1
Ecohydrological controls on grass and shrub above-ground net primary
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productivity in a seasonally-dry climate
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Supplemental Information: Model validation and flux data.
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6
Data
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Model performance is assessed by comparison to observed soil moisture and surface CO2
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and H2O fluxes during the 2007-2008 growing season (Figures S1 and S2 below). The
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model is forced by precipitation and potential evapotranspiration as described in the text.
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Soil and vegetation parameters were chosen from typical published values or calibrated to
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match observations (see Tables 2 and 3 in the text). Observations are as follows:
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Soil moisture. Volumetric water content was recorded at 2–4 week intervals by time
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domain reflectometry using 15 cm waveguides (MiniTrase, ICT International). Probes
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were installed vertically at the soil surface, providing an estimate of average water
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content over the upper 15 cm of soil.
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Gross primary productivity (GPP) and evapotranspiration (ET). Surface CO2 and
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H2O fluxes were measured by eddy covariance (see Goulden et al., 2012 for details on
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experimental set-up and data processing).
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Discussion
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The model provides a reasonable representation of the seasonal water and carbon
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dynamics for both grasses and shrubs at Loma Ridge. There are a number of caveats,
25
however, that limit interpretation of the modeling results and are noted below.
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First, note that the model under-predicts the observed cumulative water vapor flux in
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grassland (Figure S1b). The model predicts 173 mm evapotranspiration, whereas the
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estimate from the eddy covariance data is 300±60 mm. Precipitation was 223 mm
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between October 2007 and September 2008. Therefore, the model predicts an ET/P ratio
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of approximately 0.78 and the observations provide an estimate of 1.35. The modeled
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ET/P ratio is on the low end, but consistent with modeled and observed values in other
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seasonally-dry, semi-arid ecosystems (Potter et al., 2005; Feng et al., 2012; Gentine et al.,
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2012). On the other hand, the observations suggest a water vapor source in addition to the
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current year precipitation. The water source may be stored water from previous years,
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transpired by deeper rooted vegetation, or re-evaporation of morning dew, which is
36
frequently observed at the site. However, additional information is needed to distinguish
37
between these 2 hypotheses.
38
Modeled GPP, ET, and soil moisture match the observations well in the shrub plots
39
(Figure S2). Although the model predicts total GPP at the end of the growing season, the
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seasonal dynamics of carbon accumulation are delayed in the model relative to the
41
observations. This is likely due to a storage effect, enhanced by the choice of a large zr
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for the shrub simulations, which effectively slows the model dynamics. This effect can
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also be identified in the modeled soil moisture trajectory, which exhibits less variability
44
than the 15 cm surface measurements. Such inconsistencies in the time-scale of soil
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moisture depletion is a well-known artifact in the lumped root zone model employed here
46
(e.g. Guswa et al., 2004); however, it does not seem to affect the prediction at the annual
47
scale.
48
The effect of rooting depth on model results provides some additional insight into the
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ET discrepancy in the grass simulations. The ET/P ratio increases with rooting depth.
50
Both observed and modeled shrub ET resulted in estimated ET/P ratios of 1.04 and 1.02,
51
respectively, consistent with such semi-arid ecosystems. Modeled ET in grass does
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respond positively to increased zr, which increases soil moisture storage and the supply of
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water to ET. However, water stress increases and productivity decreases as well (data not
54
shown). Therefore, the model assumptions contain a trade-off between accuracy of ET
55
and ANPP measurements; and the parameters chosen here effectively balance that trade-
56
off in the results.
57
58
59
References
60
Feng, X., G. Vico, and A. Porporato. (2012). On the effects of seasonality on soil water
61
balance and plant growth. Water Resources Research, Vol. 48, W05543,
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doi:10.1029/2011WR011263.
63
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Gentine, P., P. D’Odorico, B.R. Litner, G. Sivandran, and G. Salvucci. (2012).
65
Interdependence of climate, soil, and vegetation as constrained by the Budyko curve.
66
Geophysical Research Letters, Vol. 39, L19404, doi:10.1029/2012GL053492.
67
68
Goulden, M.L., R.G. Anderson, R.C. Bales, A.E. Kelly, M. Meadows, and G.C. Winston.
69
(2012). Evapotranspiration along an elevation gradient in California’s Sierra Nevada.
70
Journal of Geophysical Research Biogeosciences, Vol. 117, G03028,
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doi:10.1029/2012JG002027.
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Guswa, A.J., M.A. Celia, and I. Rodriguez-Iturbe. (2004). Effect of vertical resolution on
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predictions of transpiration in water-limited ecosystems. Advances in Water Resources,
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27, 467-480.
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Potter, N.J., L. Zhang, P.C.D. Milly, T.A. McMahon, and A.J. Jakeman. (2005). Effects
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of rainfall seasonality and soil moisture capacity on mean annual water balance for
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Australian catchments. Water Resources Research, Vol. 41, W06007,
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doi:10.1029/2004WR003697.
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Figures
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Figure S1. Observed and modeled water balance and carbon dynamics for Loma Ridge
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grassland during the 2007-2008 growing season: (a) gross primary productivity; (b)
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evapotranspiration; (c) soil moisture; and (d) daily precipitation and potential
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evapotranspiration. The gray regions in panels (a) and (b) represent +/- 20% of the
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cumulative sums of GPP and ET.
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Figure S2. Observed and modeled water balance and carbon dynamics for Loma Ridge
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shrubland during the 2007-2008 growing season: (a) gross primary productivity; (b)
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evapotranspiration; (c) soil moisture; and (d) daily precipitation and potential
101
evapotranspiration. The gray regions in panels (a) and (b) represent +/- 20% of the
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cumulative sums of GPP and ET.
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Figure S3. Gross ecosystem exchange (GEE) for Loma Ridge grasses (open symbols,
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solid line) and shrubs (closed symbols, dashed line) estimated from eddy covariance
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observations. Linear regressions are: GEE=-0.0263*Pa-1.665 (r2=0.917) for shrubs and
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GEE=-0.0109*Pa-1.467 (r2=0.538) for grasses.