Landscape Controls on Structural Variation in Eucalypt Vegetation Communities: Woronora Plateau,Australia

Quantification of landscape-based vegetation structural variation and pattern is a significant goal for a variety of ecological, monitoring and biodiversity studies. Vegetation structural metrics, derived from airborne laser scanning (ALS or aerial light detection and ranging—LiDAR) and QuickBird satellite imagery, were used to establish the degree of plot-based vegetation variation at a hillslope scale. Topographic position is an indicator of energy and water availability, and was quantified using DEM-based insolation and topographic wetness, respectively, stratifying areas into hot-warm-cold and wet-moist-dry topographic classes. A range of vegetation metrics—maximum and modal canopy height, crown cover, foliage cover, NDVI and semivariance—were compared among randomly selected plots from each topographic class. NDVI increases with increasing landscape wetness, whereas ALS-derived foliage cover decreases with increasing insolation. Foliage cover is well correlated with crown cover (R ² =0.65), and since foliage cover is readily calculable for whole-of-landscape application, it will provide valuable and complementary information to NDVI. Between-plot heterogeneity increases with increasing wetness and decreasing insolation, indicating that more sampling is required in these locations to capture the full range of landscape-based variability. Pattern analysis in landscape ecology is one of the fundamental requirements of landscape ecology, and the methods described here offer statistically significant, quantifiable and repeatable means to realise that goal at a fine spatial grain.     

The depletion of carbon by extra mixing in metal-poor giants

There is an apparent dichotomy between the metal-poor  ([Fe/H]≤−2)  yet carbon-normal giants and their carbon-rich counterparts. The former undergo significant depletion of carbon on the red giant branch after they have undergone first dredge-up, whereas the latter do not appear to experience significant depletion. We investigate this in the context that the extra mixing occurs via the thermohaline instability that arises due to the burning of  ³He  . We present the evolution of [C/Fe], [N/Fe] and  ¹²C/¹³C  for three models: a carbon-normal metal-poor star, and two stars that have accreted material from a  1.5 M_⊙  AGB companion, one having received  0.01 M_⊙  of material and the other having received  0.1 M_⊙  . We find the behaviour of the carbon-normal metal-poor stars is well reproduced by this mechanism. In addition, our models also show that the efficiency of carbon-depletion is significantly reduced in carbon-rich stars. This extra-mixing mechanism is able to reproduce the observed properties of both carbon-normal and carbon-rich stars.     

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.     

Numerical modeling of shallow water table behavior with Lisse effect

Air entrapment is an important consideration in environments with shallow water tables and sandy soil, like the condition of highly conductive sandy soils and flat topography in Florida, USA. It causes water table rises in soils, which are significantly faster and higher than those in soils without air entrapment. Two numerical models, Integrated Hydrologic Model (IHM) and HYDRUS-1D (a single-phase, one-dimensional Richards′ equation model) were tested at an area of west central Florida to help further understanding the shallow water table behavior during a long term air entrapment. This investigation employed field data with two modeling approaches to quantify the variation of air pressurization values. It was found that the air pressurization effect was responsible at time up to 40 cm of water table rise being recorded by the observation well for these two models. The values of air pressurization calculated from IHM and HYDRUS-1D match the previously published values. Results also indicated that the two numerical models did not consider air entrapment effect (as the predictive parameters remain uncertain) and thus results of depth to water table from these models did not compare to the observations for these selected periods. Incorporating air entrapment in prediction models is critical to reproduce shallow water table observations.