Page 29 - i1052-5173-30-12

Hunt, A.G., and Ghanbarian, B., 2016, Percolation
theory for solute transport in porous media: Geo-
chemistry, geomorphology, and carbon cycling:
Water Resources Research, v. 52, p. 7444–7459,
https://doi.org/10.1002/2016WR019289.
Figure 1. Predicted and Hunt, A.G., and Sahimi, M., 2017, Flow, transport, and
observed variability of
precipitation, P, and reaction in porous media: Percolation scaling, criti-
evapotranspiration, ET, cal path analysis and effective medium approxima-
energy
ET/P, as a function of tion: Reviews of Geophysics, v. 55, p. 993–1078,
PET/P = AI (aridity index). https://doi.org/10.1002/2017RG000558.
Data from Gentine et al. Hunt, A.G., Faybishenko, B.A., Ghanbarian, B.,
(2012). Figure is modi-
fied from Hunt et al. Egli, M., and Yu, F., 2020, Predicting water cycle
(2020). characteristics from percolation theory and obser-
vation: International Journal of Environmental
Research and Public Health, v. 17, no. 3, p. 734,
https://doi.org/10.3390/ijerph17030734.
Levang-Brilz, N., and Biondini, M.E., 2003,
Growth rate, root development and nutrient up-
take of 55 plant species from the Great Plains
Grasslands, USA: Plant Ecology, v. 165, p. 117–
144, https://doi.org/10.1023/A:1021469210691.
values d > 3 that generate ET > P are not be important in evaluation of sequestering Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham,
f
used). What is new is the representation of carbon and coupling global water and carbon V., and Cox, D.R., 1999, Probabilistic modelling of
predicted variability in ET based on experi- cycles. Incorporating observations helps esti- water balance at a point: The role of climate, soil
mental d value at larger AI, not just AI = 1. mate these complementary fluxes. We found and vegetation: Proceedings of the Royal Society
f
Values of d for grasses generate almost that variability in the predicted water balance of London, Series A, v. 455, p. 3789–3805, https://
f
doi.org/10.1098/rspa.1999.0477.
the exact observed variability in ET/P at AI due to variation in plant root fractal dimen- Yang, Y., Donohue, R.J., and McVicar, T.R., 2016,
= 1, but overestimate variability at larger AI. sionality outweighs uncertainties/variation in Global estimation of effective plant rooting
We attribute the discrepancy at larger AI interception and surface run-off. Coupling depth: Implications for hydrological modeling:
mostly to the fact that low-end ET/P values our long-term percolation model with the Water Resources Research, v. 52, no. 10, https://
come from grass species with d around 1.9, short-term stochastic infiltration model (e.g., doi.org/10.1002/2016WR019392.
f
typical for nearly 2D structures, being less Rodriguez-Iturbe et al., 1999) might improve Yu, F., and Hunt, A.G., 2017, Predicting soil forma-
adapted to arid conditions, and more likely predictions of water balance components and tion on the basis of transport-limited chemical
weathering: Geomorphology, https://doi.org/
absent at larger AI. Our theoretical frame- 10.1016/ j.geomorph.2017.10.027.
work, together with experimentally deter- optimization of plant productivity. Yu, F., and Hunt, A.G., 2018, Damköhler number
mined parameters d , generates a good upper input to transport-limited chemical weather-
f
bound for ET/P from theory and its variabil- REFERENCES CITED ing calculations: ACS Earth & Space Chemis-
Budyko, M.I., 1958, The heat balance of the earth’s
ity as a function of AI. surface: Washington, DC, U.S. Department of try, v. 1, p. 30–38, https://doi.org/10.1021/
acsearthspacechem.6b00007.
The most important theoretical limitations Commerce, Weather Bureau. Yu, F., Hunt, A.G., Egli, M., and Raab, G., 2019, Com-
of applying percolation theory to water bal- Gentine, P., D’Odorico, P., Linter, B.R., Sivandran, G., parison and contrast in soil depth evolution for
ance modeling arise from the partitioning of and Salvucci, G., 2012, Interdependence of climate, steady-state and stochastic erosion processes: Pos-
surface run-off and subsurface flow (and soil, and vegetation as constrained by the Budyko sible implications for landslide prediction: Geo-
transpiration and interception), because these curve: Geophysical Research Letters, v. 39, chemistry, Geophysics, Geosystems, v. 20, p. 2886–
L19404, https://doi.org/10.1029/2012GL053492.
processes are not obviously regulated by Hunt, A.G., 2017, Spatio-temporal scaling of vegeta- 2906, https://doi.org/10.1029/2018GC008125.
plants for optimizing NPP. The ability to pre- tion growth and soil formation: Explicit predic- Manuscript received 7 June 2020
dict contributions of surface run-off, plant tions: Vadose Zone Journal, https://doi.org/10.2136/ revised Manuscript received 27 July 2020
interception, and subsurface flow would also vzj2016.06.0055. Manuscript accepted 5 aug. 2020

www.geosociety.org/gsatoday 29