Are peatlands in different states with respect to their thermodynamic behaviour? A simple test of peatland energy and entropy budgets

Whilst all ecosystems must obey the second law of thermodynamics, these physical bounds and controls on ecosystem evolution and development are largely ignored across the ecohydrological literature. To unravel the importance of these underlying restraints on ecosystem form and function, and their power to inform our scientific understanding, we have calculated the entropy budget of a range of peat ecosystems. We hypothesize that less disturbed peatlands are ‘near equilibrium’ with respect to the second law of thermodynamics and thus respond to change by minimizing entropy production. This ‘near equilibrium’ state is best achieved by limiting evaporative losses. Alternatively, peatlands ‘far-from-equilibrium’ respond to a change in energy inputs by maximizing entropy production which is best achieved by increasing evapotranspiration. To test these alternatives this study examined the energy balance time series from seven peatlands across a disturbance gradient. We estimate the entropy budgets for each and determine how a change in net radiation (ΔR_n) was transferred to a change in latent heat flux (ΔλE). The study showed that: (i) The transfer of net radiation to latent heat differed significantly between peatlands. One group transferred up to 64% of the change in net radiation to a change in latent heat flux, while the second transferred as little as 27%. (ii) Sites that transferred the most energy to latent heat flux were those that produced the greatest entropy. The study shows that an ecosystem could be ‘near equilibrium’ rather than ‘far from equilibrium’.     

Stromatoporoid growth forms and Devonian reef fabrics in the Upper Devonian Alexandra Reef System,Canada – Insight on the challenges of applying Devonian reef facies models

Existing facies models for Devonian reef systems can be divided into high‐energy and low‐energy types. A number of assumptions have been made in the development of these models and, in some cases, criteria that distinguish important aspects of the models are poorly defined. The Upper Devonian Alexandra Reef System contains a variety of reef fabrics from different depositional environments and is ideal for studying the range of environments in which stromatoporoids thrived and the facies from these different environments. A wide variety of stromatoporoid growth forms including laminar, tabular, anastamosing laminar and tabular, domal, bulbous, dendroid, expanding conical, concave‐up whorled‐laminar, concave‐up massive tabular and platy‐multicolumnar are present in the Alexandra Reef System. The whorled‐laminar and massive tabular concave‐up growth forms are virtually undocumented from other Devonian reefs but were common in the reef front of the Alexandra, where they thrived in a low‐energy environment around and below fair‐weather wave base. In contrast, high‐energy parts of the reef margin were dominated by bioclastic rubble deposits with narrow ribbon‐like discontinuous bodies of laminar stromatoporoid framestone. In the lagoon, laminar stromatoporoids formed steep‐sided sediment‐dominated bioherms in response to sea‐level rise and flooding. Relying mostly on the different reef facies in the Alexandra system, a new classification scheme for Devonian reef fabrics has been developed. Devonian reef fabrics can be classified as being: (i) sediment‐laden metazoan dominated; (ii) metazoan–microbial dominated (boundstone); (iii) metazoan dominated (framestone); or (iv) metazoan–marine cement dominated. Distinction of these fabrics carries important sedimentary and palaeoecological implications for reconstructing the depositional environment. With examples from the Alexandra Formation, it is demonstrated that reef facies accumulated in a range of depositional environments and that the simple observation of massive stromatoporoids with or without microbial deposits does not automatically imply a high‐energy reef margin, as otherwise portrayed in a number of the existing facies models for these systems.     

Effects of grain morphology on critical state: a computational analysis

We introduce a new DEM scheme (LS-DEM) that takes advantage of level sets to enable the inclusion of real grain shapes into a classical discrete element method. Then, LS-DEM is validated and calibrated with respect to real experimental results. Finally, we exploit part of LS-DEM potentiality by using it to study the dependency of critical state (CS) parameters such as critical state line (CSL) slope \(\lambda \), CSL intercept \(\varGamma \), and CS friction angle \(\varPhi _{\mathrm{CS}}\) on the grain’s morphology, i.e., sphericity, roundness, and regularity. This study is carried out in three steps. First, LS-DEM is used to capture and simulate the shape of five different two-dimensional cross sections of real grains, which have been previously classified according to the aforementioned morphological features. Second, the same LS-DEM simulations are carried out for idealized/simplified grains, which are morphologically equivalent to their real counterparts. Third, the results of real and idealized grains are compared, so the effect of “imperfections” on real particles is isolated. Finally, trends for the CS parameters (CSP) dependency on sphericity, roundness, and regularity are obtained as well as analyzed. The main observations and remarks connecting particle’s morphology, particle’s idealization, and CSP are summarized in a table that is attempted to help in keeping a general picture of the analysis, results, and corresponding implications.     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.