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Regolith Water in Zero-Order Chaparral and Perennial Grass Watersheds Four Decades after Vegetation Conversion

^a Dep. of Geosciences, Univ. of the Pacific, Stockton, CA 95211
^b Los Alamos National Lab., MS J495 EES-2: Earth and Environmental Sciences Division, Los Alamos, NM 87545
^c Dep. of Environmental Science, Univ. of California, Riverside, CA 92521
^d USDA-ARS, U.S. Salinity Lab., 450 W. Big Springs Rd., Riverside, CA 92507

View larger version (22K): [in a new window]   Fig. 1. (a) Location map of the San Dimas Experimental Forest (SDEF) and the study area. (b) Schematic of a zero-order watershed showing watershed positions. The boundary between the soil and weathered rock indicates the irregularity of this contact and that it does not always follow surface topography.

View larger version (24K): [in a new window]   Fig. 2. Depth profiles of regolith water potential. Three watersheds of each vegetation type were sampled at each position. Mean data are reported for 10-cm depth increments. (a) Vegetation means derived from all profiles under each vegetation type (n = 9). Note the significant changes at depths 70 and 100 cm under grass vegetation and that water potential is significantly different between the two vegetation types below 100 cm. (b) Position means for chaparral watersheds (n = 3). (c) Position means for grass watersheds (n = 3). Standard error is shown for each depth increment.

View larger version (25K): [in a new window]   Fig. 3. Depth profiles of regolith water Cl– concentrations. Water Cl– was measured on the same samples used in the water potential analysis. (a) Vegetation means derived from all profiles under each vegetation type (n = 9). The same significant changes are present under grass vegetation at 70 and 100 cm. The high spike at 145 cm for chaparral is due to a single depth increment at one site and suggests local water uptake by a nearby root. (b) Position means for chaparral watersheds (n = 3). Note that the Cl– concentrations from the summit element are significantly lower within the upper 50 cm. (c) Position means for grass watersheds (n = 3). Standard error is shown for each depth increment.

View larger version (30K): [in a new window]   Fig. 4. Cumulative Cl– vs. cumulative water plots from one watershed of each vegetation type. Chloride (Clswi) and water (v) accumulation with depth were used to identify portions of the regolith with different fluxes. The calculated moisture flux (m yr–1) is reported next to the appropriate straight-line segment (e.g., 0.0031 for the A and Bw horizons of the chaparral channel). Inflection points in the cumulative curves were compared with soil horizon boundaries based on the known depth of each sample point. Soil horizons and boundary depths (cm) are indicated. Arrows indicate the transition between input and leaching zones for each profile (averaged in Table 3). Note that minor inflection points frequently coincide with horizon boundaries.

View larger version (41K): [in a new window]   Fig. 5. (a) Histogram of regolith water flux in the input (n = 9) and leaching (n = 12) zones under chaparral and grass. Note that the 0.01 to 0.015 interval includes an observation from both the chaparral input and the chaparral leaching zones. (b) Histogram of regolith water age at the deepest part of the input (n = 9 for chaparral and grass) and leaching zones (n = 7 for chaparral and n = 9 for grass). The transition between flux rates was interpreted as the base of the input zone. The base of the leaching zone is the deepest interval sampled (as allowable by hand auger).

View larger version (15K): [in a new window]   Fig. 6. Depth of soil horizon boundaries vs. transitions in residual flux rate. Depths of inflection points in the cumulative plots (Fig. 4), signifying minor differences in flux, were compared with depths of soil horizon boundaries described in the field. Data are included from all nine profiles for each vegetation type. The chaparral data have a higher coefficient of determination. However, correlation for the 0- to 53-cm depth (the depth range for which data are available for grass) is similar to that for grass. Neither regression equation has a slope that is statistically differentiable from one.