Ecological Archives A017-052-A1

S. V. Smith, S. H. Bullock, A. Hinojosa-Corona, E. Franco-Vizcaíno, M. Escoto-Rodríguez, T. G. Kretzschmar, L. M. Farfán, and J. M. Salazar-Ceseña. 2007. Soil erosion and significance for carbon fluxes in a mountainous Mediterranean-climate watershed. Ecological Applications 17:1379–1387.

Appendix A. Detailed methodology of RUSLE calculations and metabolism estimates.

 

RUSLE Calculations

The following sections discuss parameter estimations for the Revised Universal Soil Loss Equation (RUSLE), as applied in this study.

Precipitation: R

The Comisión Nacional del Agua maintains long-term weather data for Mexico. Rainfall near sea level in Ensenada averages about 280 mm/yr. A 100-year record of annual rainfall in Ensenada has a range of approximately 100–600 mm/yr. From 1960–1990 precipitation data at 23 stations within or near the watershed, precipitation is estimated to have been 315 ± 73 mm/yr. Calculation of R for 5 years of automatic precipitation data, using 10-min rainfall intensities for each storm event during each year (the procedure in Renard et al. 1996) gives a value of 46,000 MJ mm ·km-2 ·hr-1 ·yr-1. Use of the Renard and Freimund (1994) equation for the catchment annual average precipitation gives 51,000. We use a rounded value of 50,000 MJ mm ·km-2 ·hr-1 ·yr-1.

Soils: K and soil OC

Soil types in the watershed are dominantly Lithosols (rock contact shallower than 10 cm; 49% of the area), Phaeozems (or Mollisols; dark, humus- and nutrient-rich surface horizon; 23%), and Regosols (Entisols; little horizon development; 21%) (FAO 1998; USDA NRCS 2006). K varies from 0.00 to 0.04 t hr ·MJ-1 ·mm-1 across the study area, with a mean of 0.022 ± 0.003. The data are of limited value for characterizing detailed spatial variation of soil types, although we do use the map as a regional approximation of variations in K. We also used the soil profile data (INEGI, 2004) to estimate soil OC, as discussed in the Appendix of Smith et al. (2005). The average soil OC (0.6 ± 0.1%) was used as an additional multiplier in the RUSLE equation to estimate OC erosion.

Slope: L and S

Specific solution of L and S for field conditions according to RUSLE are reported in Renard et al. (1996). The RUSLE formulation for LS is more amenable to GIS analysis than the USLE formulation; in our analysis, this is the major significant departure between the two versions of the erosion model. We have followed the procedure outlined by Griffin et al. (1988) and further explained by Moore and Wilson (1992) to calculate LS from digital elevation data (Peter Kinnell, unpublished manuscript). Based on 1 arc-second gridded elevation data from INEGI, converted to 30-m pixels, LS varies from <0.03 (the lowest non-0 value, based on resolution of the DEM) to 53. The mean is 5.0 ± 5.6, with 97% of the values <20.

Land cover: NDVI and C

De Jong and his colleagues (unpublished technical reports by de Jong 1994a; de Jong et al. 1998, 1994b), calibrated NDVI (defined by Sabins 1996) to estimate C for a region in France, with a similar climate and vegetation regime to northwestern Baja California. Their original equation is:

C = 0.431 – 0.805* NDVI.                             (A.1a)

Van der Knijff et al. (1999) objected to the equation, because it “…is unable to predict C-values over 0.431.” They offered an alternative equation, which predicts C above 0.45 for NDVI less than 0.28; at higher NDVI (and setting the minimum allowable C derived from Equation (2a) to 0), the two equations converge. While the objection may be valid in areas where bare soils (NDVI £ 0) are recently tilled (C near 1.0), we consider the de Jong formulation to be conceptually realistic for the predominantly natural (or “disturbed natural”) vegetation of Baja California. We adopt a modification of the de Jong equation (changing the intercept from 0.431 to 0.45, in order to conform to nominal maximum C for non-tilled land):

C = 0.45 – 0.805* NDVI.                               (A.1b)

We used a single cloud-free Landsat scene for 9 May 2005, at the end of an unusually wet winter, in order to estimate what we consider to be maximum NDVI for our area. Our rationale for this approach, rather than using a time series of NDVI data, is consistent with van der Knijff et al. (1999), the de Jong reports, and discussions in both Wischmeier and Smith (1978) and Renard et al. (1996). Dry live or dead vegetation should provide as good erosion-prevention cover as green vegetation, but the dry vegetation would be indistinguishable by NDVI from bare soil. During dry years (or dry seasons) cover by dry vegetation would be greater. We therefore chose a Landsat scene from the end of a wet season during a wet year to represent our best estimate of green + live dry + dead plant cover. Raindrop fall height from thick shrub canopy cover and from most typical local agricultural vegetation (the two locally dominant cover types) is <25 cm, so 100% plant cover can be considered to be dominated by an approximation to ground cover, rather than a high canopy, and reduce C to 0.05 or below.

Negative NDVI values (0.8% of the 2005 data) were set to 0 before solution of Eq. A.1b. Negative values for C, as derived from Eq. A.1b (NDVI > 0.56; 0.3% of the data), were set to 0, yielding C = 0.45 according to Eq. A.1b. However, within agricultural areas (as determined from CONAFOR digital land cover maps for 2000) NDVI values adjusted to 0 were assumed to be tilled and were set to C = 1.0. Adjusted NDVI for water (0.007% of the total area) is 0, and C was set to 0.  Urban areas (assumed to be largely paved or otherwise covered; 1.3% of the total area) were assigned C = 0.02. C for the remaining area (97.6%) was calculated according to Eq. A.1b. There are local rock outcrops that would have NDVI =0.0; in principle, these should also be assigned C = 0.0. In practice, we have assumed that such cover is a negligible proportion of the region, and we have not attempted this correction. C (from Eq. A.1b, with the described adjustments) ranged from 0.0 to 1.0 across the watershed, with a mean of +0.27 ± 0.10, at the time of our Landsat image.

Unadjusted NDVI for the image used to calculate C ranged from –0.7 to +0.8 (+0.22 ± 0.10). In qualitative support of our use of the 9 May 2005 “maximum NDVI image” to estimate C, comparable Landsat-derived NDVI for about the same periods in earlier years (6 May 2001, 23 April 2002) after winters with near-average and below-average rainfall, respectively, showed about the same NDVI ranges (-0.9 to +0.8; -0.6 to +0.8, respectively), but lower average values (+0.13 ± 0.10; +0.00 ± 0.08). Constant minimum and maximum NDVI should be obtained with bare ground and irrigated areas, while varying mean values reflect the proportion of green vegetation across the landscape. Further field investigation of C would help test both equation 1b and our use of maximum NDVI for estimations of C.


Metabolism Estimates

Because ground-based field data on ecosystem metabolism in the Todos Santos watershed were not available, we compared estimates of productivity from field studies in similar ecosystems in California with three global models (two based on temperature, precipitation, and evapotranspiration; the other based on NDVI data along with climatic data); and we also calculated respiration from a climate model. We used estimates of annual NPP from a global model with a daily remote perception base, averaged over 2000–2005 for each 1-km pixel in the watershed (MOD17A3, Heinsch et al. 2003). The average, for both hillslope and alluvial areas was ~490 g C ·m-2 ·yr-1. The mechanistic, field-calibrated MEDECS model provided NPP for chaparral at the Echo Valley site in California, about 114 km north of Ensenada (Miller 1981, Oechel and Lawrence 1981). The MEDECS model estimated NPP ~150 g C ·m-2 ·yr-1; because mean precipitation at that California site was  ~50% higher than for our area, we would expect the NPP to be lower in the Todos Santos watershed. However, results from Echo Valley differed notably by method (Miller 1981, Kummerow et al. 1981) such that it’s NPP might be substantially higher. We did not apply coastal scrub results from California because the CONAFOR vegetation inventory indicated it covered <3% of shrublands, although this is certainly an underestimate. Productivity trends in chaparral in the long-term absence of fire are still controversial (Hanes 1971, Gray and Schlesinger 1981, Black 1987, Keeley 1992, Minnich 2001).We also calculated both NPP and soil respiration from empirical, globally-calibrated models. These models use only annual precipitation and annual mean temperature as the independent variables. Unfortunately, although there have been at least 23 weather stations in the watershed, the records are quite fragmentary, so we used only the long-term averaged from Ensenada to represent the watershed. NPP calculated from two different models  in Lieth 1975 averaged about 250 g C ·m-2 ·yr-1. Soil respiration (Rs) calculated from monthly means for these same climatic variables at monthly time steps (Raich et al. 2002) averaged  310 C ·m-2 ·yr-1.

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