[1] Terrain attributes based on upslope contributing area, A, are used widely in distributed hydrologic models. Several grid-based algorithms are available for estimating A. In this study, five algorithms (D8, 8, MFD, DEMON, and D1) were compared quantitatively on two undulating agricultural fields (63 and 109 ha) in northeastern Colorado. Global positioning system (GPS) data (0.02-m accuracy) were used to generate grid digital elevation models (DEMs) at 5-, 10-, and 30-m cell sizes. Relative differences between A values estimated using single-and multiple-direction algorithms increased with decreasing grid cell size. Relative differences were greatest along ridges and side slopes, and differences decreased where the terrain became more convergent. Multiple-direction algorithms (MFD, DEMON, and D1), allowing for flow divergence, are recommended on these undulating terrains for 5-and 10-m grids where A is most sensitive to the algorithm selection.

Abstract:The need to transfer information across a range of space-time scales (i.e. scaling) is coupled with the need to predict variables and processes of interest across landscapes (i.e. distributed simulation). Agricultural landscapes offer a unique set of problems and space-time data availability with the onset of satellite-based positioning and crop yield monitoring. The present study addresses quantification of the spatial variability of rainfed crop yield and near-surface soil water at farm field scales using two general methods: (1) geostatistical and fractal analyses; and (2) univariate linear regression using topographic attributes as explanatory variables. These methods are applied to 2 years of crop yield data from three fields in eastern Colorado, USA, and to soil-water content (depth-averaged over the top 30 cm) in one of these fields. Method 1 is useful for scaling each variable, and variogram shapes and their associated fractal dimensions of crop yield are related to those of topographic attributes. A new measure of fractal anisotropy is introduced and estimated from field data. Method 2 takes advantage of empirical and process knowledge of topographic controls on water movement and microenvironments. Topographic attributes, estimated from a digital elevation model at some scale (10 m by 10 m spacing here), help explain the spatial variability in crop yield. The topographic wetness index, for example, explained from 38 to 48% of the spatial variance in 1997 wheat yield. Soil water displays more random spatial variability, and its dynamic nature makes it difficult to predict in both space and time. Despite such variability, spatial structure is evident and can be approximated by simple fractals out to lag distances of about 450 m. In summary, these data and spatial analyses provide a basis and motivation for estimating the fractal behaviour, spatial statistics, and distributed patterns of crop yield from landscape topographic information.

Terrain attributes are commonly used to explain the spatial variability of agronomic, pedologic, and hydrologic variables. The terrain attributes studied here (elevation, slope, aspect, and curvature) are estimated readily from digital elevation models (DEMs), but questions remain about how the accuracy and sample spacing of the elevation data affect the estimated attributes. The main objective of this study was to quantify differences in each terrain attribute due to factors affecting DEM accuracy and grid cell size. Three data sources were compared: (i) real‐time kinematic global positioning system (RTKGPS); (ii) satellite‐differentially corrected global positioning system (DGPS); and (iii) U.S. Geological Survey (USGS) 30‐m DEMs. The GPS data from three undulating agricultural fields in northeastern Colorado were interpolated onto 5‐, 10‐, 20‐, and 30‐m grid DEMs. The DGPS and USGS DEMs produced similar elevation differences relative to RTKGPS DEMs, but elevation differences in USGS DEMs were more spatially correlated. Estimates of curvature were highly sensitive to DEM differences and the sensitivity of slope, aspect, and curvature estimates decreased as grid cell size increased. The impacts of DEM accuracy and grid cell size were investigated using correlations between wheat (Triticum aestivum L.) grain yields and estimated terrain attributes. The highest correlation coefficients were obtained using RTKGPS data, and decreasing the sample spacing or grid cell size below 30 m did not consistently improve the correlations. These analyses on agricultural lands indicate the importance of accurate elevation data for detailed terrain analyses on grid cell sizes of 30 m or less.

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