiration as affected by heterogeneities in vegetation, topography, and soil texture

“…In the distributed case, soil in depressed areas also tends to accumulate water, giving rise to higher ET at these locations, and this spatial process is missed in the lumped calculation. Interestingly, using another hydrological model (RHESSys) that accounts for topographical effects on surface hydrology, Mitchell et al (2005) have also reported that distributed calculations yielded larger ET rates than those obtained from lumped calculations in a grassland ecosystem i and j represent the non-dominant and the dominant soil types, respectively. The subscripts sc, ls, and p denote silty-clay, loamy-sand and peat soils, respectively.…”

“…This yields some situations where the land cover of a grid cell might be represented by a land cover type that cover less than half of the total area of the grid cell, which may inevitably cause model predictions to be significantly biased (e.g., Chen, 1999;Gower et al, 2001;Rastetter et al, 1992Rastetter et al, , 2003Strayer et al, 2003). Several studies have illustrated for example that the overlook or the oversimplification of land surface complexity may cause ecosystem models simulations of surface hydrology to be Remote Sensing of Environment 102 (2006) 33 -51 www.elsevier.com/locate/rse considerably biased (Arora et al, 2001;Band et al, 1993;Grant, 2004;Haddeland et al, 2002;Kimball et al, 1999;Mackay et al, 2002;Mitchell et al, 2005).…”

Assessing the Impact of Parameter Uncertainty on Modeling Grass Biomass Using a Hybrid Carbon Allocation Strategy

“…Our work builds upon initial research on grassland productivity using RHESSys (Mitchell & Csillag, 2001;Mitchell et al, 2005;Zierl et al, 2007) by assessing uncertainty related to C allocation and selection of model parameters. We demonstrate a role for both resource-limitation and plant growth status in simulating C allocation between above and belowground grass biomass.…”

“…Moreover, researchers have used RHESSys in studies focusing on both climate change impacts including increased atmospheric CO 2 (Baron et al, ; Christensen et al, ; Tague et al, ; Zierl & Bugmann, ) and natural resource management (Grant et al, ). RHESSys' applicability in grasslands has also been investigated (Mitchell et al, ), but this paper provides new insight on C allocation and parameter sensitivity with respect to grasslands. Previous work has evaluated the impact of C allocation strategy for forests and sensitivity of simulating C cycling estimates to RHESSys physiological parameters (Garcia et al, ).…”

Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China

“…Such a simplification results in large uncertainties due to spatial heterogeneity, especially in topographically complex terrains. Some researchers have shown that the oversimplification of landscape complexities may inevitably cause model simulations to be considerably biased [8][9][10]. Landscape complexities typically present high spatial heterogeneity, and spatial heterogeneity is scaled by multiple factors, including endogenous and exogenous factors [11,12].…”