102 research outputs found
Social Sensing of Floods in the UK
"Social sensing" is a form of crowd-sourcing that involves systematic
analysis of digital communications to detect real-world events. Here we
consider the use of social sensing for observing natural hazards. In
particular, we present a case study that uses data from a popular social media
platform (Twitter) to detect and locate flood events in the UK. In order to
improve data quality we apply a number of filters (timezone, simple text
filters and a naive Bayes `relevance' filter) to the data. We then use place
names in the user profile and message text to infer the location of the tweets.
These two steps remove most of the irrelevant tweets and yield orders of
magnitude more located tweets than we have by relying on geo-tagged data. We
demonstrate that high resolution social sensing of floods is feasible and we
can produce high-quality historical and real-time maps of floods using Twitter.Comment: 24 pages, 6 figure
Spatial correlation increase in single-sensor satellite data reveals loss of Amazon rainforest resilience
The Amazon rainforest (ARF) is threatened by deforestation and climate
change, which could trigger a regime shift to a savanna-like state. Previous
work suggesting declining resilience in recent decades was based only on local
resilience indicators. Moreover, previous results are potentially biased by the
employed multi-sensor and optical satellite data and undetected anthropogenic
land-use change. Here, we show that the spatial correlation provides a more
robust resilience indicator than local estimators and employ it to measure
resilience changes in the ARF, based on single-sensor Vegetation Optical Depth
data under conservative exclusion of human activity. Our results show an
overall loss of resilience until around 2019, which is especially pronounced in
the southwestern and northern Amazon for the time period from 2002 to 2011. The
demonstrated reliability of spatial correlation in coupled systems suggests
that in particular the southwest of the ARF has experienced pronounced
resilience loss over the last two decades
pyveg: A Python package for analysing the time evolution of patterned vegetation using Google Earth Engine
Periodic vegetation patterns (PVP) arise from the interplay between forces that drive the
growth and mortality of plants. Inter-plant competition for resources, in particular water,
can lead to the formation of PVP. Arid and semi-arid ecosystems may be under threat due
to changing precipitation dynamics driven by macroscopic changes in climate. These regions
display some noteable examples of PVP, for example the “tiger bush” patterns found in West
Africa.
The morphology of the periodic pattern has been suggested to be linked to the resilience of
the ecosystem (Mander et al., 2017; Trichon et al., 2018). Using remote sensing techniques,
vegetation patterns in these regions can be studied, and an analysis of the resilience of the
ecosystem can be performed.
The pyveg package implements functionality to download and process data from Google Earth
Engine (GEE), and to subsequently perform a resilience analysis on the aquired data. PVP
images are quantified using network centrality metrics. The results of the analysis can be
used to search for typical early warning signals of an ecological collapse (Dakos et al., 2008).
Google Earth Engine Editor scripts are also provided to help researchers discover locations of
ecosystems which may be in decline.
pyveg is being developed as part of a research project looking for evidence of early warning
signals of ecosystem collapse using remote sensing data. pyveg allows such research to be
carried out at scale, and hence can be an important tool in understanding changing arid and
semi-arid ecosystem dynamics. An evolving list of PVP locations, obtained through both
literature and manual searches, is included in the package at pyveg/coordinates.py. The
structure of the package is outlined in Figure 1, and is discussed in more detail in the following
sections
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Virtual learning environment engagement and learning outcomes at a 'bricks-and-mortar' university
In this study, we analyse the relationship between engagement in a virtual learning environment (VLE) and module grades at a ‘bricks-and-mortar’ university in the United Kingdom. We measure VLE activity for students enrolled in 38 different credit-bearing modules, each of which are compulsory components of six degree programmes. Overall we find that high VLE activity is associated with high grades, but low activity does not necessarily imply low grades. Analysis of individual modules shows a wide range of relationships between the two quantities. Grouping module-level relationships by programme suggests that science-based subjects have a higher dependency on VLE activity. Considering learning design (LD), we find that VLE usage is more important in modules that adopt an instruction-based learning style. We also test the predictive power of VLE usage in determining grades, again finding variation between degree programmes and potential for predicting a student's final grade weeks in advance of assessment. Our findings suggest that student engagement with learning at a bricks-and-mortar university is in general hard to determine by VLE usage alone, due to the predominance of other “offline” learning activities, but that VLE usage can nonetheless help to predict performance for some disciplines
Quantitatively monitoring the resilience of patterned vegetation in the Sahel
Patterning of vegetation in drylands is a consequence of localized feedback mechanisms. Such feedbacks also determine ecosystem resilience—i.e. the ability to recover from perturbation. Hence, the patterning of vegetation has been hypothesized to be an indicator of resilience, that is, spots are less resilient than labyrinths. Previous studies have made this qualitative link and used models to quantitatively explore it, but few have quantitatively analysed available data to test the hypothesis. Here we provide methods for quantitatively monitoring the resilience of patterned vegetation, applied to 40 sites in the Sahel (a mix of previously identified and new ones). We show that an existing quantification of vegetation patterns in terms of a feature vector metric can effectively distinguish gaps, labyrinths, spots, and a novel category of spot–labyrinths at their maximum extent, whereas NDVI does not. The feature vector pattern metric correlates with mean precipitation. We then explored two approaches to measuring resilience. First we treated the rainy season as a perturbation and examined the subsequent rate of decay of patterns and NDVI as possible measures of resilience. This showed faster decay rates—conventionally interpreted as greater resilience—associated with wetter, more vegetated sites. Second we detrended the seasonal cycle and examined temporal autocorrelation and variance of the residuals as possible measures of resilience. Autocorrelation and variance of our pattern metric increase with declining mean precipitation, consistent with loss of resilience. Thus, drier sites appear less resilient, but we find no significant correlation between the mean or maximum value of the pattern metric (and associated morphological pattern types) and either of our measures of resilience
Student engagement and wellbeing over time at a higher education institution
Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students' subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records
Report on the 2022 excavations at Wogan Cavern (Pembroke, Pembrokeshire, UK)
Publisher Copyright: © 2023 British Cave Research Association. All rights reserved.Peer reviewe
Committed global warming risks triggering multiple climate tipping points
Many scenarios for limiting global warming to 1.5°C assume planetary-scale carbon dioxide removal sufficient to exceed anthropogenic emissions, resulting in radiative forcing falling and temperatures stabilizing. However, such removal technology may prove unfeasible for technical, environmental, political, or economic reasons, resulting in continuing greenhouse gas emissions from hard-to-mitigate sectors. This may lead to constant concentration scenarios, where net anthropogenic emissions remain non-zero but small, and are roughly balanced by natural carbon sinks. Such a situation would keep atmospheric radiative forcing roughly constant. Fixed radiative forcing creates an equilibrium “committed” warming, captured in the concept of “equilibrium climate sensitivity.” This scenario is rarely analyzed as a potential extension to transient climate scenarios. Here, we aim to understand the planetary response to such fixed concentration commitments, with an emphasis on assessing the resulting likelihood of exceeding temperature thresholds that trigger climate tipping points. We explore transients followed by respective equilibrium committed warming initiated under low to high emission scenarios. We find that the likelihood of crossing the 1.5°C threshold and the 2.0°C threshold is 83% and 55%, respectively, if today's radiative forcing is maintained until achieving equilibrium global warming. Under the scenario that best matches current national commitments (RCP4.5), we estimate that in the transient stage, two tipping points will be crossed. If radiative forcing is then held fixed after the year 2100, a further six tipping point thresholds are crossed. Achieving a trajectory similar to RCP2.6 requires reaching net-zero emissions rapidly, which would greatly reduce the likelihood of tipping events
Early warning signals of simulated Amazon rainforest dieback
Copyright © The Author(s) 2013. This article is published with open access at Springerlink.comWe test proposed generic tipping point early warning signals in a complex climate model (HadCM3) which simulates future dieback of the Amazon rainforest. The equation governing tree cover in the model suggests that zero and non-zero stable states of tree cover co-exist, and a transcritical bifurcation is approached as productivity declines. Forest dieback is a non-linear change in the non-zero tree cover state, as productivity declines, which should exhibit critical slowing down. We use an ensemble of versions of HadCM3 to test for the corresponding early warning signals. However, on approaching simulated Amazon dieback, expected early warning signals of critical slowing down are not seen in tree cover, vegetation carbon or net primary productivity. The lack of a convincing trend in autocorrelation appears to be a result of the system being forced rapidly and non-linearly. There is a robust rise in variance with time, but this can be explained by increases in inter-annual temperature and precipitation variability that force the forest. This failure of generic early warning indicators led us to seek more system-specific, observable indicators of changing forest stability in the model. The sensitivity of net ecosystem productivity to temperature anomalies (a negative correlation) generally increases as dieback approaches, which is attributable to a non-linear sensitivity of ecosystem respiration to temperature. As a result, the sensitivity of atmospheric CO2 anomalies to temperature anomalies (a positive correlation) increases as dieback approaches. This stability indicator has the benefit of being readily observable in the real world.NERCJoint DECC/Defra Met Office Hadley Centre Climate ProgrammeUniversity of
Exete
Optimising care for patients with cognitive impairment and dementia following hip fracture
The global shift in demographics towards aging populations is leading to a commensurate increase in age-related disease and frailty. It is essential to optimise health services to meet current needs and prepare for anticipated future demands. This paper explores issues impacting on people living with cognitive impairment and/or dementia who experience a hip fracture and are cared for in acute settings. This is important given the high mortality and morbidity associated with this population. Given the current insufficiency of clear evidence on optimum rehabilitation of this patient group, this paper explored three key themes namely: recognition of cognitive impairment, response by way of training and education of staff to optimise care for this patient group and review of the importance of outcomes measures. Whilst there is currently insufficient evidence to draw conclusions about the optimal ways of caring for patients living with dementia following hip fracture, this paper concludes that future research should improve understanding of healthcare staff education to improve the outcomes for this important group of patients
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