Estimating groundwater evapotranspiration by a subtropical pine plantation using diurnal water table fluctuations: implications from night-time water use

Exotic pine plantations have replaced large areas of the native forests for timber production in the subtropical coastal Australia. To evaluate potential impacts of changes in vegetation on local groundwater discharge, we estimated groundwater evapotranspiration (ET) by the pine plantation using diurnal water table fluctuations for the dry season of 2012 from August 1st to December 31st. The modified White method was used to estimate the ET, considering the night-time water use by pine trees (T). Depth-dependent specific yields were also determined both experimentally and numerically for estimation of ET. Night-time water use by pine trees was comprehensively investigated using a combination of groundwater level, sap flow, tree growth, specific yield, soil matric potential and climatic variables measurements. Results reveal a constant average transpiration flux of 0.02 mm h at the plot scale from 23:00 to 05:00 during the study period, which verified the presence of night-time water use. The total ET for the period investigated was 259.0 mm with an accumulated T of 64.5 mm, resulting in an error of 25% on accumulated evapotranspiration from the groundwater if night-time water use was neglected. The results indicate that the development of commercial pine plantations may result in groundwater losses in these areas. It is also recommended that any future application of diurnal water table fluctuation based methods investigate the validity of the zero night-time water use assumption prior to use

Estimating groundwater recharge and evapotranspiration from water table fluctuations under three vegetation covers in a coastal sandy aquifer of subtropical Australia

To evaluate potential hydrological impacts of changes in vegetation over a shallow sandy aquifer in subtropical Australia, we estimated groundwater recharge and discharge by evapotranspiration (. ET) under three vegetation covers. Estimates were obtained over two years (November 2011-October 2013) using the water table fluctuation method and the White method, respectively. Depth-dependent specific yields were determined for estimation of recharge and ET. Our results show that the average annual gross recharge was largest at the sparse grassland (~52% of net rainfall), followed by the exotic pine plantation (~39% of net rainfall) and then the native banksia woodland (~27% of net rainfall). Lower recharge values at forested sites resulted from higher rainfall interception and reduced storage capacity of the vadose zone due to lower elevations when the water table approaches the soil surface. During 169 rain-free days when the White method was applied, pine trees extracted nearly twice as much groundwater through ET as the banksia, whereas no groundwater use by grasses was detected. Groundwater use is largely controlled by meteorological drivers but further mediated by depth to water table. The resulting annual net recharge (gross recharge minus ET) at the pine plantation was comparable to that of the banksia woodland but only half of the corresponding value at the grassland. Vegetation cover impacts potential groundwater recharge and discharge, but in these subtropical shallow water table environments estimates of potential recharge based on rainfall data need to take into account the often limited recharge capacity in the wet season

The Impact of Modifications in Forest Litter Inputs on Soil N2O Fluxes: A Meta-Analysis

Although litter can regulate the global climate by influencing soil N2O fluxes, there is no consensus on the major drivers or their relative importance and how these impact at the global scale. In this paper, we conducted a meta-analysis of 21 global studies to quantify the impact of litter removal and litter doubling on soil N2O fluxes from forests. Overall, our results showed that litter removal significantly reduced soil N2O fluxes (−19.0%), while a doubling of the amount of litter significantly increased soil N2O fluxes (30.3%), based on the results of a small number of studies. Litter removal decreased the N2O fluxes from tropical forest and temperate forest. The warmer the climate, the greater the soil acidity, and the larger the soil C:N ratio, the greater the impact on N2O emissions, which was particularly evident in tropical forest ecosystems. The decreases in soil N2O fluxes associated with litter removal were greater in acid soils (pH 15. Litter removal decreased soil N2O fluxes from coniferous forests (−21.8%) and broad-leaved forests (−17.2%) but had no significant effect in mixed forests. Soil N2O fluxes were significantly reduced in experiments where the duration of litter removal was <1 year. These results showed that modifications in ecosystem N2O fluxes due to changes in the ground litter vary with forest type and need to be considered when evaluating current and future greenhouse gas budgets.Beijing Academy of Agriculture and Forestry Sciences (BAAFS)Natural Science Foundation of Changsh

Soil-water content characterisation in a modified Jarvis-Stewart model: a case study of a conifer forest on a shallow unconfined aquifer

Groundwater-vegetation-atmosphere fluxes were monitored for a subtropical coastal conifer forest in South-East Queensland, Australia. Observations were used to quantify seasonal changes in transpiration rates with respect to temporal fluctuations of the local water table depth. The applicability of a Modified Jarvis-Stewart transpiration model (MJS), which requires soil-water content data, was assessed for this system. The influence of single depth values compared to use of vertically averaged soil-water content data on MJS-modelled transpiration was assessed over both a wet and a dry season, where the water table depth varied from the surface to a depth of 1.4 m below the surface

Unlocking High-Efficiency Methane Oxidation with Bimetallic Pd–Ce Catalysts under Zeolite Confinement

Catalytic complete oxidation is an efficient approach to reducing methane emissions, a significant contributor to global warming. This approach requires active catalysts that are highly resistant to sintering and water vapor. In this work, we demonstrate that Pd nanoparticles confined within silicalite-1 zeolites (Pd@S-1), fabricated using a facile in situ encapsulation strategy, are highly active and stable in catalyzing methane oxidation and are superior to those supported on the S-1 surface due to a confinement effect. The activity of the confined Pd catalysts was further improved by co-confining a suitable amount of Ce within the S-1 zeolite (PdCe0.4@S-1), which is attributed to confinement-reinforced Pd-Ce interactions that promote the formation of oxygen vacancies and highly reactive oxygen species. Furthermore, the introduction of Ce improves the hydrophobicity of the S-1 zeolite and, by forming Pd-Ce mixed oxides, inhibits the transformation of the active PdO phase to inactive Pd(OH)2 species. Overall, the bimetallic PdCe0.4@S-1 catalyst delivers exceptional outstanding activity and durability in complete methane oxidation, even in the presence of water vapor. This study may provide new prospects for the rational design of high-performance and durable Pd catalysts for complete methane oxidation

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001-2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day-1, NRMSE = 0.250 and MAE = 0.508 mm day-1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models