Digital terrain analysis and simulation modeling to assess spatial variability of soil water balance and crop production

Terrain characteristics and landscape position control soil physical properties. They often modify environmentally sensitive processes such as leaching, erosion and sedimentation, as well as dynamic factors affecting crop production. The likelihood of soils becoming saturated increases at the base of slopes and in the depression where there is a convergence of both surface and subsurface flow. The objective of this study was to combine a conventional, one-dimensional soil water balance model with a terrain analysis model to evaluate the hydrological and agricultural processes occurring on sloping land surfaces. A new digital terrain model, TERRAE-SALUS was developed to study and model how the terrain affects the vertical and lateral movement of water occurring on the land surface and in the shallow, subsurface regimes. This study evaluated the capability of TERRAE-SALUS applied at a field scale with rolling terrain where the soil water content was measured. The model was able to partition the landscape into an interconnected series of element network from a grid DEM. TERRAE-SALUS was evaluated using three different scenarios to gain a better understanding of the factors affecting the runoff-runon processes. The high elevation point consistently showed lower water contents compared to the upper and lower saddles and depressions. The subsurface lateral flow was highest on the saddles between two peaks, indicating the correct performance of the model in predicting the contribution of water from the elements located on the peaks. The RMSE between measured and simulated soil water content varied from 0.22 cm to 0.68 cm. A second experiment was carried out applying the crop simulation model CROPGRO in combination with remote sensing data to evaluate the ability of the model to identify factors responsible for the yield variation in a spatially variable landscape. Results from this study showed that the combination of crop simulation modeling and remote sensing can identify management zones and causes for yield variability, which are prerequisites for zone-specific management prescription.