Volumetric soil-water depth profiles at nine sample locations on a 2-ha hillslope were monitored throughout the 1997-1998 El Nino and the 1998-1999 La Nina cycles. A hydrological model integrating the soil-water measurements with digital terrain analysis and a one-dimensional water balance model was developed to map dominant hydrological patterns of soil-water storage and lateral flow redistribution. Statistical correlations between hydrologic behavior and the compound topographic index (CTI) generated from a digital elevation model (DEM) were used to generate spatially distributed input parameters of initial water storage and soil-controlled evapotranspiration utilized in the model. The results suggest that differences in water storage and availability are highly modified by climatic conditions and local topography. Nearly three times higher than normal rainfall in the El Nino year caused deeper infiltration of water and led to significant subsurface water redistribution into the concave hillslope positions which remained moist throughout the 1998 summer. Water infiltration and distribution was diminished considerably in the drier than normal La Nina, and led to a complete dry-down in 1999. Actual evapotranspiration was 87% of total precipitation during the El Nino, compared with 100% in the subsequent La Nina. The good correlation between modeled and measured water storage shows that even a simple one-dimensional model combined with the appropriate input parameters Is a suitable tool for estimating changes in soil-water content on hillslopes where lateral flow is a significant functional component of the soil hydrology.

Soil-landscape patterns result from the integration of short- and long-term pedogeomorphic processes. A 2-ha hillslope catena in California shows short-distance variation in A horizon depth from 8 to 80 cm and in soil depth from 8 to >450 cm in convex to concave positions. Similar variations in net primary productivity (NPP) and soil C represent significant information often not captured by soil survey maps. Strong correlations between these measured soil-landscape variables and explanatory digital terrain attributes are used to develop quantitative soil-landscape models. We were able to account for between 52 and 88% of soil property variance using easily computed terrain variables such as slope and flow accumulation. Spatial implementation of the models suggest lateral redistribution processes resulting in differential accumulation of C and soil mass in convergent and divergent landscape positions. The models are explicit and quantitative, which enables their use for testing hypotheses about the spatial distribution of fine-scale landscape and ecosystem processes and for parameterizing spatially distributed hydrological and ecosystem simulation models.