„Symbolic Boundaries“ als Konzept zur Analyse ethnischer und klassenspezifischer Ungleichheit in der Gegenwartsgesellschaft

Die Metapher der „symbolischen Grenze“ hat sich in den letzten zehn Jahren als ein beliebtes und häufig angewandtes Konzept der soziologischen Analyse ethnischer und klassenspezifischer Ungleichheit etabliert. Obwohl der Anspruch erhoben wird, damit jedwede Ungleichheitskonstellation in den Blick nehmen zu können, zeigen sich sowohl in den historischen Entwicklungslinien sowie den aktuellen Anwendungen gewichtige Unterschiede, je nachdem, ob ethnische oder klassenspezifische Grenzen betrachtet werden. Diese bislang kaum beachteten Unterschiede möchten wir in unserem Vortrag zum Gegenstand machen und zeigen, dass nur unter Berücksichtigung dieser Differenzen das Konzept symbolischer Grenzen auch im Rahmen intersektionaler Analysen zum Einsatz kommen kann.Hierzu wollen wir zunächst mit Andreas Wimmers Grenzziehungsperspektive und Michèle Lamonts kultursoziologischer Interpretation soziostruktureller Ungleichheiten zwei der prominentesten Ansätze der Boundary-Forschung diskutieren. Während Wimmer unter Rückgriff auf Fredrik Barth für eine Ethnizitätsforschung plädiert, die die Beschreibung und Erklärung der Herstellung und Aufrechterhaltung ethnischer Grenzen ins Zentrum der Analyse rückt, geht es Lamont um die Weiterentwicklung des Bourdieu’schen Programms einer durch und mit Kultur stabilisierten Sozialstruktur. Bemerkenswert erscheint nun, dass im Anschluss an diese beiden Autor/-innen von einer prinzipiellen Übertragbarkeit ihrer jeweiligen Erklärungsmodelle auf andere Determinanten sozialer Ungleichheit ausgegangen wird, ohne systematisch darüber zu reflektieren, dass eine solche Übertragung den spezifischen Eigenarten ethnischer bzw. klassenspezifischer Grenzziehungen letztlich nicht gerecht werden kann. Um das Potential der „Grenze“ als Konzept voll ausschöpfen zu können, erscheint es uns zielführend, die (sowohl den unterschiedlichen theoretischen Herangehensweisen sowie die dem Gegenstand selbst geschuldeten) Differenzen systematisch zu beleuchten. Erst dann ist es unseres Erachtens möglich, auch die Überlagerungen ethnischer und klassenspezifischer Grenzen – wie sie etwa bereits in Gordons Konzept der „ethclasses“ angedacht wurden – systematisch in die Analyse symbolischer Grenzziehungen zu inkludieren

The use of semi-structured interviews for the characterisation of farmer irrigation practices

For the development of sustainable and realistic water security, generating information on the behaviours, characteristics, and drivers of users, as well as on the resource itself, is essential. In this paper we present a methodology for collecting qualitative and quantitative data on water use practices through semi-structured interviews. This approach facilitates the collection of detailed information on actors’ decisions in a convenient and cost-effective manner. Semi-structured interviews are organised around a topic guide, which helps lead the conversation in a standardised way while allowing sufficient opportunity for relevant issues to emerge. In addition, they can be used to obtain certain types of quantitative data. While not as accurate as direct measurements, they can provide useful information on local practices and users’ insights. We present an application of the methodology on farmer water use in two districts in the state of Uttar Pradesh in northern India. By means of 100 farmer interviews, information was collected on various aspects of irrigation practices, including irrigation water volumes, irrigation cost, water source, and their spatial variability. Statistical analyses of the information, along with data visualisation, are also presented, indicating a significant variation in irrigation practices both within and between districts. Our application shows that semi-structured interviews are an effective and efficient method of collecting both qualitative and quantitative information for the assessment of drivers, behaviours, and their outcomes in a data-scarce region. The collection of this type of data could significantly improve insights on water resources, leading to more realistic management options and increased water security in the future

A meta-model for understanding ‘green-red loop’ social-water interactions at a global scale

Within the coupled human and natural systems (CHANS), social-water interactions exhibit the ‘green-loop’ and ‘red-loop’ patterns, where social development is dependent on the local and external environment for resource provision, respectively. Sustainable management requires coordinating the interactions between water and the other socio-environmental systems, which we term ‘systems water management (SYWM)’. To understand the generic social-water interactions and obtain management implications for green-red loop systems, we develop a SYWM meta-model that conceptualises high-level social-environmental components and formulates their interactions into casual loops. This meta-model is evaluated via structural equation modelling using global datasets. Red- and green- loop scenarios are constructed to investigate the SYWM differences, and mediation analysis is conducted to quantify the reciprocal effects through the loops. Results show a critically weak causal link (0.22) from environmental state to quality of life, implying that the current water management performances have not been successfully incorporated for guiding further social development. Green- and red- loop systems have different direct causal effects in each link, which drives the corresponding behaviour. Red-loop systems have stronger total effects in causal links within the human system, depicting their strengths in socio-economic and water management decision-making. In contrast, green-loop systems have stronger total effects within the natural system, which symbolises receiving more environmental feedback. A sustainable SYWM that enhances both decision-making and feedback is recommended for green-red loop systems. The meta-model provides a platform for interdisciplinary collaborations and practical tools development that implements systems thinking in water management for sustainable development

Including farmer irrigation behavior in a sociohydrological modeling framework with application in north India

Understanding water user behavior and its potential outcomes is important for the development of suitable water resource management options. Computational models are commonly used to assist water resource management decision making; however, while natural processes are increasingly well modeled, the inclusion of human behavior has lagged behind. Improved representation of irrigation water user behavior within models can provide more accurate and relevant information for irrigation management in the agricultural sector. This paper outlines a model that conceptualizes and proceduralizes observed farmer irrigation practices, highlighting impacts and interactions between the environment and behavior. It is developed using a bottom‐up approach, informed through field experience and farmer interaction in the state of Uttar Pradesh, northern India. Observed processes and dynamics were translated into parsimonious algorithms, which represent field conditions and provide a tool for policy analysis and water management. The modeling framework is applied to four districts in Uttar Pradesh and used to evaluate the potential impact of changes in climate and irrigation behavior on water resources and farmer livelihood. Results suggest changes in water user behavior could have a greater impact on water resources, crop yields, and farmer income than changes in future climate. In addition, increased abstraction may be sustainable but its viability varies across the study region. By simulating the feedbacks and interactions between the behavior of water users, irrigation officials and agricultural practices, this work highlights the importance of directly including water user behavior in policy making and operational tools to achieve water and livelihood security

Flux tracking of groundwater via integrated modelling for abstraction management

In systems where surface water and groundwater interact, management of the water resource often involves conflicting objectives between water supply and baseflow maintenance. Balancing such objectives requires understanding of the role of groundwater in integrated water systems to inform the design of an efficient strategy to minimise abstraction impacts. This study first develops a reduced-complexity, processed-based groundwater model within the water systems integration modelling framework (WSIMOD). This model is applied to the Lea catchment, UK, as a case study and evaluated against monitored groundwater level and river flow data. A flux tracking approach is developed to reveal the origins of both river baseflow at a critical assessment point and abstracted groundwater across the systems. The insights obtained are used to design two strategies for groundwater abstraction reduction. Results show that the model has good performance in simulating the groundwater and river flow dynamics. Three aquifer bodies that contribute the most to the river baseflow in the dry season at the assessment point are identified; contributions being 17 %, 15 %, and 5 %. The spatial distribution of abstracted groundwater originating from these aquifer bodies is illustrated. Compared to the default equal-ratio reduction, the strategy prioritising abstraction reduction in these three aquifer bodies increases a similar amount of baseflow (13 %) by reducing much less abstraction (23 %). The other strategy, which further decreases abstraction in the adjacent aquifer bodies, increases more baseflow (16 %) with a similar abstraction reduction (30 %). Both strategies can more efficiently improve the baseflow. The flux tracking approach can be further implemented to trace water from other origins, including runoff, stormwater, and wastewater, to enable coordinated management for better systems-level performance

Integrating the Budyko framework with the emerging hot spot analysis in local land use planning for regulating surface evapotranspiration ratio

Land use planning regulates surface hydrological processes by adjusting land properties with varied evapotranspiration ratios. However, a dearth of empirical spatial information hampers the regulation of place-specific hydrological processes. Therefore, this study proposed a Local Land Use Planning framework for EvapoTranspiration Ratio regulations (ETR-LLUP), which was tested for the developments of spatially-varied land use strategies in the Dongjiang River Basin (DRB) in Southern China. With the first attempt at integrating the Emerging Hot Spots Analysis (EHSA) with the Budyko framework, the spatiotemporal trends of evapotranspiration ratios based on evaporative index and dryness index, from 1992 to 2018, were illustrated. Then, representative land-cover types in each sub-basin were defined using Geographically Weighted Principal Component Analysis, in two wet years (1998 and 2016) and three dry years (2004, 2009, and 2018), which in turn were identified using the Standard Precipitation Index. Finally, Geographically Weighted Regressions (GWRs) were used to detect spatially-varied relationships between land-cover proportions and evaporative index in both dry and wet climates. Results showed that the DRB was consistently a water-limited region from 1992 to 2018, and the situation was getting worse. We also identified the upper DRB as hotspots for hydrological management. Forests and croplands experienced increasingly water stress compared to other vegetation types. More importantly, the spatial results of GWR models enabled us to adjust basin land use by 1) expanding and contracting a combination of ‘mosaic natural vegetation’ and ‘broadleaved deciduous trees’ in the western and eastern parts of the basin, respectively; and 2) increasing ‘broadleaved evergreen trees’ in the upstream parts of the basin. These spatially-varied land use strategies based on the ETR-LLUP framework allow for place-specific hydrological management during both dry and wet climates

Integrated Modelling to Support Analysis of COVID-19 Impacts on London's Water System and In-river Water Quality

Due to the COVID-19 pandemic, citizens of the United Kingdom were required to stay at home for many months in 2020. In the weeks before and months following lockdown, including when it was not being enforced, citizens were advised to stay at home where possible. As a result, in a megacity such as London, where long-distance commuting is common, spatial and temporal changes to patterns of water demand are inevitable. This, in turn, may change where people's waste is treated and ultimately impact the in-river quality of effluent receiving waters. To assess large scale impacts, such as COVID-19, at the city scale, an integrated modelling approach that captures everything between households and rivers is needed. A framework to achieve this is presented in this study and used to explore changes in water use and the associated impacts on wastewater treatment and in-river quality as a result of government and societal responses to COVID-19. Our modelling results revealed significant changes to household water consumption under a range of impact scenarios, however, they only showed significant impacts on pollutant concentrations in household wastewater in central London. Pollutant concentrations in rivers simulated by the model were most sensitive in the tributaries of the River Thames, highlighting the vulnerability of smaller rivers and the important role that they play in diluting pollution. Modelled ammonia and phosphates were found to be the pollutants that rivers were most sensitive to because their main source in urban rivers is domestic wastewater that was significantly altered during the imposed mobility restrictions. A model evaluation showed that we can accurately validate individual model components (i.e., water demand generator) and emphasised need for continuous water quality measurements. Ultimatly, the work provides a basis for further developments of water systems integration approaches to project changes under never-before seen scenarios

Quantifying land use heterogeneity on drought conditions for mitigation strategies development in the Dongjiang River Basin, China

Spatially-invariant land use and cover changes (LUCC) are not suitable for managing non-stationary drought conditions. Therefore, developing a spatially varying framework for managing land resources is necessary. In this study, the Dongjiang River Basin in South China is used to exemplify the significance of spatial heterogeneity in land planning optimization for mitigating drought risks. Using ERA5 that is the 5th major atmospheric reanalysis from the European Centre for Medium-Range Weather Forecast, we computed the Standardized Runoff Index (SRI) to quantify the hydrologic drought during 1992 to 2018. Also, based on Climate Change Initiative land use product, The Geographically Weighted Principal Component Analysis was used to identify the most dominant land types in the same period. Then, we used the Emerging Hot Spots Analysis to characterize the spatiotemporal evolution of historical LUCC and SRI. The spatially varying coefficients of Geographically and Temporally Weighted Regression models were used to reveal the empirical relationships between land types and the SRI. Results indicated that rainfed cropland with herbaceous cover, mosaic tress and shrub, shrubland, and grassland were four land types having statistical correlations with drought conditions over 27 years. Moreover, since 2003, the DRB was becoming drier, and the northern areas generally experienced severer hydrologic drought than the south. More importantly, we proposed region-specific land-use strategies for drought risk reductions. At a basin scale, we recommended to 1) increase rainfed herbaceous cropland and 2) reduce mosaic tree and shrub. At a sub-basin scale, the extents of shrub and grassland were suggested to increase in the northern DRB but to reduce in the south. Region-specific land use planning, including suitable locations, scales, and strategies, will contribute to handling current ‘one-size-fits-all’ LUCC. Planners are suggested to integrate spatial characteristics into future LUCC for regional hydrologic management

Deep learning semantic segmentation for water level estimation using surveillance camera

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities

VENTURA - virtual decision rooms for water neutral planning

The aim of VENTURA is to create and test a prototype digital tool for collaborative early-stage strategic planning for future water management. VENTURA is a 24-month applied research project that started in September 2021 focusing on study areas in Greater Manchester and Enfield, London. The project is funded by the Engineering and Physical Research Council as part of their Digital Economies: Sustainable Digital Societies1 programme. This Briefing Note covers work in Greater Manchester; a separate note has been produced for the Enfield study. VENTURA is an interdisciplinary team of systems thinking and digital geoscience researchers from Imperial College London (ICL), University College London (UCL), and British Geological Survey (BGS). The Greater Manchester study is supported by Greater Manchester Combined Authority (GMCA), United Utilities (UU) and the Environment Agency (EA), and focuses on the Upper Mersey Catchment. This group formed an integrated water management trilateral partnership prior to VENTURA in 20212. Their aim is to influence and deliver sustainable growth in Greater Manchester by improving flood risk resilience, enhancing the environment, driving circular economy approaches, and supporting regeneration