Atmospheric carbon dioxide enrichment (eCO 2) can enhance plant carbon uptake and growth 1,2,3,4,5 , thereby providing an important negative feedback to climate change by slowing the rate of increase of the atmospheric CO 2 concentration 6. While evidence gathered from young aggrading forests has generally indicated a strong CO 2 fertilization effect on biomass growth 3,4,5 , it is unclear whether mature forests respond to eCO 2 in a similar way. In mature trees and forest stands 7,8,9,10 , photosynthetic uptake has been found to increase under eCO 2 without any apparent accompanying growth response, leaving an open question about the fate of additional carbon fixed under eCO 2 4,5,7,8,9,10,11. Here, using data from the first ecosystemscale Free-Air CO 2 Enrichment (FACE) experiment in a mature forest, we constructed a comprehensive ecosystem carbon budget to track the fate of carbon as the forest responds to four years of eCO 2 exposure. We show that, although the eCO 2 treatment of ambient +150 ppm (+38%) induced a 12% (+247 g C m-2 yr-1) increase in carbon uptake through gross primary production, this additional carbon uptake did not lead to increased carbon sequestration at the ecosystem level. Instead, the majority of the extra carbon was emitted back into the atmosphere via several respiratory fluxes, with increased soil respiration alone accounting for ~50% of the total uptake surplus. Our results call into question the predominant thinking that the capacity of forests to act as carbon sinks will be generally enhanced under eCO 2 , and challenge the efficacy of climate mitigation strategies that rely on ubiquitous CO 2 fertilization as a driver of increased carbon sinks in global forests. Main text Globally, forests act as a large carbon sink, absorbing a significant portion of the anthropogenic CO 2 emissions 1,12 , an ecosystem service that has tremendous social and

AbstractAtmospheric carbon dioxide enrichment (eCO2) can enhance plant carbon uptake and growth1,2,3,4,5, thereby providing an important negative feedback to climate change by slowing the rate of increase of the atmospheric CO2 concentration6. While evidence gathered from young aggrading forests has generally indicated a strong CO2 fertilization effect on biomass growth3,4,5, it is unclear whether mature forests respond to eCO2 in a similar way. In mature trees and forest stands7,8,9,10, photosynthetic uptake has been found to increase under eCO2 without any apparent accompanying growth response, leaving an open question about the fate of additional carbon fixed under eCO24, 5, 7,8,9,10,11. Here, using data from the first ecosystem-scale Free-Air CO2 Enrichment (FACE) experiment in a mature forest, we constructed a comprehensive ecosystem carbon budget to track the fate of carbon as the forest responds to four years of eCO2 exposure. We show that, although the eCO2 treatment of ambient +150 ppm (+38%) induced a 12% (+247 gCm-2yr-1) increase in carbon uptake through gross primary production, this additional carbon uptake did not lead to increased carbon sequestration at the ecosystem level. Instead, the majority of the extra carbon was emitted back into the atmosphere via several respiratory fluxes, with increased soil respiration alone contributing ∼50% of the total uptake surplus. Our results call into question the predominant thinking that the capacity of forests to act as carbon sinks will be generally enhanced under eCO2, and challenge the efficacy of climate mitigation strategies that rely on CO2 fertilization as a driver of increased carbon sinks in standing forests and afforestation projects.

Training image-based approaches for stochastic simulations have recently gained attention in surface and subsurface hydrology. This family of methods allows the creation of multiple realizations of a study domain, with a spatial continuity based on a training image (TI) that contains the variability, connectivity, and structural properties deemed realistic. A major drawback of these methods is their computational and/or memory cost, making certain applications challenging. It was found that similar methods, also based on training images or exemplars, have been proposed in computer graphics. One such method, image quilting (IQ), is introduced in this paper and adapted for hydrogeological applications. The main difficulty is that Image Quilting was originally not designed to produce conditional simulations and was restricted to 2-D images. In this paper, the original method developed in computer graphics has been modified to accommodate conditioning data and 3-D problems. This new conditional image quilting method (CIQ) is patch based, does not require constructing a pattern databases, and can be used with both categorical and continuous training images. The main concept is to optimally cut the patches such that they overlap with minimum discontinuity. The optimal cut is determined using a dynamic programming algorithm. Conditioning is accomplished by prior selection of patches that are compatible with the conditioning data. The performance of CIQ is tested for a variety of hydrogeological test cases. The results, when compared with previous multiplepoint statistics (MPS) methods, indicate an improvement in CPU time by a factor of at least 50.

Vapour pressure deficit (D) is projected to increase in the future as temperatures rise. In response to increased D, stomatal conductance (gs) and photosynthesis (A) are reduced, which may result in significant reductions in terrestrial carbon, water, and energy fluxes. It is thus important for gas exchange models to capture the observed responses of gs and A with increasing D. We tested a series of coupled A-gs models against leaf gas exchange measurements from the Cumberland Plain Woodland (Australia), where D regularly exceeds 2 kPa and can reach 8 kPa in summer. Two commonly used A-gs models (Leuning 1995 and Medlyn et al. 2011) were not able to capture the observed decrease in A and gs with increasing D at the leaf scale. To explain this decrease in A and gs, two alternative hypotheses were tested: hydraulic limitation (i.e., plants reduce gs and/or A due to insufficient water supply) and non-stomatal limitation (i.e., downregulation of photosynthetic capacity). We found that the model that incorporated a non-stomatal limitation captured the observations with high fidelity and required the fewest number of parameters. While the model incorporating hydraulic limitation captured the observed A and gs, it did so via a physical mechanism that is incorrect. We then incorporated a non-stomatal limitation into the stand model, MAESPA, to examine its impact on canopy transpiration and gross primary production. Accounting for a non-stomatal limitation reduced the predicted transpiration by ~19%, improving the correspondence with sap flow measurements, and gross primary production by ~14%. Given the projected global increases in D associated with future warming, these findings suggest that models may need to incorporate non-stomatal limitation to accurately simulate A and gs in the future with high D. Further data on non-stomatal limitation at high D should be a priority, in order to determine the generality of our results and develop a widely applicable model.

The textile dyeing and washing industry plays an important role in the economical growth as well as the environmental sectors of Bangladesh. The textile dyeing industries has been condemned as being one of the world's most offenders in terms of pollution. There are many dyeing industries in Bangladesh which are mainly located at Gazipur and Narayanganj industrial area. This study was aimed at the dyeing industries to assess the present situation of environmental impacts arising from the activities of dyeing industries in Bangladesh. This was done by analyzing numerous data obtained from different laboratory test concerning a range of water quality parameters of Bangladesh. Important water quality parameters like pH, turbidity, TSS, BOD, COD and presence of metals were measured by testing samples. The samples were collected from effluent water of a renowned and international buyer recognized industry named UNIQUE Washing and Dyeing industry Limited in Gazipur. The results show that all the water quality parameters are within the permissible limits. Though the water test report shows no vulnerable change in water quality for this particular industry, but the overall EIA report shows the highest negative impact on physico-ecological environment. The human interest related factors make the total EIV positive.

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Abstract. Limestone aeolianites constitute karstic aquifers covering much of the western and southern Australian coastal fringe. They are a key groundwater resource for a range of industries such as winery and tourism, and provide important ecosystem services such as habitat for stygofauna. Moreover, recharge estimation is important for understanding the water cycle, for contaminant transport, for water management, and for stalagmite-based paleoclimate reconstructions. Caves offer a natural inception point to observe both the long-term groundwater recharge and the preferential movement of water through the unsaturated zone of such limestone. With the availability of automated drip rate logging systems and remote sensing techniques, it is now possible to deploy the combination of these methods for largerscale studies of infiltration processes within a cave. In this study, we utilize a spatial survey of automated cave drip monitoring in two large chambers of Golgotha Cave, southwestern Western Australia (SWWA), with the aim of better understanding infiltration water movement and the relationship between infiltration, stalactite morphology, and unsaturated zone recharge. By applying morphological analysis of ceiling features from Terrestrial LiDAR (T-LiDAR) data, coupled with drip time series and climate data from 2012 to 2014, we demonstrate the nature of the relationships between infiltration through fractures in the limestone and unsaturated zone recharge. Similarities between drip rate time series are interpreted in terms of flow patterns, cave chamber morphology, and lithology. Moreover, we develop a new technique to estimate recharge in large-scale caves, engaging flow classification to determine the cave ceiling area covered by each flow category and drip data for the entire observation period, to calculate the total volume of cave discharge. This new technique can be applied to other cave sites to identify highly focussed areas of recharge and can help to better estimate the total recharge volume.

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