Effects of detailed soil spatial information on watershed modeling across different model scales

Hydro-ecological modelers often use spatial variation of soil information derived from conventional soil surveys in simulation of hydro-ecological processes over watersheds at mesoscale (10–100 km²). Conventional soil surveys are not designed to provide the same level of spatial detail as terrain and vegetation inputs derived from digital terrain analysis and remote sensing techniques. Soil property layers derived from conventional soil surveys are often incompatible with detailed terrain and remotely sensed data due to their difference in scales. The objective of this research is to examine the effect of scale incompatibility between soil information and the detailed digital terrain data and remotely sensed information by comparing simulations of watershed processes based on the conventional soil map and those simulations based on detailed soil information across different simulation scales. The detailed soil spatial information was derived using a GIS (geographical information system), expert knowledge, and fuzzy logic based predictive mapping approach (Soil Land Inference Model, SoLIM). The Regional Hydro-Ecological Simulation System (RHESSys) is used to simulate two watershed processes: net photosynthesis and stream flow. The difference between simulation based on the conventional soil map and that based on the detailed predictive soil map at a given simulation scale is perceived to be the effect of scale incompatibility between conventional soil data and the rest of the (more detailed) data layers at that scale. Two modeling approaches were taken in this study: the lumped parameter approach and the distributed parameter approach. The results over two small watersheds indicate that the effect does not necessarily always increase or decrease as the simulation scale becomes finer or coarser. For a given watershed there seems to be a fixed scale at which the effect is consistently low for the simulated processes with both the lumped parameter approach and the distributed parameter approach.     

The Gaia Hypothesis: Fact, Theory, and Wishful Thinking

Organisms can greatly affect their environments, and the feedback coupling between organisms and their environments can shape the evolution of both. Beyond these generally accepted facts, the Gaia hypothesis advances three central propositions: (1) that biologically mediated feedbacks contribute to environmental homeostasis, (2) that they make the environment more suitable for life, and (3) that such feedbacks should arise by Darwinian natural selection. These three propositions do not fare well under close scrutiny. (1) Biologically mediated feedbacks are not intrinsically homeostatic. Many of the biological mechanisms that affect global climate are destabilizing, and it is likely that the net effect of biological feedbacks will be to amplify, not dampen, global warming. (2) Nor do biologically mediated feedbacks necessarily enhance the environment, although it will often appear as if this were the case, simply because natural selection will favor organisms that do well in their environments – which means doing wellunder the conditions that they and their co-occurring species have created. (3) Finally, Gaian feedbacks can evolve by natural selection, but so can anti-Gaian feedbacks. Daisyworld models evolve Gaian feedback because they assume that any trait that improves the environment will also give a reproductive advantage to its carriers (over other organisms that share the same environment). In the real world, by contrast, natural selection favors any trait that gives its carriers a reproductive advantage over its non-carriers, whether it improves or degrades the environment (and thereby benefits or hinders its carriers and non-carriers alike). Thus Gaian and anti-Gaian feedbacks are both likely to evolve.     

Mechanisms of coal metamorphism: case studies from Paleozoic coalfields

Controls on coal metamorphism can be complex. In this paper, we examine four Paleozoic coalfields: the western Kentucky portion of the Illinois Basin, the Pennsylvania anthracite fields, the South Wales Coalfield, and the Bowen Basin. An increase in temperature with depth of burial is certainly a factor in coal metamorphism. In many coalfields, however, including the coalfields reviewed here, it has become apparent that such a simple mechanism does not explain the coal rank patterns observed. The flow of hydrothermal fluids through the coals has been proposed as a cause of coal metamorphism. Evidence includes inverted rank gradients, elevated CFL as an indicator of brine fluids, isotopic evidence for hydrothermal fluids, and vein and cleat mineral assemblages. In any case, multiple hypotheses must often be evaluated in the examination of any coalfield since the simple paradigm of coal rank increases with a simple increase in temperature with increasing depth does not fit the evidence observed in many cases.     

Aptian to Coniacian (Early–Late Cretaceous) palynostratigraphy of the Gustav Group, James Ross Basin, Antarctica

The Gustav Group of the James Ross Basin, Antarctic Peninsula, forms part of a major Southern Hemisphere Cretaceous reference section. Palynological data, chiefly from dinoflagellate cysts, integrated with macrofaunal evidence and strontium isotope stratigraphy, indicate that the Gustav Group, which is approximately 2.6 km thick, is Aptian–Coniacian in age. Aptian–Coniacian palynofloras in the James Ross Basin closely resemble coeval associations from Australia and New Zealand, and Australian palynological zonation schemes are applicable to the Gustav Group. The lowermost units, the coeval Pedersen and Lagrelius Point formations, have both yielded early Aptian dinoflagellate cysts. Because the overlying Kotick Point Formation is of early to mid Albian age, the Aptian/Albian boundary is placed, questionably, at the Lagrelius Point Formation–Kotick Point Formation boundary on James Ross Island, and this transition may be unconformable. Although the Kotick Point Formation is largely early Albian on dinoflagellate cyst evidence, the uppermost part of the formation appears to be of mid Albian age. This differentiation of the early and mid Albian has refined the age of the formation, previously considered to be Aptian–Albian, based on macrofaunal evidence. The Whisky Bay Formation is of late Albian to latest Turonian age on dinoflagellate cyst evidence and this supports the macrofaunal ages. Late Albian palynofloras have been recorded from the Gin Cove, lower Tumbledown Cliffs, Bibby Point and the lower–middle Lewis Hill members. However, the Cenomanian age of the upper Tumbledown Cliffs and Rum Cove members, based on molluscan evidence, is not supported by the dinoflagellate cyst floras and further work is required on this succession. The uppermost part of the Whisky Bay Formation in north-west James Ross Island is of mid to late Turonian age and this is confirmed by strontium isotope stratigraphy. The uppermost unit, the Hidden Lake Formation, is Coniacian in age on both palaeontological and strontium isotope evidence. The uppermost part of the formation appears to be early Santonian based on dinoflagellate cysts, but strontium isotope stratigraphy constrains this as being no younger than late Coniacian. This refined palynostratigraphy greatly improves the potential of the James Ross Basin as a major Cretaceous Southern Hemisphere reference section.     

Carbon budget for a subtropical seagrass dominated coastal lagoon: How important are seagrasses to total ecosystem net primary production?

It has been assumed that because seagrasses dominate macrophyte biomass in many estuaries they also dominate primary production. We tested this assumption by developing three carbon budgets to examine the contribution of autotrophic components to the total ecosystem net primary production (TENPP) of Lower Laguna Madre, Texas. The first budget coupled average photosynthetic parameters with average daily irradiance to calculate daily production. The second budget used average photosynthetic parameters and hourly in situ irradiance to estimate productivity. The third budget integrated temperature-adjusted photosynthetic parameters (using Q₁₀=2) and hourly in situ irradiance to estimate productivity. For each budget TENPP was calculated by integrating production from each autotroph based on the producers’ areal distribution within the entire Lower Laguna Madre. All budgets indicated that macroalgae account for 33–42% of TENPP and seagrasses consistently accounted for about 33–38%. The contribution by phytoplankton was consistently about 15–20%, and the contribution from the benthic microalgae varied between 8% and 36% of TENPP, although this may have been underestimated due to our exclusion of the within bed microphytobenthos component. The water column over the seagrass beds was net heterotrophic and consequently was a carbon sink consuming between 5% and 22% of TENPP, TENPP ranged between 5.41×10¹⁰ and 2.53×10¹¹ g C yr⁻¹, depending on which budget was used. The simplest, most idealized budget predicted the highest TENPP, while the more realistic budgets predicted lower values. Annual production rates estimated using the third budget forHalodule urightii andThalassia testudinum compare well with field data. Macroalgae and microalgae contribute 50–60% of TENPP, and seagrass may be more important as three-dimensional habitat (i.e., structure) than as a source of organic carbon to the water column in Lower Laguna Madre.     

Empirical models relating viscosity and tracer diffusion in magmatic silicate melts

The Adam-Gibbs equations describing relaxation in silicate melts are applied to diffusion of trace components of multicomponent liquids. The Adam-Gibbs theory is used as a starting point to derive an explicit relation between viscosity and diffusion including non-Arrhenian temperature dependence. The general form of the equation is D_iη = A_iexp{Δ(s_cE_i)/TS_c}, where D is diffusivity, η is melt viscosity, T is absolute temperature, Δ(s_cE_i) is the difference between the products of activation energies and local configurational entropies for viscous and diffusive relaxation, A_i is a constant that depends on the characteristics of the diffusing solute particles, and S_c is configurational entropy of the melt. The general equation will be impractical for most predictive purposes due to the paucity of configurational entropy data for silicate melts. Under most magmatic conditions the proposed non-Arrhenian behaviour can be neglected, allowing the general equation to be simplified to a generalized form of the Eyring equation to describe diffusion of solutes that interact weakly with the melt structure: D_iη/T = Q_iexp{ΔE_i/RT}, where Q_i and ΔE_i depend on the characteristics of the solute and the melt structure. If the diffusing solute interacts strongly with the melt structure or is a network-forming cation itself, then ΔE_i = 0, and the relation between viscosity and diffusion has the functional form of the classic Eyring and Stokes-Einstein equations; D_iη/T = Q_i. If the diffusing solute can make diffusive jumps without requiring cooperative rearrangement of the melt structure, the diffusivity is entirely decoupled from melt viscosity and should be Arrhenian, i.e., D_i = Q_iexp{B_i/T}. A dataset of 594 published diffusivities in melts ranging from the system CAS through diopside, basalt, andesite, anhydrous rhyolite, hydrous rhyolite, and peralkaline rhyolite to albite, orthoclase, and jadeite is compared with the model equations. Alkali diffusion is completely decoupled from melt viscosity but is related to melt structure. Network-modifying cations with field strength Z_i²/r between 1 and 10 interact weakly with the melt network and can be modelled with the extended form of the Eyring equation. Diffusivities of cations with high field strength have activation energies essentially equal to that of viscous flow and can be modelled with a simple reciprocal Eyring-type dependence on viscosity. The values of Q_i, ΔE_i and B_i for each cation are different and can be related to the cation charge and radius as well as the composition of the melt through the parameters Z_i²/r, M/O, and Al/(Na + K + H). I present empirical fit parameters to the model equations that permit prediction of cation diffusivities given only charge and radius of the cation and temperature, composition and viscosity of the melt, for the entire range of temperatures accessible to magmas near to or above their liquidus, for magmas ranging in composition from basalt through andesite to hydrous or anhydrous rhyolite. Pressure effects are implicitly accounted for by corrections to melt viscosity. Ninety percent of diffusivities predicted by the models are within 0.6 log units of the measured values.