Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

International audienceSocial media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20-29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets • Novel application of word embeddings in flooding context identified relevant tweets • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim) Keywords Flood management; Twitter; volunteered geographic information; natural language processing; word embeddings; social geodata. Hardware required: Intel i3 or mid-performance PC with multicore processor and SSD main drive, 8Gb memory recommended. Software required: Python and library dependencies specified in Appendix A1.2.1, (viii) environment.yml Software availability: All source code can be found at GitHub public repositorie

A geospatial framework to support integrated biogeochemical modelling in the United Kingdom

Anthropogenic impacts on the aquatic environment, especially in the context of nutrients, provide a major challenge for water resource management. The heterogeneous nature of policy relevant management units (e.g. catchments), in terms of environmental controls on nutrient source and transport, leads to the need for holistic management. However, current strategies are limited by current understanding and knowledge that is transferable between spatial scales and landscape typologies. This study presents a spatially-explicit framework to support the modelling of nutrients from land to water, encompassing environmental and spatial complexities. The framework recognises nine homogeneous landscape units, distinct in terms of sensitivity of nutrient losses to waterbodies. The functionality of the framework is demonstrated by supporting an exemplar nutrient model, applied within the Environmental Virtual Observatory pilot (EVOp) cloud cyber-infrastructure. We demonstrate scope for the use of the framework as a management decision support tool and for further development of integrated biogeochemical modelling

The wavelet packet transform: A technique for investigating temporal variation of river water solutes

Understanding factors influencing river water quality is of increasing importance. We are now able to intensively monitor water variables resulting in large time series which can be used to facilitate this understanding. These time series represent the aggregation of many complex processes driven by exter- nal factors and occurring at different temporal scales. The challenge is to use the time series to elucidate the dominant climatic, hydrological and biogeochemical processes occurring at each temporal scale (or frequency). The time series are typically non-stationary and so classical methods, such as Fourier analysis, are not suitable. In this paper we demonstrate that the Discrete wavelet packet transform (DWPT) and an adaptation of this (the Maximal Overlap DWPT—MODWPT) are appropriate tools for ana- lysing these complex signals. We exemplify this by considering measurements of nitrate and chloride concentration, temperature and discharge from the Taw River, Devon, UK. The wavelet analysis is able to distinguish frequency specific behaviour as well as intermittent events that were not visually apparent in the original time series. We find supporting evidence for observations made on similar systems by other workers and make some additional observations. We conclude that the MODWPT is an important tool which can help hydrologists and biogeochemists gain insight into the complex behaviour of catch- ment systems

Stable carbon isotope analysis of fluvial sediment fluxes over two contrasting C4-C3 semi-arid vegetation transitions

Globally, many drylands are experiencing the encroachment of woody vegetation into grasslands. These changes in ecosystem structure and processes can result in increased sediment and nutrient fluxes due to fluvial erosion. As these changes are often accompanied by a shift from C(4) to C(3) vegetation with characteristic δ(13) C values, stable isotope analysis provides a promising mechanism for tracing these fluxes.Input vegetation, surface sediment and fluvially eroded sediment samples were collected across two contrasting C(4) -C(3) dryland vegetation transitions in New Mexico, USA. Isotope ratio mass spectrometric analyses were performed using a Carlo Erba NA2000 analyser interfaced to a SerCon 20-22 isotope ratio mass spectrometer to determine bulk δ(13) C values.Stable isotope analyses of contemporary input vegetation and surface sediments over the monitored transitions showed significant differences (p <0.05) in the bulk δ(13) C values of C(4) Bouteloua sp. (grama) grassland, C(3) Larrea tridentata (creosote) shrubland and C(3) Pinus edulis/Juniperus monosperma (piñon-juniper) woodland sites. Significantly, this distinctive δ(13) C value was maintained in the bulk δ(13) C values of fluvially eroded sediment from each of the sites, with no significant variation between surface sediment and eroded sediment values.The significant differences in bulk δ(13) C values between sites were dependent on vegetation input. Importantly, these values were robustly expressed in fluvially eroded sediments, suggesting that stable isotope analysis is suitable for tracing sediment fluxes. Due to the prevalent nature of these dryland vegetation transitions in the USA and globally, further development of stable isotope ratio mass spectrometry has provided a valuable tool for enhanced understanding of functional changes in these ecosystems

Understanding spatial variability of soil properties: a key step in establishing field- to farm-scale agro-ecosystem experiments

The spatial variability of soil properties is poorly understood, despite its importance in designing appropriate experimental sampling strategies. As preparation for a farm-scale agro-ecosystem services monitoring project, the 'North Wyke Farm Platform', there was a need to assess the spatial variability of key soil chemical and physical properties.The field-scale spatial variability of soil chemical (total N, total C, soil organic matter), soil physical properties (bulk density and particle size distribution) and stable isotope ratios (δ(13) C and δ(15) N values) was studied using geostatistical approaches in an intensively managed grassland.The scales over which stable isotopes vary (ranges: 212-258 m) were larger than those of the total nutrients, soil organic matter and bulk density (ranges: 84-170 m). Two visually and statistically distinct areas of Great Field (north and south) were identified in terms of co-occurring high/low values of several soil properties.The resulting patterns of spatial variability suggest lower spatial variability of stable isotopes than that of total nutrients, soil organic matter and bulk density. Future sampling regimes should be conducted in a grid with 5 years) on the patterns of spatial variability