No abstract

Logistic models were developed to spatially predict the probability of drainage classes in a humid tropical area (58900 ha) using sampled terrain attributes from a digital elevation model, and vegetation indices from a LANDSAT‐5 Thematic Mapper image. Soil drainage classes were assigned on the basis of the local water table regime depth, determined by soil morphological indicators, to 295 pseudo‐randomly selected soil auger hole observations (calibration data set) and 72 soil pedon observations (validation data set). Six drainage classes were identified: excessively (D1), well (D2), moderately well (D3), imperfectly (D4), poorly (D5), and very poorly (D6). A nested dichotomous modeling strategy of progressively separating the six drainage classes was adopted, and resulted in five multivariate logistic models. The best performing model, predicting the probability of nonhydric (D1D2) soils versus hydric (D3D4D5D6) soils had a concordance of 99%, and the worst performing model, predicting the probability of imperfectly (D4) drained soils versus moderately well (D3) drained soils had a concordance of 65%. The most important spatial determinants were: elevation, slope, distance‐to‐the‐river channel (DC), and vegetation indices. The logistic models were combined in a geographic information system (GIS) to derive soil drainage class maps using the gridded data sets of the significant variables. The results showed that digital elevation models and vegetation indices from LANDSAT‐5 Thematic Mapper provide complementary information for developing statistical models to spatially predict and map soil drainage classes.

Abstract:TOPMODEL, a semi-distributed, topographically based hydrological model, was applied to simulate continuously the runoff hydrograph of a medium-sized (379 km 2 ), humid tropical catchment. The objectives were to relate hydrological responses to runoff generation mechanisms operating in the catchment and to estimate the uncertainty associated with runoff prediction. Field observations indicated that water tables were not parallel to the surface topography, particularly at the start of the wet season. A reference topographic index REF was therefore introduced into the TOPMODEL structure to increase the weighting of local storage deficits in upland areas. The model adaptation had the effect of deepening water tables with distance from the river channel. The generalized likelihood uncertainty estimation (GLUE) framework was used to assess the performance of the model with randomly selected parameter sets, and to set simulation confidence limits. The model simulated well the fast subsurface and overland flow events superimposed on the seasonal rise and fall of the baseflow. The top ranked parameter sets achieved modelling efficiencies of 0Ð943 and 0Ð849 in 1994 and 1995 respectively. The GLUE analysis showed that the exponential decay parameter m, controlling the baseflow and the local storage deficit, was the most sensitive parameter. There was increased uncertainty in the simulations of storm events during the early and late phases of the season, which was due to a combination of: errors in detecting the rainfall depths for convectional rainfall events; the treatment of rainfall as a catchment areal value; and, the strong seasonality in runoff response in the humid tropics.

No abstract

No abstract