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The proliferation of river infrastructure projects has altered aquatic longitudinal connectivity, posing a growing threat to riverine biodiversity and ecosystem processes worldwide. Effective methods to quantify loss of river connectivity across spatiotemporal scales and in data-limited landscapes are important to understand and inform basin-wide conservation and development planning. Here we introduce a Catchment Area-based Fragmentation Index (CAFI) and its derivative, the Catchment Area- and Rainfall-based Fragmentation Index (CARFI) as new metrics to quantify river fragmentation. These indices use catchment area as a proxy for riverine habitat availability, avoiding the drawbacks of existing metrics that rely on river length and associated derivatives. CAFI/CARFI can be computed across spatiotemporal scales, incorporate barrier passability values, assesses the cumulative impact of multiple barriers, and be applied even in data-limited environments. We first applied CAFI and CARFI to a simulated network to illustrate their properties with respect to the number and location of barriers and compared these results to the widely applied Dendritic Connectivity Index (DCI). While all indices varied with barrier addition, CAFI and CARFI were more sensitive to both barrier number and location. Next, we illustrated the utility of CAFI and CARFI through case studies in two contrasting settings: the Klamath River in California, where dam building has ceased (and dam removals are being considered) and the Netravathi River in India, where dam building is ongoing, with 65 dams proposed for future development. Results indicate that CAFI and CARFI can effectively quantify trends in fragmentation across spatial scales and temporal scenarios of dam development (i.e. descriptive applications) and can aid the prioritization of sites for dam removal, restoration, or conservation (i.e. prescriptive applications). Overall, these indices can quantify the impacts of individual dams and a

Rivers continue to be harnessed to meet humanity’s growing demands for electricity, water, and flood control. While the socioecological impacts of river infrastructure projects (RIPs) have been well-documented, methodological approaches to quantify river fragmentation and flow alteration vary widely in spatiotemporal scope, required data, and interpretation. In this review, we first present a framework to visualise the effects of different kinds of RIPs on river fragmentation and flow alteration. We then review available methods to quantify connectivity and flow alteration, along with their data requirements, scale of application, advantages, and disadvantages. Finally, we present decision-making trees to help stakeholders select among these methods based on their objectives, resource availability, and the characteristics of the project(s) being evaluated. Thematic searches of peer-reviewed literature using topic-relevant keywords were conducted on Google Scholar. The bibliography of selected papers was also reviewed, resulting in the selection of 79 publications. Papers that did not define or apply a specific metric were excluded. With respect to fragmentation, we selected papers focused on instream connectivity and excluded those dealing with overland hydrologic connections. For flow alteration, we selected papers that quantified the extent of alteration and excluded those aimed at prescribing environmental flows. The expected hydrological consequences of various RIP types were ‘mapped’ on a conceptual fragmentation-flow alteration plot. We compiled 29 metrics of river fragmentation and 13 metrics to flow alteration, and used these to develop decision-making trees to facilitate method selection. Despite recent advances in metric development, further work is needed to better understand the relationships between and among metrics, assess their ecological significance and spatiotemporal scale of application, and develop more informative methods that can be effectivel

Lead-free piezoelectric composites with polymeric matrices offer a scalable and eco-friendly solution to sensing and energy harvesting applications. Piezoelectric polymers such as PVDF are particularly interesting because of the possibility to engineer the performance of these materials through addition of higher-performance piezoelectric inclusions and nanomaterials and to scalably manufacture such composites by emerging techniques such as 3D printing. This work makes two contributions, namely towards composite design and towards development of accurate effective property models. In the context of composite design, we evaluate the piezoelectric performance of PVDF modified by the addition of polycrystalline-BaTiO3 and multiwalled carbon nanotubes. Firstly, the addition of BaTiO3 dramatically improves the electric field within the composite offering significant advantages specially at low BaTiO3 concentrations. Secondly, the addition of carbon nanotubes to the matrix, particularly at higher BaTiO3 loadings, leads to an order of magnitude increase in the piezoelectric flux generation. Further enhancement in the flux generation is also possible by tuning the polycrystallinity of the BaTiO3 inclusions. However, these behaviours are inclusion-driven and the piezoelectric behaviour of the matrix does not contribute to this improvement. Importantly, a small addition of BaTiO3 and CNT into the PVDF matrix, away from percolation, can simultaneously improve flux and electric field generation. In this part of the work, we assume an isotropic PVDF matrix. Given that PVDF is elastically anisotropic, the second aspect of this work is the development of an effective property model for CNT-modified PVDF, taking into account the elastic anisotropy of poled PVDF, to predict the elastic coefficients of CNT-modified PVDF matrices, thus undertaking a key step towards modelling anisotropic piezoelectric composites. We show that the anisotropy-based model makes similar predictions in the

Piezoelectric materials are employed in a wide range of sensing devices. At present, the vast majority of piezoelectric materials used for industrial applications are ceramic and exhibit a high lead content in their composition. However, the development of lead-free piezoelectric materials is attracting great attention in recent years, with the aim of enabling technologies that are environmentally friendly. This paper discusses the computational design of microstructured lead-free polymer-based piezoelectric nanocomposites suitable for production by 3D printing. Results are presented and further analyzed for various configurations using BaTiO3 inclusions in polymeric matrices enriched with the addition of carbon nanotubes (CNT). By incorporating carbon nanotubes in the matrix, we anticipate a quite significant improvement in the piezoelectric response of the composite, through simultaneous hardening of the matrix and improvement in its permittivity.

Alluvial sediments in the Indo-Gangetic Basin (IGB) form one of the world’s largest and most heavily used aquifer systems. There is clear evidence for overabstraction and/or groundwater contamination in some parts of the IGB, but not in others. The hydrogeology and external influences on groundwater across the aquifer are complex and diverse. Of these influences, the role of leakage from irrigation canals is now a fundamental part of groundwater dynamics in the IGB aquifer. In some areas, aquifer recharge contains a high proportion of canal leakage (Joshi et al. 2018), but in others it is much less (Lapworth et al. 2015). The Gandak, in NE India, is a sub catchment of the IGB and provides an excellent laboratory for studying the evolution of groundwater-river-canal dynamics in the centre of the basin. Our study, carried out as part of the joint UK-India funded CHANSE project, is the first detailed investigation of the hydrochemistry, stable isotopes and residence time of groundwater in the Gandak aquifer, to quantify the contribution of recharge to the aquifer from canal leakage, the Gandak River and local monsoon rainfall; and to characterise groundwater flow and mixing behaviour.

Piezoelectric composites are a class of smart materials which can be manufactured in a scalable manner by additive processes, while catering to a wide range of applications. Recent efforts are directed towards composites of lead-free piezoelectric materials with a goal of achieving performances comparable to lead-based composites. While there has been extensive research in fabrication methodologies such as 3D printing, which can manufacture complex piezoelectric structures in a scalable manner, there are important remaining questions as to how the performance of lead-free piezoelectric composites can be further improved. Fundamental to this is the understanding of key factors underlying piezoelectric performance: the electro-elastic interactions between the piezoelectric material and the matrix, the effects of the polycrystalline microstructure of the piezoelectric inclusions, the effect of randomly shaped polycrystalline fillers, and the effect of the volume fraction of the piezoelectric material in the matrix. A strong motivation for using polycrystalline fillers is that they can exhibit enhanced piezoelectric and mechanical properties compared to single crystalline materials. Moreover, polycrystalline materials are amenable to scalable manufacturing. We computationally investigate these important aspects of piezoelectric composite design and performance by taking into account for the first time the polycrystalline nature of lead-free piezoelectric inclusions, in the context of a matrix-inclusion composite. We achieve this by dispersing randomly shaped polycrystalline inclusions at random positions in the matrix which allows us to better understand the behavior of practical composite architectures. In such cases, our analysis reveals that although polycrystalline piezoelectric materials, in isolation, can outperform their single crystal counterparts, in a composite architecture these enhancements are not straightforward. We identify the sources of loss which preve

Piezoelectric matrix-inclusion composites based on lead-free ceramics have attracted attention due to the possibility of manufacturing environmentally friendly devices using scalable emerging technologies such as 3D printing. However, lead-free materials lag lead-based piezo-composites in terms of performance, thus necessitating new design strategies to escalate piezoelectric response. Here, we build a modeling paradigm for improving the piezoelectric performance through improved matrices and optimal polycrystallinity in the piezoelectric inclusions. By incorporating carbon nanotubes in the matrix, we demonstrate 2–3 orders of improvement in the piezoelectric response, through simultaneous hardening of the matrix and improvement in its permittivity. By tuning the polycrystallinity of the piezoelectric inclusions, we show considerable improvements exceeding 50% in the piezo-response, compared to single crystal inclusions. We further analyze the influence of carbon nanotube agglomerations at supramolecular length scales, as well as vacancy defects in the nanotubes at the atomic level, on composite performance. Although nanomaterial agglomeration is conventionally considered undesirable, we show that, near nanotube percolation, clustering of nanotubes can lead to better matrix hardening and higher permittivities, leading to improvements exceeding 30% in the piezoelectric response compared to non-agglomerated architectures. We further demonstrate that although atomic vacancy defects in nanotubes effectively soften the matrix, this can be compensated by agglomeration of nanotubes at larger length-scales.

Intense rainstorms are a prevalent feature of current weather. Evidence is presented showing that simulation of flood hydrographs shown to be dominated by subsurface flow requires watershed model parameterisation to vary between periods of different rainstorm intensity, in addition to varying with antecedent basin storage. The data show an emerging global relation between flood response and the intensity of rainstorms. Flood responses are quantified as watershed residence times (strictly time constants of nonlinear transfer-function models) identified directly from information contained within 15-min rainfall and streamflow observations. The emerging monotonic, curvilinear relation indicates that (subsurface) watershed residence time decreases as mean intensity rises, and is seen over a wide range of synoptic conditions from temperate and tropical climates. Projected increases in rainstorm intensity would then result in a greater likelihood of river floods in subsurface-dominated watersheds than is currently simulated by systems models omitting this additional nonlinearity.

Identifying factors that underlie invasive species colonisation and change in density could provide valuable insights into the mechanisms of biological invasions and for invasive species management. We examined a suite of factors potentially influencing the landscape-level invasion of Lantana camara L., one of the most ubiquitous invasive species in South Asia. These factors included disturbance factors like forest fires, historical habitat modification, and edge effects, in addition to factors like propagule pressure and habitat suitability. We examined the relative importance of these factors on the colonisation and change in density of L. camara in the Biligiri Rangaswamy Temple Tiger Reserve, Western Ghats, India. We used extensive (1997–2008) datasets tracking the presence and abundance of L. camara and combined these with corresponding data on disturbances, propagule pressure, and habitat suitability. We used an information-theoretic model selection approach to determine the relative importance of each factor on the colonisation and change in density of L. camara. Colonisation was mainly a function of proximity to already established populations (i.e. propagule pressure), whereas increase in L. camara density appeared to be constrained by high fire frequency. Research and management efforts need to recognize the multidimensional nature of mechanisms underlying L. camara’s success during different invasion phases when strategizing interventions to mitigate its effects.

There is an urgent need for techniques to rapidly and periodically measure biodiversity and ecosystem services over large landscapes. Conventional vegetation classification and mapping approaches are based on discrete arbitrary classes which do not capture gradual changes in forest type (and corresponding biodiversity and ecosystem services values) from site to site. We developed a simple multi-date NDVI based Mahalanobis distance measure (called eco-climatic distance) that quantifies forest type variability across a moisture gradient for complex tropical forested landscapes on a single ecologically interpretable, continuous scale. This Mahalanobis distance, unlike other distance measures takes into account the variability in the reference class and shared information amongst bands as it is based on the covariance matrix, and therefore is most useful to summarize ecological distance of a pixel to a reference class in multi-band remotely sensed space In this study we successfully apply this measure as a surrogate for tree biodiversity and ecosystem services at two nested scales for the Western Chats Bio-diversity hotspot. Data from over 500 tree-plots and forest type maps was used to test the ability of this remotely sensed distance to be a surrogate for abundance based tree-species compositional turn-over and as a continuous measure of forest type and ecosystem services. Our results suggest a strong but scale dependant relationship between the remotely-sensed distance measure and floristic distance between plots. The multi-date NDVI distance measure emerges as very good quantitative surrogate for forest type and is a useful complement to existing forest classification systems. This surrogate quantifies forest type variability on a single, continuous quantitative scale and has important applications in conservation planning and mapping and monitoring of hydrologic and carbon storage and sequestration services., authorCount :4