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Finding an explicit solution to the widely used Green–Ampt (G–A) one‐dimensional infiltration model has been subject of efforts for more than half a century. We derived an explicit semiempirical approach that combines accuracy with simplicity, a concept that has been generally neglected in previous studies. The equation is F=Kst(0.70635+0.324151+9.43456(S2Ks2t))$F\; = {K_s}\;t\;({0.70635 + 0.32415\sqrt {1 + 9.43456({\frac{{{S^2}}}{{{K_s}^2t}}})} })$, with F (L), Ks (L/T), S (L/T0.5) and t (T) being cumulative infiltration, saturated hydraulic conductivity, sorptivity, and time, respectively. Relative errors (ɛ) by the application of this equation generally do not exceed ±0.3% in most applied infiltration problems faced by water resources engineering today. We show both numerically and mathematically that │ɛ│> 0.3% could only occur if Kst/F > 0.904, a criterion that could apply to sand and loamy sand soils (i.e., coarse texture) and if they experience infiltration rates for over 6 h and 19 h, respectively. Hence, we also derived a simple linear adjustment in the model as Fadj ≅ 0.9796 F + 0.335 S2/Ks to address these longer infiltration rates, and to assure that ɛ remains within the expected ±0.3% range of uncertainty. A linearized regression technique was also developed to accurately estimate S and Ks when the G–A model is used. We numerically demonstrated that our fitting method could be used even when the G–A approach is less valid (diffusive soils), provided that the actual value of the capillary length (λ) is initially known. An added benefit of our approach is that by setting λ equal to 1/3 and 2/3, it can significantly limit the range of initializing, unknown, a priori values of S and Ks, as these two parameters are estimated through the inverse solution of implicit infiltration models. Due to the model's simplicity and accuracy, our solution should find application among hydrologists, natural resource scientists, and engineers who wish to easily derive accurate estimations from the G–A infiltration approach and/or estimate sorptivity and hydraulic conductivity without encountering divergence problems. Core Ideas: A new explicit form of the one‐dimensional Green–Ampt infiltration model was developed.This model combined simplicity, accuracy, and computational efficiency.A new linearized regression solution was derived to estimate sorptivity and hydraulic conductivity.This linearized fitting method can eliminate the risk of convergence associated with other numerical approaches. [ABSTRACT FROM AUTHOR]

Changes in land use across the semiarid grasslands of Northern Mexico have driven a decline of plant cover and alteration of plant species composition. A number of different plant communities have resulted from these changes, however, their implications on the carbon cycle and regional carbon balance are still poorly understood. Here, we examined the effects of plant cover loss and changes in species composition on net ecosystem CO2 exchange (NEE) and their biotic and abiotic controls. Five typical plant community types were examined in the semiarid grassland by encasing the entire above-ground ecosystem using the geodesic dome method. Sites included an oat crop (crop), a moderately grazed grassland (moderate grazing), a 28 yr-old grazing exclosure (exclosure), an overgrazed site with low perennial grass cover (overgrazed), and an overgrazed site presenting shrub encroachment (shrub encroachment). For natural vegetation, rates of daytime NEE for sites with a high plant cover (exclosure and moderate grazing) were similar (P > 0.05) as compared to sites with low plant cover (overgrazed and shrub encroachment). However, night time NEE (carbon loss) was more than double (P < 0.05) for sites with high plant cover compared to sites with low cover, resulting into slight C sinks for the low plant cover sites and neutral or sources for the high plant cover sites on an annual basis. Differences in plant cover and its associated biomass defined the sensitivity to environmental controls. Thus, daytime NEE in low plant cover sites reached light compensation points at lower PPFD values than those from high plant cover sites. Differences in species composition did not influence NEE rates even though there were transient or permanent changes in C3 vs. C4 functional groups. [ABSTRACT FROM AUTHOR]

Accelerated release of carbon from soils is one of the most important feedbacks related to anthropogenically induced climate change. Studies addressing the mechanisms for soil carbon release through organic matter decomposition have focused on the effect of changes in the average temperature, with little attention to changes in temperature variability. Anthropogenic activities are likely to modify both the average state and the variability of the climatic system; therefore, the effects of future warming on decomposition should not only focus on trends in the average temperature, but also variability expressed as a change of the probability distribution of temperature. Using analytical and numerical analyses we tested common relationships between temperature and respiration and found that the variability of temperature plays an important role determining respiration rates of soil organic matter. Changes in temperature variability, without changes in the average temperature, can affect the amount of carbon released through respiration over the long-term. Furthermore, simultaneous changes in the average and variance of temperature can either amplify or dampen the release of carbon through soil respiration as climate regimes change. These effects depend on the degree of convexity of the relationship between temperature and respiration and the magnitude of the change in temperature variance. A potential consequence of this effect of variability would be higher respiration in regions where both the mean and variance of temperature are expected to increase, such as in some low latitude regions; and lower amounts of respiration where the average temperature is expected to increase and the variance to decrease, such as in northern high latitudes. [ABSTRACT FROM AUTHOR]