Distributed modeling of storm flow generation in an Amazonian rain forest catchment: Effects of model parameterization

We describe a process-based storm flow generation model, Topog_SBM consisti ng of a simple bucket model for soil water accounting, a one-dimensional ki nematic wave overland flow scheme, and a contour-based element network for routing surface and subsurface flows. aside from topographic data and rainf all the model has only six input parameters: soil depth (z), saturated hydr aulic conductivity at the soil surface (K-0), the rate of decay in K-0 with depth (m), the Manning surface roughness parameter (n), the maximum (satur ated) soil water content (theta(s)), and the minimum (residual) soil water content (theta(r)). However, the model is fully distributed, so these value s can vary in magnitude across space. The model was applied to La Cuenca, a very small rainforest catchment in western Amazonia that has been well cha racterized in several hydrometric and hydrochemical investigations. Total r unoff, peak runoff, time of rise, and lag time were predicted for 34 events of varying magnitudes and antecedent moisture conditions. We compared resu lts for eight different model parameterizations or ''sets"; four of these w ere freely calibrated to yield the best possible model fit to runoff data, whereas the other four were constrained (in various ways) by the use of act ual K-0 data gathered for the catchment. The eight sets were calibrated on either one of three events or on the three events jointly to illustrate the importance of calibration event selection on model performance. Model perf ormance was evaluated by comparing observed and predicted (1) storm flow hy drograph attributes and (2) spatiotemporal patterns of overland flow occurr ence across the catchment. The model generally predicted the right amount o f runoff but usually underpredicted the peak runoff rate and overpredicted the time of rise. The "best" parameterization could credibly predict hydrog raphs for only about half of the events. Significant, and sometimes gross, errors were encountered for about one fourth of the events modeled, raising concerns in our minds about the a priori simulation of events that diverge too far from the conditions that the model was calibrated for. For the bes t parameterization we were able to predict an overland flow frequency distr ibution that accorded with field observations, though the model almost alwa ys overpredicted the spatial extent of overland flow. We concluded that mod el performance for the La Cuenca conditions could be enhanced by adding a " fast" subsurface flow pathway and/or by modifying the K-0 versus depth deca y function.